Longitudinal Patterns of HBA1c Trajectories in Patients with Type 1 Diabetes

Le résumé fourni par la source

Introduction: Type 1 diabetes is a chronic condition that affects adolescents’ quality of life and raises the risk of developing mental health concerns and diabetes-related complications. Measuring glycated hemoglobin (HbA1c) over time is the standard of care within the management of type 1 diabetes; however, the determinants of different HbA1c trajectories remain poorly understood. In the secondary analysis of the data collected for the Integrated Care Model (1), we aimed to identify groups of HbA1c trajectories with similar trends and examine the association between these groups and demographic and psychosocial variables. Methods: HbA1c data were collected at 4 consecutive time points with a gap of 3±1 months. We used Leffondré’s method (2) and Group-based trajectory modeling (GBTM) (3) to derive the groups of HbA1c trajectories among 91 adolescents. Baseline characteristics of the adolescents included in the groups were analyzed by univariate analysis. Results: Leffondré’s method identified three groups of trajectories: stable (63%), decreasing (17%), and increasing (20%). The baseline HbA1c levels for the three groups were 8.00±0.93, 10.07±1.63, and 8.21±1.14, respectively. Among the baseline characteristics, only the treatment method distinguished the groups of adolescents with similar trajectories of HbA1c over time (p=0.015). The GBTM method identified similar groups: stable (67%), decreasing (18%) and increasing (15%). The baseline HbA1c levels for the three groups were 7.89±0.88, 9.81±1.61, and 9.00±1.38, respectively. Groups produced by GBTM were also distinguished by treatment modality at baseline (p=0.022). Discussion: We identified three distinct patterns of HbA1c trajectories in adolescents. The only baseline characteristic that significantly distinguished these trajectories was the treatment modality. References J. Versloot, et al., An Integrated Care Model to Support Adolescents With Diabetes-related Quality-of-life Concerns: An Intervention Study. Can J Diabetes (2022). K. Leffondré, et al., Statistical measures were proposed for identifying longitudinal patterns of change in quantitative health indicators. J Clin Epidemiol 57, 1049–1062 (2004). D. S. Nagin, Group-based trajectory modeling: an overview. Ann Nutr Metab 65, 205–210 (2014).

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.