April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
Theoretical analysis of the relationship between changes in retinal perfusion and tissue metabolic demand
Author Affiliations & Notes
  • Simone Cassani
    Mathematics, Indiana University Purdue Univ, Indianapolis, IN
  • Julia Arciero
    Mathematics, Indiana University Purdue Univ, Indianapolis, IN
  • Giovanna Guidoboni
    Mathematics, Indiana University Purdue Univ, Indianapolis, IN
    Ophthalmology, Indiana Univ Sch of Medicine, Indianapolis, IN
  • Brent A Siesky
    Ophthalmology, Indiana Univ Sch of Medicine, Indianapolis, IN
  • Alon Harris
    Ophthalmology, Indiana Univ Sch of Medicine, Indianapolis, IN
  • Footnotes
    Commercial Relationships Simone Cassani, None; Julia Arciero, None; Giovanna Guidoboni, None; Brent Siesky, None; Alon Harris, Adom (I), Alcon (R), Biolight (C), MSD (R), Nano Retina (C), ONO Pharmaceuticals (C), Pharmalight (C), Sucampo (C)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 4322. doi:
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    • Get Citation

      Simone Cassani, Julia Arciero, Giovanna Guidoboni, Brent A Siesky, Alon Harris; Theoretical analysis of the relationship between changes in retinal perfusion and tissue metabolic demand. Invest. Ophthalmol. Vis. Sci. 2014;55(13):4322.

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

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Abstract
 
Purpose
 

Alterations in retinal perfusion are associated with diseases such as glaucoma, age-related macular degeneration and diabetes. The retina is capable of metabolic autoregulation, which is the ability to adjust perfusion in response to altered tissue demand. In cases of exercise or flicker stimulation, the metabolic demands of the retina are observed to increase, whereas in pathological cases, retinal perfusion may not meet oxygen demand. Here, a mathematical model is used to investigate the relationship between retinal perfusion and tissue demand

 
Methods
 

The retinal vasculature is represented by compartments for large arterioles, small arterioles, capillaries, small venules, and large venules. The model is based on passive and active length-tension relationships of smooth muscle that determine arteriolar diameters. Passive tension is described by an exponential function, and active tension is represented by the product of maximally active tension (given by a Gaussian function) and a factor between zero and one that indicates the level of smooth muscle activation. When all mechanisms of metabolic autoregulation are functioning, the arterioles are assumed to respond to changes in pressure, shear stress, carbon dioxide, and the downstream metabolic state. If autoregulation is not functioning, arteriolar vascular tone is assumed to be constant

 
Results
 

When autoregulation is functional, the model predicts that retinal perfusion increases nearly two-fold in response to a three-fold increase in oxygen demand. The model predicts a 14% decrease in perfusion if demand is decreased by 50%, and a 33% increase in perfusion if demand is increased by 50%. In the absence of autoregulation, the model shows a constant level of perfusion that does not respond to changes in oxygen demand

 
Conclusions
 

The model results suggest that impaired autoregulation might increase the risk of retinal ischemic damage in conditions of elevated demand (as in flicker stimulation), since the vasculature is unable to adequately respond to the increase in demand. The model also suggests that autoregulation allows for a greater degree of compensation for increased demand than for decreased demand. In particular, the model predicts only a moderate decrease in perfusion in response to a decrease in metabolic demand (as in retinal ganglion cell loss)

  
Keywords: 473 computational modeling • 688 retina • 436 blood supply  
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