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• What Is a Case-Control Study? | Definition & Examples

What Is a Case-Control Study? | Definition & Examples

Published on February 4, 2023 by Tegan George . Revised on June 22, 2023.

A case-control study is an experimental design that compares a group of participants possessing a condition of interest to a very similar group lacking that condition. Here, the participants possessing the attribute of study, such as a disease, are called the “case,” and those without it are the “control.”

It’s important to remember that the case group is chosen because they already possess the attribute of interest. The point of the control group is to facilitate investigation, e.g., studying whether the case group systematically exhibits that attribute more than the control group does.

Table of contents

When to use a case-control study, examples of case-control studies, advantages and disadvantages of case-control studies, other interesting articles, frequently asked questions.

Case-control studies are a type of observational study often used in fields like medical research, environmental health, or epidemiology. While most observational studies are qualitative in nature, case-control studies can also be quantitative , and they often are in healthcare settings. Case-control studies can be used for both exploratory and explanatory research , and they are a good choice for studying research topics like disease exposure and health outcomes.

A case-control study may be a good fit for your research if it meets the following criteria.

• Data on exposure (e.g., to a chemical or a pesticide) are difficult to obtain or expensive.
• The disease associated with the exposure you’re studying has a long incubation period or is rare or under-studied (e.g., AIDS in the early 1980s).
• The population you are studying is difficult to contact for follow-up questions (e.g., asylum seekers).

Retrospective cohort studies use existing secondary research data, such as medical records or databases, to identify a group of people with a common exposure or risk factor and to observe their outcomes over time. Case-control studies conduct primary research , comparing a group of participants possessing a condition of interest to a very similar group lacking that condition in real time.

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See an example

Case-control studies are common in fields like epidemiology, healthcare, and psychology.

You would then collect data on your participants’ exposure to contaminated drinking water, focusing on variables such as the source of said water and the duration of exposure, for both groups. You could then compare the two to determine if there is a relationship between drinking water contamination and the risk of developing a gastrointestinal illness. Example: Healthcare case-control study You are interested in the relationship between the dietary intake of a particular vitamin (e.g., vitamin D) and the risk of developing osteoporosis later in life. Here, the case group would be individuals who have been diagnosed with osteoporosis, while the control group would be individuals without osteoporosis.

You would then collect information on dietary intake of vitamin D for both the cases and controls and compare the two groups to determine if there is a relationship between vitamin D intake and the risk of developing osteoporosis. Example: Psychology case-control study You are studying the relationship between early-childhood stress and the likelihood of later developing post-traumatic stress disorder (PTSD). Here, the case group would be individuals who have been diagnosed with PTSD, while the control group would be individuals without PTSD.

Case-control studies are a solid research method choice, but they come with distinct advantages and disadvantages.

Advantages of case-control studies

• Case-control studies are a great choice if you have any ethical considerations about your participants that could preclude you from using a traditional experimental design .
• Case-control studies are time efficient and fairly inexpensive to conduct because they require fewer subjects than other research methods .
• If there were multiple exposures leading to a single outcome, case-control studies can incorporate that. As such, they truly shine when used to study rare outcomes or outbreaks of a particular disease .

Disadvantages of case-control studies

• Case-control studies, similarly to observational studies, run a high risk of research biases . They are particularly susceptible to observer bias , recall bias , and interviewer bias.
• In the case of very rare exposures of the outcome studied, attempting to conduct a case-control study can be very time consuming and inefficient .
• Case-control studies in general have low internal validity  and are not always credible.

Case-control studies by design focus on one singular outcome. This makes them very rigid and not generalizable , as no extrapolation can be made about other outcomes like risk recurrence or future exposure threat. This leads to less satisfying results than other methodological choices.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

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A case-control study differs from a cohort study because cohort studies are more longitudinal in nature and do not necessarily require a control group .

While one may be added if the investigator so chooses, members of the cohort are primarily selected because of a shared characteristic among them. In particular, retrospective cohort studies are designed to follow a group of people with a common exposure or risk factor over time and observe their outcomes.

Case-control studies, in contrast, require both a case group and a control group, as suggested by their name, and usually are used to identify risk factors for a disease by comparing cases and controls.

A case-control study differs from a cross-sectional study because case-control studies are naturally retrospective in nature, looking backward in time to identify exposures that may have occurred before the development of the disease.

On the other hand, cross-sectional studies collect data on a population at a single point in time. The goal here is to describe the characteristics of the population, such as their age, gender identity, or health status, and understand the distribution and relationships of these characteristics.

Cases and controls are selected for a case-control study based on their inherent characteristics. Participants already possessing the condition of interest form the “case,” while those without form the “control.”

Keep in mind that by definition the case group is chosen because they already possess the attribute of interest. The point of the control group is to facilitate investigation, e.g., studying whether the case group systematically exhibits that attribute more than the control group does.

The strength of the association between an exposure and a disease in a case-control study can be measured using a few different statistical measures , such as odds ratios (ORs) and relative risk (RR).

No, case-control studies cannot establish causality as a standalone measure.

As observational studies , they can suggest associations between an exposure and a disease, but they cannot prove without a doubt that the exposure causes the disease. In particular, issues arising from timing, research biases like recall bias , and the selection of variables lead to low internal validity and the inability to determine causality.

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

George, T. (2023, June 22). What Is a Case-Control Study? | Definition & Examples. Scribbr. Retrieved June 24, 2024, from https://www.scribbr.com/methodology/case-control-study/

Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis (Monographs in Epidemiology and Biostatistics, 2) (Illustrated). Oxford University Press.

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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?

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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Statistics By Jim

Making statistics intuitive

Case Control Study: Definition, Benefits & Examples

By Jim Frost 2 Comments

What is a Case Control Study?

A case control study is a retrospective, observational study that compares two existing groups. Researchers form these groups based on the existence of a condition in the case group and the lack of that condition in the control group. They evaluate the differences in the histories between these two groups looking for factors that might cause a disease.

By evaluating differences in exposure to risk factors between the case and control groups, researchers can learn which factors are associated with the medical condition.

For example, medical researchers study disease X and use a case-control study design to identify risk factors. They create two groups using available medical records from hospitals. Individuals with disease X are in the case group, while those without it are in the control group. If the case group has more exposure to a risk factor than the control group, that exposure is a potential cause for disease X. However, case-control studies establish only correlation and not causation. Be aware of spurious correlations!

Case-control studies are observational studies because researchers do not control the risk factors—they only observe them. They are retrospective studies because the scientists create the case and control groups after the outcomes for the subjects (e.g., disease vs. no disease) are known.

This post explains the benefits and limitations of case-control studies, controlling confounders, and analyzing and interpreting the results. I close with an example case control study showing how to calculate and interpret the results.

Learn more about Experimental Design: Definition, Types, and Examples .

Related posts : Observational Studies Explained and Control Groups in Experiments

Benefits of a Case Control Study

A case control study is a relatively quick and simple design. They frequently use existing patient data, and the experimenters form the groups after the outcomes are known. Researchers do not conduct an experiment. Instead, they look for differences between the case and control groups that are potential risk factors for the condition. Small groups and individual facilities can conduct case-control studies, unlike other more intensive types of experiments.

Case-control studies are perfect for evaluating outbreaks and rare conditions. Researchers simply need to let a sufficient number of known cases accumulate in an established database. The alternative would be to select a large random sample and hope that the condition afflicts it eventually.

A case control study can provide rapid results during outbreaks where the researchers need quick answers. They are ideal for the preliminary investigation phase, where scientists screen potential risk factors. As such, they can point the way for more thorough, time-consuming, and expensive studies. They are especially beneficial when the current state of science knows little about the connection between risk factors and the medical condition. And when you need to identify potential risk factors quickly!

Cohort studies are another type of observational study that are similar to case-control studies, but there are some important differences. To learn more, read my post about Cohort Studies .

Limitations of a Case Control Study

Because case-control studies are observational, they cannot establish causality and provide lower quality evidence than other experimental designs, such as randomized controlled trials . Additionally, as you’ll see in the next section, this type of study is susceptible to confounding variables unless experimenters correctly match traits between the two groups.

A case-control study typically depends on health records. If the necessary data exist in sources available to the researchers, all is good. However, the investigation becomes more complicated if the data are not readily available.

