April 2011
Volume 52, Issue 14
Free
ARVO Annual Meeting Abstract  |   April 2011
Noninvasive Assessment Of Parafoveal Capillaries In Type 2 Diabetes Prior To Onset Of Diabetic Retinopathy
Author Affiliations & Notes
  • Johnny Tam
    Joint Graduate Group in Bioengineering, University of California, Berkeley and University of California, San Francisco, Berkeley, California
  • Kavita P. Dhamdhere
    School of Optometry, University of California, Berkeley, Berkeley, California
  • Pavan Tiruveedhula
    School of Optometry, University of California, Berkeley, Berkeley, California
  • Silvestre Manzanera
    School of Optometry, University of California, Berkeley, Berkeley, California
  • Shirin Barez
    School of Optometry, University of California, Berkeley, Berkeley, California
  • Marcus A. Bearse, Jr.
    School of Optometry, University of California, Berkeley, Berkeley, California
  • Anthony J. Adams
    School of Optometry, University of California, Berkeley, Berkeley, California
  • Austin Roorda
    Joint Graduate Group in Bioengineering, University of California, Berkeley and University of California, San Francisco, Berkeley, California
    School of Optometry, University of California, Berkeley, Berkeley, California
  • Footnotes
    Commercial Relationships  Johnny Tam, None; Kavita P. Dhamdhere, None; Pavan Tiruveedhula, None; Silvestre Manzanera, None; Shirin Barez, None; Marcus A. Bearse, Jr., None; Anthony J. Adams, None; Austin Roorda, University of Rochester, University of Houston (P)
  • Footnotes
    Support  NDSEG Fellowship, NSF Graduate Research Fellowship, NIH EY014375, JDRF grant 8-2008-823
Investigative Ophthalmology & Visual Science April 2011, Vol.52, 4416. doi:
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      Johnny Tam, Kavita P. Dhamdhere, Pavan Tiruveedhula, Silvestre Manzanera, Shirin Barez, Marcus A. Bearse, Jr., Anthony J. Adams, Austin Roorda; Noninvasive Assessment Of Parafoveal Capillaries In Type 2 Diabetes Prior To Onset Of Diabetic Retinopathy. Invest. Ophthalmol. Vis. Sci. 2011;52(14):4416.

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

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Abstract

Purpose: : To investigate the appearance of the parafoveal capillary network in adult subjects with Type 2 diabetes prior to the onset of diabetic retinopathy, using adaptive optics scanning laser ophthalmoscopy (AOSLO).

Methods: : 12 adult subjects with Type 2 diabetes and no diabetic retinopathy (age 55.5 +/- 7.8) were compared to 11 control subjects with no diabetes (age 52.2 +/- 10.6). AOSLO videos were acquired in the parafoveal region of one eye from each subject; for the subjects with diabetes, the absence of retinopathy was confirmed by grading of color fundus photographs by a retina specialist. Images of the parafoveal capillary network were generated using offline motion contrast analysis. These images were used to compare the two groups. The comparison included qualitative assessment of vascular features (loops and microaneurysms) as well as quantitative assessment of foveal avascular zone (FAZ) size and shape, capillary density (CD), and tortuosity of arterio-venous (AV) channels. AV channels were identified as the least tortuous capillary paths connecting arteries to veins.

Results: : Loops and microaneurysm-like features were observed in both groups. In general, diabetic retinas (D) had more total features than control retinas (C). Comparing D to C, FAZ size was increased, but not significantly (p=0.42; n=12D,11C); FAZ shape was altered, but not significantly (p=0.48; n=9D,11C); and capillary density within 0.15 degrees of the edge of the FAZ was decreased, but not significantly (p=0.48; n=9D,8C). The tortuosity of AV channels was significantly increased (p<0.05; n=11D,8C).

Conclusions: : It is often difficult to find consistent changes in the microvasculature due to large intersubject variability. However, AOSLO imaging can be used to noninvasively visualize parafoveal capillaries and identify AV channels, which appear to be altered in Type 2 diabetes even before onset of diabetic retinopathy.

Keywords: diabetic retinopathy • imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound) • image processing 
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