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Retinal vasculature of different diameters and plexuses exhibit distinct vulnerability in varying severity of diabetic retinopathy

Abstract

Objectives

To study the changes in vessel densities (VD) stratified by vessel diameter in the retinal superficial and deep vascular complexes (SVC/DVC) using optical coherence tomography angiography (OCTA) images obtained from people with diabetes and age-matched healthy controls.

Methods

We quantified the VD based on vessel diameter categorized as <10, 10–20 and >20 μm in the SVC/DVC obtained on 3 × 3 mm2 OCTA scans using a deep learning-based segmentation and vascular graph extraction tool in people with diabetes and age-matched healthy controls.

Results

OCTA images obtained from 854 eyes of 854 subjects were divided into 5 groups: healthy controls (n = 555); people with diabetes with no diabetic retinopathy (DR, n = 90), mild and moderate non-proliferative DR (NPDR) (n = 96), severe NPDR (n = 42) and proliferative DR (PDR) (n = 71). Both SVC and DVC showed significant decrease in VD with increasing DR severity (p < 0.001). The largest difference was observed in the <10 μm vessels of the SVC between healthy controls and no DR (13.9% lower in no DR, p < 0.001). Progressive decrease in <10 μm vessels of the SVC and DVC was seen with increasing DR severity (p < 0.001). However, 10–20 μm vessels only showed decline in the DVC, but not the SVC (p < 0.001) and there was no change observed in the >20 μm vessels in either plexus.

Conclusions

Our findings suggest that OCTA is able to demonstrate a distinct vulnerability of the smallest retinal vessels in both plexuses that worsens with increasing severity of DR.

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Fig. 1: Workflow to facilitate deep-learning-based segmentation of real OCTA images without the need for human annotations. Initially, we modelled synthetic retinal vessel networks using a physiological simulation.
Fig. 2: Optical coherence tomography angiograms (first and third columns) and extracted vascular graphs (second and fourth columns) of the superficial and deep vascular complexes of the retina in healthy subjects and patients with diabetes mellitus (DM).

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Sabanayagam C, Banu R, Chee ML, Lee R, Wang YX, Tan G, et al. Incidence and progression of diabetic retinopathy: a systematic review. Lancet Diabetes Endocrinol 2019;7:140–9.

    Article  PubMed  Google Scholar 

  2. De Carlo TE, Chin AT, Bonini Filho MA, Adhi M, Branchini L, Salz DA, et al. Detection of microvascular changes in eyes of patients with diabetes but not clinical diabetic retinopathy using optical coherence tomography angiography. Retina. 2015;35:2364–70.

    Article  PubMed  Google Scholar 

  3. Cao D, Yang D, Huang Z, Zeng Y, Wang J, Hu Y, et al. Optical coherence tomography angiography discerns preclinical diabetic retinopathy in eyes of patients with type 2 diabetes without clinical diabetic retinopathy. Acta Diabetol. 2018;55:469–77.

    Article  PubMed  Google Scholar 

  4. Fayed AE, Abdelbaki AM, El Zawahry OM, Fawzi AA. Optical coherence tomography angiography reveals progressive worsening of retinal vascular geometry in diabetic retinopathy and improved geometry after panretinal photocoagulation. PLoS One. 2020;14:e0226629.

    Article  Google Scholar 

  5. Stitt AW, Lois N, Medina RJ, Adamson P, Curtis TM. Advances in our understanding of diabetic retinopathy. Clin Sci. 2013;125:1–17.

    Article  Google Scholar 

  6. Ansari P, Tabasumma N, Snigdha NN, Siam NH, Panduru RV, Azam S, et al. Diabetic retinopathy: an overview on mechanisms, pathophysiology and pharmacotherapy. Diabetology. 2022;3:159–75.

    Article  Google Scholar 

  7. Soares M, Neves C, Marques IP, Pires I, Schwartz C, Costa MÂ, et al. Comparison of diabetic retinopathy classification using fluorescein angiography and optical coherence tomography angiography. Br J Ophthalmol. 2017;101:62–8.

