MO522BIOMARKERS OF KIDNEY FUNCTION AS POTENTIAL PROGNOSTIC FACTORS FOR CARDIOVASCULAR DISEASE IN TYPE 2 DIABETIC PATIENTS
Authors: Roumeliotis Stefanos , Stamou Aikaterini , Roumeliotis Athanasios , Panagoutsos Stylianos , D'Arrigo Graziella , Tripepi Giovanni Luigi , Liakopoulos Vassilios 
Published: May 2021

Background and Aims

Developing risk predictive models in high-risk populations, such as patients with type 2 diabetes (T2DM), might provide an individualized risk prediction. During the past decade, the studies assessing carotid intima media thickness (cIMT) as a novel risk factor for cardiovascular (CV) disease, produced controversial results. In this study, we assessed the prognostic value of various risk factors, including cIMT and biomarkers of kidney function, to predict incident CV events in T2DM patients.

Method

A total of 158 adults, with a history of type 2 diabetes for&gt;10 years were recruited. At baseline, urinary albumin to creatinine ratio (UACR), eGFR and cIMT were evaluated. All patients were followed for a period of 7 years with fatal or nonfatal CV events as the primary endpoint. By adopting the Fine and Gray model, which takes into account the competitive risk of death, we assessed the associations between CV events and various candidate prognostic factors collected at baseline.

In multiple Cox Fine and Gray models, we included all variables that were correlated with the study outcome in univariate analysis, (full model with 9 variables). The prognostic performance of the full model was compared with that of a simpler (nested) model based only on three prognostic variables (simplified model).

To compare the data fitting of the 2 models, we used −2 log likelihood (−2 log L) statistics. The calibration and the discrimination abilities of both models were assessed by Hosmer-Lemeshow test and by receiver operating characteristic (ROC) curve, respectively.

Results

During the follow-up period (median 57.5 months, range 7–84 months), 75 patients experienced CV events (33 fatal, 42 nonfatal), and 13 died of causes other than CV. In univariate competitive risk analysis, hemoglobin (SHR 0.85, 95% CI 0.74–0.98, P = 0.02), female sex (SHR 0.50, 95% CI 0.30–0.82, P = 0.006), eGFR (SHR 0.98, 95% CI 0.97–0.99, P &lt;0.0001), serum albumin (SHR 0.43, 95% CI 0.26–0.74, P = 0.002), and HDL cholesterol (SHR 0.97, 95% CI 0.95–0.99, P = 0.02) were inversely associated with the incident rate of CV events, whereas the duration of type 2 diabetes (SHR 1.03, 95% CI 1.00–1.06, P = 0.04), background CVD (SHR 5.47, 95% CI 2.45–12.23, P &lt;0.0001), UACR (SHR 1.01, 95% CI 1.00–1.01, P &lt;0.0001), and cIMT (SHR 7.45, 95% CI 3.46–16.04, P &lt;0.0001) were directly related to the same endpoint. In a multivariate Fine and Gray model including all 9 univariate correlates of CV events (full model), only history of CVD (SHR 5.86, 95% CI 2.15–13.36, P &lt;0.0001), eGFR (SHR 0.99, 95% CI 0.98–0.99, P = 0.009), UACR (SHR 1.01, 95% CI 1.00–1.01, P &lt;0.0001), and cIMT (SHR 3.92, 95% CI 1.24–12.35, P = 0.02) remained significantly associated to the study outcome. In the simplified model only history of CVD (SHR 6.47, 95% CI 2.69–15.55, P &lt;0.0001), UACR (SHR 1.01, 95% CI 1.00–1.01, P &lt;0.0001), and eGFR (SHR 0.98, 95% CI 0.97–0.99, P &lt;0.0001) maintained an independent relationship with CV outcomes.

The data fitting of the two models did not significantly differ (χ2 = 9.48, 6 Df, P = 0.15) and the assessment of the areas under the ROC curves (Figure 1) confirmed that the two models had almost identical accuracy to predict the study outcome (full model 87%, 95% CI 0.81–0.92, P &lt;0.001, simplified model 84%, 95% CI 0.78–0.90, P &lt;0.001). Moreover, the Hosmer-Lemeshow test showed that the simplified model was better calibrated than the full model, because the P value of this test in the simplified model is farther from statistical significance (χ2 = 9.24, P = 0.32) than that of the full model (χ2 = 11.09, P = 0.20).

Conclusion

We propose a simple model for CV event prediction that includes only three easy-to-measure variables. eGFR, albuminuria, and history of CVD can be used for prognosis of CVD, whereas cIMT adds little to the accuracy of this prediction.