Table 2

Multivariable linear regression models for significant AGEs and OxPs as predictors of subclinical atherosclerosis

β ± SEP
Dependent variable is CIMT
 Model 1G-H1 (continuous)0.09 ± 0.040.01
 Model 2G-H1 (quartile IV vs. I–III)0.06 ± 0.020.01
Dependent variable is CAC
 Model 3G-H1 (quartile IV vs. I–III)5.53 ± 2.290.01
 Model 43DG-H (quartile IV vs. I–III)3.74 ± 2.240.09
 Model 5CEL (continuous)4.27 ± 2.480.08
 Model 62-AAA (continuous)6.08 ± 2.740.03
 Model 72-AAA (quartile IV vs. I–III)6.84 ± 2.21<0.01
Dependent variable is AAC
 Model 8CEL (continuous)13.77 ± 5.63<0.01
  • Rows show results for the prediction of subclinical atherosclerosis by individual AGEs and OxPs (models 1–8). All models are adjusted for age, duration of diabetes, prior CVD, history of hypertension, pack-years of smoking, on-trial variables (GFR, HbA1c, HDL cholesterol, and triglycerides). Both continuous variables (log-transformed) and dichotomous variables (quartile IV vs. quartiles I, II, and III combined) of AGEs and OxPs were used in different models. Replacing GFR with the albumin-to-creatinine ratio did not change the results.