Skip to main content

MicroRNA 146a is associated with diabetic complications in type 1 diabetic patients from the EURODIAB PCS



MicroRNA-146a-5p (miR-146a-5p) is a key regulator of inflammatory processes. Expression of miR-146a-5p is altered in target organs of diabetic complications and deficiency of miR-146a-5p has been implicated in their pathogenesis. We investigated if serum miR-146a-5p levels were independently associated with micro/macrovascular complications of type 1 diabetes (DM1).


A nested case–control study from the EURODIAB PCS of 447 DM1 patients was performed. Cases (n = 294) had one or more complications of diabetes, whereas controls (n = 153) did not have any complication. Total RNA was isolated from all subjects and miR-146a-5p levels measured by qPCR. Both the endogenous controls U6 snRNA and the spike (Cel-miR-39) were used to normalize the results. Logistic regression analysis was carried out to investigate the association of miR-146a-5p with diabetes complications.


MiR-146a-5p levels were significantly lower in cases [1.15 (0.32–3.34)] compared to controls [1.74 (0.44–6.74) P = 0.039]. Logistic regression analysis showed that levels of miR-146a-5p in the upper quartile were inversely associated with reduced odds ratio (OR) of all complications (OR 0.34 [95% CI 0.14–0.76]) and particularly with cardiovascular diseases (CVD) (OR 0.31 [95% CI 0.11–0.84]) and diabetic retinopathy (OR 0.40 [95% CI 0.16–0.99]), independently of age, sex, diabetes duration, A1c, hypertension, AER, eGFR, NT-proBNP, and TNF-α.


In this large cohort of DM1 patients, we reported an inverse and independent association of miR-146a-5p with diabetes chronic complications and in particular with CVD and retinopathy, suggesting that miR-146a-5p may be a novel candidate biomarker of DM1 complications.


Patients with type 1 diabetes mellitus (DM1) are at high risk of developing micro (retinopathy, nephropathy, neuropathy) and macrovascular complications of diabetes. Discovery of new biomarkers of complications is crucial to improve prediction, early diagnosis, and prognosis.

There is increasing evidence that abnormally expressed microRNAs (miRNAs) in target organs of diabetic complications are involved in the pathogenesis of vascular complications of diabetes [1,2,3]. MiRNAs are also present in body fluids and circulating miRNAs hold good promise as non-invasive clinical biomarkers. Indeed, serum miRNAs are very stable as they are either packaged in extracellular vesicles (EVs) or bound to proteins that protect them from endogenous RNases activity [4,5,6,7].

There is relatively poor knowledge on circulating miRNAs as biomarkers in the context of DM1 complications [2, 8,9,10]. However, serum levels of a panel of specific TGF-β1-regulated miRNAs predict progression to end-stage renal failure (ESRF) in DM1 patients with proteinuria [9]. Moreover, circulating miR-27b, miR-320a, and miR-126 levels were found independently associated with diabetic retinopathy [11, 12].

miR-146a-5p is a key negative regulator of inflammatory processes. Specifically, NF-kB, a pro-inflammatory transcription factor, induces miR-146a-5p expression, which in turn represses target genes important in NF-kB activation [13, 14]. Therefore, miR-146a-5p expression is a mechanism by which NF-κB restrains its inflammation-promoting activity and favors resolution of inflammation. In addition, miR-146a-5p prevents inflammasome activation and macrophage shift to the proinflammatory M1 phenotype [15]. Preclinical studies have shown that insufficient miR-146a-5p expression fuels inflammation thereby contributing to the pathogenesis of diabetes vascular complications [15,16,17,18,19].

Whether serum miR-146a-5p levels are associated with vascular complications of DM1 diabetes is unknown; however, miR-146a-5p was one of the 25 differentially expressed miRNAs in a profiling analysis performed in pooled serum samples from DM1 patients with and without complications [20].

The aim of the present study was to confirm these profiling results and to assess if elevated miR-146a-5p levels affected odds ratios (ORs) of micro/macrovascular complications in DM1 patients from the EURODIAB PCS nested case–control study.


Patient sample

The EURODIAB Type 1 Diabetes Complications Study (1989–1991) was designed to discover risk factors of diabetes complications in 3,250 subjects with DM1[21]. Seven years later study participants were invited for re-examination in the EURODIAB Prospective Complication Study (PCS 1997–1999) [22] and a nested case–control study was designed at follow-up. Cases had at least one complication among cardiovascular diseases (CVD), retinopathy, and albuminuria; whereas controls were free of complications [23,24,25,26]. The present study included 460 subjects (300 cases and 160 controls, ratio 1.9) with available serum samples and data on complications. Cases and controls were unmatched and adjustments were made at the analysis stage. Risk factors [cholesterol, hypertension, body mass index (BMI), triglycerides, A1c] were assessed as previously described [26]. Retinopathy was evaluated and graded from retinal photographs using the EURODIAB protocol [22]. Albumin excretion rate (AER) was measured on two 24-h urine collections by immunoturbidimetry and classified as normo- (< 20 μg/min), micro- (20–200 μg/min), or macro-albuminuria (≥ 200 μg/min). Glomerular filtration rate (eGFR) was estimated using the four-component equation from the Modification of Diet in Renal Disease study [27]. CVD was defined as a physician-diagnosed positive history of myocardial infarction, coronary artery bypass graft surgery, angina, stroke and/or ischemic changes on a centrally Minnesota-coded EKG.

The study was approved by the Ethical Committee of Turin University. The procedures were in accordance with the Helsinki Declaration. Written consent has been obtained from each patient or subject after full explanation of the purpose and nature of all procedures used.


Tumor necrosis factor α (TNF-α) and interleukin 6 (IL-6) levels were measured by ELISA assays (R&D Systems) as described previously [23, 26]. NT-proBNP was measured by two-site sandwich electrochemiluminescence immunoassay (Elecsys proBNP II; Roche, Mannheim, Germany) [25].

