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What Is A Case Control Study?

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A case-control study is a research method where two groups of people are compared – those with the condition (cases) and those without (controls). By looking at their past, researchers try to identify what factors might have contributed to the condition in the ‘case’ group.

Explanation

A case-control study looks at people who already have a certain condition (cases) and people who don’t (controls). By comparing these two groups, researchers try to figure out what might have caused the condition. They look into the past to find clues, like habits or experiences, that are different between the two groups.

The “cases” are the individuals with the disease or condition under study, and the “controls” are similar individuals without the disease or condition of interest.

The controls should have similar characteristics (i.e., age, sex, demographic, health status) to the cases to mitigate the effects of confounding variables .

Case-control studies identify any associations between an exposure and an outcome and help researchers form hypotheses about a particular population.

Researchers will first identify the two groups, and then look back in time to investigate which subjects in each group were exposed to the condition.

If the exposure is found more commonly in the cases than the controls, the researcher can hypothesize that the exposure may be linked to the outcome of interest.

Figure: Schematic diagram of case-control study design. Kenneth F. Schulz and David A. Grimes (2002) Case-control studies: research in reverse . The Lancet Volume 359, Issue 9304, 431 – 434

Quick, inexpensive, and simple

Because these studies use already existing data and do not require any follow-up with subjects, they tend to be quicker and cheaper than other types of research. Case-control studies also do not require large sample sizes.

Beneficial for studying rare diseases

Researchers in case-control studies start with a population of people known to have the target disease instead of following a population and waiting to see who develops it. This enables researchers to identify current cases and enroll a sufficient number of patients with a particular rare disease.

Useful for preliminary research

Case-control studies are beneficial for an initial investigation of a suspected risk factor for a condition. The information obtained from cross-sectional studies then enables researchers to conduct further data analyses to explore any relationships in more depth.

Limitations

Subject to recall bias.

Participants might be unable to remember when they were exposed or omit other details that are important for the study. In addition, those with the outcome are more likely to recall and report exposures more clearly than those without the outcome.

Difficulty finding a suitable control group

It is important that the case group and the control group have almost the same characteristics, such as age, gender, demographics, and health status.

Forming an accurate control group can be challenging, so sometimes researchers enroll multiple control groups to bolster the strength of the case-control study.

Do not demonstrate causation

Case-control studies may prove an association between exposures and outcomes, but they can not demonstrate causation.

A case-control study is an observational study where researchers analyzed two groups of people (cases and controls) to look at factors associated with particular diseases or outcomes.

Below are some examples of case-control studies:

• Investigating the impact of exposure to daylight on the health of office workers (Boubekri et al., 2014).
• Comparing serum vitamin D levels in individuals who experience migraine headaches with their matched controls (Togha et al., 2018).
• Analyzing correlations between parental smoking and childhood asthma (Strachan and Cook, 1998).
• Studying the relationship between elevated concentrations of homocysteine and an increased risk of vascular diseases (Ford et al., 2002).
• Assessing the magnitude of the association between Helicobacter pylori and the incidence of gastric cancer (Helicobacter and Cancer Collaborative Group, 2001).
• Evaluating the association between breast cancer risk and saturated fat intake in postmenopausal women (Howe et al., 1990).

Frequently asked questions

1. what’s the difference between a case-control study and a cross-sectional study.

Case-control studies are different from cross-sectional studies in that case-control studies compare groups retrospectively while cross-sectional studies analyze information about a population at a specific point in time.

In  cross-sectional studies , researchers are simply examining a group of participants and depicting what already exists in the population.

2. What’s the difference between a case-control study and a longitudinal study?

Case-control studies compare groups retrospectively, while longitudinal studies can compare groups either retrospectively or prospectively.

In a  longitudinal study , researchers monitor a population over an extended period of time, and they can be used to study developmental shifts and understand how certain things change as we age.

In addition, case-control studies look at a single subject or a single case, whereas longitudinal studies can be conducted on a large group of subjects.

3. What’s the difference between a case-control study and a retrospective cohort study?

Case-control studies are retrospective as researchers begin with an outcome and trace backward to investigate exposure; however, they differ from retrospective cohort studies.

In a  retrospective cohort study , researchers examine a group before any of the subjects have developed the disease, then examine any factors that differed between the individuals who developed the condition and those who did not.

Thus, the outcome is measured after exposure in retrospective cohort studies, whereas the outcome is measured before the exposure in case-control studies.

Boubekri, M., Cheung, I., Reid, K., Wang, C., & Zee, P. (2014). Impact of windows and daylight exposure on overall health and sleep quality of office workers: a case-control pilot study. Journal of Clinical Sleep Medicine: JCSM: Official Publication of the American Academy of Sleep Medicine, 10 (6), 603-611.

Ford, E. S., Smith, S. J., Stroup, D. F., Steinberg, K. K., Mueller, P. W., & Thacker, S. B. (2002). Homocyst (e) ine and cardiovascular disease: a systematic review of the evidence with special emphasis on case-control studies and nested case-control studies. International journal of epidemiology, 31 (1), 59-70.

Helicobacter and Cancer Collaborative Group. (2001). Gastric cancer and Helicobacter pylori: a combined analysis of 12 case control studies nested within prospective cohorts. Gut, 49 (3), 347-353.

Howe, G. R., Hirohata, T., Hislop, T. G., Iscovich, J. M., Yuan, J. M., Katsouyanni, K., … & Shunzhang, Y. (1990). Dietary factors and risk of breast cancer: combined analysis of 12 case—control studies. JNCI: Journal of the National Cancer Institute, 82 (7), 561-569.

Lewallen, S., & Courtright, P. (1998). Epidemiology in practice: case-control studies. Community eye health, 11 (28), 57–58.

Strachan, D. P., & Cook, D. G. (1998). Parental smoking and childhood asthma: longitudinal and case-control studies. Thorax, 53 (3), 204-212.

Tenny, S., Kerndt, C. C., & Hoffman, M. R. (2021). Case Control Studies. In StatPearls . StatPearls Publishing.

Togha, M., Razeghi Jahromi, S., Ghorbani, Z., Martami, F., & Seifishahpar, M. (2018). Serum Vitamin D Status in a Group of Migraine Patients Compared With Healthy Controls: A Case-Control Study. Headache, 58 (10), 1530-1540.

Further Information

• Schulz, K. F., & Grimes, D. A. (2002). Case-control studies: research in reverse. The Lancet, 359(9304), 431-434.
• What is a case-control study?

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Study Design 101: Case Control Study

• Case Report
• Case Control Study
• Cohort Study
• Randomized Controlled Trial
• Practice Guideline
• Systematic Review
• Meta-Analysis
• Helpful Formulas
• Finding Specific Study Types

A study that compares patients who have a disease or outcome of interest (cases) with patients who do not have the disease or outcome (controls), and looks back retrospectively to compare how frequently the exposure to a risk factor is present in each group to determine the relationship between the risk factor and the disease.

