parkinson's uk research support network

Research for Parkinson’s Disease

Unlike any other Parkinson’s charity in the UK, Parkinson’s Care and Support UK funds non-pharmaceutical research into managing, reversing, reducing and curing Parkinson’s Disease. This involves looking at what we put into the body and how this can affect someone’s lived experience of Parkinson’s.

With little advancement with prescription drugs over the past 50 years coupled with unpleasant side effects, PCSUK believes that our focus should be on tackling Parkinson’s with natural remedies and therapies.

We have entered an era where people are more conscious now more than ever about the effects that natural remedies have on their bodies. From eating well and having a gut-friendly lifestyle to natural supplements, research surrounding this topic has been increasingly popular and hopeful. This is an exciting and ground-breaking area of Parkinson’s research in which we will lead the way.

Privacy Overview

Register now or sign in to access the full podcast.

Username or Email Address

Remember Me

Create an account? Click Here

How did you hear about Parkinson’s Care and Support UK?

If You Have Already Account. Click Here

Research Support Network

The Parkinson’s Research Support Network is run by Parkinson’s UK and brings together people driven to find better treatments and a cure for Parkinson’s.

Get connected

Our Research Support Network connects you to all the latest Parkinson’s research news and opportunities.

Everyone is welcome, from researchers to people with Parkinson’s – all you need is an email address to get started.

Sign up to our Research Support Network  to receive regular emails about how to get involved in Parkinson’s research.

You will receive PUK’s monthly  Research Roundup , as well as opportunities to take part and have a say in research in your local area.

The Guildford and South Surrey Branch is affiliated to Parkinson’s UK and is run by volunteers to support those living in the Guildford and South Surrey area who have Parkinson’s and their families and friends.

Parkinson’s UK is the operating name of the Parkinson’s Disease Society of the United Kingdom, a registered charity in England and Wales (258197) and in Scotland (SC037554). Registered office: 215 Vauxhall Bridge Road, London, SW1V 1EJ.

• Oxford Branch
• OxWAG: younger people with Parkinson's
• Branch committee
• Branch meetings
• Future events
• Special events
• Sponsored challenges
• ParkyPals - our NEW phone chat service
• Support and assistance
• Exercise, therapy and learning opportunities
• Exercise and voice class timetables
• Support for younger and working-age people with Parkinson’s
• Respite care
• Recently diagnosed?
• Make a donation
• Our supporters
• Raising awareness
• Fundraising
• For professionals
• Member log in

What’s in the research pipeline?

Dr Kevin McFarthing is Clinical Trials Highlights editor of the Journal of Parkinson's Disease. That journal’s most-read article of 2022 is Kevin’s regular update on the clinical trials pipeline for promising drug therapies. Unlike many academic articles it’s open-access and so anybody can read it on the journal website at this link .

The importance of this work was highlighted in this recent editorial by Prof Bas Bloem and Dr Lorraine Kalia .

Informative articles by our own members

We are beginning to build a collection of research and information material written by Branch members. We hope you will find it useful.

From dogfish to Gila monsters - Parkinson’s treatments of tomorrow

Our Research Officer, Dr Kevin McFarthing, gave a presentation at our March 2020 branch meeting with an overview of the search for new treatments for Parkinson’s. Branch members also learnt the collective noun for groups of dogfish and lizards. You can find a copy of the slides here .

What goes wrong in Parkinson’s?

This short, easy-to-read article about the mechanisms in our brain cells that are affected by Parkinson’s was written by our Research Officer, Dr Kevin McFarthing, in April 2018. If you're unfamiliar with some of the scientific jargon and ideas that we often hear about when discussing Parkinson’s research and treatments, this will help! Download the article as a PDF document here, or preview it in your web browser using this link .

Regularly updated The Hope List: Parkinson’s Therapies in Development

Kevin also maintains a document listing all the drugs and other therapies that are being developed or in trial, along with some whose development has been abandoned. Download the Excel spreadsheet here, or preview it in your web browser using this link . Some of the information in Kevin's spreadsheet is taken from the PDTrialTracker.info website , which you may also find interesting.

Kevin presented some of his work on The Hope List at our Branch meeting in January 2019. His talk is available as a video screencast at this link .

Keeping up with Parkinson’s research

Anyone who's tried to stay up-to-date with research in the Parkinson’s field will know that there is a huge amount of information appearing every week, and unless you're an active researcher it's difficult to follow it all. There are many blogs, digests and other resources available on the web, but their quality is hard to assess and some have a very limited focus. We would like to suggest some resources that we know to be trustworthy and broad in scope:

Science of Parkinson’s blog (open in new tab)

Dr Simon Stott, a former Cambridge University Parkinson’s researcher who is now Deputy Director of Research at Cure Parkinson’s Trust , maintains a fascinating, wide-ranging and brilliantly written blog. He updates it two or three times a week. Some of the articles are quite technical, but they're always explained clearly and with humour. Highly recommended.

You can go straight to Simon's blog at this link . However, if you're new to it, his review of 2019 research may be a good starting point.

To receive regular updates on Simon's blog, you will need to have a Wordpress account so that you can "follow" it. If you don't want to sign up with Wordpress, you can simply check back and look at the blog every week or so – or, if you're a Twitter user, you can follow his account @ScienceofPD where he highlights new articles.

Parkinson’s UK Research Support Network

The "Parky RSN" provides regular email updates on research and how you can get involved. You can find out more by following this link .

The research blog of Parkinson’s UK (open in new tab)

Our parent charity Parkinson’s UK always takes huge care to provide accurate, up-to-date and clear information. Claire Bale, Beckie Port and other research staff at the charity maintain an interesting collection of material, with new items added every few days or so, at their research blog which you can find at this link . It covers current developments in research, but also includes material on patient experiences, living well with Parkinson’s, and background articles of general interest.

If you sign up for an account with the Medium.com website, you can "follow" Parkinson’s UK to get regular updates on new content.

The Oxford Parkinson’s Disease Centre

We are very lucky to have a world-renowned centre for Parkinson’s research on our doorstep in Oxford, the OPDC. Led by Professors Michele Hu and Richard Wade-Martins, it conducts basic research into the causes of Parkinson’s and carries out clinical studies, both to learn more about the condition and to test potential new therapies. You can find out more here on OPDC’s website .

Taking part in a clinical trial

If you’re interested in taking part in a clinical trial, first you will need to find out which trials are recruiting people with Parkinson’s. There are several good sites that provide information on such studies:

• Parkinson’s UK
• UK NHS Clinical Trials Gateway
• Cure Parkinson’s Trust
• Fox Trial Finder
• European Parkinson’s Disease Association

Parkinson's UK is the operating name of the Parkinson's Disease Society of the United Kingdom.

A registered charity in England and Wales (258197) and in Scotland (SC037554). Registered office: 215 Vauxhall Bridge Road, London SW1V 1EJ.

Welcome to the Research Support Network forum

• Articles that you have written and would like to make available to people in other regions to share
• Your experience of organising or attending research events or project visits or research speakers to your branch/group
• Your experience as a branch/group Research Champion and any advice you have on what has worked for you and what hasn’t
•  Updates of any regional research meetings
•  General sharing of thoughts and experience on RSN activity

Thanks and welcome to the forum. I have been a member of the RSN at least as long as I have been a member of Parkinson's UK. I am constantly discovering things that RSN members and others are doing to make Parkinson's research faster and more effective. Much of this goes on without fanfare which means we miss out on spreading the learning to other branches, regions or projects. I hope we can use the forum to share experiences

I think that supporting research is possibly the most important thing that people affected by Parkinson's can do. We all want the cure to come quickly. The person who discovers the cure(s) will be a researcher so it makes sense that we do all we can to speed them on their way. There are many opportunities to help make the research task easier and quicker ranging from participating in studies and trials, speaking to groups,  writing newsletters to spread knowledge and understanding about research, advising researchers, reviewing grant applications and more.

It's time to start shouting a bit more about the things we are doing or the things we wish we were doing.

Elegant Fowl

The meeting in Birmingham that you and your team, Claire, organised on Friday and Saturday last week went very well.  There were so many ideas about how the RSN could develop and how members can get together to do things in the regions in addition to responding to opportunities disseminated from National Office.  I hope we can talk about what we are doing throughout the country in this Forum and so maybe encourage people to do similar things - or radically different ones - in support of the research effort.

My involvement is with the East Midlands RSN group so I'd like draw your attention to our latest newsletter which I hope you will find interesting:  https://www.parkinsons.org.uk/sites/default/files/emrsn_newsletterapril2014.pdf .  Anyone is welcome to use any of the articles in their own newsletters if they want.  I hope more regional RSNs could have pages on the Parkinson's site so we could be inspired by what everyone is doing.  Ours is at http://www.parkinsons.org.uk/content/east-midlands-research-support-network .

Hello and thank you to both of you for helping start what I hope will be some really fruitful discussions. 

I completely agree that's it's time to start shouting about what we do. It is clear to me from our meeting that we have an incredibly engaged and willing group of people with some excellent thoughts and ideas on how we can take the Research Support Network to the next level.

We'll be working with some of our volunteers to ensure we've captured these thoughts and also building a plan of putting them into action. I hope that people feel they are able to follow up on any action/next steps from their own perspective but also that the Parkinson's UK Research Support Network team (staff and volunteers) are here to offer support if needed.  

It's a very exciting time for the Research Support Network and I look forward to working with you all.

Check out the latest East Midlands RSN News at https://www.parkinsons.org.uk/sites/default/files/emrsn_newsletterjuly2014.pdf

• Follow Follow

Research Support Network Newsletters

Written by Julie Neale

0 comment(s), published: may 19, 2023.

Please see below the latest Research Roundup letters from Parkinson’s UK

Research Roundup Thank you for your continued support of Parkinson’s research, and a warm welcome if you are new to the Research Support Network. Study reveals benefits of physical activity for Parkinson’s symptoms Researchers compared results from over 150 studies to understand how different types of physical activity can be used to manage Parkinson’s symptoms. Being physically active can have a positive impact on Parkinson’s symptoms, both physically and mentally. Research has shown that aiming for 2.5 hours of physical activity a week can help people with Parkinson’s take control of their condition. Read more about the benefits of physical activity on our website. While there are many different types of physical activity, some will naturally suit some people better than others. However, it’s unknown whether some specific activities might be useful to target particular symptoms. Or whether exercises should be advised at different stages of the progression of the condition. What did the researchers do? The research team conducted a form of study called a Cochrane review. They analysed results from 156 different studies involving different types of physical activity including dance, aqua-based training and weight training. They looked at feedback from participants in these studies to assess for changes in quality of life, and other common tests to monitor progression of the condition. Overall, the researchers found that taking part in physical activity had benefits for people with Parkinson’s in terms of movement or improved quality of life, when compared with people who had not been active. It was not clear whether specific forms of physical activity were better than others for people with Parkinson’s. The team did find some evidence that linked taking part in dance classes with an improvement in balance and other symptoms associated with movement. They also found that aqua-based training was linked to improvements in quality of life, although whether this was due to physical activity or social interaction at exercise classes was unclear. What does this mean? This study, combining results collected from over 7,000 people with Parkinson’s, offers great evidence that taking part in most types of physical activity can be beneficial for people with Parkinson’s. Importantly, there was very little evidence that physical activity resulted in harm, or worsening of symptoms, for any participants. Dr Becky Jones, Research Communications Officer at Parkinson’s UK, said: “Reviews like this are a great example of how looking across results from many different studies can provide us with a clearer picture of whether a treatment or an activity could be beneficial for people with Parkinson’s. The study adds to our existing knowledge that physical activity can be an important way for people with Parkinson’s to take control of the condition. “The Cochrane review highlights exciting areas that need further study. More research into the particular benefits of dance or aqua-based training could help guide people with Parkinson’s to try out activities that could have the most impact on symptoms. “As always, we recommend that anyone wishing to make a change to their lifestyle speak with their healthcare provider for advice before starting.” Early results offer new hope for dyskinesia treatment We’re excited to announce the positive results of a trial for a potential new treatment for people with Parkinson’s with levodopa-induced dyskinesia. This is the first completed clinical trial funded by the Parkinson’s Virtual Biotech. Dyskinesia is a debilitating side effect of current Parkinson’s medication, with around half (40 to 50%) of all people with Parkinson’s experiencing it after 5 years of taking levodopa, the main drug used to treat the condition. Up to 80% experience it after 10 years. With dyskinesia everyday tasks, such as eating, writing and walking, can become extremely difficult. In fact, uncontrolled movement was voted the third most important issue to be addressed by research in a recent Parkinson’s UK survey on quality of life. The main medication available to manage dyskinesia is amantadine, which can have side effects and does not work for everyone. The study tested whether a drug called NLX-112 is safe to use in people with Parkinson’s. It also looked at how effective it may be at reducing dyskinesia in people who take levodopa to manage their Parkinson’s. Its safety has previously been confirmed in people with other conditions. Serotonin cells in the brain have the ability to convert levodopa into dopamine. These cells are thought to contribute to the development of dyskinesia when they start to release dopamine erratically. NLX-112 works by targeting serotonin cells inside the brain, and decreasing the amount of dopamine the cells release. Supported by the Parkinson’s Virtual Biotech The clinical trial was co-funded by The Parkinson’s Virtual Biotech, the drug discovery arm of Parkinson’s UK, and The Michael J Fox Foundation for Parkinson’s Research. The projects the Parkinson’s Virtual Biotech funds are entirely driven by the Parkinson’s community and their priorities. This is the first completed clinical trial funded by the Parkinson’s Virtual Biotech, a global partnership with the Parkinson’s Foundation. The promising results show that this innovative way of working to fast-track the most promising breakthroughs through the drug development pipeline is bringing us closer to new treatments. What did the research set out to do? The phase 2a clinical trial investigated how safe and well tolerated the drug was on a small number of people with Parkinson’s. The trial also took a first look at the drug’s efficacy, which is how well it does its job. What did the clinical trial involve? 22 participants with Parkinson’s with levodopa-induced dyskinesia completed the 8-week trial in Sweden. 15 participants received NLX-112 and 7 participants received a dummy drug. Participants either received NLX-112 or the dummy drug in increasing doses during the initial 4 weeks, to minimise the potential side effects. They stayed on the maximum dose for 2 weeks, and then were weaned off the drug over 2 weeks. What were the results? The results achieved the first objective to suggest that NLX-112 was safe and well tolerated in people with Parkinson’s. The second aim of the study was to show that NLX-112 was effective in treating dyskinesia. The results suggest participants who received NLX-112 showed a significant reduction in their scores for dyskinesia, whereas those who received the dummy drug did not show a significant reduction in their scores. The side effects of participants who received NLX-112 were mild, which confirmed previous results. What’s next? Now that the phase 2a clinical trial has been completed, the researchers will complete a full analysis of the results. They will then progress to a phase 2b clinical trial where they will investigate the safety and efficacy of the drug in a larger group. Dr Arthur Roach, Director of Research at Parkinson’s UK, said: “We’re incredibly proud and excited by these early results from Neurolixis. They were one of the first companies that the Parkinson’s Virtual Biotech invested in, in partnership with The Michael J Fox Foundation, so to see it making positive progress just reiterates why this brave and innovative approach is right for the Parkinson’s community. It really is bringing us closer to new treatments that address the symptoms that the Parkinson’s community have told us are the most urgent and levodopa-induced dyskinesias is one of those. “Further studies will be necessary for regulatory approval and routine clinical use of NLX-112. But now people with Parkinson’s can have hope that a much-needed new treatment for levodopa-induced dyskinesias may be coming to them soon, and know that their support of the Parkinson’s Virtual Biotech has made this possible.” Best wishes, Becky Jones Research Communications Officer

