A clinical and in-silico analysis of hsa-miR-21 and Growth Differentiation factor-15 expression in Diabetic Nephropathy

A clinical and in-silico analysis of hsa-miR-21 and Growth Differentiation factor-15 expression in 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 A clinical and in-silico analysis of hsa-miR-21 and Growth Differentiation factor-15 expression in Diabetic Nephropathy Riddhi Girdhar Agarwal, Purvi Purohit, Manoj Khokhar, Anupama Modi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-396068/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), a microvascular complication, is a major cause of end-stage renal disease (ESRD). GDF-15 and hsa-miR-21 are closely associated with endothelial dysfunction and inflammation. Methods: In-silico analysis was used to identify GDF-15 and insulin related protein-protein interaction (PPI) network and a common set of GDF-15 regulating transcription factors. Common targeting miRNA of GDF-15 regulating transcription factors were investigated in miRNet and TargetScan. Further, 30 type 2 DN patients and 30 healthy controls were included for clinical chemistry analysis, to analyze serum GDF-15 levels by ELISA and to evaluate the fold change expression (FCE) of circulating hsa-miR-21 by RT-PCR. Results: In the PPI network of IRS1, IRS2, INSR, IGF1R, INS, AKT1, PPARG, CEBPB, EGR1, TP53, KLF4, ATF3, GDF15, TWIST2, the common nodes between insulin and GDF-15 were identified. MicroRNA-21 was bioinformatically observed to directly target GDF-15 downregulating transcription factors KLF4, TP-53, and CEBPB. Serum GDF-15 was nearly ten (10) folds higher in DN patients (p˂0.0001) as compared to healthy controls. A positive and significant correlation of serum GDF-15 was found with HbA1c, HOMA-IR, serum urea and serum creatinine. The FCE of hsa-miR-21 was 9.18 folds higher in DN patients. Conclusion: Raised serum GDF-15 and circulating hsa-miR-21 can serve as clinically important therapeutic targets and biomarkers of progressive renal disorder. Molecular Biology Biomarkers Diabetic Nephropathy MicroRNA type 2 diabetes mellitus Transforming Growth Factor-β Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Diabetes is a chronic metabolic disease characterized by hyperglycemia caused due to insulin insufficiency in the body. The prevalence of diabetes is highest in China and India, together accounting for nearly 180 million diabetic adults. The data is so alarming that the global prevalence of diabetes is anticipated to exceed 700 million by 2025 [1]. Type 2 Diabetes mellitus (T2DM) is a complex, multifactorial, and mostly preventable disease [2]. It can progress into micro and macro-vascular complications. One such complication isdiabetic nephropathy (DN), mainly characterized by albuminuria (≥300mg/day) and reduced glomerular filtration rate (GFR) [3]. At the molecular level, DN develops as a result of metabolic interactions, which activate intracellular signalling pathways, leading to extracellular matrix (ECM) protein accumulation, vessel permeability alteration, and proteinuria [4]. In DN, the hyper-glycaemic condition is associated with the upregulation of glucose transport-1 that also causes overexpression of transforming growth factor- β (TGF-β) in mesangial tubular cells or infiltrating renal cells [5]. This over-expressed TGF-β has been recognised as a key determinant of progressive renal function loss [6]. Furthermore, along with inducing the expression of TGF-β in mesangial cells, these factors also accelerate mesangial cell hypertrophy and ECM accumulation; as a consequence, GFR decreases and engenders chronic renal failure [6]. GDF-15, a member of the TGF- β superfamily, has been found to be associated with T2DM. Further, an elevated serum level of GDF-15 has been reported as a clinically valuable marker for predicting progression of diabetic kidney disease (DKD) and for individual risk stratification in DN patients, with normo-albuminuria and microalbuminuria [7]. Moreover, higher levels of GDF-15 corresponds to faster deterioration of kidney function and morbidity in DN patients [8]. Hence, it is important to validate GDF-15 as an individual biomarker for the progression of DN in humans. Several findings have also highlighted that plasma GDF-15 level increases with the Mogensen stage, and is significantly correlated with microalbumin (mAlb) and eGFR, suggesting its value in early diagnosis, evaluation and prediction of the outcomes of DN [9]. A series of miRNAs, a class of short non-coding RNAs that regulate gene ex­pression at the post-transcriptional level by binding to the 3’-untranslated regions of their target mRNAs [10], play a critical role in the regulation of many cellular and physiological activities [11]. These miRNAs either get up or downregulated in different kidney diseases [12], and can therefore serve as potential biomarkers in targeted therapy protocols [13]. In a study done on β cells, miR-21 inhibition was found to maintain the sugar level in patients with diabetic complications [5]. The expression of miR-21 is also known to be regulated by TGF-β1 and has been broadly studied in cancer biology because of its antiapoptotic effects and critical role in tumorigenesis [14]. Further, in human kidney biopsies, miR-21 was reported to increase phosphatase and tensin homolog (PTEN), and miR-21 antagonism resulted in decreased interstitial fibrosis, podocyte loss, inflammatory gene expression, albuminuria, macrophage infiltration and mesangial cell expansion [15]. In our previous review article, we have also highlighted the role of PTEN in the regulation of renal tubulointerstitial fibrosis and epithelial-mesenchymal transition (EMT) [16]. miR21 levels have also been reported to elevate with increasing disease pathology in human renal biopsies and in experimental mouse models of early/late DN [17]. However, miR-21 is regulating which member of TGF-β family, has not been investigated earlier in diabetes. Therefore, based upon previous literature we anticipated a common link between miR-21 and TGF--β family member GDF-15, therefore we investigated their interaction using bioinformatics and computational analysis, that has emerged as an efficient tool to study pathways involved in differentially expressed genes, protein-protein interaction (PPI) networks and to predict miRNA-mRNA targets [18]. Further, the expression level of serum GDF-15 was investigated in T2DM DN patients and healthy controls and was correlated with various biochemical and anthropometric parameters of the study population. Additionally, we also analysed the levels of circulating hsa-miR-21 and its correlation with serum GDF-15. Our results support the potential of a raised serum GDF-15 and circulating miR-21 to serve as therapeutic targets of progressive renal deterioration. 2. Methods 2.1 In silico investigation of the regulation of GDF-15 and its interaction with Insulin 2.1.1 Protein-Protein interaction (PPI) network The interconnecting networks between GDF-15 and its nearest neighbours were investigated in diabetes using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) [19]. The protein IDs of insulin and GDF-15 were submitted to STRING to obtain functional interpretations of their targets in Homo sapiens (Figure 1). Kyoto encyclopedia of genes and genomes (KEGG) components were analysed to obtain pathway enrichment by STRING statistics taking confidence of 0.4, and the significantly enriched pathways were identified based on a value of P < 0.05. Plug-in of Cytoscape tool was employed [20] for cluster formation and molecular complex detection (MCODE). 2.1.2 Database Search of regulatory transcription factors of GDF 15 TRRUST transcription factors 2019, TRANSFAC and JASPAR PWMs [21], ENCODE and ChEA Consensus TFs from ChIP-X ( https://genome.ucsc.edu/ENCODE/ ), miRNet Version: 2 [22], and Enrichr [23] were used to identify GDF-15 regulating transcription factors. The common transcription factors were selected by Venn diagram. 2.1.3 Functional annotation and enrichment analyses The functional features of all the genes related to T2DM involved in PPI networks were analysed by two different web servers DAVID (Database for Annotation, Visualization and Integrated Discovery) [24] and STRING Version 11.0 [19]. DAVID was used for functional enrichment such as KEGG pathways and significance of gene ontology (GO) to assess the biological process, cellular components and molecular functions. Significantly enriched GO terms and KEGG pathways were selected based on p<0.05 and further, a false discovery rate of <0.05 to avoid false positives. 2.1.4 Construction of MicroRNA-mRNA target regulatory network Common transcription factors were utilized to construct PPI networks, and miRNet Version: 2, a well-established miRNA target prediction database was used to predict the target miRNA of genes involved in the PPI networks [22, 25]. Considering the literature search and predicted miRs against targeted transcription factors, miR-21 was scrutinized. Further, Targetscan (http: www.targetscan.org) was used to predict the target mRNA according to miR-21 seed region. A heatmap of genes and proteins involved in PPI Networks, representing their expression in various tissues, was obtained using FunRich (Functional Enrichment analysis tool) [26]. 2.2 Study population Thirty DN patients visiting the outpatient department (OPD) of the Department of Nephrology, All India Institute of Medical Sciences (AIIMS) Jodhpur, and thirty healthy volunteers working as healthcare professionals were recruited in the study. The inclusion criteria for patients was HbA1c > 6.5% and urinary mAlb >300 mg/dl. Inclusion criteria for controls were non-diabetic subjects without any inflammatory disease or history of renal disorder. Since GDF-15 gets upregulated due to damaged vascular endothelium, patients diagnosed with type 1 diabetes mellitus (T1DM), hypothyroidism, coronary artery disease, chronic obstructive pulmonary disease, malignancies, hepatic disorders, rheumatoid arthritis and thalassemia were excluded. In hemochromatosis, GDF-15 is secreted by erythroblasts [27], and in pregnancy, GDF-15 levels are raised by the placenta, therefore these patients were also excluded. Venous blood samples of the study population were collected under the ethical approval of the Institutional ethics committee (IEC) of AIIMS, Jodhpur to obtain whole blood, serum and plasma. Informed consent was taken from all the participants. Urine samples were collected from the study subjects for the estimation of urinary mAlb. 2.3 Study protocol On the same day of blood sampling and handing out of 24hr urine samples, anthropometric measurements of all the participants were recorded. Height (by a stadiometer), weight (by a weighing machine), and waist-and hip circumference (by non-elastic measuring tape) were measured. These measurements were then used to calculate body mass index (BMI) and waist-hip ratio (WHR). Systolic blood pressure (SBP) and diastolic blood pressure (DBP) was recorded using a sphygmomanometer. 2.4 Biochemical analysis Enzymatic colour test of human serum was performed on Beckman Coulter AU analyser using End-point method, for the quantitative determination of Fasting Blood Sugar (FBS), Low-Density Lipoprotein (LDL), High-Density Lipoprotein (HDL), cholesterol, urea and creatinine.. The Serum electrolytes sodium, potassium and chloride were analysed on the Beckman Coulter AU analyser using the Ion-Selective Electrode (ISE) principle. HbA1c was measured by latex agglutination inhibition assay. Serum insulin was analysed using the fully automated chemiluminescence analyser Diasorin Liasion. Urinary mAlb was estimated by immunoprecipitation method. The GFR of the study population was calculated using the MDRD formula GFR (mL/min/1.73 m²) = 175 × (serum creatinine in mg/dl) -1.154 × (Age) -0.203 × (0.742 in females). 2.5 Molecular Analysis 2.5.1 Analysis of serum GDF-15 Thermo Scientific Pierce Human GDF-15 ELISA kit was used to quantify human GDF-15 in serum, according to manufacturer’s instructions. The concentration of the analyte (GDF-15) was used to obtain a standard curve. The concentration of serum GDF-15 in the samples was analysed using the standard curve by BioTek Gen5 Data analysis software. 2.5.2 Analysis of circulating hsa-miR-21 Whole blood samples of the study population were used to isolate total RNA using RBC lysis buffer in a ratio of 1:3 for Blood: RBC lysis buffer. The sample was centrifuged at 1300 rpm at 4˚C for 15 minutes and 1 ml PBS was added to dissolve the pellet, followed by centrifugation at 3000 rpm for 2 minutes. Trizol was added to the pellet (in a ratio of 10:1 for blood: trizol) to obtain a homogenous solution. Isopropanol was used to remove the aqueous layer (in a ratio of 1:1 for aqueous layer: isopropanol) and the contents were briefly vortexed. The pellet was then dissolved in 1 mL of 1% ethanol and centrifuged at 12000 x g for 5 mins. 20 µL of DEPC treated water was added after air-drying the pellet followed by incubation at 65˚C for 3-4 minutes. The sample was stored at -80˚C till further processing. Thermo-scientific NanoDrop One c was used to quantify the extracted total human RNA. The absorption maxima of RNA lies at 260 nm, and DEPC treated water was used as blank. An absorbance of ~2.0 at a ratio of 260 nm and 280 nm was accepted as “pure” for RNA. 2.5.3 Reverse transcription PCR c-DNA conversion of RNA extracted, was done by Reverse Transcription PCR (RT-PCR) using Eppendorf nexus gradient Master cycler and Qiagen miScript Reverse Transcription kit as per the manufacturer’s instructions. 2.5.4 Quantification of circulating hsa-miR-21 expression by Real-Time PCR BioRad CFX96 Real-Time System and BioRad CFX Manager Software were used for the real time expression analysis of miR21. The real time expression of miR-21 in human whole blood samples was observed using Qiagen miScript SYBR Green PCR Kit along with miScript miRNA PCR Arrays as per the manufacturer’s instructions. The fold change expression (FCE) of miR-21 in comparison to RNU6 (internal control/housekeeping gene) was evaluated using double delta/∆∆ CT method for the cases and controls. 