Abstract
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 = 5.8×10−12, 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.
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.
Plain Language Summary
Chronological age does not fully reflect a person’s health or resilience. We used data from Canadians aged 45–85 to develop a biomarker of biological age based on metabolites—small molecules in the blood that reflect body processes. We focused on 178 health-related metabolites and identified “sweet spots,” or optimal levels, for 74 of them. Our model, the Sweet Spot Clock, was strongly related to mortality and age-related diseases. These findings held up in an independent group of Canadians aged 85 and older. By focusing on health status and accounting for non-linear patterns, our approach offers a reproducible and interpretable way to measure biological aging and understand why some people age more healthfully than others.
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Data availability
Data are available from the Canadian Longitudinal Study on Aging (www.clsa-elcv.ca) for researchers who meet the criteria for access to de-identified CLSA data. The Super Seniors dataset is stored at Genome Sciences Center, BC Cancer, Canada. For inquiries about this dataset, or for requests to collaborate on projects involving this dataset, please contact Dr. Angela Brooks-Wilson at abrooks-wilson@bcgsc.ca. The list of metabolites used for analysis is provided in Supplementary Data 1. Source data for recreating figures are available at https://zenodo.org/records/1759410665 and in Supplementary Data Files 2–7: The source data for Fig. 2a, b is in Supplementary Data 2, the source data for Fig. 2c is in Supplementary Data 3, the source data for Fig. 3 is in Supplementary Data 4, the source data for Fig. 4d is in Supplementary Data 6, and the source data for Fig. 4e is in Supplementary Data 7. The list of predictors included in the Sweet Spot Clock model is in Supplementary Data 5.
Code availability
All code used in this study is available at https://zenodo.org/records/1759410665. The data analysis was performed in R v. 4.4.2.
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Acknowledgements
This research is funded by the Canadian Institutes of Health Research (grant # PAD 179760) to A Brooks-Wilson and LT Elliott. LT Elliott’s research is supported by a Michael Smith Health Research BC Scholar Award. This research was made possible using the data collected by the Canadian Longitudinal Study on Aging (CLSA). Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference: LSA 94473 and the Canada Foundation for Innovation, as well as the following provinces: Newfoundland, Nova Scotia, Quebec, Ontario, Manitoba, Alberta, and British Columbia. This research has been conducted using the CLSA Baseline Comprehensive Dataset version 7.0, Follow-up 1 Comprehensive version 4.0, Follow-up 2 Comprehensive version 1.0, Genomic data version 3.0, Epigenetics version 1.1, Metabolomics version 2.0 under Application Number 2206033. The CLSA is led by Drs. Parminder Raina, Christina Wolfson, and Susan Kirkland. The AB SCREEN™ II assessment tool is owned by Dr. Heather Keller. Use of the AB SCREEN™ II assessment tool was made under license from the University of Guelph. The opinions expressed in this manuscript are the authors’ own and do not reflect the views of the Canadian Longitudinal Study on Aging.
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L.T.E. and A.B.W. developed and directed the project. O.V. developed and performed the modeling pipeline and wrote the original draft. S.L. processed and aliquoted study samples and curated data. J.M., X.S., and K.R. provided critical feedback on the study and reviewed/edited the manuscript. O.V. and L.T.E. prepared the manuscript with input from all authors, and all authors approved the final manuscript.
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Vishnyakova, O., Min, J., Leach, S. et al. Metabolomic sweet spot clock predicts mortality and age-related diseases in the Canadian Longitudinal Study on Aging. Commun Med (2026). https://doi.org/10.1038/s43856-026-01375-2
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DOI: https://doi.org/10.1038/s43856-026-01375-2
