| Title: | A deep learning sex-specific body composition ageing biomarker using dual-energy X-ray absorptiometry scan |
| Journal: | Communications Medicine |
| Published: | 13 May 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40360687/ |
| DOI: | https://doi.org/10.1038/s43856-025-00850-6 |
| URL: | https://www.nature.com/articles/s43856-025-00850-6.pdf |
Publication 15349
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Abstract
BackgroundChronic diseases are closely linked to alterations in body composition, yet there is a need for reliable biomarkers to assess disease risk and progression. This study aimed to develop and validate a biological age indicator based on body composition derived from dual-energy X-ray absorptiometry (DXA) scans, offering a novel approach to evaluating health status and predicting disease outcomes.MethodsA deep learning model was trained on a reference population from the UK Biobank to estimate body composition biological age (BCBA). The model's performance was assessed across various groups, including individuals with typical and atypical body composition, those with pre-existing diseases, and those who developed diseases after DXA imaging. Key metrics such as c-index were employed to examine BCBA's diagnostic and prognostic potential for type 2 diabetes, major adverse cardiovascular events (MACE), atherosclerotic cardiovascular disease (ASCVD), and hypertension.ResultsHere we show that BCBA strongly correlates with chronic disease diagnoses and risk prediction. BCBA demonstrated significant associations with type 2 diabetes (odds ratio 1.08 for females and 1.04 for males, p < 0.0005), MACE (odds ratio 1.10 for females and 1.11 for males, p < 0.0005), ASCVD (odds ratio 1.07 for females and 1.10 for males, p < 0.0005), and hypertension (odds ratio 1.06 for females and 1.04 for males, p < 0.0005). It outperformed standard cardiovascular risk profiles in predicting MACE and ASCVD.ConclusionsBCBA is a promising biomarker for assessing chronic disease risk and progression, with potential to improve clinical decision-making. Its integration into routine health assessments could aid early disease detection and personalised interventions.</p>
5 Authors
- Jie Lian
- Pei Cai
- Fan Huang
- Jianpan Huang
- Varut Vardhanabhuti
1 Application
| Application ID | Title |
|---|---|
| 78730 | Activity Tracking and Whole-Body MRI For The Prediction of Individual Wellness -Comparative Analysis with Chinese Cohorts |
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