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

Modeling recurrent heart failure risk in type 2 diabetes: impact of flexible HbA1c trajectories using nonhomogeneous Poisson processes

Background

Many clinical trials yielded inconsistent results regarding the effect of intensive glycated hemoglobin control on cardiovascular diseases in type 2 diabetes. We identified distinct HbA1c trajectories and their association with the recurrent hospitalization of heart failures (HHF) for patients with type 2 diabetes starting from the date of diabetes diagnosis.

Methods

In this study, we included 194,258 patients who entered the SingHealth Diabetes Registry from 2013 to 2020. Their diagnoses of type 2 diabetes spanned the years 1960-2020, encompassing HbA1c measurements, records of HHF, and other cardiovascular complications. Latent class growth models (LCGM) with splines were used to extract the subgroups with distinct HbA1c trajectories. The association between HbA1c trajectories and the recurrent risk of HHF was investigated by nonhomogeneous Poisson processes (NHPP).

Results

Eight distinct HbA1c trajectories were identified as follows: low stable (LowS, 22.2%), moderate low ascending (ModLowA, 12.7%), moderate high ascending (ModHighA, 11.5%), moderate low descending (ModLowD, 17.2%), moderate high descending (ModHighD, 10.1%), moderate high volatility (ModHighV, 10.1%), high with a sharp decline (HighSD, 8.0%), and high volatility (HighV, 10.2%). Using the Class LowS as a reference, the hazard ratios for recurrent HHF for the other classes are as follows: 0.79 for ModLowA, 1.30 for ModHighA, 1.17 for ModLowD, 1.89 for ModHighD, 1.94 for ModHighV, 1.25 for HighSD, and 2.88 for HighV. Considering recurrent HHFs, our NHPP model demonstrated predictive capability for type 2 diabetes patients' future HHF events.

Conclusions

Low baseline HbA1c levels are associated with a lower risk of recurrent HHF, while poor glycemic control significantly increases this risk. Our application of LCGM with splines effectively captures flexible, long-term HbA1c trajectories, while the innovative use of the NHPP model allows for precise modeling of HHF recurrence risk. This approach provides a foundation for personalized risk predictions and future HF management by incorporating dynamically updated risk factors.

Association and pathways of baseline and longitudinal hemoglobin A1c with the risk of incident stroke: A nationwide prospective cohort study

AIMS

To investigate the association of baseline and long-term mean hemoglobin A1c (HbA1c) with the risk of stroke.

METHODS

A total of 11,220 participants aged over 45 years and without stroke at baseline were enrolled from the China Health and Retirement Longitudinal Study. Mean HbA1c was calculated as the mean of HbA1c at all previous visits before stroke occurred or the end of follow-up. Multivariable-adjusted Cox regressions and Bayesian network were used for the analysis.

RESULTS

During a median follow-up of 7.50 years, a total of 626 cases of stroke occurred. The risk of stroke increased with quintiles of baseline and mean HbA1c, the hazard ratio (HR) in Q5 versus Q1 was 1.30 (95 % confidence interval [CI],1.03-1.64) and 1.79 (95 % CI, 1.38-2.34), respectively. Per 1 unit increase in baseline and mean HbA1c was associated with 10 % (HR, 1.10; 95 % CI, 1.02-1.18) an 12 % (HR, 1.12; 95 % CI, 1.05-1.19) higher risk of stroke. Bayesian network analysis showed that the pathway from HbA1c to stroke was through hypertension, dyslipidemia, obesity, and inflammation.

CONCLUSIONS

Elevated levels of both baseline and long-term HbA1c were associated with increased risk of stroke, and hypertension and obesity played an important role in the pathway.