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Published on: August 15, 2020
CardioMetAge estimates cardiometabolic aging and predicts disease outcomes
Yucan Li1,2, Xinming Xu3, Yi Zheng1
1State Key Laboratory of Genetic Engineering, ZhangjiangFudan International Innovation Center, Human Phenome Institute, Fudan University, Shanghai, 201203, China.
Insights
A new aging clock, CardioMetAge, better predicts cardiometabolic diseases (CMDs) and mortality than existing models. It also identifies biological drivers and intervention effects, like caloric restriction, on aging.
Area of Science:
- Biogerontology
- Cardiovascular Medicine
- Metabolic Health
Background:
- Existing aging clocks often miss alterations critical for cardiometabolic diseases (CMDs).
- There is a need for aging clocks specifically designed to predict CMD-related outcomes.
Purpose of the Study:
- To develop and validate the CardioMetAge model, an aging clock for predicting CMD outcomes.
- To assess CardioMetAge's associations with CMD mortality, incidence, and disease progression.
- To explore biological determinants and modifiable factors influencing cardiometabolic aging.
Main Methods:
- Developed CardioMetAge using chronological age and 12 clinical biomarkers.
- Trained and validated the model in NHANES-III, continuous NHANES, and UK Biobank datasets.
- Examined associations with CMD mortality, incidence, disease transitions, and proteomic pathways.
Main Results:
- CardioMetAge deviation (CardioMetAgeDev) showed stronger associations with CMD mortality and incidence than PhenoAge.
- CardioMetAgeDev predicted 10-year CMD incidence and disease progression more effectively.
- Proteomic analyses linked CardioMetAgeDev to inflammation and metabolic disorders; lifestyle and socioeconomic status partially mediated CMD risk via CardioMetAgeDev.
Conclusions:
- CardioMetAge is a user-friendly aging clock that outperforms existing models in predicting CMD outcomes.
- It offers insights into cardiometabolic aging mechanisms and the impact of interventions like caloric restriction.
- CardioMetAge has potential for clinical monitoring and evaluating intervention effectiveness.
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.
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