Depression increases the risk of asthma: Evidence from a cross-sectional study and two-sample Mendelian randomization analysis
Year: 2025
Type: article
Study design: Observational Study
Abstract: Depression and asthma are both serious public health issues and are prone to comorbidity. However, the correlation and causality between these conditions remain unclear. Data from the 2005 to 2023 National Health and Nutrition Examination Survey were analyzed. Weighted multivariate logistic regression was used to assess the depression-asthma association, including both crude (unadjusted) and adjusted models (controlling for age, sex, and other covariates). Subgroup analyses were performed to explore risk variations. Mendelian randomization (MR) analyses were primarily conducted using genetic instruments derived from European populations, with the random effect inverse-variance weighted method as the main approach, supplemented by weighted median, MR-Egger, simple mode, and weighted mode analyses. Sensitivity analyses examined heterogeneity, horizontal pleiotropy, and result robustness. The observational study included 38,127 participants. Moderate (OR = 1.56, 95% CI 1.35-1.80, P < .001), moderately severe (OR = 1.97, 95% CI 1.59-2.44, P < .001), and severe (OR = 2.02, 95% CI 1.48-2.76, P < .001) depression were associated with increased asthma risk (P for trend < .001). Interaction effects were noted by age and gender. MR analysis indicated genetically predicted depression causally increases asthma risk (OR = 1.29, 95% CI 1.19-1.40, P < .001) with no significant horizontal pleiotropy in two-sample MR studies. Depression may be a causal factor for asthma. Increased attention is needed for asthma risk in depressed patients, especially men and the elderly. Further validation in large, prospective cohort studies is required.
Source: Medicine
Authors: Manli Ran, Yu Zhang, Wang Zhang, Minjian Wang, Siying Luo
Institution: Chongqing City Mental Health Center
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,950
OpenAlex ID: W4414858809