title: Long-Term BMI Trajectories and Health in Older Adults: Hierarchical Clustering of Functional Curves.
abstract: OBJECTIVE
  This project contributes to the emerging research that aims to identify distinct body mass index (BMI) trajectory types in the population. We identify clusters of long-term BMI curves among older adults and determine how the clusters differ with respect to initial health.
  
  
  METHODS
  Health and Retirement Study cohort (N = 9,893) with BMI information collected in up to 10 waves (1992-2010) is analyzed using a powerful cutting-edge approach: hierarchical clustering of BMI functions estimated via the Principal Analysis by Conditional Expectations (PACE) algorithm.
  
  
  RESULTS
  Three BMI trajectory clusters emerged for each gender: stable, gaining, and losing. The initial health of the gaining and stable groups in both genders was comparable; the losing cluster experienced significantly poorer health at baseline.
  
  
  CONCLUSIONS
  BMI trajectories among older adults cluster into distinct types in both genders, and the clusters vary substantially in initial health. Weight loss but not gain is associated with poor initial health in this age group.
authors.affiliation: University of Wyoming, Laramie, USA zajacova@uwyo.edu.
authors.forename: Anna
authors.initials: A
authors.lastname: Zajacova
authors.affiliation: University of Wyoming, Laramie, USA.
authors.forename: Snehalata
authors.initials: S
authors.lastname: Huzurbazar
authors.affiliation: Montana State University, Bozeman, USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Greenwood
authors.affiliation: Ohio State University, Columbus, USA.
authors.forename: Huong
authors.initials: H
authors.lastname: Nguyen
mesh_terms: D000203:Activities of Daily Living; D000368:Aged; D015992:Body Mass Index; D016000:Cluster Analysis; D015331:Cohort Studies; D005260:Female; D006304:Health Status; D006306:Health Surveys; D006801:Humans; D008297:Male; D008875:Middle Aged
keywords: BMI trajectories; BMI trajectory clusters; functional data analysis; health; older adults