Cholesterol levels and development of cardiovascular disease in Koreans with type 2 diabetes mellitus and without pre-existing cardiovascular disease.

BACKGROUND: The aim of the present study was to identify a threshold for the cholesterol level at which the risk of cardiovascular disease (CVD) begins to increase in people with type 2 diabetes mellitus (DM). METHODS: Using the Korean National Health Insurance Service database, 2,077,135 people aged ≥ 40 years with type 2 DM who underwent regular health checks between 2009 and 2012 were included. Subjects with previous CVD were excluded. Cox regression analyses were performed to estimate the risk of CVD for each low-density lipoprotein cholesterol (LDL-C) group using the < 70 mg/dL as the reference group. RESULTS: There were 78,560 cases of stroke (3.91%), and 50,791 myocardial infarction (MI, 2.53%) during a median follow-up of 7.1 years. Among participants not taking statins, LDL-C levels of 130-159 mg/dL and ≥ 160 mg/dL were significantly associated with the risk of MI: the hazard ratios (HRs) (95% confidence interval) were 1.19 (1.14-1.25) and 1.53 (1.46-1.62), respectively. Among participants taking statins, all categories of LDL-C level ≥ 70 mg/dL were significantly associated with increased risk of stroke and MI. CONCLUSIONS: We identified an increased risk of CVD in people with an LDL-C level ≥ 130 mg/dL among individuals with type 2 DM not taking statins. The risk of CVD was significantly higher in those taking statins with an LDL-C level ≥ 70 mg/dL.

Consistency of the Glycation Gap with the Hemoglobin Glycation Index Derived from a Continuous Glucose Monitoring System.

BACKGROUND: Discordances between glycated hemoglobin (HbA1c) levels and glycemic control are common in clinical practice. We aimed to investigate the consistency of the glycation gap with the hemoglobin glycation index (HGI). METHODS: From 2016 to 2019, 36 patients with type 2 diabetes were enrolled. HbA1c, glycated albumin (GA), and fasting blood glucose levels were simultaneously measured and 72-hour continuous glucose monitoring (CGM) was performed on the same day. Repeated tests were performed at baseline and 1 month later, without changing patients' diabetes management. The HGI was calculated as the difference between the measured HbA1c and the predicted HbA1c that was derived from CGM. The glycation gap was calculated as the difference between the measured and GA-based predicted HbA1c levels. RESULTS: Strong correlations were found between the mean blood glucose (MBG)-based HGI and the prebreakfast glucose-based HGI (r=0.867, P<0.001) and between the glycation gap and the MBG-based HGI (r=0.810, P<0.001). A close correlation was found between the MBG-based HGI at baseline and that after 1 month (r=0.729, P<0.001), with a y-intercept of 0 and a positive slope. CONCLUSION: The HGI and glycation gap were highly reproducible, and the magnitudes of repeated determinations were closely correlated. Patients with similar mean glucose levels may have significantly different HbA1c levels.