
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-24-05714
00096
10.1097/MD.0000000000039301
3
4400
Research Article
Observational Study
Causal relationship between educational attainment and chronic pain: A Mendelian randomization study
https://orcid.org/0000-0003-4473-8237
Liu Shuning MD 13930500003@163.com
a
Xu Debin PhD a*
a School of Marxism, Changchun University of Chinese Medicine, Changchun, Jilin, China.
* Correspondence: Debin Xu, Changchun University of Chinese Medicine, Changchun, Jilin Province 130117, China (e-mail: 1351659782@qq.com).
13 9 2024
13 9 2024
103 37 e3930122 5 2024
10 7 2024
23 7 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Educational attainment (EA) is often used as a symbol of socioeconomic status and is associated with several diseases. However, uncertainty remains regarding the potential relationship between EA and chronic pain. This study aimed to evaluate the potential causal association between EA and chronic pain. The primary method employed in Mendelian randomization (MR) analysis was inverse-variance weighted method. Additionally, MR-Egger intercept, Cochran Q, and MR-PRESSO statistical analyses were conducted to assess potential pleiotropy and heterogeneity. The MR analysis provided evidence that genetically predicted additional education significantly reduced the risk of chronic pain. Specifically, this genetic factor may reduce multisite chronic pain by 27.6%, and chronic widespread pain by 3.8%. The results of sensitivity analysis indicated the reliability of our causal estimates. Higher levels of EA may provide protection against chronic pain risk. Enhancing education, narrowing social and economic disparities may help alleviate the burden of chronic pain.

chronic pain
educational attainment
Mendelian randomization
OPEN-ACCESSTRUE
SDCT
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pmc1. Introduction

Chronic pain is typically defined as persistent or intermittent pain lasting longer than 3 months and has emerged as a significant public health concern worldwide. Multisite chronic pain (MCP) is a chronic pain phenotype that quantifies the number of chronic pain sites in the body on a scale from 0 to 7.[1] Chronic widespread pain (CWP), an independent yet related chronic pain phenotype, is a symptom of fibromyalgia and its prevalence significantly increases with age.[2] Unlike acute pain, chronic pain is persistent, lasting for years or even longer, and does not have a reasonable threshold or cutoff point, instead existing on a “continuum of widespreadness.”[3] Patients with chronic pain experience a significant decline in their quality of life and often suffer from mental and neurodevelopmental disorders, leading to the onset of chronic diseases and mental illnesses such as depression, anxiety, and substance abuse. This results in excess mortality and high social costs, imposing a heavy burden on individual health and the socioeconomic landscape.[4] Additionally, research indicates that high-income countries have higher prevalence rates than low and middle-income countries, underscoring the crucial role of societal factors in individual lives.[5]

In recent years, increasing attention has been paid to observational studies that explore the impact of socioeconomic factors on health. Serving as a “model phenotype” for behavioral traits, educational attainment (EA) has moderate heritability and significant correlations with many social, economic, and health outcomes, playing a crucial role in health promotion.[6] However, there has been little research on the direct causal association between EA and chronic pain. Finding a causal association between EA and chronic pain will aid in understanding the disease etiology, providing effective information for disease prevention or intervention, and alleviating the increasing disease burden.

Mendelian randomization (MR) analysis is a research method that uses genetic variants randomly allocated in a population as instrumental variables (IVs) to evaluate causal associations between IVs, exposure and outcomes.[7] Because genetic variation is randomly allocated during meiosis and alleles are fixed once generated and are no longer influenced by confounding factors, it can simulate the effects of randomized controlled trials (RCTs) (Fig. 1).

Figure 1. Schematic diagram of MR analysis.

2. Methods

2.1. Study design

This study employed a MR analysis to investigate the potential causal association between EA and chronic pain. The IVs which Select single-nucleotide polymorphisms (SNPs) strongly associated with EA in the MR analysis must satisfy 3 assumptions: there is a robust relationship between the IVs and the exposure factor, the IVs were independent of confounding factors, and genetic variation can only affect the occurrence of the outcome through exposure factors and cannot exert an effect on the outcome through other pathways.[8]

2.2. Data sources

EA is defined as the number of years an individual has completed in school, with each educational unit equivalent to 4.2 years.[9] Each participant was categorized based on the International Standard Classification of Education (2011), representing various levels of school education. We obtained SNPs for EA from summary data, which were meta-analyzed across 71 discovery cohorts comprising 766,345 individuals of European ancestry.

