post phd fatigue

The Savvy Scientist

Experiences of a London PhD student and beyond

PhD Burnout: Managing Energy, Stress, Anxiety & Your Mental Health

PhDs are renowned for being stressful and when you add a global pandemic into the mix it’s no surprise that many students are struggling with their mental health. Unfortunately this can often lead to PhD fatigue which may eventually lead to burnout.

In this post we’ll explore what academic burnout is and how it comes about, then discuss some tips I picked up for managing mental health during my own PhD.

Please note that I am by no means an expert in this area. I’ve worked in seven different labs before, during and after my PhD so I have a fair idea of research stress but even so, I don’t have all the answers.

If you’re feeling burnt out or depressed and finding the pressure too much, please reach out to friends and family or give the Samaritans a call to talk things through.

Note – This post, and its follow on about maintaining PhD motivation were inspired by a reader who asked for recommendations on dealing with PhD fatigue. I love hearing from all of you, so if you have any ideas for topics which you, or others, could find useful please do let me know either in the comments section below or by getting in contact . Or just pop me a message to say hi. 🙂

This post is part of my PhD mindset series, you can check out the full series below:

PhD Burnout: Managing Energy, Stress, Anxiety & Your Mental Health (this part!)
PhD Motivation: How to Stay Driven From Cover Letter to Completion
How to Stop Procrastinating and Start Studying

What is PhD Burnout?

Whenever I’ve gone anywhere near social media relating to PhDs I see overwhelmed PhD students who are some combination of overwhelmed, de-energised or depressed.

Specifically I often see Americans talking about the importance of talking through their PhD difficulties with a therapist, which I find a little alarming. It’s great to seek help but even better to avoid the need in the first place.

Sadly, none of this is unusual. As this survey shows, depression is common for PhD students and of note: at higher levels than for working professionals.

All of these feelings can be connected to academic burnout.

The World Health Organisation classifies burnout as a syndrome with symptoms of:

– Feelings of energy depletion or exhaustion; – Increased mental distance from one’s job, or feelings of negativism or cynicism related to one’s job; – Reduced professional efficacy. Symptoms of burnout as classified by the WHO. Source .

This often leads to students falling completely out of love with the topic they decided to spend years of their life researching!

The pandemic has added extra pressures and constraints which can make it even more difficult to have a well balanced and positive PhD experience. Therefore it is more important than ever to take care of yourself, so that not only can you continue to make progress in your project but also ensure you stay healthy.

What are the Stages of Burnout?

Psychologists Herbert Freudenberger and Gail North developed a 12 stage model of burnout. The following graphic by The Present Psychologist does a great job at conveying each of these.

I don’t know about you, but I can personally identify with several of the stages and it’s scary to see how they can potentially lead down a path to complete mental and physical burnout. I also think it’s interesting that neglecting needs (stage 3) happens so early on. If you check in with yourself regularly you can hopefully halt your burnout journey at that point.

PhDs can be tough but burnout isn’t an inevitability. Here are a few suggestions for how you can look after your mental health and avoid academic burnout.

Overcoming PhD Burnout

Manage your energy levels, maintaining energy levels day to day.

Eat well and eat regularly. Try to avoid nutritionless high sugar foods which can play havoc with your energy levels. Instead aim for low GI food . Maybe I’m just getting old but I really do recommend eating some fruit and veg. My favourite book of 2021, How Not to Die: Discover the Foods Scientifically Proven to Prevent and Reduce Disease , is well worth a read. Not a fan of veggies? Either disguise them or at least eat some fruit such as apples and bananas. Sliced apple with some peanut butter is a delicious and nutritious low GI snack. Check out my series of posts on cooking nutritious meals on a budget.
Get enough sleep. It doesn’t take PhD-level research to realise that you need to rest properly if you want to avoid becoming exhausted! How much sleep someone needs to feel well-rested varies person to person, so I won’t prescribe that you get a specific amount, but 6-9 hours is the range typically recommended. Personally, I take getting enough sleep very seriously and try to get a minimum of 8 hours.

A side note on caffeine consumption: Do PhD students need caffeine to survive?

In a word, no!

Although a culture of caffeine consumption goes hand in hand with intense work, PhD students certainly don’t need caffeine to survive. How do I know? I didn’t have any at all during my own PhD. In fact, I wrote a whole post about it .

By all means consume as much caffeine as you want, just know that it doesn’t have to be a prerequisite for successfully completing a PhD.

Maintaining energy throughout your whole PhD

Pace yourself. As I mention later in the post I strongly recommend treating your PhD like a normal full-time job. This means only working 40 hours per week, Monday to Friday. Doing so could help realign your stress, anxiety and depression levels with comparatively less-depressed professional workers . There will of course be times when this isn’t possible and you’ll need to work longer hours to make a certain deadline. But working long hours should not be the norm. It’s good to try and balance the workload as best you can across the whole of your PhD. For instance, I often encourage people to start writing papers earlier than they think as these can later become chapters in your thesis. It’s things like this that can help you avoid excess stress in your final year.
Take time off to recharge. All work and no play makes for an exhausted PhD student! Make the most of opportunities to get involved with extracurricular activities (often at a discount!). I wrote a whole post about making the most of opportunities during your PhD . PhD students should have time for a social life, again I’ve written about that . Also give yourself permission to take time-off day to day for self care, whether that’s to go for a walk in nature, meet friends or binge-watch a show on Netflix. Even within a single working day I often find I’m far more efficient when I break up my work into chunks and allow myself to take time off in-between. This is also a good way to avoid procrastination!

Reduce Stress and Anxiety

During your PhD there will inevitably be times of stress. Your experiments may not be going as planned, deadlines may be coming up fast or you may find yourself pushed too far outside of your comfort zone. But if you manage your response well you’ll hopefully be able to avoid PhD burnout. I’ll say it again: stress does not need to lead to burnout!

Everyone is unique in terms of what works for them so I’d recommend writing down a list of what you find helpful when you feel stressed, anxious or sad and then you can refer to it when you next experience that feeling.

I’ve created a mental health reminders print-out to refer to when times get tough. It’s available now in the resources library (subscribe for free to get the password!).

Below are a few general suggestions to avoid PhD burnout which work for me and you may find helpful.

Exercise. When you’re feeling down it can be tough to motivate yourself to go and exercise but I always feel much better for it afterwards. When we exercise it helps our body to adapt at dealing with stress, so getting into a good habit can work wonders for both your mental and physical health. Why not see if your uni has any unusual sports or activities you could try? I tried scuba diving and surfing while at Imperial! But remember, exercise doesn’t need to be difficult. It could just involve going for a walk around the block at lunch or taking the stairs rather than the lift.
Cook / Bake. I appreciate that for many people cooking can be anything but relaxing, so if you don’t enjoy the pressure of cooking an actual meal perhaps give baking a go. Personally I really enjoy putting a podcast on and making food. Pinterest and Youtube can be great visual places to find new recipes.
Let your mind relax. Switching off is a skill and I’ve found meditation a great way to help clear my mind. It’s amazing how noticeably different I can feel afterwards, having not previously been aware of how many thoughts were buzzing around! Yoga can also be another good way to relax and be present in the moment. My partner and I have been working our way through 30 Days of Yoga with Adriene on Youtube and I’d recommend it as a good way to ease yourself in. As well as being great for your mind, yoga also ticks the box for exercise!
Read a book. I’ve previously written about the benefits of reading fiction * and I still believe it’s one of the best ways to relax. Reading allows you to immerse yourself in a different world and it’s a great way to entertain yourself during a commute.

* Wondering how I got something published in Science ? Read my guide here .

Talk It Through

Meet with your supervisor. Don’t suffer in silence, if you’re finding yourself struggling or burned out raise this with your supervisor and they should be able to work with you to find ways to reduce the pressure. This may involve you taking some time off, delegating some of your workload, suggesting an alternative course of action or signposting you to services your university offers.

Also remember that facing PhD-related challenges can be common. I wrote a whole post about mine in case you want to cheer yourself up! We can’t control everything we encounter, but we can control our response.

A free self-care checklist is also now available in the resources library , providing ideas to stay healthy and avoid PhD burnout.

Top Tips for Avoiding PhD Burnout

On top of everything we’ve covered in the sections above, here are a few overarching tips which I think could help you to avoid PhD burnout:

Work sensible hours . You shouldn’t feel under pressure from your supervisor or anyone else to be pulling crazy hours on a regular basis. Even if you adore your project it isn’t healthy to be forfeiting other aspects of your life such as food, sleep and friends. As a starting point I suggest treating your PhD as a 9-5 job. About a year into my PhD I shared how many hours I was working .
Reduce your use of social media. If you feel like social media could be having a negative impact on your mental health, why not try having a break from it?
Do things outside of your PhD . Bonus points if this includes spending time outdoors, getting exercise or spending time with friends. Basically, make sure the PhD isn’t the only thing occupying both your mental and physical ife.
Regularly check in on how you’re feeling. If you wait until you’re truly burnt out before seeking help, it is likely to take you a long time to recover and you may even feel that dropping out is your only option. While that can be a completely valid choice I would strongly suggest to check in with yourself on a regular basis and speak to someone early on (be that your supervisor, or a friend or family member) if you find yourself struggling.

I really hope that this post has been useful for you. Nothing is more important than your mental health and PhD burnout can really disrupt that. If you’ve got any comments or suggestions which you think other PhD scholars could find useful please feel free to share them in the comments section below.

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PhD and Postdoc Life / What's it like? / Work-Life Balance

Post PhD Submission Fatigue. Part 2

by Sarah Lang · Published 01/05/2022 · Updated 08/01/2024

In a post last year about Post PhD Submission Fatigue I promised to follow up and then I never did. Post Phd Submission fatigue got me 😀 So now, you’ll get both an update on where I am now and some more info on the phenomenon itself (What can I expect and how long will it take?).

The state of things almost a year post submission

I never finished the list of things you’re suggested to do to make it better (that I promised in the last post). Now I am almost a year past submission (May 2021) and a good half year post defense (September 2021). My fatigue has been getting better and better. A big improvement happened after the defense was actually done. I think a contributing factor was that I didn’t work/push as hard during my fellowship in Philadelphia (3 months) and then took a vacation after the defense and before I started my new job as a PostDoc. Since I was fortunate to get a relatively long-term, full-time PostDoc position at my old workplace, a lot of worries fell away from me.

But that doesn’t solve years of overwork, of course. Feeling refreshed after taking time off, I got overly motivated in October and November (the first two months on the new job), trying to do it all. I had (and still have now) many guilt items on my to do list which had been postponed during the final phase of the PhD submission sprint. They are still not done. It’s getting better but new tasks have appeared. I took on new responsibilities and took time to work on other pet projects (long overdue, good for the soul but not that good for making due with those old todos which get worse the older they get). Then I was overwhelmed again in December and shocked at myself how I had gotten back into my old (overworking) habits again and it took me two months (!) to even notice that I had fallen back into that rut. That was a sobering realization for me. I tried to make as much time as possible to reevaluate my life, goals and work ethic over a Christmas holiday and I think some stuff got a little better. I now have a clearer idea of how I should ideally live my life (which I still don’t do, of course). But, all in all, I think things are improving. It’s astounding just how many little things have piled up that I couldn’t handle during the PhD submission phase or my fellowhips (just simple stuff like repairing sth at my appartment, doing taxes way too late and so on). I feel like I have at least gotten more or less of a handle on the private stuff. Work todos are still piling up but I’m trying to focus on trusting that I’m on the right path and improving a little every day. I’m in all over my head and do still get very tired at times (probably a normal response that I just had a habit of suppresing in the past, I fear) but all in all, I feel more in control again.

Recently, my university has lifted most covid measures, so I work from the office again. That has been a nice change. But you also sometimes waste a lot of time when not productive. In the end, neither working from home nor from the office are cure-alls. You still get procrastination and bad days. I think the goal to handling them is to just acknowledge that they happen, forgive yourself and not try to control them.

With all that said, here are a few paragraphs I wrote around last summer where I was still deeper in the recovery process. Many of them are from intel I had gathered from fellow academics who had gone through the process before me. In the following post (Part III), I will try to get more into possible reasons for Post PhD burnout, what to do about it and share a “state of things” paragraph from the recovery process last year, so you get as much first-hand experiences as possible.

What symptoms can I expect?

In this section, I want to share a few symptoms which either we epigrammetrists have experienced ourselves or which have been related to us by other people. I will try to distinguish between things I was told almost everybody will experience and more rare or individual responses.

[This part of the post was written last year, so you don’t get confused.]

Celebrate your achievement . It means get drunk with your friends if that’s someting you like to do. Get out the barbecue. Whatever applies and works for you. This really helped un-paralyze me and make me realize what had happened. Don’t put it off for too long. The time before this event was really weird for me. It got immediately better afterwards (at least a bit better).
Take as much time off as possible. An extended holiday would be best.
Set an “out-of-office” reminder in your email stating that you are not available right now and emails will only be checked after the period indicated. If something’s truly urgent, tell people to call or leave an audio message. This should help you not feel guilty or like you have to check your emails constantly (thankfully, my email only works on VPN in the US so I need to be actually at my computer to check emails at the moment). If you really can’t take time off or be unavailabe, at least set boundaries on the weekends and be offline.
Sleep. You will be very tired and that’s ok. You can also binge-watch a few seasons of your favourite show while having take-out (guilt-free!). But also, frankly, I feel that too much passive media consumption isn’t the best thing for me.
Get moving (preferably in nature). Go for a walk, go to the beach, go the the gym. Anything that’s available to you. Move your body, think with your body for a while. Breathe. No phone.
Induldge without guilt. Screens are not great but give yourself permission to pursue brainless recreation like binge-watching Netflix or whatever it is that you enjoy doing as your guilty pleasure. That will drive you in some sort of a slump but you’ll get bored of it eventually. You can still get into nature after that.
Travel, even if it’s just a day-trip. I know that’s a luxury thing that not everybody can afford – but I have seen the biggest improvement after travelling. It’s both relaxing and inspiring. That was just what I needed to learn to “live” again. To understand that I am more than my work and I don’t have to think about it constantly. For me that happened during a short trip to New York. I walked those streets and realized that I am more than my work. I exist beyond it. I have interests beyond it and that’s a relief.
Make lists so you know what’s essential and urgent. Be prepared that nothing except the most essential and urgent to dos will get done. Accept that this is ok. You just had a great accomplishment, you don’t need to be super-productive now. Give it time. However, having that list (and keeping it up-to-date) helped me to stop worrying constantly about forgetting something critical. I also had a list for the months to come so I wouldn’t mindlessly push things off to a future month that’s already taken. Even if that meant that I had to do the task now even though I didn’t want to and didn’t have the energy. But also, sometimes you don’t meet your own deadlines and realize it wasn’t that urgent after all. Tomorrow’s another day. All other deadlines are not as urgent as that PhD submission. You can relax a bit more.
Do it now. In the beginning, I was litterally physically unable to look at work or emails. I made myself do it anyway and agonized about it. Now I’m finally able to use the “Do it now” approach again on some work. Like incoming emails. I feel that it’s often easier to tackle something as it comes up. The longer you put it off, the more stressful it becomes. During the PhD phase, I had deliberately gotten out of the habit to respond (immediately or at all) to anything non-thesis-related. While helpful for finishing the thesis, the pile-up of neglected to dos was overwhelming after the thesis was done. That’s why I decided I needed to get back into doing stuff as soon as it comes up. I’m still in the process of getting stuff done I postponsed during the thesis sprint [edit: still true almost a year post-submission]. But answering that email straightaway at least means that I don’t need to put it on my to do list. The old items on the to do list are the most painful. Tackle those. But also avoid generating this procrastination spiral for new items or it’s just going to get worse and worse.
[Addition from May 2022] Learn to enjoy research again. Honestly, looking back, I was seriously that close to a full-on burnout last year. The fact that we had a few successes (like decrypting that alchemical cipher ) made it look on the outside like I was my productive normal self – but I really wasn’t, that was just an illusion. It’s a year later now and only recently am I feeling like I’m getting my energy back again, finding an effective and productive rhythm (also my responsibilities have obviously changed from PhD to PostDoc position, so that needed some adapting to as well). My boss says I should focus more on publishing my digital edition (asap!) and I totally agree (yes, too many conferences are part of the problem) but I also needed to find my energy and love for research again. Last year was such a pain because I’m definitely juggling too many projects to not be on full energy and I wasn’t for at least those three months in Philadelphia. I have to admit that. But in order to get back into full steam (not that you have to if you don’t want to), I needed to travel a lot – both for work and for conferences (which I realize is a luxury that I’m very grateful to be able to afford). In the meantime, I have been to RSA in Dublin meeting early modernist and alchemist friends and last weekend, to give the very first-ever LaTeX Ninja workshop at Harvard . Of course, conference travel is tiring (hello jetlag!) but I also haven’t felt so energized and positive in a long time. I know that I’m behind on so many old projects but I also know that I’m slowly progressing. I can believe in the fact that I can do it if I just “live in day-tight compartments” (Dale Carnegie), i.e. focus on what I can do today to get stuff done (the stuff that actually needs to get done today but not everybody’s artificial deadlines). Harvard was, of course, straining in a way but I also haven’t felt this invigourated and excited about research and doing things in a long time!

How long does it take?

From what I’ve heard, the first week post submission is full of being confused, tired and your brain feeling like mush. I had that, many friends confirmed it. It guess it’s a perfectly reasonable response from your body after completing such a big and stressful project. Now [at the time of initially writing this post] I’m in between month one and two past submission and I feel that there’s an up-and-down. I have moments of productivity again. Since I luckily had a fellowship for funding where I’m financially supported but don’t have an extreme lot of obligations (the ones I have are mostly old ones I postponed from pre-submission and some of my fellowship research), my workloads have varied from 10 hour workweeks where it felt almost impossible to open either email or an urgent work project, to 30 hour weeks where I feel that I’m pretty much back to normal, just taking it a little more slowly than I have been before. And that’s alright too since I’m in a different country and also trying to make the most of my experience here (as far as the occasional fatigue allows 😉 ). I think I have a good balance of hanging out with people, doing the occasional sightseeing, making the most of my currently two parallel climbing gym memberships (will say more about how that happend in the upcoming review posts) and watching some TV.

And also, giving myself the time to just exist and experience the process of relearning what I enjoy and relearning to listen to what I currently want to do (in the beginning, I was pretty blank on that). Oh, and as you can see, I have taken up blogging again and remembered that I actually enjoy it [edit: also then totally ignored the blogs for a few more months and I think that was an important part of my recovery process]. In the beginning of the post submission stage, I felt like my blogging life was so far away, I wasn’t even sure I still liked it. [Now in 2022, I know that I still do but I have to set it up in a way that doesn’t feel like a burden, so less blogging for me right now.]

So, that’s it for now. Stay tuned for part 3!

Good luck on your journey!

Cite this blog post Sarah Lang (2022, May 1). Post PhD Submission Fatigue. Part 2. Epigrammetry . Retrieved May 19, 2024, from https://doi.org/10.58079/og85

Tags: burnout fatigue feeling tired fellowship Post PhD Submission Fatigue recovery stress

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by Astrid Schmölzer · Published 06/02/2022 · Last modified 06/11/2022

Conferencing – Nightmare vs. Highlight

by Astrid Schmölzer · Published 07/07/2019

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Hey there! We are the Epigrammetrists, Astrid and Sarah, two friends, vibing academics between postdoclife (but also former PhD-life) and trying to help all people new on this path. You are not alone in this adventure - if you have questions on how to academia, we got you covered!

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Voices of Academia

Improving mental health in academia by giving you a voice., at breaking point: burnout and its consequences post-phd by marissa edwards.

As I leaned against the wall of my apartment, tears running down my face, one thought kept circling around and around in my head: “This shouldn’t be happening.”

Indeed, from all outside appearances, my life was pretty good. I had completed my PhD with no major difficulties, I had a supportive family and a wonderful circle of friends, I had no major health problems, I had a job that I loved and knew I was a strong candidate for a tenure-track position in the near future, and had no significant financial difficulties. I was incredibly privileged and still recognize how lucky I was. So why was I crying so hard, and why couldn’t I leave my apartment?

A Precarious Situation

Looking back, a series of events had brought me to this moment. I am, without question, someone who feels the need to perform to a high standard at work all the time. I am a life-long perfectionist. At this point in my career, the self-imposed pressure and culture of academia meant I was perfectly willing to sacrifice many things that I loved in order to land that elusive tenure-track position. I worked extremely long hours and had been rewarded for it with excellent teaching evaluations, international conference presentations, awards, and had even edited a book while completing my PhD.

It was an additive feedback loop; the more I worked, the more I pushed myself to do more and be more. But the more I ‘succeeded’, the work I worked, and so on. Yet as I found out, pushing yourself to the limit only works for a discrete period of time.

In addition to my excessive work hours, I had faced a series of stressful events in a short period of time that sent me spiralling into uncertainty and fear. I had lost my beloved dog of fourteen years, and the grief was utterly overwhelming. (Anyone who has known and loved a pet will know what this feels like) . Less than a month later, a close friend died by suicide. Again, the grief was all-consuming . It was also the beginning of the teaching semester, and prior to this I had agreed to take on a much higher teaching load than ever before, meaning that collectively I was teaching just under 1000 students in several different courses. (At my institution in Australia, we can have up to 600 or more students enrolled in a single course!). And as a casual (adjunct) employee, I was not eligible for paid bereavement leave, though I doubt I would have taken it. In fact, taking any leave didn’t even cross my mind; instead, I decided to work even harder.

The Slow Slide Down

Unsurprisingly, things started to unravel over the next few months and I began to experience symptoms of burnout . The World Health Organization recently defined burnout as “a syndrome conceptualized as resulting from chronic workplace stress that has not been successfully managed.” It has been studied extensively in the work stress literature, and is characterized by symptoms such as emotional exhaustion, cynicism about one’s job, irritability, and a range of physical ailments. In my case, I believe that my job contributed to my burnout, but the intense losses I experienced compounded the situation.

Almost immediately I stopped sleeping through the night. For me, this was the single worst part of the experience, because it was only when I was sleeping that I felt any respite from the overwhelming fear. Often I would fall asleep and wake up many times during the night; some nights I couldn’t sleep at all. There is compelling evidence that key areas of the brain that help to regulate emotions are sensitive to sleep deprivation , and that missing even one night of sleep can increase anxiety the next day. This went on for months, and soon I was becoming terrified of everything.

Although I was somehow still performing well at work, I was struggling enormously. My menstrual cycle became erratic and after Googling (which is never a good idea) I managed to convince myself I was dying. A series of examinations and blood tests revealed that I wasn’t dying but in fact was experiencing a “flare” of Graves’ Disease , an autoimmune illness associated with insomnia, weight loss and – interestingly – increased anxiety. I had been diagnosed several months earlier and had assumed that it been treated successfully. Clearly not, and there is evidence that stress may play a role in the development and exacerbation of such conditions.

But I knew that my condition wasn’t only due to the Graves’ Disease. Somehow I was still managing to function effectively at work, but I was sick to my stomach, gradually losing weight, in tears almost daily, and not coping.

Receiving Support and Seeking Help

At this point, a few friends took me aside and did the most important thing they could do: listen . One friend took me for regular day trips to the beach, even when I didn’t want to leave my apartment. This taught me the importance of “opposite actions”; sometimes, the thing that is best for us is the very thing we don’t want to do. Another friend often invited me for coffee and simply listened to me talk about the constant insomnia, grief, and fear; just expressing how I felt made me feel so much better (momentarily). Yet another friend came to my apartment unannounced and chatted with me while making me dinner and made sure I was eating properly. These and other friends encouraged me to speak to my GP (primary care doctor), which was the first step in accessing help.

I think it’s important to say here that it took me a long time to admit that I wasn’t coping. It took months of gentle encouragement from others that forced me to (reluctantly) take action. In retrospect, I wish I had sought assistance earlier. I wish that the shame and stigma around mental illness hadn’t prevented me from reaching out for professional help. And I wish that I hadn’t succumbed to the pervasive belief in academia that we are defined by our professional success, and that if we aren’t working constantly, we simply aren’t good enough.

When we support others with mental illness, it is often the case that they have to come to the realisation themselves that they needed help – this was no different for me. And to do that, I had to acknowledge first that I was deserving of help.

From here, the story gets much better. My GP referred me to a number of different health professionals to help with the insomnia and anxiety, and things started to improve slowly. Again, I recognise that I was very privileged to have the access and financial means to get help relatively quickly. If I hadn’t been in that position, I could have been waiting months for assistance, and I am still grateful. With the help of melatonin and other medication, I began sleeping through the night again. It took around five months for the insomnia to resolve completely, but it was a huge relief and had an immediate impact on my well-being.

The academic year finished, classes ended, and finally I took weeks of recreation leave. For once, I didn’t think about work. I read books. I took naps. Over time, I started to relax and enjoy myself. I went travelling to beautiful places, including the south island of New Zealand. I spent time with friends. Eventually, my family and I rescued a beautiful older dog, Ziggy, who still brings constant joy every day.

Lessons Learnt

In closing, this experience taught me many lessons that I would like to share here:

If you are struggling, please reach out for help. It doesn’t necessarily have to be to a psychologist or psychiatrist, but please try to talk to someone. Talking and getting the emotions off your chest alone can help. And vice versa, if you have a friend or colleague who is struggling, offer to listen to them (if they are willing). Listening and validating their experience is so important.
Just because you are coping successfully at work does not mean that you are okay. I still remind myself of this constantly. It’s important that we check in on our colleagues and their mental health, even those who look like they have it together.
Find a good primary care doctor . They are in the best position to refer you for additional help if needed, which may include medication, therapy, or both. If you don’t like the first doctor you try, keeping looking until you find one you “click” with. A good doctor will also do bloods to rule out any physical issues, such as low iron, low Vitamin D, etc.
Don’t underestimate the importance of sleep, nutrition, and exercise. These are some of the “building blocks” of wellness and can have a significant impact on your mental health. And exercise doesn’t mean that you have to go the gym! I am not a gym person, but I love taking long walks. If you are out of the house and moving, that’s a good start.
Social support is absolutely crucial to recovery . I will be forever grateful to my friends who were there for me when I was grieving and unwell. Research suggests that having supportive friends can help people deal with life stressors more effectively , and I think that there is some truth to the argument that depression is a disease of loneliness.

Finally, it’s important to remember that your list of publications and other work achievements won’t be there to hold your hand when you are struggling, but chances are some of your family and friends will.

I have to admit that disengaging from work and taking the time to rest is still a constant struggle for me, even now that I am tenured. In academia, there is always another paper to write, a project to start, a grant to apply for, etc. I agree strongly with those who have argued that cultural change is needed in academia. This will take time and will likely be a process of small, incremental changes. In the meantime, there are still steps we can take to look after our mental health.

When we face extremely distressing life events, turning to work as a coping mechanism is unlikely to be effective. Talking to others and reaching out for support are among the best things you can ever do for yourself.

We also have to recognise we are not machines, and physiologically and psychologically we are not designed to work 24 hours a day, seven days a week. We are human beings, deserving of rest, support and compassion. Ultimately, please be kind to yourself: you are worth it.

Marissa Edwards is an Education-Focused lecturer at the UQ Business School at the University of Queensland. Her major research interests are mental health and mental illness in academia, PhD student well-being, and voice and organisational justice. She is currently lead guest editor of a forthcoming Special Issue of the Journal of Management Education focused on mental health and well-being among management students and educators. In her spare time, Marissa loves travelling, seeing live music and spending time with her rescue dog Ziggy.

The Research Whisperer

Just like the thesis whisperer – but with more money, post-phd depression.

The author of this post has chosen to remain anonymous and they hope that sharing their post-PhD challenges will be helpful for others who may be going through the same things, or who are supporting those who are.

For those who mentor or manage Early Career Researchers, especially new postdocs, it may be useful to have this post’s perspective in the contextual mix.

———————

When I submitted my thesis, I was hit by post-submission blues, which I was already aware of. What I didn’t expect was that the cloud didn’t lift with completion and graduation. I pretended otherwise, but the moments of genuine excitement and happiness were fleeting. I felt confused and ashamed, compounding my emotions.

Wondering if anyone else had ever felt this way, I Googled it. It turns out that I’m not alone in experiencing post-PhD depression and it is a lot more common than I thought.

Alarmingly, I had never heard of it.

This post shines some light on post-PhD depression so that we can better prepare PhD candidates for life during and after completion and provide the best support that we can to graduates.

The PhD journey changes people

Even if your experience was overwhelmingly positive, a PhD changes people by virtue of its length and nature. Completion can trigger reflection on your experience. It takes time to understand and accept how you’ve changed; this can be confronting and surface as an identity crisis.

Sacrifices made might be a source of pride, grief, or both. You may struggle with poorer mental and/or physical health. Catching up with ‘normal life’ can be nice but also a constant reminder of what you missed.

Processing the emotional and mental impact of a PhD can be particularly confronting for those who faced trauma during their PhD (whether coincidentally and/or because of it). Candidates might have turned to coping mechanisms that have become unhealth, in hindsight. When life suddenly changes due to completion, trauma can surface, as can the reality of the mechanisms used to cope.

There’s a lot of good-byes

For most people, the lifestyle, environment, and relationships that are part of the PhD journey change significantly or come to an end along with the PhD itself. The loss of things you loved can be intense and overwhelming. It can take time to grieve and let go.

The future is uncertain

PhD candidates who submit and graduate are often asked, ‘What next?’.

The post-doctoral job market is highly competitive, and non-academic career pathways can be difficult to establish. Graduates – even if they know what they want to do next – can struggle to find a suitable position, especially if they are part of a marginalised group and/or are primary caregivers.

There can be a range of internal and external pressures shaping decisions. Graduates might apply for particular roles purely because they feel that is what is expected of them. They might suffer from imposter syndrome, and question whether their success was deserved, and whether they are capable of continuing to succeed (‘maybe I just got lucky’). Others might feel trapped in a particular pathway due to their life circumstances.

What can help

It can really help to know you’re not alone! Acknowledge and accept what you feel: your feelings are valid.

Be gentle with yourself. Adjusting to life post-PhD takes time and that’s ok. It can help to do other things that you enjoy, like hobbies and making the most of relationships with family and friends. Engage in ways that feel safe and are less triggering. Set goals to help give you the buzz of completing things but be aware that it’s normal to be underwhelmed by these when compared to a PhD thesis.

When you can, reflect on what you enjoyed most throughout your PhD and investigate how you can continue to do that. Perhaps you loved data analysis, writing, interviewing participants, or tutoring students. These are all skills which are used in other career pathways, such as business analytics and teaching – the specifics might be different, but the process is the same.

There will be a range of opportunities that might be available to you which aren’t immediately obvious – so don’t be afraid to ask people, from your personal and academic circles, to point them out.

Of course, that can all be easier said than done. Consider talking about what you are going through with trusted family and friends and seeking professional help where appropriate. It’s ok to ask for support.

How to help someone else struggling with post-PhD depression

It’s nice to congratulate people when they submit and complete their degree but be mindful that they might not be feeling excited. Allow this to inform how you interact with people throughout their PhD journey.

For example, consider avoiding directly asking what they’re doing next, as this can be triggering (even if well-intentioned). Instead, consider asking, ‘What are you looking forward to next?’ – it gives space for the graduate to answer however they are comfortable. If you have a closer relationship with the graduate, you could also ask, ‘What were the highlights of your journey?’ and ‘How can we support you during this next stage?’.

Consider being open about your own post-PhD experience, too. Even a casual remark can help de-stigmatise post-PhD depression. Something like ‘I realised after I finished that I actually really missed working in the laboratory, so much so that I decided to volunteer to do outreach in high schools’, for example.

