Circulating Levels of miR-155 and CTBP1-AS2 as a promising biomarker for early detection of diabetic nephropathy 

Circulating Levels of miR-155 and CTBP1-AS2 as a promising biomarker for early detection of diabetic nephropathy | 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 Circulating Levels of miR-155 and CTBP1-AS2 as a promising biomarker for early detection of diabetic nephropathy Arezoo Rahimi, Shekoofeh Nikooei, Khatere Roozbehi, Davood Semirani, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5768406/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 nephropathy (DN) is one of the most frequent complications of diabetes mellitus. Since the early diagnosis of DN is crucial to prevent the progression of the disease towards renal failure, many efforts have been made in recent years to introduce new diagnostic biomarkers. Recent studies suggest that non-coding RNAs could act as a novel diagnostic biomarker for the early detection and prediction of DN progress. Accordingly, in the current study we investigated the expression levels of miR-155 and CTBP1-AS2 in type 2 diabetes (T2D), DN patients and control subjects and evaluated their diagnostic potential for DN. Method A total of 189 age and sex-matched subjects including 65 T2D patients with normo-albuminuria, 61 DN patients who had a history of albuminuria, and 63 control subjects were included in this case-control study. The expression levels of miR-155 and CTBP1-AS2 were determined using QRT-PCR. Results The results revealed that the expression level of miR-155 was significantly reduced in T2D patients. In addition, miR-155 level was significantly higher in DN patients with macroalbuminuria compared to DN patients with microalbuminuria and T2D patients with normo-albuminuria. The expression level of CTBP1-AS2 in T2D without proteinuria was higher than DN subjects with macroalbuminuria. The results also showed that there was a significant positive correlation between the miR-155 level with DBP, TG, TC, SCr and, BUN levels and a negative correlation with HDL-C and eGFR values. Conclusion Deregulation levels of miR-155 and CTBP1-AS2 may represent useful novel diagnostic biomarkers for DN. Long non-coding RNAs microRNA Type 2 diabetes Diabetic nephropathy miR-155 CTBP1-AS2 Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Diabetes mellitus (DM) is the most common metabolic disorder, affecting almost 463 million worldwide, and is estimated to affect 578 million globally by 2030 and 783.2 million by 2045 [ 1 , 2 ]. Type 2 diabetes (T2D) represents approximately 98% of global DM diagnoses [ 3 ]. Studies estimate that the global prevalence of T2D will increase to 7079 individuals per 100,000 by 2030 [ 3 ]. Diabetic nephropathy (DN) is one of the most frequent complications of DM which is associated with glomerular sclerosis and end-stage renal disease (ESRD), causing the rising morbidity and mortality in diabetic patients [ 4 , 5 ]. Studies indicate that prompt diagnosis and intervention are needed to improve health outcomes and prevent the onset and progression of DN [ 6 ]. The most popular traditional clinical biomarkers to assess kidney function and identification of diabetic kidney disease (DKD), include serum creatinine (SCr), glomerular filtration rate (eGFR), urinary albumin-creatinine ratio (UACR), and albuminuria detection [ 7 , 8 ]. Even with the presence of these traditional markers, there are significant obstacles to the timely and precise diagnosis of DN. Recent studies revealed that approximately 30% of DN patients do not have albuminuria [ 9 ]. Furthermore, in DN patients, particularly in T2D, a decrease in GFR has been reported in the absence of albuminuria. Instead, these patients progress to chronic kidney disease (CKD) with severely decreased GFR, without a transition from microalbuminuria to overt proteinuria [ 10 ]. Moreover, is not specific for the presence of DKD, and could also occur in other diseases [ 11 ]. Since the early diagnosis of DN is crucial to prevent the progression of the disease towards renal failure, many efforts have been made in recent years to introduce new diagnostic markers of DN. Recent studies suggest that non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long-non coding RNAs (lncRNAs) are involved in the onset and progression of DN [ 12 – 14 ]. The interaction between lncRNAs and miRNAs, known as miRNA sponge or competing endogenous RNAs (ceRNA), could diminish the inhibitory effects of miRNAs on mRNAs, thereby preventing the target gene repression [ 15 ]. Furthermore, ncRNAs could act as a novel sensitive and noninvasive diagnostic biomarker for the prediction of DN progress, because of their high stability in body fluids and tissue and cell-specific expression profiles [ 16 , 17 ]. Numerous lines of evidence suggest that the pathophysiology of T2D and its related complications such as DN, are linked to deregulated levels of miRNA-155 (miR-155) [ 13 ]. This miRNA exerts some of its biological functions through interaction with lncRNAs such as C-terminal binding protein 1 antisense RNA 2 (CTBP1-AS2) [ 18 ]. In this context, it has been shown that TBP1-AS2 suppresses high glucose-induced cell injury through oxidative stress, inhibiting cell proliferation, via the CTBP1-AS2/miR-155/FOXO1 axis [ 18 ]. Moreover, deregulation of the expression level of miR-155 and lncRNA CTBP1-AS2 in T2D and DN have been reported by in vivo and invitro studies [ 12 , 13 ]. However, the expression levels of these biomarkers vary in either an up or down direction, and their ability to differentiate DN patients from T2D patients and healthy controls have not been well defined. Moreover, studies suggest that determination of miRNAs and lncRNAs co-expression could improve their sensitivity and specificity values. Accordingly, in the current study we investigated the co-expression levels of miR-155 and CTBP1-AS2 in TD2 and DN patients and control subjects and evaluated the diagnostic potential of these biomarkers. Additionally, we determined the correlation between miR-155 and CTBP1-AS2 levels, and clinical and anthropometric parameters. 2. Methods 2.1 Study design and subjects’ selection A total of 189 age and sex-matched subjects including 65 T2D patients with normo-albuminuria, 61 DN patients who had a history of albuminuria, and 63 control subjects were included in this case-control study. The control group was chosen among the healthy volunteers who had a fasting blood sugar (FBS) level of < 100 mg/dL and no history of diabetes. Subjects have either of the following criteria were diagnosed as having T2D: FBS level of 126 mg/dL or higher, and UACR < 30 mg/g. The DN group was classified according to the presence of albuminuria into two groups: 1) microalbuminuria group (n = 31), defined as an UACR = 30–300 mg/g; 2) macroalbuminuria group (n = 30), defined as an UACR > 300 mg/g. The exclusion criteria were the following: liver dysfunction, inflammatory diseases, malignancies, autoimmune diseases and other endocrine diseases. 2.2 Clinical and anthropometric data collection Demographic characteristics and anthropometric measurements including gender, age, body height, body weight, systolic blood pressure (SBP), diastolic blood pressure (DBP) was collected on admission. Body mass index (BMI) was estimated using the formula: BMI = body weight/body height 2 (kg/m 2 ). Blood samples were withdrawn from the vein after overnight fasting. FBS, triglycerides (TG), high-density lipoprotein-cholesterol (HDL‐C), low‐density lipoprotein-cholesterol (LDL-C), total-cholesterol (TC), SCr, blood urea nitrogen (BUN) and HbA1c levels were analyzed by routine laboratory methods. MDRD equation was used to calculate the estimated glomerular filtration rate (eGFR) for each subject: eGFR= (mL/min/1.73 m 2 ) = 175 × ( S cr ) −1.154 × (age) −0.203 × (0.742 if female) 2.3 Isolation and assessment of miR-155 and CTBP1-AS2 levels Total RNA extracted from the peripheral blood sample using a miRNeasy Mini kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. The purity and concentration of the total RNA were evaluated using a NanoDrop Lite spectrophotometer. RNA was reverse-transcribed into complementary DNA (cDNA) using Primescript RT reagent kit (TaKaRa, Japan). Real-time PCR (RT-PCR) analysis was done using the Applied Biosystems StepOnePlus Real-Time PCR System and SYBR Premix Ex TaqTM II (Takara, Japan) as described previously [ 12 , 19 ]. PCR efficiency was evaluated by LinRegPCR software. The relative expression of ncRNAs estimated using the 2 −ΔCt method and normalized using the GAPDH and U6 as the internal control for CTBP1-AS2 and miR-155, respectively. 2.4 Statistical analysis All statistical analysis was performed by SPSS Statistical Software Package (version 20.0). The assessment of normality performed by the Kolmogorov-Smirnov test. The comparisons of the quantitative variables between groups calculated by one-way analysis of variance (ANOVA) for the normally distributed data or Kruskal–Wallis test for the nonparametric data. Spearman’s correlation analysis carried out to determine the correlation between the expression levels of CTBP1-AS2 and miR-155 and parametric and nonparametric variables. Risk factors for DN were identified using binary logistic regression, and odds ratios (OR) and corresponding 95% confidence intervals (CI) were calculated. A receiver operating characteristic (ROC) curve provided by MedCalc software was used to assess the feasibility of using CTBP1‐AS2 and miR-155 as a diagnostic marker for the DN. The best sensitivity and specificity values were chosen based on the maximum Youden Index. P value < .05 was considered to be statistically significant. 