Abstract P120: Latent Group Trajectory Methods To Study Type 2 Diabetes Epidemiology: A Review Of The Literature

Abstract

Introduction: The progression of type 2 diabetes (T2D) is unique to each patient and can be depicted through individual temporal trajectories. Latent group trajectory methods (latent growth mixture models [LGMM] or latent class growth analysis [LCGA]) can be used to classify similar individual trajectories in a priori non-observed groups (latent groups), sharing common characteristics. Although increasingly used in the field of T2D epidemiology, many questions remain regarding the utilization of these methods. Objective: To review the literature of longitudinal studies using latent group trajectory methods among individuals with T2D. Methods: MEDLINE (Ovid), EMBASE, CINAHL and Web of Science were searched through August 25 th , 2021. Data were collected on the type of method used (LGMM or LGCA), characteristics of studies and quality of reporting using the GRoLTS-Checklist. Results: From the 4,694 citations screened, a total of 38 studies were included. The studies were published between 2010 and 2021. Total follow-ups ranged from 8 weeks to 11 years, with 95% (36 out of 38) studies with a follow-up of 1 year of more. The characteristics of studies are presented in Table 1. Regarding the quality of reporting, trajectory groups were adequately presented, however many studies failed to report important decisions made for the trajectory group identification. Many studies considered trajectory groups as exposures to a subsequent outcome. Yet, issues in relation with selection bias, immortal bias and residual confounding were suspected and poorly addressed. Conclusions: Although LCGA were preferred, the context of utilization, data sources and research questions were diverse and unrelated to the type of method used. We recommend authors to clearly report the decisions made in trajectory groups identification.