Case control studies are observational because no intervention is attempted and no attempt is made to alter the course of the disease. The goal is to retrospectively determine the exposure to the risk factor of interest from each of the two groups of individuals: cases and controls. These studies are designed to estimate odds.

Case control studies are also known as "retrospective studies" and "case-referent studies."

• Good for studying rare conditions or diseases
• Less time needed to conduct the study because the condition or disease has already occurred
• Lets you simultaneously look at multiple risk factors
• Useful as initial studies to establish an association
• Can answer questions that could not be answered through other study designs

Disadvantages

• Retrospective studies have more problems with data quality because they rely on memory and people with a condition will be more motivated to recall risk factors (also called recall bias).
• Not good for evaluating diagnostic tests because it's already clear that the cases have the condition and the controls do not
• It can be difficult to find a suitable control group

Design pitfalls to look out for

Care should be taken to avoid confounding, which arises when an exposure and an outcome are both strongly associated with a third variable. Controls should be subjects who might have been cases in the study but are selected independent of the exposure. Cases and controls should also not be "over-matched."

Is the control group appropriate for the population? Does the study use matching or pairing appropriately to avoid the effects of a confounding variable? Does it use appropriate inclusion and exclusion criteria?

Fictitious Example

There is a suspicion that zinc oxide, the white non-absorbent sunscreen traditionally worn by lifeguards is more effective at preventing sunburns that lead to skin cancer than absorbent sunscreen lotions. A case-control study was conducted to investigate if exposure to zinc oxide is a more effective skin cancer prevention measure. The study involved comparing a group of former lifeguards that had developed cancer on their cheeks and noses (cases) to a group of lifeguards without this type of cancer (controls) and assess their prior exposure to zinc oxide or absorbent sunscreen lotions.

This study would be retrospective in that the former lifeguards would be asked to recall which type of sunscreen they used on their face and approximately how often. This could be either a matched or unmatched study, but efforts would need to be made to ensure that the former lifeguards are of the same average age, and lifeguarded for a similar number of seasons and amount of time per season.

Real-life Examples

Boubekri, M., Cheung, I., Reid, K., Wang, C., & Zee, P. (2014). Impact of windows and daylight exposure on overall health and sleep quality of office workers: a case-control pilot study. Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine, 10 (6), 603-611. https://doi.org/10.5664/jcsm.3780

This pilot study explored the impact of exposure to daylight on the health of office workers (measuring well-being and sleep quality subjectively, and light exposure, activity level and sleep-wake patterns via actigraphy). Individuals with windows in their workplaces had more light exposure, longer sleep duration, and more physical activity. They also reported a better scores in the areas of vitality and role limitations due to physical problems, better sleep quality and less sleep disturbances.

Togha, M., Razeghi Jahromi, S., Ghorbani, Z., Martami, F., & Seifishahpar, M. (2018). Serum Vitamin D Status in a Group of Migraine Patients Compared With Healthy Controls: A Case-Control Study. Headache, 58 (10), 1530-1540. https://doi.org/10.1111/head.13423

This case-control study compared serum vitamin D levels in individuals who experience migraine headaches with their matched controls. Studied over a period of thirty days, individuals with higher levels of serum Vitamin D was associated with lower odds of migraine headache.

Related Formulas

• Odds ratio in an unmatched study
• Odds ratio in a matched study

Related Terms

A patient with the disease or outcome of interest.

Confounding

When an exposure and an outcome are both strongly associated with a third variable.

A patient who does not have the disease or outcome.

Matched Design

Each case is matched individually with a control according to certain characteristics such as age and gender. It is important to remember that the concordant pairs (pairs in which the case and control are either both exposed or both not exposed) tell us nothing about the risk of exposure separately for cases or controls.

Observed Assignment

The method of assignment of individuals to study and control groups in observational studies when the investigator does not intervene to perform the assignment.

Unmatched Design

The controls are a sample from a suitable non-affected population.

Now test yourself!

1. Case Control Studies are prospective in that they follow the cases and controls over time and observe what occurs.

a) True b) False

2. Which of the following is an advantage of Case Control Studies?

a) They can simultaneously look at multiple risk factors. b) They are useful to initially establish an association between a risk factor and a disease or outcome. c) They take less time to complete because the condition or disease has already occurred. d) b and c only e) a, b, and c

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Quantitative study designs: Case Control

Quantitative study designs.

• Introduction
• Cohort Studies
• Randomised Controlled Trial

Case Control

• Cross-Sectional Studies
• Study Designs Home

In a Case-Control study there are two groups of people: one has a health issue (Case group), and this group is “matched” to a Control group without the health issue based on characteristics like age, gender, occupation. In this study type, we can look back in the patient’s histories to look for exposure to risk factors that are common to the Case group, but not the Control group. It was a case-control study that demonstrated a link between carcinoma of the lung and smoking tobacco . These studies estimate the odds between the exposure and the health outcome, however they cannot prove causality. Case-Control studies might also be referred to as retrospective or case-referent studies. 

Stages of a Case-Control study

This diagram represents taking both the case (disease) and the control (no disease) groups and looking back at their histories to determine their exposure to possible contributing factors.  The researchers then determine the likelihood of those factors contributing to the disease.

(FOR ACCESSIBILITY: A case control study is likely to show that most, but not all exposed people end up with the health issue, and some unexposed people may also develop the health issue)

Which Clinical Questions does Case-Control best answer?

Case-Control studies are best used for Prognosis questions.

For example: Do anticholinergic drugs increase the risk of dementia in later life? (See BMJ Case-Control study Anticholinergic drugs and risk of dementia: case-control study )

What are the advantages and disadvantages to consider when using Case-Control?

* Confounding occurs when the elements of the study design invalidate the result. It is usually unintentional. It is important to avoid confounding, which can happen in a few ways within Case-Control studies. This explains why it is lower in the hierarchy of evidence, superior only to Case Studies.

What does a strong Case-Control study look like?

A strong study will have:

• Well-matched controls, similar background without being so similar that they are likely to end up with the same health issue (this can be easier said than done since the risk factors are unknown). 
• Detailed medical histories are available, reducing the emphasis on a patient’s unreliable recall of their potential exposures. 

What are the pitfalls to look for?

• Poorly matched or over-matched controls.  Poorly matched means that not enough factors are similar between the Case and Control. E.g. age, gender, geography. Over-matched conversely means that so many things match (age, occupation, geography, health habits) that in all likelihood the Control group will also end up with the same health issue! Either of these situations could cause the study to become ineffective. 
• Selection bias: Selection of Controls is biased. E.g. All Controls are in the hospital, so they’re likely already sick, they’re not a true sample of the wider population. 
• Cases include persons showing early symptoms who never ended up having the illness. 