Case-control studies can incorporate biases from the underlying data sources. For example, researchers frequently obtain patient data from hospital records. The population of hospital patients is likely to differ from the general population. Even the control patients are in the hospital for some reason—they likely have serious health problems. Consequently, the subjects in case-control studies are likely to differ from the general population, which reduces the generalizability of the results.

A case-control study cannot estimate incidence or prevalence rates for the disease. The data from these studies do not allow you to calculate the probability of a new person contracting the condition in a given period nor how common it is in the population. This limitation occurs because case-control studies do not use a representative sample.

Case-control studies cannot determine the time between exposure and onset of the medical condition. In fact, case-control studies cannot reliably assess each subject’s exposure to risk factors over time. Longitudinal studies, such as prospective cohort studies, can better make those types of assessment.

Related post : Causation versus Correlation in Statistics

Use Matching to Control Confounders

Because case-control studies are observational studies, they are particularly vulnerable to confounding variables and spurious correlations . A confounder correlates with both the risk factor and the outcome variable. Because observational studies don’t use random assignment to equalize confounders between the case and control groups, they can become unbalanced and affect the results.

Unfortunately, confounders can be the actual cause of the medical condition rather than the risk factor that the researchers identify. If a case-control study does not account for confounding variables, it can bias the results and make them untrustworthy.

Case-control studies typically use trait matching to control confounders. This technique involves selecting study participants for the case and control groups with similar characteristics, which helps equalize the groups for potential confounders. Equalizing confounders limits their impact on the results.

Ultimately, the goal is to create case and control groups that have equal risks for developing the condition/disease outside the risk factors the researchers are explicitly assessing. Matching facilitates valid comparisons between the two groups because the controls are similar to cases. The researchers use subject-area knowledge to identify characteristics that are critical to match.

Note that you cannot assess matching variables as potential risk factors. You’ve intentionally equalized them across the case and control groups and, consequently, they do not correlate with the condition. Hence, do not use the risk factors you want to evaluate as trait matching variables.

Learn more about confounding variables .

Statistical Analysis of a Case Control Study

Researchers frequently include two controls for each case to increase statistical power for a case-control study. Adding even more controls per case provides few statistical benefits, so studies usually do not use more than a 2:1 control to case ratio.

For statistical results, case-control studies typically produce an odds ratio for each potential risk factor. The equation below shows how to calculate an odds ratio for a case-control study.

Notice how this ratio takes the exposure odds in the case group and divides it by the exposure odds in the control group. Consequently, it quantifies how much higher the odds of exposure are among cases than the controls.

In general, odds ratios greater than one flag potential risk factors because they indicate that exposure was higher in the case group than in the control group. Furthermore, higher ratios signify stronger associations between exposure and the medical condition.

An odds ratio of one indicates that exposure was the same in the case and control groups. Nothing to see here!

Ratios less than one might identify protective factors.

Learn more about Understanding Ratios .

Now, let’s bring this to life with an example!

Example Odds Ratio in a Case-Control Study

The Kent County Health Department in Michigan conducted a case-control study in 2005 for a company lunch that produced an outbreak of vomiting and diarrhea. Out of multiple lunch ingredients, researchers found the following exposure rates for lettuce consumption.

By plugging these numbers into the equation, we can calculate the odds ratio for lettuce in this case-control study.

The study determined that the odds ratio for lettuce is 11.2.

This ratio indicates that those with symptoms were 11.2 times more likely to have eaten lettuce than those without symptoms. These results raise a big red flag for contaminated lettuce being the culprit!

Learn more about Odds Ratios.

Epidemiology in Practice: Case-Control Studies (NIH)

Interpreting Results of Case-Control Studies (CDC)

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Reader Interactions

January 18, 2022 at 7:56 am

Great post, thanks for writing it!

Is it possible to test an odds ration for statistical significance?

January 18, 2022 at 7:41 pm

Hi Michael,

Thanks! And yes, you can test for significance. To learn more about that, read my post about odds ratios , where I discuss p-values and confidence intervals.

Comments and Questions Cancel reply

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

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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When is a case-control study a case-control study?

Affiliation.

• 1 Department of Medicine, Division of Clinical Epidemiology, McGill University, Quebec, Canada. [email protected]
• PMID: 19247539
• DOI: 10.2340/16501977-0341

Rehabilitation professionals rarely ask questions about the etiology of health events or outcomes and may not have formal training or relevant experience in the design of studies whose intent is to identify causal factors. The case-control study, which is one design used to answer questions about etiology, is particularly difficult to understand and research has shown that this study design label is often used incorrectly. This paper outlines the main features of case-control studies, with a particular focus on sampling strategies. The goal is to educate clinical rehabilitation colleagues about the fundamental principles of this powerful epidemiologic design. Examples illustrate how the parameters of cumulative incidence, incidence-density, and prevalence are estimated and the effect of sampling strategy on these parameters. Also shown is how sampling strategy affects conclusions drawn about the effects of an exposure on outcome. Even when used appropriately, case-control studies are methodologically complex to design and analyze to ensure an unbiased answer to the research question. The hypothetical and real-life examples given here could be used as course material to educate rehabilitation researchers.

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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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A Clinical Diagnostic Test for Calcium Release Deficiency Syndrome

• 1 Libin Cardiovascular Institute, Department of Physiology and Pharmacology, University of Calgary, Calgary, Alberta, Canada
• 2 Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
• 3 Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, and Hebrew University Faculty of Medicine, Jerusalem, Israel
• 4 Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, England
• 5 Oxford Heart Centre, John Radcliffe Hospital, Oxford, England
• 6 Department of Cardiology, Faculty of Medicine and Health Sciences, Antwerp University Hospital, Antwerp, Belgium
• 7 Cardiovascular Research, Departments of Genetics, Pharmacology and Physiopathology of Heart, Blood Vessels and Skeleton, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
• 8 Member of the European Reference Network for Rare, Low Prevalence, and Complex Diseases of the Heart (ERN GUARD-Heart)
• 9 Department of Clinical Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
• 10 Heart Failure and Arrhythmias, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
• 11 Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Western University, London, Ontario, Canada
• 12 Montreal Heart Institute and Université de Montréal, Montreal, Quebec, Canada
• 13 Department of Cardiology, Aarhus University Hospital, Aarhus N, Denmark
• 14 Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Quebec City, Quebec, Canada
• 15 Department of Cardiac Pacing and Electrophysiology, Hopital Cardiologique du Haut-Leveque, Centre Hospitalier Universitaire de Bordeaux, Pessac, France
• 16 Division of Cardiology and Centre for Cardiovascular Innovation, University of British Columbia, Vancouver, Canada
• 17 Department of Molecular Cardiology, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
• 18 Department of Molecular Medicine, University of Pavia, Pavia, Italy
• 19 Windland Smith Rice Genetic Heart Rhythm Clinic, Division of Heart Rhythm Services, Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
• 20 Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
• 21 Section of Cardiac Electrophysiology, Division of Cardiology, University of Washington Medical Center, Seattle
• 22 Population Health Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada
• 23 Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, University of California, San Francisco
• 24 Windland Smith Rice Sudden Death Genomics Laboratory, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota
• 25 Division of Pediatric Cardiology, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota
• 26 Inherited Arrhythmia and Cardiomyopathy Program, Arrhythmia Service, Division of Cardiology, Toronto General Hospital and the University of Toronto, Toronto, Ontario, Canada
• 27 Leviev Heart Institute, Chaim Sheba Medical Center, Ramat Gan, Israel
• 28 Tel Aviv University, Tel Aviv, Israel
• 29 Oxford Biomedical Research Centre and Wellcome Centre for Human Genetics, University of Oxford, Oxford, England
• 30 Department of Clinical Medicine, Aarhus University, Aarhus C, Denmark
• 31 Heart Institute, Hadassah University Hospital, Jerusalem, Israel
• Editor's Note Clinical Test for Calcium Release Deficiency Syndrome? Gregory M. Marcus, MD, MAS; Gregory Curfman, MD; Kirsten Bibbins-Domingo, PhD, MD, MAS JAMA

Question   Cardiac arrest frequently occurs without explanation, even after a thorough clinical evaluation. Can a simple maneuver clinically diagnose calcium release deficiency syndrome (CRDS), a newly described cause of sudden death?