The MCP is a quantitative phenotype defined as self-reported pain lasting at least 3 months across 7 different body regions (head, face, neck/shoulders, back, stomach/abdomen, hips, and knees), with scores ranging from 0 to 7.[10] The CWP is regarded as a supplementary assessment tool for MCP.[11] It is described as a combination of self-reported pain lasting at least 3 months, occurring on both sides of the body, above and below the waist, and in the axial skeleton, and is associated with fibromyalgia. As derived phenotypes of chronic pain, MCP and CWP share common identification points in terms of pain location and quantity.[12] They are currently considered strong prognostic indicators of persistent chronic pain progression (Table 1). All data are publicly available datasets; therefore, no additional ethical approval was required.

Table 1 Overview of GWAS data.

Trait	PM ID	Participants	SNP	Authors	Year	
Year of schooling	30038396	1,131,881	10,101,242	Lee et al	2018	
Multisite chronic pain	33830993	387,649	9,926,106	Johnston et al	2021	
Chronic widespread pain	33926923	6914/242,929	7,616,783	Rahman et al	2021	
GWAS = genome-wide association study, SNP = single-nucleotide polymorphism.

2.3. Instrument selection and data harmonization

First, we selected SNPs closely associated with exposure from the GWAS as IVs (P < 5 × 10−8). Second, SNPs were pruned to reduce linkage disequilibrium by setting thresholds (r2 < 0.001 and distance >10,000 kb).[13,14] Lastly, SNPs effects were harmonized between the exposure and outcome data through allele harmonization, removing ambiguous palindromic SNPs with moderate effect allele frequencies. Additionally, we collected information on the major allele, allele frequency, β-value, P-value, and standard error for each SNP. Weak IVs with F < 10 were removed based on F-test values.[15,16] Table S1, Supplemental Digital Content, http://links.lww.com/MD/N506 provides detailed information on the IVs.

2.4. Statistical analyses

We conducted an inverse-variance weighted (IVW) method as the primary analysis method, with MR-Egger regression, Weighted Median, Simple Mode, and Weighted Mode as supplementary analysis methods.[17] The IVW method ignores the influence of intercept terms and combines the results and specific effects of each SNP to give SNPs with smaller variances greater weight, when the total number is valid, this is usually the most reliable.[18] The advantage of Weighted Median is that it can provide a robust causal effect estimate when effective IVs reach 50%.[19] MR-Egger regression takes into account the possibility of heterogeneity in IVs and provides corrected estimates of causal effects, capable of correcting for pleiotropic bias in IVs.[20] Simple Mode is used when there is only 1 instrumental variable, directly estimating the causal effect using the instrumental variable.[21] The weighted mode adjusts for the influence of differences in genotype frequency by weighting different genotypes, which can better control for the impact of genotype frequency differences on the analysis results, improving the robustness and accuracy of the analysis.[22] Finally, the 5 methods are compared, and for directional effects, important substantive results require consistency in results obtained from 5 different methods.

We utilized Cochran Q-value and employed the MR-Egger and IVW methods to verify heterogeneity, where P > .05 indicates no heterogeneity. If heterogeneity existed, we estimated the causal relationship using a random-effects model in the MR analysis.[23] Additionally, MR-Egger and MR-PRESSO analyses were conducted to rectify the potential pleiotropy of IVs. A substantial deviation of the intercept from 0 and P < .05 for the regression intercept indicates the presence of pleiotropy. The MR-PRESSO test corrects for pleiotropy by excluding outliers, and P > .05 is considered indicative of no pleiotropy. Furthermore, leave-one-out analysis was performed to verify the potential impact of SNPs.[24]

3. Results

3.1. Effect of EA on chronic pain

The results revealed that EA decreased the risk of chronic pain (Table 2). Specifically, an increase in EA was significantly associated with lower MCP (OR = 0.724, 95% CI = 0.701–0.75, P < .001). The estimates provided by the other methods were not as accurate as those provided by the IVW analysis. Simultaneously, an increase in EA was significantly associated with lower CWP (OR = 0.962, 95% CI = 0.9567–0.967, P < .001). To verify the robustness of the results, reverse MR analyses were executed for exposure and outcome, no evidence of reverse causation was found. Specifically, MCP with EA (P = .32) and CWP with EA (P = .08). The forest and scatter plot of the causal effect values shows consistent directions for each result, with colored lines representing the fitted results of methods (Fig. 2).

Table 2 MR results for the effect of educational attainment on the chronic pain.

Exposure	Outcome	Method	OR (95% CI)	β	SE	P	
EA	Multisite chronic pain	IVW	0.724 (0.701–0.75)	−0.3219	0.0176	1.2087e−16	
Weighted Median	0.729 (0.701–0.757)	−0.3159	0.0195	9.9702e−09	
MR Egger	0.748 (0.661–0.846)	−0.2901	0.0628	5.3911e−06	
Weighted mode	0.694 (0.605–0.796)	−0.3454	0.065	1.829e−07	
Simple mode	0.707 (0.623–0.804)	−0.3645	0.0698	2.9365e−07	
Chronic widespread pain	IVW	0.962 (0.956–0.967)	−0.0383	0.0028	1.4251e−14	
Weighted Median	0.962 (0.955–0.969)	−0.0381	0.0036	1.0273e−08	
MR Egger	0.964 (0.945–0.983)	−0.0364	0.0101	3.4779e−04	
Weighted mode	0.961 (0.941–0.982)	−0.039	0.0109	4.1571e−04	
Simple mode	0.96 (0.935–0.986)	−0.0399	0.0135	3.4881e−03	
CI = confidence interval, MR = Mendelian randomization, OR = odds ratio.

