Evidence map›Paper›PMID 42329515›Full record

ArticleScience China. Life sciences2026

A clinically practical aging clock (physical clock) for healthy aging: development, validation, and application for health assessment and intervention.

Xiao-Ming Wang, Jing Pang, Xue Yu, Xuewei Zhang, Shi-Bo Wang, Qi Pan, Aiqun Chen, Lengnan Xu, Nan Li, Xuefei Li and 20 more

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Article in Science China. Life sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

30 authors.

Xiao-Ming Wang *Department of Basic Innovation Research, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Beijing Key Laboratory of Aging Mechanism and Intervention Research on Aging-Related Diseases; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Jing Pang *Department of Basic Innovation Research, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Beijing Key Laboratory of Aging Mechanism and Intervention Research on Aging-Related Diseases; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Xue Yu *Department of Cardiology, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Xuewei Zhang *Health Management Center, Xiangya Hospital, Central South University, Changsha, 410008, China.
Shi-Bo WangDepartment of Basic Innovation Research, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Beijing Key Laboratory of Aging Mechanism and Intervention Research on Aging-Related Diseases; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Qi PanDepartment of Endocrinology, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Aiqun ChenDepartment of Nephrology, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Lengnan XuDepartment of Nephrology, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Nan LiState Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Xuefei LiState Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Yao WangDepartment of Basic Innovation Research, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Beijing Key Laboratory of Aging Mechanism and Intervention Research on Aging-Related Diseases; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Yujia GuInstitute of Statistics and Big Data, Renmin University of China, Beijing, 100872, China.
Shiping ChenSchool of Government, Shenzhen University, Shenzhen, 518055, China.
Wei MaInstitute of Statistics and Big Data, Renmin University of China, Beijing, 100872, China.
Li-Qun ZhangDepartment of Basic Innovation Research, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Beijing Key Laboratory of Aging Mechanism and Intervention Research on Aging-Related Diseases; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Chi ZhangDepartment of Basic Innovation Research, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Beijing Key Laboratory of Aging Mechanism and Intervention Research on Aging-Related Diseases; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Wensong LiuDepartment of Laboratory Medicine, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Chuan-Bao LiDepartment of Laboratory Medicine, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Tao ShenDepartment of Basic Innovation Research, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Beijing Key Laboratory of Aging Mechanism and Intervention Research on Aging-Related Diseases; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Da-Peng DaiDepartment of Basic Innovation Research, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Beijing Key Laboratory of Aging Mechanism and Intervention Research on Aging-Related Diseases; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Fang WangDepartment of Cardiology, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Yongjun LiDepartment of Vascular Surgery, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Yi LiDepartment of Traditional Chinese Medicine (TCM), Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Jun DongDepartment of Basic Innovation Research, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Beijing Key Laboratory of Aging Mechanism and Intervention Research on Aging-Related Diseases; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Wenxiang ChenNational Center for Clinical Laboratories, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Yao YaoCenter for Healthy Aging Transdisciplinary Sciences (CHATS), China Center for Health Development Studies, Peking University, Beijing, 100871, China.
Baohua LiuShenzhen Key Laboratory for Systemic Aging and Intervention (SKL-SAI), School of Basic Medical Sciences, Shenzhen University, Shenzhen, 518055, China.
Jian-Ping CaiDepartment of Basic Innovation Research, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Beijing Key Laboratory of Aging Mechanism and Intervention Research on Aging-Related Diseases; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Tiemei ZhangDepartment of Basic Innovation Research, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Beijing Key Laboratory of Aging Mechanism and Intervention Research on Aging-Related Diseases; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Ju CuiDepartment of Basic Innovation Research, Beijing Hospital, National Center for Gerontology; National Clinical Research Center for Gerontology; The Key Laboratory of Geriatrics of NHC; Beijing Key Laboratory of Aging Mechanism and Intervention Research on Aging-Related Diseases; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China. juzi.cui@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aging is the primary risk factor for most chronic diseases, yet substantial heterogeneity highlights a fundamental gap between chronological age and biological aging processes. Despite the high predictive accuracy of multi-omics clocks, their clinical implementation remains limited by cost and complexity, underscoring the need for scalable and clinically actionable alternatives. Here, we developed a physical clock that integrates routine clinical biomarkers with multidimensional physical performance using ElasticNet regression in the large-scale PENG ZU cohort. This model was further reduced to a simplified 11-marker model, primarily capturing key domains of kidney function, glucose metabolism, and physical performance. Accelerated aging, as measured by physical clock-derived ΔPhysicalAge, was strongly associated with chronic disease incidence, functional decline, reduced intrinsic capacity, and mortality. Across independent validation in UKB Biobank and NHANES, it robustly predicted disease onset, disease-specific, and all-cause mortality. Notably, ΔPhysicalAge outperformed ΔPhenoAge in predicting major age-related diseases and enabled effective risk stratification even among individuals conventionally classified as low risk. We observed an association between postmenopausal HRT use and lower ΔPhysicalAge, providing preliminary evidence supporting the validity and responsiveness of the physical clock. To facilitate clinical translation, we established age- and sex-specific healthy reference intervals (HRI) derived from individuals with normal or decelerated aging, demonstrating that maintenance within these ranges is associated with substantially reduced mortality risk. Furthermore, modifiable lifestyle and psychosocial factors, including healthy diet, physical activity, health consciousness, and social engagement, were associated with slower aging, highlighting actionable intervention pathways. Together, the physical clock bridges the gap between biological aging assessment and real-world clinical practice, offering a clinically accessible tool for risk stratification and personalized intervention to promote healthy aging.

Indexed as

AgingHealthy AgingAgedBiomarkersChronic DiseaseFemaleHumansMaleMiddle AgedRisk FactorsBiomarkershealth reference intervalhealthy agingmodifiable factorsphysical clock

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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.