Research Roundup Thank you for your continued support of Parkinson’s research, and a warm welcome if you are new to the Research Support Network. Brain Awareness Week — studying the power of the brain’s self-cleaning system In March we marked Brain Awareness Week, a celebration of research being undertaken to understand how the brain works, and crucially ways to help when things go wrong. At Parkinson’s UK, almost all of the research we fund relates to the brain in some way. We know the symptoms of Parkinson’s are caused by a loss of a brain chemical called dopamine. This is linked to the death of brain cells which produce it. So whether the research is studying the brain more closely to look at what causes this, studying the impact of certain drugs for treating symptoms. Or even studying the effects of certain activities such as physical activity on mental health, it would be tricky to study Parkinson’s without considering the brain along the way. Some of the projects we fund look a bit more closely at how the condition develops in the brain. One such project is being led by Dr Ian Harrison and Professor Mark Lythgoe at University College London. Ian and Mark are interested in how our brains normally get rid of waste products which build up throughout the day. Failed clearance of these waste products can lead to them building up in the brain, which can stop the cells from being able to carry out their job as usual. Many different neurodegenerative conditions are associated with a build up of brain waste, normally in the form of clumps of sticky protein. In Parkinson’s, the troublesome protein is called alpha-synuclein. Strands of the protein start to tangle together and clog up brain cells, causing damage which ultimately results in cell death. Flushing away the waste In our brains, we need a way to clear away the waste protein which builds up throughout the day. Luckily, we have a built-in self-cleaning system, called the glymphatic system, which kicks in while we sleep. The glymphatic system is a network of fluid-filled spaces and water channels that can carry this accumulated waste out of the brain. It uses cerebrospinal fluid, a clear liquid which surrounds the brain, to wash away the toxic proteins and dead cells that have built up during the day. But in Parkinson’s and other neurodegenerative conditions such as Alzheimer’s, the system is not able to clear away the toxic clumps of alpha-synuclein effectively. So in 2019, alongside Alzheimer’s UK, we co-funded a project led by Ian to try and understand whether there are ways to boost this system. The project involves studying mice that have been injected with alpha-synuclein. This injection triggers the alpha-synuclein already in the brain to start clumping together, which then starts to accumulate in the brain. As alpha-synuclein clumps form, they cause damage, and the clumps start to spread around other cells. This means that the mice start to develop some of the symptoms associated with Parkinson’s, such as movement problems. To understand how the glymphatic system might be involved in the development of Parkinson’s symptoms, Ian has been using a drug which stops the system working. The drug targets a protein called aquaporin-4, which previous research has shown is important in making sure the glymphatic system works correctly. Ian’s results so far show that when the mice are given this drug, they experience more problems with movement, and develop more clumps of alpha-synuclein in areas of the brain. This suggests that the glymphatic system is important in Parkinson’s — when it’s not working at all, the symptoms appear worse. So is there a way to boost the glymphatic system? Using the same mice, Ian is looking to address this question. This time, he’s using a different drug, which can speed up the glymphatic system. It does this by increasing the function of aquaporin-4. If it works, then he should see that the mice given this drug have fewer issues with movement and fewer clumps of alpha-synuclein in their brain cells. This work is still ongoing, but Ian is excited to see where this will lead. From mice to humans and back again While using mice in research can be a really helpful tool to study what’s going on in the brain during Parkinson’s, it doesn’t quite tell us the whole story of what’s happening in humans. We need studies of human brains to understand the whole picture. Which is why Ian is working with Mark Lythgoe, Professor of Biomedical Imaging at University College London, to find out more about what’s happening in the brains of people with Parkinson’s. Using tissue provided by the Parkinson’s UK Brain Bank, Ian and Mark are comparing areas of the brain in people who had early and late stage Parkinson’s, alongside people who didn’t have Parkinson’s. They are looking for aquaporin-4, and for any clues that it might be linked to increases in alpha-synuclein build up. When it’s working properly, aquaporin-4 should be found concentrated in one area of a particular type of brain cell. This helps it perform its main function — moving cerebrospinal fluid into the brain, so that the fluid can power the glymphatic system and clear away waste proteins. But when it’s not working properly, aquaporin-4 can be found spread throughout the cell, rather than concentrated to the one area. Looking at the brain tissue has helped Ian and Mark piece together more of the story that began by looking at the mice. In brain samples from people with late stages of Parkinson’s, there is less aquaporin-4 when compared to samples from people without Parkinson’s. And the aquaporin-4 that is there, isn’t where it should be. All this suggests the glymphatic system might not be working properly. However in people with early stages of Parkinson’s, there seems to be more aquaporin-4 than in people without Parkinson’s. This seems counterintuitive, but it could just mean that the body is trying harder to clear away the clumps of alpha-synuclein that have already started to form. By increasing aquaporin-4, the brain is trying to encourage the glymphatic system to work overtime, but unfortunately this is not enough to clear away all the clumps of protein. What’s the next stage? Working with the Brain Bank tissue has helped Ian and Mark confirm some of what they are seeing in their experiments with mice. Studying brain tissue donated by people who have lived with Parkinson’s is invaluable to furthering our understanding of Parkinson’s. And finding potential new treatments. As well as looking for aquaporin-4, Ian and Mark have also been able to take samples of alpha-synuclein from the Brain Bank tissue. This means that they have real protein from people with Parkinson’s, which can be used for further studies. One of the ways they hope to use this is to further their work in mice. Using this alpha-synuclein, donated from people who had Parkinson’s, might help make the study more representative of what’s going on in humans, instead of relying on an artificial form of alpha-synuclein. They also hope to use these mice to work out the best time to give a treatment which would boost the glymphatic system, making it the most useful for people with Parkinson’s. The work so far has shed light on how the glymphatic system might be involved in Parkinson’s, and ways that we might be able to harness its power to pave the way for new treatments to slow, or even stop, the progression of Parkinson’s. Study reveals benefits of physical activity for Parkinson’s symptoms Researchers compared results from over 150 studies to understand how different types of physical activity can be used to manage Parkinson’s symptoms. Being physically active can have a positive impact on Parkinson’s symptoms, both physically and mentally. Research has shown that aiming for 2.5 hours of physical activity a week can help people with Parkinson’s take control of their condition. Read more about the benefits of physical activity on our website. While there are many different types of physical activity, some will naturally suit some people better than others. However, it’s unknown whether some specific activities might be useful to target particular symptoms. Or whether exercises should be advised at different stages of the progression of the condition. What did the researchers do? The research team conducted a form of study called a Cochrane review. They analysed results from 156 different studies involving different types of physical activity including dance, aqua-based training and weight training. They looked at feedback from participants in these studies to assess for changes in quality of life, and other common tests to monitor progression of the condition. Overall, the researchers found that taking part in physical activity had benefits for people with Parkinson’s in terms of movement or improved quality of life, when compared with people who had not been active. It was not clear whether specific forms of physical activity were better than others for people with Parkinson’s. The team did find some evidence that linked taking part in dance classes with an improvement in balance and other symptoms associated with movement. They also found that aqua-based training was linked to improvements in quality of life, although whether this was due to physical activity or social interaction at exercise classes was unclear. What does this mean? This study, combining results collected from over 7,000 people with Parkinson’s, offers great evidence that taking part in most types of physical activity can be beneficial for people with Parkinson’s. Importantly, there was very little evidence that physical activity resulted in harm, or worsening of symptoms, for any participants. Dr Becky Jones, Research Communications Officer at Parkinson’s UK, said: “Reviews like this are a great example of how looking across results from many different studies can provide us with a clearer picture of whether a treatment or an activity could be beneficial for people with Parkinson’s. The study adds to our existing knowledge that physical activity can be an important way for people with Parkinson’s to take control of the condition. “The Cochrane review highlights exciting areas that need further study. More research into the particular benefits of dance or aqua-based training could help guide people with Parkinson’s to try out activities that could have the most impact on symptoms. “As always, we recommend that anyone wishing to make a change to their lifestyle speak with their healthcare provider for advice before starting.” Early results offer new hope for dyskinesia treatment We’re excited to announce the positive results of a trial for a potential new treatment for people with Parkinson’s with levodopa-induced dyskinesia. This is the first completed clinical trial funded by the Parkinson’s Virtual Biotech. Dyskinesia is a debilitating side effect of current Parkinson’s medication, with around half (40 to 50%) of all people with Parkinson’s experiencing it after 5 years of taking levodopa, the main drug used to treat the condition. Up to 80% experience it after 10 years. With dyskinesia everyday tasks, such as eating, writing and walking, can become extremely difficult. In fact, uncontrolled movement was voted the third most important issue to be addressed by research in a recent Parkinson’s UK survey on quality of life. The main medication available to manage dyskinesia is amantadine, which can have side effects and does not work for everyone. The study tested whether a drug called NLX-112 is safe to use in people with Parkinson’s. It also looked at how effective it may be at reducing dyskinesia in people who take levodopa to manage their Parkinson’s. Its safety has previously been confirmed in people with other conditions. Serotonin cells in the brain have the ability to convert levodopa into dopamine. These cells are thought to contribute to the development of dyskinesia when they start to release dopamine erratically. NLX-112 works by targeting serotonin cells inside the brain, and decreasing the amount of dopamine the cells release. Supported by the Parkinson’s Virtual Biotech The clinical trial was co-funded by The Parkinson’s Virtual Biotech, the drug discovery arm of Parkinson’s UK, and The Michael J Fox Foundation for Parkinson’s Research. The projects the Parkinson’s Virtual Biotech funds are entirely driven by the Parkinson’s community and their priorities. This is the first completed clinical trial funded by the Parkinson’s Virtual Biotech, a global partnership with the Parkinson’s Foundation. The promising results show that this innovative way of working to fast-track the most promising breakthroughs through the drug development pipeline is bringing us closer to new treatments. What did the research set out to do? The phase 2a clinical trial investigated how safe and well tolerated the drug was on a small number of people with Parkinson’s. The trial also took a first look at the drug’s efficacy, which is how well it does its job. What did the clinical trial involve? 22 participants with Parkinson’s with levodopa-induced dyskinesia completed the 8-week trial in Sweden. 15 participants received NLX-112 and 7 participants received a dummy drug. Participants either received NLX-112 or the dummy drug in increasing doses during the initial 4 weeks, to minimise the potential side effects. They stayed on the maximum dose for 2 weeks, and then were weaned off the drug over 2 weeks. What were the results? The results achieved the first objective to suggest that NLX-112 was safe and well tolerated in people with Parkinson’s. The second aim of the study was to show that NLX-112 was effective in treating dyskinesia. The results suggest participants who received NLX-112 showed a significant reduction in their scores for dyskinesia, whereas those who received the dummy drug did not show a significant reduction in their scores. The side effects of participants who received NLX-112 were mild, which confirmed previous results. What’s next? Now that the phase 2a clinical trial has been completed, the researchers will complete a full analysis of the results. They will then progress to a phase 2b clinical trial where they will investigate the safety and efficacy of the drug in a larger group. Dr Arthur Roach, Director of Research at Parkinson’s UK, said: “We’re incredibly proud and excited by these early results from Neurolixis. They were one of the first companies that the Parkinson’s Virtual Biotech invested in, in partnership with The Michael J Fox Foundation, so to see it making positive progress just reiterates why this brave and innovative approach is right for the Parkinson’s community. It really is bringing us closer to new treatments that address the symptoms that the Parkinson’s community have told us are the most urgent and levodopa-induced dyskinesias is one of those. “Further studies will be necessary for regulatory approval and routine clinical use of NLX-112. But now people with Parkinson’s can have hope that a much-needed new treatment for levodopa-induced dyskinesias may be coming to them soon, and know that their support of the Parkinson’s Virtual Biotech has made this possible.” Best wishes, Becky Jones Research Communications Officer

You may also like…

Disability services advocacy team.

