Journal of Obesity & Metabolic Syndrome



J Obes Metab Syndr 2023; 32(1): 64-76

Published online March 30, 2023

Copyright © Korean Society for the Study of Obesity.

Analyzing the Association of Visceral Adipose Tissue Growth Differentiation Factor-15 and MicroRNA in Type 2 Diabetes Mellitus

Dipayan Roy1,2,3, Purvi Purohit1,* , Manoj Khokhar1, Anupama Modi1, Ravindra Kumar Gayaprasad Shukla4, Ramkaran Chaudhary5, Shrimanjunath Sankanagoudar1, Praveen Sharma1

1Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Jodhpur; 2Indian Institute of Technology (ITT)-Madras, Chennai; 3School of Humanities, Indira Gandhi National Open University (IGNOU), New Delhi; 4Department of Endocrinology and Metabolism, All India Institute of Medical Sciences (AIIMS), Jodhpur; 5Department of General Surgery, All India Institute of Medical Sciences (AIIMS), Jodhpur, India

Correspondence to:
Purvi Purohit
Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Medical College Building, Basni Industrial Area Phase 2, Jodhpur, Rajasthan, 342005, India
Tel: +91-9928388223

Received: February 12, 2022; Reviewed : April 7, 2022; Accepted: March 7, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: Growth differentiation factor-15 (GDF-15) is involved in insulin resistance and diabetes. In this study, we determine the associations of GDF-15 with miR-181b-5p, miR-330-3p, mothers against decapentaplegic homolog 7 (SMAD7), and insulin resistance in visceral adipose tissue (VAT) and peripheral blood mononuclear cells (PBMCs) in type 2 diabetes mellitus (T2DM) patients.
Methods: Sixty patients, equally divided into those with T2DM and non-diabetic controls, were recruited for gene expression analysis. Protein-protein interaction (STRING), target prediction (miRNet), and functional enrichment were conducted accordingly.
Results: Our study showed that VAT and PBMCs had similar expression profiles, where GDF-15 and miR-181b-5p were upregulated, whereas SMAD7 and miR-330-3p were downregulated. Serum GDF-15 could differentiate between T2DM and non-diabetic patients (P<0.001). Target prediction revealed a microRNA (miRNA)-messenger RNA regulatory network, transcription factors, and functional enrichment for the miRNA that suggested involvement in T2DM pathogenesis.
Conclusion: VAT GDF-15 is associated with insulin resistance and is possibly regulated by miR-181b-5p, miR-330-3p, and SMAD7 in T2DM.

Keywords: Diabetes mellitus, type 2, Growth differentiation factor 15, Intra-abdominal fat, MicroRNAs, SMAD7

Type 2 diabetes mellitus (T2DM), an all-compassing health issue, is a complex, multifactorial disease responsible for several chronic comorbidities, including cardiovascular and renal complications. According to the 2021 International Diabetes Federation (IDF) Diabetes Atlas, 537 million adults aged 20 to 79 years have diabetes, and the number is predicted to increase to 783 million by 2045.1 Insulin resistance (IR) and β-cell dysfunction are the primary mechanisms behind T2DM pathogenesis. The development of IR and impaired glucose tolerance is further attributed to an increased visceral fat depot, an independent risk factor for T2DM and cardiometabolic disorder.2,3 According to recent findings, unhealthy adipose tissue (AT) expansion results in deposition of M1 macrophages and excess production of pro-inflammatory cytokines.4 However, the interaction of excess visceral adipose tissue (VAT) with obesity-related metabolic complications like IR and T2DM is not fully understood.

Growth differentiation factor 15 (GDF-15), belonging to the transforming growth factor-β (TGF-β) superfamily, is expressed in various human tissues and macrophages. GDF-15 is responsible for energy intake and maintenance through the gut-brain axis.5 Elevated level of GDF-15 is observed in individuals with diabetes and obesity. Further, elevated serum GDF-15 level is positively associated with the risk of diabetes and IR.6-8 GDF-15 is also released from adipocytes; as a stress-inducible cytokine, it is upregulated through several inflammatory proteins,9 increasing its level in AT and circulation. Mothers against decapentaplegic homolog 7 (SMAD7) is a protein that negatively regulates the TGF-β/SMAD signalling pathway by forming hetero-complexes with receptor-activated SMADs (R-SMADs) and directly inhibiting their activity.10 Conditional expression of SMAD7 enhances IR by reducing insulin (INS) expression and β-cell-related nuclear factors in pancreatic β-cells,11 enhancing IR. Furthermore, SMAD7 inhibits murine protein serine/ threonine kinase 38 (MPK38) activity, significantly contributing to obesity.12

MicroRNAs (miRNAs) are implicated in diabetic pathology. Various miRNAs have been considered potential circulatory markers and therapeutic targets in T2DM.13 Transcription factors and miRNA can mutually regulate each other and jointly regulate their shared target genes to form feed-forward loops, playing decisive roles in various pathophysiological processes.14 In white adipose tissue (WAT) of mice, miR-181b improved glucose homeostasis and INS sensitivity by enhancing INS-mediated protein kinase B (AKT)-phosphorylation.15 Circulatory miR-330-3p is increased in gestational diabetes patients with an aggressive diabetic phenotype and worse outcomes.16 MiR-330-3p is downregulated in the VAT of T2DM patients.17 Moreover, its target genes are significant modulators of β-cell proliferation and INS secretion.16

We hypothesized that the deregulated expression of miRNAs miR-181b-5p and miR-330-3p and of SMAD7 and GDF-15 in VAT might contribute to the development of IR in T2DM individuals. The serum GDF-15 level was quantified, and the VAT and peripheral blood mononuclear cells (PBMCs) of the study population were analyzed to explore the associations between GDF-15, SMAD7, miRNA expression levels, and IR indices. We integrated the experimental findings in silico to identify and explore the roles of these molecules in diabetic pathology.