3. Results 3.1 Characteristics of the study population The clinical and biochemical characteristics of study subjects (n = 189) are summarized in Table 1 . There was no significant difference between age (P = 0.09), gender (P = 0.75) and BMI (P = 0.41) among the three studied groups. Significant differences were observed for the distribution of other variables including, SBP, DBP, FBS, TC, LDL-C, HDL-C, HbA1c, SCr, BUN and eGFR. Moreover, demographic and clinical information of DN patients, who are classified according to proteinuria into two groups: microalbuminuria (n = 31) and macroalbuminuria (n = 30) are provided in Table 2 . Results revealed that DN with macroalbuminuria have higher values ​​of SBP (P = 0.004), SCr (P < 0.001), BUN (P = 0.03) and lower values of eGFR (P < 0.001) compared to DN subjects with microalbuminuria. Table 1 Demographic and laboratory data of T2D patients, DN subjects and controls Parameter Groups (n = 189) P T2D (n = 65) DN (n = 61) Control (n = 63) Gender (Male, Female) 31/34 33/28 33/30 0.75 Age 57.27 ± 8.10 60.67 ± 9.6 59 ± 8.10 0.09 BMI 26.36(22.07–26.98) 26.36(24-28.36) 25.35(23-27.48) 0.41 SBP 130(120–140) 130(120–140) 106(100–120) < 0.001 DBP 90(80–90) 90(80-97.5) 80(70–84) < 0.001 TG 119(89–184) 216(148–274) 120(101–141) < 0.001 TC 140(130.5–172) 192(158–224) 143(132–168) < 0.001 LDL-C 77(61–91) 83(69.5-103.5) 86(68–100) 0.11 HDL-C 40.96 ± 7.6 32.67 ± 8.8 43.22 ± 8.9 < 0.001 FBS 137(122-165.5) 142(128–173) 91(88–98) < 0.001 HbA1C 7.1(6.7-8) 7.1(6.9-8) 4.5(4.3–4.7) < 0.001 SCr 0.9(0.9–1.1) 2.3(1.5–3.8) 0.9(0.9-1) < 0.001 BUN 12(9–14) 18(14–27) 10(9–14) < 0.001 eGFR 82.27(66.78–90.08) 29.15(16.3–47.8) 86.07(75.22–99.28) < 0.001 Data presented as mean ± SD (standard deviation) or median (interquartile range), depending on whether the data were normally distributed. Data was analyzed with one-way analysis of variance (ANOVA) for the normally distributed data or Kruskal–Walls test for the nonparametric data. LDL-C: Low-density lipoprotein-cholesterol; HDL‐C: High‐density lipoprotein-cholesterol; TG: Triglycerides; TC: Total-cholesterol; FBS: Fasting blood Sugar; HbA1c: hemoglobin A1c; SCr: Serum creatinine; BUN: Blood urea nitrogen; eGFR: estimated glomerular filtration rate. Table 2 Demographic and clinical characteristics of the DN patients. Parameter DN subjects (n = 61) P Microalbuminuria (n = 31) Macroalbuminuria (n = 30) Gender (Male, Female) 20/11 13/17 0.09 Age 60.61 ± 10.24 60.73 ± 9.1 0.38 BMI 26.09(23.4–28) 26.38(24-29.48) 0.573 SBP 120(120–140) 130(130-142.5) 0.004 DBP 90(80–90) 90 (88.75–100) 0.06 TG 220(162–331) 209 (137.75-266.75) 0.64 TC 193(155–227) 191(159.25–213) 0.87 LDL-C 83(60-109.3) 83(69.5-103.5) 0.48 HDL-C 30.58 ± 9.5 34.83 ± 7.67 0.16 FBS 137(125–162) 147(129.75-178.25) 0.36 HbA1C 7 (6.9–7.7) 7.25(6.85–8.3) 0.44 SCr 1.53 (1.2-2) 3.8(2.57–4.52) < 0.001 BUN 17(13–24) 24(16–32) 0.03 eGFR 46(32.91–62.4) 16.6(12.6-26.13) < 0.001 Data presented as mean ± SD (standard deviation) or median (interquartile range), depending on whether the data were normally distributed. Data was analyzed using t-Student and U-Mann-Whitney test for the nonparametric data. LDL-C: Low-density lipoprotein-cholesterol; HDL‐C: High‐density lipoprotein-cholesterol; TG: Triglycerides; TC: Total-cholesterol; FBS: Fasting blood Sugar; HbA1c: hemoglobin A1c; SCr: Serum creatinine; BUN: Blood urea nitrogen; eGFR: estimated glomerular filtration rate. 3.2 Circulating levels of miR-155 and CTBP1-AS2 in controls, T2D and DN patients Our analysis revealed that the expression level of miR-155 was significantly reduced in T2D patients compared to healthy participants and DN patients (P < 0.001). In addition, the circulating level of miR-155 was significantly increased in DN patients than that in control subjects (P = 0.018) ( Fig. 1 A ) . The expression of CTBP1-AS2 has been decreased in the DN group, when compared to the T2D patients (Fig. 1 B). A sub-set analysis was performed in T2D patients with normo-albuminuria (n = 65), and DN patients with microalbuminuria (n = 31) or macroalbuminuria (n = 30) to evaluate the association of circulating miR-155 and CTBP1-AS2 levels with the degree of albuminuria. miR-155 levels were significantly higher in DN patients with macroalbuminuria compared to DN patients with microalbuminuria and T2D patients with normo-albuminuria (P < 0.001). Moreover, the expression level of miR-155 in DN subjects with microalbuminuria was higher than T2D patients with normo-albuminuria (P = 0.021) (Fig. 2 A). The expression level of CTBP1-AS2 in the peripheral blood of T2D without proteinuria was higher compared to DN subjects with macroalbuminuria (P = 0.004) (Fig. 2 B). Finally, a binary logistic regression was performed to determine the effects of BMI, FBS, miR-155, CTBP1-AS2, HbA1c and eGFR. The results revealed that miR-155 could serve as potential predictor marker for the development of DN in diabetic patients, along with eGFR (Table 3 ) . Table 3. Logistic regression of significant predictor parameters for prediction of DN Variable Wald test P OR 95% C.I.) Lower Upper BMI 1.388 0.24 1.1 0.93 1.31 FBS .120 0.73 1.0 0.98 1.02 miR-155 9.580 0.002 0.45 0.27 0.74 CTBP1‐AS2 .659 0.41 1.16 0.81 1.66 HbA1c 1.582 0.208 1.45 0.81 2.59 eGFR 21.917 0.000 0.89 0.85 0.93 3.3 Diagnostic value of miR-155 and CTBP1-AS2 in DN The ROC curve was applied to evaluate the diagnostic potential of miR-155 and CTBP1-AS2 in DN. An AUC above 0.8 was considered good performance and above 0.9 was considered excellent performance. Our data revealed that miR-155 has good diagnostic performance (AUC greater than 0.8) to discriminate DN from T2D patients and discriminate DN patient with macroalbuminuria from T2D cases (Fig. 3 and Table 4 ). Moreover, CTBP1‐AS2 showed poor discrimination with AUC value equal to 0.6 to distinguish the DN from T2D patients (95% CI 0.51–0.68, sensitivity of 83% and a specificity of 36, %. P = 0. 04), and discriminate DN patient with macroalbuminuria from T2D subjects (95% CI 0.58–0.78, sensitivity of 60% and a specificity of 73%. P = 0.002) (Fig. 4 ). Table 4 The diagnostic efficiency of the miR-155 for distinguishing the studied groups miR-155 expression level Associated criterion Sensitivity Specificity 95% CI AUC P T2D vs. Control ≤ 5.3 68.25 56.92 0.54–0.72 0.63 0.006 DN vs. Control ≤ 3.07 49.18 85.71 0.62–0.78 0.70 4.75 67.69 83.61 0.72–0.86 0.80 < 0.001 T2D vs. Microalbuminuria ≤ 4.75 74.19 67.69 0.62–0.81 0.72 < 0.001 T2D vs. Macroalbuminuria ≤ 3.92 80.00 84.00 0.78–0.93 0.88 < 0.001 Macroalbuminuria vs. Microalbuminuria ≤ 2.93 66.67 83.87 0.60–0.83 0.73 < 0.001 3.4 Correlation of miR-155 and CTBP1-AS2 levels with clinical and anthropometrical parameters Spearman correlation analysis was applied to determine the correlation of miR-155 and CTBP1-AS2 and clinical parameters using 2 −ΔCt values as input data. The results showed that there was a significant positive correlation between the miR-155 level with DBP, TG, TC, SCr and, BUN levels and a negative correlation with HDL-C and eGFR values. Moreover, a significant negative correlation was found between CTBP1‐AS2 and BMI levels (Table 5 ). Table 5 The correlation of miR-155 and CTBP1-AS2 expression levels with clinical and anthropometrical parameters Parameters miR-155 CTBP1-AS2 R P R P Age -0.01 0.79 -0.03 0.59 BMI 0.003 0.96 -0.19 0.008 SBP 0.12 0.09 -0.07 0.33 DBP 0.16 0.029 -0.12 0.09 HbA1C 0.05 0.48 -0.08 0.25 FBS 0.12 0.08 -0.07 0.28 TG 0.20 0.005 -0.1 0.13 TC 0.17 0.019 -0.09 0.17 HDL-C -0.21 0.003 0.01 0.83 LDL-C 0.03 0.64 -0.02 0.77 SCr 0.33 < 0.001 -0.08 0.25 BUN 0.26 < 0.001 -0.01 0.8 eGFR -0.36 < 0.001 0.08 0.26 CTBP1-AS2 -0.02 0.78 - - miR-155 - - -0.02 0.78 4. Discussion Our findings revealed that the relative expression level of miR-155 is decreased in the T2D group compared to controls. In line with our results, it has been reported that the levels of miR-155 is reduced in T2D subjects compared to the healthy group [ 20 – 22 ]. Catanzaro et al. found a significant decrease in the expression level of miR-155 in T2D patients compared to healthy participants [ 23 ]. Moreover, miR-155 has shown downregulation in obese T2D patients [ 24 ]. Samaloty et al. found lower levels of miR-155 in the serum of patients with hepatitis C and insulin resistance, compared to hepatitis C patients whom did not have insulin resistance [ 25 ]. Several studies have investigated the role of miR-155 in the pathogenesis of T2D. Lin et al. reported that miR-155 deficiency causes hyperglycemia, impaired glucose tolerance and insulin resistance in mice. They also revealed that overexpressing of miR-155 enhances glycolysis, as well as the phosphorylation of insulin-stimulated AKT and IRS-1 in liver, adipose tissue or skeletal muscle in diabetic mice [ 26 ]. A recent study demonstrated that blocking a macrophage-derived exosomal miR-155 reduced glucose intolerances in high-fat-diet feeding and T2D db/db mice [ 27 ]. Furthermore, it has been shown that the overexpression of miR-155 significantly enhanced insulin signaling and glucose uptake in trophoblasts by targeting CEBPB [ 27 ]. Studies have shown that miR-155 takes a role in the pathogenesis of diabetic complications including DN. The results of our study corroborate previous investigation, demonstrating significantly higher circulation level of miR-155 in DN patients compared to T2D patients and control subjects [ 13 , 28 ]. In addition, current study revealed that the expression levels of miR-155 in patients with macroalbuminuria were higher than patients with microalbuminuria and T2D subjects with normo-albuminuria. Consistent with our results, Wang et al . reported that the expression levels of miR-155 in patients with macroalbuminuria was higher than patients with microalbuminuria. They suggested that higher levels of miR-155 at the time of progression of DN could indicate the potential