If possible, don’t cut off support immediately, whether it’s at a personal, professional, or institutional level.

Most importantly, prevention is better than a cure. It helps to encourage a strong identity for doctoral researchers beyond academia, including maintaining connections with their family, friends, and hobbies. Supervisors and other doctoral support teams can help by openly discussing work-life balance and encouraging it for their researchers.

Take the time to learn about mental health and the PhD journey, and implement best practice for yourself, your colleagues, and for PhD candidates more generally. The ‘Managing you mental health during your PhD: A survival guide’ by Dr Zoë Ayres is a fantastic resource for candidates and academics (and it’s available through many university libraries for free).

A PhD is a life-changing journey culminating in an extraordinary accomplishment. Everyone’s journey is different, including completion and what life after may bring – and that’s ok. We can all benefit from learning to better support each other regardless of what our journeys and futures look like.

How to stay productive and motivated during your PhD

In these resources, we discuss the emotional challenges of doing a PhD, and offer tips to help you stay engaged and motivated.

Consolations For PhD Imposter Syndrome

Welcome to the land of PhD Imposter Syndrome... We all feel like imposters from time to time. We can carry with us a pervasive sense that, at our core, we’re intellectual phonies. That we don’t deserve to be on the PhD programme, and that any day now we’re going to...

Should you quit your PhD?

Should you quit your PhD? There wasn’t a day that went by during my PhD when I didn’t think about quitting at least once. I’d ask myself why I was putting myself through the roller coaster, and whether I had made a mistake. It’s comforting - or perhaps also a...

Leaving Academia After A PhD

Congratulations, Doctor; academic work is NOT your only option. If you’ve just completed your PhD, the grass may be greener outside the university gates. In this post, we offer advice and guidance for those thinking of leaving academic after a PhD. The Dream of a...

Common PhD Viva Questions (To Help You Prepare For Yours)

There are some common PhD viva questions that come up again and again in PhD oral exams. Learn what they are in this insider guide.

How Decision Fatigue Impacts Your PhD

Every day you are tasked with many choices, but making decisions saps your energy. Learn how to beat decision fatigue as a PhD student.

10 Reading Strategies for PhD Students To Improve Long-Term Accessibility of Information

We read hundreds of articles and books for our PhDs, but do we keep all the information? Learn which strategies benefit long-term memory.

Tips for working from home during your PhD

Remote learning is here to stay and you can get to the top without ever stepping on campus. Learn tips for working from home during your PhD.

Working With a Not-So-Great PhD Supervisor

The first year of supervised training for PhD students is extremely daunting. But what if you have a not-so-great PhD supervisor?

With age comes wisdom – how to succeed as a mature PhD student

LIfe as a mature PhD student can throw up challenges. Here we offer our advice on how mature students can thrive on the PhD journey.

How to prepare for PhD supervision meetings

Supervision and doctoral committee meetings are a necessary part of your PhD journey. To make the most of these meetings, there are a few important things to remember.

How to deal with unexpected PhD supervisor feedback

Devastated by unexpected constructive feedback? Take time to rage against the feedback then look after yourself and start again. Supervisors only give you feedback to help you pass your PhD, hard though it is to hear.

How to Stop Procrastinating and Complete Your PhD Thesis: 10 Strategies to Consider

“I’ll do it tomorrow…..” Stop saying and start doing with these strategies on how to stop procrastinating your PhD thesis.

80 things I wish I knew when I was doing my PhD

Often, the difference between unnecessary worry and successfully muddling through is having someone on your side who can tell you that what you’re feeling is normal and reassure you with good, practical advice that speaks to the core of you. That is the goal of this article.

Impressing the Examiners: How to Prepare for Your PhD Viva

Completing your PhD thesis is a huge moment, but there’s still another hurdle to clear. Read on to learn how to prepare for your PhD viva.

What to do if you lack motivation in your PhD

Motivation is elusive. Some days you have it and others you don’t. Well, having fluctuations in your motivation is normal and to be expected. If you took ten PhD students, how many do you think would say they’re highly motivated all the time? Not many, I imagine.

10 Things You Can Prioritise If You’re A Self-Isolating PhD Student

Whilst being based from home and self-isolating will undoubtedly have a big impact on how you work, it doesn’t mean your PhD comes to a stop. There are other things you can work on once you re-prioritise your workload and shift your projects around.

What you should know if you are self-isolating during your PhD

This post has been published in direct response to recent calls by governments around the world to encourage more people to work from home. We wanted to create a resource that talked specifically to PhD students in order to advise them on steps they could take to ease the transition to home-working. Towards the end of these guidelines are specifically tips for those who currently teach as part of their PhD workload.

5 Things New PhD Students Should Know

Noting can ever fully prepare you for the intellectual, physical and emotional assault that comes with doing a PhD. Everyone does a PhD for very personal reasons, and everyone finds them challenging in unique and varied ways. What for one person may seem like a death-blow may to others be nothing more than a minor inconvenience. However, in this post I want to discuss the six things that every new PhD student should know. They’re things that I learnt the hard way during my PhD, and which I’ve seen time and time again in my career as an academic, PhD thesis proofreader and PhD coach.

What makes a good PhD supervisor? Top tips for managing the student-supervisor relationship.

We explain what makes a good PhD supervisor, what they should and shouldn’t be doing, and how to make the most of your supervision meetings.

39 PhD Mindfulness Exercises To De-Stress Your PhD

Research has shown how mindfulness exercises can be important in lowering PhD stress. Here we present 39 mindfulness exercises to help you towards a successful submission.

Five tips to improve your PhD thesis

Regardless of what stage of the writing process you are at, there are five overarching tips you need to keep in mind if you want to improve your PhD thesis.

How to deal with post-viva PhD thesis corrections

We like to think that the viva is the end of the doctoral process; the final step in the long journey to a PhD. But, for most, it isn’t the final hurdle. The outcome of the viva in most cases is another three to sixth months work to deal with corrections.

People like me will never do things like that: PhD imposter syndrome and academic anxiety

To those who think they’re not good enough I say two things. First, good enough for what? To be an academic? Well, you are one. You’re a trainee. Second, you’re not good enough yet. There’s a big distinction.

Writing your PhD begins with writing fieldnotes

For anyone writing ethnography – or doing any participant observation at all – writing a thesis begins in earnest in the field, with notes: an absolute heap of them.

7 Ways To Beat PhD Writer’s Block

Then the deadline grows shorter. You start to worry, “I’m so far behind. Can I ever catch up? I’m a good student. So why haven’t I been more productive?”

A Handy PhD Submission Checklist

Your work isn’t finished when you’ve written your thesis and had it proofread. There is still a surprising amount of administrative work to do before you are ready to submit. Don’t underestimate the amount of time it will take to turn your finished text into a final, bound copy.

Explore Other PhD Knowledge Base Collections

Eight collections of free resources to help you along the phd journey.

Mastering your theory and literature review chapters

How to structure and write every chapter of the PhD

How to stay motivated and productive

Techniques to improve your writing and fluency

Advice on maintaining good mental health

Resources designed for non-native English speakers

Explore our back-catalogue of motivational advice

Each week we send out a short, motivational email to over 4,000 students. Here you can sign up and access the archive.

A free one-page PhD structure template

How to write a PhD in a hundred steps (or more)

A workingmumscholar's journey through her phd and beyond, phd fatigue.

So, I have written and submitted the first draft. It is a huge achievement because I can see that this really will get done now; I will finish this year. But reaching this milestone has meant working every day, seven days a week (for at least part of each day) for the last month or so at least. Which means I have not really had weekends or evenings to just chill out, and even when I have been chilling I have been unable to get my mind to stop running over arguments and data and possible conclusions and changes I need to make and clever turns of phrase to add here or there and on and on. And even though the draft is in, it is far from done – the conclusion is not finished because I literally ran out of steam, my brain unable to continue to create coherent sentences or thoughts any more, and there is still a lot of ‘panelbeating’ to do on the thesis before I will feel okay enough about it to sign it away to my examiners. And that makes me feel tired too; the anticipation of more work and more thinking to come.

And I am tired. More tired than I feel I have ever been, particularly in mental terms. I have kids, so I know fatigue well. But that kind of physical and emotional fatigue feels different to this. My brain feels like it has been replaced with woolly stuffing, and I feel kind of fuzzy around the edges, not sharp, not clear. I forget words and I can’t type straight. I think words that come out differently when I type them or write them down, and there are so many typos in everything I am trying to write this week that I need a lot more spellchecker help than usual. My brain feels untrustworthy right now because it forgets even the simplest things, like calling the plumber or why I wrote ‘notes’ on my TO DO list (what notes?) or why I went into the kitchen. This is an odd feeling for me. I’ve always been a writer and a reader and someone who thinks a lot about things (probably too much, some would say) so my brain and I have always been close; I have always trusted it far more than any other part of me, like my heart or my gut. But now, at this point in this PhD journey I find it has gone all fluffy and marshmallowy and I cannot really count on it to remember things or to get things right. It doesn’t feel good.

I am sure this will not be a permanent condition – once the final draft is handed in and I have had a long holiday over Christmas and New Year doing little more stressful than laundry or baking or reading in the hammock, I am sure my brain and body will rest and recover and I will start 2014 with a sharper, clearer brain. But now, in the middle of this, I feel like I will never really completely get rid of this tiredness, this feeling of fuzziness. I was totally unprepared for this. I thought I would feel tired and strung out at the very end, not now when I still need to keep going and thinking and writing. I worry that I don’t have enough in me to finish the revisions really well, and that I will make silly changes and not be able to see these errors before it’s too late and the thing is out of my hands. I hope I will find it in me – I must – but boy, this is one part of the PhD process people are awfully quiet about. Maybe, like pregnancy and childbirth, people can tell you how it was for them, and it could be like that for you or it could be very different. I am putting this out there anyway, because it may be like this for you, or it may be different. Either way, it would have been nice to be a little more prepared. Onwards I go, but maybe a nap first -_- .

Outcomes of a Fatigue Management Intervention for People With Post COVID-19 Condition

Affiliation.

1 From the Discipline of Occupational Therapy, School of Medicine, Trinity Centre for Health Sciences, Trinity College Dublin, Dublin, Ireland Trinity College Dublin, Dublin, Ireland (TS, DC); Department of Occupational Therapy, St James's Hospital, James's Street, Dublin, Ireland (LN, KC, AO'G); Department of Infectious Diseases, St James's Hospital, Dublin, Ireland, Dublin (CB, PC, CK, PN); Department of Clinical Medicine, School of Medicine, Trinity College, Dublin, Ireland (CB, BK); and Respiratory Medicine, St James's Hospital, Dublin, Ireland (BK, PN).
PMID: 38014889
DOI: 10.1097/PHM.0000000000002368

Objective: Fatigue is identified as one of the most prevalent and persistent problems reported by people with post COVID-19 condition that negatively impacts on everyday living and resumption of pre-COVID-19 lifestyle. A pilot occupational therapy fatigue management intervention was designed for patients presenting with post COVID-19 condition fatigue.

Design: A retrospective analysis was carried out after the delivery of the fatigue management intervention. Self-reported measures of fatigue, well-being, and health status were taken at baseline and repeated at 2 wks after intervention. Baseline and postintervention scores were compared using nonparametric analysis.

Results: Sixty participants (73% female), median age 50.5 yrs (range, 17-74), 93% reporting symptoms persisting for 12 wks or longer, completed the fatigue management intervention. All participants reported moderate to severe fatigue impacting on everyday activity at baseline. The greatest impact of fatigue was on engagement in leisure and work activity. Statistically significant improvement in fatigue ( P < 0.001), well-being ( P < 0.001), and health status ( P < 0.001) were noted after the intervention.

Conclusions: Findings indicate the potential of occupational therapy fatigue management interventions to enable self-management strategies and reduce the negative impact of fatigue among people with post COVID-19 condition.

Activities of Daily Living
Chronic Disease
Fatigue / etiology
Fatigue / therapy
Middle Aged
Post-Acute COVID-19 Syndrome
Retrospective Studies
Self-Management*

The highs and lows of a PhD in ME research

23rd September 2022

Katharine Seton has recently completed her PhD at the Quadram Institute studying Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS).

A woman with dark hair smiling, holding papers with the title "Investigating Immune Reactivity to the Intestinal Microbiome in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome"

Myalgic encephalomyelitis is a long-term debilitating condition. It has a wide range of symptoms including extreme post-exertional fatigue, cognitive difficulties and mobility difficulties which are worsened after physical or mental exertion. Katharine’s PhD in the Simon Carding group was funded by the UK charity Invest in ME Research . Her project focused on the autoimmune aspects of the condition. We spoke to Katharine to find out more.

“I chose to do my PhD project in ME/CFS research as I wanted to contribute to and increase the amount of research into this illness. I was diagnosed with ME in January 2009.

I studied Biomedical Science at Newcastle University for my undergraduate degree. I really enjoyed my final year research project on innate immune cell signalling, which inspired me to pursue a PhD. When searching for ME/CFS focused PhD projects only two were available, both at the Quadram Institute and both funded by Invest in ME Research.

I chose to apply for the PhD project which focused on immunology as this was my favourite undergraduate module. I found it interesting how the immune system can contribute both to health (by killing pathogens) and disease (through attacking the body).

The first year of my PhD involved setting up a human study to recruit severe ME/CFS patients and the people they live with as household controls.

One of the biggest challenges I faced was participant recruitment onto the human study. There were many logistical challenges I had to overcome. Due to the nature of the illness sample collection had to be undertaken at participants’ homes. It was initially difficult to find a phlebotomist to attend home visits and then schedule a time where they were available to collect blood sample donations. This led to the biggest frustration I had within my PhD as I was unable to recruit the target sample size. However, with the samples I collected I carried out experiments to learn more about the immunology of ME.

I developed and optimised research methods such as enzyme linked immunosorbent assays (ELISA) to measure antibody levels in biological samples. I also developed flow cytometry methods to both measure the levels of antibody binding to gut microbes, and to determine which gut microbes are recognised by the immune system.

The biggest highlight of my PhD was when I attended and presented at conferences, such as the annual international Biomedical Research into ME Colloquiums hosted by Invest in ME Research. These meetings aim to stimulate discussions between researchers working on ME.

I also enjoyed mentoring college and undergraduate students, teaching them about both the theory behind my experiments and how to use the equipment involved. During my PhD I mentored four people in total, including high school students as part of the Nuffield Research Placement Programme and a trainee clinical scientist from the Norfolk and Norwich University Hospital.

There were times during my PhD where I struggled with my own ME/CFS. I would often work long days which resulted in me “burning out”.

Overall, I thoroughly enjoyed my PhD at the Quadram Institute as there is a great student community who hosts events and activities throughout the year.

Throughout my PhD I gained a lot of practical knowledge and experience. I learnt how to be both an independent researcher and work as a team.

I plan to stay in academia and pursue a postdoctoral position in ME/CFS research. I hope to have my own research group one day.”

Related People

Dr Katharine Seton

Prof. Simon Carding

Related Targets

ME/Chronic Fatigue Syndrome

Related Research Groups

Simon Carding

Related Research Areas

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Quadram Institute

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[email protected]

+44 (0)1603 255000

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Amsterdam institute for Immunology and Infectious Diseases

Post-COVID Fatigue Linked to Physical Causes

Researchers from Amsterdam UMC and Vrije Universiteit Amsterdam (VU) have identified a physical basis for the enduring fatigue experienced by post-COVID patients. Professor of infectious diseases at Amsterdam UMC, Michèle van Vugt, notes, "We observe muscle changes in these patients." The study's findings were published in Nature Communications.

The research involved 25 post-COVID patients and 21 healthy control participants. Each participant underwent a 15-minute maximum exercise bike test, triggering prolonged symptom exacerbation known as post-exertional malaise (PEM). PEM manifests as extreme fatigue following physical, cognitive, or emotional exertion, with the exertion threshold varying among individuals.

Energy depletion

Examining blood and muscle tissue one week before and one day after the exercise test, VU movement scientist Rob Wüst reports, "We observed various abnormalities in the muscle tissue of the patients. At the cellular level, we noted decreased functioning of mitochondria, the cell's energy factories, resulting in less energy production." Van Vugt emphasizes, "The fatigue is biologically rooted. Muscles require energy for movement. This discovery allows us to explore effective treatments for post-COVID individuals."

Coronavirus presence

Contrary to a hypothesis suggesting lingering coronavirus particles, Van Vugt states, "We currently find no evidence of this in the muscles." The researchers also confirm normal heart and lung function in the patients, indicating the reduced condition is unrelated to cardiac or pulmonary abnormalities.

Counterproductive exercise

Exercise may not always benefit post-COVID patients. Infectious diseases physician-researcher Brent Appelman advises, "We recommend these patients monitor and avoid surpassing their physical limits. Opt for light exercise that does not worsen symptoms, such as walking or cycling on an electric bike, to maintain physical condition. Recognize that each patient has a unique limit." Van Vugt cautions, "Given symptom exacerbation after physical exertion, some traditional rehabilitation and physiotherapy approaches can hinder recovery in these patients."

Post-COVID symptoms

While the majority recover within weeks after SARS-CoV-2 infection, a small group develops post-COVID symptoms. These symptoms, alongside PEM, include severe cognitive problems (brain fog), fatigue, and exercise intolerance.

Read the post-COVID research published in Nature Communications here .

Read our previously published articles about post-covid syndrome:

Doctors advocate for large post-COVID study (October 2023)

Plenty of theories, but long covid remains a mystery (June 2022)

The COVID-19 aftermath: where are we in finding a cure for post-COVID patients? (May 2022)

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Open access
Published: 21 February 2024

Deep phenotyping of post-infectious myalgic encephalomyelitis/chronic fatigue syndrome

Brian Walitt 1 ,
Komudi Singh ORCID: orcid.org/0000-0002-6413-541X 2 ,
Samuel R. LaMunion ORCID: orcid.org/0000-0002-7290-5189 3 ,
Mark Hallett ORCID: orcid.org/0000-0002-3180-6811 1 ,
Steve Jacobson 1 ,
Kong Chen ORCID: orcid.org/0000-0002-0306-1904 3 ,
Yoshimi Enose-Akahata ORCID: orcid.org/0000-0002-7102-9703 1 ,
Richard Apps 4 ,
Jennifer J. Barb ORCID: orcid.org/0000-0003-3022-0154 5 ,
Patrick Bedard ORCID: orcid.org/0000-0001-9447-6779 1 ,
Robert J. Brychta ORCID: orcid.org/0000-0001-9491-7968 3 ,
Ashura Williams Buckley 6 ,
Peter D. Burbelo ORCID: orcid.org/0000-0003-1717-048X 7 ,
Brice Calco 1 ,
Brianna Cathay 8 ,
Li Chen 9 ,
Snigdha Chigurupati 10 ,
Jinguo Chen 4 ,
Foo Cheung 4 ,
Lisa M. K. Chin 5 ,
Benjamin W. Coleman 11 ,
Amber B. Courville ORCID: orcid.org/0000-0002-1419-3937 3 ,
Madeleine S. Deming 5 ,
Bart Drinkard 5 ,
Li Rebekah Feng ORCID: orcid.org/0000-0003-0445-4088 12 ,
Luigi Ferrucci ORCID: orcid.org/0000-0002-6273-1613 13 ,
Scott A. Gabel 14 ,
Angelique Gavin 1 ,
David S. Goldstein ORCID: orcid.org/0000-0002-5709-9940 1 ,
Shahin Hassanzadeh 2 ,
Sean C. Horan ORCID: orcid.org/0009-0005-2375-4133 15 ,
Silvina G. Horovitz 1 ,
Kory R. Johnson ORCID: orcid.org/0000-0002-6693-3378 1 ,
Anita Jones Govan 1 ,
Kristine M. Knutson ORCID: orcid.org/0000-0003-4626-4514 1 ,
Joy D. Kreskow 16 ,
Mark Levin ORCID: orcid.org/0000-0002-2241-9828 2 ,
Jonathan J. Lyons ORCID: orcid.org/0000-0002-2346-8189 17 ,
Nicholas Madian ORCID: orcid.org/0000-0001-5325-546X 18 ,
Nasir Malik 1 ,
Andrew L. Mammen ORCID: orcid.org/0000-0003-3732-3252 19 ,
John A. McCulloch ORCID: orcid.org/0000-0001-7107-6561 20 ,
Patrick M. McGurrin ORCID: orcid.org/0000-0002-6962-9582 1 ,
Joshua D. Milner ORCID: orcid.org/0000-0002-3913-3869 21 ,
Ruin Moaddel 13 ,
Geoffrey A. Mueller ORCID: orcid.org/0000-0001-8361-5323 14 ,
Amrita Mukherjee 4 ,
Sandra Muñoz-Braceras 19 ,
Gina Norato 1 ,
Katherine Pak 19 ,
Iago Pinal-Fernandez ORCID: orcid.org/0000-0001-6338-9218 19 ,
Traian Popa ORCID: orcid.org/0000-0003-1160-4830 1 ,
Lauren B. Reoma 1 ,
Michael N. Sack 2 ,
Farinaz Safavi 1 , 17 ,
Leorey N. Saligan ORCID: orcid.org/0000-0001-9481-7836 16 ,
Brian A. Sellers 4 ,
Stephen Sinclair 6 ,
Bryan Smith 1 ,
Joseph Snow 6 ,
Stacey Solin 5 ,
Barbara J. Stussman 1 , 18 ,
Giorgio Trinchieri 20 ,
Sara A. Turner 5 ,
C. Stephenie Vetter ORCID: orcid.org/0000-0002-4694-677X 22 ,
Felipe Vial 23 ,
Carlotta Vizioli ORCID: orcid.org/0000-0002-5066-7386 1 ,
Ashley Williams 24 ,
Shanna B. Yang 5 ,
Center for Human Immunology, Autoimmunity, and Inflammation (CHI) Consortium &
Avindra Nath ORCID: orcid.org/0000-0003-0927-5855 1

Nature Communications volume 15 , Article number: 907 ( 2024 ) Cite this article

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Diseases of the nervous system
Infectious diseases
Neurological disorders

Post-infectious myalgic encephalomyelitis/chronic fatigue syndrome (PI-ME/CFS) is a disabling disorder, yet the clinical phenotype is poorly defined, the pathophysiology is unknown, and no disease-modifying treatments are available. We used rigorous criteria to recruit PI-ME/CFS participants with matched controls to conduct deep phenotyping. Among the many physical and cognitive complaints, one defining feature of PI-ME/CFS was an alteration of effort preference, rather than physical or central fatigue, due to dysfunction of integrative brain regions potentially associated with central catechol pathway dysregulation, with consequences on autonomic functioning and physical conditioning. Immune profiling suggested chronic antigenic stimulation with increase in naïve and decrease in switched memory B-cells. Alterations in gene expression profiles of peripheral blood mononuclear cells and metabolic pathways were consistent with cellular phenotypic studies and demonstrated differences according to sex. Together these clinical abnormalities and biomarker differences provide unique insight into the underlying pathophysiology of PI-ME/CFS, which may guide future intervention.

Microbiota in health and diseases

Metabolic rewiring promotes anti-inflammatory effects of glucocorticoids

Sepsis-trained macrophages promote antitumoral tissue-resident T cells

Introduction.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is the commonly used term to describe a disorder of persistent and disabling fatigue, exercise intolerance, malaise, cognitive complaints, and physical symptoms with significant socioeconomic consequences. The physiological mechanism responsible for the persistence of fatigue and related symptoms has yet to be determined. Immunologic 1 , bioenergetic 2 , 3 , 4 , 5 , and physiologic 6 alterations have been reported. However, many of these findings have been inconsistent and both their clinical relevance and relation to each other are unclear. Hence, there are currently no effective disease modifying treatments for ME/CFS, and even developing and testing of potential new treatments is hampered by difficulty in defining cases or tracking response through symptoms or biomarkers.

A major obstacle to rigorous ME/CFS research is case assessment due to the absence of a diagnostic biomarker. Over 20 different diagnostic criteria underscore the difficulty in defining the clinical symptom content of ME/CFS 7 . The usual symptoms are non-specific and overlap with other diseases, hence misattribution is frequent and ME/CFS is typically a diagnosis of exclusion 8 . ME/CFS often occurs following an acute infection, designated post-infectious-ME/CFS (PI-ME/CFS), with an estimated incidence of 10–12% after certain infections 9 , 10 .The most well-known association of PI-ME/CFS has been with the Epstein-Barr Virus 11 but as the full extent of the sequelae of the COVID-19 pandemic are better understood, SARS-CoV-2 may become an even stronger correlate 12 .

In 2016, the National Institutes of Health (NIH) launched an initiative to study ME/CFS. The NIH Division of Intramural Research developed an exploratory clinical research program to perform deep phenotyping on a cohort of PI-ME/CFS participants and healthy volunteers (HV) as controls. Prior to the SARS-CoV-2 pandemic, this study recruited a cohort of well-characterized PI-ME/CFS patients and applied modern broad and deep scientific measures to describe their biophenotype compared to HVs. The aim was to identify relevant group differences that could generate new hypotheses about the pathogenesis of PI-ME/CFS and provide direction for future research. Over 75 scientists and clinicians across 15 of the 27 institutes that comprise the NIH contributed to this multi-disciplinary work. Importantly, we developed rigorous inclusion criteria which comprised detailed medical and psychological evaluations to minimize diagnostic misattribution. A relatively homogenous population was recruited in whom symptoms were initiated after infection. This study aimed to investigate the underlying pathophysiological mechanisms. The participants underwent a multi-dimensional evaluation that included a wide range of physiological measures, physical and cognitive performance testing, and biochemical, microbiological, and immunological assays of blood, cerebrospinal fluid, muscle, and stool. Measurement techniques were developed to query issues such as physical capacity, effort preference, and deconditioning that may confound the results. Multi-omic measurements of gene expression, proteins, metabolites, and lipids were performed in parallel on collected samples. This report summarizes and contextualizes the main findings arising from this work.

Data are reported in the following formats: (HV mean ± standard deviation versus PI-ME/CFS mean ± standard deviation, p -value). Odds ratio (OR) and relative odds ratio (ROR) are reported as: HV:PI-ME/CFS ratio [95% confidence interval]. Additional analysis and a glossary of abbreviations are provided in the Supplementary Information.

Case ascertainment

Study recruitment occurred between December 2016 and February 2020 (Fig. 1a ). Of 484 ME/CFS inquiries, 217 individuals underwent detailed case reviews entailing telephone interviews and medical record review (Supplementary Data S 2 ). Of these, 27 underwent in-person research evaluation and 17 were determined to have PI-ME/CFS by a panel of clinical experts with unanimous consensus. Twenty-one comparator healthy volunteers were recruited separately. Additional recruitment was terminated due to the COVID-19 pandemic.

a Diagram showing the procedure followed to recruit adjudicated Post-infectious Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (PI-ME/CFS) and matched healthy volunteers (HV) and measurements undertaken for deep phenotyping of the cohorts. The number of participants at each stage of recruitment are noted. b Comparisons of age, sex, and BMI distribution in HV (blue; n = 21 independent participants) and PI-ME/CFS (red; n = 17 independent participants) using unadjusted two-sided t-tests for independent samples. c Distribution of the response of HV (blue; n = 21 independent participants) and PI-ME/CFS (red; n = 17 independent participants) to the indicated patient reported outcome questionnaires. Group comparisons performed using unadjusted two-sided Mann–Whitney-U tests. CSF cerebrospinal fluid, PBMC peripheral blood mononuclear cell, BMI body mass index, PROMIS Patient-Reported Outcomes Measurement Information System, PHQ-15 Patient Health Questionnaire – 15, MASQ Multiple Ability Self-Report Questionnaire. Figure 1A created with Biorender.com. Source data are provided as a Source Data file.

Cohort characteristics

Characteristics of the participants are detailed in Fig. 1 b, c and Supplementary Data S 5 ; there were no group differences in age, sex, or BMI. There were no group differences in performance validity testing (Supplementary Data S 6 ); a method for determining whether neuropsychological test performances are overly impacted by non-cognitive factors 13 . Patient reported outcome measurement information system (PROMIS) and other questionnaires for fatigue, emotional distress, sleep disturbances, anxiety and other symptoms were greater ( p < 0.05) in PI-ME/CFS than HVs (Fig. 1c , Supplementary Data S 7 ). There were no clinically relevant findings on physical examination, psychological evaluation, or laboratory testing in either group (Supplementary Data S 8 , 9 ). Measurements of small nerve fiber density and neuronal injury markers in blood and cerebrospinal fluid were not different between groups (Supplementary Fig. S1 ). Polysomnography did not reveal clinically relevant findings (Supplementary Information, Sleep dysfunction). The groups did not differ in composition by whole-body dual energy X-ray absorptiometry or in slow-to-fast muscle fiber atrophy 14 as measured by Type 2: Type 1 muscle fiber median Feret diameter ratio (Type2:1 mFd) using ATPase pH 9.4 stain of the vastus lateralis (Supplementary Figs. S2 and S3D ).

Autonomic dysfunction as measured by multiple parameters in PI-ME/CFS

Head-up tilt table testing for up to 40 min showed no group differences in frequency of orthostatic hypotension, excessive orthostatic tachycardia, or tilt-related symptoms requiring test cessation. Twenty-four hour ambulatory ECG showed that PI-ME/CFS participants had diminished heart rate variability (HRV) in SDNNi, rMSSD, and pNN50 time domain indices (Fig. 2a–d ). PI-ME/CFS participants also had altered frequency domain differences, with decreased high frequency (HF) and low frequency (LF) power (Fig. 2e, f ). Non-linear analyses adjusted by hour of the day further demonstrated group differences in ( p = 1.1E−12), SD2 ( p = 2.1E−05), and SD1/SD2 ( p = 3.9E−07). Similarly, group heart rates displayed two notable trends (Fig. 2g ). Increased heart rate in PI-ME/CFS participants throughout the course of a day suggests comparatively increased sympathetic activity. PI-ME/CFS participants also had a diminished drop in nighttime heart rate suggesting diminished parasympathetic activity, consistent with observed differences in rMSSD, HF power, pNN50, and SD1. A decrease in baroslope (Fig. 2h ) and longer blood pressure recovery times after the Valsalva maneuver (3.0 ± 0.2 versus 4.1 ± 0.4 sec, p = 0.014) in PI-ME/CFS reflect decreased baroreflex-cardiovagal function. Considered together, these data suggest that there is an alteration in autonomic tone, implying central nervous system regulatory change.

a Table of time and frequency domain heart rate variability measurements. Group comparisons for panel a were performed with unadjusted two-sided Mann–Whitney U tests. Box plots comparing HV (blue; n = 19 independent participants) and PI-ME/CFS (red; n = 14 independent participants) for ( b ) SDNNI (msec) ( c ) rMSSD (msec, p = 0.019, unadjusted two-sided t-test for independent samples with equal variance) ( d ) pNN50 (%, p = 0.017, unadjusted two-sided Mann–Whitney U test) ( e ) lnHF(ms 2 ) ( f ) lnLF (ms 2 ). Box plots depict the median (horizontal line) within quartiles 1–3 (bounds of box). Whiskers extend to minimum and maximum values g : Mean heart rate of HV (blue; n = 20 independent participants) and PI-ME/CFS (red; n = 13 independent participants) of 5-min segmented intervals over a 24-h period graphed over 24-h period. Error bars represent ±SE for each 5-min time block for each group. Note HV graph (blue) demonstrates fluctuations throughout the day with subject heart rates displaced slightly higher, suggesting increased sympathetic activity. Similarly, the typical sinusoidal drop in heart rate over sleeping hours is diminished in subjects also suggesting diminished parasympathetic and/or increased sympathetic activity. h Box plot of baroreflex-cardiovagal gain as measured by mean baroslope (ms/mmHg). HVs (blue; n = 19 independent participants) and PI-ME/CFS (red; n = 16 independent participants) are compared using an unadjusted two-sided t-test for independent samples with equal variance ( p = 0.015). Box plot H depicts the median (horizontal line) within quartiles 1–3 (bounds of box). Whiskers extend to minimum and maximum values. SDNNi standard deviation of the average NN intervals for each 5 min segment of a 24 h HRV recording, rMSSD root mean square of successive differences between normal heartbeats, pNN50 proportion of NN50 divided by the total number of NN (R-R) intervals, HF high frequency, LF low frequency, SD1 standard deviation of Poincaré plot of RR intervals perpendicular to the line-of-identity, SD2 standard deviation of the Poincaré plot of RR intervals along the line-of-identity. Source data are provided as a Source Data file.

