Background

Diabetes mellitus (DM) is a multifaceted and complicated metabolic disorder characterized by heightened glucose levels in the bloodstream due to impaired secretion of insulin, insulin activity, or both. There exists a correlation between diabetes-related chronic hyperglycemia and enduring dysfunction, injury, and failure of multiple systems, particularly neurological, optic, renal, and cardiovascular systems [1].

Diabetic nephropathy (DN) is a prominent sequel of DM distinguished by enlargement of glomeruli, deposits of extracellular matrix, and thickening of the membranes of the basement membrane, tubules and glomeruli. Chronic renal failure ultimately ensues as a consequence of these alterations, which manifest as tubulointerstitial and glomerular fibrosis and sclerosis [2, 3]. The International Diabetes Federation (IDF) estimates that 40% of diabetics may get end-stage renal failure [4].

TGF-β is an extremely versatile regulator that exerts control over an extensive array of cellular processes, including but not limited to adhesion, migration, extracellular matrix formation, and cell proliferation. The physiological presence of TGF-β is essential for support of normal development, tissue healing, and organ functionality [5].

Individuals with diabetes, irrespective of the form of the disease (type I or type II), demonstrate elevated levels of TGF-β expression in the tubules and glomeruli throughout the disease's early and late stages. A direct correlation has been identified between the level of glycemic control achieved by individuals with diabetes and the synthesis of TGF-β [6].

Intracellular signaling is initiated when TGF-β binds to receptor complexes composed of type I and type II transmembrane serine/threonine kinases that are closely related TβR-I and TβR-II, respectively. Smads function as intracellular mediators of TGF-β signaling by acting in the direction opposite to the TβR-I receptors [7, 8]. TGF- β1 acts as a transcriptional regulator of renal inflammation and fibrosis by stimulating signaling molecules, such as Smad3 and noncoding RNAs dependent on Smad3. This process is inhibited by Smad7 [9].

MiRNAs are endogenous single-stranded noncoding RNAs that are involved in post-transcriptional control of a variety of cellular biological functions. Base-pairing with the target messenger RNAs (mRNAs), commonly in the 3ˋUTR, might contribute to this control. When a miRNA attaches to the target mRNA, it usually causes translational repression as well as exonucleolytic mRNA degradation [10]. Prior studies have indicated that microRNAs (miRNAs) may influence the course of diabetic kidney disease, specifically by regulating fibrosis via the action of TGF-β1 [11].

An analysis of microRNA expression patterns has revealed a majority of a specific cluster of miRNAs in the kidneys of adult humans: miRNA 215, miRNA 146a, and miRNA 886. Furthermore, the kidney contains greater quantities of additional miRNAs, including miRNA 192, miRNA 194, miRNA 21, miRNA 200a, and miRNA 204, in comparison with the other organs [12]. Through the establishment of an intricate network comprising targeted genes and signaling cascades, miRNA-21 contributes to the advancement of DN. That network facilitates various biological processes, including fibrosis, inflammation, extracellular matrix deposition and epithelial-to-mesenchymal transition [Analysis of the expression of miRNA genes

Serum RNA was extracted utilizing the miRNeasy Mini Kit (Catalog no. 217004) to profile miRNA expression. miRNA complementary DNA (cDNA) was synthesized via reverse transcription of RNA using the stem-loop RT primer and the TaqMan® MicroRNA Reverse Transcription reagent. This enabled the reverse transcription of synthetic controls and target miRNAs simultaneously. Real-time PCR was conducted utilizing TaqMan microRNA assays that are specific to the mature sequence under evaluation. miR-21 (hsa-miR-21-3p) MIMAT0004494 with mature sequence CAACACCAGUCGAUGGGCUGU, miR-192 (hsa-miR-192-3p) MIMAT0007017 with mature sequence CUGCCAAUUCCAUAGGUCACAG and Cel-miR-39 (Cel-mir-39-3p) MIMAT0000010 with mature sequence UCACCGGGUGUAAAUCAGCUUG.

