Retinal Neurodegeneration Associated With Peripheral Nerve Conduction and Autonomic Nerve Function in Diabetic Patients

Accepted Manuscript Retinal neurodegeneration associated with peripheral nerve conduction and autonomic nerve function in diabetic patients Kiyoung Kim, Seung-Young Yu, Hyung Woo Kwak, Eung Suk Kim PII:

S0002-9394(16)30312-9

DOI:

10.1016/j.ajo.2016.06.038

Reference:

AJOPHT 9802

To appear in:

American Journal of Ophthalmology

Received Date: 14 December 2015 Revised Date:

8 June 2016

Accepted Date: 25 June 2016

Please cite this article as: Kim K, Yu S-Y, Kwak HW, Kim ES, Retinal neurodegeneration associated with peripheral nerve conduction and autonomic nerve function in diabetic patients, American Journal of Ophthalmology (2016), doi: 10.1016/j.ajo.2016.06.038. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Abstract Objective: In this study, we evaluated the correlation of retinal thickness with peripheral nerve conduction and autonomic nerve function in diabetic patients. Design: Cross-sectional study. Methods: Medical records of 160 patients (mean age, 63.61 ± 12.52 years) with diabetes without diabetic retinopathy or mild non-proliferative diabetic retinopathy (NPDR) were reviewed. The mean retinal thickness of the parafoveal area and ganglion cell/inner plexiform layer (GC-IPL) thickness in six macular regions were measured using optical coherence tomography. Peripheral nerve conduction studies were conducted on peroneal and posterior tibial motor nerves, and the sural sensory nerve. Five cardiovascular autonomic function tests were performed. We classified patients into groups by severity of peripheral neuropathy and autonomic dysfunction and analyzed the correlations with mean retinal thickness. Results: The mean retinal thickness of the parafovea was 315.05 ± 12.70 µm; and mean macular GC-IPL thickness was 79.89 ± 4.70 µm. Macular GC-IPL thickness showed significant correlation with peripheral nerve conduction (no peripheral neuropathy vs. definite peripheral neuropathy: 82.0 ± 4.8 µm vs. 75.2 ± 3.8 µm, p < 0.001). GC-IPL thickness decreased with severity of autonomic nerve dysfunction (no/mild dysfunction vs. severe dysfunction: 81.2 ± 6.6 µm vs. 77.6 ± 5.9 µm, p = 0.005). There was no significant correlation between the retinal thickness of the parafovea and electrodiagnostic tests. Conclusion: The decrease of GC-IPL thickness was positively correlated with both peripheral nerve conduction and autonomic nerve function in diabetic patients who presented with no diabetic retinopathy or mild NPDR.

Retinal neurodegeneration and neuropathy in early diabetes

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Retinal neurodegeneration associated with peripheral nerve conduction and autonomic nerve function in diabetic patients Short title: Retinal neurodegeneration and neuropathy in early diabetes Kiyoung Kim, Seung-Young Yu, Hyung Woo Kwak, Eung Suk Kim

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Department of Ophthalmology, Kyung Hee University Hospital, Kyung Hee University, Seoul, Korea

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Corresponding Author: Eung Suk Kim, Department of Ophthalmology, Kyung Hee University Hospital, 23, Kyungheedae-ro, Dongdaemun-gu, Seoul, Republic of Korea. Phone and fax numbers: 82-02-958-8455, 82-02-966-7340. E-mail: [email protected]

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Retinal neurodegeneration and neuropathy in early diabetes

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Introduction Diabetic retinal neurodegeneration is a progressive and degenerative process in the retina and is thought to occur prior to clinically detectable microvascular damage.1-3 Histologically, this can lead to neural apoptosis and glial cell activation that may predominately affect the retinal ganglion cells. Cell death of the ganglion cell layer may lead to thinning of the ganglion cell-inner plexiform layer (GC-IPL) that might be detected on optical coherence tomography (OCT) examination.4-7 Recent studies have revealed that inflammation and consequent neuronal dysfunction have significant roles in the early pathogenesis of diabetic retinopathy.813 Genetic predisposition, which influences various cellular pathways, is also responsible for the development and progression of diabetic retinopathy.14 Some epigenetic modifications have been found to be prospective marker of proliferative diabetic retinopathy (PDR).15 However, to date, the underlying mechanism and association between retinal neurodegeneration and vascular complications remain unclear. 50% of diabetic patients develop diabetic polyneuropathy (DPN), but only about 20% have clinical features of neuropathy at the time of diabetes diagnosis, since it is often under-diagnosed owing to diverse and vague symptoms.16-19 Cardiac autonomic neuropathy (CAN), defined as impairment of autonomic control of the cardiovascular system, is the most common known manifestation owing to life-threatening complications.16,20 Assessment with a CAN score at a subclinical stage can lead to earlier detection and treatment, preventing further systemic complications. DPN probably results from metabolic pathways triggered by hyperglycemia, which share features with microvascular abnormality in the retina in diabetic retinopathy. Consequently, a neuronal apoptotic signal is activated and glial reactivity increases in addition to the endothelial injury and microvascular dysfunction leading to nerve ischemia.16-18,21 The purpose of this study was to investigate the clinical correlation between retinal GC-IPL thickness and the severity of peripheral neuropathy and autonomic nerve dysfunction in patients with no or minimal diabetic retinopathy. Furthermore, we hypothesized that OCT finding of thinner GC-IPL can be an early sign of diabetic neurodegeneration and requires careful clinical evaluation regarding diabetic neuropathy.

