Aging biomarkers are essential for quantifying molecular, physiological, or functional features that vary among individuals as they age. These biomarkers can enhance the prediction of health outcomes beyond mere chronological age. Recent advancements in plasma proteomics have allowed researchers to estimate the biological ages of various human organs. However, there has been a lack of systematic benchmarking against established ageing biomarkers regarding their association with mortality risk.
The study conducted by Pyrgioti et al. aimed to address this gap by pursuing two primary objectives. First, the researchers benchmarked proteomic organ clocks against multimodal ageing biomarkers to assess their associations with 16-year all-cause mortality in the Lothian Birth Cohort 1936 (LBC1936). This cohort included 861 participants, of whom 444 had died by the end of the study period. The biomarkers evaluated included the epigenetic clock GrimAge2, telomere length, physical function measures (such as grip strength, walk time, and respiratory function), neuroimaging data, and cognitive assessments.
Among the proteomic clocks analyzed, accelerated ageing of the liver, immune system, and heart were found to be significantly associated with increased mortality risk. Specifically, the hazard ratios for these associations were 1.43 for liver ageing, 1.42 for immune ageing, and 1.38 for heart ageing. Notably, other factors such as older epigenetic age, reduced brain volume, diminished respiratory function, and poorer cognitive performance exhibited even stronger associations with mortality, with hazard ratios ranging from 1.44 to 1.62.
The second aim of the study involved survival analyses of 9703 plasma protein targets, leading to the identification of 368 proteins associated with mortality risk. The analysis revealed that high-risk proteins were predominantly enriched for immune functions, while low-risk proteins were associated with genomic stability and cellular maintenance. Among the leading proteomic candidates identified were GDF15, WFDC2, and TIMP1, with hazard ratios of 1.56, 1.47, and 1.45, respectively.
What this study does not show
This study does not establish a causal relationship between the identified biomarkers and mortality risk, nor does it provide direct evidence of how interventions based on these biomarkers may affect health outcomes.
Evidence scorecard
- Analytic validity: Established
- Clinical validity: Partial
- Clinical utility: Not established
The evidence is based on primary research involving human participants.