Characterizing long-term patterns of weight change in China using latent class trajectory modeling

Area of Science:

• Public Health
• Epidemiology
• Obesity Research

Background:

• Obesity-related diseases are a major health concern in China, contributing significantly to morbidity and mortality.
• Understanding weight change patterns is crucial for predicting disease risk and informing prevention strategies.

Purpose of the Study:

• To analyze weight change trajectories in Chinese adults using latent class trajectory modeling.
• To identify associations between weight change patterns and baseline characteristics like BMI and urbanization.

Main Methods:

• Latent class trajectory modeling applied to China Health and Nutrition Survey (CHNS) data (n=12,611).
• Analysis stratified by age and sex due to differential weight gain patterns.
• Generalized linear mixed effects models used to examine covariate associations.

Main Results:

• Six distinct weight change trajectories (loss, maintenance, gain) identified across age-sex subgroups.
• Baseline BMI was a significant predictor of trajectory membership for all groups.
• Baseline overweight/obesity increased the likelihood of 'initial loss with maintenance' trajectories.

Conclusions:

• Weight change patterns differ significantly across age and gender subgroups in China.
• Baseline urbanization status was not found to be protective against specific weight trajectories.
• Age-specific analyses are essential for a nuanced understanding of weight change and associated risks.