| Title: | A Machine Learning Analysis of Big Metabolomics Data for Classifying Depression: Model Development and Validation |
| Journal: | Biological Psychiatry |
| Published: | 22 Dec 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38142718/ |
| DOI: | https://doi.org/10.1016/j.biopsych.2023.12.015 |
| Citations: | 1 (1 in last 2 years) as of 8 Aug 2024 |
Publication 9133
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Abstract
BACKGROUND: Many metabolomics studies of depression have been performed, but these have been limited by their scale. A comprehensive in silico analysis of global metabolite levels in large populations could provide robust insights into the pathological mechanisms underlying depression and candidate clinical biomarkers.</p>
METHODS: Depression-associated metabolomics was studied in 2 datasets from the UK Biobank database: participants with lifetime depression (N = 123,459) and participants with current depression (N = 94,921). The Whitehall II cohort (N = 4744) was used for external validation. CatBoost machine learning was used for modeling, and Shapley additive explanations were used to interpret the model. Fivefold cross-validation was used to validate model performance, training the model on 3 of the 5 sets with the remaining 2 sets for validation and testing, respectively. Diagnostic performance was assessed using the area under the receiver operating characteristic curve.</p>
RESULTS: In the lifetime depression and current depression datasets and sex-specific analyses, 24 significantly associated metabolic biomarkers were identified, 12 of which overlapped in the 2 datasets. The addition of metabolic features slightly improved the performance of a diagnostic model using traditional (nonmetabolomics) risk factors alone (lifetime depression: area under the curve 0.655 vs. 0.658 with metabolomics; current depression: area under the curve 0.711 vs. 0.716 with metabolomics).</p>
CONCLUSIONS: The machine learning model identified 24 metabolic biomarkers associated with depression. If validated, metabolic biomarkers may have future clinical applications as supplementary information to guide early and population-based depression detection.</p>
12 Authors
- Simeng Ma
- Xinhui Xie
- Zipeng Deng
- Wei Wang
- Dan Xiang
- Lihua Yao
- Lijun Kang
- Shuxian Xu
- Huiling Wang
- Gaohua Wang
- Jun Yang
- Zhongchun Liu
1 Application
| Application ID | Title |
|---|---|
| 87530 | Combined analysis of multi-source heterogeneous data for depression detection and diagnosis |
Enabling scientific discoveries that improve human health