Figure 2. Forest and scatter plot of the MR results, the colored lines depict the fitting results of 5 MR analysis methods. (A) Multisite chronic pain; (B) chronic widespread pain.

3.2. MR sensitivity analyses

For the sensitivity analysis of EA and chronic pain, the MR-Egger test did not indicate pleiotropy (intercepts were close to zero, P > .05). Through MR-PRESSO examination, no abnormal SNP or horizontal pleiotropy effects were detected between EA and chronic pain (P > .05). Additionally, Cochran Q test revealed significant heterogeneity (Table 3). Accordingly, we used the IVW method under fixed and random effects. The effectiveness of MR estimates remains unaffected owing to the combined heterogeneity. Visualizing the study’s heterogeneity with a funnel plot, the analysis showed that all SNPs were distributed symmetrically in the funnel plot, with relatively minor influence from potential factors, indicating robust causality. Leave-one-out analysis was used to examine the impact of each SNP locus on overall causal association. After excluding 1 SNP at a time, the remaining SNPs all fell on 1 side of the null line, indicating that no SNP significantly influenced causal association (Fig. 3).

Table 3 Sensitivity analysis results.

Trait	Pleiotropy test	Heterogeneity test	
Intercept	MR-Egger P	PRESSO P	Q-statistic	Q-df	Q-P	
Multisite chronic pain	−4.09 × 10−3	.597	.595	950.12	395	<.01	
Chronic widespread pain	−2.49 × 10−5	.842	.822	541.26	349	.171	

Figure 3. Funnel plot for chronic pain using primary genetic instruments. (A) Multisite chronic pain; (B) chronic widespread pain.

4. Discussion

We used MR analysis to investigate the causal relationship between EA and chronic pain. Specifically, EA was significantly associated with a decrease in MCP and CWP (OR = 0.724, 95% CI = 0.701–0.75, P < .001), (OR = 0.962, 95% CI = 0.9567–0.967, P < .001), respectively. According to the MR analysis results, all analytical methods showed consistent slopes, and sensitivity analysis indicated reliable results. However, no reverse causal relationship was found between chronic pain and EA.

Consistent with traditional observational study results, higher EA is protective for health. In the clinical management of rheumatology, there is often a focus on tracking the proximal causes of pain, such as injury and inflammation, while the societal and environmental aspects of chronic pain are easily overlooked. The relationship between education and chronic pain may be closely linked to socioeconomic status.[25] As an overall measure of economic and social status, including education, income, and occupation, adverse socioeconomic factors are considered predictors of chronic pain development.[26] The mainstream explanation of the relationship between education and socioeconomic status originates from Weberian theory, which suggested that education has become a popular singular indicator of socioeconomic status primarily due to its association with various lifestyle characteristics and its ease of data collection.[27] The most common measure of education is the number of completed school years, and for most adults, EA is a crucial factor of occupation and income, more stable and effective than either occupation or income alone. Formal education is typically completed during young adulthood, and the quantity of education and knowledge individuals acquire can influence their behaviors and practices by affecting their lifestyles and social networks, therefore its impact on individuals’ future careers and income can be profound.[28] Different educational groups occupy different socioeconomic statuses due to differences in occupation and income, which also affect access to healthcare resources and opportunities for decent housing. Those in higher social strata may have greater access to health information and opportunities, making them more likely to adopt healthier behaviors or communicate effectively with healthcare providers to receive appropriate medical services.[29] From this perspective, education extends to cognitive functions, lifestyle behaviors, problem-solving abilities and values, which have an impact on socioeconomic status and individual health. Additionally, education often serves as a qualification for certain occupations and incomes, hence serving as a substitute indicator in the socioeconomic field.[30]