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Methods STUDY DESIGN AND POPULATION In this study, the medical records of 160 diabetic patients from Kyung Hee University Hospital, Seoul, Korea, were retrospectively reviewed. The study was approved by the Kyung Hee University Hospital Institutional Review Board and conformed to the Declaration of Helsinki. Inclusion criteria included known diagnosis of type 2 diabetes, age between 55 and 76 years, no sign of diabetic retinopathy or mild nonproliferative diabetic retinopathy (NPDR), and patients whose OCT examination and both peripheral and autonomic nerve function test were performed within 6 months of time frame. Exclusion criteria were duration of diabetes >10 years, diagnosis of any peripheral neurologic disease except diabetes related neuropathy, medication for peripheral neuropathy prior to the nerve conduction test, cardiovascular disease, clinically significant diabetic macular edema, previous diagnosis of glaucoma, ocular hypertension, uveitis, other retinal diseases, any history of retinal treatment (laser photocoagulation, intravitreal injection, vitrectomy), and diagnosed as moderate/severe NPDR or PDR. Additionally, age-matched 60 patients without diabetes were included for comparison of GC-IPL thickness as control group. Alasil T et al. reported an average reduction of 1.5 µm in the thickness of global retinal nerve fiber layer (RNFL) per increasing decade of age.22 To minimize the effect of age-related inner retinal thinning, we included patients aged 55–75 years. Since the duration of diabetes contributes to the prevalence of various systemic complications which might influence electrodiagnostic nerve tests, we excluded longstanding diabetes to reduce confounding effects.

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OPHTHALMIC EXAMINATION In each patient, the eye with the lowest visual acuity was selected for examination, unless this eye did not meet the eligibility criteria. Patients were classified as no diabetic retinopathy or mild NPDR on the basis of fundus photography images. No diabetic retinopathy was defined as the absence of all features of diabetic retinopathy, and mild NPDR was defined as the presence of a microaneurysm, retinal dot hemorrhage or hard exudates according to the ETDRS severity scale. We analyzed the mean retinal thickness (µm) of the parafoveal area within diameters 1–3 mm and the GC-IPL thickness in 6 macular regions was analyzed using Cirrus HD-OCT (Carl Zeiss Meditec, Dublin, CA). A color-coded macular thickness map, i.e., the macular cube (512 × 128 scan) was obtained with Cirrus HDOCT. The alignment was properly positioned to the macula in the center of the scan using the iris and fundus viewports. The 9 standard ETDRS subfields consisted of 3 concentric circular areas centered on the fovea/fixation points. The diameters of the circular areas were 1, 3 and 6 mm (Figure 1). The built-in algorithms of the Cirrus HD-OCT software (version 6.5.0.772) are capable of automatically identifying the outer boundary of the macular RNFL and the outer boundary of the inner plexiform layer (IPL). The difference between the RNFL and the IPL outer boundary segmentation yields the GC-IPL thickness (Figure 2, top left). The average, minimum, and 6-sectoral (superior temporal, superior, superior nasal, inferior nasal, inferior, and inferior temporal) GC-IPL thicknesses are measured in an elliptical annulus with 3

Retinal neurodegeneration and neuropathy in early diabetes

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a vertical outer diameter of 4.0 mm and horizontal diameter of 4.8 mm (Figure 2, top right, bottom).23,24 In this study, the average CC-IPL thickness of 6 sectors was used for statistical analysis. Intra-session and inter-examiner variability of GC-IPL thickness measurement were determined by calculating coefficients of variance (CV).

