November 2016
Volume 57, Issue 14
Open Access
Retina  |   November 2016
Retinal Neurodegeneration in Diabetic Patients Without Diabetic Retinopathy
Author Affiliations & Notes
  • Joana Tavares Ferreira
    Department of Ophthalmology, Central Lisbon Hospital Center, Lisbon, Portugal
    NOVA Medical School, Universidade NOVA de Lisboa, Lisbon, Portugal
  • Marta Alves
    Epidemiology and Statistics Unit, Research Centre, Central Lisbon Hospital Center, Lisbon, Portugal
  • Arnaldo Dias-Santos
    Department of Ophthalmology, Central Lisbon Hospital Center, Lisbon, Portugal
    NOVA Medical School, Universidade NOVA de Lisboa, Lisbon, Portugal
  • Lívio Costa
    Department of Ophthalmology, Central Lisbon Hospital Center, Lisbon, Portugal
  • Bruno Oliveira Santos
    Department of Ophthalmology, Associação Médica Olhar bem, Lisbon, Portugal
    CEris-ICIST, Instituto Superior Técnico, Lisbon University, Lisbon, Portugal
  • João Paulo Cunha
    Department of Ophthalmology, Central Lisbon Hospital Center, Lisbon, Portugal
    NOVA Medical School, Universidade NOVA de Lisboa, Lisbon, Portugal
  • Ana Luísa Papoila
    NOVA Medical School, Universidade NOVA de Lisboa, Lisbon, Portugal
    Epidemiology and Statistics Unit, Research Centre, Central Lisbon Hospital Center, Lisbon, Portugal
    CEAUL (Center of Statistics and Applications), Lisbon University, Lisbon, Portugal
  • Luís Abegão Pinto
    Department of Ophthalmology, Northern Lisbon Hospital Center, Lisbon, Portugal
    Visual Sciences Study Center, Faculty of Medicine, Lisbon University, Lisbon, Portugal
  • Correspondence: Joana Tavares Ferreira, Department of Ophthalmology, Hospital de Santo António dos Capuchos, Alameda de Santo António dos Capuchos, 1169–050 Lisbon, Portugal; joanaptf@gmail.com
Investigative Ophthalmology & Visual Science November 2016, Vol.57, 6455-6460. doi:10.1167/iovs.16-20215
  • Views
  • PDF
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Joana Tavares Ferreira, Marta Alves, Arnaldo Dias-Santos, Lívio Costa, Bruno Oliveira Santos, João Paulo Cunha, Ana Luísa Papoila, Luís Abegão Pinto; Retinal Neurodegeneration in Diabetic Patients Without Diabetic Retinopathy. Invest. Ophthalmol. Vis. Sci. 2016;57(14):6455-6460. doi: 10.1167/iovs.16-20215.

      Download citation file:


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

      ×
  • Supplements
Abstract

Purpose: To compare the thickness of all retinal layers between a nondiabetic group and diabetic patients without diabetic retinopathy (DR).

Methods: Cross-sectional study, in which all subjects underwent an ophthalmic examination including optical coherence tomography. After automatic retinal segmentation, each retinal layer thickness (eight separate layers and overall thickness) was calculated in all nine Early Treatment Diabetic Retinopathy Study (ETDRS) areas. The choroidal thickness (CT) also was measured at five locations. Generalized additive regression models were used to analyze the data.

Results: A total of 175 patients were recruited, 50 nondiabetic subjects and 125 diabetic patients without DR, stratified into three groups according to diabetes duration: group I (<5 years, n = 55), group II (5–10 years, n = 39), and group III (>10 years, n = 31). Overall, groups I and III of diabetic patients had a decrease in the photoreceptor layer (PR) thickness, when compared with the nondiabetic subjects in six ETDRS areas (P < 0.0007). Patients with more recent diagnosis (group I) had thinner PR than those with moderate duration (group II). Interestingly, patients with longer known disease (group III) had the thinnest PR values. There were no overall differences in the remaining retinal parameters.

