An In Silico Modelling Approach to Predict Hemodynamic Outcomes in Diabetic and Hypertensive Kidney Disease.

Area of Science:

• Computational modeling
• Biomarker discovery
• Nephrology

Background:

• Early diagnosis of kidney disease is challenging, hindering timely intervention.
• Diabetes and hypertension are leading causes of kidney disease, often co-occurring.
• Distinguishing between diabetic and hypertensive kidney disease typically requires invasive biopsy.

Purpose of the Study:

• To develop a computational modeling approach to simulate renal hemodynamics in virtual populations.
• To identify potential non-invasive biomarkers for early kidney disease detection.
• To validate the model against existing in vivo data for aging, diabetic, and hypertensive populations.

Main Methods:

• Developed a computational model simulating blood velocity, flow rate, and pressure wave propagation in arterial networks.
• Validated the model using literature data on aging populations for pressure, flow rate, and waveform indices.
• Calibrated the model with in vivo data on diabetic and hypertensive nephropathy.

Main Results:

• Identified potential biomarkers from renal blood flow rate and pulsatility.
• Quantified resistive index changes in early and severe diabetic nephropathy (0.69–0.74) and hypertensive nephropathy (0.65–0.73).
• Reported mean renal blood flow rates differentiating disease stages in diabetic (317–329 ml/min) and hypertensive (388–443 ml/min) nephropathy.

Conclusions:

• The developed modeling approach shows promise for identifying novel biomarkers for early kidney disease diagnosis.
• This non-invasive method can aid in distinguishing between diabetic and hypertensive nephropathy.
• The model facilitates biomarker discovery and the design of future clinical trials for kidney disease.