2.6 Statistical Analysis IBM SPSS Statistics 21.0 for Windows (SPSS Inc, Chicago, IL) was used to analyse the skewness and kurtosis of various study parameters. Mann-Whitney U test was used to test the research hypothesis and Spearman correlation and logistic regression analysis were used to evaluate the relation between variables. Microsoft Excel software was used for graphical representation of the results. 3. Results 3.1 PPI network construction and analysis Using STRING, GDF15 and insulin were found to interact with IRS1 (Insulin receptor substrate 1), IRS2, INSR (insulin receptor), IGF1R (insulin-like growth factor 1 receptor), AKT1 (alpha serine/threonine-protein kinase), PPARG (Peroxisome proliferator-activated receptor-gamma), CEBPB (CCAAT/enhancer-binding protein beta), EGR1 (early growth response protein 1), TP53, KLF4 (Krueppel-like factor 4), ATF3 (cyclic AMP-dependent transcription factor ATF-3), and TWIST2 ( Twist-related protein 2). Cytoscape software was used to represent the established PPI network (Figure 3). 3.2 Cluster formation by MCODE One of the significant clusters within the PPI included INS, IGF1R, AKT1, IRS1, INSR, IRS2 and hsa-miR-21, which was selected using MCODE Score = 6.000, node- 06, edges- 15. These genes are mainly associated with the TGF-β receptor signalling and mammalian target of rapamycin (mTOR) pathway. 3.3 GO enrichment analysis Classification of genes, involved in PPI network, into the following three classes, was used for GO analysis (Figure 2): Cellular component class : the genes were enriched for the cellular components like INSR complex, protein-containing complex, nuclear transcription factor complex, receptor complex and plasma membrane protein complex. Biological Process (BP) Class: The proteins were enriched in the cellular response to organonitrogen, peptide, response to insulin, regulation of transferase, kinase, phosphorylase activity of proteins, signal transduction, cellular response to growth factor, and signalling pathways of insulin receptor, phosphatidylinositol 3-kinase (PI3K), IGFR and mitogen-activated protein kinase (MAPK). Molecular Function (MF) class: The proteins were enriched in the activation of INSR binding, PI3K binding, IGFR binding, RNA polymerase II regulatory region, DNA-binding transcription activator activity, RNA polymerase II-specific insulin binding, IRS binding, PI3K activity, transmembrane receptor protein tyrosine kinase activity, phosphatidylinositol-4,5-bisphosphate 3-kinase activity. 3.4 KEGG pathway enrichment analysis PPI networks of genes were enriched (Figure 2 C) for adenosine monophosphate-activated protein kinase (AMPK) signalling pathway, forkhead box (FoxO) signalling pathway, insulin resistance, insulin signalling pathway, T2DM, mTOR signalling pathway, PI3K-AKT signalling pathway, MAPK signalling pathway, Rap1 signalling pathway, Ras signalling pathway, adipocytokine signalling pathway, adherens junction, pancreatic cancer, tyrosine kinase inhibitor resistance, AGE-RAGE signalling pathway in diabetic complications, and TNF signalling pathway. 3.5 Prediction of transcription factor and construction of miRNA regulatory networks The target miRNAs of genes of interests were predicted by miRNet Version: 2 (an integrated platform linking miRNAs, targets & functions) and miRDB. The regulatory relationship of identified miR-21 with GDF-15 and its transcription factor was investigated by miRNet and miRDB. Cytoscape was used to construct networks based on the correlation between the target genes and their regulating transcriptome. The identified genes were ATF3, CEBPB, EGR1, KLF4, PPARG, TP53 and TWIST2, which can regulate the levels of GDF-15. In the metabolic regulatory process of T2DM and insulin resistance, IRS1, IRS2, INSR, IGF1R, INS, AKT1, PPARG, CEBPB, EGR1, TP53, KLF4, ATF3, GDF15, TWIST2 served as important nodes in the PPI networks. MicroRNA-21 was observed as a multi-targeting miRNA as it interacted with KLF4, TP-53, and CEBPB transcription factors, which were also linked with GDF-15. Additionally, hsa-miR-21 directly targeted IGF1R. Further, the interactions also suggested a correlation between GDF-15 and miR-21, supposedly through the common transcription factors. 3.6 Demographic characteristics of the study population In the present study, 30 DN patients and 30 healthy controls were included. Among the patients, 70% were males and 30% were females (Table 1), which reflects a higher prevalence of DN in males in the study population. This higher frequency of DN in males is predominantly due to the cross-sectional nature of the study design and does not indicate a gender-based risk of males towards DN. The age distribution of DN (Figure 2) revealed a near gaussian distribution of DN in the cases (40-80 years). The prevalence of DN in central age group (50-70 years) can be due to the fact that DN is a late onset disease, and usually advances with long standing diabetes, therefore a smaller number of patients belonged to the other age groups. 3.7 Anthropometric characteristics of the study population A highly significant difference (p = 0.002) in the WHR was found between healthy controls and DN patients, wherein DN patients were found to be overweight (Table 1). DN patients also had a significantly high systolic and diastolic BP as compared to controls (Table 1). High BP of DN patients can be reasoned for hypertension (a common complication of diabetes) and a progressive cardiac myopathy condition, which is another micro-vascular complication associated with T2DM. 3.8 Clinical characteristics of the study population The levels of FBS (184.49 ± 49.05 mg/dL), insulin (27.02 ± 28.49 µIU/mL), HbA1c levels (8.25 ± 1.70%) and HOMA-IR (12.19 ± 2.81) were found to be significantly higher in DN patients than healthy controls (Table 2). The values of GFR (96.19 ± 14.16 ml/min/1.73m 2 ) in the DN patients, along with highly elevated values of urea (79.31 ± 37.69 mg/dL), creatinine (2.729 ± 1.99 mg/dL) and urinary microalbumin (227.29 ± 221.21 mg/24hrs)) in the cases confirmed a comprised renal state in the DM patients. Further, in the values of lipid profile (Cholesterol + TGL + LDL + HDL), (Table1),TGL (180.26 ± 92.23 mg/dL) level was borderline high among the DN patients. Among electrolytes (Na, K and Cl), only the value of Cl was lower than the normal (101-109 mmol/L) in DN patients. An extremely significant difference was found in the values of FBS, insulin, HOMA-IR, urea, creatinine, Na, Cl, TGL, urinary microalbumin and GDF-15, with p ≤ 0.001 between DN patients and healthy controls. A highly significant difference in the value of HbA1c (p ≤ 0.05) was also found between the groups in the study population. 3.9 Levels of GDF-15 assessed by ELISA and its correlation The median value of serum GDF-15 was found to be nearly 10 folds higher in DN patients as compared to healthy controls (p˂0.0001). The levels of GDF-15 also increased with advancement of stages of chronic kidney disease (CKD) (Figure 4C). A positive and highly significant correlation of serum GDF-15 was found with HbA1c (r = 0.747**, p = 0.000), HOMA-IR (r = 0.396*, p = 0.004), serum urea (r = 0.756*, p = 0.000) and serum creatinine (r = 0.727*, p = 0.000) (Table 2), which are the most widely accepted parameters to diagnose diabetic condition and renal disorders . The Spearman’s correlation analysis of only DN cases (Table 2), revealed a positive correlation of GDF-15 with serum urea (r = 0.505*, p = 0.004) and creatinine (r = 0. 653*, p = 0.000), . 3.10 Analysis of miR-21 after quantification by RT-PCR The FCE of circulating hsa-miR-21, assessed using RT-PCR in the study population, was found to be 9.18 folds higher in DN patients as compared to healthy controls (Table 3). Additionally, serum GDF-15 had a positive correlation withcirculating hsa-miR-21. 4. Discussion At the molecular level, the factors that contribute to DN such as ECM protein accumulation, vessel permeability alteration, and proteinuria, develop as a result of interactions between various metabolic factors, which activate intracellular signalling pathways responsible for triggering inflammatory mediators and release of growth factors [5] like TGF-β1. The levels of TGF-β1 have been reported to increase under diabetic conditions in renal cells, including mesangial cells , hence TGF-β has been recognised as a key determinant of progressive renal function loss [6]. GDF-15, a member of the TGF-β superfamily, has been recently reported as an early marker of IR and mitochondrial dysfunction. An elevated serum level of GDF-15 was found to be associated with T2DM [7]. Several findings have also highlighted that plasma GDF-15 level increases with the Mogensen stage as an independent risk factor for increased microalbuminuria in DN patients. GDF-15 is also significantly correlated with mAlb and eGFR, suggesting its value in early diagnosis, evaluation and prediction of the outcomes of DN. Furthermore, according to our in-silico findings, and previous literature, TP53 and its family members, p63 and p73 have been reported as transcriptional regulators of GDF15, as its promoter region contains two p53-type response elements, RE1 and RE2, wherein RE2 confers p53-specific transactivation [28]. KLF4 acts as a transcriptional repressor of p53 [29]. In ovarian cancer cells, the expression of CEBPB was positively correlated with GDF15 expression and CEBPB was identified to bind with GDF15 gene promoter through luciferase reporter assay. Thus, CEBPB caused a positive regulation of GDF15 expression in cancer cells through epigenetic modification [30]. In another study, the CpG locus in MIR21 promoter was observed to be a conserved binding site of transcription factors CEBPB, MEIS3, and TEAD4, which were co-expressed with miR-21 in tumors [31]. Therefore, KLF4, CEBPB and miR-21 jointly augment EMT via the AKT/ERK1/2 pathway by upregulating the levels of GDF-15 [32]. EMT plays an important role in renal interstitial fibrosis (RIF) with DN [33]. Our in-silico findings have also suggested the association of insulin with GDF-15 and IGF1R, which implies the role of increased GDF-15 in insulin resistance along with endothelial dysfunction, metabolic derangement and inflammation as proposed in vascular injuries and cardiovascular complications elsewhere [34]. In another study, p53 dependent expression of GDF-15 has been suggested as the link between obesity and insulin resistance, wherein activation of p53 in adipose tissue led to increased production of proinflammatory cytokines, subsequently leading to insulin resistance, and diabetes [34, 35]. Therefore, the in-silico findings of this study reiterate the connection of GDF-15 with insulin in diabetes, and along with the previous literature highlights the likelihood of GDF-15 mediated microvascular complications in DN. In a previous finding, diabetic dyslipidaemia has been reported to promote progression of DN, with few studies suggesting that TG-rich lipoprotein particles predominantly containing apolipoproteins (apos) E, C and B serve as major promoters of DN. Further, in DN patients, plasma TG levels were also suggested to be high, due to the reduced activity of hepatic lipase (HL) and lipoprotein lipase (LPL), which hydrolyze TG [35]. This is in accordance with our finding of elevated TGL levels in the patient population. The reduced serum Cl levels observed in the current study may be due to elevated serum ketoacids as observed by Yasuda K et al., wherein they have reported that in diabetics, hypochloremia can result due to elevated serum ketoacids and the ratio of Cl/ Na in DN patients was found to be significantly lower in their study as well [36]. In our molecular analysis, we found the serum levels of GDF-15 (Table 1) to be nearly ten (10) folds higher in DN patients (median 5507 pg/ml) as compared to healthy controls (567 pg/ml), (p<0.0001) (Figure 4A). Further, GDF-15 levels were also observed to increase with stages of DN. The highest level corresponded to Stage V (ERSD), thus confirming increasing GDF-15 to be associated with advancing stages of DN. Spearman correlation analysis of the study population (Table 2) showed a positive correlation of serum GDF-15 with HbA1c (r = 0.747, p = 0.000), HOMA-IR (r = 0.396, p = 0.004), serum urea (r = 0.756, p = 0.000) and serum creatinine (r = 0.727, p = 0.000). Hence, serum GDF-15 levels can serve as a novel marker in indicating progression of DN and a deteriorating glycemic control in these patients. Further, the Spearman’s correlation analysis of only DN cases revealed a positive correlation of GDF-15 with serum urea (r = 0.505, p = 0.004) and creatinine (r = 0. 