Critical appraisal tools 

To assist with critically appraising case control studies there are some tools / checklists you can use.

CASP - Case Control Checklist

JBI – Critical appraisal checklist for case control studies

CEBMA – Centre for Evidence Based Management  – Critical appraisal questions (focus on leadership and management)

STROBE - Observational Studies checklists includes Case control

SIGN - Case-Control Studies Checklist

NCCEH - Critical Appraisal of a Case Control Study for environmental health

Real World Examples

Smoking and carcinoma of the lung; preliminary report

• Doll, R., & Hill, A. B. (1950). Smoking and carcinoma of the lung; preliminary report.  British Medical Journal ,  2 (4682), 739–748. Retrieved from  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2038856/
• Key Case-Control study linking tobacco smoking with lung cancer
• Notes a marked increase in incidence of Lung Cancer disproportionate to population growth.
• 20 London Hospitals contributed current Cases of lung, stomach, colon and rectum cancer via admissions, house-physician and radiotherapy diagnosis, non-cancer Controls were selected at each hospital of the same-sex and within 5 year age group of each.
• 1732 Cases and 743 Controls were interviewed for social class, gender, age, exposure to urban pollution, occupation and smoking habits.
• It was found that continued smoking from a younger age and smoking a greater number of cigarettes correlated with incidence of lung cancer.

Anticholinergic drugs and risk of dementia: case-control study

• Richardson, K., Fox, C., Maidment, I., Steel, N., Loke, Y. K., Arthur, A., . . . Savva, G. M. (2018). Anticholinergic drugs and risk of dementia: case-control study. BMJ , 361, k1315. Retrieved from  http://www.bmj.com/content/361/bmj.k1315.abstract .
• A recent study linking the duration and level of exposure to Anticholinergic drugs and subsequent onset of dementia.
• Anticholinergic Cognitive Burden (ACB) was estimated in various drugs, the higher the exposure (measured as the ACB score) the greater likeliness of onset of dementia later in life.
• Antidepressant, urological, and antiparkinson drugs with an ACB score of 3 increased the risk of dementia. Gastrointestinal drugs with an ACB score of 3 were not strongly linked with onset of dementia.
• Tricyclic antidepressants such as Amitriptyline have an ACB score of 3 and are an example of a common area of concern.

Omega-3 deficiency associated with perinatal depression: Case-Control study 

• Rees, A.-M., Austin, M.-P., Owen, C., & Parker, G. (2009). Omega-3 deficiency associated with perinatal depression: Case control study. Psychiatry Research , 166(2), 254-259. Retrieved from  http://www.sciencedirect.com/science/article/pii/S0165178107004398 .
• During pregnancy women lose Omega-3 polyunsaturated fatty acids to the developing foetus.
• There is a known link between Omgea-3 depletion and depression
• Sixteen depressed and 22 non-depressed women were recruited during their third trimester
• High levels of Omega-3 were associated with significantly lower levels of depression.
• Women with low levels of Omega-3 were six times more likely to be depressed during pregnancy.

References and Further Reading

Doll, R., & Hill, A. B. (1950). Smoking and carcinoma of the lung; preliminary report. British Medical Journal, 2(4682), 739–748. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2038856/

Greenhalgh, Trisha. How to Read a Paper: the Basics of Evidence-Based Medicine, John Wiley & Sons, Incorporated, 2014. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/deakin/detail.action?docID=1642418 .

Himmelfarb Health Sciences Library. (2019). Study Design 101: Case-Control Study. Retrieved from https://himmelfarb.gwu.edu/tutorials/studydesign101/casecontrols.cfm   

Hoffmann, T., Bennett, S., & Del Mar, C. (2017). Evidence-Based Practice Across the Health Professions (Third edition. ed.): Elsevier. 

Lewallen, S., & Courtright, P. (1998). Epidemiology in practice: case-control studies. Community Eye Health, 11(28), 57.  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1706071/  

Pelham, B. W. a., & Blanton, H. (2013). Conducting research in psychology : measuring the weight of smoke /Brett W. Pelham, Hart Blanton (Fourth edition. ed.): Wadsworth Cengage Learning. 

Rees, A.-M., Austin, M.-P., Owen, C., & Parker, G. (2009). Omega-3 deficiency associated with perinatal depression: Case control study. Psychiatry Research, 166(2), 254-259. Retrieved from http://www.sciencedirect.com/science/article/pii/S0165178107004398

Richardson, K., Fox, C., Maidment, I., Steel, N., Loke, Y. K., Arthur, A., … Savva, G. M. (2018). Anticholinergic drugs and risk of dementia: case-control study. BMJ, 361, k1315. Retrieved from http://www.bmj.com/content/361/bmj.k1315.abstract

Statistics How To. (2019). Case-Control Study: Definition, Real Life Examples. Retrieved from https://www.statisticshowto.com/case-control-study/  

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Case-control and Cohort studies: A brief overview

Posted on 6th December 2017 by Saul Crandon

Introduction

Case-control and cohort studies are observational studies that lie near the middle of the hierarchy of evidence . These types of studies, along with randomised controlled trials, constitute analytical studies, whereas case reports and case series define descriptive studies (1). Although these studies are not ranked as highly as randomised controlled trials, they can provide strong evidence if designed appropriately.

Case-control studies

Case-control studies are retrospective. They clearly define two groups at the start: one with the outcome/disease and one without the outcome/disease. They look back to assess whether there is a statistically significant difference in the rates of exposure to a defined risk factor between the groups. See Figure 1 for a pictorial representation of a case-control study design. This can suggest associations between the risk factor and development of the disease in question, although no definitive causality can be drawn. The main outcome measure in case-control studies is odds ratio (OR) .

Figure 1. Case-control study design.

Cases should be selected based on objective inclusion and exclusion criteria from a reliable source such as a disease registry. An inherent issue with selecting cases is that a certain proportion of those with the disease would not have a formal diagnosis, may not present for medical care, may be misdiagnosed or may have died before getting a diagnosis. Regardless of how the cases are selected, they should be representative of the broader disease population that you are investigating to ensure generalisability.

Case-control studies should include two groups that are identical EXCEPT for their outcome / disease status.

As such, controls should also be selected carefully. It is possible to match controls to the cases selected on the basis of various factors (e.g. age, sex) to ensure these do not confound the study results. It may even increase statistical power and study precision by choosing up to three or four controls per case (2).

Case-controls can provide fast results and they are cheaper to perform than most other studies. The fact that the analysis is retrospective, allows rare diseases or diseases with long latency periods to be investigated. Furthermore, you can assess multiple exposures to get a better understanding of possible risk factors for the defined outcome / disease.