Findings   In this international, multicenter, case-control study, a provoked measure of T-wave amplitude on an electrocardiogram ascertained cases of CRDS with high accuracy. The genetic mouse models recapitulated the human findings and suggested a pathologically large systolic calcium release from the sarcoplasmic reticulum was responsible.

Meaning   These preliminary results suggest that the repolarization response on an electrocardiogram to brief tachycardia followed by a pause may effectively diagnose CRDS. Given the frequency of unexplained cardiac arrest, should these findings be confirmed in larger studies, this readily available maneuver may provide clinically actionable information.

Importance   Sudden death and cardiac arrest frequently occur without explanation, even after a thorough clinical evaluation. Calcium release deficiency syndrome (CRDS), a life-threatening genetic arrhythmia syndrome, is undetectable with standard testing and leads to unexplained cardiac arrest.

Objective   To explore the cardiac repolarization response on an electrocardiogram after brief tachycardia and a pause as a clinical diagnostic test for CRDS.

Design, Setting, and Participants   An international, multicenter, case-control study including individual cases of CRDS, 3 patient control groups (individuals with suspected supraventricular tachycardia; survivors of unexplained cardiac arrest [UCA]; and individuals with genotype-positive catecholaminergic polymorphic ventricular tachycardia [CPVT]), and genetic mouse models (CRDS, wild type, and CPVT were used to define the cellular mechanism) conducted at 10 centers in 7 countries. Patient tracings were recorded between June 2005 and December 2023, and the analyses were performed from April 2023 to December 2023.

Intervention   Brief tachycardia and a subsequent pause (either spontaneous or mediated through cardiac pacing).

Main Outcomes and Measures   Change in QT interval and change in T-wave amplitude (defined as the difference between their absolute values on the postpause sinus beat and the last beat prior to tachycardia).

Results   Among 10 case patients with CRDS, 45 control patients with suspected supraventricular tachycardia, 10 control patients who experienced UCA, and 3 control patients with genotype-positive CPVT, the median change in T-wave amplitude on the postpause sinus beat (after brief ventricular tachycardia at ≥150 beats/min) was higher in patients with CRDS ( P  < .001). The smallest change in T-wave amplitude was 0.250 mV for a CRDS case patient compared with the largest change in T-wave amplitude of 0.160 mV for a control patient, indicating 100% discrimination. Although the median change in QT interval was longer in CRDS cases ( P  = .002), an overlap between the cases and controls was present. The genetic mouse models recapitulated the findings observed in humans and suggested the repolarization response was secondary to a pathologically large systolic release of calcium from the sarcoplasmic reticulum.

Conclusions and Relevance   There is a unique repolarization response on an electrocardiogram after provocation with brief tachycardia and a subsequent pause in CRDS cases and mouse models, which is absent from the controls. If these findings are confirmed in larger studies, this easy to perform maneuver may serve as an effective clinical diagnostic test for CRDS and become an important part of the evaluation of cardiac arrest.

• Editor's Note Clinical Test for Calcium Release Deficiency Syndrome? JAMA

Read More About

Ni M , Dadon Z , Ormerod JOM, et al. A Clinical Diagnostic Test for Calcium Release Deficiency Syndrome. JAMA. Published online June 20, 2024. doi:10.1001/jama.2024.8599

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Association of interleukin-2 and interleukin-10 with the pathophysiology and development of generalized anxiety disorder: a case-control study

• Nisat Sarmin 1   na1 ,
• A. S. M. Roknuzzaman 2   na1 ,
• Rapty Sarker 1   na1 ,
• Mamun -or-Rashid 1 ,
• MMA Shalahuddin Qusar 3 ,
• Sitesh Chandra Bachar 4 ,
• Eva Rahman Kabir 5 ,
• Md. Rabiul Islam 5 &
• Zobaer Al Mahmud 1  

BMC Psychiatry volume  24 , Article number:  462 ( 2024 ) Cite this article

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Generalized anxiety disorder (GAD) is a devastating mental health condition characterized by constant, uncontrolled worrying. Recent hypotheses indicate that pro-inflammatory cytokines and chemokines are potential contributors to the pathogenesis of GAD. Here, we aimed to assess the role of interleukin-2 (IL-2) and interleukin-10 (IL-10) in the pathophysiology and development of GAD.

This study recruited 50 GAD patients diagnosed according to the DSM-5 criteria and 38 age-sex-matched healthy controls (HCs). A qualified psychiatrist evaluated all study subjects. The socio-demographic and clinical characteristics of the study population were determined using pre-structured questionnaires or interviews, and cytokine serum levels were estimated using commercially available ELISA kits.

We observed reduced serum IL-10 levels in GAD patients compared to HCs (33.69 ± 1.37 pg/ml vs. 44.12 ± 3.16 pg/ml). Also, we observed a significant negative correlation between altered IL-10 levels and GAD-7 scores ( r =-0.315, p  = 0.039). Moreover, IL-10 serum measurement exhibited good predictive value in receiver operating characteristics (ROC) analysis with an area under the curve (AUC) value of 0.793 ( p  < 0.001) with 80.65% sensitivity and 62.79% specificity at a cutoff value of 33.93 pg/ml. Conversely, we noticed elevated serum IL-2 levels in GAD patients than in HCs (14.81 ± 2.88 pg/ml vs. 8.08 ± 1.1 pg/ml); however, it failed to maintain any significant association with GAD-7 scores, implying that IL-2 might not be involved in GAD pathogenesis. The lower AUC value (0.640; p  > 0.05) exhibited by IL-2 serum measurement in ROC analysis further supported that IL-2 might not be associated with GAD.

This study provides new insights into the complex interplay between anti-inflammatory cytokines and GAD pathogenesis. Based on the present findings, we can assume that IL-10 but not IL-2 may be associated with the pathophysiology and development of GAD. However, further research with a larger population size and longitudinal design is required to confirm the potential diagnostic efficacy of IL-10.

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Generalized anxiety disorder (GAD) is a chronic neuropsychiatric disorder characterized by persistent and excessive uncontrollable fear or worry (occurs for at least 6 months) about various aspects/activities of daily life, affecting the educational, occupational, or social lives of the affected people [ 1 ]. If a person is excessively worried about anything for most days over at least 6 months, he/she is considered to have GAD. Though currently the prevalence rate of GAD is 3–6% worldwide [ 1 , 2 , 3 ], the prevalence is increasing day by day due to the complexity of modern lifestyles and thus warrants attention from national and international authorities to take interventions for mitigating and managing this disorder properly. If it remains undiagnosed or untreated, the uncontrollable and persistently intense anxiety can lead to a marked reduction in cognitive functions or a reduced capacity to work properly in all spheres of life, including educational, family, social, and individual routine work. As such, chronic GAD leads to a reduced quality of life and thereby poses a significant mental health concern globally.

Despite its high prevalence, significant morbidity, and socioeconomic burden, GAD remains poorly characterized in terms of its pathophysiology or effective treatment options. Though the precise cause and mechanism of pathogenesis are still unknown, evidence suggests that multiple factors, including disrupted serotonergic, dopaminergic, and GABAergic neurotransmission and excessive glutamatergic neurotransmission in the brain, genetic factors, family or environmental stress, chronic diseases, hyperthyroidism, childhood trauma, and special personality traits, are linked to GAD. Alterations in monoaminergic neurotransmissions in limbic systems (cingulate gyrus, hippocampus, amygdala, thalamus, and hypothalamus) due to the lower synaptic availability of serotonin, norepinephrine, and dopamine are thought to be associated with anxiety symptoms. Besides, decreased GABA-mediated inhibitory neurotransmission in the amygdala or excessive activation of excitatory glutamatergic neurotransmission are also suggested to be involved in GAD pathology.