role of miR-155 in the development of this condition, which might correlate with kidney inflammation [ 29 ]. In addition to serum and whole blood samples, some studies suggested that the urinary level of miR-155 could provide a promising novel source of noninvasive biomarkers for DN. In this regard, it has been reported that urinary expression level of miR-155 is higher in patients with DKD and is significantly associated with SCr levels [ 30 ]. A recent study also revealed that T2D patients with albuminuria showed higher urinary levels of miR-155 compared to healthy controls [ 31 ]. Additionally, in a study by Donderski et al. , increase in the relative expression of miR-155 was seen in the urine samples of CKD patients [ 32 ]. Accumulating evidence suggests that microinflammation could lead to podocyte dysfunction. It has been reported that the regulation of miR-155 expression, could protect against renal damage in DKD mice. Prieto et al . demonstrated that miR-155 is over-expressed by hyperglycemia and inflammation in cultured renal cells and kidney samples from diabetic mice. They also revealed that miR-155 inhibitor therapy gradually decreased albuminuria levels over time. Moreover, they showed that mice receiving miR-155 inhibitor presented a lower level of SCr, KIM1 and lipocalin-2 [ 14 ]. Another study reported that serum expression level of miR-155 increased in CKD population and its overexpression is positively associated with eGFR levels [ 32 ]. Wang et al . reported that miR-155 was increased in serum and kidney tissue of DN mice, as well as in cultured podocytes. They reported that inhibition of miR-155 reduced proteinuria and UACR levels, inhibited kidney inflammation by suppressing podocyte foot fusion, and reverse pathological changes in the kidney of DN mice [ 33 ]. MiR-155 can also be utilized as a therapeutic strategy for DKD, as it takes role in the regulation of the autophagic process in DKD, by stimulating the signaling loop of p53/miR-155/Sirt1[ 34 ]. Due to the critical roles of miR-155 in the incidence and progression of DN, other studies have investigated the therapeutic role of this miRNA in DN. It was previously reported that glucose-lowering drugs might exert their effect by altering the miRNAs expression level [ 35 ]. Zhou et al . reported that Metformin could stimulate the inflammation and fibrosis in individuals with DKD through TNC/TLR4/NF-κB/miR-155 inflammatory loop [ 36 ]. In addition, Dapagliflozin could inhibit podocyte pyroptosis via the miR-155/HO-1/NLRP3 axis in DM, and thereby prevent pyroptosis in the kidney [ 37 ]. Available evidence has indicated the important roles of lncRNAs in the pathophysiology of T2D and its related complications, including development of proteinuria [ 38 – 40 ]. Previous study suggests that CTBP1-AS2 could regulate pancreatic beta cell maturation by affecting MafA in human umbilical cord mesenchymal stem cells [ 41 ]. Interestingly, the interaction between lncRNAs and miRNAs could diminish the inhibitory effects of miRNAs on mRNAs, thereby preventing the target gene repression [ 15 , 42 ]. In this context, Wang et al . demonstrated that TBP1-AS2 suppress high glucose-induced cell injury, by inhibiting cell proliferation, oxidative stress, ECM accumulation, and inflammation by the CTBP1-AS2/miR-155/FOXO1 axis [ 18 ]. Consistent with Wang et al. findings, current investigation revealed that the expression levels of CTBP1-AS2 was downregulated in DN patients compared to T2D. However, the expression level of this lncRNA was not significantly different between the control and T2D groups in our study. In contrast with our findings, Omidvar et al . observed a significant downregulation in the expression level of lncRNA CTBP1‐AS2 in peripheral blood samples of T2D patients compared to controls. Moreover, they have shown that CTBP1‐AS2 expression was negatively correlated with HDL‐C and positively correlated with LDL‐C levels [ 12 ]. Finally, our results revealed that miR-155 level was positively correlated with SBP, TG, TC, SCr, BUN, eGFR and HDL-C values. Huang et al . revealed that miR-155 levels were positively correlated with SBP, and DBP [ 43 ]. It has been shown that miR-155 could improve the adverse effects of pregnancy hypertension via the upregulation of FOXO3a [ 44 ]. Nemecz et al . reported that microvesicles expression of miR-155 is positively associated with cholesterol and negatively with HDL-c levels [ 45 ]. Previous studies also demonstrated a significant negative association between miR-155 expression and insulin, HOMA-IR, and hs-CRP levels in peripheral blood mononuclear cells of obese children [ 46 , 47 ]. 5. Conclusion The most popular traditional clinical biomarkers to assess kidney function and identification of DN, include SCr, eGFR, UACR, and albuminuria detection. Even with the presence of these traditional markers, there are significant obstacles to the timely and precise diagnosis of DN. Recent studies suggest that, non-coding RNAs could act as a novel sensitive and noninvasive diagnostic biomarker for the prediction of DN progress, because of their high stability in body fluids and tissue and cell-specific expression profiles. Accordingly, in the current study we investigated the co-expression levels of miR-155 and CTBP1-AS2 in TD2 and DN patients and control subjects and evaluated the diagnostic potential of these biomarkers. The results revealed that the expression level of miR-155 was significantly reduced in T2D patients compared to healthy participants and DN patients. Indeed, miR-155 level was significantly higher in DN patients with macroalbuminuria compared to DN patients with microalbuminuria and T2D patients with normo-albuminuria. The expression level of CTBP1-AS2 in the peripheral blood of T2D without proteinuria was higher compared to DN subjects with macroalbuminuria. The results of the current study provided evidence that miR-155 and CTBP1-AS2 may represent useful novel diagnostic biomarkers for DN. However, further in vitro and in vivo investigations are needed to confirm our results. Declarations Ethical approval: The study was approved by the Yasuj University of Medical Sciences Ethics Committee (IR.YUMS.REC.1402.006). Informed consent was obtained from all subjects before enrolling in the study. Conflicts of interest: The authors declare no conflicts of interest. Funding: This work was financially supported by a grant (4010045) from the Deputy of Research, Yasuj University of Medical Sciences. Author Contribution Behnam Alipoor and Danial Gholami planned the studies; Behnam Alipoor analyzed and interpreted all experiments; Arezoo Rahimi and Shekoofeh Nikooei conducted all experiments. Behnam Alipoor, Khatereh Roozbehi and Davood Semirani wrote the manuscript; Arash Arya and Rozina Abasi Larki selected the patients and referred them to the study. Acknowledgments: We greatly appreciate all volunteers for their participation in the study. Data Availability: All data generated or analyzed during this study are included in this published article. References Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract. 2019;157:107843. 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Biochem Biophys Res Commun. 2020;532(2):308-14. Xiong Y, Chen S, Liu L, Zhao Y, Lin W, Ni J. Increased serum microRNA-155 level associated with nonresponsiveness to hepatitis B vaccine. Clin Vaccine Immunol. 2013;20(7):1089-91. Zhiping L, Chunli L, Shanna Z. Study on the correlation between PPARγ, Aβ1-42, miR-155 and the occurrence and development of diabetes. Cell Mol Biol (Noisy-le-grand). 2022;67(4):214-21. Ji H, Lu Y, Liu G, Zhao X, Xu M, Chen M. Role of Decreased Expression of miR-155 and miR-146a in Peripheral Blood of Type 2 Diabetes Mellitus Patients with Diabetic Peripheral Neuropathy. Diabetes Metab Syndr Obes. 2024;17:2747-60. Akhbari M, Khalili M, Shahrabi-Farahani M, Biglari A, Bandarian F. Expression Level of Circulating Cell Free miR-155 Gene in Serum of Patients with Diabetic Nephropathy. Clin Lab. 2019;65(8). Catanzaro G, Conte F, Trocchianesi S, Splendiani E, Bimonte VM, Mocini E, et al. Network analysis identifies circulating miR-155 as predictive biomarker of type 2 diabetes mellitus development in obese patients: a pilot study. Sci Rep. 2023;13(1):19496. Latini A, Benedittis G, Ciccacci C, Novelli G, Spallone V, Borgiani P. Low expression levels of miRNA-155 and miRNA-499a are associated with obesity in Type 2 diabetes. Epigenomics. 2024;16(2):85-91. El Samaloty NM, Hassan ZA, Hefny ZM, Abdelaziz DHA. Circulating microRNA-155 is associated with insulin resistance in chronic hepatitis C patients. Arab J Gastroenterol. 2019;20(1):1-7. Lin X, Qin Y, Jia J, Lin T, Lin X, Chen L, et al. MiR-155 Enhances Insulin Sensitivity by Coordinated Regulation of Multiple Genes in Mice. PLoS Genet. 2016;12(10):e1006308. Zhang H, Jiang Y, Zhu S, Wei L, Zhou X, Gao P, et al. MiR-155-5p improves the insulin sensitivity of trophoblasts by targeting CEBPB in gestational diabetes mellitus. Placenta. 2024;148:1-11. Li H, Qiu F, Tian F, Shi X, Gao A, Song L, Liu J. Changes of miR-155 expression in serum of uremic patients before and after treatment and risk factors analysis. Exp Ther Med. 2020;20(4):3352-60. Wang J, Wang G, Liang Y, Zhou X. Expression Profiling and Clinical Significance of Plasma MicroRNAs in Diabetic Nephropathy. J Diabetes Res. 2019;2019:5204394. Beltrami C, Simpson K, Jesky M, Wonnacott A, Carrington C, Holmans P, et al. Association of Elevated Urinary miR-126, miR-155, and miR-29b with Diabetic Kidney Disease. Am J Pathol. 2018;188(9):1982-92. González-Palomo AK, Pérez-Vázquez FJ, Méndez-Rodríguez KB, Ilizaliturri-Hernández CA, Cardona-Alvarado MI, Flores-Nicasio MV, et al. Profile of urinary exosomal microRNAs and their contribution to diabetic kidney disease through a predictive classification model. Nephrology (Carlton). 