Dataset Information

Natural History Trajectories of Frailty in Community-Dwelling Older Japanese Adults.

ABSTRACT:

Background

The gap between the average life expectancy and healthy life expectancy remains wide. Understanding the natural history of frailty development is necessary to prevent and treat frailty to overcome this gap. This study elucidated the trajectories of 5 frailty assessment components using group-based multitrajectory modeling.

Methods

Overall, 845 community-dwelling older adults (aged 65-91 years; 433 males and 412 females) who underwent longitudinal frailty assessments at least 3 times were included in the analysis. The mean follow-up period (±SD, range) was 7.1 (±2.3, 3.8-11.3) years. In each wave, the physical frailty was assessed for the following 5 partially modified components of the Cardiovascular Health Study criteria: shrinking, weakness, exhaustion, slowness, and low activity. Using group-based multitrajectory modeling, we identified subgroups that followed distinctive trajectories regarding the 5 frailty components.

Results

Five frailty trajectory groups were identified: weakness-focused frail progression group (Group 1 [G1]; 10.9%), robust maintenance group (Group 2 [G2]; 43.7%), exhaustion-focused prefrail group (Group 3 [G3]; 24.3%), frail progression group (Group 4 [G4]; 6.7%), and low activity-focused prefrail group (Group 5 [G5]; 14.4%). The Cox proportional hazards model analysis showed that G1, G4, and G5 had significantly higher mortality risks after adjusting for sex and age (G2 was the reference group).

Conclusion

Based on the natural history of frailty, the 5 distinctive trajectory groups showed that some individuals remained robust, while others remained predominantly prefrail or progressed primarily owing to physical mobility decline. Therefore, identifying individuals belonging to these progressive frailty groups and providing interventions according to the characteristics of each group may be beneficial.

SUBMITTER: Tange C

PROVIDER: S-EPMC9536447 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

Natural History Trajectories of Frailty in Community-Dwelling Older Japanese Adults.

Tange Chikako C Nishita Yukiko Y Tomida Makiko M Otsuka Rei R Ando Fujiko F Shimokata Hiroshi H Arai Hidenori H

The journals of gerontology. Series A, Biological sciences and medical sciences 20221001 10

<h4>Background</h4>The gap between the average life expectancy and healthy life expectancy remains wide. Understanding the natural history of frailty development is necessary to prevent and treat frailty to overcome this gap. This study elucidated the trajectories of 5 frailty assessment components using group-based multitrajectory modeling.<h4>Methods</h4>Overall, 845 community-dwelling older adults (aged 65-91 years; 433 males and 412 females) who underwent longitudinal frailty assessments at ...[more]

PMID: 35679612

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Project description:BackgroundThe association between frailty and adverse outcomes has been clearly defined. Frailty is associated with age, but different frailty evolution patterns might determine the incidence of adverse outcomes at older ages. So far, few observational studies have examined how distinct frailty trajectories could be associated with differences in the risk of adverse events and assessing whether frailty trajectories could define risk of death, hospitalization, worsening, and incident disability better than one-off assessment. Our hypothesis is that prospective increases in frailty levels are associated with higher risk of adverse events compared with subjects that prospectively decreased frailty levels.MethodsParticipants' data were taken from the Toledo Study of Healthy Ageing. Frailty was evaluated using the Frailty Trait Scale 5 (FTS5), being 0 the lower (the most robust) and 50 the highest (the frailest) score. FTS5 scores at baseline and follow-up (median 5.04 years) were used to construct frailty trajectories according to group-based trajectory modelling (GBTM). Multivariate Cox proportional hazard and logistic regression models were used to explore associations between frailty status and trajectory membership and the adverse outcomes. Deaths were ascertained through the Spanish National Death Index. Disability was evaluated through the Katz Index. Hospitalization was defined as first admission to Toledo Hospital.ResultsNine hundred and seventy-five older adults (mean age 73.14 ± 4.69; 43.38% men) were included. GBTM identified five FTS5 trajectories: worsening from non-frailty (WNF), improving to non-frailty (INF), developing frailty (DF), remaining frail (RF), and increasing frailty (IF). Subjects belonging to trajectories of increasing frailty scores or showing consistently higher frailty levels presented with an increased risk of mortality {DF [hazard ratio (HR), 95% confidence interval (CI)] = 2.01 [1.21-3.32]; RF = 1.92 [1.18-3.12]; IF = 2.67 [1.48-4.81]}, incident [DF (HR, 95% CI) = 2.06 (1.11-3.82); RF = 2.29 (1.30-4.03); IF = 3.55 (1.37-9.24)], and worsening disability [DF (HR, 95% CI) = 2.11 (1.19-3.76); RF = 2.14 (1.26-3.64); IF = 2.21 (1.06-4.62)], compared with subjects prospectively showing decreases in frailty levels or maintaining low FTS5 scores. A secondary result was a significant dose-response relationship between baseline FTS5 score and adverse events.ConclusionsBelonging to trajectories of prospectively increasing/consistently high frailty scores over time are associated with an increased risk of adverse outcomes compared with maintaining low or reducing frailty scores. Our results support the dynamic nature of frailty and the potential benefit of interventions aimed at reducing its levels on relevant and burdensome adverse outcomes.

Project description:BackgroundThere is increasing evidence that pre-frailty manifests as early as middle age. Understanding the factors contributing to an early trajectory from good health to pre-frailty in middle aged and older adults is needed to inform timely preventive primary care interventions to mitigate early decline and future frailty.MethodsA cohort of 656 independent community dwelling adults, aged 40-75 years, living in South Australia, undertook a comprehensive health assessment as part of the Inspiring Health cross-sectional observational study. Secondary analysis was completed using machine learning models to identify factors common amongst participants identified as not frail or pre-frail using the Clinical Frailty Scale (CFS) and Fried Frailty Phenotype (FFP). A correlation-based feature selection was used to identify factors associated with pre-frailty classification. Four machine learning models were used to derive the prediction models for classification of not frail and pre-frail. The class discrimination capability of the machine learning algorithms was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, F1-score and accuracy.ResultsTwo stages of feature selection were performed. The first stage included 78 physiologic, anthropometric, environmental, social and lifestyle variables. A follow-up analysis with a narrower set of 63 variables was then conducted with physiologic factors associated with the FFP associated features removed, to uncover indirect indicators connected with pre-frailty. In addition to the expected physiologic measures, a range of anthropometric, environmental, social and lifestyle variables were found to be associated with pre-frailty outcomes for the cohort. With FFP variables removed, machine learning (ML) models found higher BMI and lower muscle mass, poorer grip strength and balance, higher levels of distress, poor quality sleep, shortness of breath and incontinence were associated with being classified as pre-frail. The machine learning models achieved an AUC score up to 0.817 and 0.722 for FFP and CFS respectively for predicting pre-frailty. With feature selection, the performance of ML models improved by up to + 7.4% for FFP and up to + 7.9% for CFS.ConclusionsThe results of this study indicate that machine learning methods are well suited for predicting pre-frailty and indicate a range of factors that may be useful to include in targeted health assessments to identify pre-frailty in middle aged and older adults.

Dataset Information

Natural History Trajectories of Frailty in Community-Dwelling Older Japanese Adults.

Background

Methods

Results

Conclusion

Publications

Natural History Trajectories of Frailty in Community-Dwelling Older Japanese Adults.

Similar Datasets

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