Currently, available pharmacotherapies for GAD include selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs), pregabalin, and benzodiazepines, which act by reversing these altered monoaminergic neurotransmitter systems. Alongside these drug treatments, non-pharmacological therapies such as several psychological interventions, including cognitive-behavioral therapy, and the acquisition and application of stress management skills, including relaxation and mindfulness skills are also widely used for the management of GAD. However, currently, available pharmacotherapies (SSRIs, SNRIs, pregabalin, and benzodiazepines) have failed to demonstrate the required efficacy in treating anxiety disorders, as 50% of patients failed to respond to these drugs, and at least in 30% of cases, there is a recurrence of the disease following the pharmacological treatment [ 1 , 4 , 5 ]. Moreover, studies reported a higher rate of discontinuity from these pharmacotherapies with low patient adherence or compliance due to the adverse effects, including sexual dysfunction for SSRIs and SNRIs, nausea and dizziness for pregabalin, demonstrating an urgent need for searching for novel anxiolytics [ 3 ]. These findings raised questions about the validity of the currently available mechanism of pathogenesis and suggested that the altered monoaminergic neurotransmitter system might not fully explain the molecular mechanism of GAD development, suggesting other pathophysiological factors might be involved in GAD. Recently, dysregulated immune systems have attracted great interest as an important pathophysiological factor for the development of GAD [ 4 , 6 , 7 , 8 ]. Several clinical and preclinical studies suggest a link between the altered immune system and GAD pathology. Preclinical studies in mice also demonstrated that administration of pro-inflammatory cytokines (including IL-1β, TNF-α, and IL-6) in mice resulted in anxiety-like behaviors that were attenuated or normalized after injecting either anti-inflammatory cytokines or antagonists for the concerned cytokines [ 9 , 10 , 11 , 12 , 13 ]. A recent prospective cohort study conducted by Hou et al., (2019) demonstrated that administration of selective serotonin reuptake inhibitors (escitalopram or sertraline) resulted in a significant reduction in peripheral pro-inflammatory cytokines, and the authors suggested that the anxiolytic effects of these SSRIs might partly be based on their acute anti-inflammatory activities [ 14 ], implicating a significant association between dysregulated peripheral immune systems and GAD development. The development of anxiety-like symptoms in IL-4 gene knock-out mice, reduced levels of IL-4 in anxious mice, and the significant attenuation of anxiety-like behaviors following IL-4 injection demonstrated a positive association between anti-inflammatory cytokines, IL-4 levels, and anxiety pathology [ 15 , 16 , 17 , 18 ]. This immune hypothesis of GAD development is further potentiated by findings from several clinical studies that reported that GAD patients showed significantly higher levels of pro-inflammatory cytokines ( IL-1Ra, IL-1, IL-6, TNF-α, etc.) compared to healthy controls (HCs) [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ] along with decreased levels of anti-inflammatory cytokines, including IL-4 and IL-10 [ 25 ]. Besides, pro-inflammatory cytokines such as TNF-α, and IL-6 were significantly associated with anxiety scores [ 29 ]. Consistent with this, a randomized clinical trial in humans demonstrated that LPS administration resulted in enhanced anxiety scores, and the authors suggested a significant correlation between pro-inflammatory cytokine levels and anxiety severity [ 30 ]. LPS-mediated microglia activation causes enhanced release of excessive pro-inflammatory cytokines in the basolateral amygdala, which ultimately leads to neuroinflammation in mice, resulting in the development of anxiety and depression-like behaviors by modulating neuronal plasticity. The authors found that anxiety pathogenesis was due to the excessive release of excitatory neurotransmitter glutamate from presynaptic axonal terminals of the prefrontal cortex, leading to neuroplasticity [ 31 ]. However, some studies reported either no significant variation in pro-inflammatory or anti-inflammatory cytokine serum levels between GAD patients and HCs [ 32 ] or that pro-inflammatory cytokines including IL-1, IL-2, and IL-6 were significantly reduced in GAD patients than HCs [ 33 , 34 ]. This discrepancy in altered levels of inflammatory cytokines across clinical studies necessitates a further examination of the role of these cytokines in GAD pathophysiology.

Interleukin-2 (IL-2) is one of the major pro-inflammatory cytokines implicated in T cell activation, proliferation, and differentiation and is thus linked to excessive neuro-inflammatory processes [ 35 ]. IL-2 has been shown to impair synaptic plasticity and cause neuroinflammation, which ultimately leads to neuronal damage in neurocircuits associated with fear and anxiety signal transduction. IL-2 was also reported to act as a potent modulator of NMDA and kainite-mediated excitability in mesolimbic or mesostriatal systems [ 36 , 37 , 38 ] and thus affect neuroplasticity. As IL-2 was found to be positively associated with major depressive disorder [ 38 , 39 ], probably, IL-2 might also be correlated with anxiety disorders like GAD, as MDD and GAD are highly co-morbid themselves and thus might share common pathophysiological factors. Recently, a preclinical study conducted by Gilio et al., (2022) observed that IL-2 administration in experimentally healthy mice triggered marked anxiety and depression-like behaviors, and the authors suggested that inhibition of GABA-mediated synaptic inhibitory neurotransmission was involved in the pathology of anxiety [ 40 ].

Interleukin-10 (IL-10) is one of the major anti-inflammatory cytokines that is secreted from Treg cells, Th2 cells, CD4 + T cells, CD8 + T cells, monocytes, macrophages, dendritic cells, B cells, neutrophils in the peripheral nervous system, and from microglia, astrocytes in the central nervous system (CNS) [ 41 ]. IL-10 signaling triggers anti-inflammatory, immunosuppressive, and immunoregulatory activities, including downregulating the production and secretion of pro-inflammatory cytokines and chemokines from activated macrophages, neutrophils, mast cells, Th1 cells, and DCS, decreasing the expression of MHC class II and co-stimulatory molecules on macrophages, and thereby suppressing the antigen presentation capacity of APCS [ 42 , 43 , 44 , 45 , 46 ]. In the CNS, it inhibits the production of such cytokines and chemokines by activated microglia and thereby counteracts cellular and tissue damage in response to excessive neuroinflammation [ 47 , 48 ]. IL-10 has also been shown to stimulate axonal regeneration and activate wound healing through tissue repair [ 48 ]. Research also indicates its role as an inhibitor for microglial hyperactivation in response to LPS-induced inflammatory stimulus [ 49 ]. Based on its anti-inflammatory and immunoregulatory functions, researchers suggested an intricate role for IL-10 in several auto-immune and neuropsychiatric disorders. For example, Mesquita et al., (2008) observed that IL-10 KO mice developed markedly enhanced depressive-like behavior, which was attenuated after IL-10 administration, and that overexpression of IL-10 resulted in reduced depressive behaviors in mice [ 50 ]. Moreover, administration of IL-10 into rats attenuated the pro-inflammatory cytokine IL-1β-induced anxiety-like symptoms in male rats [ 10 ], demonstrating that IL-10 possesses anxiolytic activities. Preclinical research using an experimental animal model also suggests that the observed anxiolytic effect of several anti-anxiety drugs, including 3’-deoxyadenosine (3’-dA), imipramine, fluoxetine, and chlordiazepoxide, stems from their ability to upregulate anti-inflammatory cytokine (IL-4, IL-10) expression in the prefrontal cortex and locus coeruleus and simultaneous down-regulation of proinflammatory cytokine gene expression, leading to a correction of the imbalance between proinflammatory and anti-inflammatory states [ 51 , 52 ]. Though several preclinical studies suggest a potential link between IL-10 levels and anxiety disorder, there is a scarcity of clinical studies aimed at evaluating such an association between IL-10 and GAD development [ 10 ].

Currently, there is no objective and cost-effective diagnostic or prognostic biomarker for GAD, which poses challenges in early diagnosis or risk prediction and leads to misdiagnosis or underdiagnosis, hampering the proper management of the disease. Currently available diagnostic tools, including self-reported symptoms and scoring severity based on the patient’s response to the 7-item questionnaire (GAD-7 scores), are subjective. Though neuroimaging techniques such as positron emission tomography (PET) and functional MRI can be used for the proper and objective diagnosis of GAD, due to their high cost and sophistication or complexities, these diagnostic tools are not suitable for either mass-level screening or are not easy to conduct multiple times to monitor the disease progression or therapeutic drug response. As such, the investigation of cost-effective objective biomarkers for GAD is one of the major focuses of current research on GAD. Finding a suitable biomarker is essential for early diagnosis and initiating psychotherapy and pharmacotherapy as early as possible [ 3 ]. Several studies were performed investigating the potential association between altered pro-inflammatory cytokines or anti-inflammatory cytokines and the pathogenesis of GAD. However, the actual role of inflammatory cytokines in GAD patients is not well explained. Therefore, the present study aims to explore the role of pro-inflammatory cytokines (IL-2) and anti-inflammatory cytokines (IL-10) in the pathophysiology and development of GAD. Also, we aim to find the potential associations of IL-2 and IL-10 with the severity of GAD patients. We believe the present study results would help to understand the pathophysiology and development of GAD.