Study population and recruitment

This study was conducted following the principles of the Declaration of Helsinki for medical research involving human subjects, and ethical approval was obtained from the Institutional Ethics Committee (IRB approval no. AIIMS/IEC/2018/636). Informed consent was obtained for all participants before inclusion into the study.

Patients awaiting laparoscopic abdominal surgery in the Department of General Surgery at a tertiary care centre were recruited by convenience sampling. Subjects aged 18 to 60 years willing to participate by donating fasting venous blood and VAT samples were included in the study. Thirty T2DM patients were recruited after examining their clinical history and medical records from the outpatient clinics of the Endocrinology and Metabolism and General Surgery departments. Patients with diagnosed diabetic complications, acute or chronic infective conditions, endocrine disorders other than diabetes, current pregnancy, or receiving drug therapy were excluded. We recruited non-diabetic individuals as controls after investigating their diabetic profiles.

Sample collection and biochemical analysis

Fasting venous blood samples were collected in ethylenediaminetetraacetic acid (EDTA), clot activator, and fluoride vacutainers before anaesthesia induction and the surgical procedure and immediately were processed to obtain serum and plasma. The biochemical parameters were analyzed in a Clinical Chemistry Analyzer AU680 (Beckman Coulter). Fasting INS was analyzed by chemiluminescence assay in Advia Centaur XP (Siemens Healthineers). The same samples were stored at –80 °C, and serum GDF-15 was analyzed in batches by a commercially available sandwich enzymelinked immunosorbent assay (ELISA) kit (Cat. no. #EHGDF15; Thermo Fisher Scientific).

IR indices were assessed following the homeostatic model assessment (HOMA), the HOMA2 calculator (available at:, total cholesterol to high-density lipoprotein (HDL) ratio, triglyceride to HDL ratio, and triglyceride glucose (TyG) index.

RNA isolation and complementary DNA synthesis

Total RNA was isolated from PBMCs using Trizol (RNA-XPress Reagent; HiMedia Laboratories) following the manufacturer’s instructions. VAT was collected during surgery from the same patient and transferred to the laboratory in 1% phosphate buffer saline in a sterile container. Total RNA was subsequently isolated following the protocol as previously described.18 RNA samples with 260 of 280 and 260 of 230 ratios ≥1.8 were considered for further downstream processes. Total RNA was reverse transcribed using a miScript II RT Kit (cat. no. 218160; Qiagen) as per the manufacturer’s instructions. HiFlex Buffer was used to prepare cDNA and quantify mature miRNAs and messenger RNAs (mRNAs).

Real-time polymerase chain reaction

A CFX96 Real-Time System and CFX Manager Software (Bio- Rad) were used for real-time expression analysis of miR-181b-5p (Hs_miR-181b_1, cat. no. MS00006699), miR-330-3p (Hs_miR- 330-3p_2, cat. no. MS00031738), SMAD7 (cat. no. 330001 PPH 01905C), and GDF-15 (cat. no. 330001 PPH01935C) using miScript Primer Assays and RT2 qPCR Primer Assay (Qiagen) (Table 1). A miScript SYBR Green PCR Kit (cat. no. 218075; Qiagen) was used. A 10 μL volume of the reaction mixture was prepared for each sample, and real-time polymerase chain reaction (RT-PCR) was performed on a 96-well reaction plate under the following conditions: initial activation step at 95 °C for 15 minutes, followed by 40 cycles of 15 seconds at 94 °C, 30 seconds at 55 °C, and 30 seconds at 70 °C. All samples in the experimental and control groups were analysed in duplicate, and the mean value was used for analysis. The mean cycle threshold (Ct) values were normalized against the internal control genes glyceraldehyde-3-phosphate dehydrogenase (GAPDH; for mRNA) (cat. no. 330001 PPH00150F) and RNA, U6 small nuclear (RNU6; for miRNA) to calculate gene expression according to the ΔΔCt method.

Construction of protein-protein interaction network, target miRNA prediction, and functional enrichment

For protein-protein interaction (PPI), we used the Search Tool for the Retrieval of Interacting Genes/Proteins database (STRING v11.0) ( and used the search terms GDF-15, SMAD7, and INS as the list of input proteins to obtain functional interpretations of their interacting proteins in Homo sapiens. The minimum interaction score was limited to moderate confidence (0.400), and the maximum number of interactors was 10 in the first shell. The list of genes involved in the PPI network was used to predict the target miRNA and transcription factors on miRNet ( in the genes query list for Homo sapiens. The interaction network between the genes, miRNA, and transcription factors was visualized using the Cytoscape v3.8.0 network visualization tool. The functional enrichment analysis for network genes, obtained from STRING, was visualized on Microsoft Excel using radar charts.

Validation of network gene expression in microarray datasets

We searched Gene Expression Omnibus (GEO) datasets with the following keywords: “Type 2 diabetes,” “diabetes,” “DM,” “T2DM,” “visceral adipose tissue,” “VAT,” “omental adipose tissue,” and “insulin resistance.” The inclusion criteria were studies involving human tissue analysis and those using expression profiling by array. The exclusion criteria were series, sample, or platform studies; analysis on Mus musculus or Rattus norvegicus; studies involving expression profiling by massively parallel signature sequencing (MPSS)/RTPCR/ serial analysis of gene expression (SAGE)/single nucleotide polymorphism (SNP) array/genome tiling array/high throughput sequencing, genome binding/occupancy profiling, genome variation profiling, methylation profiling, non-coding RNA profiling, or protein profiling; and studies of urine, semen, saliva, cell lines, or blood.

The three datasets GSE54350, GSE16415, and GSE20950 were selected for analysis. The samples collected for GSE54350 (diabetes, n=6; non-diabetes, n=6) were AT and macrophage, whereas only omental AT was gathered for the other two datasets, GSE16415 (diabetes, n=5; healthy controls, n=6) and GSE20950 (INS-resistant, n=10; INS-sensitive, n=10).

