The study identifies cardiovascular biomarkers that can distinguish between diabetic kidney disease and hypertensive kidney disease with high accuracy.
Can cardiovascular biomarkers derived from a computational circulation model accurately differentiate diabetic kidney disease from hypertensive kidney disease?
Computational modeling suggests that non-invasive hemodynamic biomarkers, particularly pulsatility index and resistive index, can accurately differentiate diabetic from hypertensive kidney disease.
Abstract Purpose 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. Methods 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 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. Conclusion 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.
Wang et al. (Thu,) conducted a other in Chronic Kidney Disease (CKD) (n=24,000). Renal circulation model vs. Not applicable was evaluated on Area under the receiver operating characteristic curve (AUC) for differentiating DKD from HKD. The study identifies cardiovascular biomarkers that can distinguish between diabetic kidney disease and hypertensive kidney disease with high accuracy.