Nevertheless, as case-controls are retrospective, they are more prone to bias. One of the main examples is recall bias. Often case-control studies require the participants to self-report their exposure to a certain factor. Recall bias is the systematic difference in how the two groups may recall past events e.g. in a study investigating stillbirth, a mother who experienced this may recall the possible contributing factors a lot more vividly than a mother who had a healthy birth.

A summary of the pros and cons of case-control studies are provided in Table 1.

Table 1. Advantages and disadvantages of case-control studies.

Cohort studies

Cohort studies can be retrospective or prospective. Retrospective cohort studies are NOT the same as case-control studies.

In retrospective cohort studies, the exposure and outcomes have already happened. They are usually conducted on data that already exists (from prospective studies) and the exposures are defined before looking at the existing outcome data to see whether exposure to a risk factor is associated with a statistically significant difference in the outcome development rate.

Prospective cohort studies are more common. People are recruited into cohort studies regardless of their exposure or outcome status. This is one of their important strengths. People are often recruited because of their geographical area or occupation, for example, and researchers can then measure and analyse a range of exposures and outcomes.

The study then follows these participants for a defined period to assess the proportion that develop the outcome/disease of interest. See Figure 2 for a pictorial representation of a cohort study design. Therefore, cohort studies are good for assessing prognosis, risk factors and harm. The outcome measure in cohort studies is usually a risk ratio / relative risk (RR).

Figure 2. Cohort study design.

Cohort studies should include two groups that are identical EXCEPT for their exposure status.

As a result, both exposed and unexposed groups should be recruited from the same source population. Another important consideration is attrition. If a significant number of participants are not followed up (lost, death, dropped out) then this may impact the validity of the study. Not only does it decrease the study’s power, but there may be attrition bias – a significant difference between the groups of those that did not complete the study.

Cohort studies can assess a range of outcomes allowing an exposure to be rigorously assessed for its impact in developing disease. Additionally, they are good for rare exposures, e.g. contact with a chemical radiation blast.

Whilst cohort studies are useful, they can be expensive and time-consuming, especially if a long follow-up period is chosen or the disease itself is rare or has a long latency.

A summary of the pros and cons of cohort studies are provided in Table 2.

The Strengthening of Reporting of Observational Studies in Epidemiology Statement (STROBE)

STROBE provides a checklist of important steps for conducting these types of studies, as well as acting as best-practice reporting guidelines (3). Both case-control and cohort studies are observational, with varying advantages and disadvantages. However, the most important factor to the quality of evidence these studies provide, is their methodological quality.

• Song, J. and Chung, K. Observational Studies: Cohort and Case-Control Studies .  Plastic and Reconstructive Surgery.  2010 Dec;126(6):2234-2242.
• Ury HK. Efficiency of case-control studies with multiple controls per case: Continuous or dichotomous data .  Biometrics . 1975 Sep;31(3):643–649.
• von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.   Lancet 2007 Oct;370(9596):1453-14577. PMID: 18064739.

Saul Crandon

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No Comments on Case-control and Cohort studies: A brief overview

Very well presented, excellent clarifications. Has put me right back into class, literally!

Very clear and informative! Thank you.

very informative article.

Thank you for the easy to understand blog in cohort studies. I want to follow a group of people with and without a disease to see what health outcomes occurs to them in future such as hospitalisations, diagnoses, procedures etc, as I have many health outcomes to consider, my questions is how to make sure these outcomes has not occurred before the “exposure disease”. As, in cohort studies we are looking at incidence (new) cases, so if an outcome have occurred before the exposure, I can leave them out of the analysis. But because I am not looking at a single outcome which can be checked easily and if happened before exposure can be left out. I have EHR data, so all the exposure and outcome have occurred. my aim is to check the rates of different health outcomes between the exposed)dementia) and unexposed(non-dementia) individuals.

Very helpful information

Thanks for making this subject student friendly and easier to understand. A great help.

Thanks a lot. It really helped me to understand the topic. I am taking epidemiology class this winter, and your paper really saved me.

Happy new year.

Wow its amazing n simple way of briefing ,which i was enjoyed to learn this.its very easy n quick to pick ideas .. Thanks n stay connected

Saul you absolute melt! Really good work man

am a student of public health. This information is simple and well presented to the point. Thank you so much.

very helpful information provided here

really thanks for wonderful information because i doing my bachelor degree research by survival model

Quite informative thank you so much for the info please continue posting. An mph student with Africa university Zimbabwe.

Thank you this was so helpful amazing

Apreciated the information provided above.

So clear and perfect. The language is simple and superb.I am recommending this to all budding epidemiology students. Thanks a lot.

Great to hear, thank you AJ!

I have recently completed an investigational study where evidence of phlebitis was determined in a control cohort by data mining from electronic medical records. We then introduced an intervention in an attempt to reduce incidence of phlebitis in a second cohort. Again, results were determined by data mining. This was an expedited study, so there subjects were enrolled in a specific cohort based on date(s) of the drug infused. How do I define this study? Thanks so much.

thanks for the information and knowledge about observational studies. am a masters student in public health/epidemilogy of the faculty of medicines and pharmaceutical sciences , University of Dschang. this information is very explicit and straight to the point

Very much helpful

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What is a Case-Control Study?

Affiliations.

• 1 Department of Neurosurgery, University of Alabama at Birmingham.
• 2 Department of Neurosurgery, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
• 3 Department of Neurosurgery, Mobile Infirmary Medical Center, Mobile, Alabama.
• PMID: 30535401
• DOI: 10.1093/neuros/nyy590

Case-control (case-control, case-controlled) studies are beginning to appear more frequently in the neurosurgical literature. They can be more robust, if well designed, than the typical case series or even cohort study to determine or refine treatment algorithms. The purpose of this review is to define and explore the differences between case-control studies and other so-called nonexperimental, quasiexperimental, or observational studies in determining preferred treatments for neurosurgical patients.

Keywords: Case–control; Observational study; Study design.

Copyright © 2018 by the Congress of Neurological Surgeons.

• Biomedical Research
• Case-Control Studies*
• Research Design

Critical Appraisal Resources for Evidence-Based Nursing Practice

• Levels of Evidence
• Systematic Reviews
• Randomized Controlled Trials
• Quasi-Experimental Studies
• Case-Control Studies

What is a Case-Control Study?

Pro tips: case-control study checklist, articles on case-control study design and methodology.

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Case-control studies are a type of quantitative research "designed to sample a group of people with and a group of people without the disease or the outcome measure being studied" (Schmidt & Brown, 2019, p. 209).  The cases are individuals with the disease or outcome measure, and the controls are individuals without the disease or outcome measure.  The purpose of a case-control study is to test whether there is an association between an exposure and a disease, condition or outcome measure (Schmidt & Brown, 2019, p. 209). 

Schmidt N. A. & Brown J. M. (2019). Evidence-based practice for nurses: Appraisal and application of research  (4th ed.). Jones & Bartlett Learning. 

