Age Ageing. 2026 May 4;55(5):afag135. doi: 10.1093/ageing/afag135.
ABSTRACT
BACKGROUND: Global population ageing is progressing at an unprecedented rate. Early identification of subpopulations at risk of chronic diseases could mitigate deteriorating health and increasing health service use, while revealing key biological cues and therapeutic targets. We hypothesised that a multisystem biomarker signature could identify future chronic disease trajectories, multimorbidity, functional decline and mortality.
METHODS: Eighteen blood biomarkers, representing key systems that become dysregulated with ageing, were assessed among n = 4961 participants aged 50+ years from Ireland. Probabilistic clustering classified participants as belonging to one of three biomarker signatures at baseline. Biomarker signatures were designated as low, medium and high risk based on relative levels of biomarker dysregulation within the signatures and previously described associations with chronic disease and mortality. These biomarker signatures were utilised to predict 4-year functional decline; 8-year cardiovascular disease (CVD), diabetes, frailty, disability and 12-year mortality. Results were validated in a US cohort (n = 3914).
RESULTS: Low (58.5%), medium (9.2%) and high-risk (32.3%) biomarker signatures were identified in the cohort from Ireland. The high-risk signature was associated with higher 12-year mortality (HR: 1.89, P < .001); higher 8-year incidence of CVD, diabetes, multimorbidity, frailty and disability (OR range: 1.46-2.49. P < .05); and lower 4-year physical function (P < .01). Findings were corroborated in the US cohort. We identified and tracked 6 disease classes over 8 years: healthy, arthritis, diabetes/angina, hypothyroid/osteoporosis/respiratory, vision/anxiety/CVD and multisystem. Associations between the high-risk biomarker signature and two of the five 8-year incident disease classes were observed, implicating dysregulated immune and cardiometabolic pathways.
CONCLUSIONS: This study provides evidence that biomarker signatures and profiling of disease patterns can be used to risk stratify and identify ageing subpopulations that would benefit most from targeted preventative or secondary intervention strategies.
PMID:42160766 | DOI:10.1093/ageing/afag135