Great diversity in the utilization and reporting of latent growth modeling approaches in type 2 diabetes: A literature review.

Introduction: The progression of complications of type 2 diabetes (T2D) is unique to each patient and can be depicted through individual temporal trajectories. Latent growth modeling approaches (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, many questions remain regarding the utilization of these methods.
Objective: To review the literature of longitudinal studies using latent growth modeling approaches to study T2D.
Methods: MEDLINE (Ovid), EMBASE, CINAHL and Wb of Science were searched through August 25 ^th , 2021. Data was collected on the type of latent growth modeling approaches (LGMM or LCGA), characteristics of studies and quality of reporting using the GRoLTS-Checklist and presented as frequencies.
Results: From the 4,694 citations screened, a total of 38 studies were included. The studies were published beetween 2011 and 2021 and the length of follow-up ranged from 8 weeks to 14 years. Six studies used LGMM, while 32 studies used LCGA. The fields of research varied from clinical research, psychological science, healthcare utilization research and drug usage/pharmaco-epidemiology. Data sources included primary data (clinical trials, prospective/retrospective cohorts, surveys), or secondary data (health records/registries, medico-administrative). Fifty percent of studies evaluated trajectory groups as exposures for a subsequent clinical outcome, while 24% used predictive models of group membership and 5% used both. Regarding the quality of reporting, trajectory groups were adequately presented, however many studies failed to report important decisions made for the trajectory group identification.
Conclusion: Although LCGA were preferred, the contexts of utilization were diverse and unrelated to the type of methods. We recommend future authors to clearly report the decisions made regarding trajectory groups identification.
Competing Interests: The authors declare the following conflict of interests: Sarah O'Connor had received a prize award (unrelated to this work) from the Faculty of Pharmacy, Université Laval, the funds were provided by Pfizer Canada (June 2021). Dr Poirier was a member of the Clinical Practice Guidelines of the Canadian Diabetes Association (macrovascular complications). Although not relevant to this work, Dr Paul Poirier declared having received fees for CME/consultants from Abbott, Amgen, Astrazeneca, Bayer, Bausch Health, Boehringer Ingelheim, Eli Lilly, HLS Therapeutics Inc, Janssen, Novartis, NovoNordisk, Sanofi and Servier. Jacinthe Leclerc is a professor of nursing at Université du Québec à Trois-Rivières. Within her role of professor, she provides (1) Continuous Medical Education sessions for health care professionals, accredited by the Fédération des médecins omnipraticiens du Québec and its local affiliates and (2) statistical expertise on Data Safety Monitoring Board Committees managed by JSS Medical Research (both unrelated to this work). Other authors declare no conflict of interests.
(© 2022 The Author(s).)