Each JBI Checklist provides tips and guidance on what to look for to answer each question.   These tips begin on page 4. 

Below are some additional  Frequently Asked Questions  about the  C ase-Control Studies Checklist  that have been asked by students in previous semesters. 

For more help:  Each JBI Checklist provides detailed guidance on what to look for to answer each question on the checklist.  These explanatory notes begin on page four of each Checklist. Please review these carefully as you conduct critical appraisal using JBI tools. 

Dey, T., Mukherjee, A., & Chakraborty, S. (2020). A practical overview of case-control studies in clinical practice .  Chest ,  158 (1S), S57–S64. https://doi.org/10.1016/j.chest.2020.03.009

Dupépé, E. B., Kicielinski, K. P., Gordon, A. S., & Walters, B. C. (2019). What is a case-control study?   Neurosurgery ,  84 (4), 819–826. https://doi.org/10.1093/neuros/nyy590

Herbert R. (2017). Case-control studies .  Journal of physiotherapy ,  63 (4), 264–266. https://doi.org/10.1016/j.jphys.2017.08.007

Schulz, K. F., & Grimes, D. A. (2002). Case-control studies: Research in reverse .  Lancet ,  359 (9304), 431–434. https://doi.org/10.1016/S0140-6736(02)07605-5

Song, J. W., & Chung, K. C. (2010). Observational studies: Cohort and case-control studies .  Plastic and reconstructive surgery ,  126 (6), 2234–2242. https://doi.org/10.1097/PRS.0b013e3181f44abc

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ORIGINAL RESEARCH article

The positive impact of smoking on poor sleep quality is moderated by igf1 levels in cerebrospinal fluid: a case-control study among chinese adults.

• 1 Department of Anesthesiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, China
• 2 School of Mental Health, Wenzhou Medical University, Wenzhou, Zhejiang, China
• 3 Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
• 4 Zhejiang Provincial Clinical Research Center for Mental Disorders, The Affiliated Wenzhou Kangning Hospital, Wenzhou Medical University, Wenzhou, China
• 5 Department of Psychiatry, The Third Hospital of Quzhou, Quzhou, China
• 6 Psychosomatic Medicine Research Division, Inner Mongolia Medical University, Hohhot, China
• 7 Beijing Huilongguan Hospital, Peking University, Beijing, China
• 8 Infection Control Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
• 9 Department of Infectious Diseases, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China

Objective: Previous research indicates associations between cigarette smoking, insulin-like growth factor-1 (IGF1), and sleep disturbances. This study aimed to examine the association between smoking and sleep quality and investigate the moderating role of IGF1.

Methods: This case-control study involved 146 Chinese adult males (53 active smokers and 93 non-smokers) from September 2014 to January 2016. Sleep quality and disturbances were evaluated using the Pittsburgh Sleep Quality Index (PSQI), which includes seven scales. Pearson correlation analysis and logistic regression analysis were utilized to examine the link between IGF1 levels in cerebrospinal fluid (CSF) and PSQI scores. The effect of IGF1 was assessed using the moderation effect and simple slope analysis, with adjustments made for potential confounders.

Results: Active smokers exhibited significantly higher global PSQI scores and lower IGF1 levels in CSF compared to non-smokers. A significant negative correlation was observed between IGF1 and PSQI scores (â = -0.28, P < 0.001), with a stronger association in non-smokers (Pearson r = -0.30) compared to smokers (Pearson r = -0.01). Smoking was associated with higher global PSQI scores (â = 0.282, P < 0.001), and this association was moderated by IGF1 levels in CSF (â = 0.145, P < 0.05), with a stronger effect at high IGF1 levels (Bsimple = 0.402, p < 0.001) compared to low IGF1 levels (Bsimple = 0.112, p = 0.268). Four subgroup analysis revealed similar results for sleep disturbances (Bsimple = 0.628, P < 0.001), with a marginal moderation effect observed on subjective sleep quality (Bsimple = 0.150, P = 0.070). However, independent associations rather than moderating effects were observed between IGF1 and sleep efficiency and daytime disturbance.

Conclusion: We provided evidence to demonstrate the moderation effect of IGF1 on the relationship between smoking and sleep in CSF among Chinese adult males.

1 Introduction

Poor sleep quality has become increasingly prevalent in various population over the past decades, affecting more than a quarter of the population worldwide ( 1 , 2 ). Existing epidemiological surveys have reported that approximately one-third of adults suffer from one or more sleep disorders during their aggregate lifetime ( 3 ). Of note, sleep disorder often coexists with physical health conditions and psychological comorbidities ( 4 , 5 ), which in turn exacerbate the symptoms that profoundly disturb sleep quality ( 6 ). Thus, sleep disorders have gained widespread concern from all walks of life as a major public health issue and a health management challenge.

Multiple factors such as unhealthy lifestyles (smoking, drinking, sedentary, diet), chronic diseases (mental illness, metabolic diseases) housing conditions, and socializing status may contribute to abnormal sleep patterns. Among these, cigarettes smoking has been shown to be detrimental to healthy sleep in several studies. A recently published meta-analysis indicated that smoking carried a higher risk of developing sleep-related issues than non-smoking ( 7 ), and sleep disorders similarly increased the difficulties for smoking cessation ( 8 ). Nevertheless, it has been reported that sleep disorders were still prevalent among smokers despite their intense quitting attempts ( 9 ). Indeed, large amounts of nicotine contained in cigarette smoke can readily penetrate the blood-brain barrier, rapidly distribute throughout the brain ( 10 ), and stimulate nicotinic receptors to release a series of neurotransmitters that independently or interactively regulate the sleep-wake cycle, thereby exacerbating sleep disorders and affecting overall sleep quality ( 11 , 12 ).

Moreover, sleep is associated with the optimal production and secretion of hormones, modulated by neuroendocrine signals ( 13 ). Recently, increasing interest has been devoted to exploring neurotrophic factors such as Insulin-like growth factor-1 (IGF1). Based on the previous studies, IGF1 is a hormone that plays a crucial role in the regulation of cell growth, differentiation, and metabolism ( 14 ). In population-based studies, high levels of peripheral IGF1 were found to be associated with better sleep quality ( 15 ). Epidemiological studies have shown that lower levels of IGF1 have been observed in individuals with chronic insomnia, while individuals with sleep extension have significantly higher levels of IGF1 concentrations in the blood compared to individuals with habitual sleep ( 15 , 16 ). IGF1 has been shown to have both neuroprotective and neurorestorative effects ( 17 ), and several studies have suggested that IGF1 may have a protective effect against the negative effects of smoking on sleep. For example, one study found that chronic nicotine exposure has been found to cause sleep disturbance in rats ( 18 ), and IGF1 supplementation can improve sleep quality in rats ( 19 ). However, there were inconsistent associations of tobacco exposure with IGF1, as well as differences in IGF1 levels between smokers and non-smokers ( 20 – 22 ). Some studies have not found the differences in the effect of smoking on sleep quality at different levels of IGF1, indicating that the relationship between these factors may be complex and multifaceted. Therefore, the aim of this study was to investigate the effect of IGF1 in cerebrospinal fluid (CSF) on the association between smoking and sleep quality.

