Long-term glycemic variability and the risk of cardiovascular diseases in type 2 diabetic patients: Effect of hypothetical interventions using parametric g-formula in a population-based historical cohort study. - Khadije Maajani; Ensieh Nasli-Esfahani; Noushin Fahimfar; Ali Sheidaei; Mohammad Ali Mansournia; Kamran Yazdani (PLoS ONE)
Background Harmful effects of long-term HbA1c and fasting plasma glucose (FPG) variability on cardiovascular diseases (CVD) have not been causally examined. We employed a parametric g-formula to estimate the causal effect of HbA1C and fasting plasma glucose (FPG) variability on CVD. Methods This retrospective cohort study was conducted on 2078 patients with type 2 diabetes who were free of CVD and aged >18 years at the entrance to the clinic (2017-2022), with at least three HbA1c and FPG measurements. Variability was calculated using standard deviation (SD), and coefficient of variation (CV). We used the parametric g-formula to estimate the 5-year risk, risk ratio, and risk difference of CVD under different deciles of HbA1c-SD/CV, FPG-SD/CV, HbA1C levels (≤5%, 5 to ≤7%, and >7), and joint exposure to different deciles of HbA1c-SD and HbA1c values, adjusted for time-varying confounders that are affected by prior exposure. Results The observed and simulated 5-year risk of CVD under no intervention were 11.6% (95% CI: 10.3, 13.1) and 11.03% (95% CI: 10.2, 12.6) for HbA1C-SD model. The estimated 5-year risk of CVD was increased from the 8.01% (95% CI: 7.5, 10.1%) in the first decile to 15.2% (95% CI: 14.1, 17.7%) in the tenth decile of HbA1c-SD. The results for FPG-SD were similar. Within the stable level of HbA1c (5 to ≤7%) the risk ratio increased from 1.37 (95% CI: 1.19, 1.48) in the first decile to 2.76 (95% CI: 2.06, 3.09) in the tenth decile of HbA1c-SD. Under a joint intervention of HbA1c <5% and the first decile of HbA1c-SD, CVD risk decreased by 6.4% (95%CI: 4.9, 7.3%) compared to the natural course. Conclusions Even within a stable HbA1c level, long-term glycemic variability may be a strong predictor of CVD.