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Missing data significantly impacts Body Mass Index (BMI) trajectory analysis. Accounting for missing data alters the estimated BMI trajectories and reduces the predicted risk of type 2 diabetes mellitus (T2DM).

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Area of Science:

  • Gerontology
  • Epidemiology
  • Biostatistics

Background:

  • Body Mass Index (BMI) trajectories are crucial for understanding long-term health.
  • Missing data is a common limitation in longitudinal BMI studies, with limited research on its impact.
  • Understanding how missing data affects BMI trajectory estimation is vital for accurate health predictions.

Purpose of the Study:

  • To explore the influence of missing data on the estimation of Body Mass Index (BMI) trajectories.
  • To compare different methods for handling missing data in BMI trajectory analysis.
  • To assess the impact of accounting for missing data on the prediction of type 2 diabetes mellitus (T2DM) risk.

Main Methods:

  • Utilized data from the English Longitudinal Study of Ageing (ELSA).
  • Estimated distinct BMI trajectories for adults aged 50 years and over using multiple methods that account for missing data.
  • Compared the influence of different missing data handling techniques on trajectory estimation and T2DM risk prediction.

Main Results:

  • Identified four distinct BMI trajectories: stable overweight, elevated BMI, increasing BMI, and decreasing BMI.
  • Observed differences in the likelihood of individuals belonging to each trajectory across various missing data methods.
  • Found that accounting for missing data reduced the observed influence of BMI trajectory on the risk of developing type 2 diabetes mellitus (T2DM).

Conclusions:

  • Missing data significantly influences the estimation of BMI trajectories and subsequent health risk predictions.
  • The choice of method for handling missing data can alter conclusions drawn from BMI trajectory analyses.
  • Further research is needed to determine the most reliable methods for addressing missing data in BMI trajectory studies, and its impact on cost-effectiveness analyses should be investigated.