Statistical analyses

A descriptive analysis was performed describing the baseline characteristics (demographic distribution, lifestyle variables, obesity, hypertension, and biochemical parameters) in participants in each prediabetes group and in participants without prediabetes. Next, we estimated the distribution of these characteristics in the three categories of glomerular filtration (hypofiltration, normofiltration, and hyperfiltration). Data for the continuous variables were presented as means ± standard deviations (SDs) for categorical variables as percentages. Different types of analysis were used for comparison between the groups such as unpaired t-test, variance (ANOVA), or χ² test. Patients with severe chronic kidney disease have not been excluded. Considering values of glomerular filtrate of the basal stage (CDKEPI), we did not have any case with values below 30 (severe kidney failures–G4 and G5-), and only 8 cases with values <45 (moderate-severe kidney failure–G3B).”

We then created a dichotomous variable of glomerular filtration rate—hyperfiltration versus non hyperfiltration- grouping hypofiltration and normofiltration into a single category. The association between levels of FPG and hyperfiltration and between levels of HbA1c and hyperfiltration was measured using logistic regression. We used two consecutive models: the first one (model 1) was adjusted by sex and age; the second one (model 2) was adjusted for the model 1 factors plus body mass index, waist circumference, metabolic syndrome, systolic blood pressure, diastolic blood pressure, alcohol consumption, smoking status, and physical exercise. The measure of association with its 95% confidence interval was the odds ratio, taking as reference the category of participants without prediabetes. All analyses were conducted using the IBM SPSS statistical package.