Conclusions: Retinal thickness profile is not linear throughout disease duration. Even in the absence of funduscopic disease, PR layer in diabetic patients seems to differ from nondiabetic subjects, thus suggesting that some form of neurodegeneration may take place before clinical signs of vascular problems arise.

Diabetes mellitus (DM) is increasing worldwide and, accordingly, diabetic retinopathy (DR) is the leading cause of legal blindness among working-aged adults.1 Of 415 million people worldwide living with diabetes in 2015, more than one-third will develop DR in their lifetime.2 More than 93 million people currently suffer some sort of eye damage from diabetes.3 
In Portugal, the PREVADIAB study found a diabetes prevalence of 11.7%.4 If “pre-diabetes” is also considered, then approximately one-third (34.9%) of the population aged 20 to 79 years is affected.4 The RETINODIAB study, an epidemiologic study that determines the prevalence and progression incidence rates of DR based on a national screening community program in Portugal, identified a 16.3% prevalence rate of DR and a 4.6% incidence rate of any DR in the first year, in patients without retinopathy at baseline.5,6 
The International Clinical Classification of DR is based in the observation of microvascular changes. The first recognizable vascular abnormalities are microaneurysms and small hemorrhages, followed by more severe signs of vascular leakage, such as hard exudates and larger hemorrhages; vascular dropout, such as cotton wool spots; and more widespread hemorrhages and neovascularizations.7 However, retinal neurodegenerative changes have been described as including apoptosis of several populations of retinal cells (photoreceptors, bipolar cells, ganglion cells, and astrocytes) with consequent reduction in thickness of the different retinal layers, in the earliest stages of DR or even when DR cannot be detected by ophthalmologic examination.811 
Recently, optical coherence tomography (OCT) has been introduced into clinical practice as the most noninvasive and objective method to visualize the retina, showing an amount of detail that resembles histological specimens.12,13 Initially, OCT was applied to detect complications of DR (edema macular or epiretinal membrane).14 Later on, it allowed quantitative and qualitative measurements of retinal thickness and segmentation of all intraretinal layers.1518 The new Spectralis spectral-domain (SD)-OCT automatic segmentation software (software version 6.0; Heidelberg Engineering, Heidelberg, Germany) demonstrated excellent repeatability and reproducibility of each of the eight individual retinal layer thickness measurements.19 Potentially, OCT might detect early retinal changes, and thus help define which diabetic patients may be at risk to develop DR. Ultimately, it could be used to plan preventive therapy before the development of vascular lesions detectable by ophthalmoscopy.20 However, until now, the smaller scale, mostly pilot studies or only focusing on specific retinal layers on this topic in OCT image analysis did not show a temporal relationship between DM duration or arising DR and the changes observed in retinal layers. 
The present study aimed to address this unmet need, by comparing the thickness of all retinal layers, measured with SD-OCT, between nondiabetic subjects and diabetic patients without DR. 
Materials and Methods
Patients
This cross-sectional study was conducted at the Ophthalmology Department of the Central Lisbon Hospital Center, between October and December of 2014. Two groups of patients were recruited: group 1 with 50 nondiabetic subjects, and group 2, with 125 type 2 diabetic patients without DR, classified according to diabetes duration: group I (<5 years), group II (5–10 years), and group III (>10 years). Per protocol, the diagnosis of type 2 DM was made following the guidelines of the Portuguese General Health Direction.21 The inclusion criteria were to be a type 2 diabetic patient without DR, with normotensive eyes, and with ability to understand the study. The exclusion criteria were the following: refractive error >5 diopters or/and axial length >25 mm in the studied eye, known diagnosis of DR or other retinal diseases, glaucoma or ocular hypertension, uveitis, neurodegenerative disease, and significant media opacities that precluded fundus imaging. 
The study was approved by our institutional ethics committee and informed consent was obtained from patients. The principles of the Declaration of Helsinki were respected. 
The ophthalmological examination included determination of best-corrected visual acuity with Snellen scale and after conversion to logMar, anterior segment examination, Goldmann applanation tonometry and dynamic contour tonometry with Pascal digital tonometer, indirect ophthalmoscopy, and ultrasonic biometry. Last, an SD-OCT was obtained and, randomly, one eye of each subject was included in this study. 
Spectral-Domain OCT Imaging and Layer Segmentation
Tomographic images were obtained using the Spectralis SD-OCT (software version 6.0; Heidelberg Engineering), after pupillary dilation, by a single, well-trained technician (G.A.), as described previously.22 Only good-quality scans with well-focused images, without overt misalignment, continuous scan patterns without missing or blank areas, without artifacts, and a signal strength better than 20 (40 = maximum) were included in the analyses. The fast macular thickness OCT protocol was performed with measurements 20 × 20-degree raster scans (consisting of 25 high-resolution scans). The automatic real-time function was set to nine frames per B-scan. An internal fixation light was used to center the scanning area on the fovea while the eye-tracking system was activated. 
The new Spectralis automatic segmentation software was used to obtain individual retinal layer thickness measurements including overall retinal thickness (RT), retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), retinal pigment epithelium (RPE), and photoreceptor layer (PR) (Fig. 1). The OCT images obtained by a technician were assessed by an ophthalmologist (J.F.) masked to the patients' diagnosis, that verified the automatic segmentation and corrected with manual segmentation when it was not defined correctly. 
Figure 1
 
