New Diagnostic Model for the Differentiation of Diabetic Nephropathy From Non-Diabetic Nephropathy in Chinese Patients

Unveiling the utility of artificial intelligence for prediction, diagnosis, and progression of diabetic kidney disease: an evidence-based systematic review and meta-analysis

OBJECTIVE

The purpose of this study was to conduct a systematic investigation of the potential of artificial intelligence (AI) models in the prediction, detection of diagnostic biomarkers, and progression of diabetic kidney disease (DKD). In addition, we compared the performance of non-logistic regression (LR) machine learning (ML) models to conventional LR prediction models.

METHODS

Until January 30, 2024, a comprehensive literature review was conducted by investigating databases such as Medline (via PubMed) and Cochrane. Research that is inclusive of AI or ML models for the prediction, diagnosis, and progression of DKD was incorporated. The area under the Receiver Operating Characteristic Curve (AUROC) served as the principal outcome metric for assessing model performance. A meta-analysis was performed utilizing MedCalc statistical software to calculate pooled AUROC and assess the performance differences between LR and non-LR models.

RESULTS

A total of 57 studies were included in the meta-analysis. The pooled AUROC of AI or ML model was 0.84 (95% CI = 0.81-0.86, p < 0.0001) for analyzing prediction of DKD, 0.88 (95%CI = 0.84-0.92, p < 0.0001) for detecting diagnostic biomarkers, and 0.80 (95% CI = 0.77-0.82, p < 0.0001) for analyzing progression of DKD. The pooled AUROC of LR and non-LR ML models exhibited no significant differences across all categories (p > 0.05), except for the random forest (RF) model, which displayed a statistically significant increase in predictive accuracy compared to LR for DKD occurrence (p < 0.04).

CONCLUSION

ML models showed solid DKD prediction effectiveness, with pooled AUROC values over 0.8, suggesting good performance. These data demonstrated that non-LR and LR models perform similarly in overall CKD management, but the RF model outperforms the LR model, particularly in predicting the occurrence of DKD. These findings highlight the promise of AI technologies for better DKD management. To improve model reliability, future study should include extended follow-up periods as well as external validation.

A machine learning-based diagnosis modeling of IgG4 Hashimoto's thyroiditis

PURPOSE

This study aims to develop a non-invasive diagnosis model using machine learning (ML) for identifying high-risk IgG4 Hashimoto's thyroiditis (HT) patients.

METHODS

A retrospective cohort of 93 HT patients and a prospective cohort of 179 HT patients were collected. According to the immunohistochemical and pathological results, the patients were divided into IgG4 HT group and non-IgG4 HT group. Serum TgAb IgG4 and TPOAb IgG4 were detected by ELISAs. A logistic regression model, support vector machine (SVM) and random forest (RF) were used to establish a clinical diagnosis model for IgG4 HT.

RESULTS

Among these 272 patients, 40 (14.7%) were diagnosed with IgG4 HT. Patients with IgG4 HT were younger than those with non-IgG4 HT (P < 0.05). Serum levels of TgAb IgG4 and TPOAb IgG4 in IgG4 HT group were significantly higher than those in non-IgG4 HT group (P < 0.05). There were no significant differences in gender, disease duration, goiter, preoperative thyroid function status, preoperative TgAb or TPOAb levels, and thyroid ultrasound characteristics between the two groups (all P > 0.05). The accuracy, sensitivity, and specificity were 57%, 78%, and 79% for logistic regression model of IgG4 HT, 80 ± 7%, 84.7% ± 2.6%, and 75.4% ± 9.6% for the RF model and 78 ± 5%, 89.8% ± 5.7%, and 64.7% ± 5.7% for the SVM model. The RF model works better than SVM. The area under the ROC curve of RF ranged 0.87 to 0.92.

CONCLUSION

A clinical diagnosis model for IgG4 HT established by RF model might help the early recognition of the high-risk patients of IgG4 HT.

Causality between diabetes and membranous nephropathy: Mendelian randomization

BACKGROUND

Membranous nephropathy (MN) has not yet been fully elucidated regarding its relationship with Type I and II Diabetes. This study aims to evaluate the causal effect of multiple types of diabetes and MN by summarizing the evidence from the Mendelian randomization (MR) study.