    Article  PubMed  Google Scholar 

  8. Group ETDRSR. Classification of diabetic retinopathy from fluorescein angiograms: ETDRS report number 11. Ophthalmology. 1991;98:807–22.

    Article  Google Scholar 

  9. Oliver SC, Schwartz SD. Peripheral vessel leakage (PVL): a new angiographic finding in diabetic retinopathy identified with ultra wide-field fluorescein angiography. Pap Presente Semin Ophthalmol. 2010;25:27–33.

    Article  Google Scholar 

  10. Fayed AE, Nesper PL, Fawzi AA. Imaging of retinal vascular disease. In: Retinal Vascular Disease. 107–125 Springer Singapore; 2020.

  11. MacKinnon JR, McKillop G, O’Brien C, Swa K, Butt Z, Nelson P. Colour Doppler imaging of the ocular circulation in diabetic retinopathy. Acta Ophthalmol Scand. 2000;78:386–9.

    Article  CAS  PubMed  Google Scholar 

  12. Paula KY, Mehnert A, Athwal A, Sarunic MV, Yu D-Y. Use of the retinal vascular histology to validate an optical coherence tomography angiography technique. Transl Vis Sci Technol. 2021;10:29–9.

    Article  Google Scholar 

  13. Fawzi AA, Fayed AE, Linsenmeier RA, Gao J, Yu F. Improved macular capillary flow on optical coherence tomography angiography after panretinal photocoagulation for proliferative diabetic retinopathy. Am J Ophthalmol. 2019;206:217–27.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Fayed AE, Gerges TK. Optical coherence tomography angiography reveals paradoxically decreasing choroidal thickness & increasing blood flow in remitting Vogt Koyanagi Harada syndrome. RETINA. 2022;42:1788–95.

  15. Scarinci F, Nesper PL, Fawzi AA. Deep retinal capillary nonperfusion is associated with photoreceptor disruption in diabetic macular ischemia. Am J Ophthalmol. 2016;168:129–38.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Estawro RG, Fayed AE, Gerges TK, Baddar DN. Choriocapillaris Island: an optical coherence tomography angiography finding observed in central serous chorioretinopathy. Int J Retin Vitreous. 2021;7:1–8.

    Article  Google Scholar 

  17. Ashraf M, Nesper PL, Jampol LM, Yu F, Fawzi AA. Statistical model of optical coherence tomography angiography parameters that correlate with severity of diabetic retinopathy. Invest Ophthalmol Vis Sci. 2018;59:4292–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Carnevali A, Sacconi R, Corbelli E, Tomasso L, Querques L, Zerbini G, et al. Optical coherence tomography angiography analysis of retinal vascular plexuses and choriocapillaris in patients with type 1 diabetes without diabetic retinopathy. Acta Diabetol. 2017;54:695–702.

    Article  CAS  PubMed  Google Scholar 

  19. Spaide RF, Fujimoto JG, Waheed NK. Image artifacts in optical coherence angiography. Retina (Phila, Pa). 2015;35:2163.

    Article  Google Scholar 

  20. Menten MJ, Paetzold JC, Dima A, Menze BH, Knier B, Rueckert D. Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs. Paper presented at: Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VIII2022.

  21. Kreitner L, Paetzold JC, Rauch N, Chen C, Hagag AM, Fayed AE, et al. Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations. IEEE Trans Med Imaging. 2024. https://doi.org/10.1109/TMI.2024.3354408. Online ahead of print.

  22. Wang X, Wei Q, Wu X, Cao S, Chen C, Zhang J, et al. The vessel density of the superficial retinal capillary plexus as a new biomarker in cerebral small vessel disease: an optical coherence tomography angiography study. Neurol Sci. 2021;42:3615–24.

  23. Hirano T, Toriyama Y, Takamura Y, Sugimoto M, Nagaoka T, Sugiura Y, et al. Outcomes of a 2-year treat-and-extend regimen with aflibercept for diabetic macular edema. Sci Rep. 2021;11:4488.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Pak KY, Shin JP, Kim HW, Sagong M, Kim YC, Lee SJ, et al. One-year results of treatment of diabetic macular edema with aflibercept using the treat-and-extend dosing regimen: the VIBIM study. Ophthalmologica. 2020;243:255–62.

