METHODS: Our analysis included data from 45,208 UK biobank participants who had a complete record of the ten variables included in the analysis. Factor adequacy was tested using Kaiser-Meyer-Olkin, Barthelt\'s, and the determinant of matrix tests, and the number of factors was determined by the parallel analysis method. Exploratory and confirmatory factor analyses were employed to determine the structure and dimensionality of indicators. Finally, the intrinsic capacity score was generated, and its construct and predictive validities as well as reliability were assessed.
RESULTS: The factor analysis identified a multidimensional construct comprising one general factor (intrinsic capacity) and five specific factors (locomotor, vitality, cognitive, psychological, and sensory). The bifactor structure showed a better fit (comparative fit index = 0.995, Tucker Lewis index = 0.976, root mean square error of approximation = 0.025, root mean square residual = 0.009) than the conventional five-factor structure. The intrinsic capacity score generated using the bifactor confirmatory factor analysis has good construct validity, as demonstrated by an inverse association with age (lower intrinsic capacity in older age; (β) =-0.035 (95%CI: -0.036, -0.034)), frailty (lower intrinsic capacity score in prefrail participants, β = -0.104 (95%CI: (-0.114, -0.094)) and frail participants, β = -0.227 (95%CI: -0.267, -0.186) than robust participants), and comorbidity (a lower intrinsic capacity score associated with increased Charlson\'s comorbidity index, β =-0.019 (95%CI: -0.022, -0.015)). The intrinsic capacity score also predicted comorbidity (a one-unit increase in baseline intrinsic capacity score led to a lower Charlson\'s comorbidity index, β = 0.147 (95%CI: -0.173, -0.121)) and mortality (a one-unit increase in baseline intrinsic capacity score led to 25 % lower risk of death, odds ratio = 0.75(95%CI: 0.663, 0.848)).
CONCLUSIONS: The bifactor structure showed a better fit in all goodness of fit tests. The intrinsic capacity construct has strong structural, construct, and predictive validities and is a promising tool for monitoring aging trajectories.
方法:我们的分析包括来自45,208名英国生物库参与者的数据,这些参与者对分析中包含的十个变量进行了完整记录。使用Kaiser-Meyer-Olkin测试了因子充分性,Barthelt\'s,和矩阵检验的行列式,并采用平行分析法确定因素数。采用探索性和验证性因素分析来确定指标的结构和维度。最后,生成了内在容量分数,并评估了其结构和预测效价以及可靠性。
结果:因子分析确定了一个多维结构,包括一个一般因素(内在能力)和五个特定因素(运动,活力,认知,心理,和感官)。双因素结构比常规五因素结构具有更好的拟合效果(比较拟合指数=0.995,塔克·刘易斯指数=0.976,近似均方根误差=0.025,均方根残差=0.009)。使用双因素验证性因子分析生成的内在能力得分具有良好的结构效度,如与年龄成反比所证明的(老年内在能力较低;(β)=-0.035(95CI:-0.036,-0.034)),虚弱(虚弱前参与者的内在能力得分较低,β=-0.104(95CI:(-0.114,-0.094))和虚弱的参与者,β=-0.227(95CI:-0.267,-0.186)比稳健的参与者),和合并症(与Charlson的合并症指数增加相关的较低的固有能力评分,β=-0.019(95CI:-0.022,-0.015))。内在容量评分还预测了合并症(基线内在容量评分增加一个单位会导致Charlson的合并症指数降低,β=0.147(95CI:-0.173,-0.121))和死亡率(基线内在能力评分增加一个单位导致死亡风险降低25%,比值比=0.75(95CI:0.663,0.848))。
结论:双因素结构在所有拟合优度测试中显示出更好的拟合。内在能力结构具有很强的结构性,construct,和预测效价,是监测衰老轨迹的有前途的工具。