A Model of Individual BMI Trajectories

The Impact of Economic Income on BMI Trajectory Groups in Chinese Elderly Individuals: A Population-Based Longitudinal Study

OBJECTIVE

The objective of this study is to gain insights into the influence of income on the body mass index (BMI) locus in the elderly population.

METHODS

The Chinese Longitudinal Healthy Longevity and Happy Family Study (CLHLS-HF) was included at baseline (2008) for participants aged 65 years and older. The total number of participants analyzed in this study was 7555. A population-based trajectory model (GBTM) was used. The economic income level was an independent variable and adjusted for age, gender, ethnicity, education, marriage, and physical activity (Model 1), and the baseline BMI value was added in Model 2, with a quadratic of the income added in Model 3. A sensitivity analysis was adopted.

RESULTS

Three BMI trajectory groups were identified using GBTM and were labeled as "overweight", "normal", and "obesity". After adjusting for covariates (Model 1), with the increase in economic income, the risk of the elderly transitioning into the overweight trajectory group and the obesity trajectory group was relatively increased. When the baseline BMI values were adjusted for Model 2, the effect of economic income on the overweight and obesity trajectories was enhanced. A sensitivity analysis was performed, and it was found that the result of the positive impact of economic income on the BMI trajectory group was robust.

CONCLUSIONS

The higher the income, the greater the risk of Chinese elderly individuals developing the overweight or obesity trajectory. It is suggested that elderly individuals with higher economic income especially need interventions and nutrition education to help them acquire nutrition knowledge for a healthy lifestyle. The positive impact of economic income on the BMI trajectories of the elderly provides further directions for preventing and controlling obesity in the elderly.

A Model of Individual BMI Trajectories

A risk factor model of body mass index (BMI) is an important building block of health simulations aimed at estimating government policy effects with regard to overweight and obesity. We created a model that generates representative population level distributions and that also mimics realistic BMI trajectories at an individual level so that policies aimed at individuals can be simulated. The model is constructed by combining several datasets. First, the population level distribution is extracted from a large, cross-sectional dataset. The trend in this distribution is estimated from historical data. In addition, longitudinal data are used to model how individuals move along typical trajectories over time. The model faithfully describes the population level distribution of BMI, stratified by sex, level of education and age. It is able to generate life course trajectories for individuals which seem plausible, but it does not capture extreme fluctuations, such as rapid weight loss.