Background: Frailty is characterised by reduced physiological reserve and associated with ageing. However, younger individuals with chronic diseases like kidney failure (KF) also develop frailty. Whether disease-related and age-related frailty represent distinct conditions clinically and pathophysiologically remains unknown.
Methods: This cross-sectional analysis used baseline data from the Reversing Frailty in Transplantation (ReFIT) study, comprising 61 KF patients (36 transplant recipients, 25 waitlisted dialysis patients) and 50 community-dwelling older adults. Demographic, clinical, and frailty index data were analysed using regression modelling and machine learning (Random Forest, Regularised Discriminant Analysis), identifying distinguishing features between disease-related and age-related frailty.
Results: Despite similar FI scores (KF: 0.23±0.08 vs older adults: 0.23±0.09, p=0.8), groups demonstrated distinct compositional profiles. Older adults exhibited deficits in balance (54% vs 84% intact, p=0.001), continence (58% vs 97% intact, p<0.001), and instrumental activities of daily living (46% vs 95% independent, p<0.001). KF patients had greater medical complexity, with 61% having hyperpolypharmacy versus 18% in older adults (p<0.001). Regression identified condition-specific pathways: in KF, frailty was best explained by medical burden and disease-related factors (data-driven model: comorbidities, sleep, and four additional issues; adjusted R²=0.81, p<0.001), whereas functional impairments predominated in older adults (functional model: balance, elimination, strength, mobility, and nutrition; adjusted R²=0.78, p<0.001). Machine learning achieved 98.2% cross-validated accuracy (κ=0.96), identifying chronic kidney disease, polypharmacy, mobility, and sleep disturbance as discriminators.
Conclusions: Disease-related and age-related frailty appear to represent distinct conditions with differing mechanisms and deficit profiles. Aggregate frailty scores may obscure clinically meaningful heterogeneity, supporting the need for condition-specific assessment.