RNA isolation

Total RNA was prepared using the Trizol®LS reagent (Thermo Fisher, Milan, Italy) according to the manufacturer’s recommendations. In brief, 200 µl of serum was mixed thoroughly with 750 µl of TRIZOL®LS reagent. Mixtures were then gently inverted 5–8 times, and incubated at room temperature (RT) for 15 min. After which, both the spike-in Cel-miR-39 (3 µl) and 200 µl of chloroform were added and the solution was mixed vigorously. The samples were then centrifuged at 12,000xg for 15 min at 4 °C, the upper aqueous phase was carefully transferred to a new tube, upon which 500 µl isopropanol was added, and then incubated for 10 min at RT, before then being centrifuged a 4 °C, 12,000xg for 10 min. Pellets were washed with 75% ethanol, air-dried at RT for 10 min, and re-suspended in 25 μl of nuclease free H2O. RNA quality was assessed by capillary electrophoresis on an Agilent-2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA).

Reverse transcription (RT) and pre-amplification

RT reaction was performed by using TaqMan MicroRNA Reverse Transcription Kit according to the company’s recommendations. Three µl of RNA solution from the 25 µl eluate was combined with 6 µl of RT primer pool, 0.30 µl of dNTPs with dTTP (100 mmol/L), 1.50 µl of 10 × RT Buffer, 3 µl of Multiscribe Reverse Transcriptase and 0.19 µl of RNase (20U/µl) to a final volume of 15 µl. The RT-PCR reaction was set as follows: 16 °C for 30 min, 42 °C for 30 min and 85 °C for 5 min using a Veriti thermocycler (Thermo Fisher, Milan, Italy). The RT reaction products were further amplified using the Megaplex PreAmp Primers (Thermo Fisher, Milan, Italy). A 2.5 µl aliquot of the RT reaction product was combined with 12.5 µl of Pre-amplification Mastermix (2x) and 3.75 µl of Preamplification primer pool (10x) to a final volume of 25 µl. The pre-amplification reaction was performed by heating the samples at 95 °C for 10 min, followed by 12 cycles of 95 °C for 15 s and 60 °C for 4 min. Finally, samples were heated at 99.9 °C for 10 min to ensure enzyme inactivation. Pre-amplification reaction products were diluted to a final volume of 100 µl and stored at − 20 °C.

Taqman qPCR assay

qPCR was performed using Taqman reagents (Taqman miRNA Assay, Taqman Universal PCR Master Mix No AmpErase UNG). Diluted pre-amplification products (0.10 µl) were combined with 0.50 µl of Taqman miRNA Assay (20x) and 5 µl of the Taqman Universal PCR Master Mix No AmpErase UNG (2x) to a final volume of 10 µl. qPCR was performed on an Applied Biosystems 7900HT thermocycler at 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min. The comparative Ct method (2−ΔΔCt). (SDS2.2 software) was used to calculate relative expression. Results were normalized using both the endogenous control U6 snRNA and the spike in Cel-miR-39. Thirteen samples with Ct values ≥ 35 or undetermined for Cel-miR-39, miR-146a-5p and U6 ≥ 35 were excluded from the analyses.

Statistical analyses

Normally distributed variables are reported as means (standard deviation, SD), while non-normally distributed variables were log-transformed (miR-146a-5p, triglycerides, NT-proBNP, TNF-α, IL6, AER) and reported as geometric means (25°-75° percentiles). Student's t-test and ANOVA were used for comparisons. Categorical variables were compared using the Chi-Squared test. Pearson’s correlation coefficient analysis was used to assess the relationship between miR-146a-5p and clinical variables. Logistic regression analysis was employed to estimate the odds ratios (ORs) of miR-146a-5p for all chronic complications (micro-macroalbuminuria, CVD, retinopathy), independently of established risk factors and confounders. We used the likelihood ratio test to compare nested models, exploring the relevance of age, gender, diabetes duration, BMI, A1c, blood pressure, total cholesterol, AER, eGFR, NT-proBNP, IL-6, TNF-α, and smoking. Analyses were hypothesis-oriented and variables were retained in the final model if they added significantly to the likelihood of models or to the estimated coefficients of predictors. As miR-146a-5p may play a different role in various complications, models were also fitted for each complication separately. To assess ORs for increasing miR-146a-5p levels, miR-146a-5p levels were categorized by quartile distribution in controls. Because ORs in the three lower quartiles were similar, they were combined as the reference category in the final analysis and compared to the upper quartile. A p value < 0.05 was considered statistically significant. Analyses were performed using the SPSS (Version 27) software.


Patients’ clinical characteristics

The studied population (n = 447) (Fig. 1) had an average age of 39.5 ± 10.1 years, mean diabetes duration of 21.6 ± 9.7 years, and a similar percentage of men and women. Among the 294 cases, 179 individuals had nephropathy (40.8% micro- and 59.2% macroalbuminuria) and 257 individuals had retinopathy (background 48.6% and proliferative 51.4%). However, most subjects 165 (56.1%) had both microvascular complications. CVD was present in 117 subjects (39.8%) and most of them had CAD (90.6%). Table 1 shows the characteristics of both cases and controls. Overall cases had a worse risk factor profile than controls, as shown by more unfavorable levels of conventional risk factors and inflammatory cytokines.

Fig. 1

Flow chart for the study population. The map shows the European countries that participated to the EURODIAB study (dark grey)

Table 1 Characteristics of the individuals recruited in the Nested Case–Control Study of the EURODIAB PCS

miR-146a-5p expression

MiR-146a-5p was measurable in all the 447 samples and value distribution was right-skewed. Individual Ct values are reported in the Additional file 1: Table S1. There were no differences in mir146a-5p levels between men and women [men: 1.32 (0.37–4.53); women: 1.34 (0.33–4.20), p = 0.91] and across age categories [≤ 35.0: 1.35 (0.38–4.31); 35.1–45.9: 1.32 (0.32–4.23); 46.0–55.9: 1.26 (0.26–5.06) and ≥ 56.0 years: 1.34 (0.46–3.53), p = 0.95].