Feb 28, 2024

The Disability Services Advocacy Team support with raising awareness of the different Disability Services benefits and...

Online Safety

To stop individuals becoming victims of fraud and blackmail, West Sussex are running three ‘Are you scam...

Fun Dog Show

Jan 26, 2024

Come along, enter your dog, have fun and help raise funds for your local Crawley and East Grinstead branch of...

Submit a Comment Cancel reply

You must be logged in to post a comment.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• IOS Press Open Library

People with Parkinson’s Disease: What Symptoms Do They Most Want to Improve and How Does This Change with Disease Duration?

Rebecca j. port.

a Parkinson’s UK, London, UK

Martin Rumsby

b Research Support Network, London, UK

Graham Brown

Ian f. harrison.

c Centre for Advanced Biomedical Imaging, Department of Imaging, Division of Medicine, University College London, London, UK

Anneesa Amjad

Claire j. bale, associated data, background:.

Parkinson’s disease (PD) is a neurodegenerative condition with a diverse and complex pattern of motor and non-motor symptoms which change over time with disease duration.

The aims of the present study were to discover what symptoms matter most to people with the condition and to examine how these priorities change with disease duration.

A simple free-text online survey (using SmartSurvey) was developed by Parkinson’s UK, which asked participants to identify up to three aspects of the condition they would most like to see improvement in.

790 people participated reporting 2,295 issues related to PD which were grouped into 24 broad symptom domains. Of these, 1,358 (59.1%) were categorised as motor symptoms, 859 (37.4%) as non-motor issues and 78 (3.4%) as medication problems. This study reveals how certain features of PD become more or less important to patients as the condition progresses. Non-motor symptoms were highly cited from the very earliest stages of PD. Problems with walking, balance and falls, speech problems, freezing and dyskinesia become increasingly important as the condition progresses whereas tremor, stiffness and psychological health become decreasingly important as the condition progresses.

Conclusions:

The data suggest that the priorities of people affected by PD for improving life are personal and change with duration of the condition. These findings have implications for developing person-centred management and care, as well as for directing future research to improve quality of life.

INTRODUCTION

Parkinson’s disease (PD) is an extremely complex, progressive neurodegenerative condition that, in the UK, affects about 145,000 people with a male to female ratio of about 3:2 [ 1 ]. PD is characterised by a broad range of motor and non-motor symptoms [ 2–4 ]. Clinical observations suggest that two major subtypes of PD can be defined, namely tremor-dominant PD with a relative absence of other motor symptoms and non-tremor dominant PD. A subgroup of PD patients has an intermediate phenotype with several motor symptoms [ 3 ]. As the condition progresses, the number and severity of symptoms increases. The amount of medication required to manage symptoms also increases leading to a risk of side effects and fluctuations contributing to increased disability [ 4–7 ].

While PD has some common features—namely the cardinal motor symptoms of tremor, rigidity and bradykinesia—it is clinically highly heterogeneous, with each individual experiencing their own unique blend of symptoms and side effects [ 8 ]. Large-scale cohort studies are underway that aim to track and record the evolution of PD over time to try and understand this heterogeneity, and identify subtypes of the condition based upon the symptoms experienced and how these symptoms evolve over time [ 9 ]. However, alongside these studies, it is essential to understand the symptoms and complications of the condition that are most troublesome and distressing to patients and those close to them, rather than simply focusing on those that are most common.

Previous research undertaken in this area has sought to identify research priorities for PD, including the James Lind Alliance priority setting partnership carried out by Parkinson’s UK in 2014 [ 10 ]. This study revealed that the top ten research priorities for PD management included the need to address motor symptoms (balance and falls, and fine motor control), non-motor symptoms (sleep and urinary dysfunction), mental health issues (stress and anxiety, dementia and mild cognitive impairments), side effects of medications (dyskinesia) and the need to develop interventions specific to the phenotypes of PD and better monitoring methods.

Such studies have identified key areas of unmet need for the PD population but do not give us a picture of how patient priorities evolve as the condition progresses. Our present study was undertaken to examine this particular problem in more depth.

MATERIALS AND METHODS

Study design.

Participants were people with PD, partners, carers or family members answering about a person with PD. The data were collected from 11/04/2018 to 02/05/2018 using an online survey.

The study was advertised using the Parkinson’s UK Research Support Network, with an email list of around 4,800 members with a connection to PD at the time of survey advertisement. The network is primarily UK based but no exclusion was placed on location. The only inclusion criteria were that the individual should be able to read and write in English.

No ethical consent was required to carry out this study as the data were submitted anonymously and all survey respondents agreed to a disclosure statement. Members of the Parkinson’s UK Research Support Network had given prior consent to receive correspondence from the charity.

Content of the survey

The aim was to produce a survey that could be quickly and easily completed to achieve the largest possible response. The final survey was designed to be completed in under 5 minutes. Respondents were required to read and agree to a survey purpose and disclosure statement. Those who chose to proceed were presented with a series of three demographic questions answered using predefined categories—their association with PD, the age of the individual with the disease and their duration of disease.

One central question was asked. This was: What particular aspects of your Parkinson’s would, if improved, make the biggest difference to your life? This single question aimed to capture respondents’ views on the aspects of PD that have primary, secondary and tertiary importance to quality of life. These aspects could be movement or non-movement symptoms, or side effects related to their PD treatment. Respondents were asked to list up to three aspects in three free text boxes provided with the most important first. No additional prompts were presented in the three text fields for this question. An additional box was presented at the end of the survey for any other comments.

Steering group

The project’s steering group consisted of 3 staff representatives from Parkinson’s UK and 6 Patient and Public Involvement (PPI) contributors from the charity’s Research Support Network. PPI contributors all had direct experience of PD and included 5 people with the condition and 1 partner. To recruit the PPI contributors, an email and role description were sent to all members of the Parkinson’s UK network of PPI contributors. The role description outlined key responsibilities, time commitment, timelines for the project and what support was available. PPI contributors expressed their interest in the role by outlining their experience of PD, their interest in the role and any relevant experience of using Excel or analysing data. 10 PPI contributors came forward for the role; and 6 were selected based on their experience and to ensure as much diversity in terms of gender, age and ethnicity as possible. The role of the group was to assist in the interpretation and categorisation of the free-text survey responses, and to provide input on the analysis and presentation of the findings. The steering group met once a month from November 2018 - February 2019 over video-conference.

Data analysis

Duplicate responses were identified and were removed based on their IP address. The issues reported were recorded as one of 41 specific symptoms or issues related to PD, with some deemed out of scope or uncategorizable (see Supplementary Table 1 ). This interpretation and sorting exercise was reviewed and finalised by the PPI contributors.

Directed by the PPI contributors, the 41 symptoms or issues were then combined where possible into 24 symptom categories. For instance, responses that mentioned fatigue, tiredness, lack of energy or fitness were grouped together in a broader symptom group called ‘Fatigue and energy’. These groups were then organised under 3 main areas: Motor Symptoms, Non-Motor Symptoms and Medication Problems (see Table 2 ).

Responses organised by symptoms reported (n = 2295). Symptom categories are given in bold with specific symptoms included listed underneath where relevant. The numbers in brackets represent the total number of respondents that mentioned a symptom within this category. This table includes responses from bereaved partners, family members and friends

Although survey respondents were instructed to list 3 symptoms or aspects of PD in the three free-text boxes in order of importance, some reported more than one symptom in each box while others only reported one or two symptoms in total. It was agreed by the Steering Group that all symptoms reported should be treated as important priorities for patients. Therefore, all symptoms were recorded and given equal weight in the main analysis irrespective of whether they exceeded the three items requested or were reported in the primary, secondary or tertiary response fields.

Pre analysis was performed to compare the types of responses from those with PD ( n  = 678) and those identifying as a carer/partner/family member or friend ( n  = 104), which showed a high degree of similarity suggesting a clear understanding of how the condition affects the individual with PD. Therefore, responses from the carer/partner/family member or friend group were included alongside responses from people with PD within the main analysis.

The responses were subsequently analysed by the duration the individual concerned had been diagnosed with PD. Due to the lower number of respondents in these groups, responses from those that identified as having been diagnosed 11–20 years ago ( n  = 98) or more than 20 years ago ( n  = 28) were combined.

Statistical analysis

To determine whether statistically significant associations existed between each reported symptom and disease duration, Kruskal-Wallis one-way Analysis of Variance (ANOVA) was applied to each symptom dataset. Post-hoc Dunn’s multiple comparisons tests were also applied to determine significance between disease duration groups. Differences with p  < 0.05 were considered statistically significant. Symptoms which demonstrated a significant negative or positive association with disease duration are included in Figs. 3, 4 , respectively. ANOVA p values are described in the text and the results of Dunn’s multiple comparisons tests are included in figure legends. Statistical analysis was performed using GraphPad Prism (version 8.4.3 for Windows).

Symptoms of Parkinson’s disease that were reported as a priority for improvement less frequently with disease duration. Percentages show the respondents with a duration of < 2 years ( n  = 134), 2–5 years ( n  = 313), 6–10 years ( n  = 209) and 11 + years ( n  = 126) reporting (a) tremor, (b) stiffness, and (c) psychological health within their 3 priority areas. Statistical significant between duration groups (Dunn’s multiple comparisons tests) are presented as asterisks: * p  < 0.05; * * p  < 0.01; * * * p  < 0.001; * * * * p  < 0.0001. Responses from bereaved partners, family members or friends have been excluded as no duration data is available.

Symptoms or side effects of Parkinson’s disease that were reported as a priority for improvement more frequently with disease duration. Percentages show the respondents with a duration of <2 years ( n  = 134), 2–5 years ( n  = 313), 6–10 years ( n  = 209) and 11 + years ( n  = 126) reporting (a) balance and falls, (b) walking, (c) speech, (d) freezing, (e) dyskinesia, and (f) medication wearing-off within their 3 priority areas. Statistical significant between duration groups (Dunn’s multiple comparisons tests) are presented as asterisks: * p  < 0.05; * * p  < 0.01; * * * p  < 0.001; * * * * p  < 0.0001. Responses from bereaved partners, family members or friends have been excluded as no duration data is available.

Survey responses

A total of 790 responses were included in this study after removal of duplicate submissions. Sample characteristics are presented in Table 1 . Some respondents provided more than one distinct symptom or side effect within each of the three boxes provided, while others only reported one or two. Hence the 790 participants responses generated 2443 items when categorised and organised by the steering group.

Sample characteristics of survey respondents

Of the 2,443 items, 2,295 related to specific symptoms or medication problems. Of these, 1,358 items were categorised as motor symptoms of the condition, 859 items as non-motor symptoms and 78 items as issues with medication ( Table 2 ). The most frequently mentioned motor symptoms were tremor, balance and falls, movement problems, walking and stiffness. The most frequently mentioned non-motor symptoms were fatigue and energy, psychological health, sleep problems, pain and unpleasant sensations and cognitive function ( Table 2 ).

Of the remaining 148 items, 113 fell outside the intended scope of the survey. These included the need for improved treatments (46) and a cure (24), better care and management (29), and the desire to maintain independence (14). Although these responses did not directly address the primary question, the project steering group felt that it was important that these responses be reported to highlight the urgency of research to address these issues. The remaining 35 items were uncategorizable.

The importance and complexity of non-motor issues

Overall, 859 (37.4%) of in-scope items reported in the survey mentioned aspects of the condition that were categorised as non-motor symptoms ( Table 2 ). The most frequently reported non-motor symptoms were fatigue and energy ( n  = 180), psychological health ( n  = 154), sleep problems ( n  = 133), pain and unpleasant sensations ( n  = 110) and cognitive function ( n  = 110) ( Table 2 ). The complexity and multi-faceted nature of these aspects of the condition is demonstrated by the broad range of terms used to describe them by survey respondents ( Supplementary Table 1 ). This builds upon recent research which found that patient descriptors were more extensive generally for non-motor than motor symptoms [ 10 ]. Interestingly, respondents were more likely to report recognisable motor symptoms as their primary priority - in particular tremor, movement or walking problems. In contrast, non-motor symptoms were more often reported as secondary or tertiary issues ( Fig. 1 ).

Symptoms or side effects reported in response to the question “what aspect of Parkinson’s do you most wish to see improvement in?” presented by priority. Percentages show the relative frequency of symptoms or side effects reported within primary responses ( n  = 848), secondary responses ( n  = 779), and tertiary responses ( n  = 668).

How priorities change with disease duration

This study reveals how certain symptoms and medication problems related to PD become more or less important to patients as the condition progresses. The most striking of these is tremor, which is the most frequently reported symptom of PD that respondents wish to improve in this survey. The frequency of mentions of tremor is highest in those living with the condition for 2 years or less (14.4%) and is reported progressively less frequently, with 4.1% of those who have lived with PD for 11 years or more mentioning this symptom ( p  < 0.0001) ( Figs. 2, 3 ). Other symptoms that also become less frequently reported by disease duration include stiffness ( p  = 0.0026) and psychological health ( p  = 0.0344) ( Fig. 3 ).

Top 10 most frequently reported symptoms or side effects respondents wished to see an improvement in by disease duration. The area of the boxes show the relative frequency of the 10 most reported symptoms or side effects respondents diagnosed for (a) <2 years ( n  = 409), (b) 2–5 years ( n  = 648), (c) 5–10 years ( n  = 327) and (d) 11 + years ( n  = 86), wished to see an improvement in, where n is the total number of categorizable, symptoms or side effects reported. Responses from bereaved partners, family members or friends have been excluded as no duration data is available.