653, p = 0.000), therefore, in DN serum GDF-15 maybe a potential marker for deteriorating renal function. The levels of miR-21 have also been reported to be higher in human renal biopsies and in experimental mouse models of early/late DN and non-DN, further, renal miR-21 knockdown showed downregulation of TGF-β1 signalling in a mouse model of T2DM [37]. The increased miR-21 expression has also been identified in the kidney biopsy samples of renal transplant patients with fibrotic kidney disease and in the urine of fibrotic patients with IgA nephropathy [38]. Since circulating miRNAs can serve as a non-invasive marker of any disease condition, Zununi and his colleagues assessed plasma samples for circulating miRNAs and observed a correlation between expression levels of miR-21 in plasma and serum creatinine (r =-0.432, P =0.03) in renal transplant patients [39]. In another study, circulating miR-21-5p has been reported to be closely associated with endothelial dysfunction and inflammation, which are two characteristic developmentsof T2DM progression. However, there are no reports on the FCE of circulating miR-21 in relation to DN. In the present study, we observed FCE of circulating miR-21 to increase up to 9.18 folds in DN patients in comparison to healthy controls (Figure 4B). The levels of miR-21 were also found to be positively correlated with the levels of GDF-15, although the association was non-significant (which can be due to the small sample size). This positive correlation indicates the possibility of mechanistic interaction between miR-21 and GDF-15. However, GDF-15 upregulation can be reflected in any vascular endothelial damage, therefore, it can appear in diverse tissues damage responses apart from nephropathy, thus enhancing the levels of circulating GDF-15 [7]. Further, in a study done by Weronica et al., to investigate the role of GDF-15 in cardiovascular disease, GDF-15 and DNA methylation level of miR-21 promoter was found to be inversely correlated. They suggested that raised levels of GDF-15 possibly lead to demethylation of miR21 promoter, increasing its expression in cardio vascular disease [40]. However, this arena is still unexplored in DN, and a similar mechanism may be functional in DN. 5. Conclusion This study, which is the first to the best of our knowledge, has explored the role of circulating hsa-miR-21 in DN patients in relation to GDF-15, and supports the potential of raised GDF-15 and circulating miR-21 to serve as two important signature and novel biomarkers of progressive renal deterioration. Our in silico analysis also points to a possible mechanistic regulation of GDF-15 by miR-21. Further research on the underlying mechanisms of GDF-15 and miR-21 can provide insights into an un-investigated, yet important GDF-15 and miR-21 dependent pathway, that contributes to pathogenesis of DN. Abbreviations AKT: alpha serine/threonine-protein kinase; BMI: Body Mass Index; ; CEBPB: CCAAT/enhancer-binding protein beta; CT: Cycle threshold; DAVID: Database for Annotation, Visualization and Integrated Discovery; DN: Diabetic Nephropathy; DBP: Diastolic Blood Pressure; ECM: Extra cellular Matrix; EGR1: Early growth response protein 1; ELISA: Enzyme Linked Immunosorbent Assay; EMT: Epithelial-mesenchymal transition; ESRD: End stage renal disease; FCE: Fold Change Expression; FunRich: Functional Enrichment analysis tool; GDF-15: Growth Differentiation Factor -15; GFR: Glomerular filtration Rate; GO: Gene Ontology; HOMA-IR: Homeostatic model assessment – Insulin Resistance; INSR: Insulin Receptor; IRS1: Insulin receptor substrate 1; ISE: Ion Selective Electrode; KEGG: Kyoto Encyclopaedia of gene and genomes; KFT: Kidney Function Test; KLF4: Krueppel-like factor 4; MCODE: molecular complex detection; miRNA: microRNA; mTOR: mammalian target of rapamycin; PI3K: phosphatidylinositol 3-kinase; PPI: Protein- Protein Interactions; PTEN: Phosphatase and tensin homolog; SBP: Systolic Blood Pressure; TGF-β1: Transforming growth factor β1; T2DM: Type 2 Diabetes Mellitus; TP53: Cellular tumour antigen p53; TWIST2: Twist-related protein 2; WHR: Waist Hip Ratio. Declarations Funding: This study was funded and supported by All India Institute of Medical Sciences, Jodhpur, India Conflict of interests: Authors have no conflicts to report. Data availability statement: The data will be provided when on a reasonable request. Ethics approval: The study was approved by AIIMS Institutional Ethics committee prior to commencement. Consent to participate All participants were asked for an informed consent to prior to participation, with a free will to withdraw if they wished to. Authors contribution: Riddhi Girdhar Agarwal- Manuscript writing, Literature review, data analysis Manoj Khokhar- Manuscript writing, Literature review, data analysis Purvi Purohit- Study design, conceptualised, Manuscript writing, Literature review, data analysis Anupama Modi - Manuscript writing, Literature review, data analysis Nitin Kumar Bajpai – Provided samples, manuscript editing Gopal Krishna Bohra- Provided samples, manuscript editing Mahendra Kumar Garg- Provided samples, manuscript editing Praveen Sharma- manuscript editing ORCID Riddhi Girdhar Agarwal: 0000-0002-3052-4492 Manoj Khokhar: 0000-0001-7106-8545 Dr. Purvi Purohit: 0000-0001-8559-2911 Anupama Modi: 0000-0002-6488-4701 M K Garg: 0000-0001-9938-5024 Praveen Sharma: 0000-0002-8324-737X References (2016) Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants. Lancet 387:1513–1530. https://doi.org/10.1016/S0140-6736(16)00618-8 Li L, Cheng W-Y, Glicksberg BS, et al (2015) Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med 7:311ra174. https://doi.org/10.1126/scitranslmed.aaa9364 Bakris GL (2011) Recognition, pathogenesis, and treatment of different stages of nephropathy in patients with type 2 diabetes mellitus. Mayo Clin Proc 86:444–456. https://doi.org/10.4065/mcp.2010.0713 Johnson RJ, Nakagawa T, Jalal D, et al (2013) Uric acid and chronic kidney disease: which is chasing which? Nephrol Dial Transplant 28:2221–2228. https://doi.org/10.1093/ndt/gft029 Chan JA, Krichevsky AM, Kosik KS (2005) MicroRNA-21 is an antiapoptotic factor in human glioblastoma cells. Cancer Res 65:6029–6033. https://doi.org/10.1158/0008-5472.CAN-05-0137 Chang AS, Hathaway CK, Smithies O, Kakoki M (2016) Transforming growth factor-β1 and diabetic nephropathy. Am J Physiol Renal Physiol 310:F689–F696. https://doi.org/10.1152/ajprenal.00502.2015 Hong JH, Chung HK, Park HY, et al (2014) GDF15 Is a Novel Biomarker for Impaired Fasting Glucose. Diabetes Metab J 38:472–479. https://doi.org/10.4093/dmj.2014.38.6.472 Richards NT, Darby S, Howie AJ, et al (1994) Knowledge of renal histology alters patient management in over 40% of cases. Nephrol Dial Transplant 9:1255–1259 Li H, Gao F, Xue Y, Qian Y (2014) [Value of plasma growth differentiation factor-15 in diagnosis and evaluation of type 2 diabetic nephropathy]. Nan Fang Yi Ke Da Xue Xue Bao 34:387–390 Barutta F, Bellini S, Mastrocola R, et al (2018) MicroRNA and Microvascular Complications of Diabetes. Int J Endocrinol 2018:6890501. https://doi.org/10.1155/2018/6890501 Feng J, Xing W, Xie L (2016) Regulatory Roles of MicroRNAs in Diabetes. International Journal of Molecular Sciences 17:1729. https://doi.org/10.3390/ijms17101729 Candia P de, Spinetti G, Specchia C, et al (2017) A unique plasma microRNA profile defines type 2 diabetes progression. PLOS ONE 12:e0188980. https://doi.org/10.1371/journal.pone.0188980 Agarwal RG, Sharma P, Nyati KK (2019) microRNAs in Mycobacterial Infection: Modulation of Host Immune Response and Apoptotic Pathways. Immune Netw 19:e30. https://doi.org/10.4110/in.2019.19.e30 Bakirtzi K, Hatziapostolou M, Karagiannides I, et al (2011) Neurotensin Signaling Activates MicroRNAs-21 and -155 and Akt, Promotes Tumor Growth in Mice, and Is Increased in Human Colon Tumors. Gastroenterology 141:1749-1761.e1. https://doi.org/10.1053/j.gastro.2011.07.038 Shannon P, Markiel A, Ozier O, et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. https://doi.org/10.1101/gr.1239303 Khokhar M, Roy D, Modi A, et al (2020) Perspectives on the role of PTEN in diabetic nephropathy: an update. Crit Rev Clin Lab Sci 1–14. https://doi.org/10.1080/10408363.2020.1746735 Zhong X, Chung ACK, Chen HY, et al (2013) miR-21 is a key therapeutic target for renal injury in a mouse model of type 2 diabetes. Diabetologia 56:663–674. https://doi.org/10.1007/s00125-012-2804-x Ding L, Fan L, Xu X, et al (2019) Identification of core genes and pathways in type 2 diabetes mellitus by bioinformatics analysis. Mol Med Rep 20:2597–2608. https://doi.org/10.3892/mmr.2019.10522 Szklarczyk D, Gable AL, Lyon D, et al (2019) STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research 47:D607–D613. https://doi.org/10.1093/nar/gky1131 Otasek D, Morris JH, Bouças J, et al (2019) Cytoscape Automation: empowering workflow-based network analysis. Genome Biol 20:185. https://doi.org/10.1186/s13059-019-1758-4 Fornes O, Castro-Mondragon JA, Khan A, et al (2019) JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Research gkz1001. https://doi.org/10.1093/nar/gkz1001 Fan Y, Xia J (2018) miRNet—Functional Analysis and Visual Exploration of miRNA–Target Interactions in a Network Context. In: von Stechow L, Santos Delgado A (eds) Computational Cell Biology. Springer New York, New York, NY, pp 215–233 Kuleshov MV, Jones MR, Rouillard AD, et al (2016) Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 44:W90–W97. https://doi.org/10.1093/nar/gkw377 Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 4:44–57. https://doi.org/10.1038/nprot.2008.211 Fan Y, Habib M, Xia J (2018) Xeno-miRNet: a comprehensive database and analytics platform to explore xeno-miRNAs and their potential targets. PeerJ 6:e5650. https://doi.org/10.7717/peerj.5650 Pathan M, Keerthikumar S, Chisanga D, et al (2017) A novel community driven software for functional enrichment analysis of extracellular vesicles data. Journal of Extracellular Vesicles 6:1321455. https://doi.org/10.1080/20013078.2017.1321455 Tanno T, Bhanu NV, Oneal PA, et al (2007) High levels of GDF15 in thalassemia suppress expression of the iron regulatory protein hepcidin. Nat Med 13:1096–1101. https://doi.org/10.1038/nm1629 Osada M, Park HL, Park MJ, et al (2007) A p53-type response element in the GDF15 promoter confers high specificity for p53 activation. Biochemical and Biophysical Research Communications 354:913–918. https://doi.org/10.1016/j.bbrc.2007.01.089 Rowland BD, Bernards R, Peeper DS (2005) The KLF4 tumour suppressor is a transcriptional repressor of p53 that acts as a context-dependent oncogene. Nat Cell Biol 7:1074–1082. https://doi.org/10.1038/ncb1314 Guo L-L, Wang S-F (2019) Downregulated Long Noncoding RNA GAS5 Fails to Function as Decoy of CEBPB, Resulting in Increased GDF15 Expression and Rapid Ovarian Cancer Cell Proliferation. Cancer Biother Radiopharm 34:537–546. https://doi.org/10.1089/cbr.2019.2889 Lu J, Tan T, Zhu L, et al (2020) Hypomethylation Causes MIR21 Overexpression in Tumors. Mol Ther Oncolytics 18:47–57. https://doi.org/10.1016/j.omto.2020.05.011 Liu C-H, Huang Q, Jin Z-Y, et al (2017) miR-21 and KLF4 jointly augment epithelial-mesenchymal transition via the Akt/ERK1/2 pathway. International Journal of Oncology 50:1109–1115. https://doi.org/10.3892/ijo.2017.3876 Roy D, Modi A, Khokar M, et al (2020) MicroRNA 21 emerging role in diabetic complications: a critical update. CDR 16:. https://doi.org/10.2174/1573399816666200503035035 Kempf T, Guba-Quint A, Torgerson J, et al (2012) Growth differentiation factor 15 predicts future insulin resistance and impaired glucose control in obese nondiabetic individuals: results from the XENDOS trial. European Journal of Endocrinology 167:671–678. https://doi.org/10.1530/EJE-12-0466 Hirano T, Oi K, Sakai S, et al (1998) High prevalence of small dense LDL in diabetic nephropathy is not directly associated with kidney damage: a possible role of postprandial lipemia. Atherosclerosis 141:77–85. https://doi.org/10.1016/s0021-9150(98)00150-6 Yasuda K, Hayashi M, Murayama M, Yamakita N (2016) Acidosis-Induced Hypochloremic Alkalosis in Diabetic Ketoacidosis Confirmed by The Modified Base Excess Method. J Clin Endocrinol Metab 101:2390–2395. https://doi.org/10.1210/jc.2016-1324 Kölling M, Kaucsar T, Schauerte C, et al (2017) Therapeutic miR-21 Silencing Ameliorates Diabetic Kidney Disease in Mice. Mol Ther 25:165–180. https://doi.org/10.1016/j.ymthe.2016.08.001 Hennino M-F, Buob D, Van der Hauwaert C, et al (2016) miR-21-5p renal expression is associated with fibrosis and renal survival in patients with IgA nephropathy. Sci Rep 6:. https://doi.org/10.1038/srep27209 Zununi Vahed S, Poursadegh Zonouzi A, Ghanbarian H, et al (2017) Differential expression of circulating miR-21, miR-142-3p and miR-155 in renal transplant recipients with impaired graft function. Int Urol Nephrol 49:1681–1689. https://doi.org/10.1007/s11255-017-1602-2 Ek WE, Hedman ÅK, Enroth S, et al (2016) Genome-wide DNA methylation study identifies genes associated with the cardiovascular biomarker GDF-15. Hum Mol Genet 25:817–827. https://doi.org/10.1093/hmg/ddv511 Tables Table 1. Comparison of different characteristics between the study population Parameters Healthy controls (n=30) DN Patients (n=30) Mann-Whitney U Wilcoxon W Z P value Anthropometric characteristics Age 32.83 ± 8.7 60.96 ± 11.14 48.50 513.50 -5.9 0.00* Gender (Male %) 60 73.33 - - - - BMI (kg/m 2 ) 24.29 ± 2.03 26.9 ± 5.7 289 759 -2.38 0.17 WHR 0.89 ± 0.03 0.94 ± 0.06 244 699 -3.05 0.002* Clinical characteristics Systolic 120.43 ± 3.7 150.00 ± 29.59 153 618 -4.4 0.000** Diastolic 74.87 ± 2.93 83.33 ± 17.29 307.5 772.5 -2.11 0.034 Biochemical profile FBS (mg/dl) 94.8 ± 6.68 184.49 ± 49.05 19 484 -6.37 0.000** 2Insulin (µIU/ml) 11.9 ± 9.84 27.02 ± 28.49 304.5 769.5 -2.15 0.000** HbA1c (%) 5.23 ± 0.26 8.25 ± 1.70 0 465 -6.66 0.031* HOMA-IR 2.81 ± 2.38 12.19 ± 2.81 190.5 655.5 -3.83 0.000** Urea (mg/dl) 23.03 ± 5.73 79.31 ± 37.69 25 490 -6.28 0.000** Creatinine (mg/dl) GFR (ml/min/1.73m 2 ) 0.9 ± 0.16 96.19 ± 14.16 2.729 ± 1.99 36.60 ± 23.05 42 18 507 483 -6.03 -6.38 0.000** 0.00** Na (mmol/L) 139.56 ± 3.33 132.56 ± 5.05 70 535 -5.65 0.000** K (mmol/L) 5.00 ± 2.16 4.69 ± 0.58 434.5 899.5 -0.22 0.819 Cl (mmol/L) 105.06 ± 2.82 99.4 ± 6.94 231 696 -3.25 0.001** Cholesterol (mg/dl) 169.13 ± 36.82 178.16 ± 64.75 418 883 -0.47 0.636 TGL (mg/dl) 115.13 ± 57.84 180.26 ± 92.23 241 706 -3.09 0.002** HDL (mg/dl) 44.3 ± 15.93 43.96 ± 11.72 432 897 -0.26 0.79 LDL (mg/dl) 106.23 ± 35.18 113.13 ± 53.99 431 896 -0.28 0.779 Urinary microalbumin (mg/24hrs) 5.49 ± 3.97 227.29 ± 221.21 58 532 -5.79 0.000** Proteomic Profile Serum GDF-15 567.36 ± 377.91 5507 ± 2759.89 0 465 -6.65 0.000** Mann-Whitney U test scores of biochemical parameters, the significantly different variables are represented by p ≤ 0.001 = ** and p ≤ 0.05 = * Table 2. Spearman correlation of GDF-15 with various parameters Study Population (n=60) DN patients (n=30) Parameters Rho Sig (2-tailed) Rho Sig (2-tailed) FBS (mg/dl) -0.73 0.000** -0.18 0.316 Insulin (µIU/ml) 0.15 0.237 -0.24 0.196 HbA1c (%) 0.74 0.000** -0.11 0.557 HOMA-IR 0.36 0.004** -0.28 0.133 Urea (mg/dl) 0.75 0.000** 0.50 0.004** Creatinine (mg/dl) 0.72 0.000** 0.65 0.000** Na (mmol/L) -0.72 0.000** -0.34 0.065 K (mmol/L) -0.04 0.743 0.04 0.812 Cl (mmol/L) -0.28 0.027* 0.28 0.134 Cholesterol (mg/dl) 0.09 0.456 -0.22 0.239 TGL (mg/dl) 0.32 0.011* -0.08 0.655 HDL (mg/dl) 0.03 0.769 -0.16 0.391 LDL (mg/dl) 0.09 0.496 -0.17 0.36 Urinary microalbumin (mg/24hrs) 0.70* 0.000** 0.30 0.106 ∆ct miR-21 -0.05 0.697 -0.12 0.517 A [Sig.2(tailed)] p-value ≥ 0.05 indicates a non-significant difference between the variables, p ≤ 0.05(*) indicates a significant difference between the variables and p ≤ 0.005(**) indicates an extremely significant difference. Table 3. FCE of miR-21 in DN patients in comparison to healthy controls Healthy Control (∆ct) (n=30) DN patients (∆ct) (n=30) ∆∆ct Fold Change Expression 2.94 -0.26 -3.20 9.18 Supplementary Files GraphicalAbstract.png Graphical Abstract Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-396068","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":21798559,"identity":"95c63263-45db-4a3d-aed3-1738ce1eeba1","order_by":0,"name":"Riddhi Girdhar Agarwal","email":"","orcid":"","institution":"All India Institute of Medical Sciences Jodhpur","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Riddhi","middleName":"Girdhar","lastName":"Agarwal","suffix":""},{"id":21798560,"identity":"81eb72f4-f326-4fd8-9432-5c2200b7afb6","order_by":1,"name":"Purvi Purohit","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYDCCAxCKB0QwMxgwyIEFH5CgxcAYLJhAhBYGiBYGg8QGEAufFr7bB9ge8+45LGMukfvwcUHBn/T5YYcfAm2xk9NtwK5F8lwCuzHPs8M8ljPSjY1nGBjkbrydZgDUkmxsdgC7FoMzDGzSPAfSeAxupAEZIC2zE0BaDiRuI1ZLuuHs9A/EaLGBa0mQl87Bb4vkGcZ2wzlALZY9z5iNeQyMDTdI5xQcSDDA7Re+M8zHHrw5IGFvzp7G+Jjnj5y8/Oz0zR8+VNjJ4dLCwMDYBnEh3KkHULhYARuqFvkGvKpHwSgYBaNgBAIAhTVYD7agmzEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-8559-2911","institution":"All India Institute of Medical Sciences Jodphur","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Purvi","middleName":"","lastName":"Purohit","suffix":""},{"id":21798561,"identity":"3e21fd08-6491-4c39-b8a9-97406a3c0fb5","order_by":2,"name":"Manoj Khokhar","email":"","orcid":"","institution":"All India Institute of Medical Sciences jodhpur","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Manoj","middleName":"","lastName":"Khokhar","suffix":""},{"id":21798562,"identity":"50b427c6-31b2-41d1-9ae5-337d45221b31","order_by":3,"name":"Anupama Modi","email":"","orcid":"","institution":"All India Institute of Medical Sciences Jodhpur","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anupama","middleName":"","lastName":"Modi","suffix":""},{"id":21798563,"identity":"b0c1c764-296d-432c-b49a-d19122a6d444","order_by":4,"name":"Nitin Kumar Bajpai","email":"","orcid":"","institution":"All India Institute of Medical Sciences Jodhpur","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nitin","middleName":"Kumar","lastName":"Bajpai","suffix":""},{"id":21798564,"identity":"baf13765-27ff-40e4-8cb7-30e8c9a5b452","order_by":5,"name":"Gopal Krishna Bohra","email":"","orcid":"","institution":"All India Institute of Medical Sciences Jodhpur","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gopal","middleName":"Krishna","lastName":"Bohra","suffix":""},{"id":21798565,"identity":"f725c23c-b4a5-41a8-a5b6-3c12542bc49a","order_by":6,"name":"Mahendra Kumar Garg","email":"","orcid":"","institution":"All India Institute of Medical Sciences Jodhpur","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mahendra","middleName":"Kumar","lastName":"Garg","suffix":""},{"id":21798566,"identity":"7992a2ea-7895-4bc0-b20a-f0b1286060fe","order_by":7,"name":"Praveen Sharma","email":"","orcid":"","institution":"All India Institute of Medical Sciences Jodhpur","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Praveen","middleName":"","lastName":"Sharma","suffix":""}],"badges":[],"createdAt":"2021-04-05 16:10:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-396068/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-396068/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":8188263,"identity":"b893ac39-495f-4949-9f28-1f7042d33a9e","added_by":"auto","created_at":"2021-04-19 20:20:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":495171,"visible":true,"origin":"","legend":"Flow Chart of the methodology.","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-396068/v1/a07805c680dce939a833cce5.png"},{"id":8188259,"identity":"91fa963a-cebd-4b64-88c7-5cb45d7c261a","added_by":"auto","created_at":"2021-04-19 20:20:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":245022,"visible":true,"origin":"","legend":"GDF15 and Insulin related Gene Ontology enriched terms obtained from Annotation Databases; (A) Biological Process (BP); (B) Molecular Function; (C) Enriched KEGG Pathway (D) Cellular components; (E) Venn diagram of GDF15 Transcription factor of four different databases.","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-396068/v1/01796b6cd5e773179e56dea1.png"},{"id":8188262,"identity":"951d62c9-d069-4d43-822a-059ce82637d1","added_by":"auto","created_at":"2021-04-19 20:20:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":237720,"visible":true,"origin":"","legend":"Protein-Protein interaction network of different genes (A) String PPIs network of Insulin, GDF-15 and networking genes with miRNA-21; (B) PPIs network of GDF-15 and Its transcription factor with common targeting miRNA-21; (C) Merged PPIs network of participating genes, GDF15 regulating transcription factor along with common targeting miRNA-21; (D) Heat map of different expression genes (DEGs) as obtained from PPI networks, in different organ of whole body in normal condition. ","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-396068/v1/01ac1381866da243979188a9.png"},{"id":8189096,"identity":"18b9ccfa-b934-4b42-bc0f-1922124e756d","added_by":"auto","created_at":"2021-04-19 20:26:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":147106,"visible":true,"origin":"","legend":"(A) Box -whisker plot of ∆ct value of miR-21 in DN patients and Healthy controls. The mean values of ∆ct miR-21 in DN (-0.26) and healthy controls (2.38) reveals an early expression of miR-21 in DN patients. (B) Box -whisker plot of serum levels of GDF-15 in DN patients (5507 ± 2759.89) and Healthy controls (567.36 ± 377.91); (C) Bar graph representing serum levels of GDF-15 in DN patients belonging to different stages of DN.","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-396068/v1/84b9c9b1ea905fc5896c4eb8.png"},{"id":13687276,"identity":"15f39b66-18d9-461a-8227-1d3eabcdd304","added_by":"auto","created_at":"2021-09-17 12:19:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1586177,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-396068/v1/c7f35ecb-099a-4150-9c8f-f63f8d4ea847.pdf"},{"id":8188557,"identity":"32a16695-f84f-49e8-b0c3-201b5ec886f0","added_by":"auto","created_at":"2021-04-19 20:23:20","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":627983,"visible":true,"origin":"","legend":"Graphical Abstract","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-396068/v1/42ba3f61afff5eb0b0c64d00.png"}],"financialInterests":"","formattedTitle":"A clinical and in-silico analysis of hsa-miR-21 and Growth Differentiation factor-15 expression in Diabetic Nephropathy","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDiabetes is a chronic metabolic disease characterized by hyperglycemia caused due to insulin insufficiency in the body. The prevalence of diabetes is highest in China and India, together accounting for nearly 180 million diabetic adults. The data is so alarming that the global prevalence of diabetes is anticipated to exceed 700 million by 2025 [1].\u003c/p\u003e\n\u003cp\u003eType 2 Diabetes mellitus (T2DM) is a complex, multifactorial, and mostly preventable disease [2]. It can progress into micro and macro-vascular complications. One such complication isdiabetic nephropathy (DN), mainly characterized by albuminuria (\u0026ge;300mg/day) and reduced glomerular filtration rate (GFR) [3].\u003c/p\u003e\n\u003cp\u003eAt the molecular level, DN develops as a result of metabolic interactions, which activate intracellular signalling pathways, leading to extracellular matrix (ECM) protein accumulation, vessel permeability alteration, and proteinuria [4]. In DN, the hyper-glycaemic condition is associated with the upregulation of glucose transport-1 that also causes overexpression of transforming growth factor- \u0026beta; (TGF-\u0026beta;) in mesangial tubular cells or infiltrating renal cells [5]. This over-expressed TGF-\u0026beta; has been recognised as a key determinant of progressive renal function loss [6].\u0026nbsp; Furthermore, along with inducing the expression of TGF-\u0026beta; in mesangial cells, these factors also accelerate mesangial cell hypertrophy and ECM accumulation; as a consequence, GFR decreases and engenders chronic renal failure [6]. \u0026nbsp;GDF-15, a member of the TGF- \u0026beta; superfamily, has been found to be associated with T2DM. Further, an elevated serum level of GDF-15 has been reported as a clinically valuable marker for predicting progression of diabetic kidney disease (DKD) and for individual risk stratification in DN patients, with normo-albuminuria and microalbuminuria [7]. Moreover, higher levels of GDF-15 corresponds to faster deterioration of kidney function and morbidity in DN patients [8]. Hence, it is important to validate GDF-15 as an individual biomarker for the progression of DN in humans. Several findings have also highlighted that plasma GDF-15 level increases with the Mogensen stage, and is significantly correlated with microalbumin (mAlb) and eGFR, suggesting its value in early diagnosis, evaluation and prediction of the outcomes of DN [9]. A series of miRNAs, a class of short non-coding RNAs that regulate gene ex\u0026shy;pression at the post-transcriptional level by binding to the 3\u0026rsquo;-untranslated regions of their target mRNAs [10],\u0026nbsp; play a critical role in the regulation of many cellular and physiological activities [11]. These miRNAs either get up or downregulated in different kidney diseases\u0026nbsp; [12], and can therefore serve as\u0026nbsp; potential biomarkers\u0026nbsp; in targeted therapy protocols [13].\u003c/p\u003e\n\u003cp\u003eIn a study done on \u0026beta; cells, miR-21 inhibition was found to maintain the sugar level in patients with diabetic complications [5]. The expression of miR-21 is also known to be regulated by TGF-\u0026beta;1 and has been broadly studied in cancer biology because of its antiapoptotic effects and critical role in tumorigenesis [14]. Further, in human kidney biopsies, miR-21 was reported to increase phosphatase and tensin homolog (PTEN), and miR-21 antagonism resulted in decreased interstitial fibrosis, podocyte loss, inflammatory gene expression, albuminuria, macrophage infiltration and mesangial cell expansion [15]. In our previous review article, we have also highlighted the role of PTEN in the regulation of renal tubulointerstitial fibrosis and epithelial-mesenchymal transition (EMT) [16]. miR21 levels have also been reported to elevate with increasing disease pathology in human renal biopsies and in experimental mouse models of early/late DN [17]. However, miR-21 is regulating which member of TGF-\u0026beta; family, has not been investigated earlier in diabetes. Therefore, based upon previous literature we anticipated a common link between miR-21 and TGF--\u0026beta; family member GDF-15, therefore we investigated their interaction using \u0026nbsp;bioinformatics and computational analysis, that has emerged as an efficient tool to study pathways involved in differentially expressed genes, protein-protein interaction (PPI) networks and to predict miRNA-mRNA targets [18].\u0026nbsp;\u0026nbsp; Further, the expression level of serum GDF-15 was investigated in T2DM DN patients and healthy controls and was correlated with various biochemical and anthropometric parameters of the study population. Additionally, we also analysed the levels of circulating hsa-miR-21 and its correlation with serum GDF-15.\u0026nbsp; Our results support the potential of a raised serum GDF-15 and circulating miR-21 to serve as therapeutic targets of progressive renal deterioration.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e\u0026nbsp;2.1 In silico investigation of the regulation of GDF-15 and its interaction with Insulin\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.1 Protein-Protein interaction (PPI) network \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe interconnecting networks between GDF-15 and its nearest neighbours were investigated in diabetes using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) [19]. The protein IDs of insulin and GDF-15 were submitted to STRING to obtain functional interpretations of their targets in \u003cem\u003eHomo sapiens\u003c/em\u003e (Figure 1). Kyoto encyclopedia of genes and genomes (KEGG) components were analysed to obtain pathway enrichment by STRING statistics taking confidence of 0.4, and the significantly enriched pathways were identified based on a value of P \u0026lt; 0.05. Plug-in of Cytoscape tool was employed [20] for cluster formation and molecular complex detection (MCODE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.2 Database Search of regulatory transcription factors of GDF 15 \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTRRUST transcription factors 2019, TRANSFAC and JASPAR PWMs [21], ENCODE and ChEA Consensus TFs from ChIP-X (\u003ca href=\"https://genome.ucsc.edu/ENCODE/\"\u003ehttps://genome.ucsc.edu/ENCODE/\u003c/a\u003e),\u0026nbsp; miRNet Version: 2 [22], and Enrichr [23] were used to identify GDF-15 regulating transcription factors. The common transcription factors were selected by Venn diagram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;2.1.3 \u003c/strong\u003e\u003cstrong\u003eFunctional annotation and enrichment analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe functional features of all the genes related to T2DM involved in PPI networks were analysed by two different web servers DAVID (Database for Annotation, Visualization and Integrated Discovery) [24] and\u0026nbsp; STRING Version 11.0 [19]. DAVID was used for functional enrichment such as KEGG pathways and\u0026nbsp; significance of gene ontology (GO) to assess the biological process, cellular components and molecular functions. Significantly enriched GO terms and KEGG pathways were selected based on p\u0026lt;0.05 and further, a false discovery rate of \u0026lt;0.05 to avoid false positives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.4 Construction of MicroRNA-mRNA target regulatory network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCommon transcription factors were utilized to construct\u0026nbsp; PPI networks,\u0026nbsp; and miRNet Version: 2, a well-established miRNA target prediction database was used to predict the target miRNA of genes involved in the PPI networks [22, 25].