2022;27(6):484-93. Donderski R, Szczepanek J, Naruszewicz N, Naruszewicz R, Tretyn A, Skoczylas-Makowska N, et al. Analysis of profibrogenic microRNAs (miRNAs) expression in urine and serum of chronic kidney disease (CKD) stage 1-4 patients and their relationship with proteinuria and kidney function. Int Urol Nephrol. 2022;54(4):937-47. Wang X, Gao Y, Yi W, Qiao Y, Hu H, Wang Y, et al. Inhibition of miRNA-155 Alleviates High Glucose-Induced Podocyte Inflammation by Targeting SIRT1 in Diabetic Mice. J Diabetes Res. 2021;2021:5597394. Wang Y, Zheng ZJ, Jia YJ, Yang YL, Xue YM. Role of p53/miR-155-5p/sirt1 loop in renal tubular injury of diabetic kidney disease. J Transl Med. 2018;16(1):146. Parvar SN, Mirzaei A, Zare A, Doustimotlagh AH, Nikooei S, Arya A, Alipoor B. Effect of metformin on the long non-coding RNA expression levels in type 2 diabetes: an in vitro and clinical trial study. Pharmacol Rep. 2023;75(1):189-98. Zhou Y, Ma XY, Han JY, Yang M, Lv C, Shao Y, et al. Metformin regulates inflammation and fibrosis in diabetic kidney disease through TNC/TLR4/NF-κB/miR-155-5p inflammatory loop. World J Diabetes. 2021;12(1):19-46. Zhang ZW, Tang MQ, Liu W, Song Y, Gao MJ, Ni P, et al. Dapagliflozin prevents kidney podocytes pyroptosis via miR-155-5p/HO-1/NLRP3 axis modulation. Int Immunopharmacol. 2024;131:111785. Niknam N, Nikooei S, Ghasemi H, Zadian SS, Goudarzi K, Ahmadi SM, Alipoor B. Circulating Levels of HOTAIR- lncRNA Are Associated with Disease Progression and Clinical Parameters in Type 2 Diabetes Patients. Rep Biochem Mol Biol. 2023;12(3):448-57. Alipoor B, Nikouei S, Rezaeinejad F, Malakooti-Dehkordi SN, Sabati Z, Ghasemi H. Long non-coding RNAs in metabolic disorders: pathogenetic relevance and potential biomarkers and therapeutic targets. J Endocrinol Invest. 2021;44(10):2015-41. Zheng DN, Zhang CJ, Sun GP. Long non-coding RNA MNX1-AS1 promotes migration and invasion of esophageal squamous cell carcinoma by upregulating IGF2. Eur Rev Med Pharmacol Sci. 2020;24(15):7916. Xie T, Huang Q, Huang Q, Huang Y, Liu S, Zeng H, Liu J. Dysregulated lncRNAs regulate human umbilical cord mesenchymal stem cell differentiation into insulin-producing cells by forming a regulatory network with mRNAs. Stem Cell Res Ther. 2024;15(1):22. Venkatesh J, Wasson MD, Brown JM, Fernando W, Marcato P. LncRNA-miRNA axes in breast cancer: Novel points of interaction for strategic attack. Cancer Lett. 2021;509:81-8. Huang YQ, Huang C, Zhang B, Feng YQ. Association of circulating miR-155 expression level and inflammatory markers with white coat hypertension. J Hum Hypertens. 2020;34(5):397-403. Liu DF, Li SM, Zhu QX, Jiang W. The involvement of miR-155 in blood pressure regulation in pregnant hypertension rat via targeting FOXO3a. Eur Rev Med Pharmacol Sci. 2018;22(20):6591-8. Nemecz M, Stefan DS, Comarița IK, Constantin A, Tanko G, Guja C, Georgescu A. Microvesicle-associated and circulating microRNAs in diabetic dyslipidemia: miR-218, miR-132, miR-143, and miR-21, miR-122, miR-155 have biomarker potential. Cardiovasc Diabetol. 2023;22(1):260. Behrooz M, Hajjarzadeh S, Kahroba H, Ostadrahimi A, Bastami M. Expression pattern of miR-193a, miR122, miR155, miR-15a, and miR146a in peripheral blood mononuclear cells of children with obesity and their relation to some metabolic and inflammatory biomarkers. BMC Pediatr. 2023;23(1):95. Mahdavi R, Ghorbani S, Alipoor B, Panahi G, Khodabandehloo H, Esfahani EN, et al. Decreased Serum Level of miR-155 is Associated with Obesity and its Related Metabolic Traits. Clin Lab. 2018;64(1):77-84. 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. 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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-5768406","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398399195,"identity":"4e4cecd1-ce09-4dcd-afbe-0048fda73fcf","order_by":0,"name":"Arezoo Rahimi","email":"","orcid":"","institution":"Yasuj University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Arezoo","middleName":"","lastName":"Rahimi","suffix":""},{"id":398399196,"identity":"aa744b13-003f-4582-9bd3-16e6b664e790","order_by":1,"name":"Shekoofeh Nikooei","email":"","orcid":"","institution":"Yasuj University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shekoofeh","middleName":"","lastName":"Nikooei","suffix":""},{"id":398399197,"identity":"03109340-9f79-49fa-b104-49af3d170e61","order_by":2,"name":"Khatere Roozbehi","email":"","orcid":"","institution":"Yasuj University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Khatere","middleName":"","lastName":"Roozbehi","suffix":""},{"id":398399198,"identity":"10047f61-6efe-4154-985b-500e035dec00","order_by":3,"name":"Davood Semirani","email":"","orcid":"","institution":"Yasuj University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Davood","middleName":"","lastName":"Semirani","suffix":""},{"id":398399199,"identity":"29f9394d-ab97-4bb3-a3fd-82375a576d5e","order_by":4,"name":"Rozina Abasi Larki","email":"","orcid":"","institution":"Yasuj University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Rozina","middleName":"Abasi","lastName":"Larki","suffix":""},{"id":398399200,"identity":"7ec73922-d90b-463d-a118-e5baff8ccb34","order_by":5,"name":"Arash Arya","email":"","orcid":"","institution":"Yasuj University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Arash","middleName":"","lastName":"Arya","suffix":""},{"id":398399201,"identity":"0a80cfa6-f9d3-4798-b674-cbe680694545","order_by":6,"name":"Danial Gholami","email":"","orcid":"","institution":"Yasuj University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Danial","middleName":"","lastName":"Gholami","suffix":""},{"id":398399202,"identity":"d2cd155a-43aa-4996-8d15-6994f82cb8dd","order_by":7,"name":"Behnam Alipoor","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACAwYeCIOPGUzZwGV4CGphg2hJI0ULhDpM2GHm7L3HHvzMYZBnY+c9+Jh3z/nEDdeOP2D4UcMgY96AXYtlz7l0w95tDIZtzHzJxjzPbiduuJ1jwNhzjIFH5gAOh93IMZPg3cbA2MbMYybNcwCshYGBt4GBRwKXX+6/MZP8u43BHqrlHFBL+gPGv/i03ACqBNqSCNVyAKglwYAZny2WPTlm0rLbJJJBfjGccyDZeCbQL4dljkng1GLOfsZM8u02G9t+/rMHH7w5YCfbdzv94cM3NTb2uLRAAUgaEkGODUDiAESEIIBosSdG6SgYBaNgFIwsAACx21ChgNMVrQAAAABJRU5ErkJggg==","orcid":"","institution":"Yasuj University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Behnam","middleName":"","lastName":"Alipoor","suffix":""}],"badges":[],"createdAt":"2025-01-05 15:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5768406/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5768406/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73270369,"identity":"31ef77e4-dda6-48b7-a7c8-7d653e6af198","added_by":"auto","created_at":"2025-01-08 10:49:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":105857,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of [A] miR-155, and [B] CTBP1‐AS2 circulating levels between control (n=63), T2D (n=65) and DN patients (n=61). \u003c/strong\u003eData was analyzed with Kruskal–Wallis test. The horizontal lines in the box plots indicate the median and the boxes indicate the interquartile range.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5768406/v1/190a2e553deaa2db7d4e67be.png"},{"id":73269147,"identity":"669b0a57-6a0d-4e19-b901-b25d649bc10e","added_by":"auto","created_at":"2025-01-08 10:41:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105955,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of [A] miR-155, and [B] CTBP1‐AS2 circulating levels between, T2D (n=65), \u003c/strong\u003eDN subjects with microalbuminuria \u003cstrong\u003e(n=31), \u003c/strong\u003eand DN subjects with macroalbuminuria\u003cstrong\u003e (n=30).\u003c/strong\u003e Data was analyzed with Kruskal–Wallis test. The horizontal lines in the box plots indicate the median and the boxes indicate the interquartile range.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5768406/v1/3dc9925bfbbc0fc1c22c9b3b.png"},{"id":73269149,"identity":"ac739ea6-afc5-49bb-941e-6d3b96182396","added_by":"auto","created_at":"2025-01-08 10:41:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":336214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive value of miR-155 circulating levels for prognosis of DN patients.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5768406/v1/1d5fe98e78be676d143b83b5.png"},{"id":73269160,"identity":"90edab8c-1e3d-4383-b543-51ca17b4900f","added_by":"auto","created_at":"2025-01-08 10:41:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":133591,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe ROC curve analysis for the evaluation of the diagnostic performance of CTBP1‐AS2 for differentiating DN.