2 Materials and methods

2.1 study population.

Considering the low proportion of female smokers in China (2.7%) ( 23 ), males were mainly recruited for the present study. The study design and population have been described in detail previously ( 24 ). Briefly, 191 subjects without fatal diseases who were scheduled for anterior cruciate ligament (ACL) reconstruction surgery were enrolled from September 2014 to January 2016 in this study. Information on sociodemographic data (age, marriage, and living) and lifestyles was obtained using interview questionnaire. Clinical information (personal and family history of diseases, history of substance abuse and dependence) was collected based on self-report and confirmed by family members. Physical examination (height and weight) was performed by a trained nurse, and body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. After excluding those with a family history of psychiatric diseases or systemic or central neurological diseases diagnosed by the Mini International Neuropsychiatric Interview, a total of 146 eligible adult males, comprising active smokers (n = 53) and non-smokers (n = 93), were recruited finally. None of the subjects had a history of alcohol abuse or psychiatric diseases identified by the Diagnostic and Statistical Manual of Mental Disorders (4th Edition). This study was conducted following the Declaration of Helsinki, approved by the Institutional Review Board of Inner Mongolian Medical University, and all the participants provided their written informed consent.

2.2 Biosamples collection and laboratory tests

The CSF biosamples were derived from lumbar puncture, the details of which have been elaborated in the previous literature ( 24 ). On the morning before ACL reconstruction surgery, a trained and licensed anesthesiologist performed the lumbar puncture operation on the subjects under local anesthesia (using 3 mL of 0.5% ropivacaine), thus collecting 5 mL CSF samples intrathecally. Each sample was distributed into 0.5mL-tubes and immediately stored in a -80°C refrigerator for determination within 24 hours. The entire procedure from hospitalization to surgery took no more than 2 days, during which the subjects were not required to quit smoking.

The levels of IGF1 in CSF were measured using atomic absorption spectrophotometry by professional laboratory technicians. The whole process of detection was in accordance with the principle of double-blind.

2.3 Definition of smoking

Non-smokers were defined as subjects who never smoked during their whole life without a history of substance abuse or dependence. Active smokers were those who smoked at least 10 cigarettes a day lasting for over one year. Otherwise, smokers in between–those who smoked less than 10 cigarettes/day–were excluded.

2.4 Assessment of PSQI

The Pittsburgh Sleep Quality Index (PSQI) is a recognized comprehensive measurement for subjective self-assessment of sleep quality and disturbances within an interval of the past month that was widely used in clinical practice and research, which can identify good and poor sleepers with high specificity, sensitivity and accuracy ( 25 ). Guided by the Chinese version of PSQI ( 26 ), all participants had to respond on a four-point Likert scale (from 0 to 3, indicating “no difficulty” to “severe difficulty”). Nineteen individual items were integrated into seven subscales: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction ( 25 ). The sum of these seven components generated a global score ranging from 0 to 21, with higher scores indicating poorer sleep quality and vice versa. Because fewer participants took sleeping medications, the other six subgroups were included in the subsequent analysis.

2.5 Statistical analysis

Categorical variables were described as number (percentage) and compared by Chi-square test. According to the normality distribution (mainly by the Shapiro-Wilk normality test), normally and skewed distributed continuous variables were presented as mean ± standard deviation and median (interquartile range), and the differences between groups were compared using independent t-test and Mann-Whitney U test, respectively. The correlation between the IGF1 levels and PSQI scores was examined by Pearson correlation analysis in the active smoking and non-smoking groups. We conducted the traditional linear regression model to investigate the interactive effect of IGF1 and cigarette dependence on PSQI score. Subsequently, a multivariate logistic regression model was employed to elucidate the associations between IGF1 levels in CSF and six subgroups of PSQI in all the subjects. Both linear regression and logistic regression models were adjusted for age (years, continuous), BMI (kg/m 2 , continuous), marriage (married/unmarried), and living (living alone/living with one roommate/living with family members). Furthermore, a moderation effect analysis and simple slope analysis were applied to assess the moderating effect of IGF1 on the relationship between smoking and PSQI scores and the significant components of PSQI. All statistical analyses were conducted using the R software (version 4.2.0, R Foundation for Statistical Computing). The moderation analysis was performed using the “Bruce R” package. All tests were two-tailed and a P < 0.05 was considered statistically significant.

3.1 Basic characteristics of study population

The basic characteristics of the 146 participants are presented in Table 1 , with a smoking rate of 36.3%. As demonstrated in Table 1 , active smokers were more likely to be older, unmarried, living alone, and have a higher BMI (all P < 0.05). Compared with non-smokers, the levels of IGF1 in CSF were significantly lower among active smokers (median levels of 33.0 ng/mL vs. 35.1 ng/mL, P < 0.001), while significantly higher in PSQI scores were observed (4.02 ± 2.27 vs. 2.60 ± 2.46, P < 0.001), particularly for sleep disturbance, sleep latency, and sleep quality. In addition, no differences were found for blood pressure and other components of PSQI between the two groups ( P > 0.05).

Table 1 Comparisons of descriptive characteristics between non-smokers and active smokers.

3.2 Correlation between IGF1 levels in CSF and PSQI scores and fractional latitude score in different groups

In the total population, the levels of IGF1 in CSF were negatively correlated with PSQI scores (β = -0.28, P < 0.001) ( Figure 1A ). However, the correlations differed across groups, where a significant negative correlation existed in non-smokers (Pearson r = -0.30, P < 0.001) but no correlation in active smokers Pearson r = -0.01, P = 0.703) ( Figure 1B ). Furthermore, the relationships between IGF1 levels (divided into two groups by the median) and six subgroups of PSQI were assessed by logistic regression model ( Figure 1C ). After adjusting for age, BMI, marriage and living, the levels IGF1 in CSF were inversely associated with sleep disturbances (OR = 0.268, 95% CI = 0.128-0.546, P = 0.001), as well as sleep quality, habitual efficiency, and daytime dysfunction (all P < 0.05).

Figure 1 Correlation analysis between IGF1 and PSQI scores. IGF1, insulin-like growth factor-1; PSQI, Pittsburgh Sleep Quality Index; OR, odds ratio; CI, confidence interval. (A) Linear regression model was used to analyze the relationship between IGF1 levels and PSQI scores in all the groups. (B) Bivariate correlation matrix for the study variables in non-smokers and active smokers using pearson correlation analysis. * P < 0.05. (C) IGF1 (divided into two groups by the median level) and seven components of PSQI (yes/no) were included into the logistics regression model as dichotomous variables to assess the associations between the two. The linear regression and logistic regression models were adjusted for age, body mass index, marriage and living. Statistical significance ( P < 0.05) was denoted in boldface.