\u003c/p\u003e\n\u003cp\u003eConsidering the literature search and predicted miRs against targeted transcription factors, miR-21 was scrutinized. Further, Targetscan (http: www.targetscan.org) was used to predict the target mRNA according to miR-21 seed region. A heatmap of genes and proteins involved in PPI Networks, representing their expression in various tissues, was obtained using FunRich (Functional Enrichment analysis tool) [26].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2\u0026nbsp;\u0026nbsp; Study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThirty DN patients visiting the outpatient department (OPD) of the Department of Nephrology, All India Institute of Medical Sciences (AIIMS) Jodhpur, and thirty healthy volunteers working as healthcare professionals were recruited in the study. The inclusion criteria for patients was HbA1c \u0026gt; 6.5% and urinary mAlb \u0026gt;300 mg/dl. Inclusion criteria for controls were non-diabetic subjects without any inflammatory disease or history of renal disorder.\u003c/p\u003e\n\u003cp\u003eSince GDF-15 gets upregulated due to damaged vascular endothelium, patients diagnosed with type 1 diabetes mellitus (T1DM), hypothyroidism, coronary artery disease, chronic obstructive pulmonary disease, malignancies, hepatic disorders, rheumatoid arthritis and thalassemia were excluded. In hemochromatosis, GDF-15 is secreted by erythroblasts [27], and in pregnancy, GDF-15 levels are raised by the placenta, therefore these patients were also excluded.\u003c/p\u003e\n\u003cp\u003eVenous blood samples of the study population were collected under the ethical approval of the Institutional ethics committee (IEC) of AIIMS, Jodhpur to obtain whole blood, serum and plasma. Informed consent was taken from all the participants. Urine samples were collected from the study subjects for the estimation of urinary mAlb.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Study protocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn the same day of blood sampling and handing out of 24hr urine samples, anthropometric measurements of all the participants were recorded. Height (by a stadiometer), weight (by a weighing machine), and waist-and hip circumference (by non-elastic measuring tape) were measured. These measurements were then used to calculate body mass index (BMI) and waist-hip ratio (WHR). Systolic blood pressure (SBP) and diastolic blood pressure (DBP) was recorded using a sphygmomanometer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;2.4 Biochemical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEnzymatic colour test of human serum was performed on Beckman Coulter AU analyser using End-point method, for the quantitative determination of Fasting Blood Sugar (FBS), Low-Density Lipoprotein (LDL), High-Density Lipoprotein (HDL), cholesterol, urea and creatinine.. The Serum electrolytes sodium, potassium and chloride were analysed on the Beckman Coulter AU analyser using the Ion-Selective Electrode (ISE) principle. HbA1c was measured by latex agglutination inhibition assay. Serum insulin was analysed using the fully automated chemiluminescence analyser Diasorin Liasion. Urinary mAlb was estimated by immunoprecipitation method. The GFR of the study population was calculated using the MDRD formula GFR\u0026nbsp;\u003cstrong\u003e(mL/min/1.73 m\u0026sup2;)\u003c/strong\u003e\u0026nbsp;= 175 \u0026times; (serum creatinine in mg/dl) -1.154 \u0026times; (Age) -0.203 \u0026times; (0.742 in females).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Molecular Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.1 Analysis of serum GDF-15\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThermo Scientific Pierce Human GDF-15 ELISA kit was used to quantify human GDF-15 in serum, according to manufacturer\u0026rsquo;s instructions. The concentration of the analyte (GDF-15) was used to obtain a standard curve. The concentration of serum GDF-15 in the samples was analysed using the standard curve by BioTek Gen5 Data analysis software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;2.5.2 \u003c/strong\u003e\u003cstrong\u003eAnalysis of circulating hsa-miR-21\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhole blood samples of the study population were used to isolate total RNA using RBC lysis buffer in a ratio of 1:3 for Blood: RBC lysis buffer. The sample was centrifuged at 1300 rpm at 4˚C for 15 minutes and 1 ml PBS was added to dissolve the pellet, followed by centrifugation at 3000 rpm for 2 minutes. Trizol was added to the pellet (in a ratio of 10:1 for blood: trizol) to obtain a homogenous solution. Isopropanol was used to remove the aqueous layer (in a ratio of 1:1 for aqueous layer: isopropanol) and the contents were briefly vortexed. The pellet was then dissolved in 1 mL of 1% ethanol and centrifuged at 12000 x g for 5 mins. 20 \u0026micro;L of DEPC treated water was added after air-drying the pellet followed by incubation at 65˚C for 3-4 minutes. The sample was stored at -80˚C till further processing. Thermo-scientific NanoDrop One\u003csup\u003ec\u003c/sup\u003e was used to quantify the extracted total human RNA. The absorption maxima of RNA lies at 260 nm, and DEPC treated water was used as blank. An absorbance of ~2.0 at a ratio of 260 nm and 280 nm was accepted as \u0026ldquo;pure\u0026rdquo; for RNA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;2.5.3 Reverse transcription PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ec-DNA conversion of RNA extracted, was done by Reverse Transcription PCR (RT-PCR) using Eppendorf nexus gradient Master cycler and Qiagen miScript Reverse Transcription kit as per the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.4 Quantification of circulating hsa-miR-21 expression by Real-Time PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBioRad CFX96 Real-Time System and BioRad CFX Manager Software were used for the real time expression analysis of miR21. The real time expression of miR-21 in human whole blood samples was observed using Qiagen miScript SYBR Green PCR Kit along with miScript miRNA PCR Arrays as per the manufacturer\u0026rsquo;s instructions. The fold change expression (FCE) of miR-21 in comparison to RNU6 (internal control/housekeeping gene) was evaluated using double delta/∆∆ CT method for the cases and controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIBM SPSS Statistics 21.0 for Windows (SPSS Inc, Chicago, IL) was used to analyse the skewness and kurtosis of various study parameters. Mann-Whitney U test was used to test the research hypothesis and Spearman correlation and logistic regression analysis were used to evaluate the relation between variables. Microsoft Excel software was used for graphical representation of the results.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 PPI network construction and analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing STRING, GDF15 and insulin were found to interact with IRS1 (Insulin receptor substrate 1), IRS2, INSR (insulin receptor), IGF1R (insulin-like growth factor 1 receptor), AKT1 (alpha serine/threonine-protein kinase), PPARG (Peroxisome proliferator-activated receptor-gamma), CEBPB (CCAAT/enhancer-binding protein beta), EGR1 (early growth response protein 1), TP53, KLF4 (Krueppel-like factor 4), ATF3 (cyclic AMP-dependent transcription factor ATF-3), and TWIST2 ( Twist-related protein 2). Cytoscape software was used to represent the established PPI network (Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Cluster formation by MCODE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne of the significant clusters within the PPI included INS, IGF1R, AKT1, IRS1, INSR, IRS2 and hsa-miR-21, which was selected using MCODE Score = 6.000, node- 06, edges- 15. These genes are mainly associated with the TGF-\u0026beta; receptor signalling and mammalian target of\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003erapamycin\u0026nbsp;(mTOR) pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 GO enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClassification of genes, involved in PPI network, into the following three classes, was used for GO analysis (Figure 2):\u003c/p\u003e\n\u003cp\u003eCellular component\u0026nbsp; class\u003cstrong\u003e:\u003c/strong\u003e the genes were enriched for the cellular components like INSR complex, protein-containing complex, nuclear transcription factor complex, receptor complex and plasma membrane protein complex.\u003c/p\u003e\n\u003cp\u003eBiological Process (BP) Class: The proteins were enriched in the cellular response to organonitrogen, peptide, response to insulin, regulation of transferase, kinase, phosphorylase activity of proteins, signal transduction, cellular response to growth factor, and signalling pathways of insulin receptor, phosphatidylinositol 3-kinase (PI3K), IGFR and mitogen-activated protein kinase (MAPK).\u003c/p\u003e\n\u003cp\u003eMolecular Function (MF) class: The proteins were enriched in the activation of INSR binding, PI3K binding, IGFR binding, RNA polymerase II regulatory region, DNA-binding transcription activator activity, RNA polymerase II-specific insulin binding, IRS binding, PI3K activity, transmembrane receptor protein tyrosine kinase activity, phosphatidylinositol-4,5-bisphosphate 3-kinase activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 KEGG pathway enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;PPI networks of genes were enriched (Figure 2 C) for adenosine monophosphate-activated protein kinase (AMPK) signalling pathway, forkhead box (FoxO) signalling pathway, insulin resistance, insulin signalling pathway, T2DM, mTOR signalling pathway, PI3K-AKT signalling pathway, MAPK signalling pathway, Rap1 signalling pathway, Ras signalling pathway, adipocytokine signalling pathway, adherens junction, pancreatic cancer, tyrosine kinase inhibitor resistance, AGE-RAGE signalling pathway in diabetic complications, and TNF signalling pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;3.5 \u003c/strong\u003e\u003cstrong\u003ePrediction of transcription factor and construction of miRNA regulatory networks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe target miRNAs of genes of interests were predicted by miRNet Version: 2 (an integrated platform linking miRNAs, targets \u0026amp; functions) and miRDB. The regulatory relationship of identified miR-21 with GDF-15 and its transcription factor was investigated by miRNet and miRDB. Cytoscape was used to construct networks based on the correlation between the target genes and their regulating transcriptome. The identified genes were ATF3, CEBPB, EGR1, KLF4, PPARG, TP53 and TWIST2, which can regulate the levels of GDF-15. In the metabolic regulatory process of T2DM and insulin resistance, IRS1, IRS2, INSR, IGF1R, INS, AKT1, PPARG, CEBPB, EGR1, TP53, KLF4, ATF3, GDF15, TWIST2 served as important nodes in the PPI networks. MicroRNA-21 was observed as a multi-targeting miRNA as it interacted with KLF4, TP-53, and CEBPB transcription factors, which were also linked with GDF-15. Additionally, hsa-miR-21 directly targeted IGF1R. Further, the interactions also suggested a correlation between GDF-15 and miR-21, supposedly through the common transcription factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Demographic characteristics of the study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the present study, 30 DN patients and 30 healthy controls were included. Among the patients, 70% were males and 30% were females (Table 1), which reflects a higher prevalence of DN in males in the study population. This higher frequency of DN in males is predominantly due to the cross-sectional nature of the study design and does not indicate a gender-based risk of males towards DN. The age distribution of DN (Figure 2) revealed a near gaussian distribution of DN in the cases (40-80 years). The prevalence of DN in central age group (50-70 years) can be due to the fact that DN is a late onset disease, and usually advances with long standing diabetes, therefore a smaller number of patients belonged to the other age groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;3.7 Anthropometric characteristics of the study population \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA highly significant difference (p = 0.002) in the WHR was found between healthy controls and DN patients, wherein DN patients were found to be overweight (Table 1). DN patients also had a significantly high systolic and diastolic BP as compared to controls (Table 1).