TGAge: A Phenotypic Aging Clock Based on Tabular Foundation Models and the Gompertz Law for Assisted Living Facility Residents
Abstract
Biological age captures overall health status beyond chronological age, yet existing biological aging clocks are difficult to apply to assisted living facility (ALF) residents due to scarce epigenetic data. We propose TGAge, a second-generation phenotypic aging clock that estimates risk-equivalent biological age from routinely collected clinical measurements. TGAge employs a tabular foundation model to predict all-cause mortality risks over discrete time-to-death intervals and converts the resulting mortality-risk ratio into biological age acceleration via the Gompertz law. We develop and evaluate TGAge on a large cohort of ALF residents curated from a proprietary, multi-state long-term care database. TGAge outperforms clinical indices and established biological aging clocks in survival prediction. Moreover, TGAge-derived age acceleration is significantly associated with multiple aging-related comorbidities. These findings support TGAge as a practical, interpretable, and highly predictive biological aging clock for vulnerable ALF populations using only routinely collected clinical measurements.
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BibTeX
@misc{li2026tgage,
title = {{TGAge: A Phenotypic Aging Clock Based on Tabular Foundation Models and the Gompertz Law for Assisted Living Facility Residents}},
author = {Chengzhuo Li and Rongjia Zhou and Shanshan Wang and Runze Yan and Carl Yang and Xiaobo Sun and Huiwen Xu and Jiaying Lu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.32994},
eprint = {2610.32994},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.32994},
}
RIS
TY - UNPB
TI - TGAge: A Phenotypic Aging Clock Based on Tabular Foundation Models and the Gompertz Law for Assisted Living Facility Residents
AU - Chengzhuo Li
AU - Rongjia Zhou
AU - Shanshan Wang
AU - Runze Yan
AU - Carl Yang
AU - Xiaobo Sun
AU - Huiwen Xu
AU - Jiaying Lu
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.32994
UR - https://arxiv.org/abs/2610.32994
AB - Biological age captures overall health status beyond chronological age, yet existing biological aging clocks are difficult to apply to assisted living facility (ALF) residents due to scarce epigenetic data. We propose TGAge, a second-generation phenotypic aging clock that estimates risk-equivalent biological age from routinely collected clinical measurements. TGAge employs a tabular foundation model to predict all-cause mortality risks over discrete time-to-death intervals and converts the resulting mortality-risk ratio into biological age acceleration via the Gompertz law. We develop and evaluate TGAge on a large cohort of ALF residents curated from a proprietary, multi-state long-term care database. TGAge outperforms clinical indices and established biological aging clocks in survival prediction. Moreover, TGAge-derived age acceleration is significantly associated with multiple aging-related comorbidities. These findings support TGAge as a practical, interpretable, and highly predictive biological aging clock for vulnerable ALF populations using only routinely collected clinical measurements.
ER -