Altered effort preference in PI-ME/CFS

The Effort-Expenditure for Rewards Task (EEfRT) 15 was used to assess effort, task-related fatigue, and reward sensitivity (Supplementary Fig. S5A–D ). Given equal levels and probabilities of reward, HVs chose more hard tasks than PI-ME/CFS participants (Odds Ratio (OR) = 1.65 [1.03, 2.65], p = 0.04; Fig. 3a ). Two-way interactions showed no group differences in responses to task-related fatigue (Relative Odds Ratio (ROR) = 1.01 [0.99, 1.03], p = 0.53; Fig. 3a ), reward value (ROR = 1.57 [0.97, 2.53], p = 0.07), reward probability (ROR = 0.50 [0.09, 2.77], p = 0.43), and expected value (ROR = 0.98 [0.54, 1.79], p = 0.95). Effort preference, the decision to avoid the harder task when decision-making is unsupervised and reward values and probabilities of receiving a reward are standardized, was estimated using the Proportion of Hard-Task Choices (PHTC) metric. This metric summarizes performance across the entire task and reflects the significantly lower rate of hard-trial selection in PI-ME/CFS participants (Fig. 3a ). There was no group difference in the probability of completing easy tasks but there was a decline in button-pressing speed over time noted for the PI-ME/CFS participants (Slope = −0.008, SE = 0.002, p = 0.003; Fig. 3b ). This pattern suggests the PI-ME/CFS participants were pacing to limit exertion and associated feelings of discomfort 16 . HVs were more likely to complete hard tasks (OR = 27.23 [6.33, 117.14], p < 0.0001) but there was no difference in the decline in button-press rate over time for either group for hard tasks (Fig. 3b ).

a , b Effort-Expenditure for Rewards Task: a Probability of choosing the hard task is significantly more in HV (blue, n = 16 independent participants) compared with PI-ME/CFS (red; n = 15 independent participants) at the start of and throughout the task. The Odds Ratio for the probability of choosing the hard task at the start of the task is 1.65 [1.03, 2.65], p = 0.04 using Fisher’s Exact test. The lines are the curvilinear fits and the error bands are the confidence intervals. Decline rates (i.e. response to fatigue) between the groups is similar as the trial progresses. b Button press rates for easy (right) and hard (left) tasks as the trial progresses is shown for HV (blue) and PI-ME/CFS (red) participants. The lines are the linear fits and the shaded error bands are the confidence intervals. The decline in button press rate over time during the easy tasks in PI-ME/CFS did not impact easy task performance, which is supportive of pacing in PI-ME/CFS participants. c – e Grip Strength test: Box plots of ( c ) maximum grip force of HV (blue; n = 20 independent participants) and PI-ME/CFS (red; n = 16 independent participants) and ( d ) time to failure of HV (blue; n = 18 independent participants) and PI-ME/CFS (red; n = 16 independent participants), unadjusted two-sided t-test for independent samples with equal variance, p = 0.0002. Correlation between time to failure and ( e ) proportion of hard task choices in HV (blue; n = 15 independent participants) and PI-ME/CFS (red; n = 14 independent participants). For figure e , the relationship between indicated variables in x and y axis were fitted by linear regression in each group. Linear regression t-tests were used to determine non-zero slope. Exact p values of the correlations are presented on the graph. For box plots c and d , boxes depict the median (horizontal line) within quartiles 1–3 (bounds of box). Whiskers extend to minimum and maximum values. Source data are provided as a Source Data file.

Equivalent motor performance in PI-ME/CFS and healthy volunteers

On single grip task, maximal voluntary contraction (MVC) was not different between groups (Fig. 3c ) and correlated with lean arm mass but not effort preference or Type2:1 mFd (Supplementary Fig. S6A–C ). Time to failure, the inability to maintain grip force at 50% of maximum contraction, was significantly shorter ( p = 0.0002) in the PI-ME/CFS participants (Fig. 3d ) and correlated with effort preference (Fig. 3e ) in PI-ME/CFS but not in HVs. Time to failure did not correlate with lean arm mass or Type2:1 mFd in either group.

Repetitive grip testing was performed on a subgroup of participants. MVC did not differ between groups. A rapid decline in force along with a significantly lower number of non-fatigued blocks (Fig. 4a ) and a relative decrease in the slope of the Dimitrov index 17 , 18 (Fig. 4b ) occurred in PI-ME/CFS participants but both remained constant in HVs, suggesting that the decline of force was not due to peripheral fatigue or a neuromuscular disorder. Motor Evoked Potential amplitudes using transcranial magnetic stimulation of HVs decreased over the course of the task, consistent with post-exercise depression as seen in healthy and depressed volunteers 19 , while they increased in PI-ME/CFS participants (Fig. 4c ). This indicates that the primary motor cortex remained excitable for PI-ME/CFS, suggesting reduced motor engagement from this group 20 .

a – e Repetitive Grip Strength test: a Grip force normalized to maximum voluntary contraction (MVC) in the first block, the last block prior to fatigue onset, and the first three blocks after fatigue onset in HV (blue) and PI-ME/CFS cohorts (red). A significant grip force difference was noted between the groups (−1.2 ± 4 versus −6.4 ± 4 kilogram-force units, t(12) = 2.46, p = 0.03). The number of non-fatigued grip test blocks of HV (blue) and PI-ME/CFS (red) participants is also displayed. b Slope of the Dimitrov index across the first block (b 1 ), the last block prior to fatigue onset (b n ), and the first three blocks after fatigue onset (f 1 , f 2 , and f 3 ) in HV (blue; n = 6 independent participants) and PI-ME/CFS (red; n = 8 independent participants) patients. A significant difference was noted between the groups (0.2 ± 0.5 versus −0.43 ± 0.3, t(12) = 3.2, p = 0.008). c Mean and standard error of the motor evoked potential of HV (blue; n = 6 independent participants) and PI-ME/CFS (red; n = 8 independent participants) participants spanning the last five grip test blocks prior to fatigue onset. The amplitudes of the MEPs of HVs significantly decreased over the course of the task while the amplitudes of the MEPs of PI-ME/CFS participants significantly increased (−0.13 ± 0.2 versus 0.13 ± 0.2 MEP units; t(12) = 2.4, p = 0.03 D. Brain regions where Blood Oxygen Dependent (BOLD) signal decreased over grip strength blocks in PI-ME/CFS patients and increased over grip strength blocks in HVs. e Brain activation of the regions depicted in d measured in the blocks of four over the course of the experiment in HV (blue; n = 10 independent participants) and PI-ME/CFS (red; n = 8 independent participants) cohorts. For e , a two-way ANOVA was run where F(3,45) = 5.4 with a voxel threshold of p ≤ 0.01, corrected for multiple comparisons p ≤ 0.05, k > 65 ( p -values were 0.976, 0.43, 0.02 (*), and 0.02 (*) for blocks 1 to 4 respectively). Source data are provided as a Source Data file.

Decreased activation of integrative brain regions in PI/ME-CFS

Repetitive grip testing by functional brain imaging was performed on a subgroup of participants and also showed a rapid decline in force (Supplementary Fig. S7E ) and a significantly lower number of non-fatigued blocks ( p = 0.004) in PI-ME/CFS. First, we assessed commonly activated brain areas by implementing a conjunction analysis in which we took each group t-test across all blocks, thresholded voxels at p ≤ 0.01 with a multiple comparison correction at p = 0.05, k > 65 and kept voxels that were commonly activated in each group. HV and PI-ME/CFS participants showed force-related brain activation in the left M1, right cerebellum, and left putamen during the task. We next assessed group differences with t-test (at p = 0.01, k > 65), but there was no difference between the groups. We also assessed changes across blocks with a two-way ANOVA (2 groups × 4 blocks), which showed that blood oxygen level dependent (BOLD) signal of PI-ME/CFS participants decreased across blocks bilaterally in temporo-parietal junction (TPJ) and superior parietal lobule, and right temporal gyrus in contradistinction to the increase observed in HVs (F (3,45) = 5.4, voxel threshold p ≤ 0.01, corrected for multiple comparisons p ≤ 0.05, k > 65; Fig. 4 d, e). TPJ activity is inversely correlated with the match between willed action and the produced movement.

Differential cardiorespiratory performance in PI-ME/CFS

Cardiopulmonary exercise testing (CPET) was performed on eight PI-ME/CFS and nine HVs (Supplementary Fig. S8A ). During testing, all but one PI-ME/CFS participant reached the peak respiratory exchange ratio (RER) of ≥1.1 and there was no difference between the groups. Effort preference did not correlate with peak power in PI-ME/CFS participants (Supplementary Fig. S8B ). The ratio of Ventilation/VCO 2 , oxygen saturation levels in the quadricep muscle as measured by Near Infrared Spectroscopy, and gross mechanical efficiency were not different between groups (Supplementary Fig. S8C–E ). These results suggest that PI-ME/CFS participants performed to the best of their abilities.

Peak power ( p = 0.08), peak respiratory rate, peak heart rate ( p = 0.07), and peak VO2 ( p = 0.004) were all lower in the PI-ME/CFS participants (Fig. 5a–d ), a difference of approximately 3.3 metabolic equivalent of task units (METs). These peak measures did not correlate with effort preference in PI-ME/CFS. PI-ME/CFS participants performed at a lower percentage of their predicted VO 2peak (Fig. 5e ) as determined by the Wasserman-Hansen cycling equation 21 , 22 and at a lower percentage of their age-predicted maximal heart rate (Supplementary Fig. S8F ). The lower peak heart rate and higher resting heart rate for PI-ME/CFS participants led to a lower heart rate reserve (Fig. 5f ). Chronotropic incompetence, as measured by age-predicted HRR (%pHRR), was noted in five of eight PI-ME/CFS and one of nine HVs (X 2 (1, n = 17) = 4.9, p = 0.03). The slope of the heart rate response during the CPET was also lower in PI-ME/CFS participants compared to HVs (1.03 ± 0.16 versus 0.70 ± 0.27, p < 0.01; Fig. 5g ). Anaerobic threshold (AT) was achieved within a similar time period and occurred at lower VO2 levels in PI-ME/CFS (Fig. 5h ), a difference of approximately 1.4 METs. VO 2 at AT also correlated with Type2:1 mFd (Supplementary Fig. S8J ) in PI-ME/CFS but not HVs, evidence of concomitant muscular deconditioning. Salivary cortisol measurements taken at rest in the morning, at noon, and before sleep showed no group differences but were significantly lower in PI-ME/CFS participants one hour after CPET (Supplementary Fig. S8L ). Thus, despite successful CPET engagement, PI-ME/CFS participants were less likely to achieve their predicted maximal output, suggesting a differential cardiorespiratory performance related to autonomic function, hypothalamic-pituitary-adrenal axis hyporesponsiveness, and muscular deconditioning from disuse that clinically impacts activities of daily life 23 . This was supported by at-home waist actigraphy measures, demonstrating a lower step count and total activity for PI-ME/CFS participants due to less moderate intensity activity (40.64 ± 37.4 versus 6.4 ± 7.0 min/day; p = 0.007).

Cardiopulmonary Exercise Test (CPET) a – h : Box plots of ( a ) peak power attained during CPET for HV (blue; n = 9 independent participants) and PI-ME/CFS (red; n = 8 independent participants) participants, ( b ) peak respiratory rate with the error bars representing ±SD at each work rate, ( c ) peak heart rate (unadjusted two-sided t-test for independent samples with unequal variance, p = 0.072), ( d ) Peak VO2 (unadjusted two-sided t-test with equal variance, p = 0.002), ( e ) Percent of predicted VO2 achieved of HV (blue; n = 9 independent participants) and PI-ME/CFS (red; n = 8 independent participants) participants (unadjusted two-sided t-test for independent samples with equal variance, p = 0.004), and ( f ) heart rate reserve for HV (blue; n = 9 independent participants) and PI-ME/CFS (red; n = 8 independent participants) participants (unadjusted two-sided t-test for independent samples with equal variance, p = 0.011). g Heart rate as a function of % total CPET time depicted as expected for age and gender of HV ( n = 9 independent participants) and PI-ME/CFS ( n = 8 independent participants) (gray solid and dashed lines, respectively, unadjusted two-sided Mann–Whitney U test for independent samples). Mean heart rate responses from the CPET are depicted for HV (blue line) and PI-ME/CFS (red line). A significant difference was observed for the heart rate slope between PI-ME/CFS and expected (0.70 ± 0.27 versus 1.05 ± 0.12, p = 0.014), but not for HV and expected (1.03 ± 0.16 versus 1.08 ± 0.13; p = 0.479). The deviation from expected relation reflects chronotropic incompetence in the PI-ME/CFS group. h Box plot of VO2 at the anaerobic threshold (AT) in HV (blue; n = 9 independent participants) and PI-ME/CFS (red; n = 8 independent participants) using an unadjusted two-sided t-test for independent samples with equal variance ( p = 0.024). For box plots a , c – f , and h boxes depict the median (horizontal line) within quartiles 1–3 (bounds of box). Whiskers extend to minimum and maximum values. Source data are provided as a Source Data file.

Despite altered cardiorespiratory function, there were no clinically meaningful differences in dietary energy intake (Supplementary Data S 11 ) or total body energy use, sleeping energy use, or respiratory quotient between the groups before or after CPET testing (Supplementary Fig. S9A–D ; Supplementary Data S 12 ).

Increased cognitive symptoms but normal neurocognitive testing in PI-ME/CFS

PI-ME/CFS participants had more self-reported total cognitive complaints (66.9 ± 14.7 versus 92.1 ± 17.6, p = 0.00006) and in all five cognitive domains measured: attention, verbal memory, visuoperceptual, language, and visual memory (Fig. 1c ). In contrast, there were no group differences in performance of any of the 15 neuropsychological tests administered (Supplementary Data S 13 ) or differential degradation of performance over time (Supplementary Information, Cognitive Function). No correlations were noted between any of the 15 neuropsychological tests administered and effort preference.

Differential cerebrospinal fluid catechols and metabolite profile in PI-ME/CFS

In cerebrospinal fluid, the PI-ME/CFS group had statistically significant decreased levels of DOPA, DOPAC, and DHPG (Fig. 6a–c ). Levels of norepinephrine, cys-DOPA, and dopamine did not differ between the groups. These results did not change after excluding data from participants taking central-acting medications.

a – c Box plot of the indicated neurotransmitters on y axis in HV (blue; n = 21 independent participants) and PI-ME/CFS (red; n = 16 independent participants) in ( a ) unadjusted two-sided Mann–Whitney U test ( p = 0.021), ( b ), unadjusted two-sided Mann–Whitney U test ( p = 0.025), and ( c ) unadjusted two-sided t-test for independent samples with equal variance ( p = 0.006). For box plots a – c boxes depict the median (horizontal line) within quartiles 1–3 (bounds of box). Whiskers extend to minimum and maximum values. Correlation between cerebrospinal fluid norepinephrine (NE) and ( d ) time to failure on grip strength task in HV (blue; n = 18 independent participants) and PI-ME/CFS (red; n = 15 independent participants) or ( e ) proportion of hard task choices (i.e., effort preference) in HVs (blue; n = 14 independent participants) and PI-ME/CFS (red; n = 14 independent participants). f Correlation between cerebrospinal fluid dopamine and time to failure in HVs (blue; n = 17 independent participants) and PI-ME/CFS (red; n = 14 independent participants). g Correlation between cerebrospinal fluid DHPG and proportion of hard task choices (i.e., effort preference) in HVs (blue; n = 14 independent participants) and PI-ME/CFS (red; n = 14 independent participants). For figures d – g , the relationship between indicated variables in x and y axis were fitted by linear regression in each group. The exact p value of each regression is presented on the graph, linear regression t-test for nonzero slope. h PCA computed from all metabolites measured from the cerebrospinal fluid samples in the indicated groups. i Heatmap of statistically significant (false discovery rate adjusted p -value < 0.05) differentially expressed metabolites in the indicated groups on x axis and the metabolites labeled on y axis. Red: upregulated; Blue: downregulated. Supervised clustering of metabolites measured from the cerebrospinal fluid samples in ( j ) male cohorts and ( k ) female cohorts from PLSDA analysis. DHPG (S)−3,5-Dihydroxyphenylglycine, PLSDA Partial least square discriminant analysis, PC principal component. Source data are provided as a Source Data file.

Additional analysis revealed that norepinephrine correlated with both time to failure and effort preference in PI-ME/CFS participants (Fig. 6d, e ). Dopamine correlated with time to failure (Fig. 6f ) but not with effort preference (Supplementary Fig. S11B ). Among HVs, DHPG correlated with effort preference (Fig. 6g ) but not with time to failure (Supplementary Fig. S11C ). Several cognitive symptoms correlated with catechols in PI-ME/CFS participants (Supplementary Fig. S12A–I ). In contradistinction, objective neurocognitive performance showed that only the Wisconsin Card Sort Test correlated with DOPA in PI-ME/CFS participants (Supplementary Fig. S12J ). For HVs, several correlations between catechols and neuropsychological tests were observed (Supplementary Fig. S12K–O ). These correlations suggest that perception and behavior, but not cognitive performance, are related to catechol levels in PI-ME/CFS participants.

Metabolomic analysis of cerebrospinal fluid also showed group differences (Fig. 6h ). Tryptophan metabolites were among the top 15 differentially expressed and statistically significant after correction for multiple comparisons (Fig. 6i ). Decreased glutamate, dopamine 3-O-sulfate, butyrate, polyamine, and tricarboxylic acid (TCA) pathway metabolites were noted in PI-ME/CFS participants (Supplementary Data S 14A ). Threonine and glutamine were decreased in males (Fig. 6j , Supplementary Fig. S 13D , Supplementary Data S 14C ). Several tryptophan metabolites were decreased in females suggesting a decrease in serotonin signaling (Fig. 6k , Supplementary Fig. S 13E , Supplementary Data S 14D ). This was irrespective of NSRI/SSRI use.

Immune activation and sex-based differences in PI-ME/CFS

There were no group differences in percentage of CD4, CD8, and CD19 cells in both peripheral blood and cerebrospinal fluid (Supplementary Data S 15 A and Supplementary Fig. S 15C ). An increase in percentage of naïve and decrease in switched memory B-cells in blood were observed in PI-ME/CFS participants (Fig. 7a, b ). Two PI-ME/CFS participants had detectable antibody secreting B-cells in cerebrospinal fluid; these participants did not have oligoclonal bands or autoantibodies. There was no group difference in NK cell frequency, but a subset of PI-ME/CFS participants had an elevated percentage of NK CD56 bright/dim ratio in cerebrospinal fluid. No group differences were noted in NK lytic units, CD16/CD56 ratio, and plasma GDF-15 levels in blood. Discovery assays for a small panel of autoantibodies detected low levels in one PI-ME/CFS and two HVs.

a Boxplot of the B cell subset naïve (%) in HV (blue; n = 20 independent participants) and PI-ME/CFS (red; n = 17 independent participants) groups, unadjusted two-sided t-test for independent samples with equal variance ( p = 0.037). b Boxplot of B cell switched memory in HV (blue; n = 20 independent participants) and PI-ME/CFS (red; n = 16 independent participants) groups, unadjusted two-sided t-test for independent samples with equal variance ( p = 0.008). c Boxplot of the CD8 + T cell subset PD-1 (%), Mann–Whitney U test, exact p -value = 0.033. d Boxplot of the CD8 + T cell subset CD226 (%), unadjusted two-sided t-test for independent samples with equal variance ( p = 0.055). The samples used for boxplot ( d ) were collected at a separate time point than the others boxplots; HV (blue; n = 7 independent participants) and PI-ME/CFS (red; n = 8 independent participants) groups. e Boxplot of CD8 + T cell CXCR5 (%), unadjusted two-sided t-test for independent samples with equal variance ( p = 0.014). f Boxplot of CD8 + T cell naïve (%), unadjusted two-sided t-test for independent samples with equal variance ( p = 0.016). Where indicated the plots shows the measurements from female and male cohorts. Measurements in PBMC samples are shown in shaded box and cerebrospinal fluid samples in open box. For box plots a – f boxes depict the median (horizontal line) within quartiles 1–3 (bounds of box). Whiskers extend to minimum and maximum values. Source data are provided as a Source Data file.

Markers of T-cell activation, PD-1 + CD8 T-cells, were elevated in the cerebrospinal fluid of PI-ME/CFS participants (Fig. 7c ). PD-1 + CD4 T-cells did not change in blood or cerebrospinal fluid. To expand on these observations, other T-cell markers (TIGIT, CD244, and CD226) were analyzed in a subset of participants. CD226 + CD8 T-cells were decreased in blood (Fig. 7d ) of PI-ME/CFS participants with no change in expression of CD244 or TIGIT (Supplementary Data S 15B , D).

When stratified by sex, PI-ME/CFS males had increased CXCR5 expression on CD8 + T-cells in cerebrospinal fluid (Fig. 7e ). PI-ME/CFS females had increased CD8+ naïve T-cells in blood (Fig. 7f ), indicating different subpopulation of immune cells are enriched in the male and female PI-ME/CFS groups.

Analysis of PBMC gene expression for all participants by principal component analysis (PCA) revealed no outliers (Fig. 8a ). 614 differentially expressed (DE) genes clustered the samples based on disease status. However, exploration of PCA plot showed that samples clustered based on their sex (Fig. 8b ). Assessment of interaction between sex and disease status was not significant. This suggests sex is a potential confounder impacting gene expression. Hence DE analysis in male and female cohorts was performed, which identified distinct subsets of DE genes, with only 34 (<5%) genes overlapping between them (Fig. 8c , Supplementary Data S 16A, B ). PCA of DE genes in each sex cohort clustered the samples into distinct HV and PI-ME/CFS groups (Fig. 8 d, f).

a , b PCA computed from all gene expression values with indicated groups: HV (blue), PI-ME/CFS (red) clusters or males (turquoise) and females (orange) highlighted for the indicated PCs. c Venn diagram showing common DE genes identified from male and female cohorts ( p value < 0.05 as filter for DE genes using an unadjusted two-sided moderated t-test). DE analysis on the ( d , e , h , i ) male cohorts and ( f , g , j , k ) female cohorts. d , f PCA plots computed from DE genes shows robust clustering of samples based on the PI-ME/CFS status. e , g Volcano plots shows log transformed statistically significant (unadjusted p -values < 0.05 using an unadjusted two-sided moderated t-test) up (red) and down regulated (blue) genes in PI-ME/CFS male and female cohorts, respectively. h , j Heatmaps of a subset of T cell process genes in males and B cell related processes in females. i , k Pathway enrichment plots showing top 15 pathways for which the DE genes from male and female cohorts selected for. The top 15 pathways are labeled on the y axis and the color of the circle is scaled with –log-10 p -value and the size of the pathway circles inside the plot are proportional to the number of genes that overlapped with the indicated pathway. Fisher’s exact test was used in the ‘clusterProfiler’ R package to obtain the log-transformed p-values. Red color nodes: upregulated in PI-ME/CFS and blue color nodes: downregulated in PI-ME/CFS. DE differentially expressed. Source data are provided as a Source Data file.

887 DE genes in male HV and PI-ME/CFS cohorts enriched ( p < 0.05) in ubiquitin, IL-10, T-cell, and NF-kB pathways (Fig. 8h, i ). DE genes related to the STAT4-TLR9 protein-protein interactome and were upregulated in male PI-ME/CFS participants (Supplementary Fig. S14A ). 849 DE genes identified in female HV and PI-ME/CFS cohorts were enriched in B-cell proliferation processes (Fig. 8j, k ) and DE genes identified in the B-cell interactome were upregulated in female PI-ME/CFS participants (Supplementary Fig. S14B ). Additionally, DE genes were also enriched in cytokine and lymphocyte proliferation processes. These data are consistent with expansion of naïve B-cells by the STAT4-TLR9 and other B-cell pathways observed in PI-ME/CFS by flow cytometry.

PCA analysis of aptamers measured in serum and cerebrospinal fluid did not identify outliers. Univariate analysis of the aptamer datasets did not identify any FDR-corrected statistically significant differential features between PI-ME/CFS and HV participants (Supplementary Data S 17A, B ). Within each sex separately, exploratory multivariate analyses suggested a subset of aptamers were predictive of PI-ME/CFS status, which are reported for potential validation (Supplementary Fig. S15A–D , S16A–D ; Supplementary Data S 17C–F ).

Sex differences in PI-ME/CFS were also assessed in a publicly deposited ME/CFS RNASeq dataset (GSE130353) from monocytes (Supplementary Fig. S17A–D ). Of 638 DE genes in males and 420 DE genes in females, only 23 genes were common (Supplementary Fig. S17E ). These observations support that distinct biological processes are perturbed in male and female PI-ME/CFS patients.

Sex-based differences in muscle gene expression profile in PI-ME/CFS

PCA analysis of muscle gene expression did not identify any outliers (Fig. 9a ). Univariate analysis did not identify any FDR-corrected statistically significant differential features (Supplementary Data S 19A ). The PCA identified clustering based on sex (Fig. 9b ). Only 15 DE genes were common in male and female cohorts (Fig. 9c ), consistent with prior observations. In the male PCA of 593 DE genes, clustering was observed based on the disease status (Fig. 9d, e ). Genes upregulated in the PI-ME/CFS males were enriched in epigenetic changes and processing of RNA and the downregulated genes were enriched in hexose metabolism and mitochondrial processes (Fig. 9h–i ). Several genes involved in fatty acid beta-oxidation were upregulated, in an interactive network (Supplementary Fig. S18A ). In the females, PCA of the 328 DE genes showed distinct clusters of HV and PI-ME/CFS samples (Fig. 9f, g ). DE genes upregulated in the PI-ME/CFS females were enriched in growth hormone receptor signaling and ubiquitin transferase function (Fig. 9j ). Downregulated genes were involved in fatty acid oxidation and mitochondrial processes (Fig. 9k ). A subset of these downregulated fatty acid oxidation genes was highly inter-connected (Supplementary Fig. S18B ). Skeletal fatty acid oxidation is regulated during exercise as fatty acid uptake, is increased after moderate exercise 24 , 25 , and is downregulated in muscle deconditioning 26 . These results suggest sex differential in the muscular bioenergetics and muscular deconditioning of PI-ME/CFS participants 27 .

a , b PCA computed from all gene expression values in samples highlighted from the indicated groups: HV (blue) and PI-ME/CFS (red) or males (turquoise) and females (orange) for the indicated PCs. c Venn diagram showing common DE genes identified from male and female cohorts (DE genes are genes with p value < 0.05 using an unadjusted two-sided moderated t-test). DE analysis using an unadjusted two-sided moderated t-test on the ( d , e , h , i ) male cohorts and ( f , g , j , k ) female cohorts. d , f PCA plot computed from DE genes shows robust clustering of samples based on the PI-ME/CFS status. e , g Volcano plots shows log transformed statistically significant (unadjusted p -values < 0.05 using an unadjusted two-sided moderated t-test) up (red) and down regulated (blue) genes in PI-ME/CFS male and female cohorts, respectively. Pathway enrichment plot of DE genes ( h ) upregulated and ( i ) downregulated in PI-ME/CFS male cohort. Pathway enrichment plot of DE genes ( j ) upregulated and ( k ) downregulated in PI-ME/CFS female cohorts. The top 15 pathways are labeled on the y axis and the color of the circle is scaled with –log-10 p -value and the size of the pathway circles inside the plot are proportional to the number of genes that overlapped with the indicated pathway. Fisher’s exact test was used in the ‘clusterProfiler’ R package to obtain the log-transformed p -values. Red color nodes: upregulated in PI-ME/CFS and blue color nodes: downregulated in PI-ME/CFS. DE differentially expressed, PC principal component. Source data are provided as a Source Data file.

Lack of differences in lipidomics between PI-ME/CFS and healthy volunteers

Univariate analysis of the plasma lipidomic data did not identify statistically significant differences between HV and PI-ME/CFS groups (Supplementary Data S 20 ). Multivariate analysis in all participants as well as in male and female cohorts separately identified several lipids as important variables in prediction (Supplementary Fig. S19A–C ) and are consistent with a prior lipidomic analysis 28 .

Differential fecal microbiota in PI-ME/CFS by shotgun metagenomics

Differences in the alpha diversity of stool samples (Supplementary Fig. S20A, B, D ) were noted with HVs having a greater number of taxa within the samples (Number of Observed Features, p = 0.002) but no differences in average proportional abundance (Inverse Simpson Index, p = 0.79). Beta diversity, as measured by Bray–Curtis dissimilarity (Supplementary Fig. S20C ), demonstrated significant differences in microbial community composition between the groups (PERMANOVA p < 0.0081).

Validation of diagnosis and course of illness on longitudinal follow-up

Within four years of participation, four of the 17 PI-ME/CFS participants had a spontaneous full recovery. No other new medical diagnoses were reported by the PI-ME/CFS participants.

This study obtained a more extensive set of biological measurements in people with PI-ME/CFS than any previous study. Although the number of study subjects was small compared to the prior literature, it identified biological alterations and confirmed some previously reported biological alterations.

All patients had documentation of good health followed by an episode of infection that led to ME/CFS symptoms. All PI-ME/CFS participants reported clinically substantial fatigue, physical symptoms, and decreased functional status that was 1.5 to two standard deviations worse than the general population. Cases were reviewed by a panel of adjudicators that had to unanimously agree on the case validity to be included in this cohort. Only 10% of those with completed reviews were adjudicated as PI-ME/CFS cases. Even in the cases that met study criteria, diagnostic misattribution was noted in 20%, with underlying causes becoming manifest over time. This misclassification bias has important ramifications on the interpretation of the existing ME/CFS research literature 8 .

Another challenge to the characterization of ME/CFS is concerns about the validity of the manifestations due to depression or anxiety 29 . Our cohort underwent substantial performance validity testing using neuropsychological measures and demonstrated consistency of response, suggesting that their symptoms were reliable and a true representation of their disease. Even though PI-ME/CFS participants endorsed more depressive and anxiety symptoms than HVs, they did not meet psychiatric diagnostic criteria. There was also no difference in psychiatric history or reporting of traumatic events between the two groups. Thus, psychiatric disorders were not a major feature in this cohort and did not account for the severity of their symptoms.

We first determined the physiological basis of fatigue in PI-ME/CFS participants. The notion of fatigue, as we use it, is a limit on ability or a diminution of ability to perform a task. Effort preference is how much effort a person subjectively wants to exert. It is often seen as a trade-off between the energy needed to do a task versus the reward for having tried to do it successfully. If there is developing fatigue, the effort will have to increase, and the effort:benefit ratio will increase, perhaps to the point where a person will prefer to lose a reward than to exert effort. Thus, as fatigue develops, failure can occur because of depletion of capacity or an unfavorable preference.

