ArticleEuropean journal of medical research2025
Association between DHA and depression: results from the NHANES 2011-2014 and a bidirectional Mendelian randomization analysis.
Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundA great deal of research demonstrates that the pathophysiology and etiology of depression have been associated with dietary deficiencies in omega-3 polyunsaturated fatty acids (n-3 PUFAs). However, little is known about this association's common genetics and causal relationships. Therefore, we used observational studies combined with bidirectional Mendelian randomization (MR) to investigate a potential association between depression and docosahexaenoic acid (DHA).
methodsUsing data from the National Health and Nutrition Examination Survey (NHANES) in the United States from 2011 to 2014, we first conducted a cross-sectional study and analyzed the association between DHA and depression using a statistical method to adjust for confounders in logistic regression. We subsequently utilized genome-wide association study (GWAS) data in the UK to determine the causal relationship between DHA and depression by a genetic approach to assess causality for MR analysis. We used inverse variance weighting (IVW) methods to obtain the majority of the bidirectional causal estimates of MR. We additionally performed sensitivity analyses to analyze the horizontal pleiotropy and heterogeneity of MR results.
resultsThis NHANES analysis has shown that DHA is associated with depression in American adults (OR = 0.996, 95% CI 0.993-0.999, P = 0.014). Bidirectional MR analysis demonstrated a significant causal relationship between DHA and depression in the European population (OR = 0.9, 95% CI 0.84-0.97, P = 0.006).
conclusionsThis research provides new insights into the association between DHA and depression. This discovery needs to be validated in further prospective studies that require large sample sizes and sufficient follow-up time.
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