Relationship Between Degree of Diabetic Retinopathy and Serum Fractalkine (CX3CL1) in Diabetic Patients

Relationship Between Degree of Diabetic Retinopathy and Serum Fractalkine (CX3CL1) in Diabetic Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Relationship Between Degree of Diabetic Retinopathy and Serum Fractalkine (CX3CL1) in Diabetic Patients Ozgur Yilmaz, Mehmet Erdogan, Murvet Algemi, Ibrahim Kocak, Sengul Aydin Yoldemir, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6570551/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Diabetic retinopathy (DR) remains a leading cause of preventable vision loss worldwide, yet reliable biochemical markers for early detection are lacking. This study aimed to investigate the predictive role of serum Fractalkine (CX3CL1) levels in the diagnosis and severity assessment of DR. Methods A total of 140 diabetic patients were enrolled, including 32 patients without DR and 108 patients with DR. The DR group was further categorized into proliferative and nonproliferative (mild, moderate, severe) subgroups. Serum Fractalkine levels were measured using the ELISA method. Statistical analyses were performed with SPSS 26.0, and p-values <0.05 were considered significant. Results Serum Fractalkine levels were significantly higher in patients with DR compared to controls (p < 0.05). No significant difference was observed between proliferative and nonproliferative groups. However, in the nonproliferative group, fractalkine levels were significantly higher in severe cases compared to mild and moderate cases (p < 0.05). ROC analysis identified an optimal cut-off value of 0.455 pg/ml for diagnosing DR (sensitivity: 81.5%, specificity: 56.3%) and 0.720 pg/ml for detecting moderate to severe nonproliferative DR (sensitivity: 100%, specificity: 61.9%). Conclusion Elevated serum Fractalkine levels are associated with the presence and severity of diabetic retinopathy. Fractalkine may serve as a promising adjunct biomarker for the early detection and grading of DR, highlighting the need for further research into its clinical utility. Diabetic retinopathy Serum Fractalkine CX3CL1 Proliferative diabetic retinopathy Non-proliferative diabetic retinopathy Figures Figure 1 Figure 2 Background Diabetic retinopathy (DR) is one of the most serious microvascular complications of diabetes mellitus and a leading cause of vision loss in adults globally [ 1 ]. According to the World Health Organization, it is estimated that there will be 700 million diabetic patients worldwide by 2045, and approximately 30–40% of these patients will develop DR [ 2 ]. The pathogenesis of DR is characterized by complex changes at the metabolic, molecular and cellular levels caused by chronic hyperglycemia [ 3 ]. Pathological changes occurring in the retinal microvascular system constitute the basic features of DR. These include pericyte loss, basement membrane thickening, disruption of the blood-retinal barrier, capillary occlusion and retinal ischemia [ 4 , 5 ]. Complications such as proliferative changes, vitreous hemorrhages, tractional retinal detachment and diabetic macular edema that occur in later stages can lead to irreversible vision loss [ 6 ]. This progressive nature of DR necessitates early diagnosis of the disease and development of effective treatment strategies [ 7 ]. In recent years, the role of inflammatory processes in the pathogenesis of DR has been increasingly understood [ 8 ]. Chronic hyperglycemia activates the inflammatory cascade by increasing oxidative stress in the retinal tissue and leads to the release of various cytokines, chemokines and growth factors. Among these molecules, vascular endothelial growth factor (VEGF), tumor necrosis factor-alpha (TNF-α) and various chemokines play important roles [ 9 , 10 ]. Fractalkine (CX3CL1) is a bifunctional molecule belonging to the CX3C chemokine family that is structurally different from other chemokines [ 11 ]. Fractalkine, which exists in both soluble and membrane-bound forms, plays a critical role in various pathophysiological processes such as inflammation, angiogenesis, neurodegeneration, and immune modulation. Fractalkine, which is expressed especially by retinal ganglion cells in the retina, binds to the CX3CR1 receptor on microglia and plays an important regulatory role in maintaining neurovascular unit homeostasis [ 12 ]. Recent studies on the role of the fractalkine-CX3CR1 axis in the pathogenesis of DR have revealed the complex and multifaceted functions of this system [ 13 ]. Increased fractalkine expression in diabetic retina can exert both neuroprotective and proinflammatory effects by modulating microglial activation. Defects in fractalkine signaling can lead to exacerbation of retinal inflammation, increased vascular damage, and neuronal degeneration. Experimental studies have shown that fractalkine administration can reduce retinal inflammation and prevent neurovascular damage [ 14 , 15 ]. In clinical studies, it has been suggested that fractalkine levels correlate with the severity of DR and may be a potential biomarker for predicting disease progression. It has been reported that fractalkine levels are increased in vitreous samples of patients with proliferative DR. However, studies systematically examining the relationship between serum fractalkine levels and DR stages are limited [ 16 , 17 ]. This study primarily aims to investigate the relationship between the stages of diabetic retinopathy (DR) and serum fractalkine levels in patients with type 2 diabetes mellitus. Secondary objectives include evaluating the diagnostic value of serum fractalkine in predicting DR severity and analyzing its association with diabetes duration, HbA1c, and other metabolic parameters. Clarifying the role of the fractalkine–CX3CR1 axis in DR may contribute to a better understanding of disease progression, support the development of early diagnostic strategies, and aid in identifying potential therapeutic targets. Furthermore, assessing the utility of serum fractalkine as a non-invasive biomarker may help optimize clinical monitoring and treatment protocols. Methods Study Design This single-center, prospective case-control study was conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the Clinical Research Ethics Committee of the University of Health Sciences, Kanuni Sultan Süleyman Training and Research Hospital (2023.05.77). Informed consent was obtained from all individuals participating in the study before the study. Subjects This study was conducted on 140 patients who applied to our hospital's internal medicine outpatient clinics and were diagnosed with type 2 diabetes mellitus (T2DM) according to the American Diabetes Association (ADA) 2024 diagnostic criteria [18]. Patients included in the study were divided into two groups according to the presence of diabetic retinopathy (DR): non-DR group (n=32) and DR group (n=108). Patients with DR were divided into two subgroups according to the severity of the disease as non-proliferative diabetic retinopathy (NPDR) (n=106) and proliferative diabetic retinopathy (PDR) (n=32). The NPDR group was further divided into three subgroups as mild (n=23), moderate (n=40), and severe (n=13). Diabetic Retinopathy Assessment Method The diagnosis and classification of diabetic retinopathy were made based on the International Diabetic Retinopathy Severity Scale (ICDR) and the Early Treatment of Diabetic Retinopathy Study (ETDRS) criteria by the same retina specialist (with at least 5 years of experience) who was unaware of the clinical features of the patients. For diagnostic evaluation, all patients underwent standard ophthalmologic examination followed by: (1) dilated fundus examination (with +90 diopter lens), (2) 45-degree angle color fundus photographs with a Topcon TRC-50DX (Japan) fundus camera, (3) fluorescein angiography with Heidelberg Spectralis HRA-2 (Germany), and (4) macular scans with Heidelberg Spectralis OCT (Germany). The patients were classified according to the ICDR criteria; mild NPDR (microaneurysms only), moderate NPDR (microaneurysms + retinal hemorrhage + hard exudate), severe NPDR (4-2-1 rule positivity: more than 20 retinal hemorrhages, venous beading, or overt IRMA), and PDR (retinal/optic disc neovascularization). The criteria for the diagnosis of diabetic macular edema were central macular thickness >250 µm on Spectralis OCT and/or leakage detected on fluorescein angiography. Disagreements and diagnostic disagreements were resolved by a second ophthalmologist [19,20]. Exclusion Criteria Patients with the following conditions were excluded from the study to ensure a homogeneous study population: type 1 diabetes mellitus, diabetic ketoacidosis, malignancies, severe liver disease, active infections, acute renal failure, chronic inflammatory disorders, and chronic lung diseases such as chronic obstructive pulmonary disease, asthma, bronchiectasis, or pulmonary hypertension. Additional exclusion criteria included thyroid disorders, immune disorders, neurological diseases, a history of eye surgery, retinal diseases, prior treatment with anti-vascular endothelial growth factor (anti-VEGF) injections or laser photocoagulation, widespread vitamin deficiencies, malnutrition, organ transplantation, and patients undergoing hemodialysis or peritoneal dialysis. The use of systemic steroids and pregnancy or breastfeeding were also among the exclusion criteria. Data Collection Age, gender, familial diabetes history, and the duration of diabetes mellitus diagnosis were documented. Body weight was recorded with an accuracy of 0.1 kg utilizing an electronic scale (Seca digital scale, 0.1 precision, Hamburg, Germany), permitting simple underpants to be worn. Height was measured using a Harpenden stadiometer (Seca mod. 240 ce 0123, manufactured in Hamburg, Germany) with a margin of error of 0.1 cm. Height measurements were conducted in an upright position with bare feet, feet positioned together and parallel, and the shoulder and gluteal region in contact with the wall. Body mass index was computed using the method of weight in kilograms (kg) divided by height in meters (m) squared. Patients were classified based on their BMI values. Measurements of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were conducted on the right arm using a mechanical sphygmomanometer following a 15-minute rest period. The patients' medications were documented. Nephropathy, neuropathy, cardiovascular disease, cerebrovascular disease, and peripheral vascular disease were documented, if applicable. Furthermore, dyslipidemia, hypertension, and metabolic syndrome were identified. Comorbidities of the patients were acquired from the hospital automation system. Blood sampling and biochemical analysis Fasting venous blood samples were collected between 8:00 and 10:00 AM after a 10- to 12-hour overnight fast. Blood was collected from the cubital vein of the subjects and collected into serum and plasma containers. Laboratory personnel were functionally blinded. Samples were centrifuged at 2000 rpm for 20 min at 4 °C. Biochemical analyses were performed immediately after centrifugation. The supernatant was collected for determination of additional parameters, and serum aliquots were immediately frozen and stored at −80 °C until further analyses. Fasting plasma glucose, creatinine, alanine aminotransferase, albumin, lipid profile (enzymatic colorimetric method) and C-reactive protein (CRP) (immunoturbidimetric method) were measured using a Roche Cobas 8000 c 702 analyzer (Roche Diagnostics, Germany). Glycosylated hemoglobin (HbA1c) was assessed by high-performance liquid chromatography using ARKRAY/ADAMS HA-8180V (ARKRAY Inc., Japan). Measurement of Serum Fractalkine (CX3CL1) Levels Venous blood samples were collected for fractalkine and were centrifuged, and the sera were separated into Eppendorf tubes and stored at –80°C until analyses. A commercial sandwich ELISA kit (E-EL-H0044, Elabscience, China) was used to measure human fractalkine concentrations. Experiments were performed according to the manufacturer's protocol, as follows: (1) 100 μL of standard/sample was added to micro ELISA plates and incubated for 90 min at 37°C, (2) biotinylated detection antibody (1:100 dilution) was added and incubated for 60 min, (3) HRP-conjugate (1:100 dilution) was added after 3x washing and incubated for 30 min, (4) TMB substrate was added after 5x washing and incubation in the dark for 15 min to initiate the color reaction, (5) Stop solution was added and optical density (OD) was measured at 450 nm. The calibration curve was created with a 4-parametric logistic model in the range of 0.16-10 ng/mL (0.10 ng/mL sensitivity), and the repeatability of the samples was confirmed with a coefficient of variation (CV) of less than 10%. Plasma/serum samples were anticoagulated with EDTA and centrifuged at 1000×g, and high concentration samples were analyzed by gradual dilution (100-100,000 fold). All measurements were made in duplicate and inter-/intra-assay CV values were found to be <10%. No cross-reactivity was observed. Outcomes This study investigates serum fractalkine levels in diabetic retinopathy, assessing its correlation with disease severity, diagnostic performance through ROC analysis, and its potential added value beyond traditional risk factors. These findings may support the potential role of fractalkine as a biomarker for early diagnosis. Statistical Analysis IBM SPSS Statistics 25.0 ((IBM Corporation, Chicago, IL, USA) and R programming language (version 3.6.1) were used in the analysis of data, and after the normality distribution was evaluated with the Shapiro-Wilk test, Student t-test and one-way ANOVA (post-hoc Tukey test) were applied for parametric data, and Mann-Whitney U and Kruskal-Wallis tests (with Dunn post-hoc correction) were applied for nonparametric data. The relationship between serum Fractalkine levels and degree of diabetic retinopathy was examined with Pearson/Spearman correlation analyses, while logistic regression models were developed by adjusting for age, gender, BMI, diabetes duration and HbA1c values in multivariate analysis. For diagnostic performance assessment, AUC values were calculated by ROC curve analysis, optimal cut-off values were determined according to the Youden index and sensitivity-specificity analyses were performed. In data presentation, normally distributed parameters were expressed as mean ± standard deviation, nonparametric data as median (interquartile range), and p<0.05 was considered statistically significant in all tests. Results A total of 140 type 2 diabetes mellitus (T2DM) patients were included in this study. 