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ASSESSMENT OF NEUROPATHY Diagnosis of DPN by clinical tests alone is not accurate and it is difficult to detect mild neuropathy in early DPN patients.25 It is generally accepted that the most accurate diagnostic method uses a combination of clinical symptoms, physical examination, and electrodiagnostic findings.26 Electrodiagnostic evaluation generally includes a nerve conduction study (NCS), which is most informative and highly specific. Motor NCSs are performed by applying electrical stimulation at points along the course of a motor nerve while recording the electrical response from an appropriate muscle. Sensory NCSs are performed by applying electrical stimulation near a nerve and recording the response from a distant site along the nerve.25, 27 In our study, NCS was reviewed in the peroneal motor nerve and the posterior tibial motor nerve by measuring velocity, terminal latency, and F-wave latency and the velocity in the sural sensory nerve. The normal range of terminal latency is 3.2 to 4.5 ms in both the peroneal motor and posterior tibial motor nerve. The normal ranges of conduction velocity in the peroneal motor, the posterior tibial motor, and the sural sensory nerve are 43–62 m/s, 41–61 m/s and 34–49 m/s, respectively. The normal range of F-wave latency is 44.7 ± 4.7 ms in the peroneal motor and 44.9 ± 6.2 ms in the posterior tibial motor nerve.28 Despite the previously proposed NCS criteria for the diagnosis of peripheral neuropathy,25, 29-30 no established guidelines exist to date. The minimal criterion for electrodiagnostic confirmation of DPN is an abnormality of any value of nerve conduction in 2 separate nerves, one of which must be the sural nerve.30 In addition, the diagnosis of DPN was established and classified by electrophysiological criteria modified by Kwon et al.31 subsequent to the Diabetes Control and Complication Trial.32 Based on previously reported classification,25, 29 subjects in this study were subdivided into 3 groups as no neuropathy, probable neuropathy, and definite neuropathy. Patients with normal sural nerve velocity were classified as no neuropathy group (Table 1). Peripheral nerve involvement was defined when any 2 parameters (among terminal latency, F-wave latency and conduction velocity) were abnormal in each motor nerve. To evaluate autonomic nerve function, 5 parameters of CAN were analyzed.33 CAN was diagnosed according to the American Diabetes Association (ADA) guidelines. They consisted of the Valsalva ratio (changes in heart rate during the Valsalva maneuver), lying to standing heart rate (30/15 ratio), R-R interval variation (maximum-minimum heart rate during expiration and inspiration), postural hypotension (change in systolic blood pressure after standing), and sustained handgrip (increase in diastolic blood pressure after the handgrip exercise). ADA recommendations of value ranges are normal Valsalva ratio >1.2; normal lying to standing heart rate > 1.04, abnormal < 1.00; normal R-R interval variation > 15 bpm, abnormal < 10 bpm; normal postural hypotension < 10 mm Hg, abnormal >30 mm Hg; and normal sustained handgrip > 16 mm Hg, abnormal < 10mm Hg. The 4

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result of each test was sorted into either normal, borderline or abnormal, which matched a score of 0, 1 and 2, respectively. Borderline was scored when values were between the normal and abnormal ranges. Patients were then subdivided into 3 groups based on the total CAN score: No/mild dysfunction 0–3, moderate dysfunction 4–6 and severe dysfunction 7–10 (Table 2).

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STATISTICAL ANALYSIS Statistical analysis was performed with SPSS software version 14.0 (SPSS Inc., Chicago, IL, USA. Intra-session and inter-examiner repeatability of GC-IPL thickness measurement was assessed based on the intraclass correlation coefficient (ICC) and coefficient of variance (CV). The linear relationships between the GC-IPL thickness and the 3 peripheral nerve velocities were analyzed by the Pearson correlation method and the results were displayed in combined plots. Pearson correlation coefficient is used to identify linear correlation (dependence) between 2 variables, giving a value between +1 and −1, where 1 is complete positive correlation 0 is no correlation, and −1 is total negative correlation, with p value < 0.05 considered as statistical significance. Demographic data of patients were compared by one-way and two-ways analysis of variance (ANOVA), Chi-square and Fisher exact test. In cases of significant results (p < 0.05), the ANOVA was followed by a Bonferroni multiple comparisons post hoc test. Multinomial logistics regression analysis was performed to evaluate the role of confounding factors in correlation between GC-IPL thickness and peripheral and autonomic neuropathy.