    Article  CAS  PubMed  Google Scholar 

  25. Curry BA, Sanfilippo PG, Chan S, Hewitt AW, Verma N. Clinical outcomes of a treat and extend regimen with intravitreal aflibercept injections in patients with diabetic macular edema: experience in clinical practice. Ophthalmol Ther. 2020;9:87–101.

    Article  PubMed  Google Scholar 

  26. Wilkinson C, Ferris FL, Klein RE, Lee PP, Agardh CD, Davis M, et al. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology. 2003;110:1677–82.

    Article  CAS  PubMed  Google Scholar 

  27. Jia Y, Tan O, Tokayer J, Potsaid B, Wang Y, Liu JJ, et al. Split-spectrum amplitude-decorrelation angiography with optical coherence tomography. Opt Express. 2012;20:4710–25.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Campbell J, Zhang M, Hwang T, Bailey S, Wilson D, Jia Y, et al. Detailed vascular anatomy of the human retina by projection-resolved optical coherence tomography angiography. Sci Rep. 2017;7:42201.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Giarratano Y, Bianchi E, Gray C, Morris A, MacGillivray T, Dhillon B, et al. Automated segmentation of optical coherence tomography angiography images: benchmark data and clinically relevant metrics. Transl Vis Sci Technol. 2020;9:5–5.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18:203–11.

    Article  CAS  PubMed  Google Scholar 

  31. Kreitner L, Paetzold JC, Rauch N, Chen C, Hagag AM, Fayed AE, et al. Detailed retinal vessel segmentation without human annotations using simulated optical coherence tomography angiographs. arXiv preprint arXiv:230610941. 2023.

  32. Meyer-Spradow J, Ropinski T, Mensmann J, Hinrichs K. Voreen: a rapid-prototyping environment for ray-casting-based volume visualizations. IEEE Comput Graph Appl. 2009;29:6–13.

    Article  CAS  PubMed  Google Scholar 

  33. Paetzold JC, McGinnis J, Shit S, Ezhov I, Büschl P, Prabhakar C, et al. Whole brain vessel graphs: a dataset and benchmark for graph learning and neuroscience. Paper presented at: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)2021.

  34. Gardiner TA, Archer DB, Curtis TM, Stitt AW. Arteriolar involvement in the microvascular lesions of diabetic retinopathy: implications for pathogenesis. Microcirculation. 2007;14:25–38.

    Article  PubMed  Google Scholar 

  35. Stitt A, Anderson H, Gardiner T, Archer D. Diabetic retinopathy: quantitative variation in capillary basement membrane thickening in arterial or venous environments. Br J Ophthalmol. 1994;78:133–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Hogan MJ, Feeney L. The ultrastructure of the retinal vessels: II. The small vessels. J Ultrastruct Res. 1963;9:29–46.

    Article  Google Scholar 

  37. Mohammed S, Li T, Chen XD, Warner E, Shankar A, Abalem MF, et al. Density-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomography. Sci Rep. 2020;10:15937.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Sohn EH, van Dijk HW, Jiao C, Kok PH, Jeong W, Demirkaya N, et al. Retinal neurodegeneration may precede microvascular changes characteristic of diabetic retinopathy in diabetes mellitus. Proc Natl Acad Sci. 2016;113:E2655–E2664.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Harris A, Arend O, Danis RP, Evans D, Wolf S, Martin BJ. Hyperoxia improves contrast sensitivity in early diabetic retinopathy. Br J Ophthalmol. 1996;80:209–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Harris A, Arend O, Bohnke K, Kroepfl E, Danis R, Martin B. Retinal blood flow during dynamic exercise. Graefes Arch Clin Exp Ophthalmol. 1996;234:440–4.

    Article  CAS  PubMed  Google Scholar 

  41. Balaratnasingam C, An D, Hein M, Yu P, Yu D-Y. Studies of the retinal microcirculation using human donor eyes and high-resolution clinical imaging: Insights gained to guide future research in diabetic retinopathy. Prog Retin Eye Res. 2022;94:101134.