\u0026nbsp; High BP of DN patients can be reasoned for hypertension (a common complication of diabetes) and a progressive cardiac myopathy condition, which is another micro-vascular complication associated with T2DM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.8 Clinical characteristics of the study population \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe levels of FBS (184.49 \u0026plusmn; 49.05 mg/dL), insulin (27.02 \u0026plusmn; 28.49 \u0026micro;IU/mL), HbA1c levels (8.25 \u0026plusmn; 1.70%) and HOMA-IR (12.19 \u0026plusmn; 2.81) were found to be significantly higher in DN patients than healthy controls (Table 2). The values of GFR (96.19 \u0026plusmn; 14.16 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e) in the DN patients, along with highly elevated\u0026nbsp; values of urea (79.31 \u0026plusmn; 37.69 mg/dL), creatinine (2.729 \u0026plusmn; 1.99 mg/dL) and urinary microalbumin (227.29 \u0026plusmn; 221.21 mg/24hrs))\u0026nbsp; in the cases confirmed a comprised renal state in the DM patients.\u003c/p\u003e\n\u003cp\u003eFurther, in the values of\u0026nbsp; lipid profile (Cholesterol + TGL + LDL + HDL), (Table1),TGL (180.26 \u0026plusmn; 92.23 mg/dL) level was\u0026nbsp; borderline high among the DN patients. \u0026nbsp;Among electrolytes (Na, K and Cl), only the value of Cl was lower than the normal (101-109 mmol/L) in DN patients.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;An extremely significant difference was found in the values of FBS, insulin, HOMA-IR, urea, creatinine, Na, Cl, TGL, urinary microalbumin and GDF-15, with p \u0026le; 0.001 between DN patients and healthy controls. A highly significant difference in the value of HbA1c (p \u0026le; 0.05) was also found between the groups in the study population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.9 Levels of GDF-15 assessed by ELISA and its correlation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe median value of serum GDF-15 was found to be nearly 10 folds higher in DN patients as compared to healthy controls (p˂0.0001). The levels of GDF-15 also increased with advancement of stages of chronic kidney disease (CKD) (Figure 4C). A positive and highly significant correlation of serum GDF-15 was found with HbA1c (r = 0.747**, p = 0.000), HOMA-IR (r = 0.396*, p = 0.004), serum urea (r = 0.756*, p = 0.000) and serum creatinine (r = 0.727*, p = 0.000) (Table 2),\u0026nbsp; which are the most widely accepted parameters to diagnose diabetic condition and renal disorders . The Spearman\u0026rsquo;s correlation analysis of only DN cases (Table 2), revealed a positive correlation of GDF-15 with serum urea (r = 0.505*, p = 0.004) and creatinine (r = 0. 653*, p = 0.000), .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.10 Analysis of miR-21 after quantification by RT-PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe FCE of circulating hsa-miR-21, assessed using RT-PCR in the study population, was found to be 9.18 folds higher in DN patients as compared to healthy controls (Table 3).\u0026nbsp;\u0026nbsp;\u0026nbsp; Additionally, serum GDF-15 had a positive correlation withcirculating hsa-miR-21.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAt the molecular level, the factors that contribute to DN such as ECM protein accumulation, vessel permeability alteration, and proteinuria, develop as a result of interactions between various metabolic factors, which activate intracellular signalling pathways responsible for triggering inflammatory mediators and release of growth factors [5] like TGF-\u0026beta;1. The levels of TGF-\u0026beta;1 have been reported to increase under diabetic conditions in renal cells, including mesangial cells , hence TGF-\u0026beta; has been recognised as a key determinant of progressive renal function loss [6]. GDF-15, a member of the TGF-\u0026beta; superfamily, has been recently reported as an early marker of IR and mitochondrial dysfunction. An elevated serum level of GDF-15 was found to be associated with T2DM [7]. Several findings have also highlighted that plasma GDF-15 level increases with the Mogensen stage as an independent risk factor for increased microalbuminuria in DN patients. GDF-15 is also significantly correlated with mAlb and eGFR, suggesting its value in early diagnosis, evaluation and prediction of the outcomes of DN. Furthermore, according to our in-silico findings, and previous literature, TP53 and its family members, p63 and p73 have been reported as transcriptional regulators of GDF15, as its promoter region contains two p53-type response elements, RE1 and RE2, wherein RE2 confers p53-specific transactivation [28]. KLF4 acts as a transcriptional repressor of p53 [29]. In ovarian cancer cells, the expression of CEBPB was positively correlated with GDF15 expression and CEBPB was identified to bind with GDF15 gene promoter through luciferase reporter assay. Thus, CEBPB caused a positive regulation of GDF15 expression in cancer cells through epigenetic modification [30]. In another study, the CpG locus in MIR21 promoter was observed to be a conserved binding site of transcription factors CEBPB, MEIS3, and TEAD4, which were co-expressed with miR-21 in tumors [31]. Therefore, KLF4, CEBPB and miR-21 jointly augment EMT via the AKT/ERK1/2 pathway by upregulating the levels of GDF-15 [32]. EMT plays an important role in renal interstitial fibrosis (RIF) with DN [33].\u003c/p\u003e\n\u003cp\u003eOur in-silico findings have also suggested the association of insulin with GDF-15 and IGF1R, which implies the role of increased GDF-15 in insulin resistance along with endothelial dysfunction, metabolic derangement and inflammation as proposed in vascular injuries and cardiovascular complications elsewhere [34].\u0026nbsp; In another study, p53 dependent expression of GDF-15 has been suggested as the link between obesity and insulin resistance, wherein activation of p53 in adipose tissue led to increased production of proinflammatory cytokines, subsequently leading to insulin resistance, and diabetes [34, 35]. Therefore, the in-silico findings of this study reiterate the connection of GDF-15 with insulin in diabetes, and along with the previous literature highlights the likelihood of GDF-15 mediated microvascular complications in DN.\u0026nbsp;\u0026nbsp; In a previous finding, diabetic dyslipidaemia has been reported to promote progression of DN, with few studies suggesting that TG-rich lipoprotein particles predominantly containing apolipoproteins (apos) E, C and B serve as major promoters of DN. Further, in DN patients, plasma TG levels were also suggested to be high, due to the reduced activity of hepatic lipase (HL) and lipoprotein lipase (LPL), which hydrolyze TG [35]. This is in accordance with our finding of elevated TGL levels in the patient population. The reduced serum Cl levels observed in the current study may be due to elevated serum ketoacids as observed by Yasuda K et al., wherein they have reported that in diabetics, hypochloremia can result due to elevated serum ketoacids and the ratio of Cl/ Na in DN patients was found to be significantly lower in their study as well [36].\u003cbr /\u003e In our molecular analysis, we found the serum levels of GDF-15 (Table 1) to be nearly ten (10) folds higher in DN patients (median 5507 pg/ml) as compared to healthy controls (567 pg/ml), (p\u0026lt;0.0001) (Figure 4A). Further, GDF-15 levels were also observed to increase with stages of DN. The highest level corresponded to Stage V (ERSD), thus confirming increasing GDF-15 to be associated with advancing stages of DN.\u003c/p\u003e\n\u003cp\u003eSpearman correlation analysis of the study population (Table 2) showed a positive correlation of serum GDF-15 with HbA1c (r = 0.747, p = 0.000), HOMA-IR (r = 0.396, p = 0.004), serum urea (r = 0.756, p = 0.000) and serum creatinine (r = 0.727, p = 0.000). Hence, serum GDF-15 levels can serve as a novel marker in indicating progression of DN and a deteriorating glycemic control in these patients. Further, the Spearman\u0026rsquo;s correlation analysis of only DN cases revealed a positive correlation of GDF-15 with serum urea (r = 0.505, p = 0.004) and creatinine (r = 0. 653, p = 0.000), therefore, in DN serum GDF-15 maybe a potential marker for deteriorating renal function.\u003c/p\u003e\n\u003cp\u003eThe levels of miR-21 have also been reported to be higher in human renal biopsies and in experimental mouse models of early/late DN and non-DN, further, renal miR-21 knockdown showed downregulation of TGF-\u0026beta;1 signalling in a mouse model of T2DM [37]. The increased miR-21 expression has also been identified in the kidney biopsy samples of renal transplant patients with fibrotic kidney disease and in the urine of fibrotic patients with IgA nephropathy [38]. Since circulating miRNAs can serve as a non-invasive marker of any disease condition, Zununi and his colleagues assessed plasma samples for circulating miRNAs and observed a correlation between expression levels of miR-21 in plasma and serum creatinine (r =-0.432, P =0.03) in renal transplant patients [39]. In another study, circulating miR-21-5p has been reported to be closely associated with endothelial dysfunction and inflammation, which are two characteristic developmentsof T2DM progression. However, there are no reports on the FCE of circulating miR-21 in relation to DN. In the present study, we observed FCE of circulating miR-21 to increase up to 9.18 folds in DN patients in comparison to healthy controls (Figure 4B). The levels of miR-21 were also found to be positively correlated with the levels of GDF-15, although the association was non-significant (which can be due to the small sample size). This positive correlation indicates the possibility of mechanistic interaction between miR-21 and GDF-15. However, GDF-15 upregulation can be reflected in any vascular endothelial damage, therefore, it can appear in diverse tissues damage responses apart from nephropathy, thus enhancing the levels of circulating GDF-15 [7]. Further, in a study done by Weronica et al., to investigate the role of GDF-15 in cardiovascular disease, GDF-15 and DNA methylation level of miR-21 promoter was found to be inversely correlated. They suggested that raised levels of GDF-15 possibly lead to demethylation of miR21 promoter, increasing its expression in cardio vascular disease [40]. However, this arena is still unexplored in DN, and a similar mechanism may be functional in DN.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study, which is the first to the best of our knowledge, has explored the role of circulating hsa-miR-21 in DN patients in relation to GDF-15, and supports the potential of raised GDF-15 and circulating miR-21 to serve as two important signature and novel biomarkers of progressive renal deterioration. Our in silico analysis also points to a possible mechanistic regulation of GDF-15 by miR-21. Further research on the underlying mechanisms of GDF-15 and miR-21 can provide insights into an un-investigated, yet important GDF-15 and miR-21 dependent pathway, that contributes to pathogenesis of DN.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAKT: alpha serine/threonine-protein kinase; BMI: Body Mass Index; ; CEBPB: CCAAT/enhancer-binding protein beta; CT: Cycle threshold; DAVID: Database for Annotation, Visualization and Integrated Discovery; DN: Diabetic Nephropathy; DBP: Diastolic Blood Pressure; ECM: Extra cellular Matrix; EGR1: Early growth response protein 1; ELISA: Enzyme Linked Immunosorbent Assay; EMT: Epithelial-mesenchymal transition; ESRD: End stage renal disease; FCE: Fold Change Expression; FunRich: Functional Enrichment analysis tool; GDF-15: Growth Differentiation Factor -15; GFR: Glomerular filtration Rate; GO: Gene Ontology; HOMA-IR: Homeostatic model assessment \u0026ndash; Insulin Resistance; INSR: Insulin Receptor; IRS1: Insulin receptor substrate 1; ISE: Ion Selective Electrode; KEGG: Kyoto Encyclopaedia of gene and genomes; KFT: Kidney Function Test; KLF4: Krueppel-like factor 4; MCODE: molecular complex detection; miRNA: microRNA; mTOR: mammalian target of rapamycin;\u0026nbsp; PI3K: phosphatidylinositol 3-kinase; PPI: Protein- Protein Interactions; PTEN: Phosphatase and tensin homolog; SBP: Systolic Blood Pressure; TGF-\u0026beta;1: Transforming growth factor \u0026beta;1; T2DM: Type 2 Diabetes Mellitus; TP53: Cellular tumour antigen p53; TWIST2: Twist-related protein 2; WHR: Waist Hip Ratio.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding: \u003c/strong\u003eThis study was funded and supported by All India Institute of Medical Sciences, Jodhpur, India\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors have no conflicts to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement: \u003c/strong\u003eThe data will be provided when on a reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by AIIMS Institutional Ethics committee prior to commencement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants were asked for an informed consent to prior to participation, with a free will to withdraw if they wished to.