An elevated urinary albumin-to-creatinine ratio increases the risk of incident cardia-cerebrovascular disease in individuals with type 2 diabetes.

aimsWe aimed to explore the associations between urine albumin-to-creatinine ratio (uACR) and cardia-cerebrovascular disease (CVD) in Chinese population with type 2 diabetes(T2D).

methodsWe included 8975 participants with T2D but free of prevalent CVD (including myocardial infarction, ischemic and hemorrhagic stroke) at baseline from Kailuan study who were assessed with uACR between 2014 and 2016. The participants were divided into three groups based on their baseline uACR: normal (< 3 mg/mmol), microalbuminuria (3-30 mg/mmol), and macroalbuminuria (≥ 30 mg/mmol). Cox regression models and restricted cubic spline were used to evaluate the hazard ratios (HRs) and 95% confidence intervals (CIs) of incident CVD. The area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to see if incorporating uACR into existing models could improve performance.

resultsDuring a median follow-up of 4.05 years, 560 participants developed first CVD event (6.24%). After adjustment for potential confounders, participants with microalbuminuria had higher risks of CVD compared with normal uACR, with HRs of 1.57(95% CI 1.04-2.37) for myocardial infarction, 1.24(95% CI 1.00-1.54) for ischemic stroke,1.62(95% CI 0.73-3.61) for hemorrhagic stroke, and 1.30(95% CI 1.07-1.57) for total CVD. The risks gradually attenuated with uACR increase, with HRs of 2.86(95% CI 1.63-5.00) for myocardial infarction, 2.46(95% CI 1.83-3.30) for ischemic stroke, 4.69(95% CI 1.72-12.78) for hemorrhagic stroke, and 2.42(95% CI 1.85-3.15) for total CVD in macroalbuminuria. The addition of uACR to established CVD risk models improved the CVD risk prediction efficacy.

conclusionsIncreasing uACR, even below the normal range, is an independent risk factor for new-onset CVD in T2D population. Furthermore, uACR could improve the risk prediction for CVD among community based T2D patients.