Correlation of miR-146a-5p with clinical parameters and serum biomarkers

We then explored the association between miR-146a-5p and a range of relevant biochemical and clinical variables. Correlations of all variables in a matrix are reported in Additional file 2: Table S2. There was no correlation between miR-146a-5p and age even after adjustment for sex (p = 0.823). Similarly, no correlation was found between miR-146a-5p levels and diabetes duration neither in the whole population nor in cases (cases: r = 0.038, p = 0.521). Notably, serum miR-146a-5p levels were inversely correlated with diastolic blood pressure (r = -0.11, p = 0.019), triglycerides (r = -0.12, p = 0.01), TNF-α (r = -0.17, p = 0.000), and NT-proBNP (r = -0.15, p = 0.002). Moreover, miR-146a-5p levels were found to be positively correlated with renal function (eGFR r = 0.22, p = 0.000).

MiR-146a-5p levels in cases vs. controls

Levels of miR-146a-5p were significantly lower in cases [1.15 (0.32–3.32)] compared to controls [1.74 (0.44–6.11) P = 0.039] (Fig. 2A), and results were not modified after adjustment for age and sex (P = 0.042) and diabetes duration (P = 0.049). Among cases miR-146a-5p levels were similar in patients with one [1.13 (0.31–3.00)] or multiple (> 1) [1.15 (0.33–3.38)] diabetes complications. When the comparison was performed separately in each sex, miR-146a-5p levels were still lower in cases compared to controls in both men and women; but differences did not reach statistical significance [men; cases: 1.16 (0.36–3.31); controls: 1.72 (0.36–6.77), P = 0.160; women: cases 1.15 (0.28–3.39); controls: 1.76 (0.46–7.34), P = 0.209] (Fig. 2B).

Fig. 2

miR-146a-5p expression. A Comparison of miR-146a-5p levels in the serum of DM1 patients with (cases; n = 294) and without (controls; n = 153) micro-macrovascular complications (*p < 0.05 cases vs. controls). B Gender-specific miR-146a-5p expression in both cases and controls (p = ns)

Logistic regression

Logistic regression analyses were carried out to establish if miR-146a-5p changed the odds ratios (OR) of having either all or individual vascular complications, independently of risk factors and potential confounders. Levels of miR-146a-5p in the upper quartile (≥ 6.73) were associated with a 59% lower risk of having all complications (OR = 0.41, 95% CI 0.21–0.79) and this was independent of age, gender, A1c, and duration of diabetes (Model 1-Table 2). After adjustment for AER, hypertension, eGFR, smoking, the strength of this inverse association was even greater (OR = 0.32, 95% CI 0.14–0.73) (Model 2-Table 2). Following further adjustment for TNF-α and NT-proBNP, miR-146a-5p values in the upper quartile are still reduced by 66% the OR for all complications (OR = 0.34, 95% CI 0.14–0.76) (Model 3-Table 2). Models were not affected by the inclusion of total cholesterol, BMI and IL-6. Analysis performed separately in men and women showed that miR-146a-5p was inversely and independently associated with all complications in men. An inverse association was also observed in women, but did not reach statistical significance (Table 3).

Table 2 Odds ratio for diabetes complications by miR-146a-5p values [upper quartile (≥ 6.73) vs. lowest quartiles (< 6.73)]
Table 3 Odds ratio for all complications by miR-146a-5p values [upper quartile (≥ 6.73) vs. lowest quartiles (< 6.73)] stratified by sex

We also examined the associations between miR-146a-5p levels and individual diabetes complications. As shown in Table 2, results obtained in all cases were predominantly driven by cases with CVD and retinopathy. In Model 1, levels of miR-146a-5p in the upper quartile were associated with 62% and 59% reduced OR for CVD and retinopathy, respectively. The trend of OR changes across models was similar to that observed for all complications and after adjustment for age, sex, A1c, diabetes duration, AER, hypertension, eGFR, and smoking the risk reduction for CVD and retinopathy was 68% and 61%, respectively. After further adjustment for both TNF-α and NT-proBNP the strength of these associations remained statistically significant.


In this case–control study on DM1 patients from the EURODIAB PCS, we showed an independent and inverse association between miR-146a-5p and chronic complications of diabetes.

Values of miR-146a-5p were lower in cases compared to controls. Moreover, in logistic regression analysis, miR-146a-5p levels in the highest quartile distribution of control subjects were associated with a significant risk reduction of diabetic complications, independently of confounders and established risk factors. This suggests that miR-146a-5p may be a candidate marker of vascular protection in DM1. miRNA-based biomarker efficacy may differ between sexes and previous studies reported lower circulating miR-146a-5p levels in women than in men [28, 29] and an influence of sex adjustment on the relationship between circulating miR-146a-5p levels and clinical outcomes [30, 31]. In our study, miR-146a-5p levels did not differ in men and women and adjustment for sex did not modify the results. However, when analyses were performed separately in men and women, the inverse relation between miR-146a-5p levels and diabetes complications was confirmed in men, while it did not reach statistical significance in women. The reason for this gender-specific difference is unknown; however, it may be related to the confounding effect of miR-146a-5p negative modulation by estrogens [32, 33].

Previous studies have reported an age-related decline in circulating miR-146a-5p levels [28, 34]. However, in our study, miR-146a-5p levels did not correlate with age and did not differ across age categories. Moreover, adjustment for age did not modify the results. The reason for this discrepancy is unknown; however, the effect of age on miR-146a-5p levels was observed in very old subjects (> 75 years) [34] and our DM1 patients were relatively young. Moreover, the age-related decline in miR-146 was weaker [34] or even absent [35] in studies performed on patients with type 2 diabetes.

The source of serum miR-146a-5p was not established in our study; however, endothelial cells, platelets, and immune/inflammatory cells are likely candidates [36, 37]. In these cell types, miR-146a-5p expression can be modulated by diabetes. Indeed, exposure of endothelial cells to high glucose induces a persistent miR-146a-5p downregulation [38,39,40]. Moreover, diabetes induces an inflammatory response that can trigger a compensatory expression of miR-146a-5p. Therefore, the relationship between miR-146a-5p and diabetes is complex and is likely affected by many other variables besides hyperglycemia. This may explain why in our study there was no correlation between miR-146a-5p and A1c, despite the inverse association between miR-146a-5p and diabetes complications.