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRiddhi Girdhar Agarwal- Manuscript writing, Literature review, data analysis\u003c/p\u003e\n\u003cp\u003eManoj Khokhar- Manuscript writing, Literature review, data analysis\u003c/p\u003e\n\u003cp\u003ePurvi Purohit- Study design, conceptualised, Manuscript writing, Literature review, data analysis\u003c/p\u003e\n\u003cp\u003eAnupama Modi - Manuscript writing, Literature review, data analysis\u003c/p\u003e\n\u003cp\u003eNitin Kumar Bajpai \u0026ndash; Provided samples, manuscript editing\u003c/p\u003e\n\u003cp\u003eGopal Krishna Bohra- Provided samples, manuscript editing\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Mahendra Kumar Garg- Provided samples, manuscript editing\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Praveen Sharma- manuscript editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRiddhi Girdhar Agarwal:\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; 0000-0002-3052-4492\u003c/p\u003e\n\u003cp\u003eManoj Khokhar:\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; 0000-0001-7106-8545\u003c/p\u003e\n\u003cp\u003eDr. Purvi Purohit:\u0026nbsp;\u0026nbsp; 0000-0001-8559-2911\u003c/p\u003e\n\u003cp\u003eAnupama Modi:\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; 0000-0002-6488-4701\u003c/p\u003e\n\u003cp\u003eM K Garg:\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp;0000-0001-9938-5024\u003c/p\u003e\n\u003cp\u003ePraveen Sharma:\u0026nbsp;\u0026nbsp; 0000-0002-8324-737X\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e(2016) Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4\u0026middot;4 million participants. Lancet 387:1513\u0026ndash;1530. https://doi.org/10.1016/S0140-6736(16)00618-8\u003c/li\u003e\n\u003cli\u003eLi L, Cheng W-Y, Glicksberg BS, et al (2015) Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med 7:311ra174. https://doi.org/10.1126/scitranslmed.aaa9364\u003c/li\u003e\n\u003cli\u003eBakris GL (2011) Recognition, pathogenesis, and treatment of different stages of nephropathy in patients with type 2 diabetes mellitus. Mayo Clin Proc 86:444\u0026ndash;456. https://doi.org/10.4065/mcp.2010.0713\u003c/li\u003e\n\u003cli\u003eJohnson RJ, Nakagawa T, Jalal D, et al (2013) Uric acid and chronic kidney disease: which is chasing which? Nephrol Dial Transplant 28:2221\u0026ndash;2228. https://doi.org/10.1093/ndt/gft029\u003c/li\u003e\n\u003cli\u003eChan JA, Krichevsky AM, Kosik KS (2005) MicroRNA-21 is an antiapoptotic factor in human glioblastoma cells. Cancer Res 65:6029\u0026ndash;6033. https://doi.org/10.1158/0008-5472.CAN-05-0137\u003c/li\u003e\n\u003cli\u003eChang AS, Hathaway CK, Smithies O, Kakoki M (2016) Transforming growth factor-\u0026beta;1 and diabetic nephropathy. Am J Physiol Renal Physiol 310:F689\u0026ndash;F696. https://doi.org/10.1152/ajprenal.00502.2015\u003c/li\u003e\n\u003cli\u003eHong JH, Chung HK, Park HY, et al (2014) GDF15 Is a Novel Biomarker for Impaired Fasting Glucose. Diabetes Metab J 38:472\u0026ndash;479. https://doi.org/10.4093/dmj.2014.38.6.472\u003c/li\u003e\n\u003cli\u003eRichards NT, Darby S, Howie AJ, et al (1994) Knowledge of renal histology alters patient management in over 40% of cases. Nephrol Dial Transplant 9:1255\u0026ndash;1259\u003c/li\u003e\n\u003cli\u003eLi H, Gao F, Xue Y, Qian Y (2014) [Value of plasma growth differentiation factor-15 in diagnosis and evaluation of type 2 diabetic nephropathy]. Nan Fang Yi Ke Da Xue Xue Bao 34:387\u0026ndash;390\u003c/li\u003e\n\u003cli\u003eBarutta F, Bellini S, Mastrocola R, et al (2018) MicroRNA and Microvascular Complications of Diabetes. Int J Endocrinol 2018:6890501. https://doi.org/10.1155/2018/6890501\u003c/li\u003e\n\u003cli\u003eFeng J, Xing W, Xie L (2016) Regulatory Roles of MicroRNAs in Diabetes. International Journal of Molecular Sciences 17:1729. https://doi.org/10.3390/ijms17101729\u003c/li\u003e\n\u003cli\u003eCandia P de, Spinetti G, Specchia C, et al (2017) A unique plasma microRNA profile defines type 2 diabetes progression. PLOS ONE 12:e0188980. https://doi.org/10.1371/journal.pone.0188980\u003c/li\u003e\n\u003cli\u003eAgarwal RG, Sharma P, Nyati KK (2019) microRNAs in Mycobacterial Infection: Modulation of Host Immune Response and Apoptotic Pathways. Immune Netw 19:e30. https://doi.org/10.4110/in.2019.19.e30\u003c/li\u003e\n\u003cli\u003eBakirtzi K, Hatziapostolou M, Karagiannides I, et al (2011) Neurotensin Signaling Activates MicroRNAs-21 and -155 and Akt, Promotes Tumor Growth in Mice, and Is Increased in Human Colon Tumors. Gastroenterology 141:1749-1761.e1. https://doi.org/10.1053/j.gastro.2011.07.038\u003c/li\u003e\n\u003cli\u003eShannon P, Markiel A, Ozier O, et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498\u0026ndash;2504. https://doi.org/10.1101/gr.1239303\u003c/li\u003e\n\u003cli\u003eKhokhar M, Roy D, Modi A, et al (2020) Perspectives on the role of PTEN in diabetic nephropathy: an update. Crit Rev Clin Lab Sci 1\u0026ndash;14. https://doi.org/10.1080/10408363.2020.1746735\u003c/li\u003e\n\u003cli\u003eZhong X, Chung ACK, Chen HY, et al (2013) miR-21 is a key therapeutic target for renal injury in a mouse model of type 2 diabetes. Diabetologia 56:663\u0026ndash;674. https://doi.org/10.1007/s00125-012-2804-x\u003c/li\u003e\n\u003cli\u003eDing L, Fan L, Xu X, et al (2019) Identification of core genes and pathways in type 2 diabetes mellitus by bioinformatics analysis. Mol Med Rep 20:2597\u0026ndash;2608. https://doi.org/10.3892/mmr.2019.10522\u003c/li\u003e\n\u003cli\u003eSzklarczyk D, Gable AL, Lyon D, et al (2019) STRING v11: protein\u0026ndash;protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research 47:D607\u0026ndash;D613. https://doi.org/10.1093/nar/gky1131\u003c/li\u003e\n\u003cli\u003eOtasek D, Morris JH, Bou\u0026ccedil;as J, et al (2019) Cytoscape Automation: empowering workflow-based network analysis. Genome Biol 20:185. https://doi.org/10.1186/s13059-019-1758-4\u003c/li\u003e\n\u003cli\u003eFornes O, Castro-Mondragon JA, Khan A, et al (2019) JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Research gkz1001. https://doi.org/10.1093/nar/gkz1001\u003c/li\u003e\n\u003cli\u003eFan Y, Xia J (2018) miRNet\u0026mdash;Functional Analysis and Visual Exploration of miRNA\u0026ndash;Target Interactions in a Network Context. In: von Stechow L, Santos Delgado A (eds) Computational Cell Biology. Springer New York, New York, NY, pp 215\u0026ndash;233\u003c/li\u003e\n\u003cli\u003eKuleshov MV, Jones MR, Rouillard AD, et al (2016) Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 44:W90\u0026ndash;W97. https://doi.org/10.1093/nar/gkw377\u003c/li\u003e\n\u003cli\u003eHuang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 4:44\u0026ndash;57. https://doi.org/10.1038/nprot.2008.211\u003c/li\u003e\n\u003cli\u003eFan Y, Habib M, Xia J (2018) Xeno-miRNet: a comprehensive database and analytics platform to explore xeno-miRNAs and their potential targets. PeerJ 6:e5650. https://doi.org/10.7717/peerj.5650\u003c/li\u003e\n\u003cli\u003ePathan M, Keerthikumar S, Chisanga D, et al (2017) A novel community driven software for functional enrichment analysis of extracellular vesicles data. Journal of Extracellular Vesicles 6:1321455. https://doi.org/10.1080/20013078.2017.1321455\u003c/li\u003e\n\u003cli\u003eTanno T, Bhanu NV, Oneal PA, et al (2007) High levels of GDF15 in thalassemia suppress expression of the iron regulatory protein hepcidin. Nat Med 13:1096\u0026ndash;1101. https://doi.org/10.1038/nm1629\u003c/li\u003e\n\u003cli\u003eOsada M, Park HL, Park MJ, et al (2007) A p53-type response element in the GDF15 promoter confers high specificity for p53 activation. Biochemical and Biophysical Research Communications 354:913\u0026ndash;918. https://doi.org/10.1016/j.bbrc.2007.01.089\u003c/li\u003e\n\u003cli\u003eRowland BD, Bernards R, Peeper DS (2005) The KLF4 tumour suppressor is a transcriptional repressor of p53 that acts as a context-dependent oncogene. Nat Cell Biol 7:1074\u0026ndash;1082. https://doi.org/10.1038/ncb1314\u003c/li\u003e\n\u003cli\u003eGuo L-L, Wang S-F (2019) Downregulated Long Noncoding RNA GAS5 Fails to Function as Decoy of CEBPB, Resulting in Increased GDF15 Expression and Rapid Ovarian Cancer Cell Proliferation. Cancer Biother Radiopharm 34:537\u0026ndash;546. https://doi.org/10.1089/cbr.2019.2889\u003c/li\u003e\n\u003cli\u003eLu J, Tan T, Zhu L, et al (2020) Hypomethylation Causes MIR21 Overexpression in Tumors. Mol Ther Oncolytics 18:47\u0026ndash;57. https://doi.org/10.1016/j.omto.2020.05.011\u003c/li\u003e\n\u003cli\u003eLiu C-H, Huang Q, Jin Z-Y, et al (2017) miR-21 and KLF4 jointly augment epithelial-mesenchymal transition via the Akt/ERK1/2 pathway. International Journal of Oncology 50:1109\u0026ndash;1115. https://doi.org/10.3892/ijo.2017.3876\u003c/li\u003e\n\u003cli\u003eRoy D, Modi A, Khokar M, et al (2020) MicroRNA 21 emerging role in diabetic complications: a critical update. CDR 16:. https://doi.org/10.2174/1573399816666200503035035\u003c/li\u003e\n\u003cli\u003eKempf T, Guba-Quint A, Torgerson J, et al (2012) Growth differentiation factor 15 predicts future insulin resistance and impaired glucose control in obese nondiabetic individuals: results from the XENDOS trial. European Journal of Endocrinology 167:671\u0026ndash;678. https://doi.org/10.1530/EJE-12-0466\u003c/li\u003e\n\u003cli\u003eHirano T, Oi K, Sakai S, et al (1998) High prevalence of small dense LDL in diabetic nephropathy is not directly associated with kidney damage: a possible role of postprandial lipemia. Atherosclerosis 141:77\u0026ndash;85. https://doi.org/10.1016/s0021-9150(98)00150-6\u003c/li\u003e\n\u003cli\u003eYasuda K, Hayashi M, Murayama M, Yamakita N (2016) Acidosis-Induced Hypochloremic Alkalosis in Diabetic Ketoacidosis Confirmed by The Modified Base Excess Method. J Clin Endocrinol Metab 101:2390\u0026ndash;2395. https://doi.org/10.1210/jc.2016-1324\u003c/li\u003e\n\u003cli\u003eK\u0026ouml;lling M, Kaucsar T, Schauerte C, et al (2017) Therapeutic miR-21 Silencing Ameliorates Diabetic Kidney Disease in Mice. Mol Ther 25:165\u0026ndash;180. https://doi.org/10.1016/j.ymthe.2016.08.001\u003c/li\u003e\n\u003cli\u003eHennino M-F, Buob D, Van der Hauwaert C, et al (2016) miR-21-5p renal expression is associated with fibrosis and renal survival in patients with IgA nephropathy. Sci Rep 6:. https://doi.org/10.1038/srep27209\u003c/li\u003e\n\u003cli\u003eZununi Vahed S, Poursadegh Zonouzi A, Ghanbarian H, et al (2017) Differential expression of circulating miR-21, miR-142-3p and miR-155 in renal transplant recipients with impaired graft function. Int Urol Nephrol 49:1681\u0026ndash;1689. https://doi.org/10.1007/s11255-017-1602-2\u003c/li\u003e\n\u003cli\u003eEk WE, Hedman \u0026Aring;K, Enroth S, et al (2016) Genome-wide DNA methylation study identifies genes associated with the cardiovascular biomarker GDF-15. Hum Mol Genet 25:817\u0026ndash;827. https://doi.org/10.1093/hmg/ddv511\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. \u003c/strong\u003eComparison of different characteristics between the study population\u003c/p\u003e\n\u003ctable border=\"1\" width=\"624\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e\u003cstrong\u003eParameters \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cstrong\u003eHealthy controls \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n=30)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cstrong\u003eDN Patients (n=30)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003eMann-Whitney U\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e\u003cstrong\u003eWilcoxon W\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e\u003cstrong\u003eZ\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" width=\"624\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAnthropometric characteristics \u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e32.83 \u0026plusmn; 8.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e60.96 \u0026plusmn; 11.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e48.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e513.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-5.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.00*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003cp\u003e(Male %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e73.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e24.29 \u0026plusmn; 2.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e26.9 \u0026plusmn; 5.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e289\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e759\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-2.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eWHR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.89 \u0026plusmn; 0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.94 \u0026plusmn; 0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e244\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e699\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-3.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.002*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" width=\"624\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eClinical characteristics \u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eSystolic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e120.43 \u0026plusmn; 3.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e150.00 \u0026plusmn; 29.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e153\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e618\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-4.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eDiastolic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e74.87 \u0026plusmn; 2.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e83.33 \u0026plusmn; 17.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e307.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e772.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-2.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.034\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" width=\"624\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eBiochemical profile \u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eFBS (mg/dl)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e94.8 \u0026plusmn; 6.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e184.49 \u0026plusmn; 49.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e484\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-6.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e2Insulin (\u0026micro;IU/ml)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e11.9 \u0026plusmn; 9.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e27.02 \u0026plusmn; 28.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e304.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e769.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-2.