METHODS

The statistical data for MN was obtained from a GWAS study encompassing 7979 individuals. Regarding diabetes, fasting glucose, fasting insulin, and HbA1C data, we accessed the UK-Biobank, within family GWAS consortium, MAGIC, FinnGen database, MRC-IEU, and Neale Lab, which provided sample sizes ranging from 17,724 to 298,957. As a primary method in this MR analysis, we employed the Inverse Variance Weighted (IVW), Weighted Median, Weighted mode, MR-Egger, Mendelian randomization pleiotropy residual sum, and outlier (MR-PRESSO) and Leave-one-out sensitivity test. Reverse MR analysis was utilized to investigate whether MN affects Diabetes. Meta-analysis was applied to combine study-specific estimates.

RESULTS

It has been determined that type 2 diabetes, gestational diabetes, type 1 diabetes with or without complications, maternal diabetes, and insulin use pose a risk to MN. Based on the genetic prediction, fasting insulin, fasting blood glucose, and HbA1c levels were not associated with the risk of MN. No heterogeneity, horizontal pleiotropy, or reverse causal relationships were found. The meta-analysis results further validated the accuracy.

CONCLUSIONS

The MR analysis revealed the association between MN and various subtypes of diabetes. This study has provided a deeper understanding of the pathogenic mechanisms connecting MN and diabetes.

Correlation between retinal vascular geometric parameters and pathologically diagnosed type 2 diabetic nephropathy

Background

Diabetic nephropathy (DN) and diabetic retinopathy (DR) are common microvascular complications of diabetes. The purpose of this study was to investigate the correlation between retinal vascular geometric parameters and pathologically diagnosed type 2 DN and to determine the capacity of retinal vascular geometric parameters in differentiating DN from non-diabetic renal disease (NDRD).

Methods

The study participants were adult patients with type 2 diabetes mellitus (T2DM) and chronic kidney disease who underwent a renal biopsy. Univariate and multivariable regression analyses were performed to evaluate associations between retinal vessel geometry parameters and pathologically diagnosed DN. Multivariate binary logistic regression analyses were performed to establish a differential diagnostic model for DN.

Results

In total, 403 patients were examined in this cross-sectional study, including 152 (37.7%) with DN, 157 (39.0%) with NDRD and 94 (23.3%) with DN combined with NDRD. After univariate logistic regression, total vessel fractal dimension, arteriolar fractal dimension and venular fractal dimension were all found to be associated with DN. In multivariate analyses adjusting for age, sex, blood pressure, diabetes, DR and other factors, smaller retinal vascular fractal dimensions were significantly associated with DN (P < .05). We developed a differential diagnostic model for DN combining traditional clinical indicators and retinal vascular geometric parameters. The area under the curve of the model established by multivariate logistic regression was 0.930.

Conclusions

Retinal vessel fractal dimension is of great significance for the rapid and non-invasive differentiation of DN. Incorporating retinal vessel fractal dimension into the diagnostic model for DN and NDRD can improve the diagnostic efficiency.

CYSTATIN C-A Monitoring Perspective of Chronic Kidney Disease in Patients with Diabetes

Chronic kidney disease (CKD) is a microvascular complication that frequently affects numerous patients diagnosed with diabetes. For the diagnosis of CKD, the guidelines recommend the identification of the urinary albumin/creatinine ratio and the determination of serum creatinine, based on which the estimated rate of glomerular filtration (eGFR) is calculated. Serum creatinine is routinely measured in clinical practice and reported as creatinine-based estimated glomerular filtration rate (eGFRcr). It has enormous importance in numerous clinical decisions, including the detection and management of CKD, the interpretation of symptoms potentially related to this pathology and the determination of drug dosage. The equations based on cystatin C involve smaller differences between race groups compared to GFR estimates based solely on creatinine. The cystatin C-based estimated glomerular filtration rate (eGFRcys) or its combination with creatinine (eGFRcr-cys) are suggested as confirmatory tests in cases where creatinine is known to be less precise or where a more valid GFR estimate is necessary for medical decisions. Serum creatinine is influenced by numerous factors: age, gender, race, muscle mass, high-protein diet, including protein supplements, and the use of medications that decrease tubular creatinine excretion (H2 blockers, trimethoprim, fenofibrate, ritonavir, and other HIV drugs). The low levels of creatinine stemming from a vegetarian diet, limb amputation, and conditions associated with sarcopenia such as cirrhosis, malnutrition, and malignancies may lead to inaccurately lower eGFRcr values. Therefore, determining the GFR based on serum creatinine is not very precise. This review aims to identify a new perspective in monitoring renal function, considering the disadvantages of determining the GFR based exclusively on serum creatinine.

