ArticleGeroScience2025
Construction and validation of a DNN-based biological age and its influencing factors in the China Kadoorie Biobank.
Article in GeroScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Metabolic Profiling of Epigenetic Aging and Its Associations With Aging-Related Phenotypes and Modifiable Lifestyle Factors.Aging cell · 2026Article
- Commentary on "Accelerated biological aging and postoperative acute kidney injury in surgical patients".International journal of surgery (London, England) · 2026Article
- Development and validation of deep learning- and ensemble learning-based biological ages in the NHANES study.Frontiers in aging neuroscience · 2025Article
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Authors and funding
8 authors.
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Abstract
Biological age is an important measure of aging that reflects an individual's physical health and is linked to various diseases. Current prediction models are still limited in precision, and the risk factors for accelerated aging remain underexplored. Therefore, we aimed to develop a precise biological age and assess the impact of socio-demographic and behavioral patterns on the aging process.We utilized Deep Neural Networks (DNN) to construct biological age from participants with physical examinations, blood samples, and questionnaires data from the China Kadoorie Biobank (CKB) between June 2004 and December 2016. △age, calculated as the residuals between biological age and chronological age, was used to investigate the associations of age acceleration with diseases. Socio-demographics (gender, education attainment, marital status, household income) and lifestyle characteristics (body mass index [BMI], smoking, drinking, physical activity, and sleep) were also assessed to explore their impact on age acceleration. 18,261 participants aged 57 ± 10 years were included in this study. The DNN-based biological age model has demonstrated accurate predictive performance, achieving a mean absolute error of 3.655 years. △age was associated with increased risks of various morbidity and mortality, with the highest associations found for circulatory and respiratory diseases, with hazard ratios of 1.033 (95% CI: 1.023, 1.042) and 1.078 (95% CI: 1.027, 1.130), respectively. Socio-demographics, including being female, lower education, widowed or divorced, and low household income, along with behavioral patterns, such as being underweight, insufficient physical activity, and poor sleep, were associated with accelerated aging. Our DNN model is capable of constructing a precise biological age using commonly collected data. Socio-demographics and lifestyle factors were associated with accelerated aging, highlighting that addressing modifiable risk factors can effectively slow age acceleration and reduce disease risk, providing valuable insights for interventions to promote healthy aging.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.