Psychological medicine · 2026
Metabolites stratify future major depressive disorder risk in obese population: a longitudinal machine learning analysis.
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Zhai Xiaobing, Xing Abao, Deng Yaoqi, Lam Chi Kin, Wang Lihuan, Yu Hui, Tong Henry H Y, Lourenço Nuno, Zhao Li, Li Kefeng
Аннотация
BACKGROUND: Obesity 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.
METHODS: Forty-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).
RESULTS: During 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 PME < 0.05).
CONCLUSIONS: These 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.
METHODS: Forty-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).
RESULTS: During 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 PME < 0.05).
CONCLUSIONS: These 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.