Assessment of the Added Value of Intravoxel Incoherent Motion Diffusion-Weighted MR Imaging in Identifying Non-Diabetic Renal Disease in Patients With Type 2 Diabetes Mellitus

BACKGROUND

Identification of non-diabetic renal disease (NDRD) in patients with type 2 diabetes mellitus (T2DM) may help tailor treatment. Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is a promising tool to evaluate renal function but its potential role in the clinical differentiation between diabetic nephropathy (DN) and NDRD remains unclear.

PURPOSE

To investigate the added role of IVIM-DWI in the differential diagnosis between DN and NDRD in patients with T2DM.

STUDY TYPE

Prospective.

POPULATION

Sixty-three patients with T2DM (ages: 22-69 years, 17 females) confirmed by renal biopsy divided into two subgroups (28 DN and 35 NDRD).

FIELD STRENGTH/SEQUENCE

3 T/ T2 weighted imaging (T2WI), and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI).

ASSESSMENT

The parameters derived from IVIM-DWI (true diffusion coefficient [D], pseudo-diffusion coefficient [D*], and pseudo-diffusion fraction [f]) were calculated for the cortex and medulla, respectively. The clinical indexes related to renal function (eg cystatin C, etc.) and diabetes (eg diabetic retinopathy [DR], fasting blood glucose, etc.) were measured and calculated within 1 week before MRI scanning. The clinical model based on clinical indexes and the IVIM-based model based on IVIM parameters and clinical indexes were established and evaluated, respectively.

STATISTICAL TESTS

Student's t-test; Mann-Whitney U test; Fisher's exact test; Chi-squared test; Intraclass correlation coefficient; Receiver operating characteristic analysis; Hosmer-Lemeshow test; DeLong's test. P < 0.05 was considered statistically significant.

RESULTS

The cortex D*, DR, and cystatin C values were identified as independent predictors of NDRD in multivariable analysis. The IVIM-based model, comprising DR, cystatin C, and cortex D*, significantly outperformed the clinical model containing only DR, and cystatin C (AUC = 0.934, 0.845, respectively).

DATA CONCLUSION

The IVIM parameters, especially the renal cortex D* value, might serve as novel indicators in the differential diagnosis between DN and NDRD in patients with T2DM.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Establishment and validation of a diagnostic model for diabetic nephropathy in type 2 diabetes mellitus

PURPOSE

There are few studies on the establishment of diagnostic models for diabetic nephropathy (DN) in in type 2 diabetes mellitus (T2DM) patients based on biomarkers. This study was to establish a model for diagnosing DN in T2DM.

METHODS

In this cross-sectional study, data were collected from the Second Hospital of Shijiazhuang between August 2018 to March 2021. Totally, 359 eligible participants were included. Clinical characteristics and laboratory data were collected. LASSO regression analysis was used to screen out diagnostic factors, and the selected factors were input into the decision tree for fivefold cross validation; then a diagnostic model was established. The performances of the diagnosis model were evaluated by the area under the receiver operator characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. The diagnostic performance of the model was also validated through risk stratifications.

RESULTS

Totally, 199 patients (55.43%) were diagnosed with DN. Age, diastolic blood pressure (DBP), fasting blood glucose, insulin treatment, mean corpuscular hemoglobin concentration (MCHC), platelet distribution width (PDW), uric acid (UA), serum creatinine (SCR), fibrinogen (FIB), international normalized ratio (INR), and low-density lipoprotein cholesterol (LDL-C) were the diagnostic factors for DN in T2DM. The diagnostic model presented good performances, with the sensitivity, specificity, PPV, NPV, AUC, and accuracy being 0.849, 0.969, 0.971, 0.838, 0.965, and 0.903, respectively. The diagnostic model based on the stratifications also showed excellent diagnostic performance for diagnosing DN in T2DM patients.

CONCLUSION

Our diagnostic model with simple and accessible factors provides a noninvasive method for the diagnosis of DN.

