A Virtual Trial to Identify Cardiovascular Biomarkers for Differentiating Diabetic and Hypertensive Kidney Disease

A diagnostic challenge in the management of chronic kidney disease (CKD) is distinguishing diabetic kidney disease (DKD) from hypertensive kidney disease (HKD) in patients with coexisting diabetes mellitus (DM) and hypertension (HTN), because accurate diagnosis often depends on renal biopsy as a reference standard. This study proposes a modeling approach to identify cardiovascular biomarkers for differentiating DKD from HKD.

An existing whole-body circulation model of the vascular tree was extended with a detailed renal circulation network to predict biomarkers measured at different locations. The model parameterized sex, age, and disease factors and was used to conduct virtual clinical trials that identified individual and combined biomarkers for DKD-HKD differentiation. Biomarkers were identified with univariate and multivariate analysis and characterized with the area under the receiver operating characteristic curve (AUC).

Results show that the strongest individual biomarker that is commonly used in clinical practice is pulsatility index (PI) measured in the main renal artery, with an AUC of 0.87. Among all evaluated two-biomarker combinations, PI and resistive index (RI) measured in the same artery achieved the highest classification performance (AUC 0.94). In comparison, the highest performance among three-biomarker combinations (AUC 0.96) is achieved by mean blood flow rate, systolic blood flow rate, and diastolic flow rate.

This modeling work suggests that cardiovascular biomarkers can assist in differentiating DKD and HKD, and proposes specific hypotheses that form a strong rationale for targeted clinical trials. If confirmed, these methods could enable non-invasive assessment of renal vascular alterations associated with DKD and HKD, reducing reliance on kidney biopsies for diagnostic evaluation.