The biological mechanism underlying the independent and inverse association between serum miR-146a-5p levels and DM1 complications is unknown. However, low serum miR-146a-5p level may mirror a miR-146a-5p downregulation in endothelial cells, monocytes, and other cell types relevant to DM1 complications, where miR-146a-5p deficiency impairs the feedback restraints on diabetes-induced inflammation [40], NOX-4-mediated oxidative stress [41], and apoptosis [42]. Therefore, serum miR-146a-5p level may be a circulating marker of the efficacy of the anti-inflammatory response and thus of the susceptibility to develop complications. Consistent with this, miR-146a-5p is a well-established marker of inflammation and is associated with a variety of immune and non-immune inflammatory conditions [14, 43,44,45], including CVD, Alzheimer’s disease, and type 2 diabetes [34, 46,47,48,49].

On the other hand, low serum miR-146a-5p levels may also be due to reduced miR-146a-5p content in circulating EVs. As uptake of miRNA containing EVs can induce phenotypic changes in the recipient cells, this may hamper the systemic control of miR-146a-5p on inflammation and thus favor the development of DM1 complications [50, 51]. Consistent with this, therapeutic strategies delivering nanoparticles loaded with miR-146a-5p have been successfully tested in various inflammatory conditions [14, 52,53,54].

The magnitude of the association between miR-146a-5p and all complications was greater after adjustment for A1C, diabetes duration, hypertension, AER, eGFR, TNF-α, NT-pro-BNP, suggesting that miR-146a-5p measurement may have an added value over traditional risk factors in identifying patients at enhanced risk of complications. Diabetes-related risk factors induce a low-grade inflammation and they are thus expected to enhance miR-146a-5p expression (13). This may explain their negative confounding effect in the inverse relationship between miR-146a-5p and all complications. Once induced, miR-146a-5p potently inhibits inflammatory processes that contribute to diabetic complication onset [13]. Therefore, higher circulating miR-146a-5p levels may identify the subgroup of patients able to build a more effective miR-146a-5p-mediated anti-inflammatory response and thus less prone to develop complications. Both miR-146 polymorphisms and epigenetic modifications may affect the efficacy of miR-146 negative feedback loop [55].

At variance with our results, a recent study in patients with type 2 diabetes showed that miR-146a-5p levels in circulating CD31+EV were greater in individuals with vascular complications compared with subjects without complications [37]. However, miR-146a-5p CD31+EV levels were not adjusted for clinical variables that correlated with miR-146a-5p and differed between the two groups. Moreover, miR-146a-5p was measured on circulating CD31+EV rather than on serum, making direct comparison [37] between the studies difficult.

Logistic regression analysis carried out for individual complications showed that higher miR-146a-5p levels were associated with a 60% lower OR of diabetic retinopathy. In line with this finding, miR-146a-5p is downregulated in the diabetic retina [38] and intervention strategies that enhance retinal miR-146 levels either by intravitreal injection or overexpression, ameliorate experimental diabetic retinopathy [56, 57]. Besides lowering inflammation, high miR-146a-5p expression may also affect retinopathy by downregulating the proangiogenic HIF-1α-ROBO4 pathway [58].

We also found an independent and inverse association between miR-146a-5p and CVD. Mechanistically, miR-146a-5p acts as a brake on proinflammatory NF-kB signaling in both endothelial cells and monocytes and this together with miR-146a-5p anti-oxidative properties [41] may explain the link between miR-146a-5p and atheroprotection. Consistent with this, treatment with miR-146a-5p is beneficial in animal models of atherosclerosis [59, 60]. Moreover, a miR-146a-5p polymorphism, resulting in lower mature miR-146a-5p production, confers enhanced risk of coronary artery disease in humans [61].

Besides the potential relevance of miR-146a-5p as a biomarker of CVD in diabetes, serum miR-146a-5p may also have direct vascular protective effects. Indeed, serum EV carrying miR-146a-5p can enter into recipient cells and alter the cell phenotype by releasing miR-146a-5p. Consistent with this, EV-mediated transfer of miR-146a-5p has been demonstrated to modulate inflammation and to improve cardiac function after myocardial infarction [62]. Moreover, in mouse models of atherosclerosis, delivery of miR-146a-5p to the endothelium by injection of either exogenous EV or a free miR-146a-5p mimetic not only ameliorated endothelial inflammation, but also reduced atherosclerotic plaque size [59, 63].

MiR-146a-5p has also been involved in the pathogenesis of diabetic nephropathy [15, 18, 64]. However, we did not observe any significant associations between serum miR-146a-5p levels and this microvascular complication of diabetes. The reason is unknown; however, endothelial cells that are a major source of circulating miR-146a-5p may play a less relevant role in this complication than in CVD and retinopathy.

Our study has several limitations. Although EURODIAB is a prospective study, baseline serum samples were not available; therefore, miR-146a-5p could only be measured in follow-up samples. Given the cross-sectional design of the study, our results only demonstrate the presence of an independent association between miR-146a-5p and chronic diabetes, but they do not prove a clinical relevance of miR-146a-5p as a biomarker of DM1 complications. Moreover, the cross-sectional design of the study restricts our ability to investigate causal relationships and to elucidate mechanisms. However, pilot cross-sectional studies on large and well-characterized existing cohorts are an effective manner to identify miRNAs of potential interest that can be then tested in longitudinal studies. The smaller number of controls compared to cases has reduced the power of the analyses; however, the number of controls and cases with individual complications was similar, though multiple comparisons might have caused significant results due to chance. Lack of matching between cases and controls for age represents a limitation of the study as age can affect circulating miR-146a-5p levels [28, 34]; however, adjustments for age were made at the analysis stage. The study did not include a control group of non-diabetic patients as the EURODIAB study exclusively recruited DM1 patients; however, the major purpose of the study was to assess if miR-146a-5p was associated with diabetes complications rather than compare circulating miR-146a-5p levels in patients with and without diabetes. Results were not adjusted for residual β-cell function; however, the role of C-peptide as marker/therapy for chronic DM1 complications is still controversial. Moreover, C-peptide is usually detectable in the first decade of DM1 [65] and in our study only 10% of patients had a diabetes duration below 10 years. The possibility of sample degradation over time cannot be ruled out. On the other hand, samples were properly stored and miRNAs are stable in biofluids. Although the TRIZOL method for miRNA extraction may be less sensitive compared to specific miRNA extraction kit, there is no an optimal “gold standard” method for isolating miRNAs [66], the TRIZOL method is widely used, performed well in our hands, and is particularly suitable for routine clinical application given its low cost. We acknowledge that there was a considerable variability in Cel-miR-39 Ct values; however, miR-146 data were normalised using both the endogenous control (U6) and the exogenous control (Cel-miR-39). An important strength of our study is the large sample size and the possibility to adjust for the potential confounding effect of other risk factors and complications. Moreover, patients were from a representative sample of European DM1 patients, and results are, thus generalizable.


