Longitudinal HbA1c trajectory modelling reveals the association of HbA1c and risk of hospitalization for heart failure for patients with type 2 diabetes mellitus

BackgroundInconsistent conclusions in past studies on the association between poor glycaemic control and the risk of hospitalization for heart failure (HHF) have been reported largely due to the analysis of non-trajectory-based HbA(1c) values. Trajectory analysis can incorporate the effects of HbA(1c) variability across time, which may better elucidate its association with macrovascular complications. Furthermore, studies analysing the relationship between HbA(1c) trajectories from diabetes diagnosis and the occurrence of HHF are scarce. MethodsThis is a prospective cohort study of the SingHealth Diabetes Registry (SDR). 17,389 patients diagnosed with type 2 diabetes mellitus (T2DM) from 2013 to 2016 with clinical records extending to the end of 2019 were included in the latent class growth analysis to extract longitudinal HbA(1c) trajectories. Association between HbA(1c) trajectories and risk of first known HHF is quantified with the Cox Proportional Hazards (PH) model. Results5 distinct HbA(1c) trajectories were identified as 1. low stable (36.1%), 2. elevated stable (40.4%), 3. high decreasing (3.5%), 4. high with a sharp decline (10.8%), and 5. moderate decreasing (9.2%) over the study period of 7 years. Poorly controlled HbA(1c) trajectories (Classes 3, 4, and 5) are associated with a higher risk of HHF. Using the diabetes diagnosis time instead of a commonly used pre-defined study start time or time from recruitment has an impact on HbA(1c) clustering results. ConclusionsFindings suggest that tracking the evolution of HbA(1c) with time has its importance in assessing the HHF risk of T2DM patients, and T2DM diagnosis time as a baseline is strongly recommended in HbA(1c) trajectory modelling. To the authors' knowledge, this is the first study to identify an association between HbA(1c) trajectories and HHF occurrence from diabetes diagnosis time.