The plasma metabolome as a predictor of biological aging in humans

Abstract

Chronological age is an important predictor of morbidity and mortality; however, it is unable to account for heterogeneity in the decline of physiological function and health with advancing age. Several attempts have been made to instead define a "biological age" using multiple physiological parameters in order to account for variation in the trajectory of human aging; however, these methods require technical expertise and are likely too time-intensive and costly to be implemented into clinical practice. Accordingly, we sought to develop a metabolomic signature of biological aging that could predict changes in physiological function with the convenience of a blood sample. A weighted model of biological age was generated based on multiple clinical and physiological measures in a cohort of healthy adults and was then applied to a group of healthy older adults who were tracked longitudinally over a 5-10-year timeframe. Plasma metabolomic signatures were identified that were associated with biological age, including some that could predict whether individuals would age at a faster or slower rate. Metabolites most associated with the rate of biological aging included amino acid, fatty acid, acylcarnitine, sphingolipid, and nucleotide metabolites. These results not only have clinical implications by providing a simple blood-based assay of biological aging, but also provide insight into the molecular mechanisms underlying human healthspan.

Keywords: Biological aging, Metabolomics, Healthspan, Precision medicine.

Fig. 1

Biological age and chronological age are significantly correlated in our training cohort of 604 healthy adults (R² = 0.68, P value < 0.0001)

Fig. 2

ageDiff was calculated, and although most individual’s biological age is within 5 years of their chronological age, many demonstrate greater differences between biological age and chronological age

Fig. 3

a Change in biological age, calculated from clinical and physiological measures, in our longitudinal cohort. Although the change in biological age is significant (P < 0.01), the trajectories of aging are highly variable. b Significant relation between ageDiff at baseline and follow-up (P < 0.001)

Fig. 4

a Rate of biological aging for each individual in the longitudinal cohort. A ratio smaller than one indicates slower aging, while a ratio greater than one indicates faster aging. b Heatmap of the change in abundance of 28 metabolites significantly associated with rate of biological aging. Individuals are aligned in columns, metabolites in rows. (See Table S2 for list of metabolites and associated pathways).