ArticleAge and ageing2025
Sex- and system-specific analysis of blood-based biomarkers and frailty in older adults-the Activity and Function of the Elderly study.
Article in Age and ageing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Frailty before chronic disease: multisystem physiological differences in middle-aged adults from the UK Biobank.GeroScience · 2026Article
- Integrating frailty and cumulative lipid burden for stroke risk stratification: a machine learning-guided Athero-Frailty Score from the CHARLS cohort.Lipids in health and disease · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUnderlying pathophysiological mechanisms behind frailty are not fully understood.
objectiveTo evaluate the sex- and system-specific association of 35 blood-based biomarkers with frailty.
methodBaseline data from the population-based Activity and Function of the Elderly study (≥65 years), collected between March 2009 and April 2010, was used. Frailty was defined through a frailty index (FI). Biomarkers associations with frailty were analysed sex-, and organ-/system-specific. Frailty models were built using backwards selection in Generalized Linear Models (GLM) for continuous and logistic regression (LR) for dichotomized FI (FI ≥0·2 frail), adjusting for age, education, smoking and alcohol intake, with further adjustment for medications when needed. Residual mean squared error (RMSE), area under the curve (AUC), sensitivity, specificity, positive and negative predictive value (PPV, NPV) were estimated.
resultsAmong 1180 participants (57·9% men) GLMs showed a good fit of the data with gamma-glutamyl transferase, high-density lipoprotein-, low-density lipoprotein-cholesterol and growth differentiation factor 15 overall, and sex-specific transferrin, alanine transaminase, testosterone, vitamin D, lactate dehydrogenase, NT-proBNP in men (RMSE 0·064, specificity 0·96, NPV 0·86), and leucocytes, cystatin C, DHEA, fT3, hs-cTnT in women (RSME 0·074, specificity 0·94, NPV 0·87). LR models included less biomarkers with similar properties (AUC 0·83, specificity 0·80, 0·93 NPV in men; AUC 0·85, specificity 0·72, NPV 0·94 in women).
conclusionObtained models provide insight into sex-specific differences related to frailty. Surprisingly, inflammation does not play an important role when taking all other biomarkers into account. Obtained models offer a good framework for the identification of blood-based biomarkers to be used in frailty prediction models.
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