000162525 001__ 162525
000162525 005__ 20251017144653.0
000162525 0247_ $$2doi$$a10.1038/s41598-025-14580-1
000162525 0248_ $$2sideral$$a145087
000162525 037__ $$aART-2025-145087
000162525 041__ $$aeng
000162525 100__ $$aGonzález-Martos, Raquel
000162525 245__ $$aUnsupervised clustering of biochemical markers reveals health profiles associated with function and survival in active aging
000162525 260__ $$c2025
000162525 5060_ $$aAccess copy available to the general public$$fUnrestricted
000162525 5203_ $$aThis study explores the relationships between biochemical phenotypes identified using machine learning, and key health outcomes, including body composition, physical function, and mortality risk. Data were collected from 536 physically active Spanish participants aged over 65 years (76.5% women) enrolled in the EXERNET cohort (2017–2018), with a 6-year mortality follow-up. Principal component analysis, and hierarchical and k-means clustering was used to identify distinct biochemical profiles. Associations between clusters and health outcomes were assessed using analysis of covariance and Cox proportional hazards models. Three distinct clusters emerged: ‘Healthy’, characterized by biochemical values within the normal range and used as the reference group; ‘Metabolic’, marked by dysregulated metabolic parameters; and ‘Hepatic’, which exhibited impaired liver function markers. Notably, all clusters showed subclinical levels of dysfunction. The ‘Healthy Cluster’ demonstrated the highest levels of organized physical activity (90%, p < 0.001), whereas the ‘Metabolic Cluster’ showed poorer body composition and reduced physical performance. Both the ‘Metabolic’ and ‘Hepatic’ clusters demonstrated a higher mortality risk, as confirmed through Cox regression analyses. Adjusted hazard ratios were significantly elevated when considering physical activity and adiposity, with values of 3.45 and 3.71 for the ‘Metabolic Cluster’, and 3.01 and 3.85 for the ‘Hepatic Cluster’ (p < 0.05). This study underscores the strong link between metabolic health, physical activity, body composition and 6-years mortality risk in older adults. Machine learning techniques for identifying phenotypic clusters offers a promising tool for early detection and targeted interventions to improve aging outcomes.
000162525 536__ $$9info:eu-repo/grantAgreement/ES/AEI/RED2022-134800-T$$9info:eu-repo/grantAgreement/EC/FP7/305483/EU/Utility of omic-based biomarkers in characterizing older individuals at risk for frailty, its progression to disability and general consequences to health and well-being - The FRAILOMIC Initiative/FRAILOMIC$$9info:eu-repo/grantAgreement/ES/MECD/EXERNET-DEP2005-00046$$9info:eu-repo/grantAgreement/ES/MECD/09-UPB-19$$9info:eu-repo/grantAgreement/ES/MECD/27-UPB-21$$9info:eu-repo/grantAgreement/ES/MECD/29-UPB-22$$9info:eu-repo/grantAgreement/ES/MECD/45-UPB-20$$9info:eu-repo/grantAgreement/ES/MECD/5-UPB-23
000162525 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000162525 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000162525 700__ $$aGaleano, Javier
000162525 700__ $$aRamirez-Castillejo, Carmen
000162525 700__ $$aGusi, Narcis
000162525 700__ $$aGesteiro, Eva
000162525 700__ $$0(orcid)0000-0002-4303-4097$$aVicente-Rodriguez, German$$uUniversidad de Zaragoza
000162525 700__ $$0(orcid)0000-0002-2854-6684$$aAra, Ignacio
000162525 700__ $$aGuadalupe-Grau, Amelia
000162525 7102_ $$11006$$2245$$aUniversidad de Zaragoza$$bDpto. Fisiatría y Enfermería$$cÁrea Educación Física y Depor.
000162525 773__ $$g15, 1 (2025), [12 pp.]$$pSci. rep. (Nat. Publ. Group)$$tScientific reports (Nature Publishing Group)$$x2045-2322
000162525 8564_ $$s1885238$$uhttps://zaguan.unizar.es/record/162525/files/texto_completo.pdf$$yVersión publicada
000162525 8564_ $$s2496835$$uhttps://zaguan.unizar.es/record/162525/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000162525 909CO $$ooai:zaguan.unizar.es:162525$$particulos$$pdriver
000162525 951__ $$a2025-10-17-14:37:22
000162525 980__ $$aARTICLE