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Results In total, 160 patients with diabetes were included in the analysis. Baseline demographic and metabolic/neurological data were shown according to peripheral neuropathy and autonomic nerve function, respectively (Table 3, 4). The mean time interval between the OCT examination and nerve function tests in each patient was 3.88±1.69 months (range 0–6). The mean age was 63.6 ± 9.43 years, the mean retinal thickness of the parafovea was 315.05 ± 12.70 µm, and mean macular GCIPL thickness was 79.89 ± 4.70 µm. Of the 160 patients, 96 (60%) were classified as no neuropathy, 21 (13%) as probable neuropathy, and 43 (27%) as definite neuropathy (Table 3). As GC-IPL thickness showed significant thinning with age (R value = –0.600, p < 0.001), an age-adjusted ANCOVA method was applied. Age, past medical history, and anthropometric data were not significantly different among the 3 groups. GC-IPL thickness and peripheral nerve conduction values differed significantly between the 3 peripheral neuropathy groups (p = 0.002), but the retinal thickness of the parafoveal, within 1–3 mm, showed no significant difference among the 3 groups (p = 0.873). Of the 160 patients, 42 (26%) were classified as no/mild autonomic dysfunction, 69 (43%) as moderate autonomic dysfunction, and 49 (31%) as severe autonomic dysfunction (Table 4). GC-IPL thickness also showed significant difference between the 3 autonomic nerve function groups (p = 0.005); whereas, retinal thickness of parafoveal, within 1–3mmfno, was not significantly different (p = 0.674). Additionally, factorial two-way ANOVA was performed to measure the effect of DPN and CAN grade on GC-IPL thickness simultaneously. The res ult revealed both significant main effect of DPN grade (F=30.11, p<0.001) and CAN grade (F=4.183, p=0.017) on GC-IPL thickness and the interaction effect was shown to be insignificant (F=0.72, p=0.58). Analysis of GC-IPL thickness and each of the peripheral nerve velocities of the peroneal motor, posterior tibial motor, and sural sensory nerve indicated significant positive correlations between GC-IPL thickness and all 3 peripheral nerve velocities (R values were 0.330, 0.298, and 0.292, respectively, Figure 3). Figure 4 displays a comparison of the GC-IPL thickness by peripheral neuropathy, autonomic dysfunction grade, and control of non-diabetics, respectively. There were significant differences in the GC-IPL thickness between the control, no neuropathy vs. probable neuropathy (82.8 ± 3.8 µm, 82.0 ± 5.8 µm vs. 78.8 ± 5.0 µm, p= 0.009, 0.011), probable neuropathy vs. definite neuropathy (78.8 ± 5.0 µm vs. 75.2 ± 4.2 µm, p = 0.010), and control, no neuropathy vs. definite neuropathy (82.8 ± 3.8 µm, 82.0 ± 5.8 µm vs. 75.2 ± 4.2 µm, both p < 0.001, Figure 4, left). There were significant differences between the control, no/ mild dysfunction vs. severe dysfunction (82.8 ± 3.8 µm, 82.2 ± 6.6 µm vs. 76.6 ± 5.9 µm, p=0.002, 0.005), and moderate dysfunction vs. severe dysfunction (81.8 ± 3.9 µm vs. 76.6 ± 5.9 µm, p = 0.038). The difference between no/mild dysfunction and moderate dysfunction was not significant (82.2 ± 6.6 µm vs. 81.8 ± 3.9 µm, p = 0.530, Figure 4, right). The multinomial logistics regression results were shown in Tables 5 and 6. After controlling for confounding factor, including age, BMI, presence of HTN, duration of diabetes, HbA1c, creatinine, cholesterol, history of insulin treatment, and CAN score, GC-IPL thickness was significantly correlated with severity of peripheral neuropathy 6

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in the presented model (Table 5). After controlling for age, BMI, presence of HTN, and duration of diabetes, the provided model showed significant correlation between GC-IPL thickness and severity of autonomic neuropathy (Table 6). Both intra-session and inter-examiner variability in GC-IPL thickness measurement showed high level of agreement with ICC value of 0.979 (95% CI: 0.958–0.989), 0.968 (95% CI: 0.948–0.979), and CV of 4.8%, 5.3%, respectively.