Retinal layer segmentation.
Figure 1
 
Retinal layer segmentation.
In all layers, the thickness values were calculated for the nine Early Treatment Diabetic Retinopathy Study (ETDRS) areas.23 An ETDRS plot consists of three concentric rings of 1-, 3-, and 6-mm diameter centered at the fovea. The two outer rings are divided into quadrants by two intersecting lines. Each sector was designated C, S3, S6, T3, T6, I3, I6, N3, and N6, according to Figure 2. The ETDRS grid was positioned automatically by the Spectralis OCT software, enabling the capture and extraction of the macular thickness values. 
Figure 2
 
Representative Spectralis SD-OCT scans of macular thickness map (ETDRS protocol).
Figure 2
 
Representative Spectralis SD-OCT scans of macular thickness map (ETDRS protocol).
The fast macular thickness OCT protocol scans were then again performed in enhanced depth imaging mode according to the previously reported method.24 The choroidal thickness (CT) was manually measured from the outer portion of the hyperreflective line (corresponding to the RPE) to the hyporeflective line (corresponding to the sclerochoroidal interface). These measurements were made in the subfoveal choroid and at 1000 μm superior, inferior, nasal, and temporal of the fovea (five locations). 
Statistical Analysis
Demographics and clinical characteristics of patients were described using the mean (SD) or median (interquartile range: 25th percentile–75th percentile) for continuous variables, and the frequencies (percentages) for categorical variables. Generalized additive regression models were used to identify the variables that explain the variability of thickness of retinal layers considering diabetic and nondiabetic groups. All the multivariable regression models included age, IOP–Pascal, axial length, and sex to adjust the association among the four groups, classified according to diabetes duration, and the layer thickness. The continuous covariates were modeled with splines due to their nonlinear association with the thickness of all retinal layers. In particular, multivariable regression models of RPE and PR layers in sectors C, S3, I3, N3, and T3, also considered the variable CT subfoveal, 1000 μm superior, inferior, nasal, and temporal of the fovea, respectively. Normality assumption of the residuals was verified using Kolmogorov-Smirnov goodness-of-fit test. A level of significance of α = 0.05 was considered. Bonferroni adjustment for multiple testing was applied. Data were analyzed using the Statistical Package for the Social Science for Windows (released 2013, IBM SPSS Statistics for Windows, Version 22.0; IBM Corp., Armonk, NY, USA) and R (R: A Language and Environment for Statistical Computing, R Core Team, 2014; R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org.) 
Results
Patient Demographics and Clinical Characteristics
A total of 125 diabetic patients with type 2 DM without DR (63 males), and 50 nondiabetic subjects (20 males), were included in this study. The diabetic patients were classified into three groups, according to the duration of diabetes: group I (up to 5 years, n = 55), group II (5–10 years, n = 39), and group III (more than 10 years, n = 31). The remaining demographic, clinical and ophthalmologic characteristics are summarized in Table 1, and mean retinal layer thicknesses of groups are presented in Figure 3
Table 1
 