Issues that appear to become increasingly important as the condition progresses include; problems with walking ( p  = 0.048); balance and falls ( p  = 0.0035); speech problems ( p  = 0.0009); freezing ( p  = 0.0002); dyskinesia ( p  < 0.0001); medication wearing-off ( p  < 0.0001) ( Fig. 4 ). Indeed, balance and falls rises to become the most important issue for people who have lived with the condition for 11 years or more ( Fig. 2 ).

Non-motor symptoms were highly cited from the very earliest stages of PD. Problems with psychological health, fatigue and energy, cognitive function, and pain and unpleasant sensations all appear in the 10 most frequently reported issues by respondents who had lived with PD for 2 years or less ( Fig. 2 ). Non-motor symptoms continue to be important throughout the course of PD. Interestingly, only one non-motor symptom, psychological health, showed any association with duration, and difficulties in this domain were reported less frequently with disease duration ( Fig. 3 ).

The results of this survey provide further evidence that the priorities of people affected by PD for improving life are diverse, personal and change substantially with duration of the condition. They reinforce previous research identifying the importance of non-motor issues to quality of life in PD [ 12–14 ], and underscore that PD is much more than a movement disorder. However, unlike some previous studies which have suggested that non-motor symptoms become increasingly important to patients over time [ 15 ], our findings indicate that non-motor symptoms are important to people affected by PD right from the earliest stages of the condition.

Interestingly, respondents to this survey were likely to mention a motor aspect of the condition as their primary priority, and non-motor symptoms were more frequently reported as secondary or tertiary issues. Our data show that motor symptoms remain important priorities for improving life for people with the condition. Tremor was the most frequently reported aspect of the condition that respondents wished to see an improvement in, in this survey, and is of particular concern to those at an early stage of the disease. This echoes another recently published US study in which tremor was found to be the most bothersome symptom for people with PD [ 16 ] alongside research demonstrating that for people living with early stage PD, tremor may cause embarrassment, limit social interactions, and interfere with the ability to perform activities of daily living and simple tasks at home and work [ 17 ]. This was also reflected in the free-text comments from respondents in the present survey with many identifying tremor as one of the most noticeable symptoms of the condition and therefore a source of embarrassment and stigma.

This survey indicates that motor symptoms continue to be important for people with PD throughout the course of the condition. Tremor is mentioned progressively less frequently with disease duration ( Figs. 2 and 3 ) while other motor symptoms become more frequently reported with disease duration including balance and falls, walking, freezing, speech and dyskinesia ( Fig. 4 ). Previous research has suggested that tremor progresses more slowly than other motor features [ 6 ] and may therefore be overtaken in importance as other issues become more impactful with disease duration.

Where our data adds to existing knowledge is in highlighting the urgent need for better recognition, treatment and management of non-motor symptoms for people living with the condition particularly in the very early stages. Previous research has demonstrated that non-motor symptoms have a significant impact on quality of life [ 12, 18, 19 ] and that non-motor symptoms may be present from the very earliest stages, indeed even prodromally [ 4, 20 ]. However, the full impact of these issues for patients and their families during these early stages is perhaps less well understood. In this survey, aspects including psychological health, fatigue and energy, cognitive problems, and pain and unpleasant sensations all appear within the top 10 most frequently reported aspects for respondents diagnosed for 2 years or less.

After tremor, issues related to psychological health were the second most frequently reported aspect of the condition that respondents who had been diagnosed with PD for 2 years or less wish to see an improvement in ( Fig. 2a ). The presence of issues related to psychological health, particularly anxiety and depression, from the very earliest stages of PD is well established [ 21 ]. Our data highlight the impact these issues have on individuals and their families and identifies improved treatment, management and support for this aspect of the condition as an urgent priority at diagnosis.

Issues related to cognitive function, including cognitive impairment, memory problems and dementia, were the eighth most reported issue in those diagnosed for two years or less in this survey. Once again, previous research has demonstrated that changes in cognitive function are relatively common in the early stages of PD [ 4 ]. The Parkinson’s Progression Markers Initiative’s published data suggests that 10% of people with early, untreated PD may already have cognitive problems [ 21 ]. The responses to the present survey reinforce the presence of cognitive changes in the very early stages of the condition and demonstrate the impact and worry these cause people with PD and their families. Interestingly, the results from our survey suggest that cognitive function is more of a concern in those very recently diagnosed than in those who have lived with PD for more than a decade. Respondents diagnosed for two years or less described difficulties with brain fog, mental fuzziness, memory, clarity of thought, and the ability to concentrate and multi-task. These early subtle cognitive changes may impact on many aspects of life as well as causing people to worry about the potential for further deterioration in their cognitive abilities.

Non-motor aspects categorised as ‘fatigue and energy’ and ‘pain and unpleasant sensations’ were frequently reported by those recently diagnosed, as well as by those who had lived with PD for longer. A recent review concluded that fatigue is a frequent symptom in PD which appears early and persists as the condition advances, and called for better recognition including standardised diagnostic criteria for fatigue in PD [ 22 ]. Similarly, sleep issues are known to be prevalent in PD and to have a significant impact on quality of life [ 23 ]. Our study adds weight to previous calls for greater research efforts to develop better treatments to help manage these debilitating symptoms.

Finally, our data suggest that for people living for 11 years or more with PD, motor aspects of the condition are often the most pressing priorities for improving life. The most frequently reported aspect in this group was balance and falls, followed by walking and speech difficulties. These findings corroborate previous studies which have highlighted the increasing burden and complexity of motor symptoms as the condition progresses [ 4, 6, 7 ] and emphasise their importance to patients. It is vital that research focuses on developing improved treatments and management strategies that are so important to maintaining independence and quality of life.

The main limitation of this study was the simplicity of the survey. To encourage the maximum possible number of responses the survey was kept deliberately very short. As a result, very limited demographic information was collected regarding age and years since diagnosis, and no data on sex or ethnicity. This study was designed to capture most troublesome symptoms for people with PD with the assumption that they were already receiving appropriate treatment. As a result, there was no information collected regarding medication or other therapies, and we are unable to comment on how treatment may influence symptom priorities. We recommend that future studies collect more detailed demographic information and consider collecting information about medication to enable the relationship between symptoms and medication to be interrogated.

The sample population surveyed, the Parkinson’s UK Research Support Network, is a community of individuals who have an active interest in PD research. Separate surveys conducted with this network suggest that they represent a younger, highly educated and predominantly white population whose responses may not be representative of the experiences of the broader PD population in the UK.

A significant challenge in this study was interpreting and appropriately categorizing the free text survey responses, which were wide-ranging. Many responses were ambiguous and required extensive discussion to agree how best to categorise them. This was why it was so important to involve people with PD in this interpretation exercise and is one of the strengths of this study.

One potential confounder of the results of this study is that comorbidities and polypharmacy may account for some of the issues reported in this survey. One recent study using a Scottish primary care database found that only 7.4% of people with PD had no other recorded conditions [ 24 ]. In the present survey, data on comorbidities was not collected but some respondents mentioned that they had difficulties assigning problems to PD rather than to their other conditions.

Overall, this study emphasises the importance of the thorough and ongoing assessment of symptoms throughout the development of the condition as called for by Shin et al. [ 25 ]. Each person with PD experiences the condition differently and their own personal priorities for improving life must be at the centre of their care. This also further demonstrates the urgent need for objective measurement tools to accurately capture the most bothersome symptoms for patients to ensure these are properly recognised both in the clinic and in future research [ 26 ].

Our data suggest that there should be a greater emphasis upon non-motor issues from the point of diagnosis, especially psychological health, fatigue and energy, pain and unpleasant sensations and cognitive function. Tremor remains an intractable motor symptom that requires improved treatment approaches. This survey identifies problems with balance, walking and falls as the most pressing concerns for patients and their families in the later stages of the condition.

We hope these data will stimulate further research to improve treatments, care and support for people with PD that addresses these important aspects of the condition. We also hope that these research efforts will involve people affected by the condition, their partners and families in developing, designing and conducting these studies to ensure they are truly focused on what matters most to those living with PD.

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

Supplementary Material

Acknowledgments.

We are grateful to the membership of the Parkinson’s UK Research Support Network for their participation in this survey and providing their personal perspectives on living with PD. Thanks to the PPI contributors who helped to analyse and categorise the survey responses. Thanks to IH for his help in producing the figures and to Professor David Dexter for editorial assistance and advice.

SUPPLEMENTARY MATERIAL

The supplementary material is available in the electronic version of this article: https://dx.doi.org/10.3233/JPD-202346 .

Parkinson's Excellence Network Mental Health Hub re-launch meeting

After a few years hiatus, the Mental Health Hub is holding an in-person meeting to re-launch the Hub and its offer to all healthcare professionals working or with an interest in Parkinson's and mental health.

The meeting aims to bring together professionals working in Parkinson’s and mental health, to network, share updates, hear the latest research and discuss the aims of the Mental Health Hub and the ways in which it can support the community of professionals working to improve mental health services for people with Parkinson’s.

The event will be led by Metal Health Hub Lead, Dr Jennifer Foley, Clinical Neuropsychologist at University College Hospitals London NHS Foundation Trust.

Through expert speaker sessions and workshops, the day will support delegates with their continuing professional development (CPD) and enable them to advise, support, signpost, and improve outcomes for people with Parkinson’s who may be experiencing issues with their mental health.

The following topics will be covered: 

• Patients’ experiences of the impact of Parkinson’s mental health issues.
• The current state of UK psychological services for people with Parkinson’s: the workforce and its development needs.
• Clinical and economic evidence for supporting NHS investment in integrated mental health services for people with Parkinson’s.
• Overview of updated professional guidance on the assessment and treatment of complex mental health issues in Parkinson's.
• The Parkinson’s Excellence Network Mental Health Audit and training available for mental health professionals.

More conferences for professionals

Explore a range of conferences and events for professionals.

Your web browser is out of date.

Partnerships and endorsements, parkinson's excellence network, parkinson's excellence network.

The Excellence Network is working to transform health and care services for people with Parkinson's across the UK.

Supported, funded and facilitated by Parkinson’s UK , they bring together and support health and social care professionals to better care for people with Parkinson's. They share best practice, resources, education and support improvements to services for people with Parkinson’s and their families.

Better support, better services, better care. Every day. Together, the Excellence Network is transforming Parkinson’s care.

What do they do?

The Excellence Network works directly with health and social care professionals to assess and improve Parkinson’s services across the UK.

They provide education programmes , for all levels of staff from all professional backgrounds, to improve care for people with Parkinson’s. These courses are developed alongside health and social care professionals.

They run the UK Parkinson’s Audit which is the recognised quality improvement tool for Parkinson’s services. It gives all services that see patients about their Parkinson's the opportunity to be involved in a recognised continuous improvement cycle. It also allows these services to be part of a larger Parkinson's community committed to service quality.

In response to the Audit findings, they identify service improvement priorities which form their national priority programmes . These are led by national experts and bring together teams of clinicians and members of the Parkinson’s community across the UK. They're working together to transform Parkinson’s services through better access to Parkinson’s nurses and multidisciplinary care, improved management of bone health, and medication optimisation and management.

They provide the latest updates and create opportunities for health and social care professionals to share best practice, network and collaborate, and receive support from peers.

Be part of the Excellence Network

Get the latest news sent directly to your inbox by signing up to the monthly e-newsletter . Or join Basecamp, the dedicated online collaboration platform for health and social care professionals. Email [email protected] to join.

Are you a health or social care professional looking for financial support to improve your service or towards your continuous professional development? Explore the funding opportunities .

Share research opportunities with your patients

People with Parkinson’s want to hear more from healthcare professionals about research. As healthcare professionals, you can help amplify research opportunities, and improve access to research information for people who are currently underrepresented in research.

Share Parkinson’s UK’s research leaflet with your patients, find research opportunities near your patients using their Take Part Hub and sign up to Parkinson’s UK’s Research Support Network . If you have any questions please email [email protected] .

Parkinson's Excellence Network is a partner of the following academies...

Parkinson's Academy

Our longest running course and founding Academy, we specialise in Parkinson's and other movement disorders.

Parkinson's Excellence Network is supporting forthcoming events...

Didn't find what you were looking for?

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 27 March 2024

The effects of genetic and modifiable risk factors on brain regions vulnerable to ageing and disease

• Jordi Manuello   ORCID: orcid.org/0000-0002-9928-0924 1 , 2 ,
• Joosung Min   ORCID: orcid.org/0000-0002-5541-5014 3 ,
• Paul McCarthy 1 ,
• Fidel Alfaro-Almagro 1 ,
• Soojin Lee 1 , 4 ,
• Stephen Smith 1 ,
• Lloyd T. Elliott 3   na1 ,
• Anderson M. Winkler 5 , 6   na1 &
• Gwenaëlle Douaud   ORCID: orcid.org/0000-0003-1981-391X 1  

Nature Communications volume  15 , Article number:  2576 ( 2024 ) Cite this article

14k Accesses

2192 Altmetric

Metrics details

• Genetics research
• Neuroscience
• Risk factors

We have previously identified a network of higher-order brain regions particularly vulnerable to the ageing process, schizophrenia and Alzheimer’s disease. However, it remains unknown what the genetic influences on this fragile brain network are, and whether it can be altered by the most common modifiable risk factors for dementia. Here, in ~40,000 UK Biobank participants, we first show significant genome-wide associations between this brain network and seven genetic clusters implicated in cardiovascular deaths, schizophrenia, Alzheimer’s and Parkinson’s disease, and with the two antigens of the XG blood group located in the pseudoautosomal region of the sex chromosomes. We further reveal that the most deleterious modifiable risk factors for this vulnerable brain network are diabetes, nitrogen dioxide – a proxy for traffic-related air pollution – and alcohol intake frequency. The extent of these associations was uncovered by examining these modifiable risk factors in a single model to assess the unique contribution of each on the vulnerable brain network, above and beyond the dominating effects of age and sex. These results provide a comprehensive picture of the role played by genetic and modifiable risk factors on these fragile parts of the brain.