Explainable machine learning for early predicting treatment failure risk among patients with TB-diabetes comorbidity

The present study aims to assess the treatment outcome of patients with diabetes and tuberculosis (TB-DM) at an early stage using machine learning (ML) based on electronic medical records (EMRs). A total of 429 patients were included at Chongqing Public Health Medical Center. The random-forest-based Boruta algorithm was employed to select the essential variables, and four models with a fivefold cross-validation scheme were used for modeling and model evaluation. Furthermore, we adopted SHapley additive explanations to interpret results from the tree-based model. 9 features out of 69 candidate features were chosen as predictors. Among these predictors, the type of resistance was the most important feature, followed by activated partial throm-boplastic time (APTT), thrombin time (TT), platelet distribution width (PDW), and prothrombin time (PT). All the models we established performed above an AUC 0.7 with good predictive performance. XGBoost, the optimal performing model, predicts the risk of treatment failure in the test set with an AUC 0.9281. This study suggests that machine learning approach (XGBoost) presented in this study identifies patients with TB-DM at higher risk of treatment failure at an early stage based on EMRs. The application of a convenient and economy EMRs based on machine learning provides new insight into TB-DM treatment strategies in low and middle-income countries.

A retrospective cohort study of clinical characteristics and outcomes of type 2 diabetic patients with kidney disease

Background

Type 2 diabetes mellitus (T2DM) with chronic kidney disease (CKD) poses a serious health threat and becomes a new challenge. T2DM patients with CKD fall into three categories, diabetic nephropathy (DN), non-diabetic kidney disease (NDKD), and diabetic nephropathy plus non-diabetic kidney disease (DN + NDKD), according to kidney biopsy. The purpose of our study was to compare the clinical characteristics and kidney outcomes of DN, NDKD, and DN + NDKD patients.

Methods

Data on clinical characteristics, pathological findings, and prognosis were collected from June 2016 to July 2022 in patients with previously diagnosed T2DM and confirmed DN and or NDKD by kidney biopsy at Tongji Hospital in Wuhan, China. The endpoint was defined as kidney transplantation, dialysis, or a twofold increase in serum creatinine.

Results

In our 6-year retrospective cohort research, a total of 268 diabetic patients were admitted and categorized into three groups by kidney biopsy. The 268 patients were assigned to DN (n = 74), NDKD (n = 109), and DN + NDKD (n = 85) groups. The most frequent NDKD was membranous nephropathy (MN) (n = 45,41.28%). Hypertensive nephropathy was the most common subtype in the DN+NDKD group (n = 34,40%). A total of 34 patients (12.7%) reached the endpoint. The difference between the Kaplan-Meier survival curves of the DN, NDKD, and DN + NDKD groups was significant (p < 0.05). Multifactorial analysis showed that increased SBP [HR (95% CI): 1.018(1.002-1.035), p = 0.025], lower Hb [HR(95% CI): 0.979(0.961-0.997), p = 0.023], higher glycosylated hemoglobin [HR(95% CI): 1.338(1.080-1.658), p = 0.008] and reduced serum ALB [HR(95% CI): 0.952(0.910-0.996), p = 0.032] were risk factors for outcomes in the T2DM patients with CKD.

Conclusions

This research based on a Chinese cohort demonstrated that the risk of endpoint events differed among DN, NDKD, and DN+NDKD patients. In T2DM patients with CKD, DN patients displayed worse kidney prognosis than those with NDKD or DN + NDKD. Increased SBP, higher glycosylated hemoglobin, lower Hb, and decreased serum ALB may be correlated with adverse kidney outcomes in T2DM patients.

Screening candidate diagnostic biomarkers for diabetic kidney disease

BACKGROUND

There are big differences in treatments and prognosis between diabetic kidney disease (DKD) and non-diabetic renal disease (NDRD). However, DKD patients couldn't be diagnosed early due to lack of special biomarkers. Urine is an ideal non-invasive sample for screening DKD biomarkers. This study aims to explore DKD special biomarkers by urinary proteomics.

MATERIALS AND METHODS

According to the result of renal biopsy, 142 type 2 diabetes mellitus (T2DM) patients were divided into 2 groups: DKD (n = 83) and NDRD (n = 59). Ten patients were selected from each group to define urinary protein profiles by label-free quantitative proteomics. The candidate proteins were further verifyied by parallel reaction monitoring (PRM) methods (n = 40). Proteins which perform the same trend both in PRM and proteomics were verified by enzyme-linked immunosorbent assays (ELISA) with expanding the sample size (n = 82). The area under the receiver operating characteristic curve (AUC) was used to evaluate the accuracy of diagnostic biomarkers.

RESULTS

We identified 417 peptides in urinary proteins showing significant difference between DKD and NDRD. PRM verification identified C7, SERPINA4, IGHG1, SEMG2, PGLS, GGT1, CDH2, CDH1 was consistent with the proteomic results and p < 0.05. Three potential biomarkers for DKD, C7, SERPINA4, and gGT1, were verified by ELISA. The combinatied SERPINA4/Ucr and gGT1/Ucr (AUC = 0.758, p = 0.001) displayed higher diagnostic efficiency than C7/Ucr (AUC = 0.632, p = 0.048), SERPINA4/Ucr (AUC = 0.661, p = 0.032), and gGT1/Ucr (AUC = 0.661, p = 0.029) respectively.