Demographic and Clinical Characteristics of the Patients by Group
Table 1
 
Demographic and Clinical Characteristics of the Patients by Group
Figure 3
 
Graphs showing retinal layer thickness in all groups, determined automatically by SD-OCT in nine ETDRS areas in the macula. (A) RT; (B) RNFL; (C) GCL; (D) IPL; (E) INL; (F) OPL; (G) ONL; (H) PR; (I) RPE.
Figure 3
 
Graphs showing retinal layer thickness in all groups, determined automatically by SD-OCT in nine ETDRS areas in the macula. (A) RT; (B) RNFL; (C) GCL; (D) IPL; (E) INL; (F) OPL; (G) ONL; (H) PR; (I) RPE.
Analysis of Retinal Layer Thickness
In multivariable regression models, after adjusting for age, sex, IOP, and axial length, and correcting for multiple testing, no difference in the overall RT thickness throughout the ETDRS areas was found. Interestingly, the patterns of layer distribution were not the same in the two samples. 
An exploratory analysis of the RT data showed a thicker RNFL, INL, and RPE in diabetic patients when compared with controls. This increase reached statistical significance in only a small number of locations (see detailed locations in Supplementary Tables S1–S3, for multivariable regression model results for RNFL, INL, and RPE thickness). 
Interestingly, the PR layer was the most consistent finding, with a smaller thickness in diabetic patients when compared with their nondiabetic controls (Table 2). Nevertheless, the pattern of thickness in this layer differs with disease duration. Once we stratified diabetic patients according to this parameter, the thinner layers could be found in patients with both an early (group I) and longer known diabetes diagnosis (group III) (P < 0.001). On the other hand, the thinning in PR in diabetic patients with moderate duration (group II) did not reach statistical significance when compared with the healthy controls (Table 2). 
Table 2
 
