| Title: | Metabolites stratify future major depressive disorder risk in obese population: a longitudinal machine learning analysis |
| Journal: | Psychological Medicine |
| Published: | 29 Jul 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42522228/ |
| DOI: | https://doi.org/10.1017/s0033291726105248 |
| URL: | ~Ehttps://www.cambridge.org/core/services/aop-cambridge-core/content/view/492898AD2935671E2103380B53B89A0B/S0033291726105248a.pdf/div-class-title-metabolites-stratify-future-major-depressive-disorder-risk-in-obese-population-a-longitudinal-machine-learning-analysis-div.pdf~ |
Publication 21124
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Abstract
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.</p>
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).</p>
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).</p>
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.</p>
15 Keywords
- Adult
- Female
- Humans
- Longitudinal Studies
- Machine Learning
- Major Depressive Disorder
- Male
- Mendelian Randomization Analysis
- Metabolomics
- Middle Aged
- Obesity
- Prediction Algorithms
- Predictive Learning Models
- Risk Assessment
- Risk Factors
10 Authors
- Xiaobing Zhai
- Abao Xing
- Yaoqi Deng
- Chi Kin Lam
- Lihuan Wang
- Hui Yu
- Henry H.Y. Tong
- Nuno Lourenço
- Li Zhao
- Kefeng Li
1 Application
| Application ID | Title |
|---|---|
| 99946 | Causal mediation analysis with machine learning to understand the increased risks of depression in overweight and obese population |
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