32 (22.9%) of the participants were individuals without diabetic retinopathy (DRP) and 108 (77.1%) were diagnosed with DRP. The DRP group was divided into two subgroups as proliferative (PDR) and nonproliferative (NPDR), with 32 (29.6%) PDR patients and 76 (70.4%) NPDR patients. The NPDR group was further divided into three subgroups as mild (30.3%, n=23), moderate (52.6%, n=40) and severe (17.1%, n=13). The mean age of the participants was 56.8 ± 9 years, and the duration of diabetes was 11.4 ± 8 years. The male/female ratio was 49.3%/50.7%, and the mean BMI was 29.2 ± 4.5 kg/m². Hypertension was present in 32.9% of the participants. In terms of treatment, 49.3% used oral antidiabetic drugs, 93.6% used insulin, and 41.4% used both. Antihypertensive use was 30%, and alcohol consumption was not reported. Demographic and clinical data were compared between groups with and without retinopathy, and detailed information is presented in Table 1. Tablo 1: Comparison of demographic and clinical variables between patients with and without diabetic retinopathy Without Retinopathy (n:32) With Retinopathy (n=108) p-value Age,y 53.0 59.0 0.071 DM duration, y 3.5 10.0 0.000 Gender, n(%) Female 13 40.6% 58 53.7% 0.194 Male 19 59.4% 50 46.3% Weight, kg 83.0 78.0 0.084 Height,m 1.7 1.7 0.316 BMI, kg/m2 30.2 ± 4.9 29.0 ± 4.4 0.180 HT, n(%) Absent 26 81.3% 68 63.0% 0.053 Present 6 18.8% 40 37.0% Smoking Status, n(%) No 29 90.6% 91 84.3% 0.366 Yes 3 9.4% 17 15.7% Medications, n(%) Oral Antidiabetic Drugs 23 71.9% 46 42.6% 0.004 Insulin 2 6.3% 7 6.5% 0.963 Insulin + OAD 7 21. % 51 47.2% 0.011 Antihypertensive 6 18.8% 36 33.3% 0.114 Other 1 3.1% 1 0.9% 0.406 Note:Data are presented as mean ± SD or median for non-normally distributed variables. Comparisons were made using the independent t-test for normal distribution, Mann-Whitney U test for non-normal distribution, and chi-square (χ²) test for categorical variables. Statistical significance was set at p < 0.05. DM: diabetes mellitus,OAD: oral antidiabetic drugs, BMI: body mass index, HT: hypertension Serum fractalkine levels were significantly higher in the retinopathy group than in the non-retinopathy group (p=0.0002). Fasting blood sugar (p=0.022), HbA1c (p=0.004), urea (p=0.008), creatinine (p=0.0001), urine microalbumin (p=0.010) and urine microalbumin/creatinine ratio p=0.003) values were significantly higher in the retinopathy group. While inflammation markers WBC (p=0.013) and neutrophil/lymphocyte ratio (p=0.005) were significantly higher in the retinopathy group, no statistically significant difference was observed between the groups in terms of CRP (p=0.215), C-peptide (p=0.387), HOMA-IR (p=0.156), liver function tests (AST p=0.421, ALT p=0.372), lipid parameters (LDL p=0.243, HDL p=0.412, triglyceride p=0.358) and hematological indices (hemoglobin p=0.476, platelet p=0.503). These data are presented in Table 2. Tablo 2: Comparison of serum fractalkine levels and laboratory values between patients with and without diabetic retinopathy Without Retinopathy (n:32) With Retinopathy (n=108) p-value Fractalkine, (ng/mL) 0.4 0.7 0.000 CRP, (mg/L) 2.8 3.7 0.773 Glucose, (mg/dL) 152.5 212.5 0.022 C Peptide , (pmol/L) 838.5 875.5 0.968 HbA1c,% 7.9±1.7 9.0±1.9 0.004 HOMA-IR 4.7 4.4 0.749 Urea , (mg/dL) 29.9 34.0 0.008 Microalbumin (Urine), (mg/L) 11.0 45.0 0.010 Microalbumin/Creatinine,(mg/g) 13.9 35.3 0.003 Creatinine ,(mg/dL) 0.7 0.9 0.000 AST , (U/L) 16.5 15.0 0.223 ALT , (U/L) 19.5 15.5 0.123 LDH , (U/L) 167.0 169.0 0.357 D-Dimer , (ug/mLFEU) 0.4 0.4 0.133 LDL-C ,(mg/dL) 117.9 ± 37.4 121.4±39.3 0.656 HDL-C , (mg/dL) 44.5 42.0 0.356 TG, (mg/dL) 191.5 175.5 0.580 Total Cholesterol, (mg/dL) 184.0 194.5 0.962 Hemoglobin, (g/dL) 14.0 ± 1.6 13.7±2.0 0.363 WBC, 10 3 /mm3 7.3 8.2 0.013 PLT, 10 3 /mm3 274.5 263.0 0.886 Neutrophil/Lymphocyte Ratio 1.7 2.1 0.005 Note: Data are presented as mean ± SD or median for non-normally distributed variables. Comparisons were made using the independent t-test for normal distribution, Mann-Whitney U test for non-normal distribution. Statistical significance was set at p < 0.05. CRP: c-reactive protein, HbA1c : hemoglobin A1c, HOMA-IR : homeostasis model assessment of insulin resistance, AST: aspartate aminotransferase, ALT : alanine aminotransferase, LDH : lactate dehydrogenase, LDL -C:low-density lipoprotein, HDL -C: high-density lipoprotein, TG : tryglyceride , WBC :white blood cell count, PLT : platelet count According to the logistic regression analysis results presented in Table 3, statistically significant relationships were found between the presence of diabetic retinopathy and diabetes duration (p<0.001), serum fractalkine levels (p<0.001), fasting blood glucose (p=0.032), HbA1c (p=0.005), serum urea (p=0.008), creatinine (p=0.001), leukocyte count (p=0.014) and neutrophil/lymphocyte ratio (p=0.008) in univariate analysis. In multivariate analysis, when adjusted for age, gender and other clinical parameters, it was determined that diabetes duration (p=0.001), fractalkine levels (p=0.036), HbA1c (p=0.012) and creatinine (p=0.012) levels independently predicted the presence of retinopathy. Tablo 3: Univariate and Multivariate Logistic Regression Analysis of Variables Associated with Diabetic Retinopathy Univariate Model Multivariate Model OR %95 CI p-value OR %95 CI p-value DM duration ,y 1.18 1.09 - 1.28 0.000 1.17 1.06 - 1.28 0.001 Fractalkine, (ng/mL) 25.3 4.1 - 154.4 0.000 10.2 1.2 - 89.6 0.036 Glucose, (mg/dL) 1.01 1.00 - 1.01 0.032 HbA1c,% 1.42 1.11 - 1.81 0.005 1.42 1.08 - 1.87 0.012 Urea, (mg/dL) 1.05 1.01 - 1.10 0.008 Creatinine, (mg/dL) 22.75 3.41 - 152.0 0.001 20.78 1.97 - 219.1 0.012 WBC, 10 3 /mm3 1.32 1.06 - 1.65 0.014 Neutrophil/Lymphocyte Ratio 2.13 1.21 - 3.75 0.008 Note: Logistic regression was performed using Forward LR (Likelihood Ratio) method. The odds ratio (OR) and 95% confidence interval (CI) are provided for each variable along with p-values. Statistically significant p-values (<0.05) are highlighted. HbA1c: hemoglobin A1c, WBC: white blood cell count, DM: diabetes mellitus According to the results of Spearman correlation analysis, no statistically significant correlation was found between serum fractalkine levels and demographic parameters such as age (r=-0.020, p=0.796), diabetes duration (r=0.108, p=0.153), BMI (r=-0.053, p=0.485), smoking (p>0.05) and all laboratory parameters including glucose (p>0.05), HbA1c (p>0.05), HOMA-IR (p>0.05), renal function tests (p>0.05), inflammatory markers (p>0.05) and lipid profile (p>0.05) (Table 4). Tablo 4: Spearman Correlation Analysis Between Serum Fractalkine Level and Clinical/Biochemical Parameters Variable FRAKTALKİNE LEVEL r p Age,y -0.020 0.796 Duration of diabetes,y 0.108 0.153 Weight,kg -0.077 0.308 Height,m 0.006 0.938 BMI,kg -0.053 0.485 Cigarette number, -0.226 0.289 Smoking duration,% 0.248 0.243 Glucose, (mg/dL) -0.001 0.991 C-peptide, (pmol/L) 0.006 0.935 HbA1c,% 0.067 0.377 HOMA-IR, 0.013 0.860 Urea, (mg/dL) -0.100 0.186 Creatinine, (mg/dL) 0.058 0.445 AST, (U/L) -0.017 0.820 ALT, (U/L) -0.013 0.864 LDH, (U/L) -0.110 0.145 CRP, (mg/L) 0.101 0.183 D-Dimer, (ug/mLFEU) 0.017 0.821 Urinary microalbumin, (mg/L) -0.183 0.115 Microalbumin/Creatinine, (mg/g) -0.118 0.122 LDL-C, (mg/dL) 0.026 0.727 HDL-C, (mg/dL) 0.034 0.656 Triglyceride, (mg/dL) -0.112 0.140 Total cholesterol, (mg/dL) 0.033 0.666 Hemoglobin, (g/dL) 0.105 0.167 PLT, 10 3 /mm3 0.053 0.489 WBC, 10 3 /mm3 0.042 0.578 BMI: body mass index, HbA1c: hemoglobin A1c, HOMA-IR: homeostasis model assessment of insulin resistance, AST: aspartate aminotransferase, ALT: alanine aminotransferase, LDH: lactate dehydrogenase, CRP: c-reactive protein, LDL: low-density lipoprotein, HDL: high-density lipoprotein, PLT: platelet count, WBC: white blood cell count When proliferative (n=32) and nonproliferative (n=76) DRP patients were compared, no significant difference was found between the mean fractalkine levels (0.5 ng/mL and 0.70 ng/mL; p=0.274). In addition, no statistically significant difference was found between the two groups in terms of metabolic, renal, hepatic, inflammatory, lipid and hematological parameters (Table 5). Table 5. Comparison of serum fractalkine levels and laboratory parameters between patients with proliferative and non-proliferative diabetic retinopathy Proliferative DR (n: 32) Non-Proliferative DR (n: 76) p-value Fractalkine, (ng/mL) 0.5 0.7 0.274 CRP, (mg/L) 6.6 ± 8.4 8.7 ± 26.4 0.668 Glucose, (mg/dL) 209.0 215.5 0.696 C Peptıt , (pmol/L) 887.2 ± 520.3 1007.9 ± 462.8 0.236 HbA1c,% 9.6 9.0 0.279 HOMA-IR 3.7 4.8 0.623 Urea , (mg/dL) 33.0 35.0 0.497 Urinary Microalbumin, (mg/L) 53.3 42.2 0.501 Microalbumin/Creatinine, (mg/g) 65.6 33.3 0.218 Creatinine, (mg/dL) 0.8 0.9 0.893 AST , (U/L) 16.5 14.0 0.348 ALT, (U/L) 17.0 15.0 0.625 LDH , (U/L) 185.3 ± 46.5 174.8 ± 45.6 0.277 D-Dimer, (ug/mLFEU) 0.5 0.4 0.400 LDL-C, (mg/dL) 127.1 ± 38.7 118.9 ± 39.6 0.326 HDL-C, (mg/dL) 44.1 ± 8.2 43.3 ± 11.8 0.731 TG , (mg/dL) 187.8 ± 94.5 181.8 ± 87.0 0.751 Total Cholesterol, (mg/dL) 205.5 ± 55.8 192.5 ± 46.7 0.214 Hemoglobin, (g/dL) 13.3 13.5 0.469 WBC, 10 3 /mm3 8.8 ± 2.3 8.6 ± 2.2 0.797 PLT, 10 3 /mm3 278.7 ± 77.4 279.1 ± 79.6 0.980 Note: P-values were calculated using the independent samples t-test for variables with normal distribution, and the Mann-Whitney U test for variables with non-normal distribution. Normality was assessed using the Shapiro-Wilk test. DR: diabetic retinopathy, CRP: c-reactive protein , HbA1c : hemoglobin A1c, HOMA-IR : homeostasis model assessment of insulin resistance, AST: aspartate aminotransferase, ALT : alanine aAminotransferase, LDH : lactate dehydrogenase, LDL -C:low-density lipoprotein, HDL -C: high-density lipoprotein, TG: triglyceride, WBC :white blood cell count, PLT : platelet count Nonproliferative diabetic retinopathy patients (n=76) were divided into mild (n=23), moderate (n=40) and severe (n=13) subgroups according to disease severity and laboratory parameters were compared (Table 6). Fractalkine levels were found to be significantly higher in the severe group compared to both mild and moderate groups (p=0.004). No significant difference was found between the mild and moderate groups (p>0.05). Regarding renal functions, urine microalbumin levels and microalbumin/creatinine ratio were found to be significantly higher in the severe and moderate groups compared to the mild group (p=0.017 and p=0.015, respectively). No significant difference was observed between the groups in terms of other laboratory parameters (glucose, HbA1c, HOMA-IR, urea, creatinine, liver enzymes, inflammation markers, lipid profile and hematological parameters) (p>0.05 for all) (Table 6). Tablo 6: Comparison of Serum Fractalkine Levels and Laboratory Parameters in Non-Proliferative DR Mild Non-Proliferative DR (n:23) Moderate Non-Proliferative DR (n:40) Severe Non-Proliferative DR (n:13) p-value Fraktalkine, (ng/mL) 0.68 0.63 0.83 * 0.004 CRP , (mg/L) 2.2 4.4 2.5 0.092 Glucose, (mg/dL) 218.0 197.5 244.0 0.992 C Peptit, (pmol/ 934.0 883.0 876.0 0.905 HbA1c ,% 8.8 ± 2.2 8.9 ± 1.7 9.2 ± 1.4 0.788 HOMA-IR 5.0 4.7 4.1 0.881 Urea, (mg/dL) 33.0 33.5 36.7 0.777 Urinary Microalbumin, (mg/L) 12.9 ¥ 49.7 116.7 0.017 Microalbumin/Creatinine, (mg/g) 20.8 ¥ 49.3 45.7 0.015 Creatinine ,(mg/dL) 0.8 0.9 0.9 0.589 AST, (U/L) 14.0 14.5 15.0 0.967 ALT, (U/L) 15.0 15.0 15.0 0.951 LDH, (U/L) 157.0 166.0 173.0 0.635 D-Dimer, (ug/mLFEU) 0.5 0.4 0.4 0.661 LDL-C, (mg/dL) 115.5 ± 36.5 118.7 ± 43.5 125.9 ± 33.7 0.754 HDL –C, (mg/dL) 41 40.0 43.0 0.328 TG, (mg/dL) 164.8 ± 67.7 193.5 ± 100.7 176.0 ± 69.7 0.442 Total Cholesterol, (mg/dL) 189.5 ± 46.4 187.8 ± 45.8 212.1 ± 48.9 0.251 Hemoglobin, (g/dL) 13.4 13.9 12.3 0.305 WBC, 10 3 /mm3 8.1 8.6 7.8 0.772 PLT, 10 3 /mm3 285.9 ± 78.5 273.1 ± 83.6 285.4 ± 72.8 0.794 Nötrofil /Lenfosit Oranı 2.3 ± 0.8 2.3 ± 1.1 1.9 ± 0.8 0.472 Note: P-values were calculated using the Kruskal-Wallis (K) test for non-normally distributed variables and the ANOVA (A) test for normally distributed variables. Statistical significance was set at p < 0.05. * Significant difference compared to the moderate non-proliferative group (p < 0.05). ¥ Significant difference compared to the severe non-proliferative group (p < 0.05) DR: diabetic retinopathy, CRP: c-reactive protein , HbA1c : hemoglobin A1c, HOMA-IR : homeostasis model assessment of insülin resistance, AST: aspartate aminotransferase, ALT : alanine aminotransferase, LDH : lactate dehydrogenase, LDL-C :low-density lipoprotein, HDL-C: high-density lipoprotein, TG:triglyceride, WBC :white blood cell count, PLT : platelet count According to the ROC analysis results, fractalkine levels showed significant diagnostic performance in distinguishing patients with and without retinopathy [AUC: 0.736 (95% CI: 0.634-0.838)]. With a cut-off value of 0.455 ng/mL, fractalkine determined the presence of retinopathy with 81.5% sensitivity and 56.3% specificity (positive predictive value: 86.3%, negative predictive value: 47.4%) (Table 7, Figure 1). Tablo 7: Diagnostic Performance of Serum Fractalkine Levels in Differentiating the Presence of Diabetic Retinopathy Based on ROC Analysis A. ROC Curve Analysis Variable AUC 95% CI p-value Fractalkine , (ng/mL) 0.736 0.634-0.838 <0.001 Fractalkine cutoff (0.455) 0.689 0.577-0.800 0.001 B. Diagnostic Performance at 0.455 ng/mL Cutoff DR Negative (n:32) DR Positive(n: 108) Value (%) Fractalkine < 0.455 18 20 Fractalkine ≥ 0.455 14 88 Sensitivity 81.5% Specificity 56.3% Positive Predictive Value (PPV) 86.3% Negative Predictive Value (NPV) 47.4% AUC: area under the curve. Cut-off value was determined based on the Youden Index. DR: diabetic retinopathy. ROC: receiver operating characteristic. CI: confidence interval In the ROC analysis performed to determine the disease severity in patients with nonproliferative retinopathy, fractalkine levels showed high diagnostic performance in distinguishing between mild and moderate-severe groups [AUC: 0.784 (95% CI: 0.679-0.888)]. With a cut-off value of 0.720 ng/mL, fractalkine detected moderate-severe retinopathy with 100% sensitivity and 61.9% specificity (positive predictive value: 35.1%, negative predictive value: 100%) (Table 8, Figure 2). These findings suggest that fractalkine levels can be used as a potential biomarker in determining both the presence and severity of retinopathy. Table 8: Diagnostic Performance of Serum Fractalkine Levels in Differentiating the Severity of Non-Proliferative Diabetic Retinopathy Based on ROC Analysis A. ROC Curve Analysis Variable AUC 95% CI p-value Fractalkine level (ng/mL) 0.784 0.679-0.888 0.001 Fractalkine cutoff (0.720) 0.810 0.713-0.906 <0.001 B. Diagnostic Performance at 0.720 ng/mL Cutoff NPDR Mild (n:23) NPDR Moderate-Severe (n: 53) Value (%) Fractalkine < 0.720 39 0 Fractalkine ≥ 0.720 24 13 Sensitivity 100.0% Specificity 61.9% Positive Predictive Value (PPV) 35.1% Negative Predictive Value (NPV) 100.0% AUC: area under the curve. Cut-off value was determined based on the Youden Index. NPDR: diabetic retinopathy. ROC: receiver operating characteristic. CI: confidence interval Discussion Diabetic retinopathy (DR) is the most common and potentially serious microvascular complication of diabetes mellitus that can lead to vision loss. However, currently there is no reliable and non-invasive biomarker that can be used for diagnosis and monitoring of disease progression. This study is important because it is one of the first to systematically evaluate the relationship between DR severity and serum fractalkine levels in diabetic patients. Our study provided