In conclusion, in patients with DM1, serum miR-146a-5p levels are independently associated with diabetic complications and in particular with CVD and retinopathy. Prospective studies are warranted to establish if serum miR-146a-5p can be exploited as a clinical biomarker to predict outcomes.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.



Prospective Complications Study




Type 1 diabetes


Quantitative PCR


Odds ratio


Confidence interval


Glycated hemoglobin


Albumin excretion rate


Estimated glomerular filtration rate


B-type natriuretic peptide


Tumor necrosis factor alpha


Cardiovascular disease


Transforming growth factor beta 1


Nuclear factor kappa-light-chain-enhancer of activated B cells


Body mass index


Interleukin 6


Enzyme-linked immunosorbent assay


Waist to hip ratio


Coronary artery disease


Extracellular vesicle


Hypoxia-inducible factor 1 alpha


Low-density lipoprotein


High-density lipoprotein


Systolic blood pressure


Diastolic blood pressure


Serum creatinine

Cr Cl:

Creatinine clearance


  1. 1.

    Kantharidis P, Wang B, Carew RM, Lan HY. Diabetes complications: the microRNA perspective. Diabetes. 2011;60:1832–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Barutta F, Bellini S, Mastrocola R, Bruno G, Gruden G. MicroRNA and microvascular complications of diabetes. Int J Endocrinol. 2018;2018:6890501.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Zhang Y, Sun X, Icli B, Feinberg MW. Emerging roles for microRNAs in diabetic microvascular disease: novel targets for therapy. Endocr Rev. 2017;38:145–68.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Kosaka N, Iguchi H, Yoshioka Y, Takeshita F, Matsuki Y, Ochiya T. Secretory mechanisms and intercellular transfer of microRNAs in living cells. J Biol Chem. 2010;285:17442–52.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Brase JC, Wuttig D, Kuner R, Sültmann H. Serum microRNAs as non-invasive biomarkers for cancer. Mol Cancer. 2010;9:306.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Wang J, Chen J, Sen S. MicroRNA as biomarkers and diagnostics. J Cell Physiol. 2016;231:25–30.

    CAS  PubMed  Google Scholar 

  7. 7.

    Gilad S, Meiri E, Yogev Y, Benjamin S, Lebanony D, Yerushalmi N, Benjamin H, Kushnir M, Cholakh H, Melamed N, Bentwich Z, Hod M, Goren Y, Chajut A. Serum microRNAs are promising novel biomarkers. PLoS ONE. 2008;3:e3148.

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Sebastiani G, Nigi L, Grieco GE, Mancarella F, Ventriglia G, Dotta F. Circulating microRNAs and diabetes mellitus: a novel tool for disease prediction, diagnosis, and staging? J Endocrinol Invest. 2017;40:591–610.

    CAS  PubMed  Google Scholar 

  9. 9.

    Pezzolesi MG, Satake E, McDonnell KP, Major M, Smiles AM, Krolewski AS. Circulating TGF-β1-regulated miRNAs and the risk of rapid progression to ESRD in type 1 diabetes. Diabetes. 2015;64:3285–93.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Barutta F, Bellini S, Canepa S, Durazzo M, Gruden G. Novel biomarkers of diabetic kidney disease: current status and potential clinical application. Acta Diabetol. 2021.

    Article  PubMed  Google Scholar 

  11. 11.

    Barutta F, Bruno G, Matullo G, Chaturvedi N, Grimaldi S, Schalkwijk C, Stehouwer CD, Fuller JH, Gruden G. MicroRNA-126 and micro-/macrovascular complications of type 1 diabetes in the EURODIAB Prospective Complications Study. Acta Diabetol. 2016;54:133–9.

    PubMed  Google Scholar 

  12. 12.

    Zampetaki A, Willeit P, Burr S, Yin X, Langley SR, Kiechl S, Klein R, Rossing P, Chaturvedi N, Mayr M. Angiogenic microRNAs linked to incidence and progression of diabetic retinopathy in type 1 diabetes. Diabetes. 2016;65:216–27.

    CAS  PubMed  Google Scholar 

  13. 13.

    Taganov KD, Boldin MP, Chang KJ, Baltimore D. NF-κB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses. Proc Natl Acad Sci U S A. 2006;103:12481–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Mortazavi-Jahromi SS, Aslani M, Mirshafiey A. A comprehensive review on miR-146a molecular mechanisms in a wide spectrum of immune and non-immune inflammatory diseases. Immunol Lett. 2020;227:8–27.

    CAS  PubMed  Google Scholar 

  15. 15.

    Bhatt K, Lanting LL, Jia Y, Yadav S, Reddy MA, Magilnick N, Boldin M, Natarajan R. Anti-inflammatory role of microRNA-146a in the pathogenesis of diabetic nephropathy. J Am Soc Nephrol. 2016;27:2277–88.

    CAS  PubMed  Google Scholar 

  16. 16.

