Friday, July 25, 2025

Predicting Biological Age in Large Populations Using Advanced Language Models

The recent literature highlights critical insights into the challenges of aging, emphasizing the intersection of biological, environmental, and genetic factors. Works by Partridge et al. (2018) and Kaeberlein et al. (2015) establish healthy aging as vital in preventative medicine. Advances by López-Otín et al. (2023) explore aging’s link to cancer, while Jaiswal and Libby (2020) connect clonal hematopoiesis to cardiovascular diseases. Emerging research identifies biological age predictors, with studies on DNA methylation and plasma proteomics leading to biomarkers for aging (Horvath et al., 2018; Zhang et al., 2024). Additionally, the impact of aging on chronic diseases is examined through multiomics and frailty assessments (Félix et al., 2024; Argentieri et al., 2024). The ongoing integration of these findings aims to foster public health strategies targeting longevity and quality of life, addressing both the inherent biological processes and societal implications of aging.

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