The qPCRs were performed employing an Applied Biosystems StepOne real-time PCR instrument. The cycle threshold was implemented to figure out the level of miRNA expression. To determine the degree of expression demonstrated by a particular miRNA, the CT value of that miRNA is subtracted from the average CT value of reference genes per sample in a provided set of samples. As a standard, the synthetic control gene was utilized. Using equation 2−∆∆CT, the relative expression (fold change) of each putative miRNA in every group was calculated, with Cel-miR-39 serving as the reference gene. The ΔCT value was calculated for each miRNA in each sample utilizing the subsequent formula: to determine the ΔCT sample, the CT value of the miRNA is subtracted from the CT value of Cel miR 39. Next, the ΔΔCT value was determined using the formula: ΔΔCT = (CT miRNA − CT Cel miR 39) for the patient group − (CT miRNA − CT Cel-miR 39) for the control group.

Chemicals used

TGF-β1 was measured using the Human TGF beta1 platinum ELISA kit from Affymetrix eBioscience, catalog number BMS249-4. Serum RNA extraction was done utilizing the miRNeasy Mini Kit (Catalog no. 217004). cDNA was synthesized via reverse transcription of RNA using the stem-loop RT primer and the TaqMan® MicroRNA Reverse Transcription reagent. Real-time PCR was conducted utilizing TaqMan microRNA assays and performed on an Applied Biosystems StepOne real-time PCR instrument.

Statistical analysis

For classification and data entry, version 26 of the Statistical Package for the Social Sciences (SPSS) (IBM Corp., Armonk, NY, USA) was used. The quantitative data were specified by the minimum and maximum values, the mean, standard deviation, and median. An alternative approach was used to describe the categorical data by employing frequency (count) and relative frequency (percent). The Kruskal–Wallis and Mann–Whitney tests, which are nonparametric, have been used to compare quantitative variables. Categorical data were compared utilizing the chi-squared test (χ2). The precise test was used in situations where the expected frequency was less than five. To determine the correlations between quantitative variables, the Spearman correlation coefficient was applied. We considered P-values less than 0.05 to be statistically significant. An analysis of the area under the receiver operating characteristic (ROC) curve was performed to ascertain the most effective cutoff value.

Results

Table 1 contains demographic, clinical, and biochemical information regarding the study participants.

Table 1 Demographic, clinical and biochemical characteristics of studied participants
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A statistically significant difference in miR21 expression was identified across the three groups under investigation (p value = 0.043). The median values of miRNA21 FC in the serum of the overt proteinuric group were significantly higher than those of the normoalbuminuric group (5.57 FC versus 1.11 FC, p = 0.017). There were no statistically significant variations observed in the expression of miR192 or TGF-β1 serum levels across the three groups under investigation (Tables 1, 2, 3, 4, 5, and Figs. 1, 2, 3).

Table 2 Serum expression of miR21, miR192 and serum TGF-β1 in diabetic normoalbuminuric group and diabetic microalbuminuric + proteinuric group
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Table 3 Serum expression of miR21, miR192 and serum TGF-β1 based on the absence or presence of complications (in the form of retinopathy, peripheral neuropathy, chronic kidney disease or ischemic heart disease)
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Table 4 Correlation analysis between the studied markers and both demographic and laboratory data in studied patients
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Table 5 Correlation between serum expression of miR21, miR192 and serum TGF-β1
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Fig. 1
figure 1

miR21 expression (FC) levels within the studied groups

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Fig. 2
figure 2

miR192 expression (FC) levels within the studied groups

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Fig. 3
figure 3

Serum TGF-β1 (pg/ml) levels within the studied groups

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As a potential diagnostic marker for the progression of kidney insult, serum expression of miR21 and miR192, as well as blood level of TGF1, were assessed using a receiver operating characteristic curve (ROC) analysis (Fig. 4). Serum miR21 exhibited a sensitivity of 55.9% and specificity of 88% in detecting the progression of kidney insult at a cut-off value of 3.258 FC; the AUC was 0.719 (p = 0.013, 95% CI 0.579–0.858).