Functional enrichment analysis of miRNA

The functional enrichment of miR-181b-5p and miR-330-3p was predicted using miRNA Enrichment Analysis and Annotation (miEAA) (, TAM 2.0 (, and MIENTURNET (, all interactive tools for network-based analysis of miRNA-target interactions. The thresholds for the minimum number of interactions and the false discovery rate (FDR) were 2 and 0.05, respectively. The miRTarBase database was chosen to visualize the miRNA-target interaction network. The filter was set according to the “Strong” interaction type.


Data were analyzed in Microsoft Excel and R programming platform (version 3.6.3) using RStudio. Variables were tested for normality by density plot, Q-Q plot, and Shapiro-Wilk significance test. Differences between groups were tested using a two-sample t-test, and Fisher’s exact test was used for categorical variables. For correlation analysis, Spearman’s rank correlation test was used. A twotailed P-value less than 0.05 was considered statistically significant.

Demographic profile and anthropometric and biochemical parameters

Among the 60 study subjects, 34 were men and 26 were women. As expected, significantly higher body mass index (BMI), waist-hip ratio (WHR), fasting blood sugar (FBS), glycated hemoglobin (HbA1c), total cholesterol, and triglycerides were observed in the diabetic group (Table 2). Serum GDF-15 level was significantly elevated in the diabetic group compared to the controls (Supplementary Figure 1A). Receiver operating characteristic (ROC) curve analysis revealed that serum GDF-15 had an area under the curve (AUC) of 0.82 for T2DM (Supplementary Figure 1B).

INS sensitivity and %β-cell function were lower in T2DM cases than in controls, with HOMA-β (controls: 92.78 [66.97 to 109.04]; T2DM: 46.09 [20.13 to 69.85]; P<0.001) and HOMA2-β (controls: 80.85 [71.65 to 99.30]; T2DM: 43.75 [27.20 to 63.68]; P< 0.001) being highly significant. Also, triglyceride/HDL ratio (controls: 2.80 [2.23 to 3.38]; T2DM: 4.06 [2.93 to 5.44]; P=0.026) and TyG index (controls: 8.48 [8.30 to 8.91]; T2DM: 8.48 [8.30 to 8.91]; P<0.001) were significantly higher in people with diabetes.

Correlation analysis

Serum GDF-15 was significantly and positively correlated to age, BMI, WHR, HbA1c, and triglycerides (Supplementary Table 1). The PBMC as well as VAT GDF-15 expression were significantly correlated with FBS (P=0.035, P<0.001), HOMA-%β (P=0.047, P=0.006), and HOMA2-%β (P=0.017, P=0.003). The miR- 181b-5p expression was negatively associated with β-cell function (P<0.001). The TyG index, a known marker for IR as well as cardiovascular disease risk, was significantly correlated to serum GDF- 15 (ρ=0.422, P<0.001), VAT GDF-15 expression (ρ=0.371, P= 0.012), and miR-181b-5p (ρ=0.399, P=0.007) in PBMCs. Blood GDF-15 expression was significantly and positively correlated with miR-181b-5p expression in peripheral blood (ρ=0.587, P<0.001) and VAT (ρ=0.301, P=0.019).

The expression of miR-330-3p in PBMCs and VAT was significantly and positively correlated with the respective SMAD7 expression (PBMC: ρ=0.855, P<0.001; VAT: ρ=0.290, P=0.025). In addition, the expression of both miR-330-3p (ρ=–0.374, P=0.012) and SMAD7 (ρ=–0.421, P=0.006) in VAT was significantly correlated with HbA1c.

Comparison of PBMC and VAT gene expression between study groups

In PBMCs, GDF-15 (fold change 2.75±1.76, P<0.001) and miR-181b-5p (fold change 6.25±4.09, P<0.001) showed significant upregulation, whereas SMAD7 (fold change 0.48±0.21, P< 0.001) and miR-330-3p (fold change 0.26±0.42, P<0.001) showed significant downregulation (Figure 1A). In VAT, the genes showed similar up- and downregulation, but only GDF-15 (fold change 2.32±1.21, P<0.001) and miR-181b-5p (fold change 3.12±3.55, P=0.030) were significant (Figure 1B).

Protein-protein interaction, miRNA-mRNA target regulatory network, and validation in microarray datasets

The PPI network yielded 10 predicted functional partners for the three input proteins based on known interactions from curated databases, laboratory experiments, and text mining (Figure 1C). These proteins were AKT1, insulin-like growth factor 1 receptor (IGF1R), insulin receptor substrate 1 (IRS1), insulin receptor (INSR), IRS2, IGF1, yes-associated protein 1 (YAP1), neural precursor cell expressed developmentally downregulated gene 4-like (NEDD4L), SMAD specific E3 ubiquitin protein ligase 1 (SMURF1), and SMURF2. Based on the predicted data from miRNet, miR- 181b-5p interacts with IRS2, IGF1R, SMAD7, and YAP1. MiR- 181b-5p also targets the transcription factors Finkel-Biskis-Jinkins (FBJ) murine osteogenic sarcoma (FOS), Yin Yang 1 (YY1), CCAATenhancer binding protein-β isoform (C/EBPβ), and metastasis-associated 1 (MTA1). On the other hand, miR-330-3p is involved with the genes SMURF2 and YAP1 and the transcription factors tumor protein p53 (TP53) and YY1.

INSR binding, SMAD binding, and protein binding for molecular function (Supplementary Figure 2A), as well as INSR complex, transcriptional regulator complex, and protein-containing complex for cellular components were among the top categories (Supplementary Figure 2B). The top categories in the biological processes were positive regulation of glucose import, regulation of glucose metabolic process, and INSR signalling pathway (Supplementary Figure 2C). Disease-gene association showed the network to be involved in hyperglycaemia, diabetes mellitus, glucose metabolism disease, and glucose intolerance (Supplementary Figure 2D). IR, regulation of lipolysis in adipocytes, mammalian target of rapamycin signalling pathway, and T2DM were the significant Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways (Supplementary Figure 2E). The network genes observed in the microarray datasets, CCAAT enhancer binding protein beta (CEBPB), IGF1R, INSR, IRS1, IRS2, NEDD4L, SMURF2, and YAP1, were significantly and differentially expressed in the VAT of T2DM patients (Figure 1D).