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Discussion In this study, we aimed to evaluate the correlation between segmented inner retinal thickness, particularly GC-IPL, with the properties of peripheral nerve conduction and autonomic nerve function in early diabetic patients. The results showed that peripheral nerve conduction and autonomic nerve function were closely related to GC-IPL thinning, but the mean retinal thickness of parafovea (1–3mm) was not significantly related. The findings of the study will enable further understanding of the pathophysiology of retinal neurodegeneration through its relationship with diabetic systemic neuropathy. Several previous studies reported thinning of the nerve fiber layer and increased apoptosis of ganglion cells using Otuska Long-Evans Tokushima fatty rats (OLETF) and streptozotocin (STZ) diabetic rats ahead of significant morphologic changes in the retinal capillaries.34-36 However, Toyoda et al. reported that Spontaneously Diabetic Torii (SDT) rats showed increased retinal thickness with immunostaining of glial fibrillary acidic protein.37 These results might suggest that both retinal thinning and thickening with neurodegeneration may develop in experimental animal models. In human clinical studies, structural and functional inner neural retinal layer changes occurred before clinically detectable vascular complications.10-12 We analyzed both parafoveal retinal thickness and GC-IPL thickness to isolate selective thinning of the GC-IPL of retina associated with neurodegeneration. Total retinal thickness can be varied with microvascular change of early diabetic retinopathy in individual layer including microaneurysm, retinal hemorrhage, cystic space, and these variations probably leads to lose significant association proven with GC-IPL thickness. This result suggests that evaluation of GC-IPL thickness is more predictable for diabetic neuropathy in the early stage of retinopathy. The pathophysiology of diabetic retinopathy and neuropathy shares many of the same triggering mechanisms. Hyperglycemia induces several metabolic pathways, such as non-enzymatic glycosylation, formation of advanced glycosylation endproducts, polyol pathway, hexosamine pathway, activation of diacylglycerol and protein kinase C, and production of reactive oxygen species.18,38 Emerging research has focused on the implication of chronic low-grade inflammation in pathogenetic mechanisms of diabetic retinopathy. 39-42 Moreover, genetic and epigenetic regulations such as DNA methylation, family history, and pre-diabetic condition have emerged as predisposing factors in the development of macro and microvascular diabetic complications.15,43-45 The complete mechanism of neuronal injury in the retina is not yet elucidated, but elevated levels of glutamate, Muller cell activation, and the overexpression of the renin-angiotensin system by glial cells are currently reported to play an essential role.46-48 Despite the availability of diverse human and animal studies, mechanisms of possible associations between retinal neurodegeneration and vasculopathy remain unclear and require further research. Therefore, understanding the mechanisms of neurodegeneration will be important for identifying new therapeutic targets in the prevention of advanced diabetic retinopathy. Diabetic neuropathy is classically defined as the presence of symptoms and/or signs of peripheral nerve dysfunction in people with diabetes after the exclusion of the other causes. Our results are consistent with previous data regarding the 8

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abnormalities of electrophysiological assessment in diabetic neuropathy. Selvarajah et al. have reported significant impaired nerve attributes of NCS at sural sensory, common peroneal, and posterior tibial nerve in DPN.49 Hussain et al. have reported that both motor and sensory nerve conduction velocity are decreased as the severity of neuropathy is increased with the duration of type 2 diabetes mellitus. They also have reported that sensory neuropathy first affects the lower limb.50 The NCS and CAN tests used in this study have been shown to detect peripheral neuropathy and autonomic neuropathy at an earlier stage, hopefully allowing better initial treatment. As retrospective design based on medical record, this study does not allow investigation in change of GC-IPL thickness and peripheral nerve velocity over time and unrecognized variables within each group could have affected the results. Hence, we are conducting a prospective epidemiological study of retinal neurodegeneration in diabetes to reveal subspecialized correlation with possible epigenetic factor such as family, clinical, and pharmacological history of each patient. Currently, very few studies have investigated the relationship between diabetic retinopathy and neuropathy. Some population-based studies showed that the presence of diabetic retinopathy was related to the presence of diabetic neuropathy.51,52 Shahidi et al. reported that only the inferior RNFL thinning was associated with peripheral neuropathy,53 and Srinivasan et al. recently reported that perifoveal retinal thickness and parafoveal RNFL thickness were inversely related to the severity of neuropathy in individuals with diabetes.54 These results are consistent with our study, but the degree of neuropathy was assessed with the neuropathy disability score (NDS) based on sign and symptom score methods.57 Furthermore, Choi et al. reported autonomic function test grading was not significantly associated with RNFL loss in type 2 diabetes.55 To our knowledge, this is the first study to investigate correlations between inner retinal layer including GC-IPL thickness and the degree of peripheral neuropathy and autonomic nerve function using objective diagnostic methods. The results provided significant clinical evidence that degenerative change of the peripheral and autonomic nerve occurred with neurodegeneration of the retina in early diabetic patients. In conclusion, this study should prompt ophthalmic evaluation in patients with clinical or subclinical peripheral neuropathy or autonomic dysfunction. Also, a thinner retinal nerve fiber layer noted by the ophthalmologist should prompt more detailed questioning of peripheral nerve symptoms and more stringent diabetic control is recommended in hopes of delaying future retinopathy.

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Acknowledgements / Disclosures a. Funding/Support: none. b. Financial disclosures: none for any of the authors. c. Other Acknowledgments: none.

