Latent biochemical phenotypes delineate divergent health trajectories in older adults

Ageing heterogeneity hampers prevention and care. We used routine biochemical panels and unsupervised learning to identify latent phenotypes in community-dwelling older adults. In 1491 participants from the Toledo Study for Healthy Ageing (TSHA) with ~10-11 years of follow-up, 39 blood biomarkers were dimension-reduced and clustered, yielding three phenotypes: Healthy, Metabolic (subclinical dysmetabolism), and Haematological (low erythroid/renal profile). Phenotypes differed in functional capacity, frailty, and independence at baseline (all p < 0.05 after age/sex adjustment) and predicted long-term mortality (Metabolic women HR = 1.49, p = 0.016). Sex-specific analyses revealed distinct disease-trajectory patterns (e.g., hypertension in Metabolic women HR = 1.30, p = 0.005; thrombosis in Haematological men HR = 7.20, p = 0.018; syncope in Haematological women HR = 1.88, p = 0.009). Findings are partially replicated in a cohort of physically active older adults (EXERNET), supporting the generalizability of the Metabolic phenotype. Standard laboratory data, integrated through machine learning, capture ageing-relevant biology and stratify future risk without specialised assays, enabling low-cost, scalable precision prevention.

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Publication Details

Journal
npj Aging
Published
2026-05-29
DOI
https://doi.org/10.1038/s41514-026-00415-4
Primary Topic
Health, Environment, Cognitive Aging
Type
article
Field-Weighted Citation Impact
0.00

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article

Latent biochemical phenotypes delineate divergent health trajectories in older adults

Leocadio Rodríguez‐Mañas, Carmen Ramírez‐Castillejo, Francisco J. García‐García, Raquel González‐Martos et al.
npj Aging
Health, Environment, Cognitive Aging
article

Latent biochemical phenotypes delineate divergent health trajectories in older adults

Leocadio Rodríguez‐Mañas, Carmen Ramírez‐Castillejo, Francisco J. García‐García, Raquel González‐Martos, Amelia Guadalupe-Grau, Ignacio Ara, Luis M. Alegre, Javier Galeano, Irene Rodríguez-Gómez
article en

Abstract

Ageing heterogeneity hampers prevention and care. We used routine biochemical panels and unsupervised learning to identify latent phenotypes in community-dwelling older adults. In 1491 participants from the Toledo Study for Healthy Ageing (TSHA) with ~10-11 years of follow-up, 39 blood biomarkers were dimension-reduced and clustered, yielding three phenotypes: Healthy, Metabolic (subclinical dysmetabolism), and Haematological (low erythroid/renal profile). Phenotypes differed in functional capacity, frailty, and independence at baseline (all p < 0.05 after age/sex adjustment) and predicted long-term mortality (Metabolic women HR = 1.49, p = 0.016). Sex-specific analyses revealed distinct disease-trajectory patterns (e.g., hypertension in Metabolic women HR = 1.30, p = 0.005; thrombosis in Haematological men HR = 7.20, p = 0.018; syncope in Haematological women HR = 1.88, p = 0.009). Findings are partially replicated in a cohort of physically active older adults (EXERNET), supporting the generalizability of the Metabolic phenotype. Standard laboratory data, integrated through machine learning, capture ageing-relevant biology and stratify future risk without specialised assays, enabling low-cost, scalable precision prevention.

npj Aging
Instituto de Salud Carlos III (ES), ProMedica Toledo Hospital (US), Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (ES), Hospital Universitario de Getafe (ES), Light Prescriptions Innovators (Spain) (ES), Servicio de Salud de Castilla La Mancha (ES), Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (ES), Complejo Hospitalario Universitario de Toledo (ES), European Telecommunications Standards Institute (FR), University of Castilla-La Mancha (ES), Universidad Politécnica de Madrid (ES)
Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable, Comunidad de Madrid, European Commission, Ministerio de Economía y Competitividad, Consejería de Sanidad de Castilla-La Mancha, Junta de Comunidades de Castilla-La Mancha, Instituto de Salud Carlos III
Openalex Percentile: Top 7%
Health, Environment, Cognitive Aging
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