Mental disorders and the risk of atopic dermatitis: A two-sample mendelian randomization study

Background: Growing evidence suggests that mental disorders are associated with an increased risk of atopic dermatitis (AD). But until now, the causal association between them has been unclear. We conducted a Mendelian randomization study to determine the bidirectional causal association between atopic dermatitis and mental disorders. Simultaneously, the correlation between biomechanics and atopic dermatitis (AD) has been analyzed. Objective: This two-sample Mendelian randomization (MR) study aims to assess the causal relationship between mental disorders and the incidence of atopic dermatitis (AD). Methods: Single nucleotide polymorphisms (SNPs) associated with severe depression, generalized anxiety disorder, sleep disorders, schizophrenia, and AD were selected from the genome-wide association study (GWAS) databases. Causal effects between exposure and outcomes were analyzed using methods such as inverse-variance weighted (IVW), weighted median (WM), and MR-Egger, with results primarily assessed by the P-values, odds ratios (ORs), and 95% confidence intervals (CIs) from the IVW method. Sensitivity analyses were conducted using IVW, MR-Egger, and MR-PRESSO methods. Results: The findings indicate a positive causal effect of severe depression on the risk of developing AD, with the IVW method yielding an OR of 1.177 (95% CI 1.083–1.280, P < 0.001) and the WM method showing an OR of 1.182 (95% CI 1.061–1.317, P < 0.001). Heterogeneity tests using the IVW method’s Cochran Q test resulted in a P-value of 0.132 and an I2 of 19.34%. Pleiotropy tests with MR-PRESSO showed a P-value of 0.193, and the MR-Egger regression intercept yielded a P-value of 0.009, indicating the presence of heterogeneity or pleiotropy. No causal relationships were found between generalized anxiety disorder (OR = 1.024, 95% CI 0.996–1.052, P > 0.05), sleep disorders (OR = 0.970, 95% CI 0.928–1.014, P > 0.05), or schizophrenia (OR = 1.006, 95% CI 0.983–1.029, P > 0.05) and the incidence of AD. Conclusion: Major depressive disorder exhibits a unidirectional causal influence on Alzheimer’s disease development risk. It is recommended that patients with severe depression undergo enhanced screening and prevention for AD to mitigate the risk of developing this condition. Biomechanics plays a significant role in the onset and progression of atopic dermatitis (AD).

Authors

Institutions

Publication Details

Journal
Molecular & cellular biomechanics
Published
2025-03-13
DOI
https://doi.org/10.62617/mcb1702
Primary Topic
Dermatology and Skin Diseases
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mental disorders and the risk of atopic dermatitis: A two-sample mendelian randomization study

Jiale Zheng, Y. J. Su, Zhihong Wu, Y. Fang et al.
1 links
Molecular & cellular biomechanics
Dermatology and Skin Diseases
article

Mental disorders and the risk of atopic dermatitis: A two-sample mendelian randomization study

Jiale Zheng, Y. J. Su, Zhihong Wu, Y. Fang, Liubang Li
article en

Abstract

Background: Growing evidence suggests that mental disorders are associated with an increased risk of atopic dermatitis (AD). But until now, the causal association between them has been unclear. We conducted a Mendelian randomization study to determine the bidirectional causal association between atopic dermatitis and mental disorders. Simultaneously, the correlation between biomechanics and atopic dermatitis (AD) has been analyzed. Objective: This two-sample Mendelian randomization (MR) study aims to assess the causal relationship between mental disorders and the incidence of atopic dermatitis (AD). Methods: Single nucleotide polymorphisms (SNPs) associated with severe depression, generalized anxiety disorder, sleep disorders, schizophrenia, and AD were selected from the genome-wide association study (GWAS) databases. Causal effects between exposure and outcomes were analyzed using methods such as inverse-variance weighted (IVW), weighted median (WM), and MR-Egger, with results primarily assessed by the P-values, odds ratios (ORs), and 95% confidence intervals (CIs) from the IVW method. Sensitivity analyses were conducted using IVW, MR-Egger, and MR-PRESSO methods. Results: The findings indicate a positive causal effect of severe depression on the risk of developing AD, with the IVW method yielding an OR of 1.177 (95% CI 1.083–1.280, P < 0.001) and the WM method showing an OR of 1.182 (95% CI 1.061–1.317, P < 0.001). Heterogeneity tests using the IVW method’s Cochran Q test resulted in a P-value of 0.132 and an I2 of 19.34%. Pleiotropy tests with MR-PRESSO showed a P-value of 0.193, and the MR-Egger regression intercept yielded a P-value of 0.009, indicating the presence of heterogeneity or pleiotropy. No causal relationships were found between generalized anxiety disorder (OR = 1.024, 95% CI 0.996–1.052, P > 0.05), sleep disorders (OR = 0.970, 95% CI 0.928–1.014, P > 0.05), or schizophrenia (OR = 1.006, 95% CI 0.983–1.029, P > 0.05) and the incidence of AD. Conclusion: Major depressive disorder exhibits a unidirectional causal influence on Alzheimer’s disease development risk. It is recommended that patients with severe depression undergo enhanced screening and prevention for AD to mitigate the risk of developing this condition. Biomechanics plays a significant role in the onset and progression of atopic dermatitis (AD).

Molecular & cellular biomechanicsVol. 22(4)
Guangxi University (CN), Guangxi University of Chinese Medicine (CN)
Openalex Percentile: Top 90%
Dermatology and Skin Diseases
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.