Background: Frailty, a state of increased vulnerability to adverse health outcomes, has garnered significant attention in research and clinical practice. Existing constructs aggregate clinical features or health deficits into a single score. While simple and interpretable, this approach may overlook the complexity of frailty and not capture the full range of variation between individuals. Methods: Exploratory factor analysis was used to infer latent dimensions of a frailty index constructed using survey data from the English Longitudinal Study of Ageing (ELSA), wave 9. The dataset included 58 self-reported health deficits in a representative sample of community-dwelling adults aged 65+ (N = 4971). Deficits encompassed chronic disease, general health status, mobility, independence with activities of daily living, psychological wellbeing, memory and cognition. Multiple linear regression examined associations with CASP-19 quality of life scores. Results: Factor analysis revealed four frailty subdimensions. Based on the component deficits with the highest loading values, these factors were labelled "Mobility Impairment and Physical Morbidity", "Difficulties in Daily Activities", "Mental Health" and "Disorientation in Time". The four subdimensions were a better predictor of quality of life than frailty index scores. Conclusions: Distinct subdimensions of frailty can be identified from standard index scores. A decomposed approach to understanding frailty has potential to provide a more nuanced understanding of an individual's state of health across multiple deficits.

翻译：背景：衰弱，一种对不良健康结果易感性增加的状态，已在研究和临床实践中受到广泛关注。现有构造将临床特征或健康缺陷聚合为单一分数。尽管这种方法简单且可解释，但可能忽略衰弱的复杂性，无法完全捕捉个体间的变异范围。方法：采用探索性因子分析，从英国老龄化纵向研究第9波调查数据构建的衰弱指数中推断潜在维度。数据集包含58个自报健康缺陷，样本为代表性社区居住的65岁及以上成年人（N=4971）。缺陷涵盖慢性病、总体健康状态、活动能力、日常生活活动独立性、心理健康、记忆和认知。多元线性回归检验与CASP-19生活质量评分的关联。结果：因子分析揭示四个衰弱子维度。根据载荷值最高的组成缺陷，这些因子被标记为“活动能力受损与躯体疾病”、“日常活动困难”、“心理健康”和“时间定向障碍”。四个子维度比衰弱指数分数更能预测生活质量。结论：可从标准指数分数中识别出不同的衰弱子维度。分解式理解衰弱的方法有望提供对个体跨多种缺陷健康状态的更细致认识。