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neurodegeneration in Type II diabetic Otsuka Long-Evans Tokushima fatty rats. Invest Ophthalmol Vis Sci. 2013;54(6):3844-3851. 35. 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(4):783-791. 36. Asnaghi V, Gerhardinger C, Hoehn T, Adeboje A, Lorenzi M. A role for the polyol pathway in the early neuroretinal apoptosis and glial changes induced by diabetes in the rat. Diabetes. 2003;52(2):506-511. 37. Toyoda F, Tanaka Y, Ota A, et al. Effect of ranirestat, a new aldose reductase inhibitor, on diabetic retinopathy in SDT rats. J Diabetes Res. 2014;2014:672590 38. Villarroel M, Ciudin A, Hernandez C, Simo R. Neurodegeneration: An early event of diabetic retinopathy. World J Diabetes. 2010;1(2):57-64. 39. Joussen AM, Poulaki V, Le ML, et al. A central role for inflammation in the pathogenesis of diabetic retinopathy. FASEB J. 2004;18(12):1450-2 40. Tang J, Kern TS. Inflammation in diabetic retinopathy. Prog Retin Eye Res. 2011;30(5):343-358 41. Urbančič M, Štunf Š, Milutinović Živin A, Petrovič D, GlobočnikPetrovič M, Epiretinal membrane inflammatory cell density might reflect the activity of proliferative diabetic retinopathy. Invest Ophthalmol Vis Sci. 2014;55:8576–8582 42. Semeraro F, Cancarini A, dell'Omo R, Rezzola S, Romano MR, Costagliola C. Diabetic Retinopathy: Vascular and Inflammatory Disease. J Diabetes Res. 2015;2015:582060 43. Huang YC, Lin JM, Lin HJ, et al. Genome-wide association study of diabetic retinopathy in a Taiwanese population. Ophthalmology. 2011;118(4):642-648 44. Pannacciulli N, De Pergola G, Ciccone M, Rizzon P, Giorgino F, Giorgino R. Effect of family history of type 2 diabetes on the intima-media thickness of the common carotid artery in normal-weight, overweight, and obese glucose-tolerant young adults. Diabetes Care. 2003;26(4):1230-1234 45. Ciccone MM, Scicchitano P, Cameli M, et al. Endothelial function in prediabetes, diabetes and diabetic cardiomyopathy: A Review. J Diabetes Metab 5:364. doi:10.4172/2155-6156.1000364 46. Barber AJ, Antonetti DA, Gardner TW. Altered expression of retinal occludin and glial fibrillary acidic protein in experimental diabetes. The Penn State Retina Research Group. Invest Ophthalmol Vis Sci. 2000;41(11):3561-3568. 47. Lieth E, LaNoue KF, Antonetti DA, Ratz M. Diabetes reduces glutamate oxidation and glutamine synthesis in the retina. The Penn State Retina Research Group. Exp Eye Res. 2000;70(6):723-730. 48. Li Q, Puro DG. Diabetes-induced dysfunction of the glutamate transporter in retinal Muller cells. Invest Ophthalmol Vis Sci. 2002;43(9):3109-3116. 49. Selvarajah D, Cash T, Davies J, et al. SUDOSCAN: A simple, rapid, and objective method with potential for screening for diabetic peripheral neuropathy. PLoS ONE 2015;10(10): e0138224. doi:10.1371/journal.pone.0138224 50. Hussain G, Rizvi SA, Singhal S, Zubair M, Ahmad J. Cross sectional study to evaluate the effect of duration of type 2 diabetes mellitus on the nerve conduction velocity in diabetic peripheral neuropathy. Diabetes Metab Syndr. 2014;8(1):48-52

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51. Jurado J, Ybarra J, Romeo JH, Pou JM. Clinical screening and diagnosis of diabetic polyneuropathy: the North Catalonia Diabetes Study. Eur J Clin Invest. 2009;39(3):183-189. 4756. 52. Kostev K, Jockwig A, Hallwachs A, Rathmann W. Prevalence and risk factors of neuropathy in newly diagnosed type 2 diabetes in primary care practices: a retrospective database analysis in Germany and U.K. Prim Care Diabetes. 2014;8(3):250-255. 53. Shahidi AM, Sampson GP, Pritchard N, et al. Retinal nerve fibre layer thinning associated with diabetic peripheral neuropathy. Diabet Med. 2012;29(7):e106-111. 54. Srinivasan S, Pritchard N, Vagenas D, et al. Retinal Tissue Thickness is Reduced in Diabetic Peripheral Neuropathy. Curr Eye Res. 2016 Feb 29:1-8. [Epub ahead of print]. DOI:10.3109/02713683.2015.1119855 55. Choi JA, Ko SH, Park YR, Jee DH, Ko SH, Park CK. Retinal nerve fiber layer loss is associated with urinary albumin excretion in patients with type 2 diabetes. Ophthalmology 2015;122(5):976-981

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Figure 1. Examples of Macular Cube 512 × 128 scans from spectral-domain optical coherence tomography (Cirrus HD-OCT) uses the raster scan method in a macular region of 6 mm × 6 mm × 2 mm, with 128 A-scans per side and 512 B-scans. A colored circular map provides overall average thickness in each of the nine sectors. The diameter of each circular area was 1 mm, 3 mm and 6 mm, respectively. In this study, the mean retinal thickness of the 4 sectors located between the 1-mm to 3mm parafovea area was used (Left). The 6 × 6-mm macular thickness printout shows the line scanning ophthalmoscope fundus image. This image can be overlaid with either the macular thickness map or the OCT fundus image (Right).