There were no differences in ventilatory function, muscle oxygenation, mechanical efficiency, resting energy expenditure, basal mitochondrial function of immune cells 30 , muscle fiber composition, or body composition supporting the absence of a resting low-energy state. However, substantial differences were noted in PI-ME/CFS participants during physical tasks. Compared to HVs, PI-ME/CFS participants failed to maintain a moderate grip force even though there was no difference in maximum grip strength or arm muscle mass. This difference in performance correlated with decreased activity of the right temporal-parietal junction, a part of the brain that is focused on determining mismatch between willed action and resultant movement 31 . Mismatch relates to the degree of agency, i.e., the sense of control of the movement 32 . Greater activation in the HVs suggests that they are attending in detail to their slight failures, while the PI-ME/CFS participants are accomplishing what they are intending. This was further validated by measures of peripheral muscular fatigue and motor cortex fatigue 20 , 33 that increased only in the HVs. Thus, the fatigue of PI-ME/CFS participants is due to dysfunction of integrative brain regions that drive the motor cortex, the cause of which needs to be further explored. This is an observation not previously described in this population.

Additionally, the results suggest the impact of effort preference, operationalized by the decision to choose a harder task when decision-making is unsupervised and reward values are held constant, on performance. 32–74% of the variance in time to grip failure for the PI-ME/CFS participants correlated with effort preference, which was not seen in HVs. This was accompanied by reduced brain activation in the right temporal-parietal area in PI-ME/CFS participants. Interviews with PI-ME/CFS participants revealed that sustained effort led to post-exertional malaise 34 . Conscious and unconscious behavioral alterations to pace and avoid discomfort may underlie the differential performance observed 35 .

We measured peripheral fatigue (high:low ratio) and central fatigue (post exercise depression). Both types of fatigue were seen in the HVs but not in the PI-ME/CFS participants. Moreover, testing of effort preference and the participants’ own words (Supplementary Information, p.10) are consistent with this finding. Together these findings suggest that effort preference, not fatigue, is the defining motor behavior of this illness.

Consistent with this observation, with strong encouragement during CPET, all but one of the PI-ME/CFS participants reached a respiratory exchange ratio of 1.1. The PI-ME/CFS group showed a clear reduction in cardiorespiratory fitness by reaching the AT at a lower level of work and ventilation 36 . At maximal performance, a substantial group difference in cardiorespiratory capacity became apparent, which was related to both chronotropic incompetence and physical deconditioning. These observations provide clarity to previous studies with inconsistent results 37 . Thus, incorporation of measures of autonomic tone and physical condition into studies of exercise performance is critical to understand the mechanisms of diminished cardiorespiratory function in ME/CFS 38 .

A frequent complaint in our PI-ME/CFS cohort was cognitive dysfunction. This did not correlate with anxiety or depression measures. Standard clinical laboratory tests, brain imaging, measures of brain injury, and sleep architecture were unremarkable. Neuropsychological testing showed that even though the HV and PI-ME/CFS participants started with different levels of perceived mental and physical fatigue, there were no differences in cognitive performance. Further, both cognitive performance and perceived fatigue changed at the same rate during testing in both groups. This is consistent with the absence of a homogenous cognitive deficit in ME/CFS. Previous studies suggest a small, heterogenous deficit in this population 39 which may not be evident in our study due to the small sample size. Performance validity testing is not routinely performed in the literature; inclusion of invalid performances would also bias studies toward differential performance.

Taken together, this evidence suggests that physical and cognitive fatigue may be mechanistically different. Interestingly, PI-ME/CFS participants’ catechol levels in cerebrospinal fluid correlated with grip strength and effort preference, and several metabolites of the dopamine pathway correlated with several cognitive symptoms. This suggests that central nervous system catechol pathways are dysregulated in PI-ME/CFS and may play a role in effort preference and cognitive complaints 40 . The pattern suggests decreased central catecholamine biosynthesis in PI-ME/CFS. Similarly, decreased serum catechols and their metabolites have recently been reported in Long COVID-19; 41 , 42 testing of cerebrospinal fluid has not yet been reported. Autonomic testing revealed HRV measures consistent with an increase in sympathetic and a decrease in parasympathetic activity in PI-ME/CFS, and a decreased baroslope and prolonged pressure recovery after Valsalva consistent with previous observations of autonomic dysfunction in ME/CFS 6 , 43 .

We investigated several additional biological functions in PI-ME/CFS. Previous studies have implicated abnormalities in immunity, mitochondrial function, reduction-oxidation regulation, or altered microbiome structure in this condition 1 , 3 , 44 , 45 . There were increased naïve B-cells and decreased switched memory B-cells in blood of PI-ME/CFS participants. However, contrary to prior published work 46 , there was no consistent pattern of autoimmunity across all PI-ME/CFS participants and autoantibody discovery assays did not reveal previously undescribed antibodies. PD-1, a marker of T-cell exhaustion and activation, was elevated in the cerebrospinal fluid of PI-ME/CFS participants. Although NK cell function was not different between groups in blood, they showed decreased expression of a cytolytic function marker in the spinal fluid. Previous studies suggest that NK cell function is decreased in ME/CFS 47 , 48 , 49 , 50 , 51 , which may not be evident in our study due to the small sample size.

Autonomic measures revealed an increase in sympathetic and a decrease parasympathetic activity in PI-ME/CFS that cannot be attributed to depression or anxiety, which is consistent with previous observations 6 , 43 . There was also distinct gene expression, immune cell populations, metabolite, and protein profiles in male and female PI-ME/CFS participants. In the male PI-ME/CFS cohort, PBMC gene expression profiling showed perturbations in the T-cell activation, proteasome and NF-kB pathways. Analyses of serum and cerebrospinal fluid proteins identified several molecules related to innate immunity suggesting a distinct expression pattern in male cohorts. In contrast, in the female cohorts, gene expression profiling of PBMCs identified perturbations in B-cell and leukocyte proliferation processes with a corresponding identification of plasma lymphotoxin α1β2, which may act as a proliferative signal in secondary lymphoid tissues. Additionally, elevated levels of plasminogen in serum and eosinophil protein galactin-10, the C-C motif chemokine (MDC), and the IL 18 receptor accessory protein were present in cerebrospinal fluid of female PI-ME/CFS participants. Plasminogen and lymphotoxin α1β2 are known to activate proinflammatory states via NF-kB 52 , 53 . Due to the small sample size of our study, we further confirmed sex differences in a previously published data set 54 . Notably, only 2% of the differentially expressed elements overlapped between the sex-separated cohort. This suggests that different immunological phenotypes may distinguish the PI-ME/CFS phenotype based on sex. The cause of immune dysregulation is not clear but may suggest the possibility of persistent antigenic stimulation 55 . Analysis of the gut microbiome showed differences in alpha and beta diversity consistent with previous studies 3 , 56 . Their potential role in modulating the immune profile needs further exploration.

Sex-driven group differences were also observed in gene expression profiles of muscle. Male PI-ME/CFS participants had upregulation of fatty acid beta-oxidation genes and down regulation of TRAF and MAP-kinase regulated genes. The female PI-ME/CFS participants had downregulation of fatty acid metabolism and mitochondrial processes in muscle. Thus, both males and females PI-ME/CFS samples had increased oxidative stress, but distinct pathways were perturbed in both groups.

Metabolomics of cerebrospinal fluid identified downregulation of tryptophan metabolites in the PI-ME/CFS cohort, consistent with prior ME/CFS and Long COVID-19 studies 41 , 57 , 58 , 59 . In female cohorts, tryptophan, butyrate, and tricarboxylic acid related compounds were identified as important variables in classifying HV and PI-ME/CFS groups. In male cohorts, a different subset of metabolites was identified as important classification predictors. These findings need to be validated in larger PI-ME/CFS cohorts.

This study has several limitations. This was a cross-sectional, exploratory case-control study. These data assess correlation, not causality. The sample size of the cohort was small but was very carefully characterized and was adequate for capturing group differences, noting important negative findings, and looking for patterns of consilience between measures. Consilience was noted among the multiple autonomic measures suggestive of decreased parasympathetic activity in PI-ME/CFS, among flow cytometry and RNA sequencing measures of adaptive immunity, and in the impact of sexual dimorphism across immunologic and metabolomic measurements. The small sample size may impact the precision of effect size measures, with a propensity to inflate estimates 60 . Post-hoc calculations of the effect size for a phenotyping sample of 21 and 17 participants to achieve a power of 80% is 0.94, suggesting only large effects will be noted to be statistically significant. Overall, the groups are well-matched on demographic factors, but individual HV and PI-ME/CFS participants were not always perfectly matched. Even though a major effort was made to minimize misattribution, we cannot completely exclude this possibility.

While we have focused on the positive results above, there were many results that were not different between HV and PI-ME/CFS participants (Supplementary Data S 22 ). While it is possible that small differences in the groups may be demonstrable in larger cohorts, these negative results demonstrate that these findings are not required for the PI-ME/CFS phenotype and are poor targets for clinical intervention.

Considering all the data together, PI-ME/CFS appears to be a centrally mediated disorder. We posit this hypothetical mechanism of how an infection can create a cascade of physiological alterations that lead to the PI-ME/CFS phenotype (Fig. 10 ). Exposure to an infection leads to concomitant immune dysfunction and changes in microbial composition. Immune dysfunction may be related to both innate and adaptive immune responses that are sex dependent. One possibility is that these changes are related to antigen persistence of the infectious pathogen 61 , 62 , 63 .

Diagram illustrates potential mechanisms and a cascade of events that lead to the development of ME/CFS after an infection. Exposure to an infection leads to concomitant and persistent immune dysfunction and changes in gut microbiome. Immune dysfunction affects both innate and adaptive immune systems that are sex dependent. We hypothesize that these changes are driven by antigen persistence of the infectious pathogen. These immune and microbial alterations impact the brain, leading to decreased concentrations of metabolites which impacts brain function. The catecholamine nuclei release lower levels of catechols, which impacts the autonomic nervous system and manifests with decreased heart rate variability and decreased baroreflex cardiovascular function, with downstream effects on cardiopulmonary capacity. Altered hypothalamic function leads to decreased activation of the temporoparietal junction during motor tasks, suggesting a failure of the integrative brain regions necessary to drive the motor cortex. This decreased brain activity is experienced as physical and psychological symptoms and impacts effort preferences, leading to decreased engagement of the motor system and decreases in maintaining force output during motor tasks. Both the autonomic and central motor dysfunction result in a reduction in physical activity. With time, the reduction in physical activity leads to muscular and cardiovascular deconditioning, and functional disability. All these features make up the PI-ME/CFS phenotype.

These immune and microbial alterations impact the central nervous system, leading to decreased concentrations of metabolites, including glutamate, tryptophan, spermidine, citrate, and the metabolites of dopamine (DOPAC) and norepinephrine (DHPG). The altered biochemical milieu impacts the function of brain structures. The catecholamine nuclei release lower levels of catechols, which impacts the autonomic nervous system leading to decreased heart rate variability and decreased baroreflex cardiovascular function, with downstream effects on cardiopulmonary capacity. Concomitant alteration of hypothalamic function leads to decreased activation of the temporoparietal junction during motor tasks, leading to a failure of the integrative brain regions necessary to drive the motor cortex. This decreased brain activity is experienced as physical and psychological symptoms and impacts effort preferences, leading to decreased engagement of the motor system and decreases in maintaining force output during motor tasks. Both the autonomic and central motor dysfunction result in a reduction in physical activity. With time, the reduction in physical activity leads to muscular and cardiovascular deconditioning, and functional disability. These features make up the PI-ME/CFS phenotype.

This model suggests places for potential therapeutic intervention and explains why other therapies have failed. The finding of possible immune exhaustion suggests that immune checkpoint inhibitors may be therapeutic by promoting clearance of foreign antigen. Immune dysfunction leads to neurochemical alterations that impact neuronal circuits, which may be another point of intervention. Therapeutically targeting downstream mechanisms, with exercise, cognitive behavioral therapy, or autonomic directed therapies, may have limited impact on symptom burden, as it would not address the root cause of PI-ME/CFS. However, combination therapy affecting multiple pathways could be considered. The finding of substantial physiological differences related to sex suggest that there may not be a single unified mechanism that leads to PI-ME/CFS and that successful therapy may require a personalized medicine approach.

In conclusion, PI-ME/CFS is a distinct entity characterized by somatic and cognitive complaints that are centrally mediated. Fatigue is defined by effort preferences and central autonomic dysfunction. There are distinct sex signatures of immune and metabolic dysregulation which suggest persistent antigenic stimulation. Physical deconditioning over time is an important consequence. These findings identify potential therapeutic targets for PI-ME/CFS.

Ethics statement

All research procedures were approved by the NIH Central IRB (NCT 02669212) and performed in accordance with the Declaration of Helsinki. Informed consent was obtained from all study participants.

Recruitment and screening

The PI-ME/CFS group was selected based on medical record documentation of persistent and severe fatigue and post-exertional malaise as the consequence of an acute infection within the last five years without a prior history of explanatory medical or psychiatric illness. The full inclusion/exclusion criteria can be found in Supplementary Data S1 .

Potential ME/CFS participants initially completed a telephone screening interview. Those passing the initial screen were contacted for a physician telephone interview and medical record review. This review process was iterative, completing when adequate documentation of infection and ME/CFS was provided, when exclusionary documentation was noted, or when all available medical records were exhausted. Only participants with adequate documentation were invited to NIH for a phenotyping visit for case ascertainment and to collect research measures.

The healthy volunteer (HV) group consisted of demographically matched persons without clinical fatigue and free from medical disease. HVs were recruited from referrals from the NIH Office of Patient Recruitment and responses to study advertisements. HVs were admitted to the NIH Clinical Center for a phenotyping visit to rule out occult illness and to collect research measures. HVs without occult illness were invited to return for an additional exercise stress visit.

Demographic information, including age, birth sex, and gender, for all participants can be found in the Source Data file for Fig. 1b . Birth sex and gender information were collected by self-report during an intake interview by the study physician (BW). ME/CFS is reported to occur three times more frequently in females and efforts were made in recruitment to obtain a balanced birth sex distribution. All participants were compensated consistent with National Institutes of Health guidelines.

Participants who potentially qualified based on screening were admitted to the NIH Clinical Center for a week-long case ascertainment visit. A detailed history and physical was performed on each participant in duplicate. An internal medicine nurse practitioner and a rheumatologist performed separate evaluations and consulted together after both were completed. A complete standardized neurological evaluation was performed by a board-certified neurologist. A psychiatric evaluation was performed by a licensed psychologist. Medical consultants were engaged to evaluate participants when appropriate. Laboratory and imaging studies were performed to investigate potential health issues noted during these medical evaluations. Clinical laboratory testing of blood and cerebrospinal fluid, brain magnetic resonance imaging, polysomnography, and an orthostatic challenge were also used in case ascertainment and are described in more detail below.

We excluded participants taking systemic immunomodulatory drugs. All participants were on stable medication dosages throughout the study. Medications that would interfere with study measurements were tapered off for a minimum of three half-lives prior to collection.

Case adjudication

Clinical information from the visit was compiled and reviewed by a Case Adjudication panel. Adjudicators were all recognized clinical experts in ME/CFS (Lucinda Bateman, Andy Kogelnik, Anthony Komaroff, Benjamin Natelson, Daniel Peterson). Each adjudicator performed their own independent review to assign both ME/CFS case status and temporality of ME/CFS onset to an infection. When discrepancies arose between adjudicators, a case adjudication meeting was convened. Adjudicators had to unanimously agree that a participant developed ME/CFS after a documented infection for a case to be considered adjudicated and included in the analyses. Positively adjudicated participants were also invited to return for an additional 10-day long exercise stress visit.

To be considered an adjudicated case, participants were required to be unanimously considered to be a case of PI-ME/CFS by the protocol’s adjudication committee, meet at least one of three ME/CFS criteria (1994 Fukuda Criteria 64 , 2003 Canadian Consensus Criteria for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome 65 , or the Institute of Medicine Diagnostic Criteria 66 ), have moderate to severe clinical symptom severity as determined by having a Multidimensional Fatigue Inventory (MFI) score of ≥13 on the general fatigue subscale or ≥10 on the reduced activity subscale, and functional impairment as determined by having a Short-Form 36 (SF-36) score of ≤70 on physical function subscale, or ≤50 on role physical subscale, or ≤75 on social function subscale.

Life narratives and qualitative interviews

Detailed life narratives and qualitative interviews were conducted to understand the lived experience of PI-ME/CFS, the context of the life it occurred in, and to capture the point-in-time experience of post-exertional malaise. Participants had a life narrative collected by a study investigator, an interview about the impact of PI-ME/CFS by an occupational therapist, and up to 10 brief semi-structured qualitative interviews that were developed based on data collected from a preparatory focus group study 34 . Evocative quotes from the transcripts were selected by the investigators to give a patient voice to match the findings reported.

Performance validity testing

Symptom validity tests were administered to determine if subjective reporting was consistent within individuals, to identify atypical patterns of responding, and to relate these measures to population norms. Performance validity tests were administered to determine whether neuropsychological test performances completed during the study were responded to validly, that is, they reflect neurocognitive functioning and are not unduly impacted by non-cognitive factors, such as poor engagement in testing. These tests include: Minnesota Multiphasic Personality Inventory – 2 Restructured Form (MPI2-RF): examinees have to complete true-false items that best describes themselves and was scored using various empirically-derived validity indices; 67 B Test: examinees have to rapidly discriminate between letter stimuli; 68 Dot Counting Test: examinees have to count dots as rapidly as possible; 69 Word Memory Test: a task where examinees have to view words and later remember them 70 .

Patient reported outcomes measures

The following symptom and health questionnaires were administered.

Short-Form 36 (SF-36): a standard measure of health-related quality of life outcomes that has been tested and validated extensively in a number of clinical populations; 71 CDC Symptom Inventory (CDC-SI): collects information on occurrence, frequency, and intensity of symptoms common in ME/CFS and other fatiguing illnesses; 72 Multidimensional Fatigue Inventory (MFI): a validated 20-item self-report instrument designed to measure fatigue severity; 73 Patient Reported Outcomes Measurement Information System—Short Forms (PROMIS-SF): a system of highly reliable, precise measures of patient–reported health status for physical, mental, and social wellbeing. PROMIS forms administered included Fatigue, Pain Behavior, Pain Interference, Pain Intensity, Global Health, Emotional Distress—Anxiety, Emotional Distress- Depression, Sleep-Related Impairment and Sleep Disturbances; 74 The McGill Pain Questionnaire (MPQ): a list of 20 groups of adjectives to describe sensory, affective and evaluative aspects of pain; 75 The Neuropathic Pain Scale (NPS): a questionnaire designed to assess the quality and the intensity of the neuropathic sensations; 76 Polysymptomatic Distress Scale: a self-administered instrument that determines both the distribution of painful areas across the body and an estimate of related symptom burden that can be used to define fibromyalgia; 77 Patient Health Questionnaire-15 (PHQ-15): a validated questionnaire used to assess somatic symptom severity and the potential presence of somatization and somatoform disorders; 78 Pittsburgh Sleep Quality Index (PSQI): a measure of sleep quality over a 1-month period; 79 Fatigue Catastrophizing Scale: a measure of catastrophizing related to the fatigue experience; 80 The Multiple Ability Self-Report Questionnaire (MASQ): a questionnaire that assesses the subjective appraisal of cognitive difficulties in five cognitive domains: language, visual-perceptual ability, verbal memory, visual-spatial memory, and attention/concentration; 81 and Belief about Emotions scale: A validated questionnaire designed to measure the beliefs regarding expressing negative thoughts and feelings 82 .

Brain magnetic resonance imaging

MRI was obtained on a 3.0 tesla Philips Achieva device. Sequences performed precontrast were 2D axial proton density-and T2-weighted, 15 direction diffusion tensor imaging (DTI) with b = 1000 DTI. 3D sagittal T1 magnetization prepared rapid acquisition gradient echo (MPRAGE), and T2 weighted fluid attenuated inversion recovery (FLAIR) with approximately 1 mm isotropic resolution. Gadolinium based contrast agent was injected slowly over approximately one minute while high resolution 0.55 isotropic susceptibility weighted imaging was obtained. Following this post contrast images were obtained using 3D sagittal T1 fast field echo (FFE) and T2 Weighted FLAIR techniques. DTI data was processed to generate diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) images. Scans were evaluated in duplicate. A neuroradiologist and a neurologist performed separate evaluations. Differences in interpretation were resolved through consultation.

Small fiber density measures

Two 3-mm excisional skin biopsies were collected from the distal thigh and distal leg. Samples were fixed, sectioned and immunostained for the panaxonal marker PGP9.5 by free-floating immunohistochemistry. Four skin sections from each biopsy were randomly selected, immunostained, and mounted on a single slide and epidermal nerve fibers were visualized with confocal microscopy. This method provides an accurate representation of the biopsy sample while avoiding sampling error 83 . A diagnosis of small fiber sensory neuropathy is given based upon a length-dependent loss of epidermal nerve fibers.

Measures of brain injury

Plasma and cerebrospinal fluid were analyzed for markers of brain injury by immunoassay using digital array technology, which uses a single molecule enzyme-linked immunoarray (Simoa) method 84 . The Neurology 4-Plex A platform for Nf-L, Tau, GFAP, and UCH-L1 was used. In brief, paramagnetic capture beads coated with each relevant antibody, and a biotinylated detector for each relevant antibody are combined. Antibody coated paramagnetic capture beads and labeled biotinylated detector antibody bind to the relevant molecules present in the sample. Following a washing step, a conjugate of streptavidin-beta-galactosidase (SBG) is mixed with the capture beads. The captured molecules become enzymatically labeled when the SBG binds to the biotinylated detector antibodies. A second wash is performed, and the capture beads are resuspended in a resorufin beta-D-galactopyranoside (RGP) substrate solution. This suspension is transferred to the Simoa Disc. Individual paramagnetic capture beads settle into 216,000 femtoliter-sized microwells designed to hold no more than one bead per well. The beads are sealed into the microwells while excess beads are sealed into the microwells while excess beads are washed away with a synthetic fluorinated polymer sealing oil. If the measured molecule is present in the sample and subsequently captured and labeled, the beta-galactosidase hydrolyzes the RGP substrate and produces a fluorescent signal. This signal is detected and counted by the Simoa optical system. The concentrations of relevant molecules are interpolated from a standard curve.

Clinical laboratory measurements of blood and cerebrospinal fluid

The following panel of laboratory evaluations were performed on collected blood samples: acute care panel, mineral panel, hepatic panel, complete blood count with differential, prothrombin time, international normalized ratio, partial thromboplastin time, thyroid stimulating hormone, free thyroxine, triiodothyronine, iron, ferritin, transferrin saturation, fasting lipid panel, hemoglobin A1c, anti-nuclear antibody, Rheumatoid factor, anti-cyclic citrullinated antibody, anti-Smith antibody, anti-RNP, ssA, ssB, vitamin B12, 25(OH) vitamin D, 1,25(OH) 2 vitamin D, folate, creatine kinase, c-reactive protein, erythrocyte sedimentation rate, d-dimer, quantitative immunoglobulins, flow cytometry for lymphocyte subsets, human immunodeficiency virus by enzyme-linked immunosorbent assay, Epstein-Barr virus and Cytomegalovirus by polymerase chain reaction, Epstein-Barr antibodies, C6 peptide antibodies, hepatitis panel, rapid plasma regain, and tryptase level.

Heavy metal screening was performed on urine samples collected over 24-hours in a CLIA certified laboratory using inductively-coupled plasma/mass spectrometry (ICP/MS).

Cerebrospinal fluid samples were analyzed for cell counts, glucose, protein, and oligoclonal bands.

Cerebrospinal fluid ICP-MS (inductively coupled plasma-mass spectroscopy)

ICP-MS is an analytical technique by which concentrations of elements are determined up to as low as ppt (parts per trillion) levels in liquid, solid or gaseous samples. Elements are led through a plasma source where the atomic forms of elements become ionized. These ions are then detected according to their masses.

Total iron concentrations in the cerebrospinal fluid samples were measured by ICP-MS (Agilent model 7900). For each sample, 200 µL of concentrated trace-metal-grade nitric acid (Fisher) was added to 200 µL of sample taken in a 15 mL Falcon tube. Tubes were sealed with electrical tape to prevent evaporation, taken inside a 1 L glass beaker, and then placed at 90 °C oven. After overnight digestion, each sample was diluted to a total volume of 4 mL with deionized water, and then analyzed by ICP-MS.

Dietary evaluation

Participants completed the Diet History Questionnaire (DHQII), an internet-based survey that asks 134 questions regarding dietary intake over the past year and eight questions about dietary supplement intake. Participants kept seven-day food records, which were reviewed by nutrition staff and coded into Nutrition Data Systems for Research (NDSR) software to obtain nutrient intake data.

Medication reconciliation

Medication and supplement use were collected during the history and physical exam.

Sleep measurements

PI-ME/CFS participants each had a standard clinical polysomnogram to evaluate for obstructive sleep apnea, periodic limb movements, and sleep fragmentation.

Heart rate variability

Standard three-lead ambulatory ECG monitors were used for 24-h recordings. All data were downloaded on-site and reviewed by ECG telemetry nurses and by a pediatric cardiologist with clinical electrophysiology training. The recorded data was analyzed using Spacelabs Impresario (version 3.07.0158) program. Arrhythmia and non-normal beats were detected, coded appropriately and excluded from subsequent HRV analysis. Tracings were reviewed for electrical and mechanical noise artifact and these portions of tracings were similarly excluded from subsequent analysis. Recordings with less than 22 h of data were excluded.

Orthostatic challenge

Prior to the orthostatic challenge, baseline hemodynamic measures and a blood sample were collected. The participant was then tilted head-up at a 70-degree angle. The orthostatic challenge was continued for 40 min, with hemodynamic information collected in real time and blood samples collected at four minute intervals. The orthostatic challenge was ended if a participant developed hemodynamic instability or acute symptoms. On completion of the orthostatic challenge, the participant was returned to a supine position for 10 min, at which time final hemodynamic and blood measures were made.

Baroreflex function measurements

Participants were fitted with electrodes to measure cardiac signals and electrical impedance, a respiratory belt, a pulse oximeter, a finger-cuff for beat-to-beat blood pressure measurements, an automated blood pressure cuff, and a forearm plethysmograph transducer paired with a rapid-inflation brachial cuff for forearm blood flow measurements. Deep breathing measures were collected with the participant supine breathing deeply at a rate of five to six breaths per minute for three minutes. Three or more Valsalva maneuvers were then performed, during which the participants blow against resistance for 12 s at 30 mmHg and then relaxes. For participants where a square wave phenomenon was observed, the participant was tilted at 20 degrees head up and the procedure repeated.

Cerebrospinal catechol measurements

Cerebrospinal levels of catechols were assayed by batch alumina extraction followed by liquid chromatography with series electrochemical detection as reported previously 85 , 86 . In summary, to freshly thawed CSF in a plastic sample tube, approximately 5 mg of acid washed alumina, internal standard, and TRIS/EDTA buffer were added for alumina adsorption of the catechols. The tube was shaken vigorously using a paint can shaker for about 20 min. The tube was then spun in a microfuge, the alumina forming a pellet at the bottom of the tube. The supernatant was removed, and the alumina was washed twice. Then, after removal of the supernatant, 100 µL of an acidic eluting solution was added to the tube for desorbing the catechols from the alumina. The tube was shaken in a vortex mixer and then centrifuged. The supernatant was removed manually using a pipette and transferred to a microvial and placed in the carousel of the automated injector. For most samples 90 µL was injected onto the liquid chromatography column. The column eluate was passed through 3 electrodes in series, the first set at an oxidizing potential and the third set at a reducing potential. The electrochemical signal from the reducing electrode was recorded using proprietary software, peak heights of compounds with retention times of interest were measured, and concentrations of analytes in units of pg/mL were tabulated in a spreadsheet using a macro after adjustment for analytical recovery of the internal standard. For reporting purposes concentration in pmol/mL were used.

Psychiatric evaluation

The following psychological inventories were administered: Composite International Diagnostic Interview Trauma Section (CIDI-Trauma): a validated survey that characterizes a participant’s previous traumatic experiences; 87 Post-traumatic Stress Diagnostic Scale (PDS): a validated instrument for the epidemiologic diagnosis of Post-traumatic Stress Disorder; 88 Childhood Trauma Questionnaire Short Form (CTQ-SF): a validated instrument that characterizes potential traumatic life experiences in early childhood; 89 Sexual and Physical Abuse Questionnaire (SPAQ): a validated questionnaire that characterizes the type and age of occurrence of traumatic life experiences; 90 Beck Depression Inventory –II (BDI-II): a validated self-report inventory for measuring the severity of depression; 91 Beck Anxiety Inventory (BAI): a validated self-report inventory for measuring the severity of anxiety 92 Center for Epidemiologic Studies Depression Scale—Revised (CESD-R): A validated self-report inventory for screening for depression; Minnesota Multiphasic Personality Inventory – 2 Restructured Form (MMPI2-RF): examinees have to complete true-false items that best describe themselves; Structured Clinical Interview—DSM 5 (SCID-5): History of current and past psychiatric diagnosis was assessed with the Structured Clinical Interview for DSM-5, Research Version (SCID-5-RV).

Body composition measurements

Weight (Scale-Tronix 5702 digital balance, Carol Stream, IL, USA) and height (Seca 242 stadiometer, Hanover, MD, USA) were taken at fasted conditions. Body composition, including body fat mass, lean soft tissue mass, and fat percentage was measured by dual-energy X-ray absorptiometry (iDXA scanner with Encore 15.0 software; GE Healthcare, Madison, WI, USA).

Mitochondrial extracellular flux testing

Mitochondrial function was assessed in PBMC that were isolated and measured within three hours of being collected using an extracellular flux assay (Mito Stress Test, Agilent) 93 . In brief, PBMCs were isolated from 8 ml of blood. Samples were centrifuged at 1,750 × g for 30 min at room temperature. The cloudy layer was transferred to a 15 mL conical tube where 15 mL of PBS was added and inverted five times. The sample was then centrifuged at 300 × g for 15 min at 4 °C. After discarding the supernatant, the pellet was re-suspended by adding 10 mL PBS and inverting five times. The sample was then centrifuged at 300 × g for 10 min at 4 °C. After discarding the supernatant, the cells were re-suspended in 1 mL PBS and centrifuged at 610 × g for 10 min. After removing the supernatant, the pellet was re-suspended in complete RPMI-1640 supplemented with 10% FBS, 10 mM Penicillin/Streptomycin. Cell plates were coated with tissue adhesive solution after diluting the stock solution in 0.1 M sodium bicarbonate pH 8.0 solution. 100 µL of the diluted solution was added to each well and incubated for 20 min at room temperature, washed with deionized water and air dried. On the day of the experiment, fresh assay media was prepared by adding L-glutamine, pyruvate, and glucose to base media (the same constituents as Dulbecco’s Modified Eagle’s Medium (DMEM), but without any sodium bicarbonate, glucose, glutamine, or sodium pyruvate) to make assay media and warmed to 37 °C and adjusted to a pH of 7.4. PBMCs were plated into each well to reach 80–90% confluency. Plates were centrifuged at 200 × g for 2 min, then washed with assay media. 180 µL of assay media was then added to each well and incubated in a non-CO2 37 °C incubator for 60 min prior to performing the extraceullar flux assay according to manufacturer’s directions. Assay Results of the assay were normalized to amount of live cells in the cell preparation 93 .