Study population

We recruited 88 participants for this case-control study (50 GAD patients and 38 HCs matched by age and sex). Patients were collected from the Department of Psychiatry, Bangabandhu Sheikh Mujib Medical University Hospital, Dhaka, Bangladesh, and HCs from nearby areas of Dhaka city. A professional psychiatrist diagnosed patients and evaluated HCs based on DSM-5 criteria. We applied a 7-item GAD scale to assess the severity of anxiety symptoms [ 53 ]. The total scores range from 0 to 21, and it classifies the anxiety severity into four categories: minimal anxiety (0–4 scores), mild anxiety (5–9 scores), moderate anxiety (10–14 scores), and severe anxiety (15–21 scores). We excluded subjects with a co-morbidity of other psychiatric disorders, such as MDD, panic disorder, post-traumatic stress disorder, and social phobia, from the study. Additional exclusion criteria for participants were chronic liver and kidney diseases, infectious diseases, and alcohol or substance abuse. We also excluded patients who were exposed to anxiolytics or antidepressant medications within at least two weeks prior to the study that might have an impact on cytokine levels. We recorded the sociodemographic profile of the study population using a pre-designed questionnaire. The objectives of the study were explained to each participant, and informed written consent was obtained from them before their participation in this study. The study was conducted in accordance with the Declaration of Helsinki.

Blood sample collection and serum isolation

A 5 ml blood sample was collected from the cephalic vein of each participant. The blood samples were kept at room temperature for 1 hour to ensure coagulation and were then subjected to centrifugation at 3000 rpm for 15 minutes at room temperature to collect serum samples. The collected serum was then placed in the Eppendorf tube and stored at -80 °C until further analysis.

Estimation of serum cytokine levels

We estimated the serum levels of IL-2 and IL-10 by ELISA methods (Boster Bio, USA). We followed the manufacturer’s protocol for the ELISA assays. At first, we added 100 µl of standard cytokine solution, samples, and controls to each well of a pre-coated 96-well microplate. The microplates were covered with a plate sealer and incubated for 90 min at 37⁰C. After that, the cover was removed, and the liquid in each well was discarded. Subsequently, 100 µl of biotinylated anti-IL-2 antibody or anti-IL-10 antibody was incorporated into each well and incubated for 60 min at 37⁰C. After discarding the liquid from each well and washing it three times with 300 µl of wash buffer, 100 µl of avidin-biotin-peroxidase complex was added to each well, and the microplate was then again incubated for 30 min at 37⁰C. After the incubation period, the liquid was again discarded, and the plate was washed again with 300 µl of wash buffer five times. Following the addition of 90 µl color-developing reagent (TMB) into each well, the plate was incubated in a dark place for 30 min at RT, followed by the addition of 90 µl of stop solution to each well to stop the reaction process. We measured the absorbance with a microplate reader at 450 nm. We calculated the cytokine levels using standard curves and expressed them as pg/ml.

Data presentation and statistical analysis

GraphPad Prism (version 8.0.1) and Statistical Package for the Social Sciences (version 24.0) were used for data analysis. We used descriptive statistics to find the variations in sociodemographic profiles and clinical characteristics between the groups. A T-test and a Chi-square test were employed to determine the statistical level of significance between the mean differences for variables across patients versus HC groups in the case of continuous variables and categorical variables, respectively. We used boxplot graphs for comparisons of analyzed cytokines between patients and HCs. We also generated scatter plot graphs for several clinical variables in GAD patients to show the correlations among the clinical parameters. A correlation analysis was performed to assess the potential association between several demographic and clinical variables in GAD patients. Receiver operating characteristics (ROC) analysis was conducted to determine the diagnostic efficacy of serum IL-2 or IL-10 levels in discriminating GAD patients from HCs. In all cases, statistical significance was considered at p  < 0.05.

Sociodemographic characteristics of the study population

The sociodemographic characteristics of the study population are presented in Table  1 . The GAD patients and HCs were similar in terms of their age, sex, and BMI. Among the participants, about 60% were male and from urban areas. The majority of patients (60.00%) and HCs (68.42%) were unmarried. There was no significant variation between patients and HCs for their education level, occupation, economic status, or smoking status. In contrast, there was a difference between patients and HCs for their family history and previous history of the disease. In GAD patients, 20.00% had a family history, and 40.00% had a previous history of the disease.

Clinical characteristics and laboratory findings

Clinical characteristics and laboratory analysis results are presented in Table  2 . GAD patients displayed markedly higher serum levels of IL-2 (14.81 ± 2.88 pg/ml) compared to HCs (8.08 ± 1.10 pg/ml), and the difference reached the statistically significant level ( p  = 0.037, two-tailed unpaired t-test) (Table  2 ; Fig.  1 ). Though male GAD patients exhibited markedly higher levels of IL-2 compared to male HCs ( p  = 0.048), there was no significant variation in IL-2 levels between female patients and female HCs ( p  > 0.05) (Fig.  1 ). Though some 1.8-fold higher IL-2 serum levels were observed in male GAD patients compared to female GAD patients, the difference did not reach the statistical significance level ( p  = 0.198, two-tailed unpaired t-test). In contrast to the results obtained for IL-2, IL-10 showed a statistically significant ( p  < 0.001) reduction in GAD patients (33.69 ± 1.37 pg/ml) compared to HCs (44.12 ± 3.16 pg/ml) (Fig.  1 ). Similar to the results obtained for IL-2, IL-10 levels showed a statistically significant difference between patients versus HCs when male people were considered (Fig.  1 ). In contrast, there was no significant variation in IL-10 levels between female GAD patients and female HCs ( p  > 0.05).

Distribution of serum IL-2 ( a i ) and IL-10 ( b i ) levels in GAD patients and healthy controls. Comparison of IL-2 and IL-10 levels between GAD patients and their counterparts in control subjects are showed in a i and b i . Comparison of IL-2 and IL-10 levels between male or female GAD patients and their counterparts in control subjects are presented in a ii and b ii

Correlation analysis among different study parameters

We then performed a series of correlation analyses to investigate the association of altered cytokine serum levels with several demographic and clinical variables, such as age, BMI, DSM-5, and GAD-7 scores (Table  3 ). Serum IL-2 levels did not show any positive or negative association with either DSM-5 or GAD-7 scores ( p  > 0.05), suggesting that despite its significant enhancement in GAD patients compared to HCs, IL-2 may not associate with GAD pathophysiology. We also observed no significant association between the ages of the patients and IL-2 serum levels. In contrast, the IL-2 levels of GAD patients maintained a significant and positive correlation with BMI levels of patients ( r  = 0.390, p  < 0.05) which is consistent with the intricate relationship between body mass and enhanced pro-inflammatory responses. Contrary to the results obtained for IL-2, reduced serum IL-10 levels maintained a significant but negative association with both DSM-5 scores ( r =-0.300, p  = 0.045) and GAD-7 scores ( r =-0.315, p  = 0.039), implicating that altered IL-10 levels are linked to GAD development or pathogenesis. However, the age and BMI levels of GAD patients failed to show any positive or negative association with IL-10 serum levels. Analysis also showed a significant and strong positive association between IL-2 and IL-10 serum levels ( r  = 0.471, p  = 0.011) in GAD patients, which might be due to the compensatory enhancement of anti-inflammatory cytokine, IL-10 in response to elevated pro-inflammatory cytokine, IL-2 levels. Also, we displayed these correlations among several clinical variables of GAD patients by scatter plot graphs (Fig.  2 ).