Similar content being viewed by others

APOE4/4 is linked to damaging lipid droplets in Alzheimer’s disease microglia

Michael S. Haney, Róbert Pálovics, … Tony Wyss-Coray

Single-cell multiplex chromatin and RNA interactions in ageing human brain

Xingzhao Wen, Zhifei Luo, … Sheng Zhong

Exome-wide analysis implicates rare protein-altering variants in human handedness

Dick Schijven, Sourena Soheili-Nezhad, … Clyde Francks

Introduction

The development of preventative strategies based on modifying risk factors might prove to be a successful approach in ensuring healthy ageing. Factors particularly scrutinised in dementia and unhealthy ageing have included cerebrovascular factors such as high blood pressure, diabetes and obesity, but also lifestyle ones such as alcohol consumption, and protective factors such as exercise 1 . Assessing these modifiable risk factors together makes it possible to identify the unique contribution of each of these factors on the brain or on cognitive decline. A Lancet commission, updated in 2020 to include, e.g., pollution for its possible role in the incidence of dementia 2 , examined the relative impact of 12 modifiable risk factors for dementia, and showed that these 12 factors may account for 40% of the cases worldwide 3 . Conversely, genetic factors are non-modifiable in nature, but can inform us about the mechanisms underlying the phenotypes of interest. These mechanisms sometimes can be shared across these phenotypes. For instance, genetic overlap has been found for Alzheimer’s and Parkinson’s diseases at a locus in the MAPT region 4 . Likewise, one of the most pleiotropic variants, in the SLC39A8 / ZIP8 gene, shows genome-wide associations with both schizophrenia and fluid intelligence, amongst many other phenotypes 5 , 6 .

One way to objectively and robustly assess susceptibility for unhealthy ageing is to look non-invasively at brain imaging markers 7 . Using a data-driven approach on a lifespan cohort, we previously identified an ensemble of higher-order, ‘transmodal’ brain regions that degenerates earlier and faster than the rest of the brain 8 . The very same areas also develop relatively late during adolescence, thus supporting the ‘last in, first out’ (LIFO) hypothesis, which posits that the process of age-related brain decline mirrors developmental maturation. Importantly, this network of brain regions further demonstrated heightened vulnerability to schizophrenia and Alzheimer’s disease, two disorders that impact on brain structure during adolescence and ageing respectively. Accordingly, this LIFO network was strongly associated with cognitive traits whose impairment is specifically related to these two disorders, namely fluid intelligence and long-term memory 8 .

Here, our main objective was to assess both the genetic and modifiable risk factors’ contributions to the vulnerability of these most fragile parts of the brain. We conducted a genome-wide association study on a prospective cohort of nearly 40,000 participants of the UK Biobank study who had received brain imaging, and in total evaluated the association between the LIFO brain network and 161 modifiable risk factors, classified according to 15 broad categories: blood pressure, cholesterol, diabetes, weight, alcohol consumption, smoking, depressive mood, inflammation, pollution, hearing, sleep, socialisation, diet, physical activity and education.

The vulnerable LIFO brain network in UK Biobank

Similar to our previously observed results 8 , the loadings of the LIFO brain network, i.e., the normalised grey matter volume in the network after regressing out the effects of all the other brain maps (see Methods), demonstrated a strong quadratic association with age in the UK Biobank cohort of 39,676 participants ( R 2  = 0.30, P  < 2.23 × 10 −308 , Fig.  1 ). These higher-order regions thus show an accelerated decrease of grey matter volume compared with the rest of the brain. Furthermore, these areas define a network mainly involved in behavioural tasks related to execution, working memory, and attention (Fig.  1 , Supplementary Information ).

Top left, spatial map of the LIFO network (in red-yellow, thresholded at Z  > 4 for visualisation) used to extract the loadings from every scanned participant from UK Biobank ( n  = 39,676). Top right, these LIFO loadings (in arbitrary units) show a strong quadratic association with age in the UK Biobank cohort, i.e. grey matter volume decreases quadratically with older age in these specific regions ( R 2  = 0.30, P  < 2.23 × 10 −308 ; inset: residual scatterplot). Bottom, the vulnerable network appears to encompass areas mainly involved in execution, working memory, and attention (using the BrainMap taxonomy 60 , and with the LIFO brain network thresholded at both Z  = 4 and Z  = 10, see  Supplementary Information ).

Genetic influences over the vulnerable LIFO brain network

Using a minor allele frequency filter of 1% and a –log 10 (P) threshold of 7.5, we found, in the 39,676 participants, genome-wide associations between the LIFO brain network and seven genetic clusters whose top variants were all replicated (Table  1 /Supplementary Data  1 , Fig.  2 ).

Top row, Manhattan plot showing the 7 significant genetic clusters associated with the LIFO brain network (–log 10 ( P ) > 7.5). Second and third rows, regional association plots of the top variants for each of the 5 autosomal genetic clusters: rs6540873 on chromosome (Chr) 1 ( KCNK2 ), rs13107325 on Chr4 ( SLC39A8 ), rs2677109 on Chr6 ( RUNX2 ) (as a proxy in high LD R 2  = 0.86 with indel 6:45442860_TA_T), rs12146713 on Chr12 ( NUAK1 ), and rs2532395 on Chr17 ( MAPT , KANSL1 )(highest variant after tri-allelic rs2693333; see Supplementary Data  4 for a complete list of significant variants in this 5th MAPT genetic cluster). Bottom row, regional association plots of the top variants for the two genetic clusters in the pseudo-autosomal region PAR1 of the X chromosome: rs312238 ( XG , CD99 ) and rs2857316 ( XG )(UK Biobank has no genotyped variants on the 3’ side). Based on Human Genome build hg19. P -values are derived from a two-sided linear association test.

The first autosomal genetic cluster, on chromosome 1, included two variants (lead variant: rs6540873, β  = 0.06, P  = 1.71 × 10 −8 , and rs1452628, with posterior probabilities of inclusion in the causal variant set of 0.56 and 0.45, respectively) close to, and eQTL of, KCNK2 ( TREK1 ). This gene regulates immune-cell trafficking into the central nervous system, controls inflammation, and plays a major role in the neuroprotection against ischemia. Of relevance, these two loci are in particular related in UK Biobank participants with the amount of alcohol consumed, insulin levels, inflammation with interleukin-8 levels, as well as, crucially, with late-onset Alzheimer’s disease (Table  1 /Supplementary Data  1 ).

The second autosomal genetic cluster on chromosome 4 was made of 7 loci, with the lead variant rs13107325 in an exon of SLC39A8/ZIP8 ( β  = 0.14, P  = 2.82 × 10 −13 , posterior probability: 0.99). This locus is one of the most pleiotropic SNPs identified in GWAS, and is, amongst many other associations, related in UK Biobank with cholesterol, blood pressure, weight, inflammation with C-reactive proteins levels, diabetes with insuline-like growth factor 1 levels, alcohol intake, sleep duration, and cognitive performance/impairment, including prospective memory (Table 1 /Supplementary Data  1 ).

The third locus was an indel in chromosome 6 in an intron, and eQTL, of RUNX2 (rs35187443, β  = 0.06, P  = 9.03 × 10 −9 ), which plays a key role in differentiating osteoblasts, and has been very recently shown to limit neurogenesis and oligodendrogenesis in a cellular model of Alzheimer’s disease 9 .

The fourth locus was a SNP in chromosome 12, in an intron of NUAK1 (rs12146713, β  = −0.10, P  = 1.26 × 10 −9 ), and remarkably its top association in UK Biobank was with the contrast between schizophrenia and major depressive disorder 10 , and it was also associated with insulin-like growth factor 1 levels (Table 1 /Supplementary Data  1 ).

The final genetic autosomal genetic cluster was made of 3,906 variants in the MAPT region. Its lead non-triallelic variant, rs2532395 ( β  = −0.09, P  = 3.56 × 10 −15 ) was more specifically <10 kb from KANSL1 and an eQTL of KANSL1 , MAPT and other genes in brain tissues (Table 1 /Supplementary Data  1 , Supplementary Data 4 ). This locus was also associated in UK Biobank with tiredness and alcohol intake. MAPT is in 17q21.31, a chromosomal band involved with a common chromosome 17 inversion 11 . Adding chromosome 17 inversion status as a confounder reduced the significance of the association ( β  = −0.15, P  = 8.45 × 10 −3 ). Since the genotype for rs2532395 was also strongly correlated with chromosome 17 inversion in our dataset (Pearson correlation r  = 0.98, P  < 2 × 10 −16 ), this would suggest that the association between MAPT and the LIFO network is not independent from chromosome 17 inversion. As this extended genetic region is known for its pathological association with many neurodegenerative disorders including Alzheimer’s disease, we investigated whether the LIFO brain regions mediated the effect of the MAPT genetic cluster (using the lead bi-allelic variant rs2532395) on Alzheimer’s disease (see Methods). Despite small average causal mediated effect (ACME) sizes, we found a significant effect for both the dominant model (ACME β  = 1.16 × 10 −4 ; 95% CI = [5.19 × 10 −5 , 1.99 × 10 −4 ]; P  = 4 × 10 −5 ) and the recessive model (ACME β  = 1.55 × 10 −4 ; 95% CI = [3.96 × 10 −5 , 3.74 × 10 −4 ]; P  = 4 × 10 −5 ; full output of the mediation package on the dominant and recessive models in  Supplementary Information ).

The two last genetic clusters of 8 and 9 variants respectively were found on the X chromosome, notably in a pseudo-autosomal region (PAR1), which is interestingly hit at a higher rate than the rest of the genome ( P  = 1.56 × 10 −5 , see  Supplementary Information ). The top variants for these clusters were related to two homologous genes coding for the two antigens of the XG blood group: rs312238 ( β  = −0.05, P  = 1.77 × 10 −10 ) ~ 10 kb from, and an eQTL of, CD99/MIC2 , and rs2857316 ( β  = −0.08, P  = 2.27 × 10 −29 ) in an intron and eQTL of XG  (Table 1 /Supplementary Data  1 ). Since chromosome X has hardly been explored, we carried out our own association analyses between these two top variants and non-imaging variables in UK Biobank. Intriguingly, the first of these two PAR1 loci, rs312238, was found to be significantly associated in the genotyped participants who had not been scanned (out-of-sample analysis in n  = 374,230 UK Biobank participants) with nitrogen dioxide air pollution, our ‘best’ MRF for pollution (see below), and many other environmental, socioeconomic, and early life factors (such as urban or rural setting, distance from the coast, place of birth, number of siblings, breastfed as a baby, maternal smoking around birth), as well as health outcomes (Supplementary Data  2 ). In particular, amongst the more easily interpretable findings of the most associated variables with rs312238, the T allele of this locus was associated with two increased measures of deprivation and/or disability (worse socioeconomic status), the ‘Townsend deprivation index’ and the ‘Health score’, but also with ‘Nitrogen dioxide air pollution’, ‘Maternal smoking around birth’, as well as ‘Number of full brothers’ and ‘Number of full sisters’, thus showing consistent signs of association between this variant and these phenotypes.

We found that the heritability of the LIFO network was significant, with h 2  = 0.15 (se = 0.01). The genetic co-heritability between the LIFO network and Alzheimer’s disease or schizophrenia was not statistically significant (coefficient of co-heritability = −0.12, se = 0.10; P  = 0.23; coefficient of co-heritability = −0.16, se = 0.04, P  = 0.07, respectively).

Modifiable risk factors’ associations with the vulnerable LIFO brain network

Including the modifiable risk factors (MRFs) in a single general linear model allows us to assess the unique contribution of each factor on the LIFO brain network. Not all UK Biobank participants have data available for all of the MRF variables however. An analysis limited to those with complete data for all MRFs would be biased, and based on a relatively small, low-powered sample. We addressed this issue via a two-stage analysis in which: (i) we first identified which variable within each of the 15 MRF categories best represented associations of that category with the LIFO brain network loadings (based on two criteria: significance and <5% missing values), (ii) we investigated the unique contribution of that MRF category, over and above all other categories and the dominating effects of age and sex, to the LIFO loadings.

From the first stage of our analysis, 12 of the 15 categories of MRFs had at least one ‘best’ MRF, i.e., with a significant effect on the LIFO brain network and enough non-missing values across all scanned participants to be investigated further (Table  2 /Supplementary Data  3 ). The contribution of the MRFs on the vulnerable brain network differed vastly depending on whether confounding effects of age, sex and head size were taken into account. The effect size and significance of some MRFs diminished because of some clear collinearity with the confounders. For instance, for the category of blood pressure, the most significant MRF was first “systolic blood pressure, automatic (second) reading” ( r  = −0.20, P  < 2.23 × 10 −308 ), but after regressing out the confounders, the ‘best’ MRF for this category was “medication for blood pressure” ( r  = −0.05, P  = 7.55 × 10 −22 ). Conversely, regressing out the effects of age served to unmask the significant deleterious effects of pollution on the vulnerable brain regions, such as nitrogen dioxide air pollution or particulate matter air pollution (Table  2 /Supplementary Data  3 ).

