|Cheng, F - Pennsylvania State University|
|Gao, X - Pennsylvania State University|
|Bao, L - Pennsylvania State University|
|Mitchell, D - Pennsylvania State University|
|Wood, C - Geisinger Medical Center|
|Sliwinski, M - Pennsylvania State University|
|Smiciklas-wright, H - Pennsylvania State University|
|Still, C - Geisinger Medical Center|
|Rolston, D - Geisinger Medical Center|
|Jensen, G - University Of Vermont College Of Medicine|
Submitted to: Obesity
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/28/2017
Publication Date: 5/24/2017
Citation: Cheng, F.W., Gao, X., Bao, L., Mitchell, D.C., Wood, C., Sliwinski, M.J., Smiciklas-Wright, H., Still, C.D., Rolston, D.D., Jensen, G.L. 2017. Obesity as a risk factor for developing functional limitation among older adults: A conditional inference tree analysis. Obesity. 25:1263-1269.
Interpretive Summary: Traditional regression approaches have been used to show a positive association between body mass index and functional decline among older persons yet they are limited in their ability translate results for meaningful clinical application. We demonstrated the utility of using newer statistical approaches (conditional inference tree analysis) which suggested that for an older adult of a specific age (>75.7 years), diagnosis of chronic disease (modified Charlson index >0) and higher body mass index (>38.5) are at higher risk for functional decline. This study shows the potential benefits to using data mining approaches in addition to traditional statistical methods. The data provide targets for identifying older adults at increased risk for functional decline with potential for clinically meaningful applications such as health care utilization.
Technical Abstract: Objective: To examine the risk factors of developing functional decline and make probabilistic predictions by using a tree-based method that allows higher order polynomials and interactions of the risk factors. Methods: The conditional inference tree analysis, a data mining approach, was used to construct a risk stratification algorithm for developing functional limitation based on BMI and other potential risk factors for disability in 1,951 older adults without functional limitations at baseline (baseline age 73.164.2 y). We also analyzed the data with multivariate stepwise logistic regression and compared the two approaches (e.g., cross-validation). Over a mean of 9.261.7 years of follow-up, 221 individuals developed functional limitation. Results: Higher BMI, age, and comorbidity were consistently identified as significant risk factors for functional decline using both methods. Based on these factors, individuals were stratified into four risk groups via the conditional inference tree analysis. Compared to the low-risk group, all other groups had a significantly higher risk of developing functional limitation. The odds ratio comparing two extreme categories was 9.09 (95% confidence interval: 4.68, 17.6). Conclusions: Higher BMI, age, and comorbid disease were consistently identified as significant risk factors for functional decline among older individuals across all approaches and analyses.