Scatter plot graphs for several clinical variables of GAD patients showing existence or absence of correlation between the clinical parameters. Scatter plot for serum IL-2 levels versus GAD-7 scores ( a ) or DSM-5 scores ( b ) expressing no significant association between IL-2 and both clinical parameters. Scatter plot graphs showing significant association between IL-2 levels and BMI ( c ), IL-10 levels and GAD-7 scores ( d ), IL-10 levels and DSM-5 scores and IL-10 and IL-2 levels ( f )

Receiver operating characteristic curve analysis

Serum IL-10 measurement showed a good performance in differentiating GAD patients from HCs, which was evidenced by its significantly higher area under the curve (AUC) value of 0.793 ( p  < 0.001) with 80.65% sensitivity and 62.79% specificity at a cut-off value of 33.93 pg/ml, in which the cytokine levels below this point indicate disease states (Table  4 ; Fig.  3 ). ROC analysis of serum IL-2 levels failed to discriminate GAD patients from HCs as the AUC value was below the acceptable range (AUC: 0.640; p  = 0.108) with 54.17% sensitivity and 68.18% specificity at a cut-off value of 8.83 pg/ml) (Fig.  3 ; Table  4 ).

Receiver operating characteristic curve (ROC) for serum IL-2 ( a ) and IL-10 levels ( b )

To the best of our knowledge, this is the first case-control study to investigate the potential association between the pathophysiology of GAD and the pro-inflammatory cytokine, IL-2, and the anti-inflammatory cytokine, IL-10, among the Bangladeshi population. We observed that IL-10 serum levels were significantly lower in GAD patients than in HCs, and this reduction was found to be significantly but negatively associated with both DSM-5 scores and GAD-7 scores, demonstrating potential involvement of this anti-inflammatory cytokine in disease severity and symptoms. Our results of a significant reduction in IL-10 levels in GAD patients are in good agreement with those observed in other studies [ 23 , 25 ]. In contrast, our results diverge from those reported by others [ 33 , 54 ] who either reported no significant variation in IL-10 levels between GAD patients and HCs or that IL-10 levels were enhanced in GAD patients compared to HCs. ROC analysis also demonstrated the good predictive value of IL-10 serum measurement in discriminating diseased patients from HCs, suggesting that IL-10 serum level might be a potential biomarker for diagnosis, anti-anxiety drug response monitoring, or disease progression monitoring. Recently, Hou et al. (2019) demonstrated that peripheral serum levels of the pro-inflammatory cytokine IL-6 could be used to monitor the treatment response of SSRIs in GAD [ 14 ]. Similarly, IL-10 might be used as a marker for therapeutic drug monitoring in GAD. However, further longitudinal studies are required to find any causal relationship between IL-10 and disease severity or pathogenesis. On the other hand, serum IL-2 levels were significantly elevated in GAD patients compared to HCs, but they failed to demonstrate any significant association with either DSM-5 scores or GAD-7 scores in Pearson correlation analysis, implying that IL-2 levels might not be associated with the pathophysiology and development of GAD. Consistent with this, ROC analysis showed that IL-2 levels have no significant diagnostic efficacy in differentiating GAD patients from HCs. Further analysis with a larger population size is required to explore the role of IL-2 in the context of GAD severity. Our results are consistent with those reported by Tang et al. (2018), who also observed that GAD patients exhibited significantly higher serum levels of IL-2 compared to HCs [ 19 ]. However, our results are not in agreement with those reported by others who observed either no significant variation in IL-2 levels [ 54 ] or a significant reduction in GAD patients compared to HCs [ 25 , 33 , 34 , 55 ]. We also observed a significant positive correlation between IL-2 and IL-10 levels in GAD patients, which indicates a compensatory mechanism [ 56 ].

Our study provides some valuable insights into the complex and intricate relationship between the dysregulated immune system and GAD. The observed reduction in IL-10 levels in GAD patients in our study suggests a potential immunoregulatory imbalance in GAD, with IL-10 playing a role in modulating anxiety severity. The lack of a significant association between IL-2 serum levels and anxiety severity highlights the nuanced nature of immune dysregulation in GAD, warranting further exploration into the specific mechanisms involved. Elevated levels of pro-inflammatory cytokine, IL-2, and decreased levels of anti-inflammatory cytokine, IL-10, in GAD patients compared to HCs indicate that GAD individuals of the Bangladeshi cohort are characterized by heightened inflammatory responses derived from the imbalance between pro-inflammatory and anti-inflammatory states. Our study finding provides further support for the cytokine hypothesis of anxiety disorder, which proposes that pro-inflammatory cytokine-mediated neuroinflammatory processes can lead to anxiety symptoms or behaviors by downregulating serotonin biosynthesis or enhancing the reuptake of serotonin, resulting in an altered serotonergic neurotransmitter system in the CNS [ 15 ]. The observed significant negative correlation between IL-10 and DSM-5 scores or GAD-7 scores suggests that lowering IL-10 levels might be involved in the pathogenesis of GAD. One of the major implications of our study findings is that IL-10 signaling might be targeted to explore potential novel immunological/immunomodulatory therapies against GAD. The diminished IL-10 levels and their negative correlation with GAD severity suggest a potential avenue for therapeutic intervention. IL-10 might also be used as an anti-inflammatory adjunctive therapy with other pharmacotherapies including SSRIs/SNRIs. However, at this moment, we don’t know the exact mechanism by which lowered levels of IL-10 are linked to higher anxiety severity in GAD patients.

As IL-10 has anti-inflammatory and immunoregulatory activities such as suppression of production of pro-inflammatory cytokines (IL-1β, IL-6, and TNF-α) from microglia and astrocytes, reduction in IL-10 levels in GAD patients in our study led to an imbalance between pro-inflammatory and anti-inflammatory states and resulted in enhanced pro-inflammatory responses, which might be the cause of enhanced anxiety symptoms as inflammatory cytokine-mediated neuroinflammation was reported to be linked with disrupted monoaminergic neurotransmission in the brain. Besides, elevated levels of IL-10 were shown to attenuate anxiety-like behaviors by modulating GABAergic neurotransmission in the amygdala (Patel et al., 2021). IL-10 was also reported to display some neuroprotective activities and has been shown to inhibit neuronal apoptosis and promote neurite outgrowth, axonal outgrowth, and synapse formation in the brain by the JAK1-STAT3 signaling pathway [ 57 ]. In a preclinical study, IL-4 has been shown to cause the shifting of microglia and macrophages from pro-inflammatory to anti-inflammatory neuroprotective phenotypes characterized by excessive production of arginase-1 and PPARγ receptor expression in microglia and macrophage and thereby attenuating brain-injury-mediated anxiety by inhibiting neuronal loss and nerve tracts in the limbic system [ 58 ]. A similar mechanism might be involved in IL-10-mediated anxiety symptom improvement in GAD patients. Further research is required to unravel the exact mechanisms of IL-10-mediated anxiety symptom attenuation in GAD patients.

In terms of diagnostic marker development, as IL-10 serum level measurement demonstrated good performance in discriminating GAD patients from HCs and as IL-10 levels maintained a significant and negative correlation with disease severity, IL-10 serum level raised the possibility of being an objective biomarker for GAD. However, the diagnostic efficacy of this cytokine should be investigated thoroughly using a range of longitudinal studies. Despite this, at this time we can conclude that IL-10 might be used as a risk indicator for assessment of susceptibility to anxiety disorder, resulting in early detection of the disease and prompting the initiation of intervention strategies. This early detection will reduce treatment costs and decrease the prevalence and morbidity associated with this chronic disorder.

The strength of our study is that we designed a set of inclusion and exclusion criteria for the recruitment of participants and followed those criteria in such a way that homogenous population data could be obtained. The strict study design helped us enormously to minimize the potential impact of several confounding variables, including age, sex, BMI, co-morbid diseases, and immunomodulatory drugs, on cytokine levels. However, our study also has some limitations that should be acknowledged. The major limitation of this study is the smaller sample size. We recruited 50 patients and 38 HCs, which does not represent the whole Bangladeshi demographic. It would be better if we could enroll an equal number of cases and controls. For example, we observed that cytokine levels maintained a statistically significant difference between male GAD patients and male HCs. In contrast, no significant variation in cytokine levels was observed when female data were considered. As we have included more male participants (60%) than female participants (40%), the lower sample size of female participants might generate a higher background noise, resulting in lower statistical power, warranting further studies recruiting a larger population size to investigate sex-specific differences in cytokine levels in GAD patients. Our case-control study design is inherently correlational and thus not able to evaluate the causal relationship between altered cytokine levels and GAD. So, at this moment, we cannot conclude whether the altered levels of serum cytokines are the causes of anxiety development or just the outcome of pathophysiological changes.