3.3 Moderation effect of IGF1

Based on the correlations in Figure 1 , we further estimated the moderating effect of IGF1 on the relationship between smoking and PSQI scores using moderation analysis ( Table 2 ). The first step of moderation analysis is to assess the association between independent and outcome variables. As shown in Model 1 ( Table 2 ), smoking was positively associated with PSQI scores after adjusting for potential confounders (β = 0.282, 95% CI = 0.135-0.428, R 2 =  0.109, P < 0.001). In the second step, moderating variables were introduced by including the interaction terms in the linear model. According to Model 3 ( Table 2 ), the positive impact of smoking on PSQI scores was moderated by IGF1 levels in CSF (β = 0.145, 95% CI = 0.004-0.285, R2 = 0.155, P < 0.001) ( Figure 2A ), with R2 increasing from 0.109 in Model 1 to 0.155 in model 3 and with F values increasing from 5.325 in Model 1 to 5.667 in Model 3 (ΔF = 0.342, p<0.05) ( Table 2 ). To elucidate the moderating role of IGF1 more clearly, we grouped all the subjects by the median level of IGF1 and performed simple slope analysis to investigate the impact of smoking on PSQI scores at different levels of IGF1 in CSF ( Figure 2B ). The positive predictive effect of smoking on PSQI scores was significantly increased in individuals with higher level of IGF1 (Bsimple = 0.402, P < 0.001), while it was weakened in those with a lower level of IGF1 (Bsimple = 0.112, P = 0.268). Moreover, we also assessed the moderating effect of IGF1 in four subgroups of PSQI which were significant in the logistic model ( Figure 1C ). Similar results were obtained in sleep disturbances (Bsimple of 0.628 and 0.200 in the groups with high and low IGF1 levels, P < 0.001, Figure 2C ), while a marginal moderating effect of IGF1 on sleep quality (Bsimple = 0.150, P = 0.070, Figure 3A ). However, there were independently negative associations rather than moderation between IGF1 and habitual efficiency (β = -0.14, P < 0.05) and daytime dysfunction (β = -0.24, P < 0.01, Figure 3B ).

Table 2 Linear regression analysis for the moderation effect of IGF1 on the relationship between smoking and PSQI scores.

Figure 2 The moderation by IGF1 for the association of smoking with PSQI. IGF1, insulin-like growth factor-1; PSQI, Pittsburgh Sleep Quality Index. (A) Conceptual model of moderation analysis regarding IGF1 as moderator. (B, C) Simple slope analysis for the moderation effect of IGF1 on the relationship between smoking and PSQI scores (B) as well as sleep disturbance (C) . Class (-1) referred to non-smokers and Class (+1) referred to active smokers. The two lines represented the regression line of the association of smoking with PSQI scores (B) and sleep disturbance (C) when IGF1 was at low (circle) or high (triangle) levels. All data was included in the model as numerical variables and reported as moderation analysis. All the models were adjusted for age, BMI, marriage, and living. * P < 0.05, *** P < 0.001.

Figure 3 Simple slope analysis for the moderation effect of IGF-1 on PSQI components. IGF-1, insulin-like growth factor-1. (A, B) Simple slope analysis for the moderation effect of IGF-1 on the relationship between smoking and sleep quality (A) as well as sleep efficiency and daytime dysfunction (B) . Class (-1) referred to non-smokers and Class (+1) referred to active smokers. The two lines represented the regression line of the association of smoking with PSQI components when IGF-1 was at low (circle) or high (triangle) levels. All data was included in the model as numerical variables and reported as moderation analysis. All the models were adjusted for age, BMI, marriage, and living. * P < 0.05, ** P < 0.01.

4 Discussion

In the present study, we found global PSQI scores were significantly higher, while IGF1 levels in CSF were lower in active smokers than non-smokers. In addition, there was a significant negative correlation between CSF IGF1 level and global PSQI score, especially in non-smokers. Furthermore, smoking was positively associated with global PSQI scores (β = 0.282, P < 0.001), which was moderated by IGF1 levels in CSF (β = 0.145, P < 0.05). Our study revealed that IGF1 played a moderating role in the process of smoking-induced sleep disorders, which, to some extent, could provide new insights into the association between cigarette smoking and sleep disorders.

Cigarette smoking is one of the major known contributors to sleep disorders. It has been reported that the smoking rate among poorer sleepers is significantly higher than that of the general population ( 9 ). A study dating back to the 1990s showed that smoking was significantly positively associated with sleep disorders ( 27 ), which was subsequently supported by various studies in different populations from different regions ( 28 , 29 ). Sleep quality varies with the characteristics and intensity of smoking, a conclusion further supported by the present study. In addition. there are studies that support the possible interactions between smoking, sleep quality, and respiratory problems. For example, a study by Jang et al. found that smoking was associated with an increased risk of obstructive sleep apnea (OSA), a common respiratory disorder that can lead to poor sleep quality ( 30 ). Another study by Caliri et al. found that smoking was associated with increased inflammation and oxidative stress in the airways, which could contribute to the development of respiratory problems such as chronic obstructive pulmonary disease (COPD) ( 31 ). Moreover, some studies found that smoking was associated with poor sleep quality, and that the combination of smoking and poor sleep quality was associated with increased inflammation and oxidative stress, suggesting that these factors may interact to exacerbate respiratory problems ( 32 – 34 ). Overall, the existing literature suggests that there are complex interactions between smoking, sleep quality, and respiratory problems, and that these factors may influence each other in important ways. Further research is needed to fully understand these interactions and their clinical implications.

In the present study, IGF1 levels in CSF were lower in active smokers than non-smokers. Previous studies have shown that circulating IGF1 has the ability to reach the central nervous system through either the blood-CSF barrier or the blood-brain barrier ( 35 , 36 ). Thus, the lack of an increase in peripheral IGF1 can lead to a deficiency of IGF1 in the brain ( 36 ). Smoking has been shown to reduce the level of peripheral IGF1 ( 20 , 37 ) and destroy the blood-brain barrier ( 38 , 39 ), which can further explain our results.

