Statistical methods
RS, diabetes, dyslipidemia
CARDIOVASCULAR DISEASES, DYSLIPIDEMIA, REGRESSION, HYPERTENSION, DIABETES
Population characteristics of the total population and SAF tertile groups were summarized. To balance sex and age representation among tertile groups, we obtained tertiles of residuals of SAF regressed on age in men and women separately and created sex-specific and age adjusted SAF tertile groups. SAF as a continuous variable, z-scores of IMT, blood pressure, and PWV were used in regression analyses. CAC scores were categorized into clinically meaningful categories (< 100, and ≥ 100), and also analyzed in the natural logarithm of (CAC + 1) for analyses due to their skewed distribution and excessive zeros. The associations between SAF and endophenotypes were studied using linear regression for continuous variables or (binary or multinomial) logistic regression for categorical variables, with SAF being the independent variable regardless of the potential causal direction. Three models were constructed to adjust the associations for potential confounders that are risk factors for AGE accumulation and cardiovascular diseases. Model 1 was adjusted for age, sex, and RS subcohorts. Model 2 was additionally adjusted for cardiovascular risk factors including BMI and the presence of dyslipidemia and hypertension. To explore the influence of covariates that are potential confounders but also important determinants of SAF, we additionally adjusted for smoking, diabetes, and eGFR in model 3. Theoretically, AGEs may also act as intermediates linking risk factors such as diabetes and smoking to cardiovascular risk. The directed acyclic graph (Additional file Missing data in covariates (ranging from 0.8 to 4.1%) were imputed by multiple imputations using logistic regression and predictive mean matching methods for categorical and continuous variables respectively for 10 imputations using the MICE package in R. The results were pooled from analyses of the imputed datasets. A two-sided p < 0.05 was considered statistically significant. All analyses were conducted using R Studio (version 4.1.2).