Longitudinal studies are required to investigate whether altered cytokine levels precede GAD or if it’s just a mere reflection of GAD pathology. Though we have restricted the impacts of several co-variates, other confounding variables, including genetic polymorphism in cytokine genes, the effect of lifestyle or xenobiotics, and dietary habits, were not considered, which might have modulatory effects on serum cytokine levels.

The study provides valuable insights for understanding the pathogenesis of GAD. Despite having elevated IL-2 levels in GAD patients compared to HCs, it failed to demonstrate a significant association with anxiety severity as assessed by GAD-7 scores. In contrast, serum IL-10 levels were significantly reduced in GAD patients compared to HCs and showed a significant negative correlation with anxiety severity, implicating a potential link with the GAD pathophysiology. Our results support the immune hypothesis of GAD development. Our study findings also suggest that IL-10 serum level measurement might offer an objective blood-based biomarker or risk assessment indicator for GAD. We recommend further research employing a larger population size and homogenous data from different areas of Bangladesh to confirm our study findings.

Data availability

All the relevant data and information will be available from the corresponding author upon reasonable request.

Abbreviations

Body mass index

Chronic energy deficiency

Confidence interval

Central nervous system

Diagnostic and statistical manual for mental disorders, 5th edition

Enzyme-linked immunosorbent assay

• Generalized anxiety disorder

Generalized anxiety disorder 7-item scores

Healthy control

• Interleukin-2
• Interleukin-10

Receiver operating characteristic

Standard error mean

Statistical package for social science

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Acknowledgements

The authors are thankful to all the participants of this study. They are also thankful to the staff and physicians at the Department of Psychiatry, BSMMU, for their technical and administrative support. The authors are also thankful for the laboratory support provided by the Department of Pharmacy, University of Asia Pacific, Dhaka Bangladesh.

This research received no specific grant from any funding agency. However, we received partial funding from University of Dhaka, Bangladesh (Centennial Research grant (2nd Phase) for the year of 2020–2021, project title: “Investigation of peripheral pro-inflammatory and anti-inflammatory cytokines and immune balance in Bangladeshi patients with Generalized Anxiety Disorder”).

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Nisat Sarmin, A. S. M. Roknuzzaman and Rapty Sarker contributed equally to this work.

Authors and Affiliations

Department of Clinical Pharmacy and Pharmacology, Faculty of Pharmacy, University of Dhaka, Dhaka, 1000, Bangladesh

Nisat Sarmin, Rapty Sarker, Mamun -or-Rashid & Zobaer Al Mahmud

Department of Pharmacy, University of Asia Pacific, Dhaka, 1205, Bangladesh

A. S. M. Roknuzzaman

Department of Psychiatry, Bangabandhu Sheikh Mujib Medical University, Shahabagh, Dhaka, 1000, Bangladesh

MMA Shalahuddin Qusar

Department of Pharmacy, Faculty of Pharmacy, University of Dhaka, Dhaka, 1000, Bangladesh

Sitesh Chandra Bachar

School of Pharmacy, BRAC University, Kha 224 Bir Uttam Rafiqul Islam Avenue, Merul Badda, Dhaka, 1212, Bangladesh

Eva Rahman Kabir & Md. Rabiul Islam

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NS, ASMR, RS, MRI, and ZAM: Conceptualization, Data curation, Investigation, Writing – original draft. MR, MMASQ, SCB, and ZAM: Funding acquisition, Project administration, Validation. ERK, MRI, and ZAM: Conceptualization, Formal analysis, Methodology, Supervision, Writing – review & editing.

Corresponding authors

Correspondence to Md. Rabiul Islam or Zobaer Al Mahmud .

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The research protocol was approved by the Research Ethics Committee (REC) of the University of Asia Pacific, Dhaka, Bangladesh (Ref: UAP/REC/2023/202-S). We briefed the objectives of the study to the participants, and informed consent was obtained from each of them. We conducted this investigation following the Helsinki Declaration’s guiding principles.

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Sarmin, N., Roknuzzaman, A.S.M., Sarker, R. et al. Association of interleukin-2 and interleukin-10 with the pathophysiology and development of generalized anxiety disorder: a case-control study. BMC Psychiatry 24 , 462 (2024). https://doi.org/10.1186/s12888-024-05911-z

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Epidemiology in Practice: Case-Control Studies

Introduction.

A case-control study is designed to help determine if an exposure is associated with an outcome (i.e., disease or condition of interest). In theory, the case-control study can be described simply. First, identify the cases (a group known to have the outcome) and the controls (a group known to be free of the outcome). Then, look back in time to learn which subjects in each group had the exposure(s), comparing the frequency of the exposure in the case group to the control group.

By definition, a case-control study is always retrospective because it starts with an outcome then traces back to investigate exposures. When the subjects are enrolled in their respective groups, the outcome of each subject is already known by the investigator. This, and not the fact that the investigator usually makes use of previously collected data, is what makes case-control studies ‘retrospective’.

Advantages of Case-Control Studies

Case-control studies have specific advantages compared to other study designs. They are comparatively quick, inexpensive, and easy. They are particularly appropriate for (1) investigating outbreaks, and (2) studying rare diseases or outcomes. An example of (1) would be a study of endophthalmitis following ocular surgery. When an outbreak is in progress, answers must be obtained quickly. An example of (2) would be a study of risk factors for uveal melanoma, or corneal ulcers. Since case-control studies start with people known to have the outcome (rather than starting with a population free of disease and waiting to see who develops it) it is possible to enroll a sufficient number of patients with a rare disease. The practical value of producing rapid results or investigating rare outcomes may outweigh the limitations of case-control studies. Because of their efficiency, they may also be ideal for preliminary investigation of a suspected risk factor for a common condition; conclusions may be used to justify a more costly and time-consuming longitudinal study later.

Consider a situation in which a large number of cases of post-operative endophthalmitis have occurred in a few weeks. The case group would consist of all those patients at the hospital who developed post-operative endophthalmitis during a pre-defined period.

The definition of a case needs to be very specific:

• Within what period of time after operation will the development of endophthalmitis qualify as a case – one day, one week, or one month?
• Will endophthalmitis have to be proven microbiologically, or will a clinical diagnosis be acceptable?
• Clinical criteria must be identified in great detail. If microbiologic facilities are available, how will patients who have negative cultures be classified?
• How will sterile inflammation be differentiated from endophthalmitis?

There are not necessarily any ‘right’ answers to these questions but they must be answered before the study begins. At the end of the study, the conclusions will be valid only for patients who have the same sort of ‘endophthalmitis’ as in the case definition.

Controls should be chosen who are similar in many ways to the cases. The factors (e.g., age, sex, time of hospitalisation) chosen to define how controls are to be similar to the cases are the ‘matching criteria’. The selected control group must be at similar risk of developing the outcome; it would not be appropriate to compare a group of controls who had traumatic corneal lacerations with cases who underwent elective intraocular surgery. In our example, controls could be defined as patients who underwent elective intraocular surgery during the same period of time.

Matching Cases and Controls

Although controls must be like the cases in many ways, it is possible to over-match. Over-matching can make it difficult to find enough controls. Also, once a matching variable has been selected, it is not possible to analyse it as a risk factor. Matching for type of intraocular surgery (e.g., secondary IOL implantation) would mean including the same percentage of controls as cases who had surgery to implant a secondary IOL; if this were done, it would not be possible to analyse secondary IOL implantation as a potential risk factor for endophthalmitis.

An important technique for adding power to a study is to enroll more than one control for every case. For statistical reasons, however, there is little gained by including more than two controls per case.