When considered together in a single model in the second stage of the analysis, 3 best MRFs had an effect on the LIFO brain network that remained significant beyond the dominating effects of age and sex, and of the 9 other best MRFs: diabetes (“diabetes diagnosed by doctor”, r  = −0.05, P  = 1.13 × 10 −24 ), pollution (“nitrogen dioxide air pollution in 2005”, r  = −0.05, P  = 5.39 × 10 −20 ) and alcohol (“alcohol intake frequency”, r  = −0.04, P  = 3.81 × 10 −17 ) (Table  3 ). No MRFs showed any bias in their sub-sampling distribution, i.e., any significant difference between the original sample and the reduced sample of 35,527 participants who had values for all 18 variables considered (the 12 best MRFs and 6 confounders: age, sex, age 2 , age × sex, age 2  × sex, head size; Supplementary Information ). In total, the 12 best MRFs explained 1.5% of the effect on the vulnerable brain network ( F 12;35509  = 43.5).

While 6 out of the 7 genetic clusters associated with the LIFO network were correlated with many variables related to each of the 15 MRF categories, including diabetes, alcohol consumption and traffic pollution (Supplementary Data  1 ), we also found some genetic overlap between the very specific best MRF of “alcohol intake frequency” and the LIFO network in the pleiotropic rs13107325 variant (cluster 2), as well as rs17690703, part of the large genetic cluster 5 in MAPT (Supplementary Data  4 ). No genetic overlap was found for the precise “nitrogen dioxide air pollution in 2005” or “diabetes diagnosed by doctor”, nor for approximate variables.

This study reveals, in a cohort of nearly 40,000 UK Biobank participants, the genetic and modifiable risk factors’ associations with brain regions in a ‘last in, first out’ (LIFO) network that show earlier and accelerated ageing and are particularly vulnerable to disease processes such as that of Alzheimer’s disease 8 . Seven genetic clusters, two of which in the pseudo-autosomal region of the sex chromosomes coding for two antigens of the XG blood system, were found significantly associated and replicated genome-wide. In addition, after accounting for age and sex effects, diabetes, traffic-related pollution and alcohol were the most deleterious modifiable risk factors (MRFs) on these particularly vulnerable brain regions.

Three lead variants for our significant genetic clusters have been previously associated with ageing-related brain imaging measures in recent studies: one, in cluster 1, an eQTL of KCNK2 ( TREK1 ) 12 , 13 , whose increase in expression mediates neuroprotection during ischemia 14 , the ubiquitous rs13107325 (cluster 2), and one, in cluster 4, in an intron of NUAK1 ( ARK5 ) 15 , 16 , 17 , which has been associated with tau pathology 18 (Table  1 /Supplementary Data  1 ). On the other hand, of the seven genetic clusters, three were entirely novel (clusters 3, 6 and 7), and not found in other brain imaging studies, including our most recent work that expanded on our previous GWAS of all of the brain IDPs available in UK Biobank 19 by including more participants—in fact, the same number of participants as analysed in this present work—and, crucially, by also including the X chromosome 20 (Table  1 /Supplementary Data  1 ). This suggests that, beyond the genetic hits that were meaningfully associated with the LIFO brain network and an array of relevant risk factors, lifestyle variables and brain disorders, and found in a few other imaging GWAS, some of the genetic underpinnings of the LIFO network are intrinsically specific to it and to no other pre-existing imaging phenotype.

All five autosomal genetic clusters identified through the GWAS of the LIFO phenotype had relevant associations with risk factors for dementia (Results; Supplementary Data  1 ), including precisely two of the best MRFs (for clusters 2 and 5), and three of them directly related in UK Biobank to the two diseases showing a pattern of brain abnormalities following the LIFO network: schizophrenia (clusters 2 and 4) and Alzheimer’s disease (cluster 1) (Supplementary Data  1 ). In particular, cluster 2 has its lead variant rs13107325 in an exon of one of the most pleiotropic genes ZIP8 , which codes for a zinc and metal transporter. Considering the vulnerability of the LIFO brain network to adolescent-onset schizophrenia and its significant association with fluid intelligence that we previously demonstrated 8 , it is notable that this variant has been associated genome-wide with schizophrenia 6 , as well as intelligence, educational attainment and mathematics ability 5 , 21 . In line with the LIFO brain network being both prone to accelerated ageing and susceptible to Alzheimer’s disease, this genetic locus has also been associated genome-wide with well-known risk factors for dementia. These comprise alcohol—including the exact same variable of “alcohol intake frequency” as identified as one of the best MRFs—cholesterol, weight, sleep—including “sleep duration”—and blood pressure 22 , 23 , 24 , 25 , 26 , all of which significantly contribute to modulating the LIFO brain network when considered separately (Table  2 /Supplementary Data  3 ). Of relevance, this genetic locus is also associated to an increased risk of cardiovascular death 27 . Cluster 5, a large genetic cluster in the MAPT region (Microtubule-Associated Protein Tau), comprised in total 3906 significant variants (Supplementary Data  4 ). This genetic region plays a role in various neurodegenerative disorders related to mutations of the protein tau, such as frontotemporal dementia 28 and progressive supranuclear palsy 29 , but also, of particular pertinence to the LIFO brain network, Alzheimer’s and Parkinson’s disease, with a genetic overlap between these two diseases in a locus included in our significant cluster 5 (rs393152, β  = −0.09, P  = 6.35 × 10 −14 ) 4 . Despite the relatively low number of people with diagnosed Alzheimer’s disease in the genetic discovery cohort, we were able to establish—albeit with small effect sizes—a significant mediation role for the LIFO brain regions between the lead bi-allelic variant for cluster 5 and this Alzheimer’s diagnosis, suggesting once more the importance played by these vulnerable brain areas in unhealthy ageing.

Finally, of the seven clusters, two were located in the pseudo-autosomal region (PAR1) of the sex chromosomes corresponding to the genes XG and CD99 , coding for the two antigens of the XG blood group. This blood group system has been largely neglected, its main contribution related to the mapping of the X chromosome itself, and its clinical role remains elusive 30 . In order to investigate further the possible role of these two variants of the XG blood group, we examined out-of-sample their associations with thousands of non-imaging phenotypes. This analysis revealed that the first of these two loci was significantly and consistently associated with early life factors, environmental factors and health outcomes, including particulate matter and nitrogen dioxide air pollution, the second most deleterious MRF to the LIFO brain network (Supplementary Data  2 ). Whether these associations are due to stratification or genotyping artefacts, or to the fact that this specific variant, which is inherited from a parent, has a parental impact that modulates the effect of early life environment of the UK Biobank participants, the so-called “nature of nurture”, will need further investigation 31 .

Intriguingly, an analysis revealed that the genes involved in the loci associated with the LIFO network (Table  1 /Supplementary Data  1 ) are enriched for the gene ontology terms of leucocyte extravasation, namely “positive regulation of neutrophil extravasation” ( P  = 4.75 × 10 −6 ) and “T cell extravasation” ( P  = 4.75 × 10 −6 ). This result held when removing the genes included in the MAPT extended region (with P  = 2.54 × 10 −6 and P  = 2.54 × 10 −6 , respectively). Leucocyte extravasation facilitates the immune and inflammatory response, and there has been renewed focus on the fact that a breakdown of the blood-brain barrier together with leukocyte extravasation might contribute to both Alzheimer’s disease and schizophrenia 32 , 33 . In line with the enrichment findings, 4 out of the 7 genetic clusters associated with the LIFO network are correlated in UK Biobank blood assays with percentage or count of immune cells (neutrophil, lymphocyte, platelet, monocyte, etc.; Supplementary Data  1 ).

Regarding MRFs’ effects on the LIFO brain network, diabetes and alcohol consumption have been consistently shown to be associated with both cerebral and cognitive decline 34 , 35 . On the other hand, pollution—and notably that of nitrogen oxides—has emerged more recently as a potential MRF for dementia 2 , 36 . In particular, the increase of dementia risk due to nitrogen oxide pollution, a proxy for traffic-related air pollution, seems to be enhanced by cardiovascular disease 37 . In this study, we found that nitrogen dioxide pollution has one of the most deleterious effects onto the fragile LIFO brain regions. This effect could only be unmasked by regressing out the effects of age and sex, as traffic-related air pollution is modestly inversely-correlated with age (Supplementary Data  5 ). It is also worth noting that including age and sex as confounding variables in the first stage of our analysis reduced considerably the contribution of what had appeared at first—before regression—as the most harmful risk factors: blood pressure, cholesterol and weight (Table  2 /Supplementary Data  3 ). Furthermore, the benefit of examining these MRFs in a single model in the second stage of our analysis is that we can assess the unique contribution of each of these factors on the LIFO brain network; in doing so, blood pressure, cholesterol and weight were no longer significant (Table  3 ).

One defining characteristic of the LIFO brain network is how much age explains its variance. Indeed, in the dataset covering most of the lifespan that was initially used to identify the LIFO and spatially define it 8 , age explained 50%. In the UK Biobank imaging project, where imaged participants are over 45 years old, age explained 30% (Fig.  1 ). It is thus perhaps unsurprising that, while the explained variance by each of the MRFs varies widely (Table  2 /Supplementary Data  3 ), it reduces notably once the effect of age and other confounders has been regressed out (without confounders included in the model: maximum 8.4%; with confounders: maximum 0.5%). Combined, the 12 best MRFs explained a significant 1.5% of the effect on the vulnerable brain network after regressing out age, head size and sex effects. Regarding the genetic hits, we found a significant heritability with h 2  = 0.15, in keeping with our results for structural brain phenotypes (except for subcortical and global brain volumes, which demonstrate higher heritability 19 ).

The uniqueness of this study relies on the fact that we combined the strengths of two different cohorts: the first, which revealed the LIFO grey matter network, is lifespan, demonstrating the mirroring of developmental and ageing processes in the LIFO brain areas, something that could never be achieved with UK Biobank because of its limited age range. Of note, for this initial work with the lifespan cohort 8 , we not only included grey matter partial volume images, as done in this current study, but also Freesurfer information of cortical thickness and surface area. The LIFO network showed no contribution from Freesurfer cortical thickness or area. This might hint at processes that only partial volume maps are able to detect due to the LIFO network’s specific localisation, including in the cerebellum and subcortical structures, which are not included in the area and thickness surface methods from Freesurfer.

Limitations of our study pertain to the nature of the data itself and the way each variable is encoded in the UK Biobank (binary, ordinal, categorical, continuous), the number of missing values, what is offered as variables for each modifiable risk factor category (e.g. we chose not to create any compound variables, such as the ratio of cholesterol levels or systolic and diastolic blood pressures), and the curation of each of these variables. Some of the factors might be proxies for another category, but including the ‘best’ ones in a single model alleviate these issues to some extent. Another limitation is the assumption in our models that each risk factor has a linear, additive effect on the vulnerable LIFO brain network. It is also important to note that cross-sectional and longitudinal patterns of brain ageing can differ, as has been shown for instance for adult span trajectories of episodic and semantic memory, especially in younger adults 38 . A recent study has also demonstrated a specific ‘brain age’ imaging measure to be more related to early life influences on brain structure than within-person rates of change in the ageing brain 39 . Further work will be needed to establish how the LIFO network data changes in terms of within-person trends, for instance by investigating the growing UK Biobank longitudinal imaging database. While we took care of assessing the replicability of our genetic results by randomly assigning a third of our dataset for such purposes (all our significant genetic hits were replicated), this was performed within the UK Biobank cohort that exhibits well-documented biases, being well-educated, less deprived, and healthier than the general population, especially for its imaging arm 40 . Independent replications will be needed to confirm the existence of the LIFO-associated genetic loci.

In conclusion, our study reveals the modifiable and non-modifiable factors associated with some of the most fragile parts of the brain particularly vulnerable to ageing and disease process. It shows that, above and beyond the effect of age and sex, the most deleterious modifiable risk factors to this brain network of higher-order regions are diabetes, pollution and alcohol intake. Genetic factors are related to immune and inflammatory response, tau pathology, metal transport and vascular dysfunction, as well as to the XG blood group system from the pseudo-autosomal region of the sex chromosomes, and meaningfully associated with relevant modifiable risk factors for dementia. The unprecedented genome-wide discovery of the two variants on the sex chromosomes in this relatively unexplored blood group opens the way for further investigation into its possible role in underlying unhealthy ageing.

Supplementary Information is available for this paper.

For the present work the imaging cohort of UK Biobank was used and we included 39,676 subjects who had been scanned and for whom the brain scans had been preprocessed at the time of the final set of analyses (M/F 47–53%; 44–82 years, mean age 64 ± 7 years; as of October 2020) 41 , 42 . Structural T1-weighted scans for each participant were processed using the FSL-VBM automated tool to extract their grey matter map 43 , 44 . The ‘last in, first out’ (LIFO) network of mainly higher-order brain regions was initially identified by performing a linked independent component analysis on the grey matter images of another, lifespan observational cohort of 484 subjects 8 , 45 , 46 . This map of interest, along with the other 69 generated by the analysis, was first realigned to the UK Biobank ‘standard’ space defined by the grey matter average across the first 15,000 participants, then regressed into the UK Biobank participants’ grey matter data, to extract weighted average values of grey matter normalised volume inside each of the z-maps, using the z-score as weighting factor. This made it possible to assess the unique contribution of this specific LIFO map, above and beyond all the rest of the brain represented in the other 69 maps. At the end of this process, we obtained a single imaging measure for each of the 39,676 participants, i.e. a ‘loading’ corresponding to their amount of grey matter normalised volume in the LIFO brain network.

Human participants: UK Biobank has approval from the North West Multi-Centre Research Ethics Committee (MREC) to obtain and disseminate data and samples from the participants ( http://www.ukbiobank.ac.uk/ethics/ ), and these ethical regulations cover the work in this study. Written informed consent was obtained from all of the participants.