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5768406/v1/685f843572b0d4fa4f6e519e.png"},{"id":73378807,"identity":"5b472e67-ba41-4d31-9b95-06826fbe5e6e","added_by":"auto","created_at":"2025-01-09 10:55:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1961135,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5768406/v1/7c6f275a-6ada-4e88-a415-ce835cc50ca1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Circulating Levels of miR-155 and CTBP1-AS2 as a promising biomarker for early detection of diabetic nephropathy ","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDiabetes mellitus (DM) is the most common metabolic disorder, affecting almost 463\u0026nbsp;million worldwide, and is estimated to affect 578\u0026nbsp;million globally by 2030 and 783.2\u0026nbsp;million by 2045 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Type 2 diabetes (T2D) represents approximately 98% of global DM diagnoses [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Studies estimate that the global prevalence of T2D will increase to 7079 individuals per 100,000 by 2030 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Diabetic nephropathy (DN) is one of the most frequent complications of DM which is associated with glomerular sclerosis and end-stage renal disease (ESRD), causing the rising morbidity and mortality in diabetic patients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Studies indicate that prompt diagnosis and intervention are needed to improve health outcomes and prevent the onset and progression of DN [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe most popular traditional clinical biomarkers to assess kidney function and identification of diabetic kidney disease (DKD), include serum creatinine (SCr), glomerular filtration rate (eGFR), urinary albumin-creatinine ratio (UACR), and albuminuria detection [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Even with the presence of these traditional markers, there are significant obstacles to the timely and precise diagnosis of DN. Recent studies revealed that approximately 30% of DN patients do not have albuminuria [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, in DN patients, particularly in T2D, a decrease in GFR has been reported in the absence of albuminuria. Instead, these patients progress to chronic kidney disease (CKD) with severely decreased GFR, without a transition from microalbuminuria to overt proteinuria [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, is not specific for the presence of DKD, and could also occur in other diseases [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Since the early diagnosis of DN is crucial to prevent the progression of the disease towards renal failure, many efforts have been made in recent years to introduce new diagnostic markers of DN. Recent studies suggest that non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long-non coding RNAs (lncRNAs) are involved in the onset and progression of DN [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The interaction between lncRNAs and miRNAs, known as miRNA sponge or competing endogenous RNAs (ceRNA), could diminish the inhibitory effects of miRNAs on mRNAs, thereby preventing the target gene repression [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Furthermore, ncRNAs could act as a novel sensitive and noninvasive diagnostic biomarker for the prediction of DN progress, because of their high stability in body fluids and tissue and cell-specific expression profiles [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNumerous lines of evidence suggest that the pathophysiology of T2D and its related complications such as DN, are linked to deregulated levels of miRNA-155 (miR-155) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This miRNA exerts some of its biological functions through interaction with lncRNAs such as C-terminal binding protein 1 antisense RNA 2 (CTBP1-AS2) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In this context, it has been shown that TBP1-AS2 suppresses high glucose-induced cell injury through oxidative stress, inhibiting cell proliferation, via the CTBP1-AS2/miR-155/FOXO1 axis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Moreover, deregulation of the expression level of miR-155 and lncRNA CTBP1-AS2 in T2D and DN have been reported by in vivo and invitro studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the expression levels of these biomarkers vary in either an up or down direction, and their ability to differentiate DN patients from T2D patients and healthy controls have not been well defined. Moreover, studies suggest that determination of miRNAs and lncRNAs co-expression could improve their sensitivity and specificity values. Accordingly, in the current study we investigated the co-expression levels of miR-155 and CTBP1-AS2 in TD2 and DN patients and control subjects and evaluated the diagnostic potential of these biomarkers. Additionally, we determined the correlation between miR-155 and CTBP1-AS2 levels, and clinical and anthropometric parameters.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and subjects\u0026rsquo; selection\u003c/h2\u003e \u003cp\u003eA total of 189 age and sex-matched subjects including 65 T2D patients with normo-albuminuria, 61 DN patients who had a history of albuminuria, and 63 control subjects were included in this case-control study. The control group was chosen among the healthy volunteers who had a fasting blood sugar (FBS) level of \u0026lt;\u0026thinsp;100 mg/dL and no history of diabetes. Subjects have either of the following criteria were diagnosed as having T2D: FBS level of 126 mg/dL or higher, and UACR\u0026thinsp;\u0026lt;\u0026thinsp;30 mg/g. The DN group was classified according to the presence of albuminuria into two groups: 1) microalbuminuria group (n\u0026thinsp;=\u0026thinsp;31), defined as an UACR\u0026thinsp;=\u0026thinsp;30\u0026ndash;300 mg/g; 2) macroalbuminuria group (n\u0026thinsp;=\u0026thinsp;30), defined as an UACR\u0026thinsp;\u0026gt;\u0026thinsp;300 mg/g. The exclusion criteria were the following: liver dysfunction, inflammatory diseases, malignancies, autoimmune diseases and other endocrine diseases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical and anthropometric data collection\u003c/h2\u003e \u003cp\u003eDemographic characteristics and anthropometric measurements including gender, age, body height, body weight, systolic blood pressure (SBP), diastolic blood pressure (DBP) was collected on admission. Body mass index (BMI) was estimated using the formula: BMI\u0026thinsp;=\u0026thinsp;body weight/body height\u003csup\u003e2\u003c/sup\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e). Blood samples were withdrawn from the vein after overnight fasting. FBS, triglycerides (TG), high-density lipoprotein-cholesterol (HDL‐C), low‐density lipoprotein-cholesterol (LDL-C), total-cholesterol (TC), SCr, blood urea nitrogen (BUN) and HbA1c levels were analyzed by routine laboratory methods. MDRD equation was used to calculate the estimated glomerular filtration rate (eGFR) for each subject:\u003c/p\u003e \u003cp\u003eeGFR= (mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;175 \u0026times; (\u003cem\u003eS\u003c/em\u003e\u003csub\u003ecr\u003c/sub\u003e)\u003csup\u003e\u0026minus;1.154\u003c/sup\u003e \u0026times; (age)\u003csup\u003e\u0026minus;0.203\u003c/sup\u003e \u0026times; (0.742 if female)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.3 Isolation and assessment of miR-155 and CTBP1-AS2 levels\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTotal RNA extracted from the peripheral blood sample using a miRNeasy Mini kit (Qiagen, Valencia, CA, USA) according to the manufacturer\u0026rsquo;s instructions. The purity and concentration of the total RNA were evaluated using a NanoDrop Lite spectrophotometer. RNA was reverse-transcribed into complementary DNA (cDNA) using Primescript RT reagent kit (TaKaRa, Japan). Real-time PCR (RT-PCR) analysis was done using the Applied Biosystems StepOnePlus Real-Time PCR System and SYBR Premix Ex TaqTM II (Takara, Japan) as described previously [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. PCR efficiency was evaluated by LinRegPCR software. The relative expression of ncRNAs estimated using the 2\u003csup\u003e\u0026minus;ΔCt\u003c/sup\u003e method and normalized using the GAPDH and U6 as the internal control for CTBP1-AS2 and miR-155, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analysis was performed by SPSS Statistical Software Package (version 20.0). The assessment of normality performed by the Kolmogorov-Smirnov test. The comparisons of the quantitative variables between groups calculated by one-way analysis of variance (ANOVA) for the normally distributed data or Kruskal\u0026ndash;Wallis test for the nonparametric data. Spearman\u0026rsquo;s correlation analysis carried out to determine the correlation between the expression levels of CTBP1-AS2 and miR-155 and parametric and nonparametric variables. Risk factors for DN were identified using binary logistic regression, and odds ratios (OR) and corresponding 95% confidence intervals (CI) were calculated. A receiver operating characteristic (ROC) curve provided by MedCalc software was used to assess the feasibility of using CTBP1‐AS2 and miR-155 as a diagnostic marker for the DN. The best sensitivity and specificity values were chosen based on the maximum Youden Index. P value\u0026thinsp;\u0026lt;\u0026thinsp;.05 was considered to be statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of the study population\u003c/h2\u003e \u003cp\u003eThe clinical and biochemical characteristics of study subjects (n\u0026thinsp;=\u0026thinsp;189) are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There was no significant difference between age (P\u0026thinsp;=\u0026thinsp;0.09), gender (P\u0026thinsp;=\u0026thinsp;0.75) and BMI (P\u0026thinsp;=\u0026thinsp;0.41) among the three studied groups. Significant differences were observed for the distribution of other variables including, SBP, DBP, FBS, TC, LDL-C, HDL-C, HbA1c, SCr, BUN and eGFR. Moreover, demographic and clinical information of DN patients, who are classified according to proteinuria into two groups: microalbuminuria (n\u0026thinsp;=\u0026thinsp;31) and macroalbuminuria (n\u0026thinsp;=\u0026thinsp;30) are provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Results revealed that DN with macroalbuminuria have higher values ​​of SBP (P\u0026thinsp;=\u0026thinsp;0.004), SCr (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BUN (P\u0026thinsp;=\u0026thinsp;0.03) and lower values of eGFR (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to DN subjects with microalbuminuria.