Theoretically, factors influencing chronic pain are almost entirely personal, with income and education potentially reflecting better health outcomes. People with higher EA reported lower pain risks than those with lower EA. Generally, more educated individuals tend to have better jobs, making it easier for them to access health services, which aids in more comprehensive pain care.[31] On the other hand, Individuals with lower EA typically have limited access to information and medical health knowledge, leading to confusion about whether chronic pain is distinct from other neuropathic pain. They are uncertain if abnormal sensations and discomfort should be classified as pain, and it is difficult to define pain intensity with a fixed value, which may delay treatment and worsen the condition.[32] Similarly, high-income respondents were less likely to experience pain compared to low-income individuals. Lower socioeconomic status is strongly linked to physical labor, often involving demanding physical tasks or high-stress work environments, which are associated with musculoskeletal injuries and work-related stress.[33] Additionally, lower EA was associated with ineffective pain coping strategies, such as forbearing, praying, and relying on hope. Unhealthy lifestyle factors, such as smoking, alcohol consumption, or lack of physical activity, along with other social and personal factors, are more prevalent among individuals of lower socioeconomic status.[34] Furthermore, individuals suffering from chronic pain are at risk of experiencing decreased levels of educational achievement, impaired occupational functioning and early parenthood, with chronic stressful environments leading them to prematurely join the workforce for reasons such as dropping out of school, often indicating that their work is not aligned with their long-term career aspirations, thereby increasing the risk and burden of chronic pain. Interventions aimed at keeping patients involved, assisting in planning, and attaining future educational and career objectives can be pivotal in managing and intervening with chronic pain. Education and knowledge are key components of behavioral change, and the success of chronic pain prevention and intervention depends on the patient’s motivation and ability to change behavior in daily life.[35] With a preventive medical approach to health, there is an urgent need for more health knowledge to continually benefit from healthy behaviors, prompting a shift in individual responsibility in the pursuit of health. The goal is to help individuals reduce high-intensity exposure to certain risk factors. From the health-promoting consumption patterns, we can see that different health products and lifestyles (including healthcare, socio-cultural activities, and physical activities) will have varying impacts on health. Therefore, enhancing education, improving self-management, and narrowing socioeconomic and health disparities may help to alleviate the burden of chronic pain.

Unlike previous RCTs, MR analysis describes the association between lifelong exposure to chronic pain related alleles in the general population, while RCTs can fully assess the role of EA in preventing or treating chronic pain. It avoids the influence of various confounding factors and bidirectional causality on associations, and study process considered issues such as the long-term latency of diseases and lifelong exposure to risk factors, effectively reducing the inherent bias in observational studies. The data used in this MR analysis were all from the European population, and both EA and chronic pain were based on the latest GWAS data, with larger sample sizes and higher statistical power. However, this investigation also had some limitations. First, our GWAS data lacked statistical information on participant gender ratios, environment, race, duration of disease, and clinical characteristics of participants, making it difficult to compare causal effects between subgroups. Second, this study and its analysis was not conducted in other ethnic groups. While this may reduce population stratification bias, the reliability of extrapolation to other racial populations may be limited. Although the protective effect of education against chronic pain was confirmed in this study, but the influence of related mediating factors was not investigated. Therefore, we need to acknowledge the fact that interventions solely targeting EA may not provide the optimal solution for mitigating the risk of chronic pain. This is because many other nonheritable factors, not captured in this study or accessible through GWAS, such as unemployment, psychosocial factors, poverty, educational opportunities, and access to healthcare. MR analysis is a commonly used experimental design method, it also has its limitations, and in practical applications, it needs to be combined with clinical and RCTs to be validated in more populations, considering multiple factors to ensure the reliability of the experimental results.

5. Conclusion

This investigation provides evidence of a causal association between higher EA and reduced risk of chronic pain, which may have an impact on public health systems and social welfare policies. Social intervention targeting low educated individuals, including expand the scope of education, increase educational opportunities, and raise the age of leaving school, may receive increasing attention. Meanwhile, based on people’s understanding of the correlation between educational differences and the risk of chronic pain, it may help improve the harmful effects of low EA on health outcomes.

Acknowledgments

The authors thank all participants and investigators who provided the GWAS data.

Author contributions

Conceptualization: Shuning Liu.

Data curation: Shuning Liu.

Formal analysis: Shuning Liu.

Investigation: Shuning Liu.

Methodology: Shuning Liu.

Project administration: Shuning Liu, Debin Xu.

Resources: Shuning Liu.

Software: Shuning Liu.

Supervision: Shuning Liu, Debin Xu.

Validation: Shuning Liu.

Visualization: Shuning Liu.

Writing – original draft: Shuning Liu.

Writing – review & editing: Shuning Liu, Debin Xu.

Supplementary Material

Abbreviations:

CI confidence interval

GWAS genome-wide association study

IVs instrumental variables

IVW inverse-variance weighted

MR Mendelian randomization

OR odds ratio

RCTs randomized controlled trials

SE standard error

SNP single-nucleotide polymorphism

The authors have no funding and conflicts of interest to disclose.

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Supplemental Digital Content is available for this article.

All data are publicly available datasets; therefore, no additional ethical approval was required.

How to cite this article: Liu S, Xu D. Causal relationship between educational attainment and chronic pain: A Mendelian randomization study. Medicine 2024;103:37(e39301).
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