Ma Ling-Zhi, Ge Yi-Jun, Zhang Yi, Cui Xi-Han, Feng Jian-Feng, Cheng Wei, Tan Lan, Yu Jin-Tai
Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, 266071, China.
Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, 12Th Wulumuqi Zhong Road, Shanghai, 200040, China.
Geroscience. 2025 Apr;47(2):2067-2078. doi: 10.1007/s11357-024-01395-7. Epub 2024 Oct 23.
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.
全面了解和识别导致虚弱的潜在决定因素,对于制定旨在预防或减轻虚弱的针对性干预措施至关重要。我们在469301名英国生物银行参与者队列中调查了虚弱的潜在决定因素。使用Fried指数对虚弱进行评估,该指数包括握力、步速、身体活动水平、非故意体重减轻和自我报告的疲惫程度的测量。首先进行了包括276个因素的全基因组关联研究(EWAS)。将EWAS中与虚弱相关的因素进一步合并,以生成不同领域的综合评分,并在多变量逻辑模型中评估与虚弱的联合关联。通过人群归因分数(PAFs)评估消除风险领域不利特征时对虚弱的潜在影响。共有21020名(4.4%)参与者被认为虚弱,192183名(41.0%)处于虚弱前期,256098名(54.6%)健康。规模最大的EWAS在十个领域中确定了90个可改变的虚弱因素,每个因素都独立增加了虚弱风险。在这些因素中,67个有可能对健康产生负面影响,而23个已被发现具有保护作用。当将所有不利特征转变为中等和有利特征时,潜在可改变的虚弱风险因素的总体调整PAF为85.9%,如果将所有因素转变为有利三分位数,则该比例增至86.6%。健康和病史、心理社会因素以及身体活动是最主要的因素,分别占11.9%、10.4%和10.1%。本研究为制定旨在预防虚弱的人群水平策略提供了有价值的见解。