June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
3D eye-specific analysis of factors influencing lamina cribrosa oxygen concentration
Author Affiliations & Notes
  • Yi Hua
    Department of Ophthalmology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
  • Jason Arthur Walker
    Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
  • Po-Yi Lee
    Department of Ophthalmology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
    Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
  • Bryn Brazile
    Department of Ophthalmology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
  • Ian A Sigal
    Department of Ophthalmology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
    Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
  • Footnotes
    Commercial Relationships   Yi Hua, None; Jason Walker, None; Po-Yi Lee, None; Bryn Brazile, None; Ian Sigal, None
  • Footnotes
    Support  Supported in part by National Institutes of Health R01-EY023966, R01-EY028662, P30-EY008098 and T32-EY017271 (Bethesda, MD), the Eye and Ear Foundation (Pittsburgh, PA), and Research to Prevent Blindness.
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 1021. doi:
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    • Get Citation

      Yi Hua, Jason Arthur Walker, Po-Yi Lee, Bryn Brazile, Ian A Sigal; 3D eye-specific analysis of factors influencing lamina cribrosa oxygen concentration. Invest. Ophthalmol. Vis. Sci. 2021;62(8):1021.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Oxygen in the lamina cribrosa (LC) is essential for maintaining functioning retinal ganglion cell axons and vision. Measuring LC oxygen experimentally is not yet possible and thus it is usually studied using numerical models. LC oxygen models have been highly simplified and generic (Chuangsuwanich et al., 2016), or eye-specific but 2D (Hua et al., 2020). Our goal was to leverage new 3D eye-specific models of the LC vessel network to identify the factors with the largest influence on LC oxygen.

Methods : We reconstructed a detailed 3D model of the monkey LC vessel network based on histological sections (Fig. 1). Hemodynamics boundary conditions were defined to simulate blood flow from the circle of Zinn-Haller, drainage through the central retinal vein, and interactions with the pre and retrolaminar regions. Using a Monte Carlo approach, 500 models were generated with varying (baseline ± 20%) microvessel diameter, pressures (arteriole, venule, prelaminar, and retrolaminar), inflow hematocrit, and oxygen consumption rate. Models were simulated to estimate LC neural tissue oxygen concentration. Regression analysis was used to determine factor influences on the minimum (10th percentile) oxygen concentration in the LC.

Results : The factors influencing the minimum oxygen concentration the most were: the microvessel diameter, oxygen consumption rate, and arteriole pressure; and to a less extent: the venule, retrolaminar, and prelaminar pressures, and the inflow hematocrit (Fig. 2).

Conclusions : Our models predict that the microvessel diameter, oxygen consumption rate, and arteriole pressure have the largest influence on the oxygen concentration in the LC. To understand the susceptibility to ischemia in retinal ganglion cell axon damage and vision loss, the influential factors should be better characterized.

This is a 2021 ARVO Annual Meeting abstract.

 

Fig. 1 (Left) Vessel network geometry and hemodynamics boundary conditions. (Right) Input factor baseline values and ranges used in the sensitivity analysis.

Fig. 1 (Left) Vessel network geometry and hemodynamics boundary conditions. (Right) Input factor baseline values and ranges used in the sensitivity analysis.

 

Fig. 2 Minimum LC oxygen concentration as a function of the factors. Each dot is one model. The bar chart shows the factors sorted based on linear regression analysis of influences.

Fig. 2 Minimum LC oxygen concentration as a function of the factors. Each dot is one model. The bar chart shows the factors sorted based on linear regression analysis of influences.

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