Functional enrichment for miR-181b-5p and miR-330-3p

The miRNA-target enrichment analysis according to miRTar- Base revealed 11 target genes for miR-181b-5p and miR-330-3p (adjusted P<0.05) (Supplementary Table 2). The network properties at an FDR threshold of 0.05 showed that two selected genes, E2F transcription factor 1 (E2F1) and programmed cell death 4 (PDCD4), were strongly involved. Functional enrichment using the databases KEGG, REACTOME, and Disease Ontology revealed the main pathways involved with the selected miRNA (Figure 2). Significant functions included IR, regulation of the AKT pathway, and immunity. Both miRNAs showed involvement with the transforming growth factor β1 (TGFB1) transcription factor (Supplementary Table 3).

In this study, we report the expression of GDF-15, SMAD7, miR181-5p, and miR-330-3p in the VAT and PBMCs of a T2DM population. In silico analysis showed an interaction of GDF-15 with SMAD7 and INS genes. Furthermore, it showed that miR-181-5p targets SMAD7, whereas miR-330-3p targets PDCD4 and E2F1, key genes regulating IR. The significantly elevated expression of GDF-15 and its significant association with IR indices HOMA-β and HOMA2-%β indicate a possible role in T2DM pathogenesis.

GDF-15 may regulate the pathogenesis of T2DM via SMAD signalling, and there may exist a possible crosslink between INS signalling proteins and the TGF-β/SMAD pathway, which was predicted in our in silico analysis. Increased SMAD7 protein level binds the TGF-β type I receptor and blocks R-SMAD phosphorylation. This interaction, in turn, induces receptor degradation or interferes with SMAD-DNA binding, eventually blocking TGF-β signalling.10 Hence, downregulation of SMAD7 would lead to enhanced GDF-15 expression through the TGF-β pathway. Also, TGF-β signalling regulates β-cell proliferation and pancreatic islet homeostasis. Moreover, the TGF-β inhibitor has been shown to stimulate β-cell replication in mice.19

Both miR-181b and miR-330-3p have been implicated in diabetes mellitus in animal and human studies. The significance of circulating miRNA is two-fold, as they are potential non-invasive markers and mediators of crosstalk between multiple signalling pathways and act differently in physiological and pathological conditions.20 The findings of this study suggest that miR-181b-5p and miR-330- 3p participate in the development of T2DM through an interconnected network. The miR-181 family is a critical regulator of AT function. Sun et al.15 studied AT endothelial cells and found that miR-181b reduced endothelial dysfunction in epididymal WAT, suggesting that miR-181 inhibitors can be a novel therapeutic approach to ameliorate IR. Copier et al.21 worked on a diabetic cardiomyopathy mouse model and found that miR-181-5p was downregulated two-fold, suggesting it as a biomarker (Table 3). However, miR-181b overexpression contributes to the development of diabetic nephropathy by targeting tissue inhibitor of metalloproteinase 3 (TIMP3).22 In this study, miR-181b-5p was upregulated in VAT and peripheral blood with 5.31- and 2.10-fold change, respectively. In silico analysis of miR-181b-5p showed SMAD7 as its target gene, corroborating our finding that upregulated miR-181b-5p can cause IR through SMAD7 degradation and enhanced GDF-15 signalling.

Dysregulated gut microbiota-miR-181 axis induces obesity and IR, where the miR-181 family is not only elevated in patients with obesity, but also promotes fat mass gain and WAT inflammation.23 This explains the upregulated miR-181b-5p in VAT and peripheral blood found in our study. GDF-15 and miR-181b-5p in PBMCs and GDF-15 in VAT were also significantly correlated with HOMA-%β and HOMA2-%β, implying a role in diminishing β-cell function.

MiR-330-3p in diabetes has primarily been explored in gestational diabetes mellitus (GDM).14,24,25 In GDM, increased level of miR- 330-3p was associated with a better response to treatment and was strongly associated with poor glycaemic control.24 Plasma miR- 330-3p was increased by 11.1-fold in GDM compared to non-diabetic pregnant women. E2F1 and cell division control protein 42 (CDC42), targets of miR-330-3p, are involved in β-cell growth and proliferation and glucose-dependent INS secretion. Our in silico analysis showed that both miR-181b-5p and miR-330-3p were strongly involved with E2F1 and PDCD4. E2F1 controls pancreatic β-cell function and INS secretion through the cyclin-dependent kinase 4/retinoblastoma protein/E2F1 (CDK4/pRB/E2F1) pathway.24 E2F1 was higher in the liver biopsy samples of glucose-intolerant individuals with obesity and triggers de novo lipogenesis under fatty conditions by acting as a metabolic switch.26 PDCD4 is a translation inhibitor, the deficiency of which attenuates IR in mice.27 IRS4, involved in cell growth and glucose homeostasis, was also predicted to be one of the targets of miR-330-3p. Xiao et al.25 concluded that miR-330-3p targets glucokinase leading to INS-1 cell dysfunction in GDM. However, miR-330-3p has not yet been explored in T2DM patients, and ours is the first study to observe its expression in VAT and its correlation to GDF-15 and SMAD7, suggesting their interconnected roles in T2DM pathogenesis. The overexpression of miR-330 inhibits the transcriptional regulator high-mobility group AT-hook 2 (HMGA2), reducing the TGF-β pathway inducer SMAD3 in colorectal cancer cell lines.28 A possible implication is that downregulated miR-330-3p increases HMGA2 expression and induces the TGF-β pathway. Previously, HMGA2 has been linked to abdominal AT proliferation, cell senescence, and increased risk of T2DM.29 It promotes adipogenesis through C/EBPβ-mediated peroxisome proliferator-activated receptor gamma (PPARγ) induction and promotes cell cycle proliferation through E2F1 activity.30 This suggests a possible mechanism (Figure 3A) through which the downregulation of miR-330-3p simultaneously induces adipogenesis through the C/EBPβ/PPARγ pathway and positively regulates TGF-β signalling. However, the gene expression levels are not identical in blood and VAT (Figure 3B). This variability can be attributed to the different types of cells present in blood from various tissues.

