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Background <p>Existing aging clocks, designed to quantify biological aging, primarily capture systemic changes and may overlook alterations crucial for cardiometabolic diseases (CMDs).</p> Methods <p>In this study, we developed the CardioMetAge model, an aging clock tailored to predict CMD-related outcomes. Trained in the NHANES-III, the model was applied to the continuous NHANES and UK Biobank. Its associations with cardiometabolic mortality, disease incidence, and transitions between disease states were examined, and its performance in predicting 10-year CMD incidence was also evaluated. We further investigated associations of proteomic pathways, lifestyle factors, and socioeconomic status with CardioMetAge, as well as the impact of caloric restriction intervention on its change.</p> Results <p>The final CardioMetAge was constructed as a linear combination of chronological age and 12 common clinical biomarkers. Its age deviation (CardioMetAgeDev) showed stronger associations with CMD mortality (HR per SD [95% CI]: 1.87 [1.83, 1.91]), CMD incidence (1.35 [1.33, 1.37]), and disease progression, including transitions from no CMD to first CMD (1.34 [1.32, 1.35]) and from first CMD to cardiometabolic multimorbidity (1.25 [1.21, 1.30]), compared with deviations of PhenoAge and other traditional biological age models. CardioMetAge also consistently outperformed these models in predicting 10-year CMD incidence. Our findings also highlighted the biological determinants of cardiometabolic aging, with proteomic analyses linking CardioMetAgeDev to inflammatory activation and metabolic disorders. Analysis of modifiable factors revealed that lifestyle and socioeconomic status were associated with CMD risks, partly via CardioMetAgeDev (mediation proportions: 34.5% and 10.7%, respectively). Additionally, two-year caloric restriction slowed the progression of CardioMetAge by 1.23&#xa0;years (95% CI: [0.61, 1.84]) relative to the ad libitum control.</p> Conclusions <p>CardioMetAge outperformed existing aging clocks in ease of use and in predicting CMD<b>-</b>related outcomes. It provides valuable insights into the mechanisms of cardiometabolic aging and holds potential for clinical monitoring and evaluating the effectiveness of interventions.</p> Graphical Abstract <p></p>

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CardioMetAge estimates cardiometabolic aging and predicts disease outcomes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月31日

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  • Yucan Li,
  • Xinming Xu,
  • Yi Zheng,
  • Xinyi He,
  • Jiacheng Wang,
  • Zhenqiu Liu,
  • Yanfeng Jiang,
  • Chen Suo,
  • Tiejun Zhang,
  • Xiang Gao,
  • Xingdong Chen,
  • Kelin Xu

摘要

Background

Existing aging clocks, designed to quantify biological aging, primarily capture systemic changes and may overlook alterations crucial for cardiometabolic diseases (CMDs).

Methods

In this study, we developed the CardioMetAge model, an aging clock tailored to predict CMD-related outcomes. Trained in the NHANES-III, the model was applied to the continuous NHANES and UK Biobank. Its associations with cardiometabolic mortality, disease incidence, and transitions between disease states were examined, and its performance in predicting 10-year CMD incidence was also evaluated. We further investigated associations of proteomic pathways, lifestyle factors, and socioeconomic status with CardioMetAge, as well as the impact of caloric restriction intervention on its change.

Results

The final CardioMetAge was constructed as a linear combination of chronological age and 12 common clinical biomarkers. Its age deviation (CardioMetAgeDev) showed stronger associations with CMD mortality (HR per SD [95% CI]: 1.87 [1.83, 1.91]), CMD incidence (1.35 [1.33, 1.37]), and disease progression, including transitions from no CMD to first CMD (1.34 [1.32, 1.35]) and from first CMD to cardiometabolic multimorbidity (1.25 [1.21, 1.30]), compared with deviations of PhenoAge and other traditional biological age models. CardioMetAge also consistently outperformed these models in predicting 10-year CMD incidence. Our findings also highlighted the biological determinants of cardiometabolic aging, with proteomic analyses linking CardioMetAgeDev to inflammatory activation and metabolic disorders. Analysis of modifiable factors revealed that lifestyle and socioeconomic status were associated with CMD risks, partly via CardioMetAgeDev (mediation proportions: 34.5% and 10.7%, respectively). Additionally, two-year caloric restriction slowed the progression of CardioMetAge by 1.23 years (95% CI: [0.61, 1.84]) relative to the ad libitum control.

Conclusions

CardioMetAge outperformed existing aging clocks in ease of use and in predicting CMD-related outcomes. It provides valuable insights into the mechanisms of cardiometabolic aging and holds potential for clinical monitoring and evaluating the effectiveness of interventions.

Graphical Abstract