Received: 18 March 2019; Published: 27 June 2019

Predicting the risk of diabetic retinopathy-associated macular edema in patients with type 2 diabetes mellitus

S.Yu. Mogilevskyy1, Dr Sc (Med), Prof.; Iu.O. Panchenko3, Cand Sc (Med); S.V. Ziablitsev2, Dr Sc (Med), Prof

1  Shupik National Medical Academy of Postgraduate Education; Kyiv (Ukraine)

2 Bohomolets National Medical University; Kyiv (Ukraine)

3 Kyiv Municipal Clinical Hospital “Eye Microsurgery Center”; Kyiv (Ukraine)

E-mail: sergey.mogilevskyy@gmail.com

Background: Previously, we have reported on the value of prothrombotic platelet phenotype as a factor for the development of diabetic maculopathy (DMP) and diabetic macular edema (DME) in patients with diabetic retinopathy (DR) and type 2 diabetes mellitus (DM2).

Purpose: To predict DR-associated DME in patients with DM2 based on platelet dysfunction analysis.

Materials and Methods: Ninety patients (92 eyes) with DM2 were included in the study. Of these eyes, 18, 43 and 31 were found to have, respectively, mild non-proliferative DR (NPDR), moderate or severe NPDR, and proliferative DR. Platelet aggregation agonists, adenosine diphosphate (ADP), platelet activation factor (PAF), and collagen (Sigma, St. Louis, MO), were used, and platelet aggregation was assessed with a Chrono-Log aggregometer. Methods for building logistic regression and neural network models were used to identify a set of independent variables associated with the risk for DME.

Results: The risk for DME increased with increases in adrenaline- and PAF-induced platelet aggregations (p=0.03 and p=0.02, respectively) and decreased with an increase in collagen-induced platelet aggregation (p=0.046). There was a tendency to increase in the risk for DME with a one per cent increase in angiotensin II (ANG II)-induced platelet aggregation. Neural network analysis revealed non-linear associations of this risk with three independent variables, ANG II-, PAF-, and collagen-induced platelet aggregations. A neural network model with a sensitivity of 77.1% and specificity of 78.1% was created to predict DME based on this set of independent variables.

Keywords: diabetic macular edema, type 2 diabetes mellitus, platelet dysfunction, prediction models



1.Pasyechnikova NV. [Diabetic maculopathy: current aspects of the pathogenesis, clinical manifestations, diagnosis and treatment]. Kyiv: Karbon LTD; 2010. Russian.

2.Grassi MA, Tikhomirov A, Ramalingam S, Lee KE, Hosseini SM, Klein BE, et al. Replication analysis for severe diabetic retinopathy. Invest Ophthalmol Vis Sci. 2012 Apr 30;53(4):2377-81. doi: 10.1167/iovs.11-8068.

3.Fabrikantov OV, Gurko TS. [Diabetic maculopathy: epidemiology, pathogenesis and current approaches to treatment (literature review)]. Vestnik TGU. 2014;19(2):744-7. Russian.

4.Amirov AN, Abdullaieva EA, Minkhuzina EL. [Diabetic macular edema: pathogenesis, diagnosis clinical manifestations, and treatment]. Kazanskii meditsinskii zhurnal. 2015:96(1):70-6. Russian.

5.Dabir SS, Das D, Nallathambi J, Mangalesh S, Yadav NK, Schouten JS. Differential systemic gene expression profile in patients with diabetic macular edema: Responders versus nonresponders to standard treatment. Indian J Ophthalmol. 2014 Jan;62(1):66-73. doi: 10.4103/0301-4738.126186.

6.Chen E, Looman M, Laouri M, Gallagher M, Van Nuys K, Lakdawalla D, Fortuny J. Burden of illness of diabetic macular edema: literature review. Curr Med Res Opin. 2010 Jul;26(7):1587-97. doi: 10.1185/03007995.2010.482503. Review.

7.Stefanini FR, Badaró E, Falabella P, Koss M, Farah ME, Maia M. Anti-VEGF for the management of diabetic macular edema. J Immunol Res. 2014;2014:632307. doi: 10.1155/2014/632307.

8.Mogilevskyy SIu, Panchenko IuO, Ziablitsev SV, Natrus LV. [Platelet aggregation impairment as a factor for the development of diabetic maculopathy and diabetic macular edema in patients with non-proliferative diabetic retinopathy in type 2 diabetes mellitus]. Arkhiv oftalmologii Ukrainy.  2018; 8(3):26–31. Ukrainian.

9.Hudz AS, Mogilevskyy SIu, Maksymtsiv ML. Functional status of platelets in type 2 diabetes patients showing no diabetic fundus changes. J Ophthalmol (Ukraine). 2017;1:20-4.

10.Rykov SO, Burdei AV, Ziablitsev SV, Mogilevskyy SIu. Predicting the development and progression of primary open-angle glaucoma based on the determination of GST gene polymorphisms. J Ophthalmol (Ukraine). 2018;4(483):11-6.

11.Mogilevskyy SIu, Ziablitsev SV, Denisiuk LI, Gur’ianov VG. Mathematical analysis of the effect of Pro72Arg polymorphism in the TP53 gene on the emergence and progression of POAG. J Ophthalmol (Ukraine). 2016;6:32-7.

12.Mun SA, Glushkov AN, Shternis TA. [Regression analysis in medical and biological research]. Kemerovo: KemGMA; 2012. Russian.

13.Lally DR, Shah CP, Heier JS. Vascular endothelial growth factor and diabetic macular edema. Surv Ophthalmol. 2016 Nov - Dec;61(6):759-68. doi: 10.1016/j.survophthal.2016.03.010.

14.Li JK, Wei F, Jin XH, Dai YM, Cui HS, Li YM. Changes in vitreous VEGF, bFGF and fibrosis in proliferative diabetic retinopathy after intravitreal bevacizumab. Int J Ophthalmol. 2015;8(6):1202-6.

15.Favre GA, Lebrun P, Lopez P, Butori C, Hofman P, Esnault VL, Van Obberghen E. Constitutive activation of the renin-angiotensin system reduces visceral fat and improves glucose tolerance in mice. J Renin Angiotensin Aldosterone Syst. 2014 Dec;15(4):396-409. doi: 10.1177/1470320314537695.

16.Kim JH, Kim JH, Yu YS, Cho CS, Kim KW. Blockade of angiotensin II attenuates VEGF-mediated blood-retinal barrier breakdown in diabetic retinopathy. J Cereb Blood Flow Metab. 2009 Mar;29(3):621-8. doi: 10.1038/jcbfm.2008.154.

17.Porta M, Taulaigo AV. The changing role of the endocrinologist in the care of patients with diabetic retinopathy. Endocrine. 2014 Jun;46(2):199-208. doi: 10.1007/s12020-013-0119-4. 

The authors certify that they have no conflicts of interest in the subject matter or materials discussed in this manuscript.