The bioinformatic analysis revealed transcription factors for miR-181b-5p, namely FOS, YY1, MTA1, C/EBPβ, and TGFB1. The products of the FOS gene family form the AP-1 transcription factor complex, which mediates a hyperglycaemia-induced activation of the human TGF-β1 promoter.31 C/EBPs are a family of C/ EBPβ is increased under cytokine stimulation. Rahman et al.32 further studied C/EBPβ in obesity-induced inflammation and observed that it induces pro-inflammatory genes in macrophages and adipocytes, possibly explaining its connection to the development of IR. The YY1 protein, a common transcription factor to both miR-181b-5p and miR-330-3p, has been widely studied in diabetes and acts by repressing TGF-β1. It is a potential target for fasting hyperglycaemia and treatment of diabetic nephropathy.33 The TGF-β signalling pathway has been implicated in childhood obesity in a recent in silico study.34 Functional enrichment for miRNA also identified the regulation of phosphatase and tensin homolog (PTEN) as a highly significant pathway in REACTOME. PTEN has an established role in hyperglycaemia and in the pathogenesis of diabetic nephropathy through miRNA.35-37

GDF-15 is involved in the pathogenetic mechanism of multiple disorders like diabetes, obesity, and cancer.38,39 It has shown great promise as a diagnostic and prognostic biomarker in various cancers.40,41 In T2DM, serum GDF-15 can act as a predictive adjunct marker because of its reasonable diagnostic accuracy. GDF-15 Fc fusion proteins have shown promise as therapeutic agents in obesity- related disorders and comorbidities, including T2DM.3 Moreover, an in-silico analysis pointed towards possible functionalities of GDF-15 and SMAD7 in an interconnected pathway with miR- 181b-5p and miR-330-3p towards developing IR and T2DM. Exploring and validating the regulatory pathways for these miRNAs will undercover new candidates for clinical testing and therapeutic targeting for hyperadiposity and related complications. Furthermore, connecting the dots between VAT and INS changes in obesity and T2DM will provide a better understanding of the pathogenesis of metabolic disorders. The differential expression (upregulation and downregulation) of the mRNA and miRNA in VAT were also reflected in the blood, which implies that the molecular interactions in VAT relevant to IR also are occurring in the blood. This can also be attributed to blood gene expression being influenced by other tissues.

To conclude, the current study reports, for the first time, the association of GDF-15 with SMAD7 and the regulatory miRNAs, miR-181b-5p and miR-330-3p, in Indian patients with T2DM. The upregulated GDF-15 and miR-181b-5p and downregulated SMAD7 and miR-330-3p in PBMCs and VAT of T2DM patients implicate their role in IR, as supported by the in silico analysis. However, the number of studies exploring these molecules in human VAT is limited, and their analysis has to be explored further for clarity on their mechanistic role in adipogenesis and development of IR at the VAT level.

Fig. 1. Comparison of messenger RNA (mRNA) and microRNA (miRNA) expression levels in type 2 diabetes mellitus (T2DM) patients compared to controls by SYBR Green chemistry-based quantitative polymerase chain reaction. Data are presented as a bar plot for fold change expression as normalized by 2-ΔΔCt probability density with kernel estimator for the genes (growth differentiation factor-15 [GDF-15], mothers against decapentaplegic homolog 7 [SMAD7]) and microRNAs (miR-181b-5p and miR- 330-3p) in both (A) peripheral blood mononuclear cells and (B) visceral adipose tissue in the study population. (C) Protein-protein interaction of GDF-15, SMAD7, and insulin along with their common transcription factors (in orange) with miR-181b-5p and miR-330-3p (in pink). Target prediction was performed on miRNet and network visualization in Cytoscape v3.8.0. (D) Fold change expression for the network genes in T2DM samples of microarray datasets (GSE20950, GSE16415, GSE54350) showed significant differential expression of CCAAT enhancer binding protein beta (CEBPB), insulin-like growth factor 1 receptor (IGF1R), insulin receptor (INSR), insulin receptor substrate 1 (IRS1), IRS2, neural precursor cell expressed developmentally downregulated gene 4-like (NEDD4L), SMAD specific E3 ubiquitin protein ligase 2 (SMURF2), and yes-associated protein 1 (YAP1) in T2DM. Statistical significance considered at *P< 0.05 and P< 0.001. MTA1, metastasis-associated 1; FOS, Finkel-Biskis-Jinkins murine osteogenic sarcoma; AKT1, protein kinase B; YY1, Yin Yang 1; TP53, tumor protein p53.
Fig. 2. Functional enrichment of miR-181b-5p and miR-330-3p. Top five categories according to (A) REACTOME: Regulation of phosphatase and tensin homolog (PTEN) gene transcription, extracellular matrix organization, diseases of glycosylation, phosphatidylinositol-3,4,5-triphosphate (PIP3) activates protein kinase B (AKT) signalling, and PTEN regulation; and (B) Kyoto Encyclopaedia of Genes and Genomes (KEGG): microRNAs in cancer, cellular senescence, mitophagy—animal, cell cycle, and endocrine resistance. FDR, false discovery rate.
Fig. 3. (A) Schematic diagram of possible regulation of miR-181b-5p and miR-330-3p through their transcription factors. Downregulation of miR-330-3p reduces the inhibition of high-mobility group AT-hook 2 (HMGA2),28 which induces transforming growth factor-β (TGF-β) signalling through mothers against decapentaplegic homolog 3 (SMAD3). Again, miR-181b-5p overexpression reduces SMAD7, which further induces the receptor-activated SMADs (R-SMADs). Overexpressed HMGA2 causes an increase in CCAAT enhancer binding protein beta (CEBPB).30 CEBPB, a cytokine-inducible transcription factor, is also stimulated by the presence of a pro-inflammatory state in the diseased condition.32 This has a two-fold action: inducing the miR-181b-5p promoter and aggravating the peroxisome proliferator-activated receptor gamma (PPARγ) pathway through E2F transcription factor 1 (E2F1) activity. The latter is also affected by dysregulated miR-330-3p.24,30 Phosphatase and tensin homolog (PTEN) antagonizes the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) pathway by reconverting phosphatidylinositol-3,4,5-triphosphate (PIP3) back to phosphatidylinositol 3,4-bisphosphate (PIP2). Therefore, regulation of insulin function is performed by the balance between phosphorylation and dephosphorylation. Several microRNAs (miRNAs) are known to target PTEN and overexpress and modulate TGF-β and AKT activation. Overall, this results in cellular proliferation and increased adipogenesis, β-cell destruction, and insulin resistance, leading to a type 2 diabetes mellitus (T2DM) state. (B) The gene expression for messenger RNA (mRNA) and miRNA in visceral adipose tissue (VAT) and peripheral blood. GDF-15, growth differentiation factor-15; GFRAL, glial cell-derived neurotrophic factor receptor-alpha-like; YY1, Yin Yang 1; TβR, TGF-β receptor; GLUT4, glucose transporter type 4; HOMA, homeostatic model assessment; TG, triglyceride; HDL, high-density lipoprotein; TyG, triglyceride glucose.

