July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
An Hourglass Pattern of Oxygen Distribution in the Lamina Cribrosa Predicted using a Computational Model
Author Affiliations & Notes
  • Thanadet Chuangsuwanich
    Biomedical Engineering, National University of Singapore, Singapore, Singapore
  • Liang Hwa Leo
    Biomedical Engineering, National University of Singapore, Singapore, Singapore
  • Leopold Schmetterer
    Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
    Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
  • Michael J A Girard
    Biomedical Engineering, National University of Singapore, Singapore, Singapore
    Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
  • Footnotes
    Commercial Relationships   Thanadet Chuangsuwanich, None; Liang Leo, None; Leopold Schmetterer, None; Michael Girard, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 4477. doi:
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      Thanadet Chuangsuwanich, Liang Hwa Leo, Leopold Schmetterer, Michael J A Girard; An Hourglass Pattern of Oxygen Distribution in the Lamina Cribrosa Predicted using a Computational Model. Invest. Ophthalmol. Vis. Sci. 2018;59(9):4477.

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

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Abstract

Purpose : To estimate oxygen concentration and consumption levels within the retinal ganglion cell (RGC) axons at the site of the lamina cribrosa (LC) and to rank the LC morphological factors that influence such concentrations.

Methods : We generated 10,000 artificial LC micro-capillary networks. As in our previous work (Chuangsuwanich T et al., 2016. IOVS; 57:6167-6179), we used computational fluid dynamics to simulate blood flow within the LC micro-capillaries. In addition, we used the Green’s function method to predict oxygen diffusion and consumption within the RGC axons at the LC. Across networks, we varied the average pore size of the LC (5600±1200 µm2 for the superior-inferior [S-I] regions and 2700±800 µm2 for the nasal-temporal [N-T] regions), the micro-capillary arrangement (radial, isotropic or circumferential), the LC diameter (1.9±0.3 mm), the S-I LC curvature (340±116 m-1) and the N-T LC curvature (-78±130 m-1). Blood flow was assumed to originate from the LC periphery (arterial pressure: 50±6.8 mmHg) and drainage occurred in the central retinal vein (venous pressure: 17±6.4 mmHg). Finally, we performed linear regressions to rank the influence of each factor on LC’s tissue oxygen concentration.

Results : Across all networks, we generally observed a characteristic hourglass pattern of oxygen distribution (Figure 1a) with S-I regions having less average oxygen concentration (average for all networks: 52±6.1 mmHg O2) than the N-T regions (56±5.8 mmHg O2). Interestingly, LC pore-size, which is the distinguishing morphological feature between S-I and N-T regions, was relatively insignificant in influencing oxygen concentration (Figure 1b). S-I curvature was ranked third in its impact on oxygen concentration (Figure 1b) and an increase in S-I curvature (more cupping) significantly lowered average oxygen concentration.

Conclusions : This study presents a comprehensive hemodynamics model for the LC that revealed local oxygen concentration in RGC axons. Our result showed a heterogeneous distribution of oxygen concentration within the LC that may contribute to the selective pattern of axonal loss in glaucoma. Also, it was suggested that the increase in curvature along S-I region could be the main contributing factor behind this pattern, not the differences in LC pore-size between each region. This finding could provide a clinically relevant perspective for the pathogenesis of glaucoma.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

 

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