Fig. 4
figure 4

ROC curve for miR21, miR192 and TGFβ1

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Area under the curve

p value

95% CI

Cut off

Sensitivity %

Specificity %

Lower bound

Upper bound

miR21

0.719

0.013

0.579

0.858

3.258

55.9

87.5

miR192

0.378

0.167

0.215

0.540

–

–

–

TGF-β1

0.631

0.140

0.451

0.810

–

–

–

Discussion

Serum levels of miRNA 21, miRNA 192 expression and TGF-β1 were assessed in type II diabetic patients in the current study to determine their relationship with glycemic control, metabolic abnormalities, and renal function.

The molecular pathogenesis of diabetic nephropathy is multifactorial, involving pro-fibrotic and pro-inflammatory cytokines (e.g., TGF-β), pro-inflammatory factors (as interleukin IL-1, 6 and 18), endothelin systems, in addition to protein kinase C and other biochemical aberrations [18]. Multiple pathological processes, including adhesion, migration of multiple cell types, cellular proliferation, differentiation, programmed cell death and extracellular matrix protein synthesis, are activated and regulated by TGF-β [19]. During renal fibrosis, the regulation of the expression of TGF-β dependent miRNAs (miR21, miR192, and the miR29 family) is intricately controlled by TGF-β1 through the Smad3 pathway [20]. It was found that TGF/Smad proteins are involved in the biosynthesis and regulation of microRNAs. Prior research has established the regulatory function of microRNAs in the pathophysiology and development of the kidneys. It has been demonstrated that dysregulation of several of these miRNAs contributes to the pathophysiology and progression of DN. The discovery that miRNAs are consistently detected in the bloodstream outside of cells indicates that they function as extracellular signaling molecules and potentially function as noninvasive biomarkers for an extended array of diseases [21].

There has been discussion regarding the potential of microRNAs (miRNAs) to act as therapeutic targets and diagnostic or prognostic indicators in incidences of chronic renal disease [22].

Nevertheless, the utilization of miRNA signatures in clinical settings is challenging due to the conflicting findings in the research that examines the expression profile of these miRNAs in chronic kidney disease (CKD) [11].

DN is characterized by a dramatic increase in miRNA21 expression in plasma, urine, and renal tissue; this increase has continued with the progression of DN. By interacting with multiple signaling cascades and binding to target proteins, miRNA21 establishes a complex network that promotes DN [13]. Hung et al., found that miR21 levels are upregulated in type I diabetic patients with overt proteinuria and microalbuminuria (p = 0.001, p = 0.0024, respectively) as compared to patients with normoalbuminuria [23]. In agreement, miR21 was only significantly higher in the overt proteinuric group when compared to the normoalbuminuric group in the current study. According to Fouad et al. study, when compared to ACR, plasma miRNA21 demonstrated higher sensitivity (94.1%) and specificity (100%) in diagnosing DN at a cutoff of 0.01. ACR cutoff levels of 45 mg/gm and 89% specificity and 88.2% sensitivity were observed [24].

The results of this investigation indicated that serum miR21 exhibited a sensitivity of 55.9% and specificity of 88% at a cut-off value of 3.258 FC, as measured by an AUC of 0.719 (p = 0.013, 95% CI 0.579–0.858). The inconsistency in the findings may be accounted for by the limited sample size utilized in our research and the distinct methodologies employed for miRNA retrieval, isolation, storage and analysis.

MiRNA 192 expression did not differ statistically between normoalbuminuric, microalbuminuric and overt proteinuric type II diabetic patients (p value = 0.234). In agreement with this study, Hung et al. reported that there was no significant variation in miRNA192 levels between T2DM subjects with normoalbuminuria versus subjects with microalbuminuria and overt proteinuria [23]. Furthermore, Al-Kafaji and Al-Muhtaresh found no statistically significant difference between microalbuminuric and normoalbuminuric diabetic groups in terms of miRNA192 expression. In contrast, the study authors documented a statistically significant decrease in miR192 expression between the macroalbuminuric and normoalbuminuric groups (p < 0.005) [21].