Collecting Data

After clearly defining cases and controls, decide on data to be collected; the same data must be collected in the same way from both groups. Care must be taken to be objective in the search for past risk factors, especially since the outcome is already known, or the study may suffer from researcher bias. Although it may not always be possible, it is important to try to mask the outcome from the person who is collecting risk factor information or interviewing patients. Sometimes it will be necessary to interview patients about potential factors (such as history of smoking, diet, use of traditional eye medicines, etc.) in their past. It may be difficult for some people to recall all these details accurately. Furthermore, patients who have the outcome (cases) are likely to scrutinize the past, remembering details of negative exposures more clearly than controls. This is known as recall bias. Anything the researcher can do to minimize this type of bias will strengthen the study.

Analysis; Odds Ratios and Confidence Intervals

In the analysis stage, calculate the frequency of each of the measured variables in each of the two groups. As a measure of the strength of the association between an exposure and the outcome, case-control studies yield the odds ratio. An odds ratio is the ratio of the odds of an exposure in the case group to the odds of an exposure in the control group. It is important to calculate a confidence interval for each odds ratio. A confidence interval that includes 1.0 means that the association between the exposure and outcome could have been found by chance alone and that the association is not statistically significant. An odds ratio without a confidence interval is not very meaningful. These calculations are usually made with computer programmes (e.g., Epi-Info). Case-control studies cannot provide any information about the incidence or prevalence of a disease because no measurements are made in a population based sample.

Risk Factors and Sampling

Another use for case-control studies is investigating risk factors for a rare disease, such as uveal melanoma. In this example, cases might be recruited by using hospital records. Patients who present to hospital, however, may not be representative of the population who get melanoma. If, for example, women present less commonly at hospital, bias might occur in the selection of cases.

The selection of a proper control group may pose problems. A frequent source of controls is patients from the same hospital who do not have the outcome. However, hospitalised patients often do not represent the general population; they are likely to suffer health problems and they have access to the health care system. An alternative may be to enroll community controls, people from the same neighborhoods as the cases. Care must be taken with sampling to ensure that the controls represent a ‘normal’ risk profile. Sometimes researchers enroll multiple control groups . These could include a set of community controls and a set of hospital controls.

Confounders

Matching controls to cases will mitigate the effects of confounders . A confounding variable is one which is associated with the exposure and is a cause of the outcome. If exposure to toxin ‘X’ is associated with melanoma, but exposure to toxin ‘X’ is also associated with exposure to sunlight (assuming that sunlight is a risk factor for melanoma), then sunlight is a potential confounder of the association between toxin ‘X’ and melanoma.

Case-control studies may prove an association but they do not demonstrate causation. Consider a case-control study intended to establish an association between the use of traditional eye medicines (TEM) and corneal ulcers. TEM might cause corneal ulcers but it is also possible that the presence of a corneal ulcer leads some people to use TEM. The temporal relationship between the supposed cause and effect cannot be determined by a case-control study.

Be aware that the term ‘case-control study’ is frequently misused. All studies which contain ‘cases’ and ‘controls’ are not case-control studies. One may start with a group of people with a known exposure and a comparison group (‘control group’) without the exposure and follow them through time to see what outcomes result, but this does not constitute a case-control study.

Case-control studies are sometimes less valued for being retrospective. However, they can be a very efficient way of identifying an association between an exposure and an outcome. Sometimes they are the only ethical way to investigate an association. If care is taken with definitions, selection of controls, and reducing the potential for bias, case-control studies can generate valuable information.

Case-Control Studies: Advantages and Disadvantages

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• Jason Codrington   ORCID: orcid.org/0009-0003-1490-4211 1 ,
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The proliferation of microplastics (MPs) represents a burgeoning environmental and health crisis. Measuring less than 5 mm in diameter, MPs have infiltrated atmospheric, freshwater, and terrestrial ecosystems, penetrating commonplace consumables like seafood, sea salt, and bottled beverages. Their size and surface area render them susceptible to chemical interactions with physiological fluids and tissues, raising bioaccumulation and toxicity concerns. Human exposure to MPs occurs through ingestion, inhalation, and dermal contact. To date, there is no direct evidence identifying MPs in penile tissue. The objective of this study was to assess for potential aggregation of MPs in penile tissue. Tissue samples were extracted from six individuals who underwent surgery for a multi-component inflatable penile prosthesis (IPP). Samples were obtained from the corpora using Adson forceps before corporotomy dilation and device implantation and placed into cleaned glassware. A control sample was collected and stored in a McKesson specimen plastic container. The tissue fractions were analyzed using the Agilent 8700 Laser Direct Infrared (LDIR) Chemical Imaging System (Agilent Technologies. Moreover, the morphology of the particles was investigated by a Zeiss Merlin Scanning Electron Microscope (SEM), complementing the detection range of LDIR to below 20 µm. MPs via LDIR were identified in 80% of the samples, ranging in size from 20–500 µm. Smaller particles down to 2 µm were detected via SEM. Seven types of MPs were found in the penile tissue, with polyethylene terephthalate (47.8%) and polypropylene (34.7%) being the most prevalent. The detection of MPs in penile tissue raises inquiries on the ramifications of environmental pollutants on sexual health. Our research adds a key dimension to the discussion on man-made pollutants, focusing on MPs in the male reproductive system.

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A portable pen-sized instrumentation to measure stiffness of soft tissues in vivo

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All relevant data to the current study that was generated and analyzed is available upon reasonable request from the corresponding author.

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Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, FL, USA

Jason Codrington, Alexandra Aponte Varnum, Joginder Bidhan, Kajal Khodamoradi, Aymara Evans, David Velasquez, Christina C. Yarborough, Ashutosh Agarwal, Edoardo Pozzi, Francesco Mesquita, Francis Petrella, David Miller & Ranjith Ramasamy

Institute of Coastal Environmental Chemistry, Department for Inorganic Environmental Chemistry, Helmholtz-Zentrum Hereon, Max-Planck-Str 1, 21502, Geesthacht, Germany

Lars Hildebrandt & Daniel Pröfrock

Institute of Membrane Research, Helmholtz-Zentrum Hereon, Max-Planck-Str 1, 21502, Geesthacht, Germany

Anke-Lisa Höhme & Martin Held

Dr. J.T. MacDonald Foundation BioNIUM, Miller School of Medicine, University of Miami, Miami, FL, USA

Bahareh Ghane-Motlagh

Department of Biomedical Engineering, University of Miami, Miami, FL, USA

Ashutosh Agarwal

University of Colorado, Anschutz Medical Campus, Aurora, CO, USA

Justin Achua

Vita-Salute San Raffaele University, Milan, Italy

Edoardo Pozzi

IRCCS Ospedale San Raffaele, Urology, Milan, Italy

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Contributions

Jason Codrington—conceptualization, methodology, investigation, project administration, data curation, visualization, writing—original draft, editing. Alexandra Aponte Varnum—investigation, writing—original draft, editing, data curation, visualization. Lars Hildebrandt—investigation, writing—original draft, validation, resources. Daniel Pröfrock—investigation, editing, validation, resources. Joginder Bidhan—resources, writing—original draft. Kajal Khodamoradi—project administration, resources. Anke-Lisa Höhme—investigation, visualization. Martin Held—writing—original draft, editing. Aymara Evans—writing—original draft. David Velasquez—writing—original draft. Christina C. Yarborough—writing—original draft. Bahareh Ghane-Motlagh—investigation. Ashutosh Agarwal—investigation. Justin Achua—writing—original draft. Edoardo Pozzi—editing. Francesco Mesquita—editing. Francis Petrella—writing—review. David Miller—writing—review. Ranjith Ramasamy—conceptualization, methodology, project administration, resources, supervision, editing, funding acquisition

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Correspondence to Ranjith Ramasamy .

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Dr. Edoardo Pozzi is currently an Associate Editor for the International Journal of Impotence Research.

Ethics approval

The study was approved by the Institutional Review Board of the University of Miami (Study # 20150740) and conducted following the Declaration of Helsinki. All patients provided written and informed consent to participate in the study.

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Codrington, J., Varnum, A.A., Hildebrandt, L. et al. Detection of microplastics in the human penis. Int J Impot Res (2024). https://doi.org/10.1038/s41443-024-00930-6

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Received : 21 March 2024

Revised : 29 May 2024

Accepted : 04 June 2024

Published : 19 June 2024

DOI : https://doi.org/10.1038/s41443-024-00930-6

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