ATP 9.4 characterization of muscle

Samples were collected from the vastus lateralis muscle and flash frozen. Frozen sections of muscle samples were prepared with cryostat, 10um thickness. Six slides of frozen sections were taken to Johns Hopkins University, Neurology department, Neuromuscular Laboratory for ATPase pH 9.4 stain, which can identify Type II muscle fibers.

The calcium method for myosin-ATPase demonstration, employing solutions of different pH values, has been used primarily to distinguish muscle fiber types. Muscle fibers may be broadly categorized as type I (slow, red muscle, oxidative) and type II (fast, white muscle, glycolytic). Type II muscle fibers are further subdivided as IIa (glycolytic), IIb (glycolytic/oxidative), and IIc which may be fibers that are changing types due to disease, injury, or development. The preincubation pH relatively inactivates the myosin-ATPase iso-enzyme characteristic of specific fiber types. The remaining active ATP and calcium-dependent enzyme activity releases calcium atoms which are replaced by cobalt, and finally precipitated as a black insoluble cobalt salt of ammonium sulfide. Slides were scanned at National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) Light Image department and images, 2.5X size, were acquired using NDP.view2 software from Hamamatsu.

Analysis of images was performed using Fiji (Image J) in a semi-automated way to facilitate the evaluation of cross-sectioned myofibers in the largest possible area for each image. Preprocessing and thresholding of the images using Fiji available filters and tools was first done to generate a selection of muscle fibers and to classify them according to the intensity of the staining; however, the accuracy of this evaluation was limited, and human intervention was required during the analysis. Following automatic fiber selection, a researcher blinded to sample identity modified the selection of fibers to exclude regions of the image where fibers were longitudinal or where the sample quality was suboptimal. In addition, fiber segmentation was manually corrected to properly delineate a region of interest for each fiber where automatic delimitation was not accurate. Similarly, automatic classification of the fibers was manually reviewed. The minimum Feret diameter was measured for each fiber, and the median of the measurements for type I and type II fibers in each image was calculated and used to compute a Type II/Type I ratio to assess the relative fiber size for the image.

Mitochondrial genetic analysis

Samples were collected from the vastus lateralis muscle and flash frozen. Sample analysis was performed by GeneDx.

Genomic DNA was extracted from the specimens. For the nuclear genome, the DNA is enriched for the complete coding regions and splice junctions of the genes on this panel using a proprietary targeted capture system developed by GeneDx for next-generation sequencing with CNV calling (NGS-CNV). The enriched targets were simultaneously sequenced with paired end reads on an Illumina platform. Bi-directional sequence reads were then assembled and aligned to reference sequences based on NCBI RefSeq transcripts and human genome build GRCh37/UCSC hg19. After gene specific filtering, data were analyzed to identify sequence variants and most deletions and duplications involving coding exons; however, technical limitations and inherent sequence properties effectively reduce this resolution for some genes. Alternative sequencing or copy number detection methods were used to analyze regions with inadequate sequence or copy number data by NGS. The entire mitochondrial genome from the submitted sample was also amplified and sequenced using Next Generation sequencing. DNA sequence was assembled and analyzed in comparison with the revised Cambridge Reference Sequence (rCRS GeneBank number NC_012920) and the reported variants listed in the MITOMAP database ( http://www.mitomap.org ). Next generation sequencing may not detect large-scale mtDNA deletions present at 5% heteroplasmy or lower or mtDNA point variants present at 1.5% heteroplasmy or lower. Reportable variants include pathogenic variants, likely pathogenic variants and variants of uncertain significance. Likely benign and benign variants, if present, were not reported. For the nuclear genome, the technical sensitivity of sequencing is estimated to be >99% at detecting single nucleotide events. It will not reliably detect deletions greater than 20 base pairs, insertions or rearrangements greater than 10 base pairs, or low-level mosaicism. The copy number assessment methods used with this test cannot reliably detect copy number variants of less than 500 base pairs or mosaicism and cannot identify balanced chromosome aberrations. Assessment of exon-level copy number events is dependent on the inherent sequence properties of the targeted regions, including shared homology and exon size. Due to the presence of non-functional pseudogenes, regions of the GYG2, NR2F1, PDSS1, and TSFM, and genes are not fully sequenced by this method. For the COQ7, COX8A, HTRA2, NDUFB11, RNASEH1, SCO2, SDHA, SLC25A26, SLC25A46, TFAM, TMEM126B, and TRMT10C genes, sequencing but not deletion/duplication analysis was performed. In addition, the COA5 gene deletion/duplication analysis may only be able to detect full gene events. For the mitochondrial genome, next generation sequencing can detect mtDNA point variants as low as 1.5% heteroplasmy and large-scale deletions (2 kb or larger) as low as 5% heteroplasmy. However, for large-scale mtDNA deletions observed at less than 15% heteroplasmy, a quantitative value will not be provided. This test is expected to detect greater than 98% of known pathogenic variants and deletions of the mitochondrial genome.

The mitochondrial variants identified by GeneDx were annotated using Annovar 94 .

Modified effort expenditure for rewards task

The Effort-Expenditure for Rewards Task 15 is a multi-trial game in which participants were offered a choice between two task difficulty levels for a reward (Supplementary Fig. S5A ). The task began with a one second blank screen, followed by a five second choice period in which the participant was informed of the value of the reward and the probability of winning if the task were completed successfully. After the participant chose the task, another one second blank screen was displayed. Next, the participant either completed 30 button presses in seven seconds with the dominant index finger if they chose the easy task, or 98 button presses in 21 s using the non-dominant little finger if they chose the hard task. Next, the participant received feedback on whether they completed the task successfully. Finally, the participant learned if they have won, based upon the probability of winning and the successful completion of the task. This process repeats in its entirety for 15 min.

Participants were told at the beginning of the game that they would win the dollar amount they received from two of their winning tasks, chosen at random by the computer program (range of total winnings is $2.00–$8.42).

The primary measure of the EEfRT task is Proportion of Hard Task Choices (effort preference). This behavioral measure is the ratio of the number of times the hard task was selected compared to the number of times the easy task was selected. This metric is used to estimate effort preference, the decision to avoid the harder task when decision-making is unsupervised and reward values and probabilities of receiving a reward are standardized.

Participants were instructed to wear ActiGraph GT3X+ accelerometers (ActiGraph Inc., Pensacola, FL, USA) on their waist and non-dominant wrist, continuously, for at least seven consecutive days at home. Raw tri-axial accelerometer data was recorded at 80 samples/second and subsequently filtered and aggregated into one-minute vector magnitude activity counts and steps with Actilife software (v6.13.0, ActiGraph, Pensacola, FL, USA) and customized programs in Matlab (R2021b, Mathworks, Natick, MA, USA). Periods of sixty or more consecutive minutes of zero vector magnitude counts were identified as non-wear, and daily data were considered valid if the device was worn for ≥10 h from 12 midnight to 12 midnight the next day 95 . Minutes where activity counts of the vertical axis of the waist-worn accelerometer fell between 2020 and 5998 were identified as moderate intensity activity, i.e., three to six times the resting metabolism.

Grip strength

Grip strength was measured with a hand-held dynamometer (Jamar). Each hand was tested individually with the arm, forearm, and wrist in a neutral position. First, each participant was instructed to exert a maximum possible grip force for about five seconds. After completing the first reading, this maximal grip task was repeated. After a minute of rest, the participant was asked to maintain their maximal grip for as long as possible. The time elapsed when the participant’s grip force reduced to 50% of their maximum was recorded by an investigator. All three tests were then repeated with the other hand.

Electrophysiology and repetitive grip testing

To assess physical fatigue, a grip force task was designed which required participants to try to maintain their grip at 50% of maximum voluntary contraction (MVC) in successive blocks of 30 s interspaced with 30 s rest blocks. Each participant sat with their right forearm placed in a rigid-frame dynamometer (biopac, Goleta, CA, USA). The MVC was set as the highest value of three squeezes.

Electromyography (EMG) was collected using surface electrodes (3 M, St. Paul, MN, USA) over the flexor and extensor carpi radialis (FCR, ECR) muscles and the abductor pollicis brevis (APB), using amplifiers and software from Cambridge Electronic Design, Cambridge, UK.

Transcranial magnetic stimulation (TMS) was performed to probe the excitability of the primary motor cortex (m1) via motor evoked potentials (MEPs). A 70 mm figure-8 coil was used to determine the optimal position for evoking MEPs by holding the coil tangential to the scalp and slightly displacing it until the highest MEP amplitude was recorded in the APB muscle. The positions of the participant’s head and TMS coil were tracked with a neuronavigation system (rogue Research, Cambridge, MA, USA) in order to maintain the stimulation position over the hotspot. The TMS Input-Output curve was recorded, collecting MEP responses for intensities 5–100%, in increments of 5%, of stimulation output, in order to calculate the S50. The S50 value was defined as 50% of maximum MEP amplitude. This curve value was also used to estimate resting motor threshold (rMT), which was confirmed using single pulse TMS as the stimulation intensity that would evoke a ~50 µV response in roughly 50% of pulses delivered.

The maximum M-wave was also determined prior and after the repetitive grip task by applying electrical stimulation over the nerve innervating the FCR muscle.

During the repetitive grip task, each participant repeatedly performed 30-s periods of isometric muscle contractions aiming at 50% of MVC. Generally, participants performed 16 blocks, but some quit earlier, and some continued for up to 32 blocks. After each squeeze block, there was a 30 s period of rest. During this rest period, MEPs (elicited every five seconds) were measured.

The development of muscular fatigue during the task, defined as the inability to maintain at least 40% MVC force for more than three seconds, was analyzed by comparing the 1 st block (no fatigue), the last block before fatigue onset, and three following blocks after fatigue onset or, if they did not fatigue, the last four blocks. For EMG, we used the Dimitrov index (DI) 17 , 18 to evaluate the shift in EMG frequency power within blocks.

Functional MRI repetitive grip testing

Brain activity was also assessed during the grip strength task with fMRI. This task was designed to identify, at the whole brain level, brain areas involved in fatigue. Participants lay supine in the scanner and performed repeated 30-s blocks of grip strength with a dynamometer (isometric muscle contractions) at 50% of their maximum voluntary contraction (MVC) interspaced with 30-s blocks of rest. MVC of the forearm muscles was determined from the best of three brief squeezes on the dynamometer. Participants used visual feedback from a computer to monitor force generation. Similar to the TMS study, participants performed 16 blocks, but some quit earlier, and some continued for up to 32 blocks. Subjective appraisals of muscular fatigue were measured with a VAS before and after the grip strength task.

We used a 3 T Prisma SIEMENS scanner equipped with a 64-channel head-coil in the Nuclear Magnetic Resonance Center at the National Institutes of Health. We acquired T2*-weighted EPI with TR = 2 s, TE = 30 ms, image matrix = 64 × 64, flip angle: 70˚, FoV: 100, voxel size 3.5 ×3.5 ×3.5 mm.

Cardiopulmonary exercise testing (CPET)

CPET was performed using a cycle ergometer and a computerized metabolic cart (CardiO2 Ultima; MedGraphics Corp, St.Paul, MN, USA). A ramp protocol was used where the work rate would be gradually increased until volitional fatigue was reached by each participant. A time-matching paradigm to ensure all participants exercised for between eight to twelve minutes was employed, as per American College of Sports Medicine recommendations 96 . The target endpoint was exertional intolerance defined as the participants’ expressed desire to stop cycling despite strong verbal encouragement from the testing staff. Endpoints for stopping the tests were those recommended by the American College of Sports Medicine 96 .

Breath-by-breath gas exchange and heart rate (by 12-lead ECG) were measured throughout the CPET. Peak oxygen consumption (peak VO2) was calculated as the 20 s average at the end of the CPET. The anaerobic threshold (AT) identified by the metabolic cart was verified by gas exchange analysis methods 22 . Chronotropic incompetence (CI) was calculated as % predicted heart rate reserve = [peak HR−resting HR]/[APMHR−resting HR] × 100. The slope of the heart rate response during the CPET was examined by linear fit between 15 to 100% of the CPET time. Expected heart rate responses were also generated using linear fits between predicted resting and peak heart rate values, matching sex and age of both HV and PI-ME/CFS participants. Muscle oxygenation measurements were also made at the vastus lateralis during the CPET by near infrared spectroscopy (NIRO-200NX, Hamamatsu Photonics, Japan). For further determination of maximal oxygenation values of near infrared spectroscopy measurements, a thigh occlusion test was performed prior to the CPET. Following seated rest, an occlusion cuff (Hokanson Rapid Cuff Inflator; Hokanson Inc., Belleview, WA, USA) was rapidly inflated to and held at 80 mmHg above systolic blood pressure for eight minutes.

Bioenergetic measurements

The metabolic chamber is a whole-room indirect calorimeter that allows detailed assessment of energy and nutrient balance. Measurements are conducted at stable interior (room) temperature, humidity, and barometric pressure, which are continuously measured. Airtight sampling ports and a four-way air-locking food and specimen passage are designed to allow blood draws and specimen retrievals with minimal disturbance to the chamber environment. Outside air is continuously drawn into the chamber, and the flow rate of air at the outlet is measured using a pneumotachograph with a differential manometer. A fraction of the extracted air is analyzed at one minute intervals for O2 and CO2 concentrations with a thermomagnetic O2 analyzer. This allows for a continuous assessment of oxygen consumption (V˙O2), carbon dioxide elimination (V˙CO2), and calculation of overall energy expenditure (EE). The ratio between V˙CO2 and V˙O2 (the respiratory quotient [RQ]) reflects preference for carbohydrate or fat oxidation.

Starting the day prior to CPET, each participant was placed on a metabolic diet controlled for energy and macronutrient content. Measures of energy expenditure and respiratory exchange were obtained during a 16 h (4 pm to 8 am) stay in a metabolic chamber prior to CPET and for three consecutive days afterwards.

Salivary cortisol measurements

Saliva was collected using a SARSTEDT salivette. Participants did not eat for at least 2 h or drink water 30 min prior to collection. Samples were centrifuged for 2 min at 1000 × g and then frozen at −80 °C. Samples were measured by Salimetrics using enzyme-linked immunoassays for cortisol performed in duplicate. Assay sensitivity is <0.007 ug/dL.

Neuropsychological Measures

The following neuropsychological measures were administered by a trained neuropsychometrist in the following general order: Visual Analogue Scale (Time 1): a set of scales that were administered to capture subjective effort, performance, mental fatigue, and physical fatigue. This test was immediately prior to administration of the neuropsychological testing battery; Wechsler Test of Adult Reading (WTAR) (The Psychological Corporation, 2001): a task that requires the examinee to read words aloud; 97 Hopkins Verbal Learning Test-Revised Learn (HVLT-R Learn): a task where examinees have to learn a list of words; 98 Grooved Pegboard Test: a task where examinees have to rapidly insert pegs in holes; 99 Wechsler Adult Intelligence Scale—Fourth Edition (WAIS-IV) subtests including Coding, Symbol Search and Digit Span: a task where examinees memorize strings of numbers or complete speeded tasks involving unfamiliar symbols; 100 B Test: examinees have to rapidly discriminate between letter stimuli; 68 Hopkins Verbal Learning Test-Revised Delayed Recall (HVLT-R DR): a task where examinees have to recall the list of words previously learned; 98 Brief Visual Memory Test-Revised (BVMT-R): a task where examinees have to learn a list of designs; 101 Visual Analogue Scale (Time 2): scales to capture subjective effort, performance, mental fatigue, and physical fatigue were collected at this time, approximately one hour after testing started; Wisconsin Card Sort Test (WCST-64): a task where examinees have to utilize corrective feedback to learn how to sort cards; 102 Controlled Oral Word Association Test (COWAT; FAS and Animals): a task where examinees have to generate words to various cues; 103 Paced Auditory Serial Addition Test (PASAT): a task where examinees have to rapidly perform serial addition; 104 Brief Visual Memory Test-Revised (BVMT-R) Delayed Recall: a task where examinees have to recall the prior list of designs; 101 Word Memory Test: a task where examinees have to view words; 70 Test of Variables of Attention: a task where examinees rapidly respond using a button press to certain target stimuli and not distractor stimuli; 105 Visual Analogue Scale (Time 3): scales to capture subjective effort, performance, mental fatigue, and physical fatigue were collected at this time, approximately two hours after testing started; Word Memory Test Delayed Recall: a task where examinees have to recall the words viewed earlier; 70 Dot Counting Test: examinees have to count dots as rapidly as possible; 69 MMPI-−2 RF: As described above; EEfRT test: As described above; Visual Analogue Scale (Time 4): scales to capture subjective effort, performance, mental fatigue, and physical fatigue were collected at this time, approximately three hours after testing started.

PBMC RNA sequencing

RNA was extracted from PBMC’s of participants using miRNeasy Micro Kit (QIAGEN). RNA was quantified using Qubit 3.0 fluorometer (Thermo Fisher Scientific) and its integrity confirmed using an Agilent 2100 Bioanalyzer (Agilent Technologies). Dual index libraries were constructed with at least one unique index per each patient library using the TruSeq Stranded Total RNA HT Kit (Illumina) to enable subsequent pooling of equal quantities of individual libraries. The integrity and ratio of pooled libraries was validated using Miseq system (Illumina); then, paired-end sequencing (2 × 75 base pairs (bp)) was performed on an HiSeq 3000 sequencer (Illumina) with the Illumina HiSeq 3000 SBS Hit.

Peripheral blood serum was isolated using SST tubes and cryopreserved with corresponding cerebrospinal fluid samples. Proteomic analysis used the SOMAscan 1.3k Assay (SomaLogic). This is an aptamer-based assay able to detect 1305 protein analytes, optimized for analysis of human serum 106 , 107 . Briefly, aptamers are short single-stranded DNA sequences modified to confer specific binding to target proteins and can be highly multiplexed for discovery of biomarker signatures. The proteins quantified include cytokines, hormones, growth factors, receptors, kinases, proteases, protease inhibitors, and structural proteins. The assay was performed according to manufacturer specifications for each of the serum and cerebrospinal fluid sample types. Briefly, serum samples were assayed at three dilutions (40%, 1%, and 0.005%) with each sample dilution added to a corresponding subset of the 1305 SOMAmer detection reagents binned according to manufacturer’s predicted target abundance in serum. Cerebrospinal fluid was run at a single 15% concentration dilution with protease inhibitors and polyanionic competitor reagent added. Data was then inspected using a web tool and subjected to quality control procedures as previously described 108 , 109 .

Flow cytometry of blood and cerebrospinal fluid

EDTA-treated whole blood and cerebrospinal fluid cells were used for flow cytometric analysis. Cerebrospinal fluid samples were obtained by lumbar puncture and the cerebrospinal cells were collected within an hour by centrifugation. Whole blood or cerebrospinal fluid cells were stained with CD3, CD4, CD8, CD14, CD16, CD19, CD25, CD27, CD45, CD45RA, CD56, CD152, CXCR5, IgD, HLA-DR (all from BD Biosciences), PD-1 (BioLegend) and FoxP3 (eBiosciences), as previously described 110 . All flow cytometric analysis was performed using an LSR II (BD Biosciences). The data were analyzed using FlowJo 10.6 software (FlowJo LLC).

An additional panel of T-cell markers including TIGIT, CD244, and CD226 were performed on a subset of participants. Some of these samples were collected from participants during a second lumbar puncture during a return visit months after the initial sample collected for the analysis above.

All antibodies, clones, catalog numbers, manufacturers, and dilutions used in this study are as follows: anti-human CD3 (clone: UCHT1, Cat. 558117, BD Biosciences, 1:30 dilution (1:100 dilution for CSF cells)) anti-human CD3 (clone: UCHT1, Cat. 555335, BD Biosciences, 1:100 dilution); anti-human CD4 (clone: RPA-T4, Cat. 557922, BD Biosciences, 1:30 dilution (1:100 dilution for CSF cells)); anti-human CD4 (clone: RPA-T4, Cat. 555346, BD Biosciences, 1:100 dilution); anti-human CD8 (clone: SK1, Cat. 341051, BD Biosciences, 1:30 dilution (1:100 dilution for CSF cells)); anti-human CD8 (clone: RPA-T8, Cat. 557746, BD Biosciences, 1:100 dilution) anti-human CD14 (clone: M5E2, Cat. 555399, BD Biosciences, 1:30 dilution (1:100 dilution for CSF cells)); anti-human CD16 (clone: 3G8, Cat. 338426, BD Biosciences, 1:30 dilution (1:100 dilution for CSF cells)); anti-human CD19 (clone: HIB19, Cat. 555413, BD Biosciences, 1:30 dilution (1:100 dilution for CSF cells)); anti-human CD25 (clone: M-A251, Cat. 557741, BD Biosciences, 1:30 dilution (1:100 dilution for CSF cells)); anti-human CD27 (clone: M-T271, Cat. 560222, BD Biosciences, 1:30 dilution (1:100 dilution for CSF cells)); anti-human CD45 (clone: HI30, Cat. 560777, BD Biosciences, 1:30 dilution (1:100 dilution for CSF cells)); anti-human CD45RA (clone: HI100, Cat. 555488, BD Biosciences, 1:30 dilution (1:100 dilution for CSF cells)) anti-human CD56 (clone: B159, Cat. 557747, BD Biosciences, 1:30 dilution (1:100 dilution for CSF cells)); anti-human CD226 (clone: 11A8, Cat. 338318, BioLegend, 1:30 dilution (1:100 dilution for CSF cells)); anti-human CD244 (clone: C1.7, Cat. 329522, BioLegend, 1:30 dilution (1:100 dilution for CSF cells)); anti-human PD-1 (clone: EH12.2H7, Cat. 329906, BioLegend, 1:30 dilution (1:100 dilution for CSF cells)); anti-human CXCR5 (clone: RF8B2, Cat. 558113, BD Biosciences, 1:30 dilution (1:100 dilution for CSF cells)); anti-human HLA-DR (clone: G46-6, Cat. 555811, BD Biosciences, 1:30 dilution (1:100 dilution for CSF cells)); anti-human IgD (clone: IA6-2, Cat. 561302, BD Biosciences, 1:30 dilution (1:100 dilution for CSF cells)); anti-human TIGIT (clone: A15153G, Cat. 372714, BioLegend, 1:30 dilution (1:100 dilution for CSF cells)); anti-human CD127 (clone: HIL-7R-M21, Cat. 560551, BD Biosciences, 1:30 dilution); anti-FOXP3 (clone: 236A/E7, Cat. 17-4777-42, Thermo Fisher Scientific, 1:50 dilution); anti-human CD152 (clone: BNI3, Cat. 555853, BD Biosciences, 1:50 dilution); anti-Stat5 (pY694) (clone: 47/Stat5(pY694), Cat. 612567, BD Biosciences, 1:50 dilution).

Dilutions were determined according to our own staining protocols. All the antibodies used for flow cytometry in this study are commercially available, and their specificities have been well validated by the manufacturers and other users.

NK cell function measurement

NK cell function was measured in blood within 24 h of collection by a 51 Chromium release assay 111 by the clinical laboratory at Cincinnati Children’s Hospital.

Growth differentiation factor-15 measurement

The GDF15 ELISA was performed using R&D Systems, Minneapolis, MN. Catalog No. DGD150 kit as per manufacturer instructions. The intra-assay variation was 1.8–2.8% and the interassay variation was 4.7–5.6%.

Luciferase immunoprecipitation assay

The luciferase immunoprecipitation systems (LIPS) assay provides an informative tool to explore serology as evidence of autoimmunity and infectious disease exposure due to its ability to efficiently detect antigenicity antibodies against both conformational and linear epitopes. Here, LIPS was used to assess for the presence of autoantibodies against a small diverse panel of known and potential antigens in the PI-ME/CFS and healthy volunteer participants. The previously described testing format was used to examine antibodies against the various target molecules included known autoimmune-associated proteins (Ro52, Jo-1, TPO, gastric ATPase, tyrosine hydroxylase), neurological autoantigens (GAD65, LGI1, NMDAR1, MUSK), cytokines (Interferon alpha1, Interleukin-6, CXCR4, TGFB1), muscle proteins (MPZ, PMP22), as well as against several infectious agents (HDV, HEV, Zika virus). Light units were measured in a Berthold LB 960 Centro luminometer (Berthold Technologies, Germany) using coelenterazine or furimazine substrate mix (Promega, Madison, WI). In some cases, control sera samples from known positive control autoimmune patients were used as positive controls.

Muscle RNA sequencing

RNA was extracted from muscle samples using the TRIzol protocol 112 . The integrity of the RNA was verified using a standard quality metric denominated RNA integrity number (RIN) value using the Agilent 4200 TapeStation system and the concentration was measured using the DeNovix DS-11 spectrophotometer. Five hundred nanograms of RNA were used to prepare the RNA sequencing libraries using the NEBNext Ultra II Directional RNA Library Prep Kit and sequenced using the Illumina NovaSeq 6000 sequencer. Reads were demultiplexed using bcl2fastq v. 2.20.0.

Metabolomics of cerebrospinal fluid

Metabolomics on cerebrospinal fluid samples was performed using Metabolon’s Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS). Samples were prepared using the automated MicroLab STAR® system from Hamilton Company. Several recovery standards were added prior to the first step in the extraction process for QC purposes. To remove protein, dissociate small molecules were bound to protein or trapped in the precipitated protein matrix, and to recover chemically diverse metabolites, proteins were precipitated with methanol under vigorous shaking for two minutes (Glen Mills GenoGrinder 2000) followed by centrifugation. The resulting extract was divided into five fractions: two for analysis by two separate reverse phase (RP)/UPLC-MS/MS methods with positive ion mode electrospray ionization (ESI), one for analysis by RP/UPLC-MS/MS with negative ion mode ESI, one for analysis by HILIC/UPLC-MS/MS with negative ion mode ESI, and one sample was reserved for backup. Samples were placed briefly on a TurboVap® (Zymark) to remove the organic solvent. The sample extracts were stored overnight under nitrogen before preparation for analysis.

All methods utilized a Waters ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution. The sample extract was dried then reconstituted in solvents compatible to each of the four methods. Each reconstitution solvent contained a series of standards at fixed concentrations to ensure injection and chromatographic consistency. One aliquot was analyzed using acidic positive ion conditions, chromatographically optimized for more hydrophilic compounds. In this method, the extract was gradient eluted from a C18 column (Waters UPLC BEH C18-2.1×100 mm, 1.7 µm) using water and methanol, containing 0.05% perfluoropentanoic acid (PFPA) and 0.1% formic acid (FA). Another aliquot was also analyzed using acidic positive ion conditions; however, it was chromatographically optimized for more hydrophobic compounds. In this method, the extract was gradient eluted from the same afore-mentioned C18 column using methanol, acetonitrile, water, 0.05% PFPA and 0.01% FA and was operated at an overall higher organic content. Another aliquot was analyzed using basic negative ion optimized conditions using a separate dedicated C18 column. The basic extracts were gradient eluted from the column using methanol and water, however, with 6.5 mM Ammonium Bicarbonate at pH 8. The fourth aliquot was analyzed via negative ionization following elution from a HILIC column (Waters UPLC BEH Amide 2.1 × 150 mm, 1.7 µm) using a gradient consisting of water and acetonitrile with 10 mM Ammonium Formate, pH 10.8. The MS analysis alternated between MS and data-dependent MS n scans using dynamic exclusion. The scan range varied slightly between methods but covered 70–1000 m/z.

Lipidomics of plasma

The extraction of lipids from plasma was accomplished following manufacturer’s protocol, with slight modifications 113 . To 25 µl of plasma sample, 975 µl water, 2 ml methanol, 900 µl dichloromethane (DCM) and 25 µl internal standard was added. The internal standard was prepared from a kit (Avanti Lipids) developed for the Lipidyzer platform (SCIEX) 114 , 115 . The samples were then vortexed and allowed to sit at room temperature for 30 min. 1 ml water and 900 µl DCM was then added and samples were vortexed and centrifuged for 10 min at 2000 × g . The lower organic layer was removed and placed in a separate collection tube. To the remaining aqueous layer, 1.8 ml DCM was added and the samples were vortexed and centrifuged. The lower organic layer was again removed, added to the previous organic layer, and stream dried under nitrogen. The lipids were reconstituted in 250 µl running solvent (50/50 methanol/DCM 10 mM ammonium acetate) and placed in an autosampler vial for analysis. Quality control (QC) samples and 3 QCs spiked with unlabeled internal standards from the QC Spike Standards kit (SCIEX) were run at the beginning, middle, and end of the samples. The quantitation of the lipid species was done with Lipidomics Workflow Manager (SCIEX) 1.0.5.0, which uses validated DMS-MRM acquisition methods, known internal standard concentrations, and integrated peak areas from the samples to determine the quantity of each lipid species. Lipid species were included in the data analyses if above the limit of quantification in >50% of the participants.

Microbiome stool sampling procedure

Whole fecal samples were collected in a sterile bowl at the NIH Clinical Center, aliquoted, and immediately frozen at −80 °C. Aliquots were sent to the sequencing laboratory for metagenomic and metabolomic analyses.

Stool microbiome metagenomics

For metagenomic shotgun sequencing, paired-end libraries were prepared from metagenomic DNA using the Illumina Nextera Flex kit, and then sequenced on the Illumina NovaSeq platform with a 2 ×150 bp length configuration. Bioinformatic analysis of the shotgun metagenomic samples was done using the JAMS_BW package, version 1.7.2, which is available on GitHub at https://github.com/johnmcculloch/JAMS_BW . All code for every step in the bioinformatic analysis from reads to plots is publicly available in this package.

Fastqs from each sample were processed with JAMSalpha 116 , which, briefly, entails quality trimming and adapter clipping of raw reads with Trimmomatic 0.36 117 . The reads were then aligned against the human genome with Bowtie2 v2.3.2 118 and unaligned (non-host) reads were then assembled using MEGAHIT v1.2.9 119 . For all samples evaluated, the mean sequencing depth (already discounting host reads) was 6.04 Gbp ± 0.79 Gbp, yielding a mean assembly rate of 94.06% ± 1.93%. Assembly contigs smaller than 500 bp were discarded and taxonomic classification of remaining contigs was obtained through Kraken2 120 , with a custom 96-Gb Kraken2 database built using draft and complete genomes of all bacteria, archaea, fungi, viruses, and protozoa available in the NCBI GenBank in January 2022, in addition to human and mouse genomes, built using the JAMSbuildk2db tool of the JAMS package. This JAMS-compatible kraken2 database is available for download through the URL https://hpc.nih.gov/~mccullochja/JAMSdb202201.tar.gz . Functional annotation of contigs was obtained using Prokka v1.14.6 121 . The sequencing depth of each contig was obtained by aligning reads used for assembly back to the contigs. Taxonomy was expressed as the last known taxon (LKT), being the taxonomically lowest unambiguous classification determined for each query sequence, using Kraken’s confidence scoring threshold of 5e−06 (using the --confidence parameter). The relative abundance, expressed in parts-per-million (PPM) for each LKT within each sample was obtained by dividing the number of bp covering all contigs and unassembled reads pertaining to that LKT by the total number of non-host base pairs sequenced for that sample.