    Liu XS, Fan B, Szalad A, Jia L, Wang L, Wang X, Pan W, Zhang L, Zhang R, Hu J, Zhang XM, Chopp M, Zhang ZG. MicroRNA-146a mimics reduce the peripheral neuropathy in type 2 diabetic mice. Diabetes. 2017;66:3111–21.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Wang L, Chopp M, Szalad A, Zhang Y, Wang X, Zhang RL, Liu XS, Jia L, Zhang ZG. The role of miR-146a in dorsal root ganglia neurons of experimental diabetic peripheral neuropathy. Neuroscience. 2014;259:155–63.

    CAS  PubMed  Google Scholar 

  18. 18.

    Lee HW, Khan SQ, Khaliqdina S, Altintas MM, Grahammer F, Zhao JL, Koh KH, Tardi NJ, Faridi MH, Geraghty T, Cimbaluk DJ, Susztak K, Moita LF, Baltimore D, Tharaux PL, Huber TB, Kretzler M, Bitzer M, Reiser J, Gupta V. Absence of miR-146a in podocytes increases risk of diabetic glomerulopathy via up-regulation of ErbB4 and Notch-1. J Biol Chem. 2017;292:732–47.

    CAS  PubMed  Google Scholar 

  19. 19.

    La Sala L, Prattichizzo F, Ceriello A. The link between diabetes and atherosclerosis. Eur J Prev Cardiol. 2019;26:15–24.

    PubMed  Google Scholar 

  20. 20.

    Barutta F, Bruno G, Matullo G, Chaturvedi N, Grimaldi S, Schalkwijk C, Stehouwer CD, Fuller JH, Gruden G. MicroRNA-126 and micro-/macrovascular complications of type 1 diabetes in the EURODIAB Prospective Complications Study. Acta Diabetol. 2017;54:133–9.

    CAS  PubMed  Google Scholar 

  21. 21.

    The EURODIAB IDDM Complications Study Group. Microvascular and acute complications in IDDM patients: the EURODIAB IDDM complications study. Diabetologia. 1994;37:278–85.

    Google Scholar 

  22. 22.

    Chaturvedi N, Sjoelie AK, Porta M, Aldington SJ, Fuller JH, Songini M, Kohner EM; EURODIAB prospective complications study. Markers of insulin resistance are strong risk factors for retinopathy incidence in type 1 diabetes. Diabetes Care. 2001;24:284–289.

  23. 23.

    Schram MT, Chaturvedi N, Schalkwijk CG, Fuller JH, Stehouwer CD; EURODIAB prospective complications study group. Markers of inflammation are cross-sectionally associated with microvascular complications and cardiovascular disease in type 1 diabetes: the EURODIAB prospective complications study. Diabetologia. 2005;48:370–378.

  24. 24.

    Gruden G, Bruno G, Chaturvedi N, Burt D, Pinach S, Schalkwijk C, Stehouwer CD, Witte DR, Fuller JH, Cavallo-Perin P; EURODIAB prospective complications study group. ANTI-HSP60 and ANTI-HSP70 antibody levels and micro/macrovascular complications in type 1 diabetes: the EURODIAB study. J Intern Med. 2009;266:527–536.

  25. 25.

    Gruden G, Barutta F, Chaturvedi N, Schalkwijk C, Stehouwer CD, Pinach S, Manzo M, Loiacono M, Tricarico M, Mengozzi G, Witte DR, Fuller JH, Perin PC, Bruno G. NH2-terminal probrain natriuretic peptide is associated with diabetes complications in the EURODIAB Prospective Complications Study: the role of tumor necrosis factor-α. Diabetes Care. 2012;35:1931–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Chaturvedi N., Schalkwijk C.G., Abrahamian H., Fuller J.H., Stehouwer C.D.; EURODIAB prospective complications study group. Circulating and urinary transforming growth factor beta1, Amadori albumin, and complications of type 1 diabetes: the EURODIAB prospective complications study. Diabetes Care. 2002;25:2320–2327.

  27. 27.

    Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of diet in renal disease study group. Ann Intern Med. 1999;130:461–470.

  28. 28.

    Maffioletti E, Milanesi E, Ansari A, Zanetti O, Galluzzi S, Geroldi C, Gennarelli M, Bocchio-Chiavetto L. miR-146a plasma levels are not altered in alzheimer’s disease but correlate with age and illness severity. Front Aging Neurosci. 2020;17(11):366.

    Google Scholar 

  29. 29.

    Zheng L, Zhuang C, Wang X, Ming L. Serum miR-146a, miR-155, and miR-210 as potential markers of Graves’ disease. J Clin Lab Anal. 2018;32:e22266.

    Google Scholar 

  30. 30.

    Bae SC, Lee YH. MiR-146a levels in rheumatoid arthritis and their correlation with disease activity: a meta-analysis. Int J Rheum Dis. 2018;21:1335–42.

    CAS  PubMed  Google Scholar 

  31. 31.

    Meerson A, Najjar A, Saad E, Sbeit W, Barhoum M, Assy N. Sex differences in plasma microRNA biomarkers of early and complicated diabetes mellitus in Israeli Arab and Jewish patients. Noncoding RNA. 2019;5:32.

    CAS  PubMed Central  Google Scholar 

  32. 32.

    Kangas R, Pöllänen E, Rippo MR, Lanzarini C, Prattichizzo F, Niskala P, Jylhävä J, Sipilä S, Kaprio J, Procopio AD, Capri M, Franceschi C, Olivieri F, Kovanen V. Circulating miR-21, miR-146a and Fas ligand respond to postmenopausal estrogen-based hormone replacement therapy–a study with monozygotic twin pairs. Mech Ageing Dev. 2014;15(143–144):1–8.

    Google Scholar 

  33. 33.

    Dai R, Phillips RA, Zhang Y, Khan D, Crasta O, Ahmed SA. Suppression of LPS-induced Interferon-gamma and nitric oxide in splenic lymphocytes by select estrogen-regulated microRNAs: a novel mechanism of immune modulation. Blood. 2008;112:4591–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Mensà E, Giuliani A, Matacchione G, Gurău F, Bonfigli AR, Romagnoli F, De Luca M, Sabbatinelli J, Olivieri F. Circulating miR-146a in healthy aging and type 2 diabetes: age- and gender-specific trajectories. Mech Ageing Dev. 2019;180:1–10.