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Figure 2. Examples of macular ganglion cell analysis (GCA) maps with automatic algorithms of the spectral-domain optical coherence tomography (Cirrus HD-OCT) software (version 6.5.0.772). Boundary lines are drawn to measure ganglion cellinner plexiform layer (GC-IPL) thickness. The purple line indicates the boundary between the retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL) and the yellow line indicates the boundary between the GCL and Inner plexiform layer (IPL) (top left). Dimension of an elliptical annulus (dimensions: vertical inner and outer diameter of 1.0 mm and 4.0 mm, horizontal inner and outer diameter of 1.2 mm and 4.8 mm, respectively) centered on the fovea within a macular area scanned with a macular cube scan (top right). GC-IPL significance map shows (clockwise) the thickness of the superior, superior nasal, inferior nasal, inferior, inferior temporal, and superior temporal sectors of the annulus and the average and minimum GC-IPL (box) (bottom). Figure 3. Pearson correlation between ganglion cell-inner plexiform layer (GC-IPL) thickness and the nerve conduction velocities of the peroneal nerve (left), posterior tibial motor nerve (middle), sural sensory nerves (right). R value was 0.330, 0.298 and 0.292, respectively, and the p value was < 0.001 in all 3 nerves.

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Figure 4. Comparisons of ganglion cell-inner plexiform layer (GC-IPL) thickness by peripheral neuropathy and cardiac autonomic neuropathy grade. Left panel shows significant difference of GC-IPL thickness between control, no neuropathy vs. probable peripheral neuropathy (p = 0.009, 0.011), probable vs. definite peripheral neuropathy (p = 0.010), control, no neuropathy vs. definite peripheral neuropathy (both p < 0.001). Right panel shows significant difference of GC-IPL thickness between moderate vs. severe autonomic dysfunction (p = 0.038) and control, no/mild vs. severe autonomic dysfunction (p = 0.002, 0.005). Difference between no/mild and moderate autonomic dysfunction was not significant (p = 0.530). The statistical method was ANCOVA with Tukey’s HSD post-hoc test. * p < 0.05.

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No neuropathy

No sural nerve involvement

Probable neuropathy

Sural nerve + any 1 nerve involvement

Definite neuropathy

Sural nerve + any 2 nerve involvement

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No or mild dysfunction

0–3

Moderate dysfunction

4–6

Severe dysfunction

7–10

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Probable neuropathy

Definite neuropathy

96

21

43

Age (years)

63.2±5.9

66.19±5.7

67.05±6.1

0.054

BMI (kg/㎡)

24.6±3.3

24.5±2.8

24.9±3.3

0.870

Presence of HTN (n)

71

17

Presence of Stroke (n)

4

3

History of insulin treatment (n)

11

3

Diabetic duration (years)

5.3±4.1

6.3±4.1

HbA1c (%)

7.2±1.1

7.1±0.8

Total cholesterol (mmol/L)

159.5±36.8

159.7±30.4

Retinal thickness of parafoveal 1-3 mm (µm)

314.9±20.7

GC-IPL thickness (µm)

82.0±5.8

LogMAR BCVA

0.08±0.12

BUN (mg/dl)

15.4±5.9

Creatinine (mg/dl)

Number of subjects

P-value

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Table 3. Demographic data according to peripheral neuropathy classification

0.753

3

0.811

5

0.862

5.5±4.4

0.619

7.3±1.2

0.732

160.8±33.9

0.978

314.2±20.8

314.5±17.2

0.873

78.8±5.0

75.2±4.2

0.002*

0.13±0.13

0.12±0.13

0.129

17.4±6.6

16.7±4.9

0.226

0.83±0.47

0.76±0.24

0.83±0.25

0.770

3.75±2.63

4.14±2.21

4.61±2.33

0.172

3.5±0.73

3.7±0.61

4.6±1.3

<0.0001*

45.4±5.3

41.7±3.9

37.7±3.0

<0.0001*

48.4±5.2

49.6±4.4

57.4±6.1

<0.0001*

3.3±0.49

4.0±0.94

4.3±0.82

<0.0001*

Post. tibial velocity (m/s)