important findings demonstrating the relationship between serum fractalkine levels and the presence and severity of diabetic retinopathy (DRP). Fractalkine levels were significantly higher in the group with retinopathy compared to the group without retinopathy. In particular, fractalkine levels were significantly higher in patients with severe nonproliferative DRP compared to patients with mild and moderate stages. Several mechanisms supporting the role of fractalkine in the pathogenesis of DRP have been described in the literature. Jian-Jang You et al. showed that fractalkine is a critical mediator of retinal angiogenesis and is found at high levels in patients with diabetic retinopathy [ 21 ]. Samuel A. Mills et al. showed that fractalkine impairs vasoregulation in the early stages of DRP via microglial activation [ 22 ]. These findings are consistent with the results of our study and support the idea that fractalkine plays an important role in both inflammatory and angiogenic processes. Another finding of our study is that there was no significant difference in fractalkine levels between the proliferative and nonproliferative DRP groups. This may be due to individual differences in fractalkine signaling pathways (CX3CR1 polymorphisms) or variability in ADAM10/ADAM17 metalloproteinase activities. Ahmed M. Abu El-Asrar's study has shown a strong correlation between fractalkine and VEGF levels in vitreous samples of patients with proliferative DRP [ 23 ]. These findings suggest that the fractalkine-VEGF interaction may be important in the pathogenesis of DRP. Finny Monickaraj has shown that CXCL1 levels are increased in patients with diabetic retinopathy [ 24 ], while Andrew S. Mendiola has shown that fractalkine signaling modulates microglial activity in animal models [ 21 ]. These studies support the critical role of chemokines in DRP. In our study, diabetes duration was significantly longer in patients with diabetic retinopathy (DR) compared to those without. However, no significant difference was observed in diabetes duration between non-proliferative DR (NPDR) and proliferative DR (PDR) groups or among NPDR stages. These findings align with previous studies, such as a study conducted in the United Kingdom by Stratton IM et al. showing that 36% of patients without retinopathy developed NPDR and 68% developed PDR within 5 years, and the LALES study, which reported an 8% increase in DR risk per year of diabetes duration. While our results confirm the strong association between diabetes duration and the onset of DR, they also suggest that duration alone may not sufficiently explain disease progression. This underscores the need to explore additional contributing factors, including genetic predisposition, inflammation, and vascular changes, alongside the chronic effects of hyperglycemia. The UK Prospective Diabetes Study (UKPDS 33) emphasized the clinical significance of glycemic control, showing that a 1% reduction in HbA1c was associated with a 24% decrease in microvascular complications and a 29% reduction in the need for laser coagulation [ 27 ]. Our findings align with this, supporting the critical role of glycemic control in the incidence and progression of DRP. The concept of metabolic memory, highlighted in Varadhan et al.'s retrospective study, suggests that early poor glycemic control increases the risk of retinopathy, but subsequent tight control can reduce this risk by 80% [ 28 ]. Long-term data from the EDIC study also support this, showing a higher risk of retinopathy in patients initially receiving conventional therapy compared to those in the intensive therapy group [ 29 ]. In our study, participants in the DRP group had significantly higher mean fasting glucose and HbA1c levels compared to the non-DRP group. These findings underscore the importance of timing in glycemic control for long-term outcomes. However, in our study, no significant differences were found in glucose and HbA1c levels between the NPDR and PDR groups or within NPDR substages, suggesting that glycemic control alone may not sufficiently determine disease severity once retinopathy has developed. Future studies could focus on the timing of glycemic control, glycemic variability, and experimental investigations into metabolic memory mechanisms. In conclusion, while poor glycemic control remains a key risk factor for DRP development, no direct relationship was observed between glycemic control and retinopathy severity, indicating that other factors contribute to the pathogenesis of DRP. Our study found a significant association between diabetic retinopathy (DRP) and nephropathy, with higher levels of serum creatinine, urea, urine microalbumin, and microalbumin/creatinine ratio in patients with DRP compared to those without. These results reinforce the link between diabetic microvascular complications. Previous studies, including those by El-Asrar AM et al., Li et al., Manavi, and Butt, have similarly demonstrated that the prevalence of both retinopathy and microalbuminuria increases with diabetes duration and are positively correlated [ 30 – 33 ]. Notably, we observed a progressive increase in urine microalbumin levels from mild to severe NPDR, indicating worsening renal involvement with advancing retinopathy. However, no significant differences in renal parameters were noted between NPDR and PDR groups. These findings suggest that early and intensive glycemic control, especially in patients with mild NPDR, may help slow the progression of both retinopathy and nephropathy, supporting conclusions from Rasheed et al. in long-term diabetic patients [ 34 ]. Logistic regression analyses of our study revealed the independent role of fractalkine levels in predicting the presence of diabetic retinopathy (DRP). In univariate analysis, fractalkine levels, as well as diabetes duration, glucose, HbA1c, urea, creatinine, WBC and neutrophil/lymphocyte ratio were found to have significant predictive value. In the multivariate model, fractalkine levels, diabetes duration, HbA1c and creatinine levels were found to remain as independent predictors. These findings indicate that the role of fractalkine in the pathogenesis of DRP is independent of traditional risk factors. ROC curve analysis quantitatively evaluated the diagnostic performance of fractalkine. The cut-off value of 0.455 pg/ml determined for fractalkine diagnosed DRP with 81.5% sensitivity and 56.3% specificity (AUC = 0.689). More importantly, the cut-off value of 0.720 pg/ml showed 100% sensitivity and 61.9% specificity (AUC = 0.810) in distinguishing the severity of nonproliferative DRP. These results indicate that fractalkine levels have a clinically significant predictive value, especially in assessing the severity of nonproliferative DRP. These analyses suggest that Fractalkine may be a potential marker that can be used clinically in DRP risk stratification together with traditional biochemical markers. Its ability to identify severe forms of nonproliferative DRP with high sensitivity may provide the opportunity for early intervention, while AUC values ​​also show that Fractalkine exhibits a good diagnostic performance, especially in assessing disease severity. When evaluated in light of existing literature, our findings support the role of fractalkine in the inflammatory and angiogenic pathways involved in diabetic retinopathy (DRP) progression, suggesting its potential as a predictive biomarker. Although this study is among the first to demonstrate a clinical association between serum fractalkine levels and DRP severity—particularly in nonproliferative stages—the cross-sectional design limits causal inference. A significant strength of our study is its ability to show that fractalkine levels correlate with DRP presence and severity, independent of traditional risk factors such as diabetes duration, HbA1c, and creatinine. However, several limitations should be acknowledged. The absence of a healthy control group restricted our ability to compare baseline fractalkine levels. The cross-sectional nature and relatively small sample size—especially in the PDR subgroup—may limit the generalizability and statistical power. Potential confounding factors, including glycemic control, antihypertensive use, and comorbidities, were not fully controlled. Furthermore, genetic and molecular variables such as CX3CR1 polymorphisms and ADAM10/ADAM17 activity were not assessed, and fractalkine was measured at a single time point, not capturing temporal variations. Conclusions This study is one of the first clinical investigations to demonstrate a significant association between serum fractalkine levels and both the presence and severity of diabetic retinopathy (DRP). Fractalkine emerged as an independent predictor of DRP and showed high diagnostic value, particularly in severe nonproliferative stages. These results suggest that fractalkine may play a key role in DRP pathogenesis and could serve as a promising biomarker for early diagnosis and disease monitoring. Abbreviations DR – Diabetic Retinopathy VEGF – Vascular Endothelial Growth Factor TNF-α – Tumor Necrosis Factor-alpha CX3CL1 – Fractalkine CX3CR1 – CX3C Chemokine Receptor 1 PDR – Proliferative Diabetic Retinopathy NPDR – Non-Proliferative Diabetic Retinopathy T2DM – Type 2 Diabetes Mellitus ADA – American Diabetes Association ICDR – International Clinical Diabetic Retinopathy Severity Scale ETDRS – Early Treatment Diabetic Retinopathy Study IRMA – Intraretinal Microvascular Abnormalities BMI – Body Mass Index ELISA – Enzyme-Linked Immunosorbent Assay HOMA-IR – Homeostasis Model Assessment of Insulin Resistance HbA1c – Hemoglobin A1c (Glycated Hemoglobin) ADAM10 – A Disintegrin and Metalloproteinase Domain-Containing Protein 10 ADAM17 – A Disintegrin and Metalloproteinase Domain-Containing Protein 17 LALES – Los Angeles Latino Eye Study UKPDS 33 – The United Kingdom Prospective Diabetes Study 33 EDIC – Epidemiology of Diabetes Interventions and Complications ROC – Receiver Operating Characteristic AUC – Area Under the Curve HRP – Horseradish Peroxidase TMB – Tetramethylbenzidine CRP – C-Reactive Protein Declarations Author Contributions . O.Y. and M.Ak. made the most substantial contributions to the conception, design, and supervision of the study. They also led the writing and critical revision of the manuscript. M.A. played a key role in conducting and interpreting the biochemical analyses. M.E. and I.K. contributed to data collection and statistical analysis. S.A.Y. was involved in literature review and provided valuable feedback during manuscript preparation. All authors reviewed and approved the final version of the manuscript. Acknowledgements None. Ethical Statement This study was conducted in accordance with the principles outlined in the Declaration of Helsinki. The study protocol was reviewed and approved by the Clinical Research Ethics Committee of the University of Health Sciences, Kanuni Sultan Süleyman Training and Research Hospital (Approval No: 2023.05.77). All study procedures complied with institutional and international ethical guidelines.Written informed consent was obtained from all participants prior to their inclusion in the study Consent for publication Not applicable. Funding This research received no external funding Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Clinical trial number Not applicable. Data availability statement The data that support the findings of this study are available from the corresponding author upon reasonable request. References Teo ZL, Tham YC, Yu M, Chee ML, Rim TH, Cheung N, et al. Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: Systematic Review and Meta-analysis. Ophthalmology. 2021;128(11):1580-91. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. IDF Diabetes Atlas Committee. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. 2019;157:107843. Stitt AW, Curtis TM, Chen M, Medina RJ, McKay GJ, Jenkins A, et al. The progress in understanding and treatment of diabetic retinopathy. Prog Retin Eye Res. 2016;51:156-86 Antonetti DA, Barber AJ, Bronson SK, Freeman WM, Gardner TW, Jefferson LS, et al. JDRF Diabetic Retinopathy Center Group. Diabetic retinopathy: seeing beyond glucose-induced microvascular disease. Diabetes. 2006 ;55(9):2401-11 Hammes HP, Lin J, Renner O, Shani M, Lundqvist A, Betsholtz C, et al. Pericytes and the pathogenesis of diabetic retinopathy. Diabetes. 2002 ;51(10):3107-120 Simó R, Stitt AW, Gardner TW. Neurodegeneration in diabetic retinopathy: does it really matter? Diabetologia. 2018 ;61(9):1902-12 Solomon SD, Chew E, Duh EJ, Sobrin L, Sun JK, VanderBeek BL, et al. Diabetic Retinopathy: A Position Statement by the American Diabetes Association. Diabetes Care. 2017;40(3):412-18. Tang J, Kern TS. Inflammation in diabetic retinopathy. Prog Retin Eye Res. 2011;30(5):343-58. Lee CY, Yang CH. The Role of Fractalkine in Diabetic Retinopathy: Pathophysiology and Clinical Implications. Int J Mol Sci. 2025;26(1):378. Kang Q., Yang C. Oxidative stress and diabetic retinopathy: Molecular mechanisms, pathogenetic role and therapeutic implications. Redox. Biol. 2020;37:101799. Bazan JF, Bacon KB, Hardiman G, Wang W, Soo K, Rossi D, et al. A new class of membrane-bound chemokine with a CX3C motif. Nature. 1997 13;385(6617):640-49 White GE, Greaves DR. Fractalkine: a survivor's guide: chemokines as antiapoptotic mediators. Arterioscler Thromb Vasc Biol. 2012 ;32(3):589-94 You J.J., Yang C.H., Huang J.S., Chen M.S., Yang C.M. Fractalkine, a CX3C chemokine, as a mediator of ocular angiogenesis. Investig. Ophthalmol. Vis. Sci. 2007;48:5290–98 Cardona S.M., Mendiola A.S., Yang Y.C., Adkins S.L., Torres V., Cardona A.E. Disruption of Fractalkine Signaling Leads to Microglial Activation and Neuronal Damage in the Diabetic Retina. ASN Neuro. 2015;7:1759091415608204 Mendiola A.S., Garza R., Cardona S.M., Mythen S.A., Lira S.A., Akassoglou K., et al. Fractalkine Signaling Attenuates Perivascular Clustering of Microglia and Fibrinogen Leakage during Systemic Inflammation in Mouse Models of Diabetic Retinopathy. Front. Cell. Neurosci. 2016;10:303 Rodriguez D, Church KA, Pietramale AN, Cardona SM, Vanegas D, Rorex C, et al. Fractalkine isoforms differentially regulate microglia-mediated inflammation and enhance visual function in the diabetic retina. J Neuroinflammation. 2024 4;21(1):42 Serra AM, Waddell J, Manivannan A, Xu H, Cotter M, Forrester JV. CD11b+ bone marrow-derived monocytes are the major leukocyte subset responsible for retinal capillary leukostasis in experimental diabetes in mouse and express high levels of CCR5 in the circulation. Am J Pathol. 2012 ;181(2):719-27 American Diabetes Association Professional Practice Committee; Introduction and Methodology: Standards of Care in Diabetes—2024. Diabetes Care 2024; 47 (Supplement_1): S1–4. Wilkinson, C. P., et al. (2003). Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology, 110(9), 1677-82 Early Treatment Diabetic Retinopathy Study Research Group. (1991). Grading diabetic retinopathy from stereoscopic color fundus photographs. Ophthalmology, 98(5), 786-806 You JJ, Yang CH, Huang JS, Chen MS, Yang CM. Fractalkine, a CX3C chemokine, as a mediator of ocular angiogenesis. Invest Ophthalmol Vis Sci. 2007 ;48(11):5290-8 Mills SA, Jobling AI, Dixon MA, Bui BV, Vessey KA, Phipps JA, et al. Fractalkine-induced microglial vasoregulation occurs within the retina and is altered early in diabetic retinopathy. Proc Natl Acad Sci U S A. 2021 21;118(51):e2112561118 Abu El-Asrar AM, Nawaz MI, Ahmad A, De Zutter A, Siddiquei MM, Blanter M, et al. Evaluation of Proteoforms of the Transmembrane Chemokines CXCL16 and CX3CL1, Their Receptors, and Their Processing Metalloproteinases ADAM10 and ADAM17 in Proliferative Diabetic Retinopathy. Front Immunol. 2021 20;11:601-39 Monickaraj F, Acosta G, Cabrera AP, Das A. Transcriptomic Profiling Reveals Chemokine CXCL1 as a Mediator for Neutrophil Recruitment Associated With Blood-Retinal Barrier Alteration in Diabetic Retinopathy. Diabetes. 2023 1;72(6):781-94 Stratton IM, Adler AI, Neil HA, Matthews DR, Manley SE, Cull CA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ. 2000 12;321(7258):405-12 Varma R, Torres M, Peña F, Klein R, Azen SP; Los Angeles Latino Eye Study Group. Prevalence of diabetic retinopathy in adult Latinos: the Los Angeles Latino eye study. Ophthalmology. 2004 ;111(7):1298-306 Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group. Lancet. 1998 12;352(9131):837-53. Erratum in: Lancet 1999 14;354(9178):602 Varadhan L, Humphreys T, Hariman C, Walker AB, Varughese GI. GLP-1 agonist treatment: implications for diabetic retinopathy screening. Diabetes Res Clin Pract. 2011 ;94(3):e68-71 Writing Team for the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Research Group. Effect of intensive therapy on the microvascular complications of type 1 diabetes mellitus. JAMA 2002 15;287(19):2563-69 El-Asrar AM, Al-Rubeaan KA, Al-Amro SA, Moharram OA, Kangave D. Retinopathy as a predictor of other diabetic complications. Int Ophthalmol. 2001;24(1):1-11 Li Y, Su X, Ye Q, Guo X, Xu B, Guan T, et al. The predictive value of diabetic retinopathy on subsequent diabetic nephropathy in patients with type 2 diabetes: a systematic review and meta-analysis of prospective studies. Ren Fail. 2021 ;43(1):231-40 Butt A, Mustafa N, Fawwad A, Askari S, Haque MS, Tahir B, et al. Relationship between diabetic retinopathy and diabetic nephropathy; A longitudinal follow-up study from a tertiary care unit of Karachi, Pakistan. Diabetes Metab Syndr. 2020 ;14(6):1659-63 Manaviat, M.R., Afkhami, M. & Shoja, M.R. Retinopathy and microalbuminuria in type II diabetic patients. BMC Ophthalmol 4, 9 (2004). https://doi.org/10.1186/1471-2415-4-9 Rasheed R, Pillai GS, Kumar H, Shajan AT, Radhakrishnan N, Ravindran GC. Relationship between diabetic retinopathy and diabetic peripheral neuropathy - Neurodegenerative and microvascular changes. Indian J Ophthalmol. 2021 ;69(11):3370-75 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6570551","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463545051,"identity":"ec118b14-045d-48a4-85ba-e4d8bfb220ec","order_by":0,"name":"Ozgur 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1","display":"","copyAsset":false,"role":"figure","size":123172,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve for Serum Fractalkine in Detecting Diabetic Retinopathy\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6570551/v1/3d0119eca29337443c9bf035.png"},{"id":83768295,"identity":"b20763e3-6400-4cc4-a241-7ff7eb960aa7","added_by":"auto","created_at":"2025-06-02 11:41:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":131027,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve for Serum Fractalkine in Detecting Non-Proliferative Diabetic Retinopathy\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6570551/v1/442d141ff9146780e672cb34.png"},{"id":97664851,"identity":"db19658a-475b-4249-87b1-c618ef7db7f0","added_by":"auto","created_at":"2025-12-08 09:15:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1423848,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6570551/v1/651a1f86-af8e-432e-ab19-ab3d99c5ea8a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship Between Degree of Diabetic Retinopathy and Serum Fractalkine (CX3CL1) in Diabetic Patients","fulltext":[{"header":"Background","content":"\u003cp\u003eDiabetic retinopathy (DR) is one of the most serious microvascular complications of diabetes mellitus and a leading cause of vision loss in adults globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the World Health Organization, it is estimated that there will be 700\u0026nbsp;million diabetic patients worldwide by 2045, and approximately 30\u0026ndash;40% of these patients will develop DR [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The pathogenesis of DR is characterized by complex changes at the metabolic, molecular and cellular levels caused by chronic hyperglycemia [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePathological changes occurring in the retinal microvascular system constitute the basic features of DR. These include pericyte loss, basement membrane thickening, disruption of the blood-retinal barrier, capillary occlusion and retinal ischemia [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Complications such as proliferative changes, vitreous hemorrhages, tractional retinal detachment and diabetic macular edema that occur in later stages can lead to irreversible vision loss [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This progressive nature of DR necessitates early diagnosis of the disease and development of effective treatment strategies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, the role of inflammatory processes in the pathogenesis of DR has been increasingly understood [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Chronic hyperglycemia activates the inflammatory cascade by increasing oxidative stress in the retinal tissue and leads to the release of various cytokines, chemokines and growth factors. Among these molecules, vascular endothelial growth factor (VEGF), tumor necrosis factor-alpha (TNF-α) and various chemokines play important roles [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFractalkine (CX3CL1) is a bifunctional molecule belonging to the CX3C chemokine family that is structurally different from other chemokines [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Fractalkine, which exists in both soluble and membrane-bound forms, plays a critical role in various pathophysiological processes such as inflammation, angiogenesis, neurodegeneration, and immune modulation. Fractalkine, which is expressed especially by retinal ganglion cells in the retina, binds to the CX3CR1 receptor on microglia and plays an important regulatory role in maintaining neurovascular unit homeostasis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies on the role of the fractalkine-CX3CR1 axis in the pathogenesis of DR have revealed the complex and multifaceted functions of this system [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Increased fractalkine expression in diabetic retina can exert both neuroprotective and proinflammatory effects by modulating microglial activation. Defects in fractalkine signaling can lead to exacerbation of retinal inflammation, increased vascular damage, and neuronal degeneration. Experimental studies have shown that fractalkine administration can reduce retinal inflammation and prevent neurovascular damage [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn clinical studies, it has been suggested that fractalkine levels correlate with the severity of DR and may be a potential biomarker for predicting disease progression. It has been reported that fractalkine levels are increased in vitreous samples of patients with proliferative DR. However, studies systematically examining the relationship between serum fractalkine levels and DR stages are limited [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study primarily aims to investigate the relationship between the stages of diabetic retinopathy (DR) and serum fractalkine levels in patients with type 2 diabetes mellitus. Secondary objectives include evaluating the diagnostic value of serum fractalkine in predicting DR severity and analyzing its association with diabetes duration, HbA1c, and other metabolic parameters. Clarifying the role of the fractalkine\u0026ndash;CX3CR1 axis in DR may contribute to a better understanding of disease progression, support the development of early diagnostic strategies, and aid in identifying potential therapeutic targets. Furthermore, assessing the utility of serum fractalkine as a non-invasive biomarker may help optimize clinical monitoring and treatment protocols.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy Design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis single-center, prospective case-control study was conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the Clinical Research Ethics Committee of the University of Health Sciences, Kanuni Sultan S\u0026uuml;leyman Training and Research Hospital (2023.05.77). Informed consent was obtained from all individuals participating in the study before the study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSubjects\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted on 140 patients who applied to our hospital\u0026apos;s internal medicine outpatient clinics and were diagnosed with type 2 diabetes mellitus (T2DM) according to the American Diabetes Association (ADA) 2024 diagnostic criteria [18]. Patients included in the study were divided into two groups according to the presence of diabetic retinopathy (DR): non-DR group (n=32) and DR group (n=108). Patients with DR were divided into two subgroups according to the severity of the disease as non-proliferative diabetic retinopathy (NPDR) (n=106) and proliferative diabetic retinopathy (PDR) (n=32). The NPDR group was further divided into three subgroups as mild (n=23), moderate (n=40), and severe (n=13).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDiabetic Retinopathy Assessment Method\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe diagnosis and classification of diabetic retinopathy were made based on the International Diabetic Retinopathy Severity Scale (ICDR) and the Early Treatment of Diabetic Retinopathy Study (ETDRS) criteria by the same retina specialist (with at least 5 years of experience) who was unaware of the clinical features of the patients. For diagnostic evaluation, all patients underwent standard ophthalmologic examination followed by: (1) dilated fundus examination (with +90 diopter lens), (2) 45-degree angle color fundus photographs with a Topcon TRC-50DX (Japan) fundus camera, (3) fluorescein angiography with Heidelberg Spectralis HRA-2 (Germany), and (4) macular scans with Heidelberg Spectralis OCT (Germany). The patients were classified according to the ICDR criteria; mild NPDR (microaneurysms only), moderate NPDR (microaneurysms + retinal hemorrhage + hard exudate), severe NPDR (4-2-1 rule positivity: more than 20 retinal hemorrhages, venous beading, or overt IRMA), and PDR (retinal/optic disc neovascularization). The criteria for the diagnosis of diabetic macular edema were central macular thickness \u0026gt;250 \u0026micro;m on Spectralis OCT and/or leakage detected on fluorescein angiography. Disagreements and diagnostic disagreements were resolved by a second ophthalmologist [19,20].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExclusion Criteria\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePatients with the following conditions were excluded from the study to ensure a homogeneous study population: type 1 diabetes mellitus, diabetic ketoacidosis, malignancies, severe liver disease, active infections, acute renal failure, chronic inflammatory disorders, and chronic lung diseases such as chronic obstructive pulmonary disease, asthma, bronchiectasis, or pulmonary hypertension. Additional exclusion criteria included thyroid disorders, immune disorders, neurological diseases, a history of eye surgery, retinal diseases, prior treatment with anti-vascular endothelial growth factor (anti-VEGF) injections or laser photocoagulation, widespread vitamin deficiencies, malnutrition, organ transplantation, and patients undergoing hemodialysis or peritoneal dialysis. The use of systemic steroids and pregnancy or breastfeeding were also among the exclusion criteria.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData Collection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAge, gender, familial diabetes history, and the duration of diabetes mellitus diagnosis were documented. Body weight was recorded with an accuracy of 0.1 kg utilizing an electronic scale (Seca digital scale, 0.1 precision, Hamburg, Germany), permitting simple underpants to be worn. Height was measured using a Harpenden stadiometer (Seca mod. 240 ce 0123, manufactured in Hamburg, Germany) with a margin of error of 0.1 cm. Height measurements were conducted in an upright position with bare feet, feet positioned together and parallel, and the shoulder and gluteal region in contact with the wall. Body mass index was computed using the method of weight in kilograms (kg) divided by height in meters (m) squared. Patients were classified based on their BMI values. Measurements of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were conducted on the right arm using a mechanical sphygmomanometer following a 15-minute rest period. The patients\u0026apos; medications were documented.\u0026nbsp;Nephropathy, neuropathy, cardiovascular disease, cerebrovascular disease, and peripheral vascular disease were documented, if applicable. Furthermore, dyslipidemia, hypertension, and metabolic syndrome were identified. Comorbidities of the patients were acquired from the hospital automation system.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBlood sampling and biochemical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFasting venous blood samples were collected between 8:00 and 10:00 AM after a 10- to 12-hour overnight fast. Blood was collected from the cubital vein of the subjects and collected into serum and plasma containers. Laboratory personnel were functionally blinded. Samples were centrifuged at 2000 rpm for 20 min at 4 \u0026deg;C. Biochemical analyses were performed immediately after centrifugation. The supernatant was collected for determination of additional parameters, and serum aliquots were immediately frozen and stored at \u0026minus;80 \u0026deg;C until further analyses. Fasting plasma glucose, creatinine, alanine aminotransferase, albumin, lipid profile (enzymatic colorimetric method) and C-reactive protein (CRP) (immunoturbidimetric method) were measured using a Roche Cobas 8000 c 702 analyzer (Roche Diagnostics, Germany). Glycosylated hemoglobin (HbA1c) was assessed by high-performance liquid chromatography using ARKRAY/ADAMS HA-8180V (ARKRAY Inc., Japan).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMeasurement of Serum Fractalkine (CX3CL1) Levels\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eVenous blood samples were collected for fractalkine and were centrifuged, and the sera were separated into Eppendorf tubes and stored at \u0026ndash;80\u0026deg;C until analyses.