Multivariable Regression Models Results for PR Thickness
Table 2
 
Multivariable Regression Models Results for PR Thickness
The remaining layers (ONL, OPL, INL, and GCL) showed an overall tendency toward a thicker layer in diabetic retinas when compared with nondiabetic patients, but did not reach statistical significance. 
Discussion
This cross-sectional study used SD-OCT to compare the retinal layer thickness between nondiabetic subjects and type 2 diabetic patients without DR and with different DM duration. Overall, the current analysis revealed a significant thinning of the PR layer in diabetic patients when compared with controls. 
Photoreceptors are the most metabolically active neurons in the central nervous system,25 although not usually regarded as important in the pathogenesis of early DR, perhaps due in part to the substantial distance between the photoreceptors and the retinal microvasculature that is affected by diabetes. However, a number of animal studies have reported that at least some photoreceptors degenerate in DM.2628 Furthermore, electrophysiology data suggest that photoreceptors and/or RPE also show variable impairments in diabetes.29,30 
The vasculopathy of the choriocapillary layer that nourishes photoreceptors may be the cause of photoreceptor degeneration in diabetic patients. A diabetic choroidopathy in diabetic eyes without DR was identified in histological, animal, and human studies and characterized by changes in choroidal blood flow, impaired autoregulation,31 and differences in the CT measured by OCT32 accompanied by pathologic changes like degenerative capillaries and capillary dropouts33 were described. Therefore, it is possible that these microvascular changes of the choroid may contribute to the photoreceptor degeneration described in this study. A further possible cause of photoreceptor degeneration may be the direct effects of hyperglycemia and hypoinsulinemia. Diabetes mellitus changes some elements in the insulin signalling pathway in the photoreceptors, impairing the important survival and neuroprotective signal.34,35 
One interesting finding from our study was that the pattern of thinning PR layer was not uniform throughout disease duration, with patients with a moderate duration appearing to have a smaller difference in thickness than both early and longer known diabetes. This could be interpreted as a temporary cellular swelling due to a number of reasons, ranging from the diabetic-induced hypoxia,36 oxidative stress with increased generation of superoxide, and other reactive oxygen species in the retina,37 which induces the release of proinflammatory molecules and changes in retinal vasculature. Ultimately, the continuous cellular swelling is known to lead to a cellular atrophy,36 potentially explaining the thinnest PR layers in the patients with longer disease duration. This nonlinear behavior is important, as it can explain the contradictory results in this field, as each study may be recruiting patients with a different disease duration. Additionally, it could be clinically relevant, as studies have suggested the importance of the PR layer in the development of DR, loss of PR reduced the severity of vascular degeneration in DR.38,39 Further studies would be needed to interpret such findings. 
The several clinical studies using SD-OCT to show changes that correspond to an early neurodegenerative process in DR typically analyzed the inner layers of the retina, and they either showed a decreased RNFL or GCL thickness in diabetic patients without DR17,40,41 or did not find differences in any inner layer thickness between nondiabetic and type 1 or type 2 diabetic patients even without DR.15,16 Vujosevic and Midena17 studied both inner and outer layers but in opposition to this work they did not find any differences in the RPE and PR layer thickness. However, these authors have studied the RPE and PR layers together not individualizing them in two different layers. 
This study had some limitations. First, despite being one of the largest studies in the field, including 125 diabetic patients without DR, subdividing into smaller groups for disease duration may have hampered our ability to subanalyze the RT. Nevertheless, our main outcome was the analysis of the overall RT between diabetic and nondiabetic subjects. Our interesting data regarding the subgroup analysis can provide a useful hint in future studies. Second, retinal measurements were done with automatic software. However, a manual correction was performed when the segmentation was inaccurate by an ophthalmologist masked to the patients' diagnosis. Third, our assumption for the length of disease duration is dependent on the clinical diagnosis, which may have underestimated the real time of diabetes. 