CONCLUSIONS

The combined index SERPINA4/Ucr and gGT1/Ucr can be considered as candidate biomarkers for diabetic nephropathy after adjusting by urine creatinine.

Comparison of conventional mathematical model and machine learning model based on recent advances in mathematical models for predicting diabetic kidney disease

Previous research suggests that mathematical models could serve as valuable tools for diagnosing or predicting diseases like diabetic kidney disease, which often necessitate invasive examinations for conclusive diagnosis. In the big-data era, there are several mathematical modeling methods, but generally, two types are recognized: conventional mathematical model and machine learning model. Each modeling method has its advantages and disadvantages, but a thorough comparison of the two models is lacking. In this article, we describe and briefly compare the conventional mathematical model and machine learning model, and provide research prospects in this field.

A Novel Risk Score Model for the Differential Diagnosis of Type 2 Diabetic Nephropathy: A Multicenter Study

Introduction

DN is a common complication of diabetes. However, diabetes combined with renal injury may involve DN or NDKD, with different treatment schemes. The purpose of our study was to determine the independent risk factors of DN and establish a risk score model to help differentiate DN and NDKD, providing a reference for clinical treatment.

Methods

A total of 678 T2D patients who had undergone renal biopsy in four affiliated hospitals of Peking University were consecutively enrolled. Patients were assigned to the DN group and NDKD group according to histopathological results. Seventy percent of patients from PKUFH were randomly assigned to the training group, and the remaining 30% were assigned to the internal validation group. Patients from the other three centers were assigned to the external validation group. We used univariate and multivariate logistic regression analyses to identify independent risk factors of DN in the training group and conducted multivariate logistic regression analysis with these independent risk factors in the training group to find regression coefficients "β" to establish a risk score model. Finally, we conducted internal and external validation of the model with ROC curves.

Results

Diabetic retinopathy, diabetes duration ≥ 5 years, eGFR < 30 ml/min/1.73 m2, 24 h UTP ≥ 3 g, and no hematuria were independent risk factors (P < 0.05), and each factor scored 2, 1, 1, 1, and 1. We assigned the patients to a low-risk group (0-1 points), a medium-risk group (2-3 points), and a high-risk group (4-6 points), representing unlikely DN, possibly DN, and a high probability of DN, respectively. The AUCs were 0.860, 0.924, and 0.855 for the training, internal validation, and external validation groups, respectively.

Conclusion

The risk score model could help differentiate DN and NDKD in a noninvasive manner, reduce the number of renal biopsies, and provide a reference for clinical treatment.

The relevance of the non-invasive biomarkers lncRNA GAS5/miR-21 ceRNA regulatory network in the early identification of diabetes and diabetic nephropathy

BACKGROUND

To investigate the diagnostic value of serum lncRNA growth arrest-specific transcript 5 (lncRNA GAS5) and microRNA-21 (miR-21) in patients with type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN), and elucidate their roles in the pathogenesis.

METHODS

A microarray technology was used asses lncRNA GAS5 and miR-21 expression profiles in non-anticoagulant blood from 44 patients including T2DM without DN group (DM), T2DM with DN group (DN), and healthy controls group (N), followed by real-time PCR validation. Logistic regression and receiver operating characteristic (ROC) curves were applied to evaluate the clinical indicators among normal, T2DM, and DN patients.

RESULTS

The serum lncRNA GAS5 expression in T2DM and DN patients was significantly down-regulated compared with the N group, while the expression of miR-21 was significantly up-regulated (all P < 0.05). Fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c) were negatively correlated with serum lncRNA GAS5, and FBG was independently correlated with serum lncRNA GAS5. Urinary microalbumin, total cholesterol (TC), creatinine (Cr), urea, and systolic blood pressure (SBP) were significantly positively correlated with serum miR-21. Glomerular filtration rate (GFR) and albuminuria (ALB) were negatively correlated with serum miR-21, and ALB was independently correlated with serum miR-21. Serum lncRNA GAS5, miR-21 and lncRNA GAS5/miR-21 showed good diagnostic efficiency as the "diagnostic signature" of T2DM and DN.

CONCLUSION