Stool nuclear magnetic resonance spectroscopy

Fecal extractions were based on published recommendations 122 . Samples were thawed on ice and buffer ( 2 H-phosphate buffered saline + 0.01% sodium azide; pH 7.5) was added at 5 ml/1 g of material. Samples were sonicated 2× 30 s (50% power) on ice, vortexed for 1 min, and centrifuged at 8000 rcf for 20 min at 4 °C. Supernatant was added to a 3 kDa filter concentrator to further remove particulates and proteins. The flow through was collected after about an hour which amounted to approximately 600 ul. Trimethylsilypropanesulfonate was added at 200 uM concentration as a chemical shift standard and concentration reference. NMR spectra were acquired on an 800 MHz Agilent DD2 console equipped with a cryogenically cooled probe. A 1-dimensional NOESY sequence with 4 s acquisition time, 1 s recycle delay, and 100 ms mixing time was used. Chenomx software (Alberta, Canada) was used for processing and spectral analysis. Metaboanalyst was used for statistical comparisons.

Clinical follow-up

Each participant was recontacted by a physician on the investigative team between 11/2021 and 7/2022 to inquire about changes in their clinical condition.

Statistical analysis

This research protocol utilized an exploratory design coupled with a broad and deep phenotyping approach. The assembled data represents a multidimensional description of post-infectious ME/CFS cases collected with the intent to generate new hypotheses.

Using strict case criteria and adjudication process we minimized medical misattribution and studied a homogeneous population. Since the deep phenotyping resulted in a large number of measurements, our statistical approach used a modified concept of consilience, the principle that evidence from independent, unrelated sources can converge on strong conclusions. Here, measures were purposely selected to interrogate different facets of immunologic, bioenergetic, and homeostatic physiology and determine if similar results would emerge from the different techniques employed. It also used repetition of measures to aid in the interpretation of data variance and reliability.

This exploratory approach embraces the explanatory power of negative findings. Small sample sizes can be adequate for applying logic to demonstrate that a phenomenon is not related to a particular physiological process 123 . These data can be used to estimate the futility of continuing to look for a physiological difference that likely does not exist.

The sample size for the cohort was selected for convenience; no statistical method was used to predetermine sample size. With the sample size of this cohort, we anticipated being only able to detect large effects. Post-hoc calculations of the effect size for a phenotyping sample of 21 and 17 participants to achieve a power of 80% is 0.94. Similar calculations for the exercise sample of nine and six participants is 1.27.

Inherent in the data are numerous estimates of effect size and correlation, even for variables that do not reach statistical significance. While the precision of these effect sizes may be poor, they are reported to provide a sense of the strength of relationship and would be useful for determining statistical power for future research.

Statistically, each measure in the protocol was analyzed independent from all others. The statistical testing approach for each measure is listed in Supplementary Data S 1 . Where appropriate, statistical correction for multiple comparisons was performed within the measurement analyzed. Given the exploratory nature of the study, no statistical correction for multiple comparisons was performed across the different measures or for correlational analyses.

For analysis of biological samples, two or more technical replicates were used. Clinical evaluations were performed without replication. There was no randomization in this study. There were no interventions in this study. All researchers performing the experimental analysis of biological samples were blinded to diagnostic group. Evaluating clinicians were not blinded to diagnostic group. Details about data exclusions for each analysis performed can be reviewed in Supplementary Data S 23 .

Statistical analysis of heart rate variability

A text file indicating timing in milliseconds between normal beats was exported. Rstudio 1.1.463(19) was used to remove non-normal beats and re-order data start-time to 8am. The subsequent text file was imported into Kubios HRV Version 1.0 with an artifact correction threshold of 0.3 s. HRV analysis was performed as recommended by the 1996 ESC and NASPE HRV task force and European Heart Rhythm Association. No de-trending was performed. Time-domain metrics included NN interval, pNN50, RMSSD, SDNN, SDNNi and SDANN measured over day(12-h), night(12-h), and 24-h periods. Frequency-domain metrics included VLF(0–0.04 Hz), LF(0.04–0.15 Hz), and HF(0.15–0.4 Hz) measured over day (12-h), night (12-h), 24-h, and 5-min periods. Frequency analysis was conducted using a Lomb-Scargle periodogram with a smoothing window width of 0.02 Hz. Non-linear metrics included SD1, SD2, SD1/SD2 and were measured in one hour segments. Representative traces (HR, LF, HF) were plotted using data sampled at one minute intervals.

Subsequent analyses between study participants and controls were performed using GraphPad Prism version 9.0.0 for Mac, (GraphPad Software, San Diego, California USA) and SAS 9.4 (SAS Institute, Cary, NC). Mann–Whitney tests and Chi-square tests were used to evaluate the difference between HV and PI-ME/CFS participants. Scatterplot graphs display a bar signifying the median of the distribution. Non-linear measures (SD1, SD2 and SD1/SD2) and heart rate were examined using a mixed-effects model to account for 24-h repeated measurements with adjustments for hour of day; results of these latter measures were reported and displayed as least-square mean (lsmean) +/− standard error (stand err.) for PI-ME/CFS and HVs, respectively. A p -value < 0.05 is considered statistically significant.

Statistical analysis of effort expenditure for rewards task

Following the analytic strategy described by Treadway 15 , generalized estimating equations (GEE) were used to model the effects of trial-by-trial and participant variables on hard task choice. A binary distribution and logit link function were used to model the probability of choosing the hard task versus the easy task. All models included reward probability, reward magnitude, expected value (the product of reward probability and reward magnitude), and trial number, in addition to binary categorical variables indicating participant group and sex. Emulating Treadway et al., the two-way interactions between PI-ME/CFS diagnosis and reward probability, PI-ME/CFS diagnosis and reward magnitude, and PI-ME/CFS diagnosis and expected value were also tested, as was the three-way interaction among PI-ME/CFS diagnosis, reward magnitude, and reward probability. One new two-way interaction, the interaction of PI-ME/CFS diagnosis and trial number, was tested as well in order to determine whether rate of fatigue differed by diagnostic group.

Departing from the procedures described by Treadway 15 , GEE was also used to model the effects of trial-by-trial and participant variables on task completion. A binary distribution and logit link function were again used given the binary nature of the task completion variable (i.e., success or failure). The model included reward probability, reward magnitude, expected value, trial number, participant diagnosis, and participant sex, as well as a new term indexing the difficulty of the task chosen (easy or hard). The three-way interaction of participant diagnosis, trial number, and task difficulty was evaluated in order to determine whether participants’ abilities to complete the easy and hard tasks differed between diagnostic group, and in turn whether fatigue demonstrated differential effects on probability of completion based on diagnosis and task difficulty. Additionally, GEE was used to model the effects of these independent variables and interactions on button press rate, to provide an alternative quantification of task performance. This time, the default distribution and link function were used. The model’s independent variables and interaction terms were the same as in the above task completion model.

All three sets of GEE models were performed using an exchangeable working correlation structure. Unstructured models were tested as well, but failed to converge. All GEE models were implemented in SAS 9.4.

Statistical analysis of repetitive grip testing

Grip force data was filtered with a lowpass 8 Hz butterworth of order 2 and normalized to the maximum voluntary contraction. To represent the evolution of fatigue, data collected during the 1 st block was compared to the last successful block and the three following failed blocks.

Statistical analysis of functional MRI repetitive grip testing

AFNI 124 , 125 was used to process anatomical and EPI timeseries with the afni_proc.py tool that included removing the first two volumes, despiking the timeseries, registering the EPI data to the anatomical scan, adjusting for slice timing offsets, motion correcting these timeseries referring to least outlier volume with rigid body transformations using cubic polynomial interpolation, and spatially blurring the timeseries with a 6 mm FWHM Gaussian kernel. @ANATICOR was used to remove white matter signal from the timeseries to reduce scanner-related artifacts 126 and also to remove CSF signal. The motion limit was set to 3 mm and removed volumes with more than 10% of outliers as defined with 3dToutcount tool in AFNI. The demeaned and derivatives of head motion parameters were regressed out. The anatomical and the EPI timeseries were transformed to the MNI template with nonlinear transformations.

Regression analysis with AFNI’s 3dDeconvolve tool was used, with a box car model for each 30 s block of grip force. The first 16 blocks of the task were divided into quartiles of four blocks each. Blocks were pooled together in this fashion to better estimate fatigue-related brain activation. We used a different approach than in the TMS session to represent the evolution of fatigue because we needed to pool blocks together to better estimate fatigue-related brain activation. For group analysis, linear mixed-effects (3dLME tool in AFNI) were used with two groups and four blocks and participants as a random factor. A voxel threshold of p ≤ 0.01 and a cluster threshold of p ≤ 0.05, k ≥ 65 (multiple comparisons correction) was used. We also used a t-test with the 3dMEMA tool in AFNI to assess commonly activated areas.

Statistical analysis of RNA sequencing data

RNA sequence data was obtained from the libraries using the bcl2fastq v.2.17; Illumina software. RNA sequences were subjected to quality control (FastQC, a quality control tool for high throughput sequence data and available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc ), and trimmomatic ( https://github.com/timflutre/trimmomatic ) to remove adapters, followed by alignment to the human genome (GRCh38) using STAR 127 . Gene expression levels were quantified using featuresCounts 128 . DE analysis was performed on PBMC and muscle RNAseq data using limma 129 , and genes with nominal p -value ≤ 0.05 were considered DE. BMI was used as a covariate in sex separated and combined cohorts. Pathway enrichment analysis was performed using the R package clusterProfiler 130 , which uses the fisher test to determine statistical significance. Additionally, prior known protein-protein interactions for the DE genes were extracted from the STRING resource ( https://string-db.org/ ) 131 . Protein-protein interactions with a confidence score of >0.7 were reported. The fold change information of the genes node in the PPI network are highlighted as red (for upregulated genes) and blue (for downregulated gene) color nodes in Cytoscape 132 .

Statistical analysis of aptamer-based proteomics, metabolomics, and lipidomics data

For these datasets, the data analysis is described below. Partial least squares discrimination analysis (PLSDA) (mixOmics; https://bioconductor.org/packages/mixOmics/ ) was used to analyze the data and calculate the variable importance in prediction (VIP) scores for all the metabolites measured. Briefly, variables measurements with missing values in >50% of the samples were removed. For the remaining variables, the missing values were imputed with half of the minimum measurements for the respective metabolite. All the variables with VIP score >1 were subset for either pathway level inference or were assessed individually for known functions, and their expression level between groups are shown in the selected heatmaps. The features with VIP > 1 were considered to be important because the squared sum of all VIP values is equal to the number feature and thus, the average VIP would be equal to 1 133 .

Statistical analysis of transposable element expression data

RNA sequencing data from the PBMCs was aligned to the human genome (GRCh38) using STAR (PMID: 23104886) to allow for multi-aligned sequences using the following criteria: winAnchorMultimapNmax 100 and outFilterMultimapNmax 100. The transposable elements gene transfer file (GTF) was generated from the UCSC genome database. Gene expression quantification was performed with the UCSC RepeatMasker GTF file using the TEtranscripts tool (PMID: 26206304). The gene expression count matrix was used and DEseq2 R package was used to perform DE analysis. The summary DE table is reported. In parallel, the RNAsequencing data from the PBMCs were aligned to the human genome (GRCh38) using bowtie2, and HERV elements were quantified with the author-provided GTF file using Telescope tool (PMID: 31568525). No transposable elements were quantified from the dataset using this tool.

Statistical analysis of microbiome shotgun metagenomics data

Comparisons between samples were interrogated from SummarizedExperiment objects 134 constructed using the JAMSbeta pipeline of the JAMS_BW package. Ordination plots were made with the t-distributed stochastic neighbor embedding (t-SNE) algorithm using the uwot package in R ( https://github.com/jlmelville/uwot ) and the ggplot2 library. Permanova values were obtained using the adonis function of the vegan package, with 10,000 permutations and pairwise distances calculated using Bray–Curtis distance. Heatmaps were drawn using the ComplexHeatmap package 135 . For each feature, p -values were calculated using the Mann–Whitney–Wilcoxon U-test on PPM relative abundances for that feature in samples within each group.

Reproducibility of data analyses

In an effort to promote open science and reproducibility, the final source data files were reanalyzed where appropriate and individual source data files and source code used to analyze and visualize those data have been provided where possible and are available at https://github.com/docwalitt/National-Institutes-of-Health-Myalgic-Encephalomyelitis-Chronic-Fatigue-Syndrome-Code-Repository .

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The Map ME/CFS databank (accession code: https://www.mapmecfs.org/group/post-infectious-mecfs-at-the-nih ) contains demographics, performance validity testing, patient reported outcomes, small nerve fiber measures, neuronal injury markers, clinical lab data, heart rate variability measures, orthostatic challenge data, psychological scales, body composition measures, extracellular flux measures of PBMCs, muscle fiber measures, actigraphy and strength measures, cardiopulmonary exercise test data, whole room calorimetry, neuropsychological testing, biological measures of blood and cerebrospinal fluid, 51 Chromium release assay, metabolomics, proteomics, lipidomics, mitochondrial sequencing, and stool nuclear magnetic resonance spectroscopy data. Accessing Map ME/CFS data requires signing up for an account but otherwise access to the data is unrestricted (Creative Commons BY 4.0). PBMC gene expression data ( GEO Accession viewer (nih.gov) : Accession Code GSE251872), muscle gene expression data ( GEO Accession viewer (nih.gov) : Accession Code GSE245661), and proteomics data ( GSE251790 - GEO DataSets - NCBI (nih.gov) : Accession Code: GSE251790; ( GSE254030 - GEO DataSets - NCBI (nih.gov) : Accession Code GSE254030) is available at Gene Expression Omnibus (GEO) and stool shotgun metagenomic data ( SRA Links for BioProject (Select 954397) - SRA - NCBI (nih.gov) , Accession Code SRP467038) are available at Sequence Read Archive, which are all available at BioProject ( Homo sapiens (ID 954397) - BioProject - NCBI (nih.gov) : Accession Code PRJNA954397). All neurophysiology data from transcranial magnetic stimulation and functional magnetic resonance imaging experiments are available at Pennsieve ( Deep phenotyping of Post-infectious Myalgic Encephalomyelitis-Chronic Fatigue Syndrome - Blackfynn Discover (pennsieve.io) ; DOI: 10.26275/ile7-wrsk). External datasets analyzed include GEO GSE13033 ( GEO Accession viewer (nih.gov) ) and GEO GSE156792 ( GEO Accession viewer (nih.gov) ). Source data are provided with this paper.

Code availability

Codes for the bioinformatic analysis of shotgun metagenomics are available at: https://github.com/johnmcculloch/JAMS_BW . The JAMS compatible Kraken2 taxonomic classification database used for shotgun metagenomics is available at: https://hpc.nih.gov/~mccullochja/JAMSdb202201.tar.gz . RNA sequence quality control was performed with FastQC: http://www.bioinformatics.babraham.ac.uk/projects/fastqc , trimmomatic: https://github.com/timflutre/trimmomatic , and multiqc: https://multiqc.info/ . Codes for differential gene expression analysis are available at: COVID-19_Transcriptomics/Differential_gene_expression.R at master·NHLBI-BCB/COVID-19_Transcriptomics·GitHub. Codes for Pathway enrichment analysis are available at: COVID-19_Transcriptomics/PathwayEnrichment_clusterProfiler.R at master·NHLBI-BCB/COVID-19_Transcriptomics·GitHub. Transposable Seqeunce analysis performed with STAR: https://github.com/alexdobin/STAR , samtools: https://github.com/samtools/samtools , and TEcount tool: ( https://github.com/mhammell-laboratory/TEtranscripts . Additionally, all the above codes and additional R scripts used in this study are available at: https://github.com/docwalitt/National-Institutes-of-Health-Myalgic-Encephalomyelitis-Chronic-Fatigue-Syndrome-Code-Repository .

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Acknowledgements

This research was supported (in part) by the Intramural Research Program of the NIH, including National Institute of Neurological Diseases and Stroke (NINDS), National Heart, Lung and Blood Institute (NHLBI), National Institute of Mental Health (NIMH), National Institute of Allergy and Infectious Disease (NIAID), National Institute of Diabetes, Digestion, and Kidney Disease (NIDDK), National Cancer Institute (NCI), National Institute of Aging (NIA), National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), National Institute on Drug Abuse (NIDA), National Institute of Dental and Craniofacial Research (NIDCR), National Institute of Environmental Health Sciences (NIEHS), National Institute of Nursing Research (NINR), National Center for Complementary and Integrative Health (NCCIH), and NIH Clinical Center (CC). Grant support for this project included: ZIA NS003157 (A.N.), ZIA MH002922-14 (J.S.), ZIA HL 006210 (M.L.), ZIA HL 006212 (M.L.), ZIA DK071014 (K.C.), ZIA DK071014 (K.C.), ZIA HL005199 (M.S.), ZIA ES103362 (G.A.M.), and the NIH Common Fund (A.N.). We want to thank Francis Collins and Walter Koroshetz for their support of this project. Additionally, we would like to acknowledge the following people for their advice and support for this project: Anthony Komaroff, Lucinda Bateman, Benjamin Natelson, Andy Kogolnik, Daniel Peterson, Michael Tierney, Camilo Toro, Jeffery Lewis, Ana Acevedo, Jeffery Cohen, Nicolas Grayson, Fred Gill, Wendy Henderson, Nicolaas Fourie, Rosario Jaime-Iara, Paule Joseph, Eugene Major, Adriana Marques, Bonnie Hodsdon, Susan Robertson, Leora Comis, Dardo Tomasi, Neil Young, John Tsang, Rose Hayden, Olga Carlson, John Butman, Dima Hammoud, Govind Bhagvatheeshwaran, Eleanor Goulden, Renkui Bai, Michael Polydefkis, Jessica Gill, Chen Lai, Tracey Rouault, Manik Ghosh, and Angela Walitt.

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B.W., M.H., S.J., K.C., Y.E.A, R.A., R.J.B., A.W.B., P.D.B., L.M.K.C., A.B.C., B.D., L.F., D.S.G., S.G.H., M.L., J.J.L., A.L.M., J.A.M., J.D.M., L.B.R., M.N.S., L.N.S., B.S, J.S., B.J.S., G.T, S.A.T., S.B.Y., CHI Consortium, and A.N. conceived and designed the study. B.W., S.R.L., P.B., R.J.B., B.Cal., S.C., J.C., L.M.K.C., B.W.C., A.B.C., M.S.D., B.D., A.G., D.S.G., S.C.H., S.G.H., A.J.G., K.M.K., J.D.K., N.Mad., P.M.M., A.M., T.P., L.B.R., B.S., J.S., S.Si., S.So., B.J.S., S.A.T., C.S.V., F.V., C.V., A.W., S.B.Y, and A.N. recruited participants and collected clinical data, research data, and samples. B.W., S.R.L, Y.E.A., P.B., R.J.B., A.W.B., P.D.B., B.Cal., B.Cat., L.C., S.C., L.M.K.C., B.W.C., A.B.C., B.D., L.R.F., S.A.G., A.G., D.S.G., S.H., S.C.H., S.G.H., K.M.K., M.L., N.Mad., N.Mal., P.M.M., R.M., S.M.B., G.N., K.P., I.P-F., T.P., B.A.S., S.Si., J.S., B.J.S., S.A.T., C.S.V., F.V., C.V., A.W., S.B.Y, and CHI Consortium arranged and prepared samples and/or data for analysis. B.W., K.S., S.R.L., M.H., S.J., K.C., Y.E.A., J.J.B., P.B., R.J.B., A.W.B., P.D.B. B.Cal., B.Cat., L.C., F.C., L.M.K.C., A.B.C., B.D., L.R.F., D.S.G., S.C.H., S.G.H., K.R.J., K.M.K., M.L., N.Mad., N.Mal., A.L.M., J.A.M., P.M.M., R.M., G.A.M., S.M.B., G.N., I.P.-F., M.N.S., F.S., S.Si., J.S., B.J.S., G.T., S.A.T., C.S.V., C.V, S.B.Y, CHI Consortium, and A.N. performed statistical analyses. B.W., K.S., S.R.L., M.H., S.J., K.C., R.A., P.B., R.J.B., F.C., L.M.K.C., B.D., L.F., D.S.G., S.G.H., M.L., N.Mad., A.L.M., J.A.M., G.A.M., M.N.S., J.S., B.J.S., C.V., CHI Consortium, and A.N. drafted the manuscript. All authors contributed to the revision and editing of the manuscript.

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Walitt, B., Singh, K., LaMunion, S.R. et al. Deep phenotyping of post-infectious myalgic encephalomyelitis/chronic fatigue syndrome. Nat Commun 15 , 907 (2024). https://doi.org/10.1038/s41467-024-45107-3

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DOI : https://doi.org/10.1038/s41467-024-45107-3

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Mechanisms of Post-Stroke Fatigue: A Follow-Up From the Third Stroke Recovery and Rehabilitation Roundtable

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1 Queen Square Institute of Neurology, University College London, London, UK

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3 Department of Neurology, University of Kansas Medical Center, University of Kansas Alzheimer’s Disease Research Center, Fairway, KS, MO, USA

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4 School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle, Australia Heart and Stroke Program, Hunter Medical Research Institute, Newcastle, NSW, Australia

Coralie English

5 School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Australia Heart and Stroke Program, Hunter Medical Research Institute, Newcastle, NSW, Australia

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7 IIMPACT in Health, University of South Australia, Adelaide, SA, Australia

Quentin J. Pittman

8 Department of Physiology and Pharmacology, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada

Dawn B. Simpson

9 School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Australia Heart and Stroke Program, Hunter Medical Research Institute, Newcastle, NSW, Australia

Brad A. Sutherland

10 Tasmanian School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, TS, Australia

Connie Wong

11 Centre for Inflammatory Diseases, Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia

Dale Corbett

12 Department of Cellular and Molecular Medicine, University of Ottawa Brain and Mind Institute, University of Ottawa, Ottawa, ON, Canada

Post-stroke fatigue (PSF) is a significant and highly prevalent symptom, whose mechanisms are poorly understood. The third Stroke Recovery and Rehabilitation Roundtable paper on PSF focussed primarily on defining and measuring PSF while mechanisms were briefly discussed. This companion paper to the main paper is aimed at elaborating possible mechanisms of PSF.

This paper reviews the available evidence that potentially explains the pathophysiology of PSF and draws parallels from fatigue literature in other conditions. We start by proposing a case for phenotyping PSF based on structural, functional, and behavioral characteristics of PSF. This is followed by discussion of a potentially significant role of early inflammation in the development of fatigue, specifically the impact of low-grade inflammation and its long-term systemic effects resulting in PSF. Of the many neurotransmitter systems in the brain, the dopaminergic systems have the most evidence for a role in PSF, along with a role in sensorimotor processing. Sensorimotor neural network dynamics are compromised as highlighted by evidence from both neurostimulation and neuromodulation studies. The double-edged sword effect of exercise on PSF provides further insight into how PSF might emerge and the importance of carefully titrating interventional paradigms.

The paper concludes by synthesizing the presented evidence into a unifying model of fatigue which distinguishes between factors that pre-dispose, precipitate, and perpetuate PSF. This framework will help guide new research into the biological mechanisms of PSF which is a necessary prerequisite for developing treatments to mitigate the debilitating effects of post-stroke fatigue.

Introduction

Post-stroke fatigue (PSF) is a significant symptom for stroke survivors with few effective, evidence-based interventions currently available. The lack of evidence-based interventions is largely a result of poor understanding of the phenomenon, with little agreement on its definition and measurement. Fatigue is a complex phenomenon with multiple driving factors, which requires a systematic deconstruction of the phenomenon to propel advances in the field. This aim was pursued following the recent Stroke Recovery and Rehabilitation Roundtable (SRRR) consensus process involving experts in the field, which has produced a comprehensive definition and guidelines for measurement of PSF alongside a brief exposition on the possible mechanisms of fatigue and available interventions. 1

In this companion SRRR paper we have put forward a clear definition of fatigue that incorporated both expert consensus and personal experience of stroke survivors. PSF is not mere tiredness, but a “feeling of exhaustion, weariness or lack of energy that can be overwhelming, and which can involve physical, emotional, cognitive and perceptual contributors, which is not relieved by rest and affects a person’s daily life.” Previous studies of PSF have frequently been confounded by other conditions such as depression, anxiety, and sleep disorders which often associate with fatigue. 2 While these conditions might contribute to the feeling of fatigue, they are dissociable and need to be identified at the time of diagnosis. For example, Fluoxetine relieves depression but not fatigue. 3 The consensus view of our SRRR working group was that the Fatigue Severity Scale-7 (FSS-7) represented the most commonly used fatigue measure. Despite its wide usage, this scale has several drawbacks as it does not distinguish between different domains and does not measure fatigue severity or the impact of fatigue on communication ability. It primarily captures impact and interference of fatigue in daily life. For research purposes nuanced interpretations of findings will require the use of domain specific scores from other elaborate fatigue scales summarized in the main paper. 1 Clinically, in order to ensure that PSF does not continue to be an invisible symptom, it is important that it is detected as soon as possible following stroke. We have recommended that the Stroke Fatigue Clinical Assessment Tool (SF-CAT) best meets this need. The SF-CAT can be administered via interview and should be part of clinical follow up for all stroke survivors.

The primary goal of the current current paper is to elaborate on mechanisms of PSF briefly discussed in a companion SRRR paper on PSF. 1 Here, we present a more comprehensive description of the potential processes that drive PSF in order to guide future research into the biological mechanisms of PSF and ultimately the development of new therapeutic interventions. We draw from the literature both in stroke and other diseases where fatigue is a significant symptom and put forward a model of PSF that further highlights promising avenues of future research.

We begin by presenting the idea of PSF as a cluster of disorders with potentially dissociable mechanisms. We then discuss evidence that supports inflammation and immune dysregulation as a potential process that could underpin both acute PSF and long-term PSF. Next, we discuss how dopamine (DA), a neuromodulator with diverse functions including effort perception, motivation, and memory, could be implicated in PSF, with evidence supporting dopaminergic pathways as a potential therapeutic target. Finally, we discuss whole brain neural network changes and exercise induced multi-system dynamics in the context of PSF, both mechanistically and therapeutically. Furthermore, throughout the manuscript, we present evidence from other human diseases where fatigue is a significant symptom, to identify possible overlapping mechanisms with PSF. This is based on the premise that fatigue, in the chronic stages of a disease is delinked from the primary etiopathology of the disease and commonalities in the experience of chronic fatigue indicate a common disease-independent mechanism. Finally, we present a single framework ( Figure 1 ) that links the available evidence and identifies the gaps in our knowledge about PSF.

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Unifying model of fatigue. This schematic illustrates factors that are associated with PSF and its potential role in development and maintenance of PSF. New and untested hypotheses previously proposed, are also included.

PSF: Single or Multiple Disorders?

A fundamental question is whether PSF is a uniform disorder or 1 with distinct subtypes. At an individual level, evidence from qualitative studies suggests that PSF is experienced differently, with each individual experiencing 1 or more predominant features of PSF. Such diversity of experiences alongside comorbidities in PSF, suggests there may be different subtypes that warrant phenotyping. Some stroke survivors describe PSF as physical activity requiring more effort while others define it as an inability to follow conversations, the perception of brain fog, or frequent occurrence of emotional outbursts. 4 Likewise, triggers for fatigue also vary, which results in adoption of different coping strategies. Some stroke survivors take short rest periods to relieve fatigue, others engage in physical activity to relieve fatigue while others find cognitive task switching an effective strategy. 5 There is no comprehensive study investigating a systematic relationship between triggers, experience, and coping mechanisms related to PSF. But the report of distinct features, triggers, and effective coping methods that are specific to the individual suggest there may be subtypes of PSF.

Further evidence for the potential for subtypes of fatigue comes from quantitative studies. The majority of PSF studies use the FSS which provides a single score for fatigue. Other instruments such as Modified Fatigue Impact Scale (MFIS) and Fatigue Scale for Motor and Cognitive (FSMC) functions have subscale scores. Each scale measures different aspects of fatigue and recent recommendations by the SRRR roundtable highlights the need for carefully choosing the scale to suit the aspect of fatigue being interrogated, which will allow for easy cross comparison across investigations. 6 Studies that have used scales with sub-scores show that physical impairments are associated with higher scores in the physical subscale of MFIS, while cognitive impairments relate to mental subscale score of MFIS. 7 Cognitive subscale scores of FSMC related to mental speed, working memory, and verbal short-term memory, while the physical subscale additionally correlated with upper limb physical function. Moreover, those with cortical lesions scored higher on cognitive subscale scores, and those with subcortical lesions scored higher on physical subscale scores. 8 Binocular visual dysfunction relates to both physical and cognitive fatigue, while gait disturbances are associated with cognitive fatigue. 9 Studies that measured only mental fatigue, showed a relation with several measures of cognitive impairment. 10 Those with depression are more likely to score high on the mental and motivational subscales, while physical fatigue scores are more strongly influenced by anxiety. 11 Overall, when measured using an instrument that distinguishes the dimensions of PSF, the correlation with functional measures is dissociable, suggesting that different underlying pathologies might manifest as different aspects of the experience of fatigue.

In stroke patients in the late subacute (>3 months) or chronic stroke phases (>6 months) Modafinil is effective 12 for PSF, but only in those with fronto-striatal dysfunction. 13 In a similar population, neuromodulation interventions 14 , 15 show mixed results, due to differences in methodologies, with bilateral cortical sensorimotor targets providing robust reduction in fatigue, and unilateral dorsolateral pre-frontal targets showing no effects. Furthermore, participants in the sensorimotor cohort were minimally impaired with no depression, while in the dorsolateral pre-frontal cohort although minimally impaired, depressed individuals were not excluded. Therefore, mixed results could be due to both participant and intervention characteristics as suggested by the effectiveness of both graded exercise and cognitive behavioral therapy, but only in a small subset of stroke survivors. 16 The selective effectiveness of different types of interventions suggests the presence of subtypes, each of which may respond to a different intervention.

PSF significantly overlaps with depression, anxiety, pain, and sleep disturbances 17 - 19 in stroke survivors. While the general consensus is that the primary underlying mechanisms of fatigue are independent of comorbidities, treatment strategies may need to accommodate for the presence of a significant comorbidity, or indeed there may be overlapping mechanisms which need further investigation, necessitating comorbidities-based phenotyping.