In conclusion, diabetic patients without DR have a thinning of the PR layer, when compared with a nondiabetic group. There are early changes in outer retinal layers of diabetic patients even without clinical signs of DR that probably correspond to an inflammatory and apoptotic process of the retina as a neurovascular unit. 
Acknowledgments
Special thanks to orthoptist Gonçalo Agudo for his help in obtaining tomographic images. 
Disclosure: J. Tavares Ferreira, None; M. Alves, None; A. Dias-Santos, None; L. Costa, None; B.O. Santos, None; J.P. Cunha, None; A.L. Papoila, None; L. Abegão Pinto, None 
References
Klein BEK. Overview of epidemiologic studies of diabetic retinopathy. Ophthalmic Epidemiol. 2007; 14: 179–183.
International Diabetes Federation (IDF). IDF Diabetes Atlas. 7th ed. Available at: http://www.diabetesatlas.org/. Accessed on June 3, 2016.
Yau JWY, Rogers SL, Kawasaki R, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012; 35: 556–564.
Gardete-Correia L, Boavida JM, Raposo JF, et al. First diabetes prevalence study in Portugal: PREVADIAB study. Diabet Med. 2010; 27: 879–881.
Dutra Medeiros M, Mesquita E, Papoila AL, Genro V, Raposo JF. First diabetic retinopathy prevalence study in Portugal: RETINODIAB Study—Evaluation of the screening programme for Lisbon and Tagus Valley region. Br J Ophthalmol. 2015; 99: 1328–1333.
Dutra Medeiros M, Mesquita E, Gardete-Correia L, et al. First incidence and progression study for diabetic retinopathy in Portugal, the RETINODIAB study: evaluation of the screening program for Lisbon region. Ophthalmology. 2015; 122: 2473–2481.
Albany NY, Kassoff A, David Goodman A, et al.; Early Treatment Diabetic Retinopathy Study Research Group. Grading diabetic retinopathy from stereoscopic color fundus photographs—an extension of the modified Airlie House classification. ETDRS report number 10. Ophthalmology. 1991; 98: 786–806.
Barber AJ, Lieth E, Khin SA, Antonetti DA, Buchanan AG, Gardner TW. Neural apoptosis in the retina during experimental and human diabetes. Early onset and effect of insulin. J Clin Invest. 1998; 102: 783–791.
Carrasco E, Hernández C, Miralles A, Huguet P, Farrés J, Simó R. Lower somatostatin expression is an early event in diabetic retinopathy and is associated with retinal neurodegeneration. Diabetes Care. 2007; 30: 2902–2908.
Carrasco E, Hernández C, de Torres I, Farrés J, Simó R. Lowered cortistatin expression is an early event in the human diabetic retina and is associated with apoptosis and glial activation. Mol Vis. 2008; 14: 1496–1502.
Garcia-Ramírez M, Hernández C, Villarroel M, et al. Interphotoreceptor retinoid-binding protein (IRBP) is downregulated at early stages of diabetic retinopathy. Diabetologia. 2009; 52: 2633–2641.
van Dijk HW, Verbraak FD, Stehouwer M, et al. Association of visual function and ganglion cell layer thickness in patients with diabetes mellitus type 1 and no or minimal diabetic retinopathy. Vision Res. 2011; 51: 224–228.
Fischer MD, Huber G, Beck SC, et al. Noninvasive in vivo assessment of mouse retinal structure using optical coherence tomography. PLoS One. 2009; 4: e7507.
Ceklic L, Maár N, Neubauer AS. Optical coherence tomography fast versus regular macular thickness mapping in diabetic retinopathy. Ophthalmic Res. 2008; 40: 235–240.
van Dijk HW, Verbraak FD, Kok PHB, et al. Early neurodegeneration in the retina of type 2 diabetic patients. Invest Ophthalmol Vis Sci. 2012; 53: 2715–2719.
van Dijk HW, Verbraak FD, Kok PHB, et al. Decreased retinal ganglion cell layer thickness in patients with type 1 diabetes. Invest Ophthalmol Vis Sci. 2010; 51: 3660–3665.
Vujosevic S, Midena E. Retinal layers changes in human preclinical and early clinical diabetic retinopathy support early retinal neuronal and Müller cells alterations. J Diabetes Res. 2013; 2013: 905058.
van Dijk HW, Kok PHB, Garvin M, et al. Selective loss of inner retinal layer thickness in type 1 diabetic patients with minimal diabetic retinopathy. Invest Ophthalmol Vis Sci. 2009; 50: 3404–3409.
Ctori I, Huntjens B. Repeatability of foveal measurements using Spectralis optical coherence tomography segmentation software. PLoS One. 2015; 10: e0129005.
Simó R, Hernández C. Neurodegeneration in the diabetic eye: new insights and therapeutic perspectives. Trends Endocrinol Metab. 2014; 25: 23–33.