Modifiable risk factors selection

The following 15 categories of modifiable risk factors (MRFs) for dementia were investigated based on previous literature: blood pressure, diabetes, cholesterol, weight, alcohol, smoking, depression, hearing, inflammation, pollution, sleep, exercise, diet/supplementation, socialisation, and education. These included well-documented cerebrovascular risk factors, and in particular included all of the 12 modifiable risk factors considered in the updated Lancet commission on dementia, with the sole exception of traumatic brain injury 3 . For each category, several MRF variables from UK Biobank were very minimally pre-processed ( Supplementary Information ). In total, 161 MRF variables were obtained. To optimise the interpretability of the results, and to be able to relate them to previous findings, we did not carry out any data reduction, which would have prevented us from identifying exactly which variable—and subsequently, which genetic component for this specific variable—contribute to the effect. For these same reasons, we did not create any compound variable.

Statistical analyses

Genome-wide association study.

We followed the same protocol we had developed for the first genome-wide association study (GWAS) with imaging carried out on UK Biobank 19 . Briefly, we examined imputed UK Biobank genotype data 47 , and restricted the analysis to samples that were unrelated (thereby setting aside only ~450 participants), without aneuploidy and with recent UK ancestry. To account for population stratification, 40 genetic principal components were used in the genetic association tests as is recommended for UK Biobank genetic studies 19 , 20 , 47 . We excluded genetic variants with minor allele frequency <0.01 or INFO score <0.03 or Hardy-Weinberg equilibrium –log 10 ( P ) > 7. We then randomly split the samples into a discovery set with 2/3 of the samples ( n  = 22,128) and a replication set with 1/3 of the samples ( n  = 11,083). We also examined the X chromosome with the same filters, additionally excluding participants with sex chromosome aneuploidy: 12 in non-pseudoautosomal region (PAR) and 9 in PAR for the discovery set, 3 in non-PAR and 6 in PAR for the replication set. Variants were considered significant at –log 10 ( P ) > 7.5, and replicated at P  < 0.05.

Modifiable risk factor study

In the first stage, the general linear model was used to investigate, separately, the association between each of these 161 MRFs and the LIFO network loadings in all the scanned UK Biobank participants ( n  = 39,676). We ran each model twice: once as is, and once adding 6 confounders: age, age 2 , sex, age × sex, age 2 × sex, and head size, to estimate the contribution of these MRFs on the LIFO network above and beyond the dominating effects of age and sex. Sex was based on the population characteristics entry of UK Biobank. This is a mixture of the sex the NHS had recorded for the participant at recruitment, and updated self-reported sex. For the GWAS, both sex and genetic sex were used (the sample was excluded in case of a mismatch). In total, 32 variables tailored to structural imaging had been considered as possible confounders, and we retained those with the strongest association ( R 2  ≥ 0.01; see  Supplementary Information ). Socioeconomic status via the Townsend deprivation index was also considered as a possible confounding variable but explained little variance ( R 2  < 0.001) and thus was not included as a confounder.

MRFs were not considered further if they were not significant—not surviving Bonferroni-correction, i.e., P  > 1.55 × 10 −4 —and if more than 5% of the subjects had their MRF values missing. For each category, a single ‘best’ MRF was then selected as the variable with the highest R 2 among those remaining, after regressing out the confounding effects of age and sex.

In the second stage, all these best MRFs were then included in a single general linear model, together with the same 6 confounders used in the first stage, to assess the unique contribution of each factor on the LIFO brain network loadings. A prerequisite to carry out this single general linear model analysis was to only include participants who would have values for all best MRFs and confounders. This explains the additional criterion of only including MRFs that had no more than 5% of values missing, to ensure that the final sample of participants who had values for all these best and confounding factors would not be biased compared with the original sample—something we formally tested (see  Supplementary Information )—especially as data are not missing at random in UK Biobank, and exhibit some genetic structure 48 . The sample was therefore reduced to a total of 35,527 participants for this second stage analysis (M/F 17,290–18,237; 45–82 years, mean 64 ± 7 years). The effect of these best MRFs taken altogether was considered significant with a very conservative Bonferroni correction for multiple comparisons across all combinations of every possible MRF from each of the initial 15 MRF categories ( P  < 4.62 × 10 −17 , see  Supplementary Information for more details). In addition, both full and partial correlations were computed for the same set of best MRFs and confounders, in order to assess possible relationships between variables.

Post hoc genetic analyses

Chromosome 17 inversion.

We investigated chromosome 17 inversion status of the participants in the discovery cohort by considering their genotype on 32 variants that tag chromosome 17 inversion according to Steinberg et al. 11 . Of these 32 variants, 24 were present in our genetic data. We labelled the participants homozygous inverted, heterozygous, or homozygous direct (not inverted) when all 24 of these alleles indicated the same zygosity. This yielded an unambiguous inversion status for 21,969 participants (99% of the discovery cohort). To examine if the association between the non-triallelic lead variant of the MAPT genetic cluster (rs2532395, Table  1 /Supplementary Data  1 ) and the LIFO network was independent from this common inversion, we determined inversion/direct status of the discovery cohort and: 1. repeated the association test between rs2532395 and the LIFO phenotype, with chromosome 17 inversion status added as a confounder; and 2. correlated the genotype for rs2532395 with chromosome 17 inversion.

Causality within each genetic cluster

We used CAVIAR (Causal Variants Identification in Associated Regions 49 ) to assess causality of variants that passed the genome-wide significance threshold in each of the genetic clusters we report. CAVIAR uses a Bayesian model and the local linkage disequilibrium structure to assign posterior probabilities of causality to each variant in a region, given summary statistics for an association. We did not perform CAVIAR analysis on the genetic cluster on chromosome 17, as its non-triallelic lead variant (rs2532395) was strongly correlated with chromosome 17 inversion, and the LD matrix was large and low rank. We excluded the X chromosome loci from this analysis due to the difficulty in assessing LD in this chromosome.

Enrichment analysis

Based on the genes listed in the ‘Genes’ column of Table  1 /Supplementary Data  1 , we performed an enrichment analysis for the genes associated with the LIFO brain network using PANTHER 50 . PANTHER determines whether a gene function is overrepresented in a set of genes, according to the gene ontology consortium 51 , 52 .

Mediation analysis between MAPT top variant and Alzheimer’s disease, via the LIFO brain network

As the gene MAPT is associated with Alzheimer’s disease, and as we found a significant association between MAPT and the LIFO brain network, we examined to what extent the effect of MAPT is mediated by the LIFO brain regions. We conducted a mediation analysis using the counterfactual framework in which the average indirect effect of the treatment on the outcome through the mediator is nonparametrically identified (version 4.5.0 of the R package ‘mediation' 53 ). This is a general approach that encompasses the classical linear structural equation modelling framework for causal mediation, allowing both linear and non-linear relationships. In this analysis, the genotype for the lead bi-allelic variant of the MAPT association was used as the treatment, the LIFO loadings as the mediator, and Alzheimer’s disease diagnosis as the outcome.

From the ~43 K UK Biobank participants who had been scanned, we searched for those who had been diagnosed with Alzheimer’s disease specifically, regardless of whether this diagnosis occurred before, or after their brain scans. Based on hospital inpatient records (ICD10: F000, F001, F002, F009, G300, G301, G308, and G309 and ICD9: 3310) and primary care (GP) data (Eu00., Eu000, Eu001, Eu002, Eu00z, F110., F1100, F1101, Fyu30, X002x, X002y, X002z, X0030, X0031, X0032, X0033, XaIKB, XaIKC, and XE17j), we identified 65 such cases— UK Biobank being healthier than the general population, and those scanned showing an even stronger healthy bias—of which 34 were included in the discovery set after QC.

We considered two conditions for the effect of the treatment on the outcome. First, a dominant condition in which the minor allele is assumed to be dominant and for which at least one copy of the minor allele is considered treated. Second, a recessive condition in which the minor allele is assumed to be recessive. We considered that either condition was nominally significant if the confidence interval of the average causal mediated effect did not intersect zero, and had an associated P  < 0.05 ÷ 2 (correcting for the two conditions). We assessed confidence intervals and P -values using 50,000 bootstrapped samples.

Associations between the LIFO brain network’s genetic hits and the MRFs

First, we reported in Table  1 / Supplementary Data  1 the significant associations between the LIFO genetic hits and UK Biobank variables related to the 15 categories listed for the MRFs. For this, we used the Open Targets Genetics website, which reports the GWAS carried out in UK Biobank ( https://genetics .opentargets.org/ ). Second, we assessed whether there was any genetic overlap between the known genetic components of the 3 best MRFs and the LIFO phenotype. Again, we used the Open Targets Genetics website outputs for these 3 very specific UK Biobank variables, and compared the significant hits for these 3 best MRFs within ±250 kbp of, or in high LD (>0.8) with, our own LIFO variants. If reported hits were limited, we also searched online for GWAS done on similar variables. Finally, we also included the list of significant hits for diabetes 54 , which focused on a potential genetic overlap between diabetes and Alzheimer’s disease.

Post hoc association for the sex chromosomes variants

The allele counts of each participant for two specific significant variants of the sex chromosomes not—or hardly—available in open databases such as https://genetics.opentargets.org/ 55 were further associated out-of-sample with all non-imaging phenotypes of UK Biobank ( n  = 16,924). This analysis was carried out in the entire genotyped, quality-controlled sample where participants who had been scanned were removed (final sample: 374,230 participants), taking into account the population structure (40 genetic principal components), as well as the confounding effects of age, sex, age x sex, age 2 and age 2 x sex. Results were corrected for multiple comparisons across all non-imaging phenotypes and the two variants.

Heritability

We examined the heritability of the LIFO phenotype, and the coheritability between the LIFO network and Alzheimer’s disease or schizophrenia using LDSC 56 . This method uses regression on summary statistics to determine narrow sense heritability h 2 of a trait, or the shared genetic architecture between two traits. LDSC corrects for bias LD structure using LD calculated from a reference panel (we used LD from the Thousand Genomes Project Phase 1 57 ). We obtained summary statistics for a meta-analysis of Alzheimer’s disease involving 71,880 cases and 383,378 controls 58 . The number of genetic variants in the intersection between the summary statistics was 1,122,435. For schizophrenia, the summary statistics were obtained from a meta-analysis involving 53,386 cases and 77,258 controls 59 . A total of 1,171,319 genetic variants were in the intersection with the summary statistics for LIFO. For both Alzheimer’s and schizophrenia, the X chromosome was not included in the heritability calculation, as it was excluded from the meta-analysis that we sourced the summary statistics from.

Reproducibility

No data was excluded for the MRF analyses. For the genetic analyses, these were restricted to samples that were unrelated, without aneuploidy and with recent UK ancestry (see above).

No statistical method was used to predetermine sample size. The experiments were not randomised. The Investigators were not blinded to allocation during experiments and outcome assessment.

Reporting summary

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

Data availability

All the FLICA decomposition maps − including the LIFO grey matter network − in UK Biobank standard space, the UK Biobank grey matter template, scripts, and the LIFO loadings for all of the participants are freely available on a dedicated webpage: open.win.ox.ac.uk/pages/douaud/ukb-lifo-flica/ .

Baumgart, M. et al. Summary of the evidence on modifiable risk factors for cognitive decline and dementia: a population-based perspective. Alzheimers Dement. 11 , 718–726 (2015).

Article   PubMed   Google Scholar  

Chen, H. et al. Exposure to ambient air pollution and the incidence of dementia: a population-based cohort study. Environ. Int. 108 , 271–277 (2017).

Article   CAS   PubMed   Google Scholar  

Livingston, G. et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet 396 , 413–446 (2020).

Article   PubMed   PubMed Central   Google Scholar  

Desikan, R. S. et al. Genetic overlap between Alzheimer’s disease and Parkinson’s disease at the MAPT locus. Mol. Psychiatry 20 , 1588–1595 (2015).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Hill, W. D. et al. A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence. Mol. Psychiatry 24 , 169–181 (2019).

Pardinas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50 , 381–389 (2018).

Douaud, G. et al. Preventing Alzheimer’s disease-related gray matter atrophy by B-vitamin treatment. Proc. Natl Acad. Sci. USA 110 , 9523–9528 (2013).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Douaud, G. et al. A common brain network links development, aging, and vulnerability to disease. Proc. Natl Acad. Sci. USA 111 , 17648–17653 (2014).

Nakatsu, D. et al. BMP4-SMAD1/5/9-RUNX2 pathway activation inhibits neurogenesis and oligodendrogenesis in Alzheimer’s patients’ iPSCs in senescence-related conditions. Stem Cell Rep. 18 , 1246 (2023).

Article   CAS   Google Scholar  

Peyrot, W. J. & Price, A. L. Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS. Nat. Genet. 53 , 445–454 (2021).

Steinberg, K. M. et al. Structural diversity and African origin of the 17q21.31 inversion polymorphism. Nat. Genet. 44 , 872–880 (2012).

Le Guen, Y. et al. eQTL of KCNK2 regionally influences the brain sulcal widening: evidence from 15,597 UK Biobank participants with neuroimaging data. Brain Struct. Funct. 224 , 847–857 (2019).

Jonsson, B. A. et al. Brain age prediction using deep learning uncovers associated sequence variants. Nat. Commun. 10 , 5409 (2019).

Heurteaux, C. et al. TREK-1, a K+ channel involved in neuroprotection and general anesthesia. EMBO J. 23 , 2684–2695 (2004).

Vojinovic, D. et al. Genome-wide association study of 23,500 individuals identifies 7 loci associated with brain ventricular volume. Nat. Commun. 9 , 3945 (2018).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Zhao, B. et al. Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Nat. Genet. 51 , 1637–1644 (2019).

