Purpose:
Retinal blood flow autoregulation is achieved by altering the tone of arteriolar smooth muscle cells and capillary pericytes according to myogenic, shear-dependent, and metabolic mechanisms. A failure in retinal autoregulation may be a risk factor for glaucoma, suggesting that glaucoma therapies should target vascular regulatory mechanisms. Here, a mathematical model is used to predict the relative importance of regulatory mechanisms in achieving retinal autoregulation.
Methods:
Resistance vessels are assumed to respond to local changes in pressure, shear stress, and lactate production and to the downstream metabolic state communicated via conducted responses. Arteriolar diameters are calculated based on vascular smooth muscle mechanics, and capillary diameters remain fixed unless lactate production exceeds a certain threshold. Model parameters governing wall tension are fit to data from porcine retinal arterioles and differ for each vessel type. The response to lactate production is assumed to differ in arterioles and capillaries and under conditions of normoxia or hypoxia, as has been seen experimentally.
Results:
The factor by which flow changes as the blood pressure exiting the central retinal artery is varied between 32 and 44 mmHg is used to indicate the degree of autoregulation. A factor of 1 indicates perfect autoregulation. In the presence of only myogenic and shear-dependent mechanisms, the model predicts a poor degree of autoregulation (factor of 1.74). However, including the effects of both the conducted and lactate responses in arterioles significantly improves the degree of autoregulation (factor of 1.14).
Conclusions:
The model results indicate that metabolic responses are crucial in achieving autoregulation of blood flow. This model can also be used to predict the relative roles of lactate and conducted responses in both autoregulation and metabolic flow regulation.
Keywords: computational modeling • blood supply • retina