The primers used for real-time polymerase chain reaction

Primers for mRNA

Assay name Cat. no. Unigene no. RefSeq accession no. Reference position

SMAD7 330001 PPH01905C Hs.465087 NM_005904.3 1,355
GDF-15 330001 PPH01935C Hs.616962 NM_004864.2 443
GAPDH 330001 PPH00150F Hs.592355 NM_002046.5 842

Primers for miRNA

Assay name Position Mature miRNA ID miScript primer assay ID

miR-181b-5p C16 hsa-miR-181b-5p MS00006699
miR-330-3p M03 hsa-miR-330-3p MS00031738
RNU6 P20 RNU6-2 MS00033740

mRNA, messenger RNA; SMAD7, mothers against decapentaplegic homolog 7; GDF-15, growth differentiation factor-15; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; miRNA, microRNA; RNU6, RNA, U6 small nuclear.

Baseline demographic and biochemical characteristics of the study population

Variable Controls (n = 30) T2DM (n = 30) P
Age (yr) 46.54 (40.50–54.75) 52.58 (45.25–58.00) 0.087
Sex 0.794*
Woman 12 (40.00) 14 (46.67)
Man 18 (60.00) 16 (53.33)
Body mass index (kg/m2) 23.72 (21.88–26.15) 28.28 (26.00–31.57) < 0.001
Waist-to-hip ratio 0.88 (0.86–0.92) 0.93 (0.91–1.00) < 0.001
Fasting blood sugar (mg/dL) 98.00 (92.50–107.75) 141.00 (108.00–179.75) < 0.001
HbA1c (%) 5.65 (5.5–6.0) 7.45 (6.7–8.2) < 0.001
Insulin (mIU/L) 8.75 (5.59–12.64) 8.79 (4.03–11.67) 0.442
Total cholesterol (mg/dL) 166.00 (150.25–188.50) 200 (177.75–213.75) 0.008
Triglycerides (mg/dL) 103.50 (78.75–118.00) 161.00 (129.25–186.25) < 0.001
HDL-C (mg/dL) 35.00 (32.00–42.00) 40.5 (35.25–46.75) 0.068
LDL-C (mg/dL) 110.50 (90.25–129.00) 131.00 (113.25–154.75) 0.067
GDF-15 (pg/mL) 689.57 (562.42–817.80) 1,362.19 (955.14–1,958.35) < 0.001

Values are presented as median (interquartile range) or number (%). The probability of the Wilcoxon rank sum test with continuity correction between the two groups was used for nonparametric numerical data, whereas the probability of two-sample t-test was used for parametric data after testing for equality of variances.

*Fisher’s exact test was used for qualitative and categorical data; Statistical significance indicated at P< 0.001 and P< 0.05.

T2DM, type 2 diabetes mellitus; HbA1c, glycosylated hemoglobin; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; GDF-15, growth differentiation factor-15.

Comparison of miR-181b-5p and miR-330-3p expression from this study with that of existing studies and their roles in pathways related to deranged insulin signalling and diabetes mellitus

MicroRNA Relative expression Signalling pathways/targets/sites Experimental model Reference
181b Increased TIMP3 Diabetic patients (n = 54) with and without nephropathy Zhu et al.22
181b Reduced AKT-eNOS-NO signalling Epididymal adipose tissue endothelial cells in obese mice Sun et al.15
181b-5p Reduced Circulation C57BL/6 male mice with diabetic cardiomyopathy Copier et al.21
181b Reduced NF-κB Diabetic, pre-diabetic, and healthy control human subjects Dehghani et al.20
181 family Increased Gut microbiota-miR-181-fat axis C57BL/6 obese mice Virtue et al.23
330-3p Increased Circulation GDM and non-diabetic pregnant patients Sebastiani et al.16
330-3p Increased Circulation GDM patients and non-diabetic controls Pfeiffer et al.24
330-3p Increased Circulation GDM and non-diabetic pregnant patients Xiao et al.25
181b-5p Increased Circulation, VAT T2DM and non-diabetic controls Current study
330-3p Reduced Circulation, VAT T2DM and non-diabetic controls Current study

TIMP3, tissue inhibitor of metalloproteinase 3; AKT, protein kinase B; eNOS, endothelial nitric oxide synthase; NO, nitric oxide; NF-κB, nuclear factor kappa-light-chain-enhancer of activated B cell; GDM, gestational diabetes mellitus; VAT, visceral adipose tissue; T2DM, type 2 diabetes mellitus.