Zhao, B. et al. Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits ( n = 17,706). Mol. Psychiatry https://doi.org/10.1038/s41380-019-0569-z (2019).

Lasagna-Reeves, C. A. et al. Reduction of Nuak1 decreases tau and reverses phenotypes in a tauopathy mouse model. Neuron 92 , 407–418 (2016).

Elliott, L. T. et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562 , 210–216 (2018).

Smith, S. M. et al. An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank. Nat. Neurosci. 24 , 737–745 (2021).

Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50 , 1112–1121 (2018).

Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466 , 707–713 (2010).

Sanchez-Roige, S. et al. Genome-Wide Association Study Meta-Analysis of the Alcohol Use Disorders Identification Test (AUDIT) in Two Population-Based Cohorts. Am. J. Psychiatry 176 , 107–118 (2019).

Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518 , 197–206 (2015).

Dashti, H. S. et al. Genome-wide association study identifies genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates. Nat. Commun. 10 , 1100 (2019).

International Consortium for Blood Pressure Genome-Wide Association, S. et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478 , 103–109 (2011).

Article   ADS   Google Scholar  

Johansson, A. et al. Genome-wide association and Mendelian randomization study of NT-proBNP in patients with acute coronary syndrome. Hum. Mol. Genet. 25 , 1447–1456 (2016).

Poorkaj, P. et al. Tau is a candidate gene for chromosome 17 frontotemporal dementia. Ann. Neurol. 43 , 815–825 (1998).

Baker, M. et al. Association of an extended haplotype in the tau gene with progressive supranuclear palsy. Hum. Mol. Genet. 8 , 711–715 (1999).

Johnson, N. C. XG: the forgotten blood group system. Immunohematology 27 , 68–71 (2011).

Kong, A. et al. The nature of nurture: Effects of parental genotypes. Science 359 , 424–428 (2018).

Zenaro, E., Piacentino, G. & Constantin, G. The blood-brain barrier in Alzheimer’s disease. Neurobiol. Dis. 107 , 41–56 (2017).

Pong, S., Karmacharya, R., Sofman, M., Bishop, J. R. & Lizano, P. The role of brain microvascular endothelial cell and blood-brain barrier dysfunction in schizophrenia. Complex Psychiatry 6 , 30–46 (2020).

Chatterjee, S. et al. Type 2 diabetes as a risk factor for dementia in women compared with men: a pooled analysis of 2.3 million people comprising more than 100,000 cases of dementia. Diabetes Care 39 , 300–307 (2016).

Veldsman, M. et al. Cerebrovascular risk factors impact frontoparietal network integrity and executive function in healthy ageing. Nat. Commun. 11 , 4340 (2020).

Power, M. C., Adar, S. D., Yanosky, J. D. & Weuve, J. Exposure to air pollution as a potential contributor to cognitive function, cognitive decline, brain imaging, and dementia: a systematic review of epidemiologic research. Neurotoxicology 56 , 235–253 (2016).

Grande, G., Ljungman, P. L. S., Eneroth, K., Bellander, T. & Rizzuto, D. Association between cardiovascular disease and long-term exposure to air pollution with the risk of dementia. JAMA Neurol. 77 , 801–809, (2020).

Ronnlund, M., Nyberg, L., Backman, L. & Nilsson, L. G. Stability, growth, and decline in adult life span development of declarative memory: cross-sectional and longitudinal data from a population-based study. Psychol. Aging 20 , 3–18 (2005).

Vidal-Pineiro, D. et al. Individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change. Elife https://doi.org/10.7554/eLife.69995 (2021).

Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186 , 1026–1034 (2017).

Alfaro-Almagro, F. et al. Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. NeuroImage 166 , 400–424 (2018).

Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19 , 1523–1536 (2016).

Good, C. D. et al. A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14 , 21–36 (2001).

Douaud, G. et al. Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia. Brain 130 , 2375–2386 (2007).

Groves, A. R. et al. Benefits of multi-modal fusion analysis on a large-scale dataset: life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure. NeuroImage 63 , 365–380 (2012).

Smith, S. et al. Structural variability in the human brain reflects fine-grained functional architecture at the population level. J. Neurosci. 39 , 6136–6149 (2019).

Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562 , 203–209 (2018).

Mignogna, G. et al. Patterns of item nonresponse behaviour to survey questionnaires are systematic and associated with genetic loci. Nat. Hum. Behav . https://doi.org/10.1038/s41562-023-01632-7 (2023).

Hormozdiari, F., Kostem, E., Kang, E. Y., Pasaniuc, B. & Eskin, E. Identifying causal variants at loci with multiple signals of association. Genetics 198 , 497–508 (2014).

Thomas, P. D. et al. PANTHER: Making genome-scale phylogenetics accessible to all. Protein Sci. 31 , 8–22 (2022).

Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25 , 25–29 (2000).

Gene Ontology, C. et al. The Gene Ontology knowledgebase in 2023. Genetics https://doi.org/10.1093/genetics/iyad031 (2023).

Imai, K., Keele, L. & Tingley, D. A general approach to causal mediation analysis. Psychol. Methods 15 , 309–334 (2010).

Hardy, J., de Strooper, B. & Escott-Price, V. Diabetes and Alzheimer’s disease: shared genetic susceptibility? Lancet Neurol. 21 , 962–964 (2022).

Carvalho-Silva, D. et al. Open Targets Platform: new developments and updates two years on. Nucleic Acids Res. 47 , D1056–D1065 (2019).

Gazal, S., Marquez-Luna, C., Finucane, H. K. & Price, A. L. Reconciling S-LDSC and LDAK functional enrichment estimates. Nat. Genet. 51 , 1202–1204 (2019).

Genomes Project, C. et al. A global reference for human genetic variation. Nature 526 , 68–74 (2015).

Jansen, I. E. et al. Author Correction: Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 52 , 354 (2020).

Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 604 , 502–508 (2022).

Laird, A. R. et al. ALE meta-analysis workflows via the brainmap database: progress towards a probabilistic functional brain atlas. Front. Neuroinform. 3 , 23 (2009).

Download references

Acknowledgements

We are grateful to Profs Christian K. Tamnes, Lars T. Westlye, Kristine B. Walhovd and Anders M. Fjell, and Dr Andreas Engvig for providing the lifespan cohort which was used to initially derive the original ‘last in, first out’ brain network map, and to Prof Augustine Kong for helpful discussion on the associations between the PAR hit and early life and environmental factors. G.D. was supported by a UK MRC Career Development Fellowship (MR/K006673/1) and a Wellcome Collaborative Award (215573/Z/19/Z). S.S. was supported by Wellcome (203139/Z/16/Z; 215573/Z/19/Z). L.E. was funded by NSERC grants (RGPIN/05484-2019; DGECR/00118-2019) and a Michael Smith Health Research BC Scholar Award. A.M.W. received support through the NIH Intramural Research Program (ZIA-MH002781; ZIA-MH002782). This research was funded in whole, or in part, by the Wellcome Trust (215573/Z/19/Z; 203139/Z/16/Z; 203139/A/16/Z). For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. This research was also supported by the NIHR Oxford Health Biomedical Research Centre (NIHR203316). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z and 203139/A/16/Z).

Author information

These authors contributed equally: Lloyd T. Elliott, Anderson M. Winkler.

Authors and Affiliations

FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK

Jordi Manuello, Paul McCarthy, Fidel Alfaro-Almagro, Soojin Lee, Stephen Smith & Gwenaëlle Douaud

FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy

Jordi Manuello

Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, Canada

Joosung Min & Lloyd T. Elliott

Pacific Parkinson’s Research Centre, The University of British Columbia, Vancouver, BC, Canada

National Institutes of Mental Health, National Institutes of Health, Bethesda, MD, USA

Anderson M. Winkler

Department of Human Genetics, University of Texas Rio Grande Valley, Brownsville, TX, USA

You can also search for this author in PubMed   Google Scholar

Contributions

G.D. conceived and supervised the work, and carried out some of the genetic and modifiable risk factors analyses. J.Ma. carried out most of the genetic and modifiable risk factors analyses. J.Mi., S.L., A.M.W., and L.T.E. carried out additional genetics analyses. G.D., P. McC., F.A.-A., S.S., and L.T.E. created/extracted the imaging and genetics data, and organised the non-imaging data and confound variables. L.T.E. co-supervised the genetic analyses. A.M.W. co-supervised the modifiable risk factor analyses. G.D. interpreted the results and wrote the paper. J.Ma., S.S., L.T.E., and A.M.W. revised the paper.

Corresponding author

Correspondence to Gwenaëlle Douaud .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Communications thanks Xavier Caseras and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, peer review file, description of additional supplementary files, supplementary data 1-5, reporting summary, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Manuello, J., Min, J., McCarthy, P. et al. The effects of genetic and modifiable risk factors on brain regions vulnerable to ageing and disease. Nat Commun 15 , 2576 (2024). https://doi.org/10.1038/s41467-024-46344-2

Download citation

Received : 15 February 2023

Accepted : 22 February 2024

Published : 27 March 2024

DOI : https://doi.org/10.1038/s41467-024-46344-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• Helpline 0808 800 0303
• Newly diagnosed with Parkinson's?
• Living with Parkinson's?
• Caring for someone with Parkinson's?
• Young onset Parkinson's
• Newly diagnosed To Do list
• What is Parkinson's?
• Types of Parkinson's
• What causes Parkinson's?
• Do I have Parkinson's?
• How does Parkinson's progress?
• Does Parkinson's run in families?
• Support for you
• Coronavirus support
• Our support services
• Everyday life
• Work, money, driving and legal
• Relationships and family life
• Your Magazine
• Motor symptoms
• Non-motor symptoms
• Monitoring your Parkinson's
• Treatments and therapies
• Drug treatments
• Side effects of Parkinson's drugs
• Deep brain stimulation
• Ways to give
• Give in celebration
• Give in memory
• Leave a gift in your will
• Philanthropy
• Where your money goes
• Fundraising and events
• Find an event
• Do your own local fundraising
• Corporate fundraising
• Order a fundraising pack
• Pay in your fundraising
• Find a volunteer role
• For current volunteers
• My volunteer induction
• Volunteer Awards
• Fighting for you in this crisis
• Become a campaigner
• Our benefits campaigns
• Our health and social care campaigns
• Parkinson's in Parliament
• Become a member
• AGM and trustee elections
• Explore our research
• When will there be a cure for Parkinson's?
• Improving life through research
• Our research projects
• Our achievements so far
• Get involved in research
• Take part in research
• Volunteer to help shape research
• Research events
• Parkinson's UK Brain Bank
• Donate to research appeals
• For Parkinson's researchers
• Open funding calls
• Information about our grants
• Resources and support for researchers
• Patient and Public Involvement
• Recruit participants to your study
• Research news
• Research blog
• Our research magazine
• Research Conference 2020
• Your network
• UK Parkinson's Audit
• Local network groups
• Excellence Network Awards
• Professional funding opportunities
• Professionals news
• Resource library
• Clinical tools and assessments
• Signposting from diagnosis
• Research resources
• Learning hub
• Search our learning directory
• Sign up to nurse learning pathway
• Sign up to health and care staff learning pathway
• Sign up to therapist learning pathway
• Information for your patients
• Popular resources for patients
• First Steps programme
• Find support and activities near you
• Conferences
• Helpline and local advisers
• General enquiries
• Supporter enquiries
• Accessibility statement
• Feedback and complaints
• Our strategy
• What we think
• Parkinson's UK in Scotland
• Parkinson's UK in Wales
• Parkinson's UK in Northern Ireland
• Jobs at Parkinson’s UK
• Benefits of working for us
• Working at Parkinson's UK
• News and media
• Latest news
• Media and press office

London Landmarks Half Marathon

This half marathon weaves through central london passing iconic landmarks. join team parkinson's now, and together we can fund breakthroughs for the parkinson's community., our charity places have sold out..

Our Charity Places for London Landmarks have now sold out. However, if you have purchased your own place in the event and wish to fundraise for Parkinson's UK, please join our team now.

Event details

This event is not your average half marathon! Take on this exciting closed road, central London run to raise money for Parkinson’s UK. Experience the only half marathon to go through both the City of London and the City of Westminster.

You’ll explore London’s cultural landmarks and heritage. Themed cheer stations will keep you going as you enjoy musical performances, DJs and dancers along the route.

When you sign up you'll get:

• free Parkinson's UK breathable running top 
• exclusive email updates including fundraising and training tips
• access to a range of fundraising materials
• great support team out on the route.

Entry options

Charity place entry

Charity places for the London Landmarks have now closed.

Own place entry

If you purchased your own place with the race organisers for the 2024 London Landmarks Half Marathon through the public ballot, you can still run for Team Parkinson's. Our suggested fundraising target is £150. We look forward to cheering you on at the 2024 race!

Find out more

To find out more about the event, visit the London Landmarks Half Marathon (LLHM) website . 

Contact details

Get in touch with us at [email protected]  or call 020 7936 9312.

0808 800 0303

The Parkinson's UK helpline is a free and confidential service for Parkinson' s information and support.

[email protected] Emails answered within 5 working days.

Text relay: 18001 0808 800 0303 Textphone number for textphone users only.

SUPPORT FOR YOU

• Worried you have Parkinson's?
• Carer or family member?
• Find local support
• Health or social care professionals
• Living with Parkinson's
• Recently diagnosed?

Parkinson's UK

Parkinson's UK 215 Vauxhall Bridge Road London SW1V 1EJ

Tel: 020 7931 8080

Parkinson's UK is the operating name of the Parkinson's Disease Society of the United Kingdom. 

A registered charity in England and Wales (258197) and in Scotland (SC037554).

• About our health content
• Privacy policy
• Terms of use