ArticlePsychological medicine2026
Metabolites stratify future major depressive disorder risk in obese population: a longitudinal machine learning analysis.
Article in Psychological medicine, 2026. 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
backgroundObesity is a well-established risk factor for major depressive disorder (MDD), yet the risk is not uniform, highlighting the need for precise risk stratification. This study aimed to develop a metabolomics-based prediction model to identify high-risk metabolic phenotypes among obese participants and to elucidate the causal metabolic pathways involved.
methodsForty-one-thousand-four-hundred-fifty-nine obese participants were followed for a median of 14.4 years. We integrated multiple machine learning (ML) algorithms to develop predictive models for 3-, 5-, and 9-year MDD risk, with a temporal validation within the same biobank. Furthermore, we investigated the potential causal relationships within the obesity-metabolite-MDD using mediation Mendelian randomization (MR).
resultsDuring follow-up, 3,642 incident MDD cases were documented. The optimized LightGBM model demonstrated superior predictive performance, achieving AUCs of 0.844 (95% CI: 0.773-0.914), 0.824 (95% CI: 0.771-0.875), and 0.834 (95% CI: 0.796-0.871) for 3-, 5-, and 9-year intervals, respectively, significantly outperforming existing clinical models. Temporal validation confirmed the model's robustness (AUCs: 0.738-0.776). MR analysis confirmed that key metabolites causally mediate the pathway from obesity to MDD (mediation proportions: -11.5% and -35.3%, all
conclusionsThese findings challenge the notion of a uniform obesity-MDD association, demonstrating that metabolomic signatures can effectively stratify MDD risk. We present a validated ML framework for the early identification of high-risk individuals with obesity, offering a precision medicine approach to guide targeted metabolic and psychiatric interventions.
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