Jenkins Natalie D, Hoogendijk Emiel O, Armstrong Joshua J, Lewis Nathan A, Ranson Janice M, Rijnhart Judith J M, Ahmed Tamer, Ghachem Ahmed, Mullin Donncha S, Ntanasi Eva, Welstead Miles, Auais Mohammad, Bennett David A, Bandinelli Stefania, Cesari Matteo, Ferrucci Luigi, French Simon D, Huisman Martijn, Llewellyn David J, Scarmeas Nikolaos, Piccinin Andrea M, Hofer Scott M, Muniz-Terrera Graciela
Edinburgh Dementia Prevention, University of Edinburgh, Edinburgh, UK.
Department of Epidemiology & Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC-Location VU University Medical Center, Amsterdam, The Netherlands.
Innov Aging. 2022 Jan 15;6(2):igab059. doi: 10.1093/geroni/igab059. eCollection 2022.
There is an urgent need to better understand frailty and its predisposing factors. Although numerous cross-sectional studies have identified various risk and protective factors of frailty, there is a limited understanding of longitudinal frailty progression. Furthermore, discrepancies in the methodologies of these studies hamper comparability of results. Here, we use a coordinated analytical approach in 5 independent cohorts to evaluate longitudinal trajectories of frailty and the effect of 3 previously identified critical risk factors: sex, age, and education.
We derived a frailty index (FI) for 5 cohorts based on the accumulation of deficits approach. Four linear and quadratic growth curve models were fit in each cohort independently. Models were adjusted for sex/gender, age, years of education, and a sex/gender-by-age interaction term.
Models describing linear progression of frailty best fit the data. Annual increases in FI ranged from 0.002 in the Invecchiare in Chianti cohort to 0.009 in the Longitudinal Aging Study Amsterdam (LASA). Women had consistently higher levels of frailty than men in all cohorts, ranging from an increase in the mean FI in women from 0.014 in the Health and Retirement Study cohort to 0.046 in the LASA cohort. However, the associations between sex/gender and rate of frailty progression were mixed. There was significant heterogeneity in within-person trajectories of frailty about the mean curves.
Our findings of linear longitudinal increases in frailty highlight important avenues for future research. Specifically, we encourage further research to identify potential effect modifiers or groups that would benefit from targeted or personalized interventions.
迫切需要更好地理解衰弱及其诱发因素。尽管众多横断面研究已确定了衰弱的各种风险和保护因素，但对衰弱的纵向进展了解有限。此外，这些研究方法上的差异妨碍了结果的可比性。在此，我们采用协调分析方法对5个独立队列进行研究，以评估衰弱的纵向轨迹以及3个先前确定的关键风险因素（性别、年龄和教育程度）的影响。
我们基于累积缺陷法为5个队列得出衰弱指数（FI）。在每个队列中独立拟合4种线性和二次增长曲线模型。模型对性别、年龄、受教育年限以及性别与年龄的交互项进行了调整。
描述衰弱线性进展的模型最符合数据。FI的年增长率在基安蒂地区老年化研究队列中为0.002，在阿姆斯特丹纵向衰老研究（LASA）中为0.009。在所有队列中，女性的衰弱水平始终高于男性，从健康与退休研究队列中女性平均FI增加0.014到LASA队列中增加0.046不等。然而，性别与衰弱进展率之间的关联不一。个体内部衰弱轨迹围绕平均曲线存在显著异质性。
我们关于衰弱纵向线性增加的研究结果突出了未来研究的重要方向。具体而言，我们鼓励进一步研究以确定潜在的效应修饰因素或可能从针对性或个性化干预中受益的群体。