44.2±5.4

40.4±3.5

37.8±3.9

<0.0001*

Post. tibial F wave

48.8±5.3

49.8±3.5

56.6±3.9

<0.0001*

Sural sensory velocity (m/s)

39.7±3.0

33.7±2.5

32.3±2.7

<0.0001*

Peroneal terminal latency (ms)

Peroneal F wave

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Peroneal velocity (m/s)

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CAN score

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Values are mean ± standard deviation. BMI=body mass index; GC-IPL = ganglion cell-inner plexiform layer; BCVA = best corrected visual acuity; BUN = blood urea nitrogen; CAN = cardiac autonomic neuropathy * One way Analysis Of variance (ANOVA)

ACCEPTED MANUSCRIPT Table 4. Demographic data according to classification by autonomic nerve function. No/mild dysfunction

Moderate dysfunction

Severe dysfunction

42

69

49

Age (years)

63.5±6.0

64.5±5.4

66.7±5.7

0.06

BMI (kg/㎡)

25.3±3.8

24.3±3.1

24.7±2.9

0.309

Presence of HTN (n)

26

51

Presence of Stroke (n)

2

4

History of insulin treatment (n)

4

10

Diabetic duration (years)

4.2±4.1

6.0±4.1

HbA1c (%)

7.0±0.81

7.2±1.2

Total cholesterol (mmol ⁄ L)

166.5±34.4

161.5±28.6

Retinal thickness of parafoveal 1-3mm (µm)

317.4±20.0

GC-IPL thickness (µm)

82.2±6.6

LogMAR BCVA

0.04±0.05

BUN (mg/dl)

14.8±4.2

Creatinine (mg/dl)

0.73±0.23

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Number of patients

P-value

0.105

4

0.872

5

0.625

6.0±4.2

0.052

7.5±1.2

0.165

151.8±42.3

0.119

314.1±18.8

314.5±20.9

0.674

81.8±3.9

76.6±5.9

0.005*

0.08±0.10

0.16±0.17

<0.0001*

15.6±5.4

17.7±7.00

0.033*

0.76±0.31

0.98±0.53

0.002*

3.50±0.83

3.92±1.03

3.91±1.06

0.07

45.1±5.3

42.4±5.5

41.5±5.8

0.007*

48.7±5.1

50.9±6.4

53.1±7.6

0.624

3.59±0.81

3.74±0.71

3.69±0.88

0.028*

43.1±5.5

41.4±5.9

41.1±4.8

0.176

Post. tibial F wave

49.7±5.8

51.1±6.1

52.0±5.8

<0.0001*

Sural sensory velocity (m/s)

39.5±3.8

35.9±4.3

36.3±4.5

<0.0001*

Peroneal velocity (m/s)

Post. tibial latency (ms)

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Post. tibial velocity (m/s)

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Peroneal F wave

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Peroneal terminal latency (ms)

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Values are mean ± standard deviation. BMI=body mass index, GC-IPL = ganglion cell-inner plexiform layer; BCVA = best corrected visual acuity; BUN = blood urea nitrogen, * One way Analysis Of variance (ANOVA)

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Table 5. Logistics regression model for association between ganglion cell-inner plexiform layer thickness and severity of peripheral nerve neuropathy 95% confidence Exp (B) P value interval GC-IPL thickness 1.470 1.290–1.676 <0.001 Age 1.019 0.965–1.075 0.502 BMI 0.977 0.851–1.211 0.737 Presence of HTN 0.730 0.244–2.184 0.574 Duration of diabetes 1.052 0.932–1.187 0.415 HbA1c 1.004 0.623–1.617 0.988 Creatinine 3.469 0.696–17.285 0.933 Cholesterol 1.018 1.000–1.035 0.044 History of insulin treatment 6.695 2.100–21.348 0.001 CAN score 0.991 0.811–1.212 0.933 Exp (B) = exponentiation of the B coefficient; GC-IPL = ganglion cell-inner plexiform layer; BMI=body mass index;

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Table 6. Logistics regression model for association between ganglion cell-inner plexiform layer thickness and severity of cardiac autonomic nerve dysfunction 95% confidence Exp (B) P value interval GC-IPL thickness 1.134 1.041–1.235 0.031 Age 0.922 0.874–0.973 0.003 BMI 1.050 0.912–1.210 0.497 Presence of HTN 2.316 0.828–6.475 0.109 Duration of diabetes 0.906 0.809–1.013 0.084 Exp (B) = exponentiation of the B coefficient; GC-IPL = ganglion cell-inner plexiform layer; BMI=body mass index;

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