Identifying modifiable factors and their joint effect on frailty: a large population-based prospective cohort study

Abstract

A thorough understanding and identification of potential determinants leading to frailty are imperative for the development of targeted interventions aimed at its prevention or mitigation. We investigated the potential determinants of frailty in a cohort of 469,301 UK Biobank participants. The evaluation of frailty was performed using the Fried index, which encompasses measurements of handgrip strength, gait speed, levels of physical activity, unintentional weight loss, and self-reported exhaustion. EWAS including 276 factors were first conducted. Factors associated with frailty in EWAS were further combined to generate composite scores for different domains, and joint associations with frailty were evaluated in a multivariate logistic model. The potential impact on frailty when eliminating unfavorable profiles of risk domains was evaluated by PAFs. A total of 21,020 (4.4%) participants were considered frailty, 192,183 (41.0%) pre-frailty, and 256,098 (54.6%) robust. The largest EWAS identified 90 modifiable factors for frailty across ten domains, each of which independently increased the risk of frailty. Among these factors, 67 have the potential to negatively impact health, while 23 have been found to have a protective effect. When shifting all unfavorable profiles to intermediate and favorable ones, overall adjusted PAF for potentially modifiable frailty risk factors was 85.9%, which increases to 86.6% if all factors are transformed into favorable tertiles. Health and medical history, psychosocial factors, and physical activity were the most significant contributors, accounting for 11.9%, 10.4%, and 10.1% respectively. This study offers valuable insights for developing population-level strategies aimed at preventing frailty.

Keywords: Frailty; Modifiable risk factors; PAF; Unmodifiable risk factors.

Fig. 1

Overview of modifiable risk factors analytic design. Analytical procedure to identify modifiable risk factors associated with frailty in the UK Biobank. Abbreviations: PAF, population attributable fraction

Fig. 2

The figure displays an association plot illustrating the relationship between modifiable risk factors and the incidence of frailty, with the x-axis categorized by conceptual domains and the y-axis depicting the statistical significance as − log10 of the p-value. A horizontal line is depicted to indicate the significance threshold corrected for multiple testing. To enhance readability, a subset of the most significant factors is annotated. The full set of association results is provided in the supplemental materials

Fig. 3

Summary heat map for significant factors in EWAS analysis across the full sample and subgroups. The color of cells indicates the effect sizes (OR) between each risk factor and incident risk. Asterisks in cells represent significant associations after correction for multiple testing (Bonferroni-corrected, P < 1.443e-5

Fig. 4

Associations between ten domains and frailty. The favorable profile was set as reference in each domain. The associations were estimated applying Logistic model including all ten domains mutually adjusted and with adjustment of age, sex, and assessment center, education, ethnicity, Townsend Deprivation Index, smoking status, and alcohol use. Robust and pre-frailty individuals were set as the control group. Abbreviations: OR, odds ratio