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eDemographic and laboratory data of T2D patients, DN subjects and controls\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGroups (n\u0026thinsp;=\u0026thinsp;189)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2D (n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDN (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;63)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003cp\u003e(Male, Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31/34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33/28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33/30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.27\u0026thinsp;\u0026plusmn;\u0026thinsp;8.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.67\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59\u0026thinsp;\u0026plusmn;\u0026thinsp;8.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.36(22.07\u0026ndash;26.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.36(24-28.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.35(23-27.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130(120\u0026ndash;140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130(120\u0026ndash;140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106(100\u0026ndash;120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(80\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90(80-97.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80(70\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119(89\u0026ndash;184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216(148\u0026ndash;274)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120(101\u0026ndash;141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140(130.5\u0026ndash;172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192(158\u0026ndash;224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143(132\u0026ndash;168)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77(61\u0026ndash;91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83(69.5-103.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86(68\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.96\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.22\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137(122-165.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142(128\u0026ndash;173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91(88\u0026ndash;98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.1(6.7-8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.1(6.9-8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5(4.3\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9(0.9\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3(1.5\u0026ndash;3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9(0.9-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(9\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(14\u0026ndash;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(9\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.27(66.78\u0026ndash;90.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.15(16.3\u0026ndash;47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.07(75.22\u0026ndash;99.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (standard deviation) or median (interquartile range), depending on whether the data were normally distributed. Data was analyzed with one-way analysis of variance (ANOVA) for the normally distributed data or Kruskal\u0026ndash;Walls test for the nonparametric data. LDL-C: Low-density lipoprotein-cholesterol; HDL‐C: High‐density lipoprotein-cholesterol; TG: Triglycerides; TC: Total-cholesterol; FBS: Fasting blood Sugar; HbA1c: hemoglobin A1c; SCr: Serum creatinine; BUN: Blood urea nitrogen; eGFR: estimated glomerular filtration rate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eDemographic and clinical characteristics of the DN patients.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDN subjects (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMicroalbuminuria (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMacroalbuminuria (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male, Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20/11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13/17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.61\u0026thinsp;\u0026plusmn;\u0026thinsp;10.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.73\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.09(23.4\u0026ndash;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.38(24-29.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120(120\u0026ndash;140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130(130-142.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(80\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (88.75\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220(162\u0026ndash;331)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209 (137.75-266.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193(155\u0026ndash;227)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191(159.25\u0026ndash;213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83(60-109.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83(69.5-103.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.58\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.83\u0026thinsp;\u0026plusmn;\u0026thinsp;7.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137(125\u0026ndash;162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147(129.75-178.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (6.9\u0026ndash;7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.25(6.85\u0026ndash;8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.53 (1.2-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8(2.57\u0026ndash;4.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(13\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(16\u0026ndash;32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(32.91\u0026ndash;62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.6(12.6-26.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (standard deviation) or median (interquartile range), depending on whether the data were normally distributed. Data was analyzed using t-Student and U-Mann-Whitney test for the nonparametric data. LDL-C: Low-density lipoprotein-cholesterol; HDL‐C: High‐density lipoprotein-cholesterol; TG: Triglycerides; TC: Total-cholesterol; FBS: Fasting blood Sugar; HbA1c: hemoglobin A1c; SCr: Serum creatinine; BUN: Blood urea nitrogen; eGFR: estimated glomerular filtration rate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.2 Circulating levels of miR-155 and CTBP1-AS2 in controls, T2D and DN patients\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eOur analysis revealed that the expression level of miR-155 was significantly reduced in T2D patients compared to healthy participants and DN patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, the circulating level of miR-155 was significantly increased in DN patients than that in control subjects (P\u0026thinsp;=\u0026thinsp;0.018) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. The expression of CTBP1-AS2 has been decreased in the DN group, when compared to the T2D patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA sub-set analysis was performed in T2D patients with normo-albuminuria (n\u0026thinsp;=\u0026thinsp;65), and DN patients with microalbuminuria (n\u0026thinsp;=\u0026thinsp;31) or macroalbuminuria (n\u0026thinsp;=\u0026thinsp;30) to evaluate the association of circulating miR-155 and CTBP1-AS2 levels with the degree of albuminuria. miR-155 levels were significantly higher in DN patients with macroalbuminuria compared to DN patients with microalbuminuria and T2D patients with normo-albuminuria (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, the expression level of miR-155 in DN subjects with microalbuminuria was higher than T2D patients with normo-albuminuria (P\u0026thinsp;=\u0026thinsp;0.021) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The expression level of CTBP1-AS2 in the peripheral blood of T2D without proteinuria was higher compared to DN subjects with macroalbuminuria (P\u0026thinsp;=\u0026thinsp;0.004) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFinally, a binary logistic regression was performed to determine the effects of BMI, FBS, miR-155, CTBP1-AS2, HbA1c and eGFR. The results revealed that miR-155 could serve as potential predictor marker for the development of DN in diabetic patients, along with eGFR (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \n\u003cp\u003e\u003cstrong\u003eTable 3. Logistic regression of significant predictor parameters for prediction of DN\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" colspan=\"2\" valign=\"top\" style=\"width: 8.7508%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 8.2601%;\"\u003e\n \u003cp\u003eWald test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 7.524%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 7.3605%;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 18.4829%;\"\u003e\n \u003cp\u003e95% C.I.)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 8.2601%;\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.2229%;\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0667%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 7.6876%;\"\u003e\n \u003cp\u003e1.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 7.524%;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 8.2601%;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 8.1783%;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.0705%;\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0667%;\"\u003e\n \u003cp\u003eFBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 7.6876%;\"\u003e\n \u003cp\u003e.