\u0026nbsp;A commercial sandwich ELISA kit (E-EL-H0044, Elabscience, China) was used to measure human fractalkine concentrations. Experiments were performed according to the manufacturer\u0026apos;s protocol, as follows: (1) 100 \u0026mu;L of standard/sample was added to micro ELISA plates and incubated for 90 min at 37\u0026deg;C, (2) biotinylated detection antibody (1:100 dilution) was added and incubated for 60 min, (3) HRP-conjugate (1:100 dilution) was added after 3x washing and incubated for 30 min, (4) TMB substrate was added after 5x washing and incubation in the dark for 15 min to initiate the color reaction, (5) Stop solution was added and optical density (OD) was measured at 450 nm. The calibration curve was created with a 4-parametric logistic model in the range of 0.16-10 ng/mL (0.10 ng/mL sensitivity), and the repeatability of the samples was confirmed with a coefficient of variation (CV) of less than 10%. Plasma/serum samples were anticoagulated with EDTA and centrifuged at 1000\u0026times;g, and high concentration samples were analyzed by gradual dilution (100-100,000 fold). All measurements were made in duplicate and inter-/intra-assay CV values were found to be \u0026lt;10%. No cross-reactivity was observed.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eOutcomes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study investigates serum fractalkine levels in diabetic retinopathy, assessing its correlation with disease severity, diagnostic performance through ROC analysis, and its potential added value beyond traditional risk factors. These findings may support the potential role of fractalkine as a biomarker for early diagnosis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;IBM SPSS Statistics 25.0 ((IBM Corporation, Chicago, IL, USA) and R programming language (version 3.6.1) were used in the analysis of data, and after the normality distribution was evaluated with the Shapiro-Wilk test, Student t-test and one-way ANOVA (post-hoc Tukey test) were applied for parametric data, and Mann-Whitney U and Kruskal-Wallis tests (with Dunn post-hoc correction) were applied for nonparametric data. The relationship between serum Fractalkine levels and degree of diabetic retinopathy was examined with Pearson/Spearman correlation analyses, while logistic regression models were developed by adjusting for age, gender, BMI, diabetes duration and HbA1c values in multivariate analysis. For diagnostic performance assessment, AUC values were calculated by ROC curve analysis, optimal cut-off values were determined according to the Youden index and sensitivity-specificity analyses were performed. In data presentation, normally distributed parameters were expressed as mean \u0026plusmn; standard deviation, nonparametric data as median (interquartile range), and p\u0026lt;0.05 was considered statistically significant in all tests.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 140 type 2 diabetes mellitus (T2DM) patients were included in this study. 32 (22.9%) of the participants were individuals without diabetic retinopathy (DRP) and 108 (77.1%) were diagnosed with DRP. The DRP group was divided into two subgroups as proliferative (PDR) and nonproliferative (NPDR), with 32 (29.6%) PDR patients and 76 (70.4%) NPDR patients. The NPDR group was further divided into three subgroups as mild (30.3%, n=23), moderate (52.6%, n=40) and severe (17.1%, n=13).\u003c/p\u003e\n\u003cp\u003eThe mean age of the participants was 56.8 \u0026plusmn; 9 years, and the duration of diabetes was 11.4 \u0026plusmn; 8 years. The male/female ratio was 49.3%/50.7%, and the mean BMI was 29.2 \u0026plusmn; 4.5 kg/m\u0026sup2;. Hypertension was present in 32.9% of the participants. In terms of treatment, 49.3% used oral antidiabetic drugs, 93.6% used insulin, and 41.4% used both. Antihypertensive use was 30%, and alcohol consumption was not reported.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDemographic and clinical data were compared between groups with and without retinopathy, and detailed information is presented in Table 1.\u003c/p\u003e\n\u003cp\u003eTablo 1: Comparison of demographic and clinical variables between patients with and without diabetic retinopathy\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithout Retinopathy\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n:32)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith Retinopathy (n=108)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003eAge,y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e53.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e59.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003eDM duration, y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.000\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e40.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e53.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e59.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e46.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003eWeight, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e83.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e78.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003eHeight,m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003eBMI,\u0026nbsp;kg/m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e30.2 \u0026plusmn; 4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e29.0 \u0026plusmn; 4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHT, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e81.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e63.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e18.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e37.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking Status, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e90.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e84.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e9.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e15.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedications, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eOral Antidiabetic Drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e71.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e42.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.004\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e6.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e6.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eInsulin + OAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e21.\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e47.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.011\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eAntihypertensive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e18.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e33.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote:Data\u003cem\u003e\u0026nbsp;are presented as mean \u0026plusmn; SD or median for non-normally distributed variables. Comparisons were made using the independent t-test for normal distribution, Mann-Whitney U test for non-normal distribution, and chi-square (\u0026chi;\u0026sup2;) test for categorical variables. Statistical significance was set at p \u0026lt; 0.05. DM: diabetes mellitus,OAD: oral antidiabetic drugs, BMI: body mass index, HT: hypertension\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSerum fractalkine levels were significantly higher in the retinopathy group than in the non-retinopathy group (p=0.0002). Fasting blood sugar (p=0.022), HbA1c (p=0.004), urea (p=0.008), creatinine (p=0.0001), urine microalbumin (p=0.010) and urine microalbumin/creatinine ratio p=0.003) values were significantly higher in the retinopathy group. While inflammation markers WBC (p=0.013) and neutrophil/lymphocyte ratio (p=0.005) were significantly higher in the retinopathy group, no statistically significant difference was observed between the groups in terms of CRP (p=0.215), C-peptide (p=0.387), HOMA-IR (p=0.156), liver function tests (AST p=0.421, ALT p=0.372), lipid parameters (LDL p=0.243, HDL p=0.412, triglyceride p=0.358) and hematological indices (hemoglobin p=0.476, platelet p=0.503). These data are presented in Table 2.\u003c/p\u003e\n\u003cp\u003eTablo 2: Comparison of serum fractalkine levels and laboratory values between patients with and without diabetic retinopathy\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithout Retinopathy (n:32)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith Retinopathy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=108)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eFractalkine, \u0026nbsp;(ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.000\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eCRP, \u0026nbsp;(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eGlucose, \u0026nbsp;(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e152.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e212.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.022\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eC Peptide , (pmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e838.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e875.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eHbA1c,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e7.9\u0026plusmn;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e9.0\u0026plusmn;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.004\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eUrea , \u0026nbsp;(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.008\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eMicroalbumin (Urine), (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.010\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eMicroalbumin/Creatinine,(mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e35.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.003\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eCreatinine ,(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.000\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eAST , \u0026nbsp;(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eALT , \u0026nbsp;(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eLDH , \u0026nbsp;(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e167.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e169.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eD-Dimer , (ug/mLFEU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eLDL-C \u0026nbsp;,(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e117.9 \u0026plusmn; 37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e121.4\u0026plusmn;39.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eHDL-C , \u0026nbsp;(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e44.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eTG, \u0026nbsp;(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e191.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e175.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eTotal Cholesterol, (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e184.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e194.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eHemoglobin, (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e14.0 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e13.7\u0026plusmn;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eWBC,\u0026nbsp;10\u003csup\u003e3\u003c/sup\u003e/mm3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.013\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003ePLT,\u0026nbsp;10\u003csup\u003e3\u003c/sup\u003e/mm3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e274.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e263.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eNeutrophil/Lymphocyte Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.005\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote:\u003cem\u003eData are presented as mean \u0026plusmn; SD or median for non-normally distributed variables. Comparisons were made using the independent t-test for normal distribution, Mann-Whitney U test for non-normal distribution. Statistical significance was set at p \u0026lt; 0.05.\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCRP: c-reactive protein, HbA1c : hemoglobin A1c, \u0026nbsp;HOMA-IR : homeostasis model assessment of insulin resistance, AST: aspartate aminotransferase, ALT : alanine aminotransferase, LDH : lactate dehydrogenase, LDL -C:low-density lipoprotein, HDL -C: high-density lipoprotein, TG : tryglyceride , WBC :white blood cell count, PLT : platelet count\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the logistic regression analysis results presented in Table 3, statistically significant relationships were found between the presence of diabetic retinopathy and diabetes duration (p\u0026lt;0.001), serum fractalkine levels (p\u0026lt;0.001), fasting blood glucose (p=0.032), HbA1c (p=0.005), serum urea (p=0.008), creatinine (p=0.001), leukocyte count (p=0.014) and neutrophil/lymphocyte ratio (p=0.008) in univariate analysis. In multivariate analysis, when adjusted for age, gender and other clinical parameters, it was determined that diabetes duration (p=0.001), fractalkine levels (p=0.036), HbA1c (p=0.012) and creatinine (p=0.012) levels independently predicted the presence of retinopathy.\u003c/p\u003e\n\u003cp\u003eTablo 3: Univariate and Multivariate Logistic Regression Analysis of Variables Associated with Diabetic Retinopathy\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 194px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 202px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate Model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate Model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%95 CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%95 CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eDM duration ,y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.000\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eFractalkine, (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e25.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e154.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.000\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e89.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.036\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eGlucose,\u0026nbsp;(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.032\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eHbA1c,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.005\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.012\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eUrea,\u0026nbsp;(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.008\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eCreatinine,\u0026nbsp;(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e22.