title: Characterizing long-term patterns of weight change in China using latent class trajectory modeling.
abstract: BACKGROUND
  Over the past three decades, obesity-related diseases have increased tremendously in China, and are now the leading causes of morbidity and mortality. Patterns of weight change can be used to predict risk of obesity-related diseases, increase understanding of etiology of disease risk, identify groups at particularly high risk, and shape prevention strategies.
  
  
  METHODS
  Latent class trajectory modeling was used to compute weight change trajectories for adults aged 18 to 66 using the China Health and Nutrition Survey (CHNS) data (n = 12,611). Weight change trajectories were computed separately for males and females by age group at baseline due to differential age-related patterns of weight gain in China with urbanization. Generalized linear mixed effects models examined the association between weight change trajectories and baseline characteristics including urbanicity, BMI category, age, and year of study entry.
  
  
  RESULTS
  Trajectory classes were identified for each of six age-sex subgroups corresponding to various degrees of weight loss, maintenance and weight gain. Baseline BMI status was a significant predictor of trajectory membership for all age-sex subgroups. Baseline overweight/obesity increased odds of following 'initial loss with maintenance' trajectories. We found no significant association between baseline urbanization and trajectory membership after controlling for other covariates.
  
  
  CONCLUSIONS
  Trajectory analysis identified patterns of weight change for age by gender groups. Lack of association between baseline urbanization status and trajectory membership suggests that living in a rural environment at baseline was not protective. Analyses identified age-specific nuances in weight change patterns, pointing to the importance of subgroup analyses in future research.
authors.affiliation: Department of Nutrition, Gillings School of Global Public Health at the University of North Carolina, Chapel Hill NC, United States of America.
authors.forename: Lauren
authors.initials: L
authors.lastname: Paynter
authors.affiliation: Department of Biostatistics, Gillings School of Global Public Health at the University of North Carolina, Chapel Hill NC, United States of America.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Koehler
authors.affiliation: Department of Biostatistics, Gillings School of Global Public Health at the University of North Carolina, Chapel Hill NC, United States of America; Carolina Population Center, University of North Carolina, Chapel Hill NC, United States of America.
authors.forename: Annie Green
authors.initials: AG
authors.lastname: Howard
authors.affiliation: Department of Biostatistics, Gillings School of Global Public Health at the University of North Carolina, Chapel Hill NC, United States of America; Carolina Population Center, University of North Carolina, Chapel Hill NC, United States of America.
authors.forename: Amy H
authors.initials: AH
authors.lastname: Herring
authors.affiliation: Department of Nutrition, Gillings School of Global Public Health at the University of North Carolina, Chapel Hill NC, United States of America; Carolina Population Center, University of North Carolina, Chapel Hill NC, United States of America.
authors.forename: Penny
authors.initials: P
authors.lastname: Gordon-Larsen
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D002681:China / Q000453:epidemiology; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D009749:Nutrition Surveys; D009765:Obesity / Q000453:epidemiology*; D012307:Risk Factors; D014505:Urban Population; D014507:Urbanization; D015430:Weight Gain; D055815:Young Adult