Impact of frailty phenotype on chronic disease accumulation and healthy life years lost: A multi-state analysis of the UK Biobank, 2026, Xing et al

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Impact of frailty phenotype on chronic disease accumulation and healthy life years lost: A multi-state analysis of the UK Biobank

Xing, Xiaoming; Zhao, Cong; Li, Menglin; Huang, Jiaqin; Chen, Yu; Sun, Wenting; Yu, Yanqiao; Chen, Xuenan; Qiao, Linlin; Li, Yi; Yan, Xiaoguang; Li, Ye

Background
Frailty is a systemic vulnerability syndrome linked to adverse health outcomes, yet its role in shaping the dynamic trajectory from health through incident disease to multimorbidity remains poorly understood.

Methods
Among 276,696 disease-free UK Biobank participants (mean age 55.3 years; 53.5% female; median follow-up of over 13 years), we evaluated baseline frailty phenotype (robust, pre-frail, frail) as a predictor of 43 chronic conditions using Cox models and restricted mean survival time (RMST) analysis. A four-state Markov multistate model mapped transitions across healthy, incident disease, multimorbidity, and death. Multimorbidity clustering was performed via multiple correspondence analysis and K-means. Population attributable fractions (PAFs) quantified the frailty-attributable disease burden.

Results
Frailty was significantly associated with 42 of 43 conditions (97.7%). The frail group lost 2.72 years (95% CI, 2.64–2.81) of multimorbidity-free survival over 12 years. In multistate models, frailty was strongly associated with higher hazards of transition from healthy to incident disease (HR 1.77; 95% CI, 1.72–1.83) and from incident disease to multimorbidity (HR 1.73; 95% CI, 1.68–1.79), but showed attenuated associations with post-disease mortality.

Four stable multimorbidity phenotypes were identified. PAF analysis indicated that eliminating pre-frailty and frailty could theoretically prevent 9.85% of incident disease events and 9.40% of multimorbidity progression, with pre-frailty contributing the majority of the attributable burden (7.83%).

Conclusions
Baseline frailty was strongly associated with broad-spectrum chronic disease onset and accelerated multimorbidity accumulation. Systematic screening and early intervention targeting pre-frailty, particularly in middle-aged populations, holds substantial promise for delaying the multimorbidity trajectory.

Web | DOI | PDF | The Journal of nutrition, health and aging | Open Access
 
After multivariable adjustment, frailty was significantly associated with incident risk of 42 of 43 conditions (Fig. 1); only anorexia/bulimia did not reach significance. The largest effect sizes in the frail versus robust comparison were observed for chronic fatigue syndrome (HR 15.37; 95% CI, 12.15–19.45), multiple sclerosis (HR 8.92; 95% CI, 7.01–11.35), diabetes (HR 4.18; 95% CI, 3.97–4.41), depression (HR 3.87; 95% CI, 3.65–4.11), and heart failure (HR 2.93; 95% CI, 2.69–3.20). Among pre-frail participants, the strongest associations were observed for chronic fatigue syndrome (HR 2.76), multiple sclerosis (HR 2.25), and diabetes (HR 1.97).
Notably, the diseases conferring the highest relative risks (e.g., chronic fatigue syndrome, HR 15.37; multiple sclerosis, HR 8.92) did not coincide with those responsible for the greatest absolute survival loss (pain conditions, 1.60 years; hypertension, 1.58 years).
 
Frailty was operationalised using a modified Fried frailty phenotype encompassing five standardised dimensions [15]: (1) unintentional weight loss; (2) exhaustion; (3) low physical activity, defined as sex- and age-adjusted metabolic equivalent values from the International Physical Activity Questionnaire (IPAQ) below the lowest quintile; (4) slow walking speed; and (5) low grip strength, defined as the lowest quintile after stratification by sex and body mass index (BMI). Each fulfilled dimension scored one point (range 0–5). Participants were classified as robust (0 points), pre-frail (1–2 points), or frail (≥3 points). For dose–response analyses, frailty scores were modelled simultaneously as continuous and categorical variables.
I think this explains the correlation with chronic fatigue syndrome
 
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