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e152.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e20.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e219.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.012\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eWBC,\u0026nbsp;10\u003csup\u003e3\u003c/sup\u003e/mm3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.014\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eNeutrophil/Lymphocyte Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.008\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: Logistic\u0026nbsp;\u003c/em\u003eregression\u003cem\u003e\u0026nbsp;was performed using Forward LR (Likelihood Ratio) method. The odds ratio (OR) and 95% confidence interval (CI) are provided for each variable along with p-values. Statistically significant p-values (\u0026lt;0.05) are highlighted.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHbA1c: hemoglobin A1c, WBC: white blood cell count, DM: diabetes mellitus\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the results of Spearman correlation analysis, no statistically significant correlation was found between serum fractalkine levels and demographic parameters such as age (r=-0.020, p=0.796), diabetes duration (r=0.108, p=0.153), BMI (r=-0.053, p=0.485), smoking (p\u0026gt;0.05) and all laboratory parameters including glucose (p\u0026gt;0.05), HbA1c (p\u0026gt;0.05), HOMA-IR (p\u0026gt;0.05), renal function tests (p\u0026gt;0.05), inflammatory markers (p\u0026gt;0.05) and lipid profile (p\u0026gt;0.05) (Table 4).\u003c/p\u003e\n\u003cp\u003eTablo 4: Spearman Correlation Analysis Between Serum Fractalkine Level and Clinical/Biochemical Parameters\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 336px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFRAKTALKİNE \u0026nbsp; LEVEL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eAge,y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eDuration of diabetes,y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eWeight,kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eHeight,m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eBMI,kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eCigarette number,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eSmoking duration,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eGlucose, (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eC-peptide, (pmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eHbA1c,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eHOMA-IR,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eUrea, (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eCreatinine, (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eAST, (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eALT, \u0026nbsp;(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eLDH, (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eCRP, (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eD-Dimer, (ug/mLFEU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eUrinary microalbumin, (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eMicroalbumin/Creatinine, (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eLDL-C, (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eHDL-C, (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eTriglyceride, (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eTotal cholesterol, (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eHemoglobin, (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003ePLT, 10\u003csup\u003e3\u003c/sup\u003e/mm3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eWBC, 10\u003csup\u003e3\u003c/sup\u003e/mm3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eBMI: body mass index, HbA1c: hemoglobin A1c, HOMA-IR: homeostasis model assessment of insulin resistance, \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;AST: aspartate \u0026nbsp;aminotransferase, ALT: alanine aminotransferase, LDH: lactate dehydrogenase, CRP: c-reactive protein, \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; LDL: low-density lipoprotein, HDL: high-density lipoprotein, PLT: platelet count, WBC: white blood cell count\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhen proliferative (n=32) and nonproliferative (n=76) DRP patients were compared, no significant difference was found between the mean fractalkine levels (0.5 ng/mL and 0.70 ng/mL; p=0.274). In addition, no statistically significant difference was found between the two groups in terms of metabolic, renal, hepatic, inflammatory, lipid and hematological parameters (Table 5).\u003c/p\u003e\n\u003cp\u003eTable 5. Comparison of serum fractalkine levels and laboratory parameters between patients with proliferative and non-proliferative diabetic retinopathy\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProliferative DR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n: 32)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Proliferative DR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n: 76)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eFractalkine, \u0026nbsp;(ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eCRP, \u0026nbsp;(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e6.6 \u0026plusmn; 8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e8.7 \u0026plusmn; 26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eGlucose, \u0026nbsp;(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e209.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e215.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eC Peptıt , (pmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e887.2 \u0026plusmn; 520.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1007.9 \u0026plusmn; 462.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eHbA1c,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eUrea , (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e33.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e35.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eUrinary Microalbumin, (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e53.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e42.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eMicroalbumin/Creatinine, (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e65.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eCreatinine, \u0026nbsp;(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eAST , (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eALT, \u0026nbsp;(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eLDH , (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e185.3 \u0026plusmn; 46.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e174.8 \u0026plusmn; 45.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eD-Dimer, \u0026nbsp;(ug/mLFEU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eLDL-C, \u0026nbsp;(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e127.1 \u0026plusmn; 38.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e118.9 \u0026plusmn; 39.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eHDL-C, \u0026nbsp;(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e44.1 \u0026plusmn; 8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e43.3 \u0026plusmn; 11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eTG , (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e187.8 \u0026plusmn; 94.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e181.8 \u0026plusmn; 87.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eTotal Cholesterol, (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e205.5 \u0026plusmn; 55.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e192.5 \u0026plusmn; 46.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eHemoglobin, (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eWBC,\u0026nbsp;10\u003csup\u003e3\u003c/sup\u003e/mm3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e8.8 \u0026plusmn; 2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e8.6 \u0026plusmn; 2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003ePLT,\u0026nbsp;10\u003csup\u003e3\u003c/sup\u003e/mm3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e278.7 \u0026plusmn; 77.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e279.1 \u0026plusmn; 79.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: P-values were calculated using the independent samples t-test for variables with normal distribution, and the Mann-Whitney U test for variables with non-normal distribution. Normality was assessed using the Shapiro-Wilk test.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDR: diabetic retinopathy, CRP: c-reactive protein , \u0026nbsp;HbA1c : hemoglobin A1c, \u0026nbsp;HOMA-IR : homeostasis model assessment of insulin resistance, AST: aspartate \u0026nbsp; aminotransferase, ALT : alanine aAminotransferase, LDH : lactate dehydrogenase, LDL -C:low-density lipoprotein, HDL -C: high-density lipoprotein, TG: triglyceride, WBC :white blood cell count, PLT : platelet count\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNonproliferative diabetic retinopathy patients (n=76) were divided into mild (n=23), moderate (n=40) and severe (n=13) subgroups according to disease severity and laboratory parameters were compared (Table 6). Fractalkine levels were found to be significantly higher in the severe group compared to both mild and moderate groups (p=0.004). No significant difference was found between the mild and moderate groups (p\u0026gt;0.05). Regarding renal functions, urine microalbumin levels and microalbumin/creatinine ratio were found to be significantly higher in the severe and moderate groups compared to the mild group (p=0.017 and p=0.015, respectively). No significant difference was observed between the groups in terms of other laboratory parameters (glucose, HbA1c, HOMA-IR, urea, creatinine, liver enzymes, inflammation markers, lipid profile and hematological parameters) (p\u0026gt;0.05 for all) (Table 6).\u003c/p\u003e\n\u003cp\u003eTablo 6: Comparison of Serum Fractalkine Levels and Laboratory Parameters in Non-Proliferative DR\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"607\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMild\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Proliferative DR (n:23)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Moderate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Proliferative DR (n:40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSevere\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Proliferative DR (n:13)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eFraktalkine, (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.83\u003csup\u003e*\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.004\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eCRP , (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.092\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eGlucose, (mg/dL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e218.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e197.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e244.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.992\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eC Peptit, (pmol/\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e934.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e883.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e876.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.905\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eHbA1c ,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e8.8 \u0026plusmn; 2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e8.9 \u0026plusmn; 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e9.2 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.788\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.881\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eUrea, (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e33.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e33.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e36.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.777\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eUrinary Microalbumin, (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e12.9 \u003csup\u003e\u0026yen;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e49.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e116.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.017\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eMicroalbumin/Creatinine, (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e20.8 \u003csup\u003e\u0026yen;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e49.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.015\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eCreatinine ,(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eAST, (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eALT, (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eLDH, (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e157.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e166.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e173.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eD-Dimer, (ug/mLFEU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eLDL-C, (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e115.5 \u0026plusmn; 36.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e118.7 \u0026plusmn; 43.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e125.9 \u0026plusmn; 33.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eHDL \u0026ndash;C, (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e43.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eTG, (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e164.8 \u0026plusmn; 67.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e193.5 \u0026plusmn; 100.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e176.0 \u0026plusmn; 69.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eTotal Cholesterol, (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e189.5 \u0026plusmn; 46.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e187.8 \u0026plusmn; 45.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e212.1 \u0026plusmn; 48.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eHemoglobin, (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eWBC, 10\u003csup\u003e3\u003c/sup\u003e/mm3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003ePLT, 10\u003csup\u003e3\u003c/sup\u003e/mm3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e285.9 \u0026plusmn; 78.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e273.1 \u0026plusmn; 83.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e285.4 \u0026plusmn; 72.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eN\u0026ouml;trofil /Lenfosit Oranı\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e2.3 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e2.3 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e1.9 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: P-values were calculated using the Kruskal-Wallis (K) test for non-normally distributed variables and the ANOVA (A) test for normally distributed variables. Statistical significance was set at p \u0026lt; 0.05.