Contrary to this study, Lotfy and coworkers found a statistically significant decrease in miRNA192 expression level in microalbuminuric and macroalbuminuric groups when compared to the normoalbuminuric group [25].

In this study, the median level of miRNA192 exhibited a substantial negative relationship with the duration of diabetes (r = − 0.303, p = 0.033). That was similar to El-Monem et al. who determined that there is a correlation between a reduction in miRNA192 and an increase in renal fibrosis in living organisms. Additionally, it was seen that the expression of miRNA192 decreases as the duration of the disease becomes longer which ranges from 5 to 15 years [26]. Ezzat et al. found significantly higher levels of miR192 in individuals with long-standing disease (43.5 ± 24 months), contrary to the current study [27].

In this work, there was no statistical difference in serum TGF-β1 levels between normoalbuminuric, microalbuminuric and overt proteinuria type II diabetic patients (p value = 0.225). In disagreement, **aoyu et al. showed that the levels of TGF-β1 in the overt proteinuric group were significantly higher than in the microalbuminuric and normoalbuminuric groups (p value < 0.01) [28]. Moreover, a study done by Mou et al., documented that individuals with type 2 diabetes mellitus who presented with microalbuminuria had elevated serum TGF-β1 levels in comparison with those with normoalbuminuria. Similarly, individuals with macroalbuminuria had elevated serum TGF-β1 levels in comparison with those with microalbuminuria [29]. Tianbiao et al. reached at the similar conclusions [30]. The results of the previous research contradicted the findings of the present investigation.

TGF-β1 and miRNA21 exhibited a significant positive correlation in this study (r = 0.428, p = 0.002). There are functional interactions between miRNA21 and TGFβ. Smad 7 is a direct target for miR21, leading to its inhibition and enhancement of TGFβ signaling [7,8,9]. This finding may provide insight into the etiology of DN, as there is evidence that in vitro cultured endothelial cells are susceptible to miRNA modifications induced by hypoxia and elevated glucose. The correlation between miRNA21 upregulation and mitochondrial dysfunction, as well as oxidative stress, is not unexpected. Similarly, hyperglycemia contributes to diabetic nephropathy through direct or indirect hemodynamic mechanisms that cause renal injury. Contributing to tubulointerstitial injury and glomerular sclerosis, it stimulates intrinsic glomerular cells to secrete TGF-β1, activates protein kinase C, and increases AGE production (31, 32).

There were limitations in this study that restricted the precise interpretation of the parameters tested during diabetic nephropathy progression. The previously mentioned restrictions involve a comparatively small sample size, the absence of any apparent correlation between levels of miR21 and renal histopathological condition as assessed through renal biopsies, and the absence of subsequent repeated measurements of candidate miRNAs.

Conclusions

In summary, the findings of the present investigation revealed a statistically significant increase in the expression level of miRNA21 among diabetic individuals presenting with both overt proteinuria and microalbuminuria, as opposed to those with normoalbuminuria. There was an absence of statistically significant variation observed in the serum levels of TGF-β1 and miRNA192 among the three groups that were investigated. A significant positive correlation was observed between median levels of miRNA21 and ACR, while a significant negative correlation was observed between serum expression of miRNA192 and the duration of diabetes. The examined parameters exhibited no correlation with BMI, BP, FBS, PP, HbA1c, cholesterol, triglyceride, HDL, or LDL.

In diabetic patients, miRNA21 may serve as a useful noninvasive biomarker for the early detection of ongoing endothelial dysfunction, according to our findings. That may provide insights into the diagnostic, predictive, and therapeutic role of miRNA 21 in treating DN. The current evidence highlights an important area for future research focusing on the effective biomarkers for DN, which may facilitate early diagnosis and the judgment of prognosis for DN. Here, the findings on the pivotal role of miR-21 in the pathogenesis of DN may indicate that miR-21 could serve as a potential therapeutic target.