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 phenot

Judges – Help Us Raise Funds for Student Presentation Awards Thank you for your service as a judge at ABRCMS. Your dedication has helped shape the future of countless student scientists, and we’re dee...

Synopsis. Social media has become widely used by the scientific community for a variety of professional uses, including networking and public outreach. For

It is difficult to fathom why anyone intelligent enough to be a scientist would actually choose to be one. Doing good science requires the utmost exertion of body, mind and spirit, yet is consistently...

By analysing the smartphone data of 2,112,288 participants, in particular observing and comparing the activity of the same individual in two different environments, we find that increases in the walka...

Dementia Researcher Salon - Should I stay or should I go... does research need me?

UCL, London seeks an internationally recognised leader in imaging informed cognitive neuroscience to shape research, lead teams and advance brain understanding.

The Chief Medical Officer for England’s 2023 Annual Report on Health in an Ageing Society identified that more research needs to include older people. As a result of this, the Vivensa Foundation has j...

Gaucher disease (GD), the most common lysosomal storage disorder, is caused by bi-allelic mutations in the GBA1 gene. Variants in GBA1 also represent the most frequent genetic risk factor for Parkinso...