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 7.524%;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 8.2601%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 8.1783%;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.0705%;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003emiR-155\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 7.6876%;\"\u003e\n \u003cp\u003e9.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 7.524%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 8.2601%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 8.1783%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.0705%;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0667%;\"\u003e\n \u003cp\u003eCTBP1‐AS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 7.6876%;\"\u003e\n \u003cp\u003e.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 7.524%;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 8.2601%;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 8.1783%;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.0705%;\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0667%;\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 7.6876%;\"\u003e\n \u003cp\u003e1.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 7.524%;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 8.2601%;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 8.1783%;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.0705%;\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eeGFR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 7.6876%;\"\u003e\n \u003cp\u003e21.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 7.524%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 8.2601%;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 8.1783%;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.0705%;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Diagnostic value of miR-155 and CTBP1-AS2 in DN\u003c/h2\u003e \u003cp\u003eThe ROC curve was applied to evaluate the diagnostic potential of miR-155 and CTBP1-AS2 in DN. An AUC above 0.8 was considered good performance and above 0.9 was considered excellent performance. Our data revealed that miR-155 has good diagnostic performance (AUC greater than 0.8) to discriminate DN from T2D patients and discriminate DN patient with macroalbuminuria from T2D cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Moreover, CTBP1‐AS2 showed poor discrimination with AUC value equal to 0.6 to distinguish the DN from T2D patients (95% CI 0.51\u0026ndash;0.68, sensitivity of 83% and a specificity of 36, %. P\u0026thinsp;=\u0026thinsp;0. 04), and discriminate DN patient with macroalbuminuria from T2D subjects (95% CI 0.58\u0026ndash;0.78, sensitivity of 60% and a specificity of 73%. P\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe diagnostic efficiency of the miR-155 for distinguishing the studied groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-155 expression level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociated\u003c/p\u003e \u003cp\u003ecriterion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2D vs. Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54\u0026ndash;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDN vs. Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u0026ndash;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDN vs. T2D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;4.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e67.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e83.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.72\u0026ndash;0.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2D vs. Microalbuminuria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u0026ndash;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT2D vs. Macroalbuminuria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026le;\u0026thinsp;3.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e80.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e84.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.78\u0026ndash;0.93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacroalbuminuria vs. Microalbuminuria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\u0026ndash;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation of miR-155 and CTBP1-AS2 levels with clinical and anthropometrical parameters\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis was applied to determine the correlation of miR-155 and CTBP1-AS2 and clinical parameters using 2\u003csup\u003e\u0026minus;ΔCt\u003c/sup\u003e values as input data. The results showed that there was a significant positive correlation between the miR-155 level with DBP, TG, TC, SCr and, BUN levels and a negative correlation with HDL-C and eGFR values. Moreover, a significant negative correlation was found between CTBP1‐AS2 and BMI levels (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe correlation of miR-155 and CTBP1-AS2 expression levels with clinical and anthropometrical parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003emiR-155\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCTBP1-AS2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL-C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSCr\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBUN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeGFR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTBP1-AS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiR-155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur findings revealed that the relative expression level of miR-155 is decreased in the T2D group compared to controls. In line with our results, it has been reported that the levels of miR-155 is reduced in T2D subjects compared to the healthy group [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Catanzaro \u003cem\u003eet al.\u003c/em\u003e found a significant decrease in the expression level of miR-155 in T2D patients compared to healthy participants [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Moreover, miR-155 has shown downregulation in obese T2D patients [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Samaloty \u003cem\u003eet al.\u003c/em\u003e found lower levels of miR-155 in the serum of patients with hepatitis C and insulin resistance, compared to hepatitis C patients whom did not have insulin resistance [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Several studies have investigated the role of miR-155 in the pathogenesis of T2D. Lin \u003cem\u003eet al.\u003c/em\u003e reported that miR-155 deficiency causes hyperglycemia, impaired glucose tolerance and insulin resistance in mice. They also revealed that overexpressing of miR-155 enhances glycolysis, as well as the phosphorylation of insulin-stimulated AKT and IRS-1 in liver, adipose tissue or skeletal muscle in diabetic mice [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A recent study demonstrated that blocking a macrophage-derived exosomal miR-155 reduced glucose intolerances in high-fat-diet feeding and T2D db/db mice [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, it has been shown that the overexpression of miR-155 significantly enhanced insulin signaling and glucose uptake in trophoblasts by targeting CEBPB [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies have shown that miR-155 takes a role in the pathogenesis of diabetic complications including DN. The results of our study corroborate previous investigation, demonstrating significantly higher circulation level of miR-155 in DN patients compared to T2D patients and control subjects [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In addition, current study revealed that the expression levels of miR-155 in patients with macroalbuminuria were higher than patients with microalbuminuria and T2D subjects with normo-albuminuria. Consistent with our results, Wang \u003cem\u003eet al\u003c/em\u003e. reported that the expression levels of miR-155 in patients with macroalbuminuria was higher than patients with microalbuminuria. They suggested that higher levels of miR-155 at the time of progression of DN could indicate the potential role of miR-155 in the development of this condition, which might correlate with kidney inflammation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In addition to serum and whole blood samples, some studies suggested that the urinary level of miR-155 could provide a promising novel source of noninvasive biomarkers for DN. In this regard, it has been reported that urinary expression level of miR-155 is higher in patients with DKD and is significantly associated with SCr levels [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A recent study also revealed that T2D patients with albuminuria showed higher urinary levels of miR-155 compared to healthy controls [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, in a study by Donderski \u003cem\u003eet al.