\u003c/em\u003e \u003cem\u003e\u003csup\u003e*\u003c/sup\u003eSignificant difference compared to the moderate non-proliferative group (p \u0026lt; 0.05). \u003csup\u003e\u0026yen;\u003c/sup\u003eSignificant \u0026nbsp;difference compared to the severe non-proliferative group (p \u0026lt; 0.05)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDR: diabetic retinopathy, CRP: c-reactive protein , \u0026nbsp;HbA1c : hemoglobin A1c, \u0026nbsp;HOMA-IR : homeostasis \u0026nbsp;model \u0026nbsp; assessment \u0026nbsp;of \u0026nbsp;ins\u0026uuml;lin \u0026nbsp; resistance, AST: aspartate \u0026nbsp; aminotransferase, ALT : alanine \u0026nbsp; aminotransferase, LDH : lactate \u0026nbsp; dehydrogenase, LDL-C :low-density \u0026nbsp; lipoprotein, HDL-C: high-density \u0026nbsp; lipoprotein, TG:triglyceride, WBC :white \u0026nbsp; blood cell count, PLT : platelet \u0026nbsp; count\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the ROC analysis results, fractalkine levels showed significant diagnostic performance in distinguishing patients with and without retinopathy [AUC: 0.736 (95% CI: 0.634-0.838)]. With a cut-off value of 0.455 ng/mL, fractalkine determined the presence of retinopathy with 81.5% sensitivity and 56.3% specificity (positive predictive value: 86.3%, negative predictive value: 47.4%) (Table 7, Figure 1).\u003c/p\u003e\n\u003cp\u003eTablo 7: Diagnostic Performance of Serum Fractalkine Levels in Differentiating the Presence of Diabetic Retinopathy Based on ROC Analysis\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"588\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 588px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA. ROC Curve Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003eFractalkine , (ng/mL) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.634-0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003eFractalkine cutoff (0.455)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.577-0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 225px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB. Diagnostic Performance at 0.455 ng/mL Cutoff\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 364px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDR Negative (n:32)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDR Positive(n: 108)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003eFractalkine \u0026lt; 0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003eFractalkine \u0026ge; 0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e81.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e56.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003ePositive Predictive Value (PPV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e86.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003eNegative Predictive Value (NPV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e47.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAUC: area under the curve. Cut-off value was determined based on the Youden Index. DR: diabetic retinopathy. ROC: receiver \u0026nbsp; operating \u0026nbsp;characteristic. CI: confidence \u0026nbsp;interval\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the ROC analysis performed to determine the disease severity in patients with nonproliferative retinopathy, fractalkine levels showed high diagnostic performance in distinguishing between mild and moderate-severe groups [AUC: 0.784 (95% CI: 0.679-0.888)]. With a cut-off value of 0.720 ng/mL, fractalkine detected moderate-severe retinopathy with 100% sensitivity and 61.9% specificity (positive predictive value: 35.1%, negative predictive value: 100%) (Table 8, Figure 2). These findings suggest that fractalkine levels can be used as a potential biomarker in determining both the presence and severity of retinopathy.\u003c/p\u003e\n\u003cp\u003eTable 8: Diagnostic Performance of Serum Fractalkine Levels in Differentiating the Severity of Non-Proliferative Diabetic Retinopathy Based on ROC Analysis\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 586px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA. ROC Curve Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 224px;\"\u003e\n \u003cp\u003eFractalkine level (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e0.679-0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 224px;\"\u003e\n \u003cp\u003eFractalkine cutoff (0.720)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e0.713-0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB. Diagnostic Performance at 0.720 ng/mL Cutoff\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 362px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 224px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPDR Mild\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n:23)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPDR Moderate-Severe\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n: 53)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 224px;\"\u003e\n \u003cp\u003eFractalkine \u0026lt; 0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 224px;\"\u003e\n \u003cp\u003eFractalkine \u0026ge; 0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 224px;\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 224px;\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e61.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 224px;\"\u003e\n \u003cp\u003ePositive Predictive Value (PPV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e35.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 224px;\"\u003e\n \u003cp\u003eNegative Predictive Value (NPV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAUC: area under the curve. Cut-off value was determined based on the Youden Index. NPDR: diabetic retinopathy. ROC: receiver \u0026nbsp;operating \u0026nbsp;characteristic. CI: confidence interval\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDiabetic retinopathy (DR) is the most common and potentially serious microvascular complication of diabetes mellitus that can lead to vision loss. However, currently there is no reliable and non-invasive biomarker that can be used for diagnosis and monitoring of disease progression. This study is important because it is one of the first to systematically evaluate the relationship between DR severity and serum fractalkine levels in diabetic patients.\u003c/p\u003e \u003cp\u003eOur study provided important findings demonstrating the relationship between serum fractalkine levels and the presence and severity of diabetic retinopathy (DRP). Fractalkine levels were significantly higher in the group with retinopathy compared to the group without retinopathy. In particular, fractalkine levels were significantly higher in patients with severe nonproliferative DRP compared to patients with mild and moderate stages.\u003c/p\u003e \u003cp\u003eSeveral mechanisms supporting the role of fractalkine in the pathogenesis of DRP have been described in the literature. Jian-Jang You et al. showed that fractalkine is a critical mediator of retinal angiogenesis and is found at high levels in patients with diabetic retinopathy [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Samuel A. Mills et al. showed that fractalkine impairs vasoregulation in the early stages of DRP via microglial activation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These findings are consistent with the results of our study and support the idea that fractalkine plays an important role in both inflammatory and angiogenic processes.\u003c/p\u003e \u003cp\u003eAnother finding of our study is that there was no significant difference in fractalkine levels between the proliferative and nonproliferative DRP groups. This may be due to individual differences in fractalkine signaling pathways (CX3CR1 polymorphisms) or variability in ADAM10/ADAM17 metalloproteinase activities. Ahmed M. Abu El-Asrar's study has shown a strong correlation between fractalkine and VEGF levels in vitreous samples of patients with proliferative DRP [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These findings suggest that the fractalkine-VEGF interaction may be important in the pathogenesis of DRP.\u003c/p\u003e \u003cp\u003eFinny Monickaraj has shown that CXCL1 levels are increased in patients with diabetic retinopathy [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], while Andrew S. Mendiola has shown that fractalkine signaling modulates microglial activity in animal models [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These studies support the critical role of chemokines in DRP.\u003c/p\u003e \u003cp\u003eIn our study, diabetes duration was significantly longer in patients with diabetic retinopathy (DR) compared to those without. However, no significant difference was observed in diabetes duration between non-proliferative DR (NPDR) and proliferative DR (PDR) groups or among NPDR stages. These findings align with previous studies, such as a study conducted in the United Kingdom by Stratton IM et al. showing that 36% of patients without retinopathy developed NPDR and 68% developed PDR within 5 years, and the LALES study, which reported an 8% increase in DR risk per year of diabetes duration. While our results confirm the strong association between diabetes duration and the onset of DR, they also suggest that duration alone may not sufficiently explain disease progression. This underscores the need to explore additional contributing factors, including genetic predisposition, inflammation, and vascular changes, alongside the chronic effects of hyperglycemia.\u003c/p\u003e \u003cp\u003eThe UK Prospective Diabetes Study (UKPDS 33) emphasized the clinical significance of glycemic control, showing that a 1% reduction in HbA1c was associated with a 24% decrease in microvascular complications and a 29% reduction in the need for laser coagulation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our findings align with this, supporting the critical role of glycemic control in the incidence and progression of DRP. The concept of metabolic memory, highlighted in Varadhan et al.'s retrospective study, suggests that early poor glycemic control increases the risk of retinopathy, but subsequent tight control can reduce this risk by 80% [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Long-term data from the EDIC study also support this, showing a higher risk of retinopathy in patients initially receiving conventional therapy compared to those in the intensive therapy group [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In our study, participants in the DRP group had significantly higher mean fasting glucose and HbA1c levels compared to the non-DRP group. These findings underscore the importance of timing in glycemic control for long-term outcomes.\u003c/p\u003e \u003cp\u003eHowever, in our study, no significant differences were found in glucose and HbA1c levels between the NPDR and PDR groups or within NPDR substages, suggesting that glycemic control alone may not sufficiently determine disease severity once retinopathy has developed. Future studies could focus on the timing of glycemic control, glycemic variability, and experimental investigations into metabolic memory mechanisms. In conclusion, while poor glycemic control remains a key risk factor for DRP development, no direct relationship was observed between glycemic control and retinopathy severity, indicating that other factors contribute to the pathogenesis of DRP.\u003c/p\u003e \u003cp\u003eOur study found a significant association between diabetic retinopathy (DRP) and nephropathy, with higher levels of serum creatinine, urea, urine microalbumin, and microalbumin/creatinine ratio in patients with DRP compared to those without. These results reinforce the link between diabetic microvascular complications. Previous studies, including those by El-Asrar AM et al., Li et al., Manavi, and Butt, have similarly demonstrated that the prevalence of both retinopathy and microalbuminuria increases with diabetes duration and are positively correlated [\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Notably, we observed a progressive increase in urine microalbumin levels from mild to severe NPDR, indicating worsening renal involvement with advancing retinopathy. However, no significant differences in renal parameters were noted between NPDR and PDR groups. These findings suggest that early and intensive glycemic control, especially in patients with mild NPDR, may help slow the progression of both retinopathy and nephropathy, supporting conclusions from Rasheed et al. in long-term diabetic patients [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLogistic regression analyses of our study revealed the independent role of fractalkine levels in predicting the presence of diabetic retinopathy (DRP). In univariate analysis, fractalkine levels, as well as diabetes duration, glucose, HbA1c, urea, creatinine, WBC and neutrophil/lymphocyte ratio were found to have significant predictive value. In the multivariate model, fractalkine levels, diabetes duration, HbA1c and creatinine levels were found to remain as independent predictors. These findings indicate that the role of fractalkine in the pathogenesis of DRP is independent of traditional risk factors.\u003c/p\u003e \u003cp\u003eROC curve analysis quantitatively evaluated the diagnostic performance of fractalkine. The cut-off value of 0.455 pg/ml determined for fractalkine diagnosed DRP with 81.5% sensitivity and 56.3% specificity (AUC\u0026thinsp;=\u0026thinsp;0.689). More importantly, the cut-off value of 0.720 pg/ml showed 100% sensitivity and 61.9% specificity (AUC\u0026thinsp;=\u0026thinsp;0.810) in distinguishing the severity of nonproliferative DRP. These results indicate that fractalkine levels have a clinically significant predictive value, especially in assessing the severity of nonproliferative DRP.\u003c/p\u003e \u003cp\u003eThese analyses suggest that Fractalkine may be a potential marker that can be used clinically in DRP risk stratification together with traditional biochemical markers. Its ability to identify severe forms of nonproliferative DRP with high sensitivity may provide the opportunity for early intervention, while AUC values ​​also show that Fractalkine exhibits a good diagnostic performance, especially in assessing disease severity.\u003c/p\u003e \u003cp\u003eWhen evaluated in light of existing literature, our findings support the role of fractalkine in the inflammatory and angiogenic pathways involved in diabetic retinopathy (DRP) progression, suggesting its potential as a predictive biomarker. Although this study is among the first to demonstrate a clinical association between serum fractalkine levels and DRP severity\u0026mdash;particularly in nonproliferative stages\u0026mdash;the cross-sectional design limits causal inference. A significant strength of our study is its ability to show that fractalkine levels correlate with DRP presence and severity, independent of traditional risk factors such as diabetes duration, HbA1c, and creatinine.