Longevity Papers

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.

A plethora of senolytic compounds and technologies have been identified. However, the efficacy of these treatments in eliminating senescent cells and the suppression of chronic inflammation remains to be substantiated in vivo. Here, we employ single-cell RNA sequencing and find the selective elimination of highly inflammatory fibroblasts, endothelial cells in the lung, and proximal tubule cells in the kidney from the GLS1 inhibitor BPTES-treated aged mice. The eliminated fibroblasts share typical phenotypes with in vitro human senescent cells. These cells predominantly express Dpp4 (CD26) and Cadm3, showing activated IFN signaling and lysosomal membrane damage. Cells eliminated by BPTES in lungs exhibit transcriptomes similar to those eliminated in p16-DTR mice, where DTR expression is limited in p16-expressing cells. BPTES treatment results in the T cell population shift from cytotoxic to a protective state, suggesting the suppression of age-related chronic inflammation. CellChat analysis revealed that multiple cytokine signals are transmitted from inflammatory fibroblasts and proximal tubular cells to immune cells in lungs and kidneys. These results provide evidence that GLS1 inhibitor functions as a bona fide senolytic drug to eliminate inflammatory cells, including a subset of senescent cells, and suppresses age-related chronic inflammation.

As populations age, identifying the neurobiological basis of cognitive resilience is critical for delaying or preventing Alzheimers disease (AD). While most older adults experience memory decline, a subset known as superagers (SA) maintains youthful memory into late life, offering a unique window into protective mechanisms against neurodegeneration. Here, we identified a functional connectivity (FC) signature, termed Alzheimer\'s-resilient connectome (ARC), that robustly differentiates SA from age-matched patients with AD. Using resting-state fMRI in a discovery cohort (N = 290), we identified ARC derived from machine learning classifiers that distinguished SA from AD with high accuracy (AUC = 0.85), and validated the replicability of the ARC in an independent replication cohort (N = 143). ARC involved prefrontal, temporal and insular networks and was strongly associated with brain age. When applied to cognitively unimpaired (CU) adults (discovery cohort: N = 818 and replication cohort: N = 497), ARC-based subtyping revealed SA-like and AD-like subgroups with similar baseline cognitive performance but markedly divergent longitudinal trajectories. SA-like CU individuals showed slower cognitive decline, reduced amyloid-{beta} accumulation, and lower risk of conversion to mild cognitive impairment and AD, reinforcing the ARC signature as a potential early indicator of resilience. Genome-wide association analysis identified CLYBL and FRMD6 as novel genetic modulators associated with these divergent aging phenotypes. Together, our findings position ARC as a sensitive and generalizable biomarker of resilience, enabling early risk stratification and precision prevention for AD.