ArticleNature medicine2025
Large language model-based biological age prediction in large-scale populations.
Article in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- An LLM-enhanced AI agent for rapid diagnosis of acute aortic dissection in multi-center settings.iScience · 2026Article
- Speech clocks decode dementia phenotypes, social exposome, and biological aging.Science advances · 2026Article
- A landscape of metabolic burden and its cardiovascular impact across the adult lifespan: a prospective cohort study of 7.5 million people in Northwestern China.The Lancet regional health. Western Pacific · 2026Article
- Sex-specific aging clocks from a large-scale human phenome reveal distinct aging transitions and circulating signatures.Nature aging · 2026Article
- Longitudinal alignments and syntheses of multimodal clinical data for personalized medicine with the PULSE framework.Nature computational science · 2026Article
- A Multidisciplinary Team-Based Large Language Model Framework for Predicting Postoperative Neurological Complications in Acute Type A Aortic Dissection: Model Development and Validation Study.Journal of medical Internet research · 2026Article
- A clinically practical aging clock (physical clock) for healthy aging: development, validation, and application for health assessment and intervention.Science China. Life sciences · 2026Article
- Biological aging clocks in health and disease.Nature medicine · 2026Review
- Hierarchical endpoints and win statistics for geromedicine trials.Nature aging · 2026Review
- Functional, molecular, and digital measurements of biological age.The Journal of clinical investigation · 2026Review
- Epidemiological and bioinformatics analyses of air pollution and genetic susceptibility in aortic stenosis risk.Nature communications · 2026Article
- A multimodal ocular aging index reveals proteomic pathways and predicts incident age-related eye diseases.npj aging · 2026Article
- The BHARAT study: a multi-modal, multi-omics investigation of aging signatures in the Indian population.Aging · 2026Observational
- Article
- The modifying effect of chronological age on the predictive value of vascular aging indicators for the long-term cardiovascular events risk.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Article
- A global metagenomic atlas of aging identifies a microbiota phase transition associated with disease risk.NPJ biofilms and microbiomes · 2026Article
- Advancing Precision Nutrition Through Multimodal Data and Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Evaluating Representation Embeddings from LLMs and Time-Series Foundation Models for Wearable Accelerometer-Based Health Prediction.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Nurses as guardians of time: the hidden clinical value of continuous care in geriatrics.Frontiers in public health · 2026Review
- Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance.Frontiers in cardiovascular medicine · 2026Review
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Authors and funding
22 authors.
Funding
Abstract
Accurate and convenient assessment of individual aging is crucial for identifying health risks and preventing aging-related diseases. Nonetheless, current aging proxies often face challenges such as methodological limitations, weak associations with adverse outcomes and limited generalizability. Here we propose a framework that leverages large language models (LLMs) to estimate individual overall and organ-specific aging using only health examination reports. We validated this approach across six population-based cohorts, encompassing over 10 million participants and demonstrated effectiveness and reliability. Our results showed that the LLM-predicted overall age achieved a concordance index (C-index) of 0.757 (95% CI 0.752-0.761) for all-cause mortality, significantly outperforming other aging proxies such as telomere length, frailty index, eight epigenetic ages and four machine-learning models predictions. The overall age gap was strongly associated with multiple aging-related phenotypes and health outcomes, showing a hazard ratio of 1.055 (95% CI 1.050-1.060) for all-cause mortality. For organ-specific aging, LLM-predicted ages and age gaps also demonstrated superior performance in predicting corresponding organ-specific diseases compared to machine-learning models. Additionally, we examined the dynamic aging assessment capability of LLMs and applied age gaps to identify proteomic biomarkers associated with accelerated aging and to develop risk prediction models of 270 diseases. Interpretability analyses were also conducted to explore the decision-making process of LLMs. In conclusion, our LLM-based aging assessment framework offers a precise, reliable and cost-effective approach for estimating overall and organ-specific aging. It has potential for personalized aging assessment and health management in large-scale general populations.
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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 OpenQuestion graph.