Estimation of Intraglomerular Pressure Using Invasive Renal Arterial Pressure and Flow Velocity Measurements in Humans

Abstract

Background: Glomerular hyperfiltration resulting from an elevated intraglomerular pressure (Pglom) is an important cause of CKD, but there is no feasible method to directly assess Pglom in humans. We developed a model to estimate Pglom in patients from combined renal arterial pressure and flow measurements.

Methods: We performed hemodynamic measurements in 34 patients undergoing renal or cardiac angiography under baseline conditions and during hyperemia induced by intrarenal dopamine infusion (30 μg/kg). For each participant during baseline and hyperemia, we fitted an adapted three-element Windkessel model that consisted of characteristic impedance, compliance, afferent resistance, and Pglom.

Results: We successfully analyzed data from 28 (82%) patients. Median age was 58 years (IQR, 52-65), median eGFR was 95 ml/min per 1.73 m² (IQR, 74-100) using the CKD-EPI formula, 30% had microalbuminuria, and 32% had diabetes. The model showed a mean Pglom of 48.0 mm Hg (SD=10.1) at baseline. Under hyperemia, flow increased by 88% (95% CI, 68% to 111%). This resulted in a 165% (95% CI, 79% to 294%) increase in afferent compliance and a 13.1-mm Hg (95% CI, 10.0 to 16.3) decrease in Pglom. In multiple linear regression analysis, diabetes (coefficient, 10.1; 95% CI, 5.1 to 15.1), BMI (0.99 per kg/m²; 95% CI, 0.38 to 1.59), and renal perfusion pressure (0.42 per mm Hg; 95% CI, 0.25 to 0.59) were significantly positively associated with baseline Pglom.

Conclusions: We constructed a model on the basis of proximal renal arterial pressure and flow velocity measurements that provides an overall estimate of glomerular pressure and afferent and efferent resistance in humans. The model provides a novel research technique to evaluate the hemodynamics of CKD on the basis of direct pressure and flow measurements.

Clinical trial registry name and registration number: Functional HEmodynamics in patients with and without Renal Artery stenosis (HERA), NL40795.018.12 at the Dutch national trial registry (toetsingonline.nl).

Keywords: glomerular hyperfiltration; renal hemodynamics; renal hypertension.

Graphical abstract

Figure 1.

Schematic representation of the renal Windkessel model used to estimate glomerular pressure. The upper panel depicts the Windkessel model, which is fitted to match the relation between pressure and flow in the afferent arteriole. The lower panel depicts the Windkessel embedded in the single-nephron model for the estimation of the efferent resistance R_e. After the estimation of R_a and P_glom using the Windkessel model, R_e can be derived by . P is the pressure inside the main renal artery, Z_c is the characteristic impedance of the proximal renal artery, C_a is the renal artery compliance, R_a is the afferent resistance, and R_e is the efferent resistance.

Figure 2.

Representative example of a Windkessel fit during baseline. Black indicates the measured value, and gray indicates the fitted values. The left panel shows measured pressure, measured flow, and the modeled flow. The right panel shows measured and modeled impedance. In this example, the Windkessel fit resulted in a glomerular pressure of 37.4 mm Hg, a characteristic impedance of 1.12 mm Hg/(cm/s), and a compliance of 1.06 cm/mm Hg. The measured impedance and the fitted impedance differ in the direct current component due to the subtraction of the estimated Pglom in the modeled impedance. The shape of the absolute value and the phase of the modeled impedance and estimated impedance in this example are representative of an adequate measurement.

Figure 3.

Distribution of estimated glomerular pressure under baseline (left) and hyperemic conditions (right) in patients with and without diabetes, showing a higher baseline Pglom in participants with diabetes. The boxes indicate medians and IQRs, and the bars indicate the ranges. P values show comparison of diabetes with nondiabetes mellitus corrected for renal perfusion pressure and BMI in a multiple linear regression model.