  1. International Diabetes Federation. IDF Diabetes Atlas, 10th ed [Internet]. IDF; 2021 [cited 2023 Mar 14]. Available from:
  2. Basat O, Ucak S, Ozkurt H, Basak M, Seber S, Altuntas Y. Visceral adipose tissue as an indicator of insulin resistance in nonobese patients with new onset type 2 diabetes mellitus. Exp Clin Endocrinol Diabetes 2006;114:58-62.
    Pubmed CrossRef
  3. Sachan A, Singh A, Shukla S, Aggarwal S, Mir I, Yadav R. Serum adipocytokines levels and their association with insulin sensitivity in morbidly obese individuals undergoing bariatric surgery. J Obes Metab Syndr 2020;29:303-12.
    Pubmed KoreaMed CrossRef
  4. Meister BM, Hong SG, Shin J, Rath M, Sayoc J, Park JY. Healthy versus unhealthy adipose tissue expansion: the role of exercise. J Obes Metab Syndr 2022;31:37-50.
    Pubmed KoreaMed CrossRef
  5. Xiong Y, Walker K, Min X, Hale C, Tran T, Komorowski R, et al. Long-acting MIC-1/GDF15 molecules to treat obesity: evidence from mice to monkeys. Sci Transl Med 2017;9:eaan8732.
    Pubmed CrossRef
  6. Bao X, Borné Y, Muhammad IF, Nilsson J, Lind L, Melander O, et al. Growth differentiation factor 15 is positively associated with incidence of diabetes mellitus: the Malmö Diet and Cancer-Cardiovascular Cohort. Diabetologia 2019;62:78-86.
    Pubmed KoreaMed CrossRef
  7. Roy D, Purohit P, Modi A, Khokhar M, Shukla RK, Chaudhary R, et al. Growth differentiation factor-15 as a biomarker of obese pre-diabetes and type 2 diabetes mellitus in Indian subjects: a case-control study. Curr Diabetes Rev 2022;18:e010321189862.
    Pubmed CrossRef
  8. Kempf T, Guba-Quint A, Torgerson J, Magnone MC, Haefliger C, Bobadilla M, et al. Growth differentiation factor 15 predicts future insulin resistance and impaired glucose control in obese nondiabetic individuals: results from the XENDOS trial. Eur J Endocrinol 2012;167:671-8.
    Pubmed CrossRef
  9. Modi A, Dwivedi S, Roy D, Khokhar M, Purohit P, Vishnoi J, et al. Growth differentiation factor 15 and its role in carcinogenesis: an update. Growth Factors 2019;37:190-207.
    Pubmed CrossRef
  10. Yan X, Liao H, Cheng M, Shi X, Lin X, Feng XH, et al. Smad7 protein interacts with receptor-regulated Smads (R-Smads) to inhibit transforming growth factor-β (TGF-β)/Smad signaling. J Biol Chem 2016;291:382-92.
    Pubmed KoreaMed CrossRef
  11. Smart NG, Apelqvist AA, Gu X, Harmon EB, Topper JN, MacDonald RJ, et al. Conditional expression of Smad7 in pancreatic beta cells disrupts TGF-beta signaling and induces reversible diabetes mellitus. PLoS Biol 2006;4:e39.
    Pubmed KoreaMed CrossRef
  12. Seong HA, Ha H. Thr55 phosphorylation of p21 by MPK38/MELK ameliorates defects in glucose, lipid, and energy metabolism in diet-induced obese mice. Cell Death Dis 2019;10:380.
    Pubmed KoreaMed CrossRef
  13. Roy D, Modi A, Khokhar M, Sankanagoudar S, Yadav D, Sharma S, et al. MicroRNA 21 emerging role in diabetic complications: a critical update. Curr Diabetes Rev 2021;17:122-35.
    Pubmed CrossRef
  14. Tsang J, Zhu J, van Oudenaarden A. MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals. Mol Cell 2007;26:753-67.
    Pubmed KoreaMed CrossRef
  15. Sun X, Lin J, Zhang Y, Kang S, Belkin N, Wara AK, et al. MicroRNA-181b improves glucose homeostasis and insulin sensitivity by regulating endothelial function in white adipose tissue. Circ Res 2016;118:810-21.
    Pubmed KoreaMed CrossRef
  16. Sebastiani G, Guarino E, Grieco GE, Formichi C, Delli Poggi C, Ceccarelli E, et al. Circulating microRNA (miRNA) expression profiling in plasma of patients with gestational diabetes mellitus reveals upregulation of miRNA miR-330-3p. Front Endocrinol (Lausanne) 2017;8:345.
    Pubmed KoreaMed CrossRef
  17. Purohit P, Roy D, Modi A, Choudhary R, Shukla RP, Sharma P. Circulating and visceral adipose tissue miR-330-3p is associated with insulin resistance in type II diabetes mellitus. Metabolism 2021;116(Supplement):154552.
  18. Roy D, Tomo S, Modi A, Purohit P, Sharma P. Optimising total RNA quality and quantity by phenol-chloroform extraction method from human visceral adipose tissue: a standardisation study. MethodsX 2020;7:101113.
    Pubmed KoreaMed CrossRef
  19. Dhawan S, Dirice E, Kulkarni RN, Bhushan A. Inhibition of TGF-β signaling promotes human pancreatic β-cell replication. Diabetes 2016;65:1208-18.
    Pubmed KoreaMed CrossRef
  20. Dehghani M, Aghaei Zarch SM, Vahidi Mehrjardi MY, Nazari M, Babakhanzadeh E, Ghadimi H, et al. Evaluation of miR-181b and miR-126-5p expression levels in T2DM patients compared to healthy individuals: relationship with NF-κB gene expression. Endocrinol Diabetes Nutr (Engl Ed) 2020;67:454-60.
