Using a Biomarker of Biological Aging to Predict Incident Cardiometabolic Disease.

BACKGROUND

The ability to predict cardiometabolic risk is essential to support efforts to prevent future onset of disease. Biomarkers that assess biological aging-the rate of decline in physiological functioning that occurs across the systems of the human body-have the potential to support this goal.

METHODS

We investigated the extent to which Dunedin Pace of Aging Calculated From the Epigenome (DunedinPACE), an epigenetic measure of biological aging, predicted cardiometabolic disease onset in a cohort of 2062 US military veterans free of disease at baseline and followed for an average of 14.1 years via electronic health records. Other clinical biomarkers included hemoglobin A1C, blood pressure, pulse, and body mass index.

RESULTS

DunedinPACE predicted incident cardiometabolic disease onset (hazard ratio [HR]=1.82 [95% CI, 1.64-2.04]; P<0.001) and remained associated with cardiometabolic disease onset when accounting for hemoglobin A1C, blood pressure, pulse, and body mass index (HR=1.38 [95% CI, 1.23-1.55]; P<0.001). DunedinPACE increased prediction of cardiometabolic disease (area under the receiver operating characteristic [AUROC] curve=0.76, ΔAUROC=0.04) compared with demographics alone and increased prediction when combined with clinical biomarkers, demographics, smoking, and alcohol use (AUROC=0.81, ΔAUROC=0.01, ΔΧ2 = 27.81, df=1, P<0.001).

CONCLUSIONS

These results suggest that DunedinPACE improves prediction of cardiometabolic disease compared with other clinical biomarkers and could help identify individuals who would benefit from preventative interventions that delay the onset of cardiometabolic disease.