Study: Blood Metabolite Clocks May Predict Biological Age and Risk
Blood plasma analysis of 18,476 individuals aged 1 to 96 reveals that metabolic patterns can track biological aging and mortality risk across the human lifespan, according to a longitudinal study published in Science Translational Medicine.
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Key Clinical Takeaways:
- Researchers at six centers in Shanghai analyzed plasma samples using nano-assisted laser desorption/ionization mass spectrometry to build metabolomic aging metrics.
- Participants with accelerated metabolomic age showed an increased likelihood of developing chronic conditions such as diabetes, hypertension, and fatty liver disease.
Metabolomic Profiling Across 18,476 Individuals in the Shanghai Pudong Cohort
The research team examined metabolic patterns across nearly a century of life to investigate their relationship with health and longevity. As physiological functions gradually decline with advancing age, biological age becomes increasingly decoupled from chronological age. To capture these shifts, investigators utilized plasma samples collected from participants in the Shanghai Pudong Cohort. The study initially incorporated 5,252 healthy individuals with a mean age of 55 years to develop a stacking multialgorithm ensemble model. A healthy validation group of 5,456 participants with a mean age of 55 years tested the model, while a separate disease cohort of 7,768 participants with a mean age of 68 years underwent further examination.
Baseline assessments for the cohort took place in 2019. Chronic disease status was reassessed in 2023, when 3,745 healthy participants and 4,658 disease-cohort participants completed repeat surveys and updated evaluations. Mortality tracking for the cohort continued through January 2025. During the 2023 follow-up, 1,500 participants underwent repeat plasma metabolomic profiling.
Researchers Calculate Metabolomic Age Acceleration and Mortality Deviations
In the healthy cohort, investigators estimated metabolomic age to calculate metabolomic age acceleration, a metric indicating the extent to which a person’s metabolomic age differs from their actual age. For participants with pre-existing health conditions, researchers deployed a model to develop an age deviation metric. This secondary measure tracked how metabolite-based mortality risk differed from what clinicians would expect for a specific chronological age.
A panel of 56 age-related metabolites retained most of the age-predictive information originally provided by a broader set of 232 metabolite features. Higher metabolomic age acceleration remained associated with greater mortality risk even after researchers accounted for established health measures. Participants exhibiting accelerated metabolomic age had a higher risk of death from any cause and were more likely to develop chronic illnesses.
Diet and Exercise Influence Metabolomic Age Acceleration
Higher metabolomic age acceleration values correlated with specific lifestyle, environmental, and family-history factors. These included greater consumption of organ meats, sugar, processed meat, seafood, and fried foods. Conversely, mixed grains and regular exercise linked to lower values.
Repeated analyses during the follow-up period associated increased physical activity and reduced fried-food intake with more favorable changes in metabolomic age acceleration. Among participants with pre-existing hypertension, those who achieved blood pressure readings below 140/90 mmHg by 2023 showed more favorable changes in the mortality-linked age deviation metric compared to individuals with uncontrolled blood pressure. However, researchers emphasize that these observational findings do not establish direct causality between individual foods or lifestyle choices and biological aging.
Specific Metabolites and Biological Pathways Associate with Aging
The metabolomic alterations implicated specific biological pathways associated with threonine, serine, glycine, butanoate, cysteine, and methionine metabolism. Among individual compounds, 3-methylindole, creatinine, and sebacic acid showed positive associations with aging. Leucine levels increased after midlife and associated with greater metabolomic age acceleration. In contrast, L-asparagine, glyceric acid, and the collagen-related metabolite 4-hydroxyproline demonstrated inverse associations with age.
Statistical evaluations identified 3-methylindole as having the most significant positive association with mortality risk, while 4-hydroxyproline demonstrated the most significant inverse association. In the disease cohort, the mortality-linked age deviation metric registered higher in men than in women. Among participants with comorbidities, age deviation correlated consistently with routine blood biomarkers. Elevated creatinine, uric acid, and urea nitrogen levels linked to greater age deviations, whereas hemoglobin, hematocrit, and erythrocyte counts showed opposite associations.
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