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eHbA1c (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e5.23 \u0026plusmn; 0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e8.25 \u0026plusmn; 1.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e465\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-6.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.031*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eHOMA-IR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.81 \u0026plusmn; 2.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e12.19 \u0026plusmn; 2.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e190.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e655.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-3.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eUrea (mg/dl)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e23.03 \u0026plusmn; 5.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e79.31 \u0026plusmn; 37.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e490\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-6.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eCreatinine (mg/dl)\u003c/p\u003e\n\u003cp\u003eGFR (ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.9 \u0026plusmn; 0.16\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e96.19 \u0026plusmn; 14.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.729 \u0026plusmn; 1.99\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e36.60 \u0026plusmn; 23.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e507\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e483\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-6.03\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e-6.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.000**\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.00**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eNa (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e139.56 \u0026plusmn; 3.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e132.56 \u0026plusmn; 5.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e535\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-5.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eK (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e5.00 \u0026plusmn; 2.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e4.69 \u0026plusmn; 0.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e434.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e899.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.819\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eCl (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e105.06 \u0026plusmn; 2.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e99.4 \u0026plusmn; 6.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e231\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e696\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-3.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eCholesterol (mg/dl)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e169.13 \u0026plusmn; 36.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e178.16 \u0026plusmn; 64.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e418\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e883\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.636\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eTGL (mg/dl)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e115.13 \u0026plusmn; 57.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e180.26 \u0026plusmn; 92.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e241\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e706\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-3.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.002**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eHDL (mg/dl)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e44.3 \u0026plusmn; 15.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e43.96 \u0026plusmn; 11.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e432\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e897\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eLDL (mg/dl)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e106.23 \u0026plusmn; 35.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e113.13 \u0026plusmn; 53.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e431\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e896\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.779\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eUrinary microalbumin (mg/24hrs)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e5.49 \u0026plusmn; 3.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e227.29 \u0026plusmn; 221.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e532\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-5.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" width=\"624\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eProteomic Profile \u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003eSerum GDF-15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e567.36 \u0026plusmn; 377.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e5507 \u0026plusmn; 2759.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e465\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-6.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMann-Whitney U test scores of biochemical parameters, the significantly different variables are represented by p \u0026le; 0.001 = ** and p \u0026le; 0.05 = *\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. \u003c/strong\u003eSpearman correlation of GDF-15 with various parameters\u003c/p\u003e\n\u003ctable border=\"1\" width=\"586\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"215\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"172\"\u003e\n\u003cp\u003eStudy Population\u003c/p\u003e\n\u003cp\u003e(n=60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"198\"\u003e\n\u003cp\u003eDN patients\u003c/p\u003e\n\u003cp\u003e(n=30)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"215\"\u003e\n\u003cp\u003eParameters\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003eRho\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003eSig (2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003eRho\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003eSig (2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"215\"\u003e\n\u003cp\u003eFBS (mg/dl)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e-0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e-0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e0.316\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"215\"\u003e\n\u003cp\u003eInsulin (\u0026micro;IU/ml)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.237\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e-0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e0.196\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"215\"\u003e\n\u003cp\u003eHbA1c (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e-0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e0.557\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"215\"\u003e\n\u003cp\u003eHOMA-IR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.004**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e-0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e0.133\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"215\"\u003e\n\u003cp\u003eUrea (mg/dl)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e0.004**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"215\"\u003e\n\u003cp\u003eCreatinine (mg/dl)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"215\"\u003e\n\u003cp\u003eNa (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e-0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e-0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e0.065\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"215\"\u003e\n\u003cp\u003eK (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e-0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.743\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e0.812\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"215\"\u003e\n\u003cp\u003eCl (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e-0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.027*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e0.134\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"215\"\u003e\n\u003cp\u003eCholesterol (mg/dl)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.456\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e-0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e0.239\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"215\"\u003e\n\u003cp\u003eTGL (mg/dl)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.011*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e-0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e0.655\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"215\"\u003e\n\u003cp\u003eHDL (mg/dl)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.769\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e-0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e0.391\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"215\"\u003e\n\u003cp\u003eLDL (mg/dl)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.496\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e-0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"215\"\u003e\n\u003cp\u003eUrinary microalbumin (mg/24hrs)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.70*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.000**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e0.106\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"215\"\u003e\n\u003cp\u003e∆ct miR-21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e-0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"97\"\u003e\n\u003cp\u003e0.697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"81\"\u003e\n\u003cp\u003e-0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e0.517\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA [Sig.2(tailed)] p-value \u0026ge; 0.05 indicates a non-significant difference between the variables, p \u0026le; 0.05(*) indicates a significant difference between the variables and p \u0026le; 0.005(**) indicates an extremely significant difference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. \u003c/strong\u003eFCE of miR-21 in DN patients in comparison to healthy controls\u003c/p\u003e\n\u003ctable border=\"1\" width=\"568\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eHealthy Control (∆ct)\u003c/p\u003e\n\u003cp\u003e(n=30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eDN patients (∆ct)\u003c/p\u003e\n\u003cp\u003e(n=30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e∆∆ct\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003eFold Change Expression\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e2.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e-0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e-3.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd\u003e\n\u003cp\u003e9.18\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\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":"Biomarkers, Diabetic Nephropathy, MicroRNA, type 2 diabetes mellitus, Transforming Growth Factor-β","lastPublishedDoi":"10.21203/rs.3.rs-396068/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-396068/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eDiabetic Nephropathy (DN), a microvascular complication, is a major cause of end-stage renal disease (ESRD). GDF-15 and hsa-miR-21 are closely associated with endothelial dysfunction and inflammation.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e In-silico analysis was used to identify GDF-15 and insulin related protein-protein interaction (PPI) network and a common set of GDF-15 regulating transcription factors. Common targeting miRNA of GDF-15 regulating transcription factors were investigated in miRNet and TargetScan. Further, 30 type 2 DN patients and 30 healthy controls were included for clinical chemistry analysis, to analyze serum GDF-15 levels by ELISA and to evaluate the fold change expression (FCE) of circulating hsa-miR-21 by RT-PCR.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003e\u0026nbsp;In the PPI network of IRS1, IRS2, INSR, IGF1R, INS, AKT1, PPARG, CEBPB, EGR1, TP53, KLF4, ATF3, GDF15, TWIST2, the common nodes between insulin and GDF-15 were identified. MicroRNA-21 was bioinformatically observed to directly target GDF-15 downregulating transcription factors KLF4, TP-53, and CEBPB. Serum GDF-15 was nearly ten (10) folds higher in DN patients (p˂0.0001) as compared to healthy controls. A positive and significant correlation of serum GDF-15 was found with HbA1c, HOMA-IR, serum urea and serum creatinine. The FCE of hsa-miR-21 was 9.18 folds higher in DN patients. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Raised serum GDF-15 and circulating hsa-miR-21 can serve as clinically important therapeutic targets and biomarkers of progressive renal disorder. \u003c/p\u003e","manuscriptTitle":"A clinical and in-silico analysis of hsa-miR-21 and Growth Differentiation factor-15 expression in Diabetic Nephropathy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-04-19 20:20:18","doi":"10.21203/rs.3.rs-396068/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":"e1e285f1-646a-424d-b606-b02a09321668","owner":[],"postedDate":"April 19th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":3753763,"name":"Molecular Biology"}],"tags":[],"updatedAt":"2021-05-21T07:48:02+00:00","versionOfRecord":[],"versionCreatedAt":"2021-04-19 20:20:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-396068","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-396068","identity":"rs-396068","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}