    PubMed  Google Scholar 

  35. 35.

    Baldeon RL, Weigelt K, de Wit H, Ozcan B, van Oudenaren A, Sempertegui F, Sijbrands E, Grosse L, Freire W, Drexhage HA, Leenen PJ. Decreased Serum Level of miR-146a as Sign of Chronic Inflammation in Type 2 Diabetic Patients. PLoS One. 2014;9,e115209.

  36. 36.

    Balasubramanyam M, Aravind S, Gokulakrishnan K, Prabu P, Sathishkumar C, Ranjani H, MohanImpaired V. miR-146a expression links subclinical inflammation and insulin resistance in Type 2 diabetes. Mol Cell Biochem. 2011;351:197–205.

    CAS  PubMed  Google Scholar 

  37. 37.

    Prattichizzo F, De Nigris V, Sabbatinelli J, Giuliani A, Castaño C, Párrizas M, Crespo I, Grimaldi A, Baranzini N, Spiga R, Mancuso E, Rippo MR, Procopio AD, Novials A, Bonfigli AR, Garavelli S, La Sala L, Matarese G, de Candia P, Olivieri F, Ceriello A. CD31+ extracellular vesicles from patients with type 2 diabetes shuttle a miRNA signature associated with cardiovascular complications. Diabetes. 2021;70:240–54.

    CAS  PubMed  Google Scholar 

  38. 38.

    Feng B, Chen S, McArthur K, Wu Y, Sen S, Ding Q, Feldman RD, Chakrabarti S. MiR-146a-mediated extracellular matrix protein production in chronic diabetes complications. Diabetes. 2011;60:2975–84.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Zhong X, Liao Y, Chen L, Liu G, Feng Y, Zeng T, Zhang J. The MicroRNAs in the pathogenesis of metabolic memory. Endocrinology. 2015;156:3157–68.

    CAS  PubMed  Google Scholar 

  40. 40.

    Lo WY, Peng CT, Wang HJ. MicroRNA-146a-5p mediates high glucose-induced endothelial inflammation via targeting interleukin-1 receptor-associated kinase 1 expression. Front Physiol. 2017;8:551.

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Wang HJ, Huang JL, Shih YY, Wu HY, Peng CT, Lo WY. MicroRNA-146a decreases high glucose/thrombin-induced endothelial inflammation by inhibiting NAPDH oxidase 4 expression. Mediators Inflamm. 2014;2014:379537.

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Ye EA, Steinle JJ. miR-146a suppresses STAT3/VEGF pathways and reduces apoptosis through IL-6 signaling in primary human retinal microvascular endothelial cells in high glucose conditions. Vision Res. 2017;139:15–22.

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Olivieri F, Prattichizzo F, Giuliani A, Matacchione G, Rippo MR, Sabbatinelli J, Bonafè M. miR-21 and miR-146a: the microRNAs of inflammaging and age-related diseases. Ageing Res Rev. 2021;70:101374.

    CAS  PubMed  Google Scholar 

  44. 44.

    Giuliani A, Lattanzi S, Ramini D, Graciotti L, Danni MC, Procopio AD, Silvestrini M, Olivieri F, Sabbatinelli J. Potential prognostic value of circulating inflamma-miR-146a-5p and miR-125a-5p in relapsing-remitting multiple sclerosis. Mult Scler Relat Disord. 2021;54:103126.

    PubMed  Google Scholar 

  45. 45.

    Zhang LL, Wu XX, Wang XF, Di DS, Huang Q, Liu RS, Shuai ZW, Ye DQ, Leng RX. Genetic variant in microRNA-146a gene is associated with risk of rheumatoid arthritis. Ann Med. 2021;53:824–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Wang J, Yan Y, Song D, Liu B. Reduced plasma miR-146a is a predictor of poor coronary collateral circulation in patients with coronary artery disease. Biomed Res Int. 2016.

    Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Hijmans JG, Diehl KJ, Bammert TD, Kavlich PJ, Lincenberg GM, Greiner JJ, Stauffer BL, DeSouza CA. Association between hypertension and circulating vascular-related microRNAs. J Hum Hypertens. 2018;32:440–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Dong H, Li J, Huang L, Chen X, Li D, Wang T, Hu C, Xu J, Zhang C, Zen K, Xiao S, Yan Q, Wang C, Zhang CY. Serum MicroRNA profiles serve as novel biomarkers for the diagnosis of alzheimer’s disease. Dis Markers. 2015;2015:625659.

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Giuliani A, Gaetani S, Sorgentoni G, Agarbati S, Laggetta M, Matacchione G, Gobbi M, Rossi T, Galeazzi R, Piccinini G, Pelliccioni G, Bonfigli AR, Procopio AD, Albertini MC, Sabbatinelli J, Olivieri F, Fazioli F. Circulating Inflamma-miRs as potential biomarkers of cognitive impairment in patients affected by alzheimer’s disease. Front Aging Neurosci. 2021;13:647015.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Yang J, Malone F, Go M, Kou J, Lim JE, Caughey RC, Fukuchi KI. Lipopolysaccharide-induced exosomal miR-146a Is involved in altered expression of alzheimer’s risk genes via suppression of TLR4 signaling. J Mol Neurosci. 2021;71:1245–55.

    CAS  PubMed  Google Scholar 

  51. 51.

    Milano G, Biemmi V, Lazzarini E, Balbi C, Ciullo A, Bolis S, Ameri P, Di Silvestre D, Mauri P, Barile L, Giuseppe V. Intravenous administration of cardiac progenitor cell-derived exosomes protects against doxorubicin/trastuzumab-induced cardiac toxicity. Cardiovasc Res. 2020;116:383–92.

    CAS  PubMed  Google Scholar 

  52. 52.

    Fan B, Chopp M, Zhang ZG, Liu XS. Treatment of diabetic peripheral neuropathy with engineered mesenchymal stromal cell-derived exosomes enriched with microRNA-146a provide amplified therapeutic efficacy. Exp Neurol. 2021;341:113694.

    CAS  PubMed  Google Scholar 

  53. 53.