  42. An D, Yu P, Freund KB, Yu D-Y, Balaratnasingam C. Three-dimensional characterization of the normal human parafoveal microvasculature using structural criteria and high-resolution confocal microscopy. Invest Ophthalmol Vis Sci. 2020;61:3–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Falcão M, Falcão-Reis F, Rocha-Sousa A. Diabetic retinopathy: understanding pathologic angiogenesis and exploring its treatment options. Open Circ Vasc J. 2010;3:30–42.

  44. Niki T, Muraoka K, Shimizu K. Distribution of capillary nonperfusion in early-stage diabetic retinopathy. Ophthalmology. 1984;91:1431–9.

    Article  CAS  PubMed  Google Scholar 

  45. Lavia C, Couturier A, Erginay A, Dupas B, Tadayoni R, Gaudric A. Reduced vessel density in the superficial and deep plexuses in diabetic retinopathy is associated with structural changes in corresponding retinal layers. PLoS One. 2019;14:e0219164.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. AttaAllah HR, Mohamed AAM, Ali MA. Macular vessels density in diabetic retinopathy: quantitative assessment using optical coherence tomography angiography. Int Ophthalmol. 2019;39:1845–59.

    Article  PubMed  Google Scholar 

  47. An D, Pulford R, Morgan WH, Yu D-Y, Balaratnasingam C. Associations between capillary diameter, capillary density, and microaneurysms in diabetic retinopathy: a high-resolution confocal microscopy study. Transl Vis Sci Technol 2021;10:6–6.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Nesper PL, Roberts PK, Onishi AC, Chai H, Liu L, Jampol LM, et al. Quantifying microvascular abnormalities with increasing severity of diabetic retinopathy using optical coherence tomography angiography. Invest Ophthalmol Vis Sci. 2017;58:BIO307–15.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Hirano T, Kitahara J, Toriyama Y, Kasamatsu H, Murata T, Sadda S. Quantifying vascular density and morphology using different swept-source optical coherence tomography angiographic scan patterns in diabetic retinopathy. Br J Ophthalmol. 2019;103:216–21.

    Article  PubMed  Google Scholar 

  50. Sim DA, Keane PA, Rajendram R, Karampelas M, Selvam S, Powner MB, et al. Patterns of peripheral retinal and central macula ischemia in diabetic retinopathy as evaluated by ultra-widefield fluorescein angiography. Am J Ophthalmol. 2014;158:144–153.e141.

    Article  PubMed  Google Scholar 

  51. Silva PS, Liu D, Glassman AR, Aiello LP, Grover S, Kingsley RM, et al. Assessment of fluorescein angiography nonperfusion in eyes with diabetic retinopathy using ultrawide field retinal imaging. Retina. 2022;42:1302–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

Authors

Contributions

AEF, MM AMH and SS conceptualized the project. AEF, SB and RRF collected the imaging and patient information. MM, LK, JCP and DR performed the data analysis. AMH performed the statistical analysis. AEF wrote the manuscript. All authors contributed to reviewing the manuscript.

Corresponding author

Correspondence to Alaa E. Fayed.

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Competing interests

AF: None, MM: None, LK: None, JCP: None, DR: None, SB: None, AH: Boehringer Ingelheim Ltd (E), SS: Bayer (F), Novartis (F), Abbvie (F), Roche (F), Boehringer Ingelheim (F), Optos (F), EyeBiotech (F), Biogen (F)Apellis (F), Janssen Pharmaceuticals (F), Ocular Therapeutix (F), OcuTerra (F). SS is a member of the Eye Editorial Board.

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Fayed, A.E., Menten, M.J., Kreitner, L. et al. Retinal vasculature of different diameters and plexuses exhibit distinct vulnerability in varying severity of diabetic retinopathy. Eye (2024). https://doi.org/10.1038/s41433-024-03021-4

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  • DOI: https://doi.org/10.1038/s41433-024-03021-4

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