DGS. Diagnóstico e Classificação da Diabetes Mellitus. Norma da Direção Geral da Saúde. 2011; 2: 1–13.
Jeoung JW, Kim T-W, Weinreb RN, Kim SH, Park KH, Kim DM. Diagnostic ability of spectral-domain versus time-domain optical coherence tomography in preperimetric glaucoma. J Glaucoma. 2014; 23: 299–306.
Early Treatment Diabetic Retinopathy Study Research Group. Photocoagulation for diabetic macular edema. Early Treatment Diabetic Retinopathy Study report number 1 Arch Ophthalmol. 1985; 103: 1796–1806.
Spaide RF, Koizumi H, Pozzoni MC, Pozonni MC. Enhanced depth imaging spectral-domain optical coherence tomography. Am J Ophthalmol. 2008; 146: 496–500.
Ames A, Li YY, Heher EC, Kimble CR. Energy metabolism of rabbit retina as related to function: high cost of Na+ transport. J Neurosci. 1992; 12: 840–853.
Kumar B, Gupta SK, Srinivasan BP, et al. Hesperetin rescues retinal oxidative stress neuroinflammation and apoptosis in diabetic rats. Microvasc Res. 2013; 87: 65–74.
Park S-H, Park J-W, Park S-J, et al. Apoptotic death of photoreceptors in the streptozotocin-induced diabetic rat retina. Diabetologia. 2003; 46: 1260–1268.
Enzsoly A, Szabo A, Kantor O, et al. Pathologic alterations of the outer retina in streptozotocin-induced diabetes. Invest Ophthalmol Vis Sci. 2014; 55: 3686–3699.
Holfort SK, Jackson GR, Larsen M. Dark adaptation during transient hyperglycemia in type 2 diabetes. Exp Eye Res. 2010; 91: 710–714.
Harrison WW, Bearse MA, Ng JS, et al. Multifocal electroretinograms predict onset of diabetic retinopathy in adult patients with diabetes. Invest Ophthalmol Vis Sci. 2011; 52: 772–777.
Muir ER, Renteria RC, Duong TQ. Reduced ocular blood flow as an early indicator of diabetic retinopathy in a mouse model of diabetes. Invest Ophthalmol Vis Sci. 2012; 53: 6488–6494.
Melancia D, Vicente A, Cunha JP, Abegão Pinto L, Ferreira J. Diabetic choroidopathy: a review of the current literature. Graefes Arch Clin Exp Ophthalmol. 2016; 254: 1453–1461.
Cao J, McLeod S, Merges CA, Lutty GA. Choriocapillaris degeneration and related pathologic changes in human diabetic eyes. Arch Ophthalmol. 1998; 116: 589–597.
Rajala A, Dighe R, Agbaga M-P, Anderson RE, Rajala RVS. Insulin receptor signaling in cones. J Biol Chem. 2013; 288: 19503–19515.
Reiter CEN, Wu X, Sandirasegarane L, et al. Diabetes reduces basal retinal insulin receptor signaling reversal with systemic and local insulin. Diabetes. 2006; 55: 1148–1156.
Kern TS, Berkowitz BA. Photoreceptors in diabetic retinopathy. J Diabetes Investig. 2015; 6: 371–380.
Du Y, Veenstra A, Palczewski K, Kern TS. Photoreceptor cells are major contributors to diabetes-induced oxidative stress and local inflammation in the retina. Proc Natl Acad Sci U S A. 2013; 110: 16586–16591.
Arden GB. The absence of diabetic retinopathy in patients with retinitis pigmentosa: implications for pathophysiology and possible treatment. Br J Ophthalmol. 2001; 85: 366–370.
De Gooyer TE, Stevenson KA, Humphries P, Simpson DAC, Gardiner TA, Stitt AW. Retinopathy is reduced during experimental diabetes in a mouse model of outer retinal degeneration. Invest Ophthalmol Vis Sci. 2006; 47: 5561–5568.
Chhablani J, Sharma A, Goud A, et al. Neurodegeneration in type 2 diabetes: evidence from spectral-domain optical coherence tomography. Invest Ophthalmol Vis Sci. 2015; 56: 6333–6338.
Carpineto P, Toto L, Aloia R, et al. Neuroretinal alterations in the early stages of diabetic retinopathy in patients with type 2 diabetes mellitus. Eye (Lond). 2016; 30: 673–679.
Figure 1
 
Retinal layer segmentation.
Figure 1
 
Retinal layer segmentation.
Figure 2
 
Representative Spectralis SD-OCT scans of macular thickness map (ETDRS protocol).
Figure 2
 
Representative Spectralis SD-OCT scans of macular thickness map (ETDRS protocol).
Figure 3
 
Graphs showing retinal layer thickness in all groups, determined automatically by SD-OCT in nine ETDRS areas in the macula. (A) RT; (B) RNFL; (C) GCL; (D) IPL; (E) INL; (F) OPL; (G) ONL; (H) PR; (I) RPE.
Figure 3
 
Graphs showing retinal layer thickness in all groups, determined automatically by SD-OCT in nine ETDRS areas in the macula. (A) RT; (B) RNFL; (C) GCL; (D) IPL; (E) INL; (F) OPL; (G) ONL; (H) PR; (I) RPE.
Table 1
 
Demographic and Clinical Characteristics of the Patients by Group
Table 1
 
Demographic and Clinical Characteristics of the Patients by Group
Table 2
 
Multivariable Regression Models Results for PR Thickness
Table 2
 
Multivariable Regression Models Results for PR Thickness
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×