Multi-Omic Biological Age Estimation and Its Correlation With Wellness and Disease Phenotypes: A Longitudinal Study of 3,558 Individuals

Abstract

Biological age (BA), derived from molecular and physiological measurements, has been proposed to better predict mortality and disease than chronological age (CA). In the present study, a computed estimate of BA was investigated longitudinally in 3,558 individuals using deep phenotyping, which encompassed a broad range of biological processes. The Klemera-Doubal algorithm was applied to longitudinal data consisting of genetic, clinical laboratory, metabolomic, and proteomic assays from individuals undergoing a wellness program. BA was elevated relative to CA in the presence of chronic diseases. We observed a significantly lower rate of change than the expected ~1 year/year (to which the estimation algorithm was constrained) in BA for individuals participating in a wellness program. This observation suggests that BA is modifiable and suggests that a lower BA relative to CA may be a sign of healthy aging. Measures of metabolic health, inflammation, and toxin bioaccumulation were strong predictors of BA. BA estimation from deep phenotyping was seen to change in the direction expected for both positive and negative health conditions. We believe BA represents a general and interpretable "metric for wellness" that may aid in monitoring aging over time.

Keywords: Biological age; Chronological age; Deep phenotyping; Healthy aging; Precision medicine.

Figure 1.

Scatter plots of Biological Age estimates using the Klemera–Doubal algorithm for each data type individually, and in aggregate (All Data Sources). Each point is one observation of an individual. The solid line in each plot is the ordinary least squares regressed line, and the dotted line is biological age = chronological age. The Clinical Lab plot contains estimates from two vendors: Labcorp (circle) and Quest (triangle).

Figure 2.

Forest plot of ΔAge estimates and 95% confidence intervals associated with the 40 most common health conditions, plus ever smoking, current smoking, and obesity. Each condition or behavior was modeled individually, with ΔAge as the dependent variable, the health condition/behavior as the independent variable, and further adjustment for chronological age (CA) and obesity (body mass index > 30) in Generalized Estimating Equation models clustered by client ID with an exchangeable correlation matrix to account for multiple observations from individual clients. The obesity outcome was adjusted for CA only. Biological age (BA) estimates for each data type are shown. The blue dotted line at 0 indicates no difference between BA and CA; point estimates to the right of the blue line indicate higher BA than CA associated with the health condition/behavior (eg, based on the all-data-type BA estimate, individuals with type 2 diabetes have BAs that are, on average, 6.4 years greater [95% CI: 4.6, 8.2] than their CAs, after adjustment for CA and obesity). ***p < .0003 (Bonferroni threshold); **p < .005; *p < .05. GERD = gastroesophageal reflux disease; IBS = irritable bowel syndrome; PTSD = Post-traumatic stress disorder.

Figure 3.

Shown are the strongest 20 analytes per data type by average effect for males (red) and females (blue). The y-axis demonstrates the effect in years per SD of the analyte, that is, 1 SD greater than the mean value for HbA1c in the Labcorp clinical labs would result in a roughly 4-year increase in biological age, all other values held constant.