  21. Copier CU, León L, Fernández M, Contador D, Calligaris SD. Circulating miR-19b and miR-181b are potential biomarkers for diabetic cardiomyopathy. Sci Rep 2017;7:13514.
    Pubmed KoreaMed CrossRef
  22. Zhu FX, Wu HL, Chen JX, Han B, Guo YF. Dysregulation of microRNA-181b and TIMP3 is functionally involved in the pathogenesis of diabetic nephropathy. J Cell Physiol 2019;234:18963-9.
    Pubmed CrossRef
  23. Virtue AT, McCright SJ, Wright JM, Jimenez MT, Mowel WK, Kotzin JJ, et al. The gut microbiota regulates white adipose tissue inflammation and obesity via a family of microRNAs. Sci Transl Med 2019;11:eaav1892.
    Pubmed KoreaMed CrossRef
  24. Pfeiffer S, Sánchez-Lechuga B, Donovan P, Halang L, Prehn JH, Campos-Caro A, et al. Circulating miR-330-3p in late pregnancy is associated with pregnancy outcomes among lean women with GDM. Sci Rep 2020;10:908.
    Pubmed KoreaMed CrossRef
  25. Xiao Y, Ding J, Shi Y, Lin L, Huang W, Shen D, et al. MiR-330-3p contributes to INS-1 cell dysfunction by targeting glucokinase in gestational diabetes mellitus. J Obstet Gynaecol Res 2020;46:864-75.
    Pubmed CrossRef
  26. Denechaud PD, Lopez-Mejia IC, Giralt A, Lai Q, Blanchet E, Delacuisine B, et al. E2F1 mediates sustained lipogenesis and contributes to hepatic steatosis. J Clin Invest 2016;126:137-50.
    Pubmed KoreaMed CrossRef
  27. Wang Q, Dong Z, Liu X, Song X, Song Q, Shang Q, et al. Programmed cell death-4 deficiency prevents diet-induced obesity, adipose tissue inflammation, and insulin resistance. Diabetes 2013;62:4132-43.
    Pubmed KoreaMed CrossRef
  28. Mansoori B, Mohammadi A, Naghizadeh S, Gjerstorff M, Shanehbandi D, Shirjang S, et al. miR-330 suppresses EMT and induces apoptosis by downregulating HMGA2 in human colorectal cancer. J Cell Physiol 2020;235:920-31.
    Pubmed CrossRef
  29. Markowski DN, Thies HW, Gottlieb A, Wenk H, Wischnewsky M, Bullerdiek J. HMGA2 expression in white adipose tissue linking cellular senescence with diabetes. Genes Nutr 2013;8:449-56.
    Pubmed KoreaMed CrossRef
  30. Xi Y, Shen W, Ma L, Zhao M, Zheng J, Bu S, et al. HMGA2 promotes adipogenesis by activating C/EBPβ-mediated expression of PPARγ. Biochem Biophys Res Commun 2016;472:617-23.
    Pubmed CrossRef
  31. Weigert C, Sauer U, Brodbeck K, Pfeiffer A, Häring HU, Schleicher ED. AP-1 proteins mediate hyperglycemia-induced activation of the human TGF-beta1 promoter in mesangial cells. J Am Soc Nephrol 2000;11:2007-16.
    Pubmed CrossRef
  32. Rahman SM, Janssen RC, Choudhury M, Baquero KC, Aikens RM, de la Houssaye BA, et al. CCAAT/enhancer-binding protein β (C/EBPβ) expression regulates dietary-induced inflammation in macrophages and adipose tissue in mice. J Biol Chem 2012;287:34349-60.
    Pubmed KoreaMed CrossRef
  33. Gao P, Li L, Yang L, Gui D, Zhang J, Han J, et al. Yin Yang 1 protein ameliorates diabetic nephropathy pathology through transcriptional repression of TGFβ1. Sci Transl Med 2019;11:eaaw2050.
    Pubmed CrossRef
  34. Roy D, Modi A, Ghosh R, Ghosh R, Benito-León J. Visceral adipose tissue molecular networks and regulatory microRNA in pediatric obesity: an in silico approach. Int J Mol Sci 2022;23:11036.
    Pubmed KoreaMed CrossRef
  35. Khokhar M, Roy D, Modi A, Agarwal R, Yadav D, Purohit P, et al. Perspectives on the role of PTEN in diabetic nephropathy: an update. Crit Rev Clin Lab Sci 2020;57:470-83.
    Pubmed CrossRef
  36. Khokhar M, Roy D, Tomo S, Gadwal A, Sharma P, Purohit P. Novel molecular networks and regulatory microRNAs in type 2 diabetes mellitus: multiomics integration and interactomics study. JMIR Bioinform Biotechnol 2022;3:e32437.
  37. Khokhar M, Roy D, Bajpai NK, Bohra GK, Yadav D, Sharma P, et al. Metformin mediates MicroRNA-21 regulated circulating matrix metalloproteinase-9 in diabetic nephropathy: an in-silico and clinical study. Arch Physiol Biochem 2021; [Epub].
    Pubmed CrossRef
  38. Modi A, Roy D, Purohit P, Pareek P, Vishnoi J. Analysis of serum growth differentiation factor-15 in progression of breast cancer in type II diabetes mellitus patients. Metabolism 2021;116(Supplement):154538.
  39. Modi A, Purohit P, Roy D, Vishnoi JR, Pareek P, Elhence P, et al. FOXM1 mediates GDF-15 dependent stemness and intrinsic drug resistance in breast cancer. Mol Biol Rep 2022;49:2877-88.
    Pubmed CrossRef
  40. Modi A, Purohit P, Gadwal A, Roy D, Fernandes S, Vishnoi JR, et al. A combined analysis of serum growth differentiation factor-15 and cancer antigen 15-3 enhances the diagnostic efficiency in breast cancer. EJIFCC 2021;32:363-76.
  41. Roy D, Modi A, Purohit P, Khokhar M, Goyal M, Sharma S, et al. Growth differentiation factor-15 as a candidate biomarker in gynecologic malignancies: a meta-analysis. Cancer Invest 2022;40:901-10.
    Pubmed CrossRef