The main finding of present study is that there was a significant negative correlation between the IGF1 level in CSF and PSQI score, especially in non-smokers. A low serum IGF1 level has been reported to be associated with sleep-related disease, and longer slow wave time could be associated with increased IGF1 levels ( 40 , 41 ). A recent case-control study in China indicated that serum IGF1 concentration was negatively associated with chronic insomnia, sleep disorders and anxiety scores ( 15 ). Behavioral symptoms of circadian rhythm imbalance and sleep-wake disorders were noted to be improved by increasing or releasing free IGF1 in serum ( 16 , 42 ). Similarly, animal experiments and epidemiological studies have revealed that sleep disorders might inhibit the IGF1 axis, with circulating IGF1 levels significantly declining after sustained sleep deprivation ( 43 , 44 ). The underlying mechanisms are complex. IGF1 is known for its neuroprotective properties, activating IGF1 receptor to initiate downstream phosphorylation cascades that regulate transcription, synaptic maturation, inhibits apoptosis, and promote growth, differentiation and metabolism of neuronal cells ( 35 ). Firstly, this relationship may be attributed to BDNF/IGF1 regulated neuronal plasticity changes, hypothesized to increase slow wave sleep activity ( 45 , 46 ). Moreover, IGF1 could facilitate the repair of neurons from hypoxia and improve sleep regulation ( 47 ). These studies suggest that IGF1 could improve sleep quality to some extent, which is similar to our results.

Moreover, our results imply that the level of IGF1 might differently influence the relationship between smoking and sleep quality. As mentioned, a high IGF1 level is associated with low PSQI scores in both non-smokers and all participants, indicating that IGF1, like cigarettes, leads to a direct effect on sleep quality. However, for participants with different CSF IGF1 levels, we not only found the independent effects from the two factors (β = 0.258***, t = 3.525 for smoking; β = -0.212**, t = -2.967 for IGF1). In addition, there was a complex interaction (β = 0.145*, t = 2.042). For participants with a low CSF IGF1 level, smoking did not activate sleep problems (β = 0.112, P = 0.268), but in those who with a high CSF IGF1 level, the sleep damage caused by smoking was greatly increased (β = 0.402, P < 0.001).

This finding is intriguing. Elevated IGF1 levels have the ability to regulate sleep, as mentioned above. Numerous studies have shown a positive correlation between IGF1 levels and sleep quality. Therefore, given the independent effects of smoking and IGF1, it is expected that elevated IGF1 levels will counteract the sleep disturbances induced by smoking. In the interaction model and its sub-dimensions, this effect is evident. Specifically, IGF1 exerts a significant protective effect on four dimensions of sleep disturbance: sleep quality, sleep efficiency, sleep disturbance and daytime dysfunction (sleep quality: OR = 0.389, p = 0.010; sleep efficiency: OR = 0.066, p = 0.010; daytime dysfunction: OR = 0.333, p = 0.002; sleep disturbance: OR = 0.268, p = 0.001). It is also noteworthy that an interaction similar to the PSQI results was only observed in the sleep disturbance dimension. Smoking and IGF1 had independent effects on sleep (see Figure 3 ) in the remaining dimensions.

It is likely that this effect is due to the activity of the orexin neurons. A study in 2020 clearly demonstrated that IGF1 in the central nervous system can directly influence the sleep-wake cycle of mice through the activation of orexin neurons. Orexin neurons significantly prolonged sleep duration in mice lacking IGF1 receptors, suggesting the involvement of IGF1 in wakefulness and maintenance via orexin neurons ( 48 ). In addition, previous studies have consistently shown a strong link between smoking and orexin expression. Exposure to smoke significantly increases orexin levels, thereby promoting wakefulness ( 49 – 51 ). Consequently, smokers with elevated levels of cerebrospinal fluid IGF1 may experience increased nicotine-induced stimulation of active orexin neurons, leading to this significant positive interaction.

To our knowledge, this is the first study to assess the role of IGF1 in CSF on smoking-induced sleep disorders (indicated by PSQI) in Chinese males. The effect of smoking on PSQI is moderated by different levels of IGF1 in CSF. Admittedly, there are several limitations in this study. First, causal inferences cannot be drawn from the case-control design, and a small sample size may restrict the statistical power to examine associations and moderations. Hence, evidence from prospective studies with larger sample size is warranted. Second, retrospective recall biases may occur using subjective sleep measurements and smoking assessments. Third, anterior cruciate ligament reconstructive surgery may be a potential confounder affecting smoking, sleep quality and biomarkers. Moreover, other potential confounding factor such as obstructive sleep apnea may affect our understanding of the relationship between smoking and sleep. Finally, only men were recruited due to the low smoking rate in women, resulting in limited applicability and generalizability.

5 Conclusion

The positive effect of smoking on PSQI scores and sleep disturbances were negatively moderated by the levels of IGF1 in cerebrospinal fluid in Chinese adult males. The results of this study have important clinical implications. Firstly, they highlight the importance of considering IGF1 levels in cerebrospinal fluid when assessing the relationship between smoking and sleep quality. Clinicians may need to monitor IGF1 levels in smokers who report poor sleep quality and consider interventions aimed at increasing IGF1 levels, such as exercise or nutritional supplements. Secondly, the findings suggest that targeting IGF1 may be a potential therapeutic strategy for improving sleep quality in smokers. Future studies are needed to explore the underlying mechanisms and to develop effective interventions. Overall, this study contributes to our understanding of the complex interplay between smoking, IGF1, and sleep quality. The findings have important implications for the development of targeted interventions to improve sleep quality in smokers and for the prevention of smoking-related sleep disturbances.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Institutional Review Board of Inner Mongolian Medical University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

LS: Writing – original draft. YW: Writing – original draft, Formal Analysis. JL: Writing – review & editing, Resources. MM: Writing – review & editing, Resources. XL: Writing – review & editing. KZ: Writing – original draft, Resources. WH: Writing – review & editing. YK: Writing – review & editing, Resources. FW: Writing – review & editing, Resources. YL: Writing – review & editing, Supervision. YX: Writing – review & editing, Supervision. XJ: Writing – review & editing, Supervision.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Basic Public Welfare Research Project of Zhejiang Province (TGD23H030004), Wenzhou Basic Scientific Research Project (Y20220021), Health Science and Technology Project of Zhejiang Province (2022KY887).

Acknowledgments

We thank Prof. Li Chen for her statistic help.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: smoking, PSQI score, sleep disturbances, IGF1, moderation

Citation: Shan L, Wu Y, Lao J, Ma M, Luo X, Zheng K, Hu W, Kang Y, Wang F, Liu Y, Xu Y and Jin X (2024) The positive impact of smoking on poor sleep quality is moderated by IGF1 levels in cerebrospinal fluid: a case-control study among Chinese adults. Front. Psychiatry 15:1392732. doi: 10.3389/fpsyt.2024.1392732

Received: 28 February 2024; Accepted: 24 April 2024; Published: 10 May 2024.

Reviewed by:

Copyright © 2024 Shan, Wu, Lao, Ma, Luo, Zheng, Hu, Kang, Wang, Liu, Xu and Jin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yanlong Liu, [email protected] ; Xiaoya Jin, [email protected] ; Yali Xu, [email protected]

† These authors have contributed equally to this work and share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.