Heterogeneity of Human Aging and Its Assessment

Abstract

Understanding the heterogeneity in health of older adults is a compelling question in the biology of aging. We analyzed the performance of five measures of health heterogeneity, judging them by their ability to predict mortality. Using clinical and biomarker data on 1,013 participants of the Canadian Study of Health and Aging who were followed for up to 6 years, we calculated two indices of biological age using the Klemera and Doubal method, which controversially includes using chronological age as a "biomarker," and three frailty indices (FIs) that do not include chronological age: a standard clinical FI, an FI from standard laboratory blood tests and blood pressure, and their combination (FI-combined). Predictive validity was tested using Cox proportional hazards analysis and discriminative ability by the area under the receiver-operating characteristic curves. All five measures showed moderate performance that was improved by combining measures to evaluate larger numbers of items. The greatest addition in explanatory power came from the FI-combined that showed the best mortality prediction in an age-adjusted model. More extensive comparisons across different databases are required, but these results do not support including chronological age as a biomarker.

Keywords: Biological age; Biological aging; Biomarkers; Frailty indices; Health heterogeneity.

Figure 1.

Definition of the difference Δ between the estimated biological age and chronological age (CA) of a person (indicated by the solid circle), illustrated for the jth biomarker X_j. These differences Δ_j obtained for biomarkers significantly correlated with CA (j = 1,…, m) are averaged (equation 5) with the weights depending on the slopes and residual variances.

Figure 2.

(A) The histogram of the difference (Δ) between estimated biological age BA_E from equations (1), (5), and (6) and CA; (B) BA_E estimated using equations (1), (5), and (6). (C) BA_EC estimated using equation (3) with w_CA corresponded to the $s_{\text{BC}}^2=150$. The histograms are overlaid with the normal curves with the parameter: for Δ: mean = 0.24 (SD = 16.39); for BA_E: mean = 80.9 (SD = 17.83), and for BA_EC: mean = 80.8 (SD = 11.1).

Figure 3.

BA_E is strongly associated with the FI-LAB (A) than with the FI-CSHA (B). Similarly, BA_EC is strongly associated with FI-LAB (C) than with the FI-CSHA (D) although BA_CE relationships with CA weaker than with BA_E. The dots represent individuals’ values of the BA and FI-LAB; solid lines are the least squares regressions of BA on FI-LAB (A and C) and FI-CSHA (B and D).

Figure 4.

Kaplan–Meier survival curves for grades of the BA_E (A) and BA_EC (B). The cut-points for the strata are as follows: <65, 65–75, 75–100, and >100.