\u003c/em\u003e, increase in the relative expression of miR-155 was seen in the urine samples of CKD patients [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccumulating evidence suggests that microinflammation could lead to podocyte dysfunction. It has been reported that the regulation of miR-155 expression, could protect against renal damage in DKD mice. Prieto \u003cem\u003eet al\u003c/em\u003e. demonstrated that miR-155 is over-expressed by hyperglycemia and inflammation in cultured renal cells and kidney samples from diabetic mice. They also revealed that miR-155 inhibitor therapy gradually decreased albuminuria levels over time. Moreover, they showed that mice receiving miR-155 inhibitor presented a lower level of SCr, KIM1 and lipocalin-2 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Another study reported that serum expression level of miR-155 increased in CKD population and its overexpression is positively associated with eGFR levels [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Wang \u003cem\u003eet al\u003c/em\u003e. reported that miR-155 was increased in serum and kidney tissue of DN mice, as well as in cultured podocytes. They reported that inhibition of miR-155 reduced proteinuria and UACR levels, inhibited kidney inflammation by suppressing podocyte foot fusion, and reverse pathological changes in the kidney of DN mice [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. MiR-155 can also be utilized as a therapeutic strategy for DKD, as it takes role in the regulation of the autophagic process in DKD, by stimulating the signaling loop of p53/miR-155/Sirt1[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Due to the critical roles of miR-155 in the incidence and progression of DN, other studies have investigated the therapeutic role of this miRNA in DN. It was previously reported that glucose-lowering drugs might exert their effect by altering the miRNAs expression level [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Zhou \u003cem\u003eet al\u003c/em\u003e. reported that Metformin could stimulate the inflammation and fibrosis in individuals with DKD through TNC/TLR4/NF-κB/miR-155 inflammatory loop [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In addition, Dapagliflozin could inhibit podocyte pyroptosis via the miR-155/HO-1/NLRP3 axis in DM, and thereby prevent pyroptosis in the kidney [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAvailable evidence has indicated the important roles of lncRNAs in the pathophysiology of T2D and its related complications, including development of proteinuria [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Previous study suggests that CTBP1-AS2 could regulate pancreatic beta cell maturation by affecting MafA in human umbilical cord mesenchymal stem cells [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Interestingly, the interaction between lncRNAs and miRNAs could diminish the inhibitory effects of miRNAs on mRNAs, thereby preventing the target gene repression [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In this context, Wang \u003cem\u003eet al\u003c/em\u003e. demonstrated that TBP1-AS2 suppress high glucose-induced cell injury, by inhibiting cell proliferation, oxidative stress, ECM accumulation, and inflammation by the CTBP1-AS2/miR-155/FOXO1 axis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Consistent with Wang \u003cem\u003eet al.\u003c/em\u003e findings, current investigation revealed that the expression levels of CTBP1-AS2 was downregulated in DN patients compared to T2D. However, the expression level of this lncRNA was not significantly different between the control and T2D groups in our study. In contrast with our findings, Omidvar \u003cem\u003eet al\u003c/em\u003e. observed a significant downregulation in the expression level of lncRNA CTBP1‐AS2 in peripheral blood samples of T2D patients compared to controls. Moreover, they have shown that CTBP1‐AS2 expression was negatively correlated with HDL‐C and positively correlated with LDL‐C levels [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, our results revealed that miR-155 level was positively correlated with SBP, TG, TC, SCr, BUN, eGFR and HDL-C values. Huang \u003cem\u003eet al\u003c/em\u003e. revealed that miR-155 levels were positively correlated with SBP, and DBP [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. It has been shown that miR-155 could improve the adverse effects of pregnancy hypertension via the upregulation of FOXO3a [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Nemecz \u003cem\u003eet al\u003c/em\u003e. reported that microvesicles expression of miR-155 is positively associated with cholesterol and negatively with HDL-c levels [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Previous studies also demonstrated a significant negative association between miR-155 expression and insulin, HOMA-IR, and hs-CRP levels in peripheral blood mononuclear cells of obese children [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe most popular traditional clinical biomarkers to assess kidney function and identification of DN, include SCr, eGFR, UACR, and albuminuria detection. Even with the presence of these traditional markers, there are significant obstacles to the timely and precise diagnosis of DN. Recent studies suggest that, non-coding RNAs could act as a novel sensitive and noninvasive diagnostic biomarker for the prediction of DN progress, because of their high stability in body fluids and tissue and cell-specific expression profiles. Accordingly, in the current study we investigated the co-expression levels of miR-155 and CTBP1-AS2 in TD2 and DN patients and control subjects and evaluated the diagnostic potential of these biomarkers. The results revealed that the expression level of miR-155 was significantly reduced in T2D patients compared to healthy participants and DN patients. Indeed, miR-155 level was significantly higher in DN patients with macroalbuminuria compared to DN patients with microalbuminuria and T2D patients with normo-albuminuria. The expression level of CTBP1-AS2 in the peripheral blood of T2D without proteinuria was higher compared to DN subjects with macroalbuminuria. The results of the current study provided evidence that miR-155 and CTBP1-AS2 may represent useful novel diagnostic biomarkers for DN. However, further in vitro and in vivo investigations are needed to confirm our results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical approval:\u003c/h2\u003e \u003cp\u003e The study was approved by the Yasuj University of Medical Sciences Ethics Committee (IR.YUMS.REC.1402.006). Informed consent was obtained from all subjects before enrolling in the study.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflicts of interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was financially supported by a grant (4010045) from the Deputy of Research, Yasuj University of Medical Sciences.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBehnam Alipoor and Danial Gholami planned the studies; Behnam Alipoor analyzed and interpreted all experiments; Arezoo Rahimi and Shekoofeh Nikooei conducted all experiments. Behnam Alipoor, Khatereh Roozbehi and Davood Semirani wrote the manuscript; Arash Arya and Rozina Abasi Larki selected the patients and referred them to the study.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eWe greatly appreciate all volunteers for their participation in the study.\u003c/p\u003e\u003ch2\u003eData Availability:\u003c/h2\u003e \u003cp\u003eAll data generated or analyzed during this study are included in this published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSaeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract. 2019;157:107843.\u003c/li\u003e\n\u003cli\u003eSun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. 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Clin Lab. 2018;64(1):77-84.\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":"Long non-coding RNAs, microRNA, Type 2 diabetes, Diabetic nephropathy, miR-155, CTBP1-AS2","lastPublishedDoi":"10.21203/rs.3.rs-5768406/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5768406/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDiabetic nephropathy (DN) is one of the most frequent complications of diabetes mellitus. Since the early diagnosis of DN is crucial to prevent the progression of the disease towards renal failure, many efforts have been made in recent years to introduce new diagnostic biomarkers. Recent studies suggest that non-coding RNAs could act as a novel diagnostic biomarker for the early detection and prediction of DN progress. Accordingly, in the current study we investigated the expression levels of miR-155 and CTBP1-AS2 in type 2 diabetes (T2D), DN patients and control subjects and evaluated their diagnostic potential for DN.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eA total of 189 age and sex-matched subjects including 65 T2D patients with normo-albuminuria, 61 DN patients who had a history of albuminuria, and 63 control subjects were included in this case-control study. The expression levels of miR-155 and CTBP1-AS2 were determined using QRT-PCR.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results revealed that the expression level of miR-155 was significantly reduced in T2D patients. In addition, miR-155 level was significantly higher in DN patients with macroalbuminuria compared to DN patients with microalbuminuria and T2D patients with normo-albuminuria. The expression level of CTBP1-AS2 in T2D without proteinuria was higher than DN subjects with macroalbuminuria. The results also showed that there was a significant positive correlation between the miR-155 level with DBP, TG, TC, SCr and, BUN levels and a negative correlation with HDL-C and eGFR values.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDeregulation levels of miR-155 and CTBP1-AS2 may represent useful novel diagnostic biomarkers for DN.\u003c/p\u003e","manuscriptTitle":"Circulating Levels of miR-155 and CTBP1-AS2 as a promising biomarker for early detection of diabetic nephropathy ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-08 10:41:52","doi":"10.21203/rs.3.rs-5768406/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":"27282eab-626c-484a-80ef-4ff82fe5bde4","owner":[],"postedDate":"January 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-09T10:54:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-08 10:41:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5768406","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5768406","identity":"rs-5768406","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}