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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2026年6月18日

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  • Xiao-Ming Wang,
  • Jing Pang,
  • Xue Yu,
  • Xuewei Zhang,
  • Shi-Bo Wang,
  • Qi Pan,
  • Aiqun Chen,
  • Lengnan Xu,
  • Nan Li,
  • Xuefei Li,
  • Yao Wang,
  • Yujia Gu,
  • Shiping Chen,
  • Wei Ma,
  • Li-Qun Zhang,
  • Chi Zhang,
  • Wensong Liu,
  • Chuan-Bao Li,
  • Tao Shen,
  • Da-Peng Dai,
  • Fang Wang,
  • Yongjun Li,
  • Yi Li,
  • Jun Dong,
  • Wenxiang Chen,
  • Yao Yao,
  • Baohua Liu,
  • Jian-Ping Cai,
  • Tiemei Zhang,
  • Ju Cui

摘要

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.