ArticleChina CDC weekly2024
AI-Based Hematological Age Predictors and the Association Between Biological Age Acceleration and Type 2 Diabetes Mellitus - Chongqing Municipality, China, 2015-2021.
Article in China CDC weekly, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Association of clinical biomarker-based biological age and aging trajectory with cardiovascular disease and all-cause mortality in Chinese adults: a population-based cohort study.BMC public health · 2025Article
- AI-Driven Management of Type 2 Diabetes in China: Opportunities and Challenges.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
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10 authors.
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Abstract
Introduction: Biological age (BA) can represent the actual state of human aging more accurately than chronological age (CA). Methods: Using hematological data from 112,925 participants in southwestern China, collected between 2015 and 2021, this study constructed BA predictors using 7 machine learning (ML) methods (tailored separately for male and female populations). This study then analyzed the association between BA acceleration and type 2 diabetes mellitus (T2DM) within this data using logistic regression. Additionally, it examined the impact of glycemic control on BA in individuals with diabetes. Results: Among all ML models, deep neural networks (DNN) delivered the best performance in male [mean absolute error (MAE)=6.89, r=0.75] and female subsets (MAE=6.86, r=0.74). BA acceleration showed positive correlations with T2DM in both male [odds ratio ( Discussion: Artificial intelligence (AI)-based hematological BA predictors show promise as advanced tools for assessing aging in epidemiological studies. Implementing AI-based BA predictors in public health initiatives could facilitate proactive aging management and disease prevention.
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