    Bobba CM, Fei Q, Shukla V, Lee H, Patel P, Putman RK, Spitzer C, Tsai M, Wewers MD, Lee RJ, Christman JW, Ballinger MN, Ghadiali SN, Englert JA. Nanoparticle delivery of microRNA-146a regulates mechanotransduction in lung macrophages and mitigates injury during mechanical ventilation. Nat Commun. 2021;12:289.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Wu H, Fan H, Shou Z, Xu M, Chen Q, Ai C, Dong Y, Liu Y, Nan Z, Wang Y, Yu T, Liu X. Extracellular vesicles containing miR-146a attenuate experimental colitis by targeting TRAF6 and IRAK1. Int Immunopharmacol. 2019;68:204–12.

    CAS  PubMed  Google Scholar 

  55. 55.

    Jazdzewski K, Murray EL, Franssila K, Jarzab B, Schoenberg DR, de la Chapelle A. Common SNP in pre-miR-146a decreases mature miR expression and predisposes to papillary thyroid carcinoma. Proc Natl Acad Sci U S A. 2008;105:7269–74.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Zhuang P, Muraleedharan CK, Xu S. Intraocular delivery of miR-146 inhibits diabetes-induced retinal functional defects in diabetic rat model. Invest Ophthalmol Vis Sci. 2017;58:1646–55.

    CAS  PubMed  Google Scholar 

  57. 57.

    Chen S, Feng B, Thomas A, Chakrabarti S. MiR-146a regulates glucose induced upregulation of inflammatory cytokines extracellular matrix proteins in the retina and kidney in diabetes. PLoS ONE. 2017;12:e0173918.

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Gong Q, Xie J, Li Y, Liu Y, Su G. Enhanced ROBO4 is mediated by up-regulation of HIF-1alpha/SP1 or reduction in miR-125b-5p/miR-146a-5p in diabetic retinopathy. J Cell Mol Med. 2019;23:4723–37.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Li K, Ching D, Luk FS, Raffai RL. Apolipoprotein E enhances microRNA-146a in monocytes and macrophages to suppress NF-κB-driven inflammation and atherosclerosis. Circ Res. 2015;117:e1–11.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Cheng HS, Besla R, Li A, Chen Z, Shikatani EA, Nazari-Jahantigh M, Hammoutène A, Nguyen MA, Geoffrion M, Cai L, Khyzha N, Li T, MacParland SA, Husain M, Cybulsky MI, Boulanger CM, Temel RE, Schober A, Rayner KJ, Robbins CS, Fish JE. Paradoxical suppression of atherosclerosis in the absence of microRNA-146a. Circ Res. 2017;121:354–67.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Bao MH, Xiao Y, Zhang QS, Luo HQ, Luo J, Zhao J, Li GY, Zeng J, Li JM. Meta-Analysis of miR-146a polymorphisms association with coronary artery diseases and ischemic stroke. Int J Mol Sci. 2015;16:14305–17.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Barile L, Lionetti V, Cervio E, Matteucci M, Gherghiceanu M, Popescu LM, Torre T, Siclari F, Moccetti T, Vassalli G. Extracellular vesicles from human cardiac progenitor cells inhibit cardiomyocyte apoptosis and improve cardiac function after myocardial infarction. Cardiovasc Res. 2014;103:530–41.

    CAS  PubMed  Google Scholar 

  63. 63.

    Ma S, Tian XY, Zhang Y, Mu C, Shen H, Bismuth J, Pownall HJ, Huang Y, Wong WT. E-selectin-targeting delivery of microRNAs by microparticles ameliorates endothelial inflammation and atherosclerosis. Sci Rep. 2016;6:22910.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Yu HY, Meng LF, Lu XH, Liu LH, Ci X, Zhuo Z. Protective effect of miR-146 against kidney injury in diabetic nephropathy rats through mediating the NF-kappaB signaling pathway. Eur Rev Med Pharmacol Sci. 2020;24:3215–22.

    PubMed  Google Scholar 

  65. 65.

    Rickels MR, Evans-Molina C, Bahnson HT, Ylescupidez A, Nadeau KJ, Hao W, Clements MA, Sherr JL, Pratley RE, Hannon TS, Shah VN, Miller KM, Greenbaum CJ, T1D Exchange β-Cell Function Study Group. High residual C-peptide likely contributes to glycemic control in type 1 diabetes. J Clin Invest. 2020;130:1850–62.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Laurent L, Abdel-Mageed A, Adelson P, Arango J, Balaj L, Breakefield X, Carlson E, Carter B, Majem B, Chen C, et al. Meeting report: discussions and preliminary findings on extracellular RNA measurement methods from laboratories in the NIH Extracellular RNA Communication Consortium. J Extracell Vesicles. 2015;4:26533.

    PubMed  Google Scholar 

Download references


Not applicable.


This research was funded by EFSD (European Federation for the Study of Diabetes), CRT (Fondazione Cassa di Risparmio di Torino), Turin University, Italian Ministry for Education, University and Research (Ministero dell'Istruzione, dell'Università e della Ricerca ‐ MIUR) under the program “Dipartimenti di Eccellenza 2018–2022” project D15D18000410001.

Author information




Conceptualization, FB, GG, MD; methodology, FB, GG; formal analysis, FB; investigation, BC, SB, SG; MS data curation, CS, CDS, NC, SSS; writing—original draft preparation, FB, GG, MD, writing—review and editing, GM; supervision, FB, GG; funding acquisition, GG. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Federica Barutta.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Ethical Committee of Turin University, the procedures were in accordance with the Helsinki Declaration and informed consent was obtained from all subjects involved in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1

: Table S1. Mean Ct values for miR-146a-5p, U6 snRNA and Cel-miR-39 in both controls and cases.

Additional file 2

: Table S2. Pearson correlation coefficient of clinical variables.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Barutta, F., Corbetta, B., Bellini, S. et al. MicroRNA 146a is associated with diabetic complications in type 1 diabetic patients from the EURODIAB PCS. J Transl Med 19, 475 (2021).

Download citation


  • MicroRNAs
  • Type 1 diabetes
  • Retinopathy
  • Cardiovascular diseases