\u003c/p\u003e \u003cp\u003eHowever, several limitations should be acknowledged. The absence of a healthy control group restricted our ability to compare baseline fractalkine levels. The cross-sectional nature and relatively small sample size\u0026mdash;especially in the PDR subgroup\u0026mdash;may limit the generalizability and statistical power. Potential confounding factors, including glycemic control, antihypertensive use, and comorbidities, were not fully controlled. Furthermore, genetic and molecular variables such as CX3CR1 polymorphisms and ADAM10/ADAM17 activity were not assessed, and fractalkine was measured at a single time point, not capturing temporal variations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study is one of the first clinical investigations to demonstrate a significant association between serum fractalkine levels and both the presence and severity of diabetic retinopathy (DRP). Fractalkine emerged as an independent predictor of DRP and showed high diagnostic value, particularly in severe nonproliferative stages. These results suggest that fractalkine may play a key role in DRP pathogenesis and could serve as a promising biomarker for early diagnosis and disease monitoring.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDR \u0026ndash; Diabetic Retinopathy\u003c/p\u003e\n\u003cp\u003eVEGF \u0026ndash; Vascular Endothelial Growth Factor\u003c/p\u003e\n\u003cp\u003eTNF-\u0026alpha; \u0026ndash; Tumor Necrosis Factor-alpha\u003c/p\u003e\n\u003cp\u003eCX3CL1 \u0026ndash; Fractalkine\u003c/p\u003e\n\u003cp\u003eCX3CR1 \u0026ndash; CX3C Chemokine Receptor 1\u003c/p\u003e\n\u003cp\u003ePDR \u0026ndash; Proliferative Diabetic Retinopathy\u003c/p\u003e\n\u003cp\u003eNPDR \u0026ndash; Non-Proliferative Diabetic Retinopathy\u003c/p\u003e\n\u003cp\u003eT2DM \u0026ndash; Type 2 Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eADA \u0026ndash; American Diabetes Association\u003c/p\u003e\n\u003cp\u003eICDR \u0026ndash; International Clinical Diabetic Retinopathy Severity Scale\u003c/p\u003e\n\u003cp\u003eETDRS \u0026ndash; Early Treatment Diabetic Retinopathy Study\u003c/p\u003e\n\u003cp\u003eIRMA \u0026ndash; Intraretinal Microvascular Abnormalities\u003c/p\u003e\n\u003cp\u003eBMI \u0026ndash; Body Mass Index\u003c/p\u003e\n\u003cp\u003eELISA \u0026ndash; Enzyme-Linked Immunosorbent Assay\u003c/p\u003e\n\u003cp\u003eHOMA-IR \u0026ndash; Homeostasis Model Assessment of Insulin Resistance\u003c/p\u003e\n\u003cp\u003eHbA1c \u0026ndash; Hemoglobin A1c (Glycated Hemoglobin)\u003c/p\u003e\n\u003cp\u003eADAM10 \u0026ndash; A Disintegrin and Metalloproteinase Domain-Containing Protein 10\u003c/p\u003e\n\u003cp\u003eADAM17 \u0026ndash; A Disintegrin and Metalloproteinase Domain-Containing Protein 17\u003c/p\u003e\n\u003cp\u003eLALES \u0026ndash; Los Angeles Latino Eye Study\u003c/p\u003e\n\u003cp\u003eUKPDS 33 \u0026ndash; The United Kingdom Prospective Diabetes Study 33\u003c/p\u003e\n\u003cp\u003eEDIC \u0026ndash; Epidemiology of Diabetes Interventions and Complications\u003c/p\u003e\n\u003cp\u003eROC \u0026ndash; Receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003eAUC \u0026ndash; Area Under the Curve\u003c/p\u003e\n\u003cp\u003eHRP \u0026ndash; Horseradish Peroxidase\u003c/p\u003e\n\u003cp\u003eTMB \u0026ndash; Tetramethylbenzidine\u003c/p\u003e\n\u003cp\u003eCRP \u0026ndash; C-Reactive Protein\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eO.Y. and M.Ak. made the most substantial contributions to the conception, design, and supervision of the study. They also led the writing and critical revision of the manuscript. M.A. played a key role in conducting and interpreting the biochemical analyses. M.E. and I.K. contributed to data collection and statistical analysis. S.A.Y. was involved in literature review and provided valuable feedback during manuscript preparation. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical Statement\u003c/em\u003e\u2028\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles outlined in the Declaration of Helsinki. The study protocol was reviewed and approved by the Clinical Research Ethics Committee of the University of Health Sciences, Kanuni Sultan S\u0026uuml;leyman Training and Research Hospital (Approval No: 2023.05.77). All study procedures complied with institutional and international ethical guidelines.Written informed consent was obtained from all participants prior to their inclusion in the study\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDeclaration of competing interest\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eClinical trial number\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData availability statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTeo ZL, Tham YC, Yu M, Chee ML, Rim TH, Cheung N, et al. Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: Systematic Review and Meta-analysis. Ophthalmology. 2021;128(11):1580-91.\u003c/li\u003e\n\u003cli\u003eSaeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. IDF Diabetes Atlas Committee. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. 2019;157:107843.\u003c/li\u003e\n\u003cli\u003eStitt AW, Curtis TM, Chen M, Medina RJ, McKay GJ, Jenkins A, et al. The progress in understanding and treatment of diabetic retinopathy. Prog Retin Eye Res. 2016;51:156-86\u003c/li\u003e\n\u003cli\u003eAntonetti DA, Barber AJ, Bronson SK, Freeman WM, Gardner TW, Jefferson LS, et al. JDRF Diabetic Retinopathy Center Group. Diabetic retinopathy: seeing beyond glucose-induced microvascular disease. Diabetes. 2006 ;55(9):2401-11\u003c/li\u003e\n\u003cli\u003eHammes HP, Lin J, Renner O, Shani M, Lundqvist A, Betsholtz C, et al. Pericytes and the pathogenesis of diabetic retinopathy. Diabetes. 2002 ;51(10):3107-120\u003c/li\u003e\n\u003cli\u003eSim\u0026oacute; R, Stitt AW, Gardner TW. Neurodegeneration in diabetic retinopathy: does it really matter? Diabetologia. 2018 ;61(9):1902-12\u003c/li\u003e\n\u003cli\u003eSolomon SD, Chew E, Duh EJ, Sobrin L, Sun JK, VanderBeek BL, et al. Diabetic Retinopathy: A Position Statement by the American Diabetes Association. Diabetes Care. 2017;40(3):412-18.\u003c/li\u003e\n\u003cli\u003eTang J, Kern TS. Inflammation in diabetic retinopathy. Prog Retin Eye Res. 2011;30(5):343-58. \u003c/li\u003e\n\u003cli\u003eLee CY, Yang CH. The Role of Fractalkine in Diabetic Retinopathy: Pathophysiology and Clinical Implications. Int J Mol Sci. 2025;26(1):378.\u003c/li\u003e\n\u003cli\u003eKang Q., Yang C. Oxidative stress and diabetic retinopathy: Molecular mechanisms, pathogenetic role and therapeutic implications. Redox. Biol. 2020;37:101799.\u003c/li\u003e\n\u003cli\u003eBazan JF, Bacon KB, Hardiman G, Wang W, Soo K, Rossi D, et al. A new class of membrane-bound chemokine with a CX3C motif. Nature. 1997 13;385(6617):640-49\u003c/li\u003e\n\u003cli\u003eWhite GE, Greaves DR. Fractalkine: a survivor\u0026apos;s guide: chemokines as antiapoptotic mediators. Arterioscler Thromb Vasc Biol. 2012 ;32(3):589-94\u003c/li\u003e\n\u003cli\u003eYou J.J., Yang C.H., Huang J.S., Chen M.S., Yang C.M. Fractalkine, a CX3C chemokine, as a mediator of ocular angiogenesis. Investig. Ophthalmol. Vis. Sci. 2007;48:5290\u0026ndash;98\u003c/li\u003e\n\u003cli\u003eCardona S.M., Mendiola A.S., Yang Y.C., Adkins S.L., Torres V., Cardona A.E. Disruption of Fractalkine Signaling Leads to Microglial Activation and Neuronal Damage in the Diabetic Retina. ASN Neuro. 2015;7:1759091415608204\u003c/li\u003e\n\u003cli\u003eMendiola A.S., Garza R., Cardona S.M., Mythen S.A., Lira S.A., Akassoglou K., et al. Fractalkine Signaling Attenuates Perivascular Clustering of Microglia and Fibrinogen Leakage during Systemic Inflammation in Mouse Models of Diabetic Retinopathy. Front. Cell. Neurosci. 2016;10:303\u003c/li\u003e\n\u003cli\u003eRodriguez D, Church KA, Pietramale AN, Cardona SM, Vanegas D, Rorex C, et al. Fractalkine isoforms differentially regulate microglia-mediated inflammation and enhance visual function in the diabetic retina. J Neuroinflammation. 2024 4;21(1):42\u003c/li\u003e\n\u003cli\u003eSerra AM, Waddell J, Manivannan A, Xu H, Cotter M, Forrester JV. CD11b+ bone marrow-derived monocytes are the major leukocyte subset responsible for retinal capillary leukostasis in experimental diabetes in mouse and express high levels of CCR5 in the circulation. Am J Pathol. 2012 ;181(2):719-27\u003c/li\u003e\n\u003cli\u003eAmerican Diabetes Association Professional Practice Committee; Introduction and Methodology: Standards of Care in Diabetes\u0026mdash;2024. Diabetes Care 2024; 47 (Supplement_1): S1\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eWilkinson, C. P., et al. (2003). Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology, 110(9), 1677-82\u003c/li\u003e\n\u003cli\u003eEarly Treatment Diabetic Retinopathy Study Research Group. (1991). Grading diabetic retinopathy from stereoscopic color fundus photographs. Ophthalmology, 98(5), 786-806\u003c/li\u003e\n\u003cli\u003eYou JJ, Yang CH, Huang JS, Chen MS, Yang CM. Fractalkine, a CX3C chemokine, as a mediator of ocular angiogenesis. Invest Ophthalmol Vis Sci. 2007 ;48(11):5290-8\u003c/li\u003e\n\u003cli\u003eMills SA, Jobling AI, Dixon MA, Bui BV, Vessey KA, Phipps JA, et al. Fractalkine-induced microglial vasoregulation occurs within the retina and is altered early in diabetic retinopathy. Proc Natl Acad Sci U S A. 2021 21;118(51):e2112561118\u003c/li\u003e\n\u003cli\u003eAbu El-Asrar AM, Nawaz MI, Ahmad A, De Zutter A, Siddiquei MM, Blanter M, et al. Evaluation of Proteoforms of the Transmembrane Chemokines CXCL16 and CX3CL1, Their Receptors, and Their Processing Metalloproteinases ADAM10 and ADAM17 in Proliferative Diabetic Retinopathy. Front Immunol. 2021 20;11:601-39\u003c/li\u003e\n\u003cli\u003eMonickaraj F, Acosta G, Cabrera AP, Das A. Transcriptomic Profiling Reveals Chemokine CXCL1 as a Mediator for Neutrophil Recruitment Associated With Blood-Retinal Barrier Alteration in Diabetic Retinopathy. Diabetes. 2023 1;72(6):781-94\u003c/li\u003e\n\u003cli\u003eStratton IM, Adler AI, Neil HA, Matthews DR, Manley SE, Cull CA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ. 2000 12;321(7258):405-12\u003c/li\u003e\n\u003cli\u003eVarma R, Torres M, Pe\u0026ntilde;a F, Klein R, Azen SP; Los Angeles Latino Eye Study Group. Prevalence of diabetic retinopathy in adult Latinos: the Los Angeles Latino eye study. Ophthalmology. 2004 ;111(7):1298-306\u003c/li\u003e\n\u003cli\u003eIntensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group. Lancet. 1998 12;352(9131):837-53. Erratum in: Lancet 1999 14;354(9178):602\u003c/li\u003e\n\u003cli\u003eVaradhan L, Humphreys T, Hariman C, Walker AB, Varughese GI. GLP-1 agonist treatment: implications for diabetic retinopathy screening. Diabetes Res Clin Pract. 2011 ;94(3):e68-71\u003c/li\u003e\n\u003cli\u003eWriting Team for the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Research Group. Effect of intensive therapy on the microvascular complications of type 1 diabetes mellitus. JAMA 2002 15;287(19):2563-69\u003c/li\u003e\n\u003cli\u003eEl-Asrar AM, Al-Rubeaan KA, Al-Amro SA, Moharram OA, Kangave D. Retinopathy as a predictor of other diabetic complications. Int Ophthalmol. 2001;24(1):1-11\u003c/li\u003e\n\u003cli\u003eLi Y, Su X, Ye Q, Guo X, Xu B, Guan T, et al. The predictive value of diabetic retinopathy on subsequent diabetic nephropathy in patients with type 2 diabetes: a systematic review and meta-analysis of prospective studies. Ren Fail. 2021 ;43(1):231-40\u003c/li\u003e\n\u003cli\u003eButt A, Mustafa N, Fawwad A, Askari S, Haque MS, Tahir B, et al. Relationship between diabetic retinopathy and diabetic nephropathy; A longitudinal follow-up study from a tertiary care unit of Karachi, Pakistan. Diabetes Metab Syndr. 2020 ;14(6):1659-63\u003c/li\u003e\n\u003cli\u003eManaviat, M.R., Afkhami, M. \u0026amp; Shoja, M.R. Retinopathy and microalbuminuria in type II diabetic patients. BMC Ophthalmol 4, 9 (2004). https://doi.org/10.1186/1471-2415-4-9\u003c/li\u003e\n\u003cli\u003eRasheed R, Pillai GS, Kumar H, Shajan AT, Radhakrishnan N, Ravindran GC. Relationship between diabetic retinopathy and diabetic peripheral neuropathy - Neurodegenerative and microvascular changes. Indian J Ophthalmol. 2021 ;69(11):3370-75\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diabetic retinopathy, Serum Fractalkine, CX3CL1,Proliferative diabetic retinopathy, Non-proliferative diabetic retinopathy","lastPublishedDoi":"10.21203/rs.3.rs-6570551/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6570551/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eDiabetic retinopathy (DR) remains a leading cause of preventable vision loss worldwide, yet reliable biochemical markers for early detection are lacking. This study aimed to investigate the predictive role of serum Fractalkine (CX3CL1) levels in the diagnosis and severity assessment of DR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eA total of 140 diabetic patients were enrolled, including 32 patients without DR and 108 patients with DR. The DR group was further categorized into proliferative and nonproliferative (mild, moderate, severe) subgroups. Serum Fractalkine levels were measured using the ELISA method. Statistical analyses were performed with SPSS 26.0, and p-values \u0026lt;0.05 were considered significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eSerum Fractalkine levels were significantly higher in patients with DR compared to controls (p \u0026lt; 0.05). No significant difference was observed between proliferative and nonproliferative groups. However, in the nonproliferative group, fractalkine levels were significantly higher in severe cases compared to mild and moderate cases (p \u0026lt; 0.05). ROC analysis identified an optimal cut-off value of 0.455 pg/ml for diagnosing DR (sensitivity: 81.5%, specificity: 56.3%) and 0.720 pg/ml for detecting moderate to severe nonproliferative DR (sensitivity: 100%, specificity: 61.9%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eElevated serum Fractalkine levels are associated with the presence and severity of diabetic retinopathy. Fractalkine may serve as a promising adjunct biomarker for the early detection and grading of DR, highlighting the need for further research into its clinical utility.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Relationship Between Degree of Diabetic Retinopathy and Serum Fractalkine (CX3CL1) in Diabetic Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 11:41:06","doi":"10.21203/rs.3.rs-6570551/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e889b90-a95e-405f-b209-b1a70917fc84","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T00:53:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-02 11:41:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6570551","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6570551","identity":"rs-6570551","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}