Precipitating Factors of PSF

Current evidence supporting a role for inflammation contributing to PSF is limited but evidence suggests that the acute inflammatory response may persist longer than previously thought. 20 - 22 Small studies aimed at identifying inflammatory biomarkers of PSF, have made assessments early after stroke rather than later in the subacute or chronic (>6 months) post-stroke phase. Despite this, a positive correlation between acute interleukin-1β (IL-1β; a key mediator of the inflammation response) levels and fatigue 6 months after stroke has been observed ( r = .37), whereas anti-inflammatory IL-1ra ( r = −.38) and IL-9 ( r = −.36) levels negatively correlated with fatigue at 12 months. 23 , 24 The relationship between levels of C-reactive protein (CRP) and fatigue are unclear with 1 study showing a positive relationship (odds ratio 3.435, 95% confidence interval 2.222-5.309), another a negative relationship (ρ = −.47, p < .05) and a further study finding no relationship ( r = .12). 25 - 27 However, a recent retrospective study using a high sensitivity test for CRP showed that PSF at 6 months after stroke was associated with increases in CRP at admission, indicative of systemic inflammation. 28

Stroke-induced inflammation may also lead to genomic or epigenetic changes which modulate brain energy metabolism. 25 The brain has limited energy supplies which are further taxed by brain injury and advancing age as additional neural networks are recruited to maintain function. Systemic inflammation can disturb mitochondrial function, shifting it towards a more inefficient form of metabolism; this phenomenon has also been observed in cancer and chronic fatigue syndrome. 29 Thus, it is highly possible that mitochondria may have a smaller “reserve energy capacity” after stroke, making them unable to provide sufficient ATP to meet the energy demand. This bioenergetics hypothesis may explain variation in PSF amongst patients as they respond differently to external/internal factors, such as sleep changes, stress level, and changes in co-morbidity. Microglia, the resident immune cells of the brain, become “primed” after brain injury, resulting in prolonged activation and chronic neuroinflammation. In traumatic brain injury, persistent microglial activity or inflammation promote neuronal dysfunction and impairments in neuronal homeostasis. 30 , 31 Perhaps similar perturbations occur after stroke and contribute to neuropathology in PSF. Excessive cytokine production and immune dysregulation could also have an impact on orexin-secreting neurons in the hypothalamus as well as a decreased synthesis of serotonin, DA, and norepinephrine neurotransmitters. 32 Other studies show that a loss of activity in these orexin neurons could lead to fatigue. 33 While no investigations so far have directly investigated hypothalamic–pituitary–adrenal (HPA) axis dysfunction in PSF, there is a high prevalence of pituitary dysfunction in stroke 34 and warrants a thorough investigation to determine if this could be related to inflammation. In a cross-sectional study (n = 70) of participants in the chronic phase of stroke recovery, fatigue score was significantly positively correlated with the level of both IL-6 and CRP, although this relationship was no longer statistically significant once entered into a linear regression model with cardiovascular covariables. 20 Therefore, PSF may not be a direct result of inflammation, but may be driven by a matrix of other factors such as the stroke associated cardiovascular risk factors (e.g. diabetes, hypertension). There are also genetic factors that might predispose patients to PSF as has been reported for single-nucleotide polymorphisms in genes that modulate inflammation. One study reported that PSF is associated with the C allele of IL1RN rs4251961. In addition, functional polymorphisms in the gene for toll-like receptor 4 ( TLR4 ) that render TLR4 less responsive to its ligands are associated with lower levels of PSF. 35 Given the limited research to date, it is apparent that definitive inflammatory biomarkers for PSF remain to be identified. However, the data overall support the idea that perturbations in the immune response after stroke may contribute to PSF.

Another possible contributor to PSF is post-stroke dysbiosis, which has a bidirectional relationship with host immune and inflammatory response following injury. The effects of a post-stroke inflammatory state on gut dysbiosis as a driver for PSF has not been examined but warrants investigation, especially in light of emerging evidence in support of inflammation induced gut dysbiosis as a possible driver of fatigue in other conditions such as cancer related fatigue, 36 and the beneficial effects of fecal transplantation in stroke. 37 If dysregulation of the immune response is an important factor in PSF, then an appropriate treatment strategy may be to dampen the inflammatory response, similar to work in Multiple Sclerosis. However, the exact targets need to be better elucidated given the complex network of signaling mechanisms controlling inflammation, and the complexity around the development of PSF.

DA neurons are involved in the control of movement, motivation, arousal, learning, and regulation of the immune system. 38 There is evidence that damage to DA pathways is associated with fatigue in multiple sclerosis. 39 Further, the DA reuptake inhibitors, methylphenidate and modafinil have been used to treat PSF. 12 , 40 , 41 Methylphenidate reportedly reduces fatigue in small studies of chronic fatigue, 41 Multiple Sclerosis, Parkinson’s disease, and cancer. 39 , 42 There is evidence that methylphenidate reduces the cost of mental labor (ie, perceived effort). Participants given methylphenidate engage in challenging cognitive tasks more often than leisure activity when compared to placebo controls. 43 [18F] DOPA positron emission tomography (PET) imaging showed that this effect was associated with higher striatal DA synthesis. Methylphenidate’s effectiveness in PSF has not been assessed. Modafinil shares many actions with methylphenidate and may offer some benefit in reducing PSF as reported in 2 small trials. 12 , 44 In the MIDAS trial, positive responders to modafinil treatment had previously exhibited reduced functional connectivity in the fronto-striatal-thalamic networks when compared to those who did not respond to modafinil. 13 The selective effectiveness of Modafinil in this study suggests that more than 1 mechanism might underlie PSF and non-responders might require a different intervention for reduction of PSF. A larger, multi-center clinical trial (MIDAS 2) investigating the capacity of modafinil to improve stroke patient quality of life and reduce fatigue, is underway and will throw further light on characteristics of individuals in whose PSF is reduced by modafinil.

The pharmacological evidence linking DA to PSF is thus based upon the efficacy of modafinil and methylphenidate to increase DA concentration by inhibiting its uptake. In that scenario, the DA hypothesis of fatigue is similar to the serotonin hypothesis of depression which is based upon the efficacy of the selective serotonin reuptake inhibitors (eg, Fluoxetine). Going forward, trials should include functional magnetic resonance imaging or PET imaging that could be used to relate behavioral outcomes to changes in functional network connectivity, including DA activity. An important caveat for future studies is that combination therapies are much more effective than single interventions in improving cognition, and post-stroke motor recovery. 45 , 46 For example, preclinical and clinical studies indicate that a combination of physical exercise and cognitive stimulation are more effective than either alone in improving cognition. It may be that DA reuptake inhibitors need to be combined with other interventions such as exercise or cognitive training 16 to achieve more robust reductions in PSF. This would be in line with the view that PSF consists of both peripheral (eg, disability due to stroke and physical deconditioning) and central fatigue.

Over the last 2 decades several small studies (see review 47 ) have investigated the relationship between lesion characteristics and PSF with inconclusive results partly due to the variations in scales, metrics of lesion characteristics, small sample size, and variations in time points. Recently, a meta-analysis 48 of 14 studies assessing PSF, and a large scale (n = 361) structural MRI study 49 both concluded that a lesion in the thalamus significantly increased the likelihood of reporting PSF 6 months post-stroke. Extending beyond lesion location, another study examined if structural dysconnectivity associated with the lesion explains PSF, and found no relationship. 50 The thalamus is a major sensory processing hub with functionally heterogenous nuclei and several first and second order processing units. Current studies do not differentiate between different thalamic nuclei and networks. For lesion mapping studies to meaningfully contribute to understanding the mechanisms of PSF, it is critical that future studies use fine grained methods of lesion mapping which combines both structural and functional outcomes, 51 along with use of PSF instruments that differentiate the dimensions of PSF. Such an approach may also yield better outcomes for structural and functional connectivity mapping of PSF. An equally potent, alternate method would be to combine clinically available lesion-mapping data with longitudinal follow-up of PSF associated behavior to identify functionally relevant networks involved in PSF.

Fatigue is an experience, and experiences are an emergent property of neural network activity. Investigations that have focused on neural network activity in PSF have explored the possibility of network level dysfunction as the basis of PSF. A recently proposed model, the sensory attenuation model of fatigue suggests that altered gain modulation in sensory networks, results in PSF. 52 , 53 The theory posits that task irrelevant sensory information, which is normally attenuated, is poorly suppressed leading to high task-related effort. Fatigue is an inference of high effort; therefore, poor sensory attenuation mechanistically underpins fatigue. Biomarkers of poor attenuation include changes in resting state cortical excitability and neural network connectivity, all of which are present in PSF. 54 , 55 An inability to suppress muscle contraction related afferent input manifests as resting state hyper-connectivity in primary somatosensory networks, and hypoconnectivity in motor networks in PSF. 55 Evoked potentials from M1 are diminished in PSF 54 further corroborating heightened sensory processing which has inhibitory influences on M1 excitability. Such altered resting state of sensorimotor neural networks has implications for brain states that need to be achieved in order to produce a movement. In PSF, poor modulation of pre-movement motor cortical inhibition 56 is observed which is a hallmark of poor behavioral flexibility underpinned by heightened sensory processing. Sensory processing is also reliant on inter-hemispheric interactions, and the normal left hemispheric dominance in such interactions shifts to a right hemispheric dominance 57 in people with PSF. Attention to visual and auditory sensory streams also appear to be poorly attenuated, specifically in relation to processing of distractors both in auditory 58 and visual processing 59 in PSF. This series of results support the sensory attenuation model of fatigue, however, functional connectivity studies beyond the somatosensory-motor networks do not fully support the hypothesis, 60 but indicate network level dysfunction does underpin PSF. Evidence from other diseases such as multiple sclerosis 61 , 62 also supports a neural network dysfunction hypothesis of fatigue, with white matter lesions implicated in network dysfunction. In stroke, despite no white matter involvement, small vessel disease is independently associated with greater fatigue. 63 Neuromodulation of sensorimotor networks significantly reduces fatigue both in stroke 14 , 64 and in multiple sclerosis 65 , 66 suggesting a causal link between sensorimotor dysfunction and fatigue. While more studies are needed to replicate and expand the observed results, neural network dysfunction is a possible driver of PSF and a promising interventional target. Neural networks do not function in isolation and, the impact and interaction of sensorimotor network dysfunction with other networks in relation to fatigue is unknown. Lack of studies focusing on pre-frontal and sub-cortical networks that subserve movement, attention, and motivation makes it difficult to definitively conclude the importance of sensorimotor dysfunction over others, for development of PSF. Future research must focus on exploring these other network level dysfunctions in PSF.

Exercise as a Possible Intervention for PSF

People with stroke are much less active than age-matched controls. 67 , 68 Exercise may ameliorate some of the peripheral (aerobic deconditioning and muscle atrophy) and central disturbances (alterations in cerebral blood flow, cellular energy stores, and neural circuit activity) that potentially contribute to PSF. 36 - 71 In a small RCT using a combination of 12-weeks of cognitive training and exercise, addition of exercise reduced fatigue compared to control subjects that received cognitive training alone. 16 A recent systematic review explored whether exercise impacts fatigue using the FSS. 72 Only 2 of 4930 manuscripts met the defined eligibility criteria. Regan et al 73 used a 9 to 10 weeks combination of aerobic and resistance training that significantly reduced PSF immediately following the intervention while the effect was lost 20 weeks post-intervention. Paul et al 74 also reported significant reduction in PSF following a 6-week aerobic exercise program consisting of increasing steps. Both studies suffered from small sample size, lack of details about stroke (type and location), and/or details of the exercise programs (frequency and session duration) with the additional lack of a control arm in the first study, and fatigue not the primary target of intervention in the second study. Other conditions which exhibit fatigue symptomology have also shown benefit with exercise including multiple sclerosis, 75 , 76 cancer, chronic fatigue syndrome, and other diseases. 77 , 78 For example, a randomized clinical trial reported that a 16-week combination of resistance training and high intensity interval training reduced fatigue in breast cancer patients. 78

Given that exercise increases aerobic conditioning, cerebral blood flow, release of growth factors (eg, BDNF), DA, and other monoamine neurotransmitters, neurogenesis, and decreases inflammation, 71 it is surprising that it has received so little attention as a potential treatment for PSF. While there are several pathways through which exercise could reduce fatigue, only recently have investigations in disease populations begun to investigate these pathways. For example, in Multiple Sclerosis, exercise is thought to reduce fatigue by reducing the levels of the muscle derived hormone, irisin. 79 Aerobic exercise alone, or in combination with resistance training or cognitive training, may be helpful in reducing PSF (keeping in mind that the “doses of rehabilitation” have typically been too low to engage neuroplasticity mechanisms important for post-stroke motor recovery). 80 , 81 Careful titration of dose will be required in PSF patients who are either deconditioned and/or exercise intolerant. Another potentially important consideration for exercise-PSF studies is timing. As is the case with motor recovery studies, most exercise-PSF trials have been started in the chronic phase—2 or more years post-stroke. Delaying motor rehabilitation into the chronic phase makes it progressively more difficult to achieve significant recovery. 46 , 82 PSF may benefit from interventions implemented earlier to prevent deconditioning and to preserve energy expenditures that may be limited by supply. An important consideration is that people with a history of fatigue may become resigned to their condition and resistant to engage in exercise that they perceive as potentially harmful. Interventions to mitigate against this should be explored and if it remains an insurmountable problem exercise mimetic, such as drugs that activate many of the same cellular signaling pathways as aerobic exercise 83 might be helpful as adjunctive therapies to reduce PSF. These drugs could be especially helpful in patients who are severely deconditioned or fearful of engaging in exercise.

A Unifying Model of Fatigue

Stroke sets into action a complex cascade of events that spans multiple neural control systems, with significant impact on several peripheral and central pathways. Fatigue is a complex phenomenon often not explained by any 1 dysfunctional biological process resulting from stroke. In what follows, we attempt to bring together known factors that underpin fatigue as discussed in previous sections, and present a framework that will facilitate interpretation of results, and development of effective interventions ( Figure 1 ). In this model, we follow a recently proposed principle of segregating factors associated with complex symptoms into that which pre-disposes one to experience fatigue, factors that are essential for experience of fatigue (precipitating factors), and those that perpetuate fatigue. 84 While outside the scope of this paper, there are several known pre-disposing factors associated with PSF 19 , 85 which are included in the model, and discussed in the SRRR paper. The essential conditions for development of PSF start with high levels of inflammation setting into motion changes in innate immune response and changes in metabolic pathways. Stroke profoundly disrupts neurotransmission, with specific emphasis on DA signaling. The impact of dopaminergic dys-homeostasis is reflected in whole brain networks, specifically sensory networks processing somesthetic, visual, and auditory stimuli, where gain modulation, a process dependent on DA, is compromised. Such compromise in sensory processing can present independently or as a cluster giving rise to the rich range of symptoms reported by those with PSF. Over time, with the resolution of stroke related high levels of inflammation, second order mechanisms such as alterations in predictive processing triggered by altered sensory gain, establishes itself as the primary driver of chronic fatigue. The interplay between precipitating factors and a range of perpetuating factors such as comorbid depression, pain, sleep disorders, anxiety, psychosocial factors, all result in exacerbation and maintenance of fatigue in the long-term.

To reverse fatigue the precipitating factors must first be addressed. While presently aspirational, targeted, hypothesis driven research to establish the key nodes of fatigue related dysfunction is critical in developing effective interventions. Effective interventions will likely be implemented early in the post-stroke period and entail a combination of pharmacological, neuromodulatory, and behavioral, interventions that address mechanisms at several levels. An opportunity to modulate fatigue levels presents itself in the perpetuating factors where a number of paradigms are already in trial, or in existing clinical practice. Such paradigms need to be rigorously tested and best practice widely disseminated to optimize results for stroke survivors.

In summary, PSF is a complex phenomenon which requires a multi-disciplinary approach that cuts across clinical and basic science disciplines for a comprehensive understanding. The ball has been set rolling by the SRRR roundtable with a comprehensive definition, guidelines for how to measure PSF and in the present paper an in-depth discussion of the biological correlates of PSF. We also identify promising avenues for future research and highlight the need for a hypothesis driven approach in future mechanistic investigations of PSF.

Acknowledgments

The third International Stroke Recovery and Rehabilitation Roundtables are an initiative of the International Stroke Recovery and Rehabilitation Alliance. We would like to acknowledge Prof Julie Bernhardt’s vision for SRRR; Dr Kathryn Hayward and Dr Gert Kwakkel for co-chairing and convening the third Stroke Recovery and Rehabilitation Roundtable (SRRRIII), Dr Emily Dalton for supporting SRRRIII conduct and organization of the in-person meeting. We thank our Advisory Group members: Lived Experience (Brenda Booth, Shailaja Buvaneswari, Jim Koehler, Rachel Peak, Jessica Peters, Ben Schelfhaut) and all members of the SRRRIII post-stroke fatigue taskforce.

Author Contributions: Annapoorna Kuppuswamy: conceptualization; writing—original draft; writing—review & editing. Sandra Billinger: Writing—review & editing. Kirsten G. Coupland: writing—review & editing. Coralie English: writing—review & editing. Mansur A. Kutlubaev: writing—review & editing. Lorimer Moseley: writing—review & editing. Quentin J. Pittman: writing—review & editing. Dawn B. Simpson: Writing—review & editing. Brad A. Sutherland: writing—review & editing. Connie Wong: writing—review & editing. Dale Corbett: writing—original draft; writing—review & editing.

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: AK is funded by Wellcome Trust 202346/Z/16/Z. Receipt of financial support from the Canadian Partnership for Stroke Recovery, NHMRC Center of Research Excellence to Accelerate Stroke Trial Innovation and Translation (grant no. GNT2015705) and unrestricted educational grants provided by Ipsen Pharma and Moleac.

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Statement of Endorsement: The World Stroke Organization (WSO) endorses the goals of the third international Stroke Recovery and Rehabilitation Roundtable (SRRRIII) research activity which are consistent with the mission of the WSO.

Long COVID, ME/CFS and the Importance of Studying Infection-Associated Illnesses

BY OSMAN MONEER May 13, 2024

The COVID-19 pandemic posed many unprecedented global challenges. But as reports of Long COVID cases grew, there were some patients for whom the persistent symptoms of the post-viral syndrome felt familiar.

Even before COVID-19 spread across the planet, patients were presenting with similar constellations of symptoms following other infections, which doctors and researchers collectively referred to as infection-associated chronic illnesses or post-acute infection syndromes . Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a neuroinflammatory, neuroimmune illness most commonly triggered by infection, such as Epstein-Barr virus, though patients have reported other triggers as well, such as physical accidents and environmental exposures.

ME/CFS is characterized by prolonged and severe fatigue, and by symptoms that worsen after physical or cognitive exertion (known as post-exertional malaise, or PEM), sleep disturbances, brain fog /difficulty thinking, dizziness and orthostatic intolerance (the medical term for when standing up causes symptoms), headaches, muscle weakness and pain, and more.

To Beth Pollack , an ME/CFS expert and research scientist at MIT who studies infection-associated chronic illnesses, ME/CFS is a chronic illness that affects multiple systems in the body . “Research has shown that ME/CFS involves dysfunction of the immune and nervous systems, as well as cardiovascular, connective tissue, gastrointestinal, metabolic, and mitochondrial dysfunction,” she says.

What is ME/CFS?

Fatigue is a hallmark of ME/CFS and Long COVID

ME/CFS is a severe, disabling, and life-altering disease: 75% of ME/CFS patients are too ill to work, and a quarter of patients are unable to leave their homes or, in some cases, their beds. Some physicians caring for ME/CFS patients say it’s one of the most disabling illnesses they've ever seen . According to the late William Reeves, MD, former head of Viral Diseases at the Centers for Disease Control and Prevention (CDC), “The level of functional impairment in people who suffer from CFS is comparable to multiple sclerosis, AIDS, end-stage renal failure and chronic obstructive pulmonary disease.”

Dr. Deborah F.* agrees with that assessment. She is a family physician who, like so many doctors, had COVID-19 early in the pandemic, in March 2020, before anyone really understood what it was. As a then-33-year-old “super healthy, physically active, and athletic person,” she believed that she’d recover from COVID quickly and return to seeing patients. However, she didn’t recover, and four years later she is still not well enough to return to her job. She’s tried many times but hasn't been able to do the work. Too often, she experiences severe, debilitating symptoms within an hour of work. She has had to accept that at this time she is still physically unable to work, even though she misses practicing medicine, which she loved. “I feel like I’m trying to run on an empty gas tank,” Dr. F. says. No matter how much she rests, the fatigue never really leaves her.

“I went through high school, college, medical school, and family medicine residency, doing intense, 80-plus-hour work weeks during residency,” she explains. “I always worked hard and was a total over-achiever. I thought I knew what it meant to get ‘tired’ back then, but this is so much more than that. I was used to the kind of fatigue where you can take a power nap or get a cup of coffee, and then power through. But ME/CFS isn’t like that. The fatigue drops you where you are. There is usually no pushing through. It’s so much worse. You just can’t function. And you have no idea how long the fatigue will last.”

Studies over the last few years have found that about half of people with Long COVID meet diagnostic criteria for ME/CFS, and the prevalence of ME/CFS has been growing as a result of the pandemic and Long COVID. Recent estimates suggest that there may be 3.3 million Americans living with ME/CFS, possibly growing to 5-9 million as a result of the pandemic . Findings from ME/CFS research have helped inform research directions for Long COVID.

How similar are Long COVID and ME/CFS?

Numerous studies have revealed a significant overlap in symptoms reported by patients with Long COVID and ME/CFS. For instance, stud i es comparing patients found that both groups experienced orthostatic intolerance and autonomic dysfunction, in which just standing upright induces symptoms such as dizziness or lightheadedness.

However, there are some distinctions. Long COVID is defined as symptoms persisting a minimum of four weeks post-infection , according to the CDC. It is heterogenous, with over 200 symptoms. While a subset of patients have ME/CFS, many do not. A 2022 NIH study , led by researchers at Weill Cornell Medicine, identified four major subgroups of the disease. One subgroup associated with predominantly neurological symptoms may be more aligned with ME/CFS. Many Long COVID patients experience decreased smell and taste and respiratory issues, which are not as common in ME/CFS.

How are ME/CFS and Long COVID diagnosed?

Doctors typically identify diseases by using a combination of lab tests and a pattern of unique symptoms. But given how varied symptoms can be, along with the absence of specific diagnostic lab tests, both Long COVID and ME/CFS can be difficult to diagnose. Both diseases are frequently seen as diagnoses of exclusion (meaning the diagnosis is made after ruling out other conditions that can cause similar symptoms) , contributing to delays in diagnosis and misdiagnosis. Despite substantial knowledge and documentation of disease pathologies, researchers are still studying and trying to understand the complex mechanisms underlying these diseases.

What’s next for ME/CFS and Long COVID?

What’s next for these illnesses? Historically, ME/CFS has been underfunded relative to its disease burden. More research is critical to better understand the underlying mechanisms of both Long COVID and ME/CFS and to inform future clinical trials.

Importantly, according to Pollack, ME/CFS and Long COVID commonly co-occur with a group of other overlapping conditions, including postural orthostatic tachycardia syndrome (POTS) , small fiber neuropathy, mast cell activation disorders, connective tissue disorders, and reproductive health conditions . Pollack recommends that both researchers and clinicians screen for co-occurring diseases in those diagnosed with ME/CFS or Long COVID.

“Unfortunately, the most severe and complex patients often fall through the cracks of both clinical care and research,” says Pollack. “We need to include severe patient cohorts in research, even if it means visiting them at home while they are in bed.”

Ongoing research, including work at Yale by the lab of Akiko Iwasaki, PhD , Sterling Professor of Immunobiology, seeks to clarify the basis for Long COVID and to better understand its relationship with ME/CFS. Iwasaki’s lab and MIT’s Tal Research Group, where Pollack works, are collaborating in their efforts to understand these diseases. At MIT, the Tal Research Group, led by Michal Tal, launched the MAESTRO study on Long COVID and chronic Lyme disease, and hopes to add an ME/CFS cohort. MAESTRO aims to identify biomarkers and mechanisms of illness, increase understanding of the overlaps among these illnesses, and predict who develops chronic illness after infection. In addition to the MIT-Yale collaboration, the Iwasaki lab is collaborating with the team of David Putrino, PhD, at the Mount Sinai School of Medicine, to enroll pre-pandemic ME/CFS patients to conduct deep immune phenotyping.

Pollack is hopeful that we are entering a new era of chronic illness research. There has been a series of conferences to explore the pathophysiology of Long COVID and ME/CFS, including two at the National Academies of Sciences, Engineering, and Medicine; a Keystone conference; an ECHO series , led by the Bateman Horne Center; and a series of NIH webinars bringing together world experts to summarize ME/CFS research.

Pollack chairs an NIH subgroup on “less studied pathologies” in ME/CFS, and she organized and led a four-hour NIH research webinar in January 2024 on understudied topics in the disease that greatly impact patients—the first event of its kind in the field. She encourages clinicians, researchers, students, and patients to watch some of the eight NIH Research Roadmap webinars, as they cover many ME/CFS subtopics in-depth, reviewing the current state and next steps for research.

“We are thinking about how we advance the field toward clinical trials and therapeutics,” says Pollack. “These illnesses urgently need effective treatments. We also need deep phenotyping and mechanistic research that helps us identify subsets most likely to benefit from certain treatments. When appropriate, cross-illness research such as clinical studies and clinical trials with ME/CFS and Long COVID comparator cohorts, or other cohorts like POTS, are important to consider.”

*Not her real name .

Osman Moneer is an MD candidate at Yale School of Medicine

The last word from Lisa Sanders, MD:

Fatigue is common after an illness. Recovery does take time. But ME/CFS is a whole different level of fatigue. Dr. F. is a patient of mine at the Yale New Haven Long COVID Multidisciplinary Care Center, and I’ve watched her struggle to recapture even a part of the life she used to have. She has trouble taking care of herself and when she’s really bad off she goes home to live with her parents. She sleeps 12 to 16 hours at night and naps during the day—every day. And, while she has a day when she can function every now and then, she still has to be careful. If she does too much, she can “crash,” as she calls it—and then is unable to even get out of bed for a day or two or three or four. “With Long COVID, it’s so unpredictable how my body will respond,” she explains. “One day I can do something. But when I try it another day, I will crash.”

The frustration for Dr. F. and for all the doctors who participate in her care is that we don’t know enough about this disease to help her. And so far, nothing we have tried has helped. She keeps trying. We keep trying. Right now, it’s all we can do.

If you’d like to share your experience with Long COVID for possible use in a future post (under a pseudonym), write to us at: LongCovid [email protected]

Information provided in Yale Medicine content is for general informational purposes only. It should never be used as a substitute for medical advice from your doctor or other qualified clinician. Always seek the individual advice of your health care provider with any questions you have regarding a medical condition.

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Managing Myalgic Encephalomyelitis/Chronic Fatigue Syndrome
Living with ME/CFS
Patient Toolkit
Stakeholder Engagement and Communication (SEC) calls
ME/CFS in Children
Disability and ME/CFS
ME/CFS Awareness Day
Information on CDC's ME/CFS Program
Clinical Overview
Healthcare Provider Toolkit
Training: Medscape Continuing Medical Education (CME)

Symptoms of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome symptoms are common in many other illnesses.
There is no test to confirm ME/CFS. This makes it hard to diagnose.
Symptoms are unpredictable and may change or come and go over time.
However, a combination of core symptoms are used to diagnose ME/CFS.

female doctor holding a patient clipboard to discuss and analyze the patient's condition before treating

Primary symptoms

ME/CFS has five core symptoms. You must have three main ME/CFS symptoms and at least one of the other two symptoms to be diagnosed with ME/CFS.

Less ability to do activities and fatigue (required)

People with ME/CFS have a much lower ability to do activities they could do before they got sick. This limitation comes with fatigue and lasts six months or longer.

The fatigue:

Can be severe
Isn't caused by an unusually difficult activity
Isn't relieved by sleep or rest

Worse symptoms after activity (required)

People with ME/CFS experience a worsening of their symptoms after any type of activity - physical or mental. These activities wouldn't have been a problem before they became ill. This is called Post-Exertional Malaise (PEM).

PEM can lead to a cycle of "pushing" to do more, followed by "crashing." During a crash, people with ME/CFS may have a variety of symptoms. These can include difficulty thinking, problems sleeping, sore throat, headaches, feeling dizzy, or severe tiredness.

It may take days, weeks, or longer to recover from a crash. Some people may be confined to bed or the house. As examples:

Attending a school event may leave someone house-bound for days.
Grocery shopping may require a nap in the car before driving home.
Doing errands may require getting a ride home.
Showering may leave someone bed-bound for days.
Working may mean spending nights and weekends recovering.

Sleep Problems (Required)

People with ME/CFS may not feel better or less tired, even after a full night's sleep. Some may have problems falling asleep or staying asleep.

Additional symptoms (At least 1 required)

In addition to the three required symptoms above, one of the following two symptoms is needed to be diagnosed with ME/CFS.

Memory and thinking problems

Most people with ME/CFS have trouble thinking quickly, remembering things, and paying attention to details. People with ME/CFS often say they have “brain fog” to describe this problem. This is because they feel “stuck in a fog” and not able to think clearly.

Problems being upright

People with ME/CFS often report their symptoms get worse when they are standing or sitting upright. This is called orthostatic intolerance.

People with ME/CFS may be lightheaded, dizzy, weak, or faint while standing or sitting up. They may have vision changes like blurring or seeing spots.

Other common symptoms

Many but not all people with ME/CFS have other symptoms.

Pain is very common in people with ME/CFS. The type of pain, where it occurs, and how bad it is varies a lot. The pain people with ME/CFS feel is not caused by an injury. The most common types of pain in ME/CFS are:

Muscle pain and aches
Joint pain without swelling or redness
Headaches, either new or worsening

Some people with ME/CFS may also have:

Tender lymph nodes in the neck or armpits
Frequent sore throat
Digestive issues, like irritable bowel syndrome
Chills and night sweats
Allergies or sensitivities to foods, odors, chemicals, light and noise
Muscle weakness
Shortness of breath
Irregular heartbeat
Disclaimer: This website is for informational purposes only. The information provided on this website is not intended to be a substitute for professional medical advice, diagnosis, or treatment.

ME/CFS is a serious, debilitating illness that makes it hard for people to do activities they could previously do without difficulty.

For Everyone

Health care providers.

Study protocol
Open access
Published: 15 May 2024

Comparing effectiveness of physiotherapy versus drug management on fatigue, physical functioning, and episodic disability for myalgic encephalomyelitis in post-COVID-19 condition: a study protocol of randomized control trial

Altaf Hossain Sarker 1 ,
K.M. Amran Hossain 2 ,
Md. Feroz Kabir 2 ,
Sharmila Jahan 2 ,
Md. Zahid Hossain 2 ,
Tofajjal Hossain 2 &
Iqbal Kabir Jahid 1

Trials volume 25 , Article number: 321 ( 2024 ) Cite this article

19 Altmetric

Metrics details

Physiotherapy interventions effectively improved fatigue and physical functioning in non-COVID patients with myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS). There is a research gap on the effectiveness of physiotherapy interventions versus drug management on ME/CFS in post-COVID-19 conditions (PCC).

We planned a three-arm prospective randomized control trial on 135 PCC cases with ME/CFS who are diagnosed between 20 November 2023 and 20 May 2024 from a population-based cohort. The study aims to determine the effectiveness of physiotherapy interventions as adapted physical activity and therapeutic exercise (APTE) provided in institution-based care versus telemedicine compared with drug management (DM). Participants will be assigned to three groups with the concealed location process and block randomization with an enrollment ratio of 1:1:1. The post-treatment evaluation will be employed after 2 months of interventions, and follow-up will be taken after 6 months post-intervention. The Chalder fatigue scale will measure the primary outcome of fatigue. SF-36 and the disability-adjusted life years (DALYs) will measure the secondary outcome of physical functioning and episodic disability.

This study will address the research gap to determine the appropriate approach of physiotherapy or drug management for ME/CFS in PCC cases. The future direction of the study will contribute to developing evidence-based practice in post-COVID-19 condition rehabilitation.

Trial registration

The trial is registered prospectively from a primary Clinical Trial Registry side of WHO CTRI/2024/01/061987. Registered on 29 January 2024.

Peer Review reports

Introduction

Post-COVID-19 condition (PCC) can be defined as persistent symptoms for more than 12 weeks of SARS-COV-2 diagnosis that lasts for at least 2 months and is not related to any other clinical diagnosis [ 1 ]. This clinical case definition of PCC by WHO working group is more specific in terms of diagnosis and explaining the symptom responses causing episodic disability [ 2 ]. The prevalence of post-COVID-19 condition is globally estimated at 43%, and in Asia 51%, of all COVID-19 cases [ 3 ]. Two Bangladeshi large-scale studies found a different prevalence of PCC also known as long COVID between 16.1% [ 4 ] and 24% [ 2 , 5 ]. The symptom responses of PCC are diverse; among all the symptoms, pain and fatigue are prominent [ 2 , 3 , 4 , 5 , 6 ].

Myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS) is an ongoing multisystem relapsing–remitting symptom characterized as fatigue associated with pain [ 7 ] that can be evident in any post-viral sequelae. ME/CFS impacts the cognitive, immune, and autonomic nervous systems [ 7 , 8 ]. This is a disabling condition with a poor progression that significantly impacts the activity and performance of an individual [ 9 ]. One study finds that ME/CFS is one of the prevalent conditions among PCC cases, with a global prevalence nearing 45% of all post-COVID-19 (PCC) cases [ 10 ]. In the USA, 2.5 million people suffer from ME/CFS for different reasons [ 11 ]. As per the disease burden, PCC has less research and funding [ 12 ]. ME/CFS affects a person’s daily activities and participation in livelihood, including physical and psychological state [ 10 ]. Moreover, PCC is clinically related to impaired physical functioning and reduced quality of life [ 13 ]. ME/CFS have a wide range of symptoms; the key clinical features include fatigue and pain; the additional symptoms are headache, photophobia, problems in short-term memory, reduced ability to multitask works, brain fog, and difficulty in working online or even watching television [ 14 ].

The management of ME/CFS is symptomatic, which includes managing underlying pain symptoms, fatigue, brain fog, sleep issues, musculoskeletal problems, and postural tachycardia syndrome (POTS) [ 15 , 16 ]. ME/CFS symptoms are treated with a variety of medications such as amphetamine, methylphenidate, naltrexone, duloxetine, gabapentin, intravenous solution, Dexedrine, fludrocortisone, trazodone, clonazepam, tricyclic antidepressants, ketotifen, montelukast, diphenhydramine, and metoprolol [ 15 , 16 ]. Other management by medications for ME/CFS includes treating with azithromycin, remdesivir, favipiravir, infliximab, tocilizumab, siltuximab, hydrocortisone, rituximab, rintatolimod, and intravenous immunoglobulin [ 17 , 18 ]. The overall physiotherapy management for ME/CFS aims to improve the painful status, cardiorespiratory functions, adaptive coping for fatigue, energy consumption and restoration, and improving physical and psychological well-being [ 19 ]. Physiotherapy management of ME/CFS cases includes exercise, pacing, and different indicative approaches such as cognitive behavioral therapy [ 19 , 20 ]. A wide range of exercise therapies can be prescribed for ME/CFS, including customized exercise, aerobic exercise, and adapted physical activity and therapeutic exercise programs [ 11 , 19 , 20 ]. Evidence suggests adaptive physical activity and therapeutic exercise programs are more effective than passive control or cognitive behavioral therapy for non-COVID patients having ME/CFS [ 13 ].

The physiotherapy management for ME/CFS must adhere to safe post-COVID-19 condition rehabilitation [ 21 ] and NICE guidelines [ 22 ]. A study examined the effect of exercise on patients with ME/CFS in non-COVID patients and found promising results in favor of exercise therapy [ 19 ]. The key outcome of exercise in ME/CFS is restoration of physical functioning that is significantly improved even after 12 to 24 weeks of interventions [ 22 , 23 , 24 , 25 ]. In recent studies, the adapted physical activity and therapeutic exercise programs (APTE) have been considered superior to other exercise programs, patients’ education, or active control for non-COVID cases having ME/CFS [ 26 ]. As there is no study exploring the outcome of APTE on ME/CFS in PCC cases, our hypothesis is that APTE can be more effective in reducing fatigue and improving physical functioning than drug management for PCC cases having ME/CFS. Our study aims to determine the effectiveness of adapted physical activity and therapeutic exercise programs (APTE) through institution-based care (APTE-I) versus telemedicine (APTE-T) compared with drug management (DM) on fatigue, physical functioning, and episodic disability for PCC cases having ME/CFS. The objectives are to (1) find baseline compatibility among APTE-I, APTE-T, and DM; (2) determine the among-group, among-observation outcomes on fatigue, physical function, and episodic disability for PCC patients having ME/CFS; and (3) present the post hoc within-group, within-observation outcomes of APTE-I, APTE-T, and DM.

Methodology

Study design.

The proposed study will be a three-arm randomized clinical trial (RCT) of PCC patients diagnosed with ME/CFS according to WHO working group criteria [ 1 ]. PCC cases having ME/CFS will be randomized to three separate groups: adapted physical activity and therapeutic exercise provided in an institution-based setting (APTE-I), adapted physical activity and therapeutic exercise provided through telemedicine (APTE-T), and drug management (DM) group. Participants will be enrolled from a population-based inception cohort of post-COVID-19 cases [ 2 , 6 ], with defined eligibility criteria.

Sample size calculation

The sample size was calculated using the software ClinCalc [ 27 ], and the key primary outcome was determined as the score of fatigue in the Chalder fatigue scale (CFS) [ 28 ]. Sample size has been calculated as the minimal clinically important differences (MCID) of CFS were estimated at 9.14 ± 2.73 in 0–33 Chalder fatigue scale, and superiority is considered as a 15% improvement from the baseline. The enrolment ratio will be 1:1:1. The calculation was completed with 80% power and an alpha value of 0.05. With the calculation, the total sample stands at 124, and each group will have a minimum sample of 42. For safety purposes, we considered each group sample to be 45.

Δ =| ${\mu }_{2}-{\mu }_{1}$ |= absolute difference between two means,

${\sigma }^{1},{\sigma }^{2}$ = variance of means #1 and #2

${n}_{1}$ = sample size for group #1 ${n}_{2}$ = sample size for group #2

α = probability of type I error (usually 0.05)

β = probability of type II error (usually 0.2)

z = critical Z value for a given α or β

k = ratio of sample size for group #2 to group #1

Study duration

Participants will be recruited between 21 May 2024 and 20 August 2024. Baseline compatibility will be conducted on the initial recruitment day, followed by the intervention and outcome evaluation. The post-treatment assessment will be performed after 2 months of the initial recruitment, and the follow-up will be undertaken after 6 months of post-intervention evaluations.

Study population, samples, and eligibility criteria

The post-COVID-19 condition (PCC) cases of ME/CFS will be screened from the inception cohort [ 6 ] of post-COVID cases. The respondents from Dhaka and Khulna divisions will be considered a study population. The eligibility criteria for inclusion in the study will be participants (1) aged 18 years or above, (2) COVID-19 symptoms onset at least 12 weeks and perseverance at a minimum of 8 weeks [ 1 ], (3) diagnosed PCC according to WHO working group criteria [ 1 ], (4) diagnosing ME/CFS according to 2006 Canadian consensus criteria [ 29 ], (5) willing to participate in the trial with consent of adherence with the interventions, and (6) eligible for drug management according to the physician’s assessment. Exclusion criteria will be (1) any preexisting post-exertion symptom exaggeration, (2) any preexisting clinical condition with fatigue such as cardiovascular or neurological disability, (3) any red flags or signs that are explained as contraindication according to safe post-COVID-19 condition rehabilitation guideline [ 30 ], and (4) patient drop-out within the 1st week of inclusion.

Study settings

ME/CFS patients will be recruited and treated in three specialized hospitals. The study will be conducted in BRB Hospital Limited and Specialized Physiotherapy Hospital Ltd. in the Dhaka division. In the Khulna division, the treatment center will be the Department of Physiotherapy and Rehabilitation at Jashore University of Science and Technology. The APTE-I, APTE-T, and DM groups will be recruited from any centers. To prevent cross-contamination of the data, different treatment set-ups will be arranged in each study setting, and separate personnel will be employed for each treatment group.

Study procedure

We will adopt a randomized sample enrollment and recruitment process in the trial. The respondents will be recruited from the inception cohort through the hospital-based randomization process. In group allocation, concealed allocation will be employed for APTE-I, APTE-T, and DM groups, and block randomization will be adopted. We will follow the standard criteria for maintaining the protocols as per Standard Protocol Items: Interventional Trials 2013 (SPIRIT guidelines) to ensure the rigor of designing the trial protocol (Table 1 ).

Recruitment strategies

The population of the study will be the diagnosed ME/CFS cases of inception cohort conducted in Bangladesh. We will have a pool of selected samples who have the disease and are aged more than 18 years, and we will do simple random sampling through random numbers to allocate the groups and centers by Excel “rand” function. The allocation will be computer-generated and concealed.

Interventions

The intervention protocol for APTE-I and APTE-T will be according to an e-Delphi consensus [ 31 ], systematic review [ 32 ], Cochrane review [ 13 ], clinical trial [ 26 ], and a systematic review research from a research group in Norway [ 19 ]. The APTE protocol will consist of breathing exercises and breathing control exercises [ 32 ], exercises to improve flexibility and motor activities [ 13 ], aerobic capacity exercise [ 13 , 19 , 32 ], and interventions to maintain a healthy lifestyle [ 19 , 31 , 32 ]. All the interventions will be provided as per the recommendation from Safe Post-COVID-19 condition rehabilitation [ 30 ]. To ensure a safe application of interventions, we will screen the participants for red flag signs and then prescribe personalized interventions supervised by a registered physiotherapist. In each session, patient feedback will be taken to ensure nothing is causing harm to the patient.

Institute-based adapted physical activity and therapeutic exercise (APTE-I)

APTE-I will be provided under the consultation of a consultant physiotherapist specializing in post-COVID-19 condition rehabilitation. Interventions will be provided in 45-min sessions in a one-to-one approach. There might be some home exercises or advice to follow at home. There will be continuous communication with patients, to ensure that the treatment does not deteriorate the symptoms.

Adapted physical activity and therapeutic exercise through telemedicine (APTE-T)

For APTE-T, the interventions will be provided by a consultant physiotherapist through digital media. such as Zoom, WhatsApp, Facebook Messenger, or the personalized app of the Department of Physiotherapy and Rehabilitation at Jashore University of Science and Technology. It will be a 45-min session with a one-to-one approach. The patient will be performing exercises or advice at home. The physiotherapist will explain and demonstrate procedures through cameras, and the patient will perform. There will be continuous communication with the patients to ensure that the interventions provided are performed accordingly and are safe.

Drug management (DM)

Participants of the drug management group will receive drug interventions as azithromycin, remdesivir, favipiravir, infliximab, tocilizumab, siltuximab, hydrocortisone, rituximab, rintatolimod, and intravenous immunoglobulin [ 17 , 18 ]. The drug interventions will be directly prescribed by a physician specialized in treating PCC cases. A single brand name will be prescribed for each drug. We will communicate with the patients to ensure no adverse effects of the medications. The patient will be given a choice if they are willing to join the exercise programs; they have full liberty to join the programs even after the completion of the trial.

Treatment progression

The participants of all three groups will be performing exercise or taking their treatment for 8 weeks. The exercise group will take the interventions formally twice a week for 8 weeks with continuous monitoring. In case of any adverse effect, additional sessions will be employed depending on the opinion of the consultant physiotherapist or physician. The overall treatment for ME/CFS will be provided actively for 2 months, and after that, the treatment will stop, and there will be a 6-month follow-up.

Outcome measures

Chalder fatigue scale (cfs).

Fatigue is the primary outcome of the study, and Chalder fatigue scale (CFS) [ 28 , 33 ] will be used to measure the outcome of fatigue. CFS is an 11-item questionnaire with each item corresponding to a 4-point Likert scale [ 33 ]. A higher score indicates a higher level of fatigue. CFS is a valid and reliable scale with an agreeable internal consistency and a coefficient of 0.78–0.96 [ 34 ].

Physical functioning sub-domain of SF-36

The secondary outcome will be physical functioning. It will be determined through the physical function measures of SF-36 [ 35 , 36 , 37 ]. SF-36 is a valid and reliable tool for measuring health status with eight subdomains, one of which is physical functioning items [ 38 ]. Scores in the physical functioning section range from 0 to 100 [ 39 , 40 ]. The larger scale reflects that the participants can perform all physical activities, and the lower scale indicates a limitation in physical function [ 41 ]. SF-36 has a reliability score of 0.80, and the physical functioning section reliability score is 0.90 [ 39 ].

Disability-adjusted life years (DALYs)

Another secondary outcome will be episodic disability of PCC, which will be measured by calculating disability-adjusted life years (DALYs). DALYs will be calculated by the sum of years of healthy life lost due to disability (YLDs) and years lost due to premature mortality (YLLs). For YLDs, the incidence of ME/CFS from the previous study (I), disability weight of PCC (DW), and duration of ME/CFS in weeks ( L 1 ) will be determined. The YLLs will be calculated by estimating the number of deaths due to COVID-19 in Bangladesh (N) and estimating L 2 by deducting the mean age of death people from COVID-19 from the average life expectancy in Bangladesh.

The original questionnaire was formulated in English. Then, a bilingual researcher who is not involved in this study project translates forward Bangla to backward English.

Study guideline

The study will follow the Consolidated Standards of Reporting Trials (CONSORT) guidelines mentioned in Fig. 1 .

CONSORT flow diagram of the proposed trial

Minimization of bias and blinding process

Three separate independent blinded assessors will document the baseline posttest and follow-up. All the intervention teams will be separate. The respondents of the physiotherapy groups will be blinded to the group allocations along with the treatment providers. The treatment providers will be aware of the treatment and treatment components, but they will be blinded to the group allocations. Participants will not pay for any interventions or be given any remunerations. The overall research team will not participate in any intervention or assessment. Training or demonstrations will be provided for data collectors, but no other measures will be taken.

Monitoring data quality

There will be a separate data management team and one trial manager to ensure the rigor of the entire trial procedure. Only auditing and investigating the outliers will be done, and there will be no manipulation. There will be one internal and one external data monitoring personnel to ensure good clinical practice.

Clinical monitoring team

A monitoring team consisting of two physiotherapists will monitor the trial procedure, especially monitoring the participants and ensuring that they are following the treatments. They will also ensure that there are no symptoms of exacerbation after the treatment. In case of any irregularities, they will report directly to the trial manager.

Safety measures and managing adverse effects

We anticipate no adverse effect for the physiotherapy groups except minor post-exertional symptom exacerbation. We will follow safe post-COVID-19 condition rehabilitation guidelines [ 30 ] and maintain subjective and objective analysis and plan (SOAP note) to ensure safe interventions. For drug management, the participants will be monitored closely, and in case of any adverse effects, the responsible physicians will be informed. All the adverse effects observed will be monitored, documented, and recorded during the final publication of the study.

Participate

Screening participants will be informed about the study’s aims, objectives, and intervention process and provided a written informed consent form. For online patients, informed consent will be obtained using a Google form and a verbal procedure. The full trial has ethical approval from the Institutional Review Board on the Institute of Physiotherapy, Rehabilitation & Research (IPRR) BPA-IPRR/IRB/19/01/2023/69 [13/02/2023] as part of a PhD project. The trial is registered to the Clinical Trial Registry India (CTRI), the primary trial site of the World Health Organization CTRI/2024/01/061987 [registered on: 29 January 2024]. We will follow the Helsinki Declarations’ ethical guidelines per the rules provided by the ethical approval bodies. Before enrolling, we will provide written informed consent and ensure the participation is voluntary, and they can withdraw the trial anytime during the trial. Also, the participants will be ensured that withdrawal from our study will not change their treatment process. The trial manager, principal investigator, and data auditors will have access to the final trial data set. After completing the trials, all the authors will have equal access to the anonymous data. All the hard copies and soft copies of data collection will be kept to the principal investigator, and there will not be any disclosure or access to the identification of trial patients. There will be post-trial care only if any adverse effects are noted during the trial.

Data analysis

Data will be analyzed through Statistical Packages of Social Science (SPSS) version 23 for Windows. Normality will be examined through the Kolmogorov-Simonov and Shapiro–Wilk tests [ 42 ]. The descriptive analysis will be completed using the mean and the standard deviation for continuous variables and frequency and percentage for categorical variables. A baseline compatibility test will be performed using one-way ANOVA or Friedman’s ANOVA according to the data distribution. The within-group changes among three measurements or groups will be performed using one-way ANOVA or Friedman’s ANOVA and subsequent post hoc tests [ 43 ]. The among-group, among-observation changes will be determined using MANOVA or the multivariate Kruskal–Wallis test. We will use the intention-to-treat analysis. The significance level will be set as alpha value < 0.05, and subsequent Bonferroni correction will be made for post hoc tests. No interim analysis will be done.

Trial status

Trial version number is 1, and the protocol date was 3 August 2023. This study will be recruiting participants from 21 May 2024 to 20 August 2024.

Dissemination

After the completion of the trial, the result will be presented in the seminars to the relevant stakeholders in Bangladesh. Final research records will be presented and submitted to an indexed journal for publication. There will be a dissemination session for the physiotherapist and physicians to manage ME/CFS in post-COVID-19 conditions. In accordance with the criteria set forth by the International Committee of Medical Journal Editors (ICMJE), authorship for publications regarding trial results will be determined. We have a plan to publish the unanimous data.

ME/CFS has an overall disease burden double of HIV and half of breast cancer in the USA [ 44 ]. From a global perspective, it is estimated that nearly 45% of the global PCC survivors have ME/CFS [ 13 ]. The study is designed for a significant study population. According to previous studies, COVID-19-related low back pain [ 45 ], ME/CFS-associated pain, fatigue, and impaired physical functioning impact overall activity limitation, participation restriction, and environmental and social factors proceeding towards disabilities [ 19 , 30 , 31 , 32 ]. There is limited evidence on pharmacological management [ 24 ], and a few nonpharmacological trials have been examined on the ME/CFS in non-COVID cases [ 13 , 17 , 20 , 23 , 24 ]. The proposed study will cover a significant research gap in estimating the outcome of physiotherapy compared with drug management for PCC cases having ME/CFS. In this study, we are ensuring safe treatment approaches according to existing guidelines [ 17 , 18 , 30 ]. The interventions also adhere to the Bangladeshi setup [ 31 , 32 ]. Also, the study procedure adhered to standard protocols such as SPIRIT and CONSORT. Different layers of blinding and masking will be adopted to prevent the cross-contamination of the data. We have chosen valid and reliable tools for measuring the primary and secondary outcomes. The results of the studies will be confined to specific samples derived from 2 divisions of Bangladesh. However, the samples will be framed through a randomized process so that the findings will outweigh the limitations.

Implication of the study

The study’s implication will contribute to a paradigm shift in treatment approaches for ME/CFS. The trial’s key strength is covering the recommended research gap, and this study will be implicated in the clinical management of ME/CFS for PCC cases. The study will enrich the body of knowledge of intervention protocols in post-COVID-19 condition rehabilitation or any post-viral sequel rehabilitation globally.

In conclusion, the overall study process is well-designed, synchronized, and well-planned to be executed through a complicated sequential process. We expect the findings to guide good clinical practice for managing ME/CFS for PCC cases.

Availability of data and materials

There is no available data as this is a trial protocol.

Abbreviations

Chronic fatigue syndrome

Cognitive behavioral therapy

Adapted physical activity and therapeutic exercise program

Active control

Minimal clinically important differences

Disability-adjusted life years

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We followed the Standard Protocol Items: Recommendations for Interventional Trials guideline for the study protocol. The study implication will follow Consolidated Standards of Reporting Trials guidelines.

The study received partial funding for data collection and intervention provision as a doctoral study (Grant No.: 23-FoBST-06) from Jashore University of Science and Technology through the University Grants Commission (UGC) of the People’s Republic of Bangladesh.

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Altaf Hossain Sarker & Iqbal Kabir Jahid

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K.M. Amran Hossain, Md. Feroz Kabir, Sharmila Jahan, Md. Zahid Hossain & Tofajjal Hossain

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Sarker, A.H., Hossain, K., Kabir, M.F. et al. Comparing effectiveness of physiotherapy versus drug management on fatigue, physical functioning, and episodic disability for myalgic encephalomyelitis in post-COVID-19 condition: a study protocol of randomized control trial. Trials 25 , 321 (2024). https://doi.org/10.1186/s13063-024-08077-x

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DOI : https://doi.org/10.1186/s13063-024-08077-x

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A graduating senior at a Tennessee high school launched his diploma at another student as he was being escorted out of the ceremony for allegedly showing gang signs, igniting an on-stage fight.

The caught-on-camera brawl between the two Hamilton High School graduates happened as their peers were being celebrated inside the Cannon Center in Memphis, Tenn. on Thursday night.

The incident, which stemmed from a prior incident with the two students, started when the agitator was seen throwing up gang signs toward another student, according to WREG.

A security guard caught the student’s hand gesture and promptly removed him from his seat.

As the unruly graduate was escorted off the stage, he chucked his graduation binder at his unsuspecting victim.

A graduating senior from Hamilton High School chucked his diploma at one of his classmates during their graduation ceremony on Thursday in Memphis, Tennessee.

The security guard quickly grabbed the suspected thrower and pulled him off the stage. The student that was hit, jumped over three rows of seats and chased after his attacker.

Two other students joined the chaotic scene as several security guards rushed the stage to break up the brawl.

One of the students was pepper sprayed as the security attempted to detain the students.

The ceremony was disrupted for a few minutes before the commencement ceremony resumed.

The incident started when the agitator allegedly flashed gang signs toward another student.

“We are not going to stop our graduation. We are going to continue because this is the Wildcat way,” one speaker said through the sound system.

The fight didn’t sit well with several parents who called out the student’s behavior during the momentous occasion.

“How do you fight at a graduation? It’s a time for celebration so why is everybody fighting? Why are you angry?” Quita White, whose son graduated, told WREG.

The fight didn't sit well with several parents, including Quita White, who called out the student's behavior during the momentous occasion.

All involved were given a Juvenile Summons or Misdemeanor Citation and released to their parents, the outlet reported.

One officer was injured during the scuffle.

“He has a big bruise on his left eye, they broke his glasses, the front of his foot looks like it is fractured,” the injured worker’s wife said.

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The unidentified woman blamed the school principal for ruining other students’ special day.

“Knowing that this was the end of the school year, knowing that these kids were battling to get each other,” she said. “Now they were not in school. But they gave him a packet. “You guys can come and walk,” the furious woman said.

The victim’s wife doesn’t believe the two students should have been at the ceremony in the first place.

The unidentified woman blamed the Hamilton High School principal for ruining other students’ special day.

“Memphis has a huge problem, period,” White said, “It’s a real problem with youth.”

The remaining students, whose names were still not called, got their chance on stage after the fighting settled down and the suspects were detained.

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Graham Bundy Jr. is a steadying influence for Georgetown men’s lacrosse

The graduate student is a vital piece for the Hoyas, who face top-seeded Notre Dame on Saturday in the NCAA tournament quarterfinals.

There isn’t much Graham Bundy Jr. hasn’t witnessed since he agreed to become a part of Georgetown’s men’s lacrosse team.

The graduate student committed to the program while he was a high school freshman in 2016, two years before the Hoyas began a string of six consecutive NCAA tournament appearances. (NCAA rules have since changed to limit recruiting to Sept. 1 of a player’s junior year). He has helped Georgetown win four Big East tournaments. He is the only player in program history with four 30-goal seasons.

But as much as anything else, he is a steadying force who has started on four NCAA tournament quarterfinalists, including this year, as the eighth-seeded Hoyas (13-3) head into a meeting with top-seeded Notre Dame (13-1) on Saturday in Hempstead, N.Y. A victory would vault Georgetown into the semifinals for the first time since 1999.

When Georgetown stared at an 0-3 start last year and dropped its first two this year, Bundy settled his teammates with a stay-the-course approach. That works in games, too, such as when the Hoyas rallied from fourth-quarter deficits in both of their Big East tournament games this month, or were down five in the first half of Sunday’s 12-9 win over of Penn State in the first round of the NCAA tournament.

“I take a lot of pride in being that guy who can relax everyone,” Bundy said. “I think the coaches’ job is to get everyone riled up and it’s kind of the players’ job to care for each other and find a lot of trust and right the wrongs, whether it’s the beginning of the year or the beginning of games. We don’t like finding ourselves in those situations, but we’ve found some weird comfort in it.”

Bundy led the Hoyas in points two seasons ago, setting career highs in goals (45) and assists (25). Last year, he was a more complementary but still dangerous player when the Hoyas added graduate transfers Tucker Dordevic and Brian Minicus.

He has arguably been more valuable than ever this spring. The only one of Georgetown’s top six goal-scorers from last season to return, Bundy is akin to a star offensive lineman in Coach Kevin Warne’s mind. The Hoyas had one major piece who checked a big box, and then it was a matter of filling in around him.

“It was vital he decided to use his fifth year, but also at the same time for us knowing here’s a guy we can rely on to get 30-some goals from and we kind of know what he does and build the other roles where we don’t have to ask guys to do too much, and it makes it easier,” Warne said.

But not a breeze. Georgetown aggressively added transfers last year, so much so that its top three scorers and assist leader were all graduate students who would spend only one year in the program.

The Hoyas picked up Alexander Vardaro from Princeton this year, but they also lean on Aidan Carroll, an attackman who has enjoyed a breakout senior year after spending his first three years as a reserve, and sophomore midfielders Patrick Crogan and Jordan Wray.

That newness meant Georgetown would pay a steeper penalty if it made poor decisions, and Bundy carried that message.

“We need to be a lot more consistent, and there’s not really any room for error in terms of not playing good team offense because we don’t have as many of those, ‘Holy cow, how did you just make that play?’ type of moments,” Bundy said. “It really takes all six guys that are out there to make the wheel keep turning.”

It helps to have something of a lacrosse chameleon in Bundy, a player who has grown accustomed to filling roles in which he might not always be the most comfortable, but always eagerly embraces them. Also useful is the ability to skillfully handle what is thrown his way.

“Being able to throw the ball to a guy like that who can score off the dodge, who can score inside, who can score from deep, right [or] lefty, it makes my job a lot easier and guys at the attack level a lot easier,” said attackman TJ Haley, who has assisted on 24 of Bundy’s 153 career goals, the most of any Hoya. “Graham can play attack and get the No. 1 pole and also play midfield coming out of the box. It gives our offense a lot of ability to dictate what the defense is going to do rather than them dictating it toward us.”

That calming influence extends to the coaching staff. Warne, well known in the sport for not hiding his emotions on the sideline, gave the Hoyas an earful during a timeout when they trailed 7-2 in the second quarter Sunday against Penn State.

Then he turned around to see Bundy smirking at him.

“He just starts laughing and says, ‘You okay?’” Warne said. “And I’m like, ‘Yes, I’m okay.’ He’s such a wise guy. It shows you the relationship I appreciate with him. We’re here as coaches to help these guys, but also they’re here to help us. It’s a collaborative effort. We can joke around, and it’s good when you’re able to do that where two seconds later you can joke around and say: ‘Let’s get the next one. Let’s go.’”

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Mechanisms of Post-Stroke Fatigue
Brad A. Sutherland, PhD10, Connie Wong, PhD11, and ... Post-stroke fatigue (PSF) is a significant and highly prevalent symptom, whose mechanisms are poorly understood. The third Stroke Recovery and Rehabilitation Roundtable paper on PSF focussed primarily on defining and measuring PSF while mechanisms were briefly discussed. This companion ...
Outcomes of a Fatigue Management Intervention for People With Post
Abstract. Objective: Fatigue is identified as one of the most prevalent and persistent problems reported by people with post COVID-19 condition that negatively impacts on everyday living and resumption of pre-COVID-19 lifestyle. A pilot occupational therapy fatigue management intervention was designed for patients presenting with post COVID-19 ...
The highs and lows of a PhD in ME research
Myalgic encephalomyelitis is a long-term debilitating condition. It has a wide range of symptoms including extreme post-exertional fatigue, cognitive difficulties and mobility difficulties which are worsened after physical or mental exertion. Katharine's PhD in the Simon Carding group was funded by the UK charity Invest in ME Research.
Post-COVID Fatigue Linked to Physical Causes
Researchers from Amsterdam UMC and Vrije Universiteit Amsterdam (VU) have identified a physical basis for the enduring fatigue experienced by post-COVID patients. Professor of infectious diseases at Amsterdam UMC, Michèle van Vugt, notes, "We observe muscle changes in these patients." The study's findings were published in Nature Communications.
Deep phenotyping of post-infectious myalgic encephalomyelitis ...
Post-infectious myalgic encephalomyelitis/chronic fatigue syndrome (PI-ME/CFS) is a disabling disorder, yet the clinical phenotype is poorly defined, the pathophysiology is unknown, and no disease ...
Mechanisms of Post-Stroke Fatigue: A Follow-Up From the Third Stroke
Annapoorna Kuppuswamy, PhD, 1, 2 Sandra Billinger, PhD, 3 Kirsten G. Coupland, PhD, 4 Coralie English, PhD, 5 Mansur A. Kutlubaev, ... Post-stroke fatigue (PSF) is a significant symptom for stroke survivors with few effective, evidence-based interventions currently available. The lack of evidence-based interventions is largely a result of poor ...
Should I take an iron supplement? Here's what the science says
Iron is an essential micronutrient that helps produce healthy red blood cells. People with an iron deficiency often feel exhausted. Some may find it hard to catch their breath or notice paler skin ...
Long COVID, ME/CFS and the Importance of Studying ...
The COVID-19 pandemic posed many unprecedented global challenges. But as reports of Long COVID cases grew, there were some patients for whom the persistent symptoms of the post-viral syndrome felt familiar.. Even before COVID-19 spread across the planet, patients were presenting with similar constellations of symptoms following other infections, which doctors and researchers collectively ...
PT researchers studying treatments for post-stroke fatigue
Now, two Texas Woman's professors are hoping their research can lead to treatments that can improve life for those who are dealing with post-stroke fatigue. Associate Professor Hui-Ting Goh, PhD and Professor Emeritus Elaine Jackson, both from the School of Physical Therapy at the Dallas Center, have been awarded a $372,870 three-year grant ...
Management of Fatigue in Adult Survivors of Cancer: ASCO-Society for
Web-based CBT interventions have demonstrated efficacy for fatigue in the post-treatment setting 54 and should also be evaluated during treatment. 1.3. Mindfulness-Based Programs Literature Review Update and Clinical Interpretation. ... PhD, Marlana Orloff, MD, the ASCO Evidence-Based Medicine Committee, and the Society for Integrative Oncology ...
Symptoms of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome
The fatigue: Can be severe. Isn't caused by an unusually difficult activity. Isn't relieved by sleep or rest. Worse symptoms after activity (required) ... This is called Post-Exertional Malaise (PEM). PEM can lead to a cycle of "pushing" to do more, followed by "crashing." During a crash, people with ME/CFS may have a variety of symptoms. These ...
Comparing effectiveness of physiotherapy versus drug management on
Physiotherapy interventions effectively improved fatigue and physical functioning in non-COVID patients with myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS). There is a research gap on the effectiveness of physiotherapy interventions versus drug management on ME/CFS in post-COVID-19 conditions (PCC). We planned a three-arm prospective randomized control trial on 135 PCC cases ...
Tennessee Hamilton High School graduate throws his ...
A graduating senior at a Tennessee high school launched his diploma at another student as he was being escorted out of the ceremony for allegedly showing gang signs, igniting an on-stage fight.
Spring 2024 GSRA Award Winners
The Graduate Student Research Awards are used to advance the scholarship of graduate students and faculty working in partnership. Grants up to $5,000 are awarded to the selected student/faculty pairs and are intended to help support student research, scholarship, and creative activity; give students experience writing grants; and foster the mentoring relationship between faculty and graduate ...
International student deposits in UK dive after graduate visas tightened
A letter last week co-signed by the heads of 17 local chambers of commerce warned that abolition of the graduate visa route and the resultant loss of income from international students would have ...
Graham Bundy Jr. leads Georgetown into NCAA men's lacrosse
The graduate student is a vital piece for the Hoyas, who face top-seeded Notre Dame on Saturday in the NCAA tournament quarterfinals. By Patrick Stevens May 17, 2024 at 9:22 a.m. EDT

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