Dataset Information

Trajectories of body mass indices and development of frailty: Evidence from the health and retirement study.

ABSTRACT:

Objective

To estimate trajectories of body mass index (BMI) and determine their association with incident frailty in later life.

Methods

Data come from the 2004 to 2012 waves of the Health and Retirement Study, a longitudinal survey of older adults. Analysis was restricted to respondents who were not frail at baseline (n = 10,827). BMI (kg/m(2) ) was calculated from self-reported weight and height. Incident frailty was assessed using the Frailty Index. Longitudinal growth mixture modeling was used to estimate the relationship between BMI trajectories and incident frailty over a 10-year period.

Results

Four trajectory classes were identified: weight gain (n = 162 [1.4%], mean final BMI = 42 kg/m(2) ), weight loss (n = 171 [1.7%], mean final BMI = 25.0 kg/m(2) ), consistent obesity (n = 640 [6.8%], mean final BMI = 34.7 kg/m(2) ), and consistent overweight (n = 9,864 [90.1%] mean final BMI = 26.0 kg/m(2) ). Cumulative incidence of frailty was 19.9%. Relative to the consistent overweight class, the weight gain class had the highest likelihood of incident frailty (odds ratio, OR: 3.61, 95% confidence interval, CI: 2.39-5.46). The consistent obesity (OR: 2.72, 95% CI: 2.06-3.58) and weight loss (OR: 2.81, 95% CI: 1.84-4.30) classes had similarly elevated risk of frailty.

Conclusions

Weight change and obesity are associated with risk of frailty.

SUBMITTER: Mezuk B

PROVIDER: S-EPMC5433251 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Trajectories of body mass indices and development of frailty: Evidence from the health and retirement study.

Mezuk Briana B Lohman Matthew C MC Rock Andrew K AK Payne Martha E ME

Obesity (Silver Spring, Md.) 20160629 8

<h4>Objective</h4>To estimate trajectories of body mass index (BMI) and determine their association with incident frailty in later life.<h4>Methods</h4>Data come from the 2004 to 2012 waves of the Health and Retirement Study, a longitudinal survey of older adults. Analysis was restricted to respondents who were not frail at baseline (n = 10,827). BMI (kg/m(2) ) was calculated from self-reported weight and height. Incident frailty was assessed using the Frailty Index. Longitudinal growth mixture ...[more]

PMID: 27355440

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Project description:IntroductionFrailty is a geriatric syndrome that has been defined differently with various indices. Without a uniform definition, it remains unclear how to interpret and compare different frailty indices (FIs). With the advances in index mining, we find it necessary to review the implicit assumptions about the creation of FIs. We are concerned the processing of frailty data may introduce measurement error and bias. We aim to review the assumptions, interpretability and predictive power of FIs regarding mortality.MethodsThree FIs, the Functional Domains Model proposed by Strawbridge et al. (1998), the Burden Model by Rockwood et al. (2007) and the Biologic Syndrome Model by Fried et al. (2004), were directly compared using the data from the Health and Retirement Study (HRS), a longitudinal study since 1996 mainly following up Americans aged 50 years and over. The FIs were reproduced according to Cigolle et al. (2009) and interpreted with their input variables through forward-stepwise regression. Biases were the residuals of the FIs that could not be explained by own input variables. Any four of the input variables were used to create alternative indices. Discrete-time survival analysis was conducted to compare the predictive power of FIs, input variables and alternative indices on mortality.ResultsWe found frailty a syndrome not unique to the elderly. The FIs were produced with different degrees of bias. The FIs could not be fully interpreted with the theory-based input variables. The bias induced by the Biological Syndrome Model better predicted mortality than frailty status. A complicated FI, the Burden Model, could be simplified. The input variables better predicted mortality than the FIs. The continuous FIs predicted mortality better than the frailty statuses. At least 6865 alternative indices better predicted mortality than the FIs.ConclusionFIs have been used as outcome in clinical trials and need to be reviewed for adequacy based on our findings. The three FIs are not closely linked to the theories because of bias introduced by data manipulation and excessive numbers of input variables. We are developing new algorithms to develop and validate innovative indices.

Project description:BackgroundGene-obesogenic environment interactions influence body mass index (BMI) across the life course; however, limited research examines how these interactions may differ by race and sex.MethodsUtilizing mixed-effects models, we examined the interaction effects of a polygenic risk score (PGS) generated from BMI-associated single-nucleotide polymorphisms, and environmental factors, including age, physical activity, alcohol intake, and childhood socioeconomic status on measured longitudinal BMI from the Health and Retirement Study (HRS). HRS is a population representative survey of older adults in the United States. This study used a subsample of genotyped Black (N = 1796) and White (N = 4925) men and women (50-70 years) with measured BMI.ResultsHigher PGS was associated with higher BMI. The association between PGS and BMI weakened as individuals aged among White men (Pinteraction = 0.0383) and White women (Pinteraction = 0.0514). The mean BMI difference between the 90th and 10th PGS percentile was 4.25 kg/m2 among 50-year-old White men, and 3.11 kg/m2 among the 70 years old's, i.e., a 1.14 kg/m2 (95% CI: -0.27, 2.82) difference. The difference among 50- and 70-year-old White women was 1.34 kg/m2 (95% CI: 0.09, 2.60). In addition, the protection effect of physical activity was stronger among White women with higher PGS (Pinteraction = 0.0546). Vigorous physical activity (compared with never) was associated with 1.66 kg/m2 (95% CI: 1.06, 2.29) lower mean BMI among those in the 90th PGS percentile, compared with 0.83 kg/m2 (95% CI: 0.37, 1.29) lower among those in the 10th PGS percentile. Interactions were also observed between both PGS and alcohol intake among White men (Pinteraction = 0.0034) and women (Pinteraction = 0.0664) and Black women (Pinteraction = 0.0108), and PGS and childhood socioeconomic status among White women (Pinteraction = 0.0007).ConclusionsOur findings reinforce the importance of physical activity among those with an elevated genetic risk; additionally, other detected interactions may underscore the influence of broader social environments on obesity-promoting genes.

Dataset Information

Trajectories of body mass indices and development of frailty: Evidence from the health and retirement study.

Objective

Methods

Results

Conclusions

Publications

Trajectories of body mass indices and development of frailty: Evidence from the health and retirement study.

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