BACKGROUND: Chronological age does not capture individual health or resilience. Advances in metabolomics have enabled development of molecular aging biomarkers that capture deviations between biological and chronological age, highlighting how genetics, environment, and lifestyle shape biological aging. Despite their promise, metabolomic biomarkers face challenges such as interpretability, non-linearity, and reproducibility.
METHODS: We have developed a metabolomic predictor of biological age based on untargeted metabolomic profiling of individuals aged 45-85 years from the Canadian Longitudinal Study on Aging. To enhance interpretability, we first identified metabolites related to health based on variance heterogeneity. For metabolites with identifiable optimal levels, or "sweet spots", we modeled non-linearity using deviations from these values. A penalized regression model was trained on the Frailty Index using sweet spot deviations as predictors.
RESULTS: Here we show that the Sweet Spot Clock built on 178 health-related metabolites is strongly associated with all-cause mortality (HR = 1.08, p = 5.8×10, C-index=0.841) and age-related diseases. The biomarker outperforms models trained on chronological age and those using raw metabolite levels, underscoring the importance of modeling non-linearity. It remains predictive after adjusting for age, sex, lifestyle and socioeconomic factors, though its added value over standard health and demographic measures is modest. The model generalizes to an independent cohort of individuals aged 85 years and older. -12
CONCLUSIONS: The Sweet Spot Clock provides a reproducible and interpretable measure of biological age. By modeling deviations from optimal metabolite levels and training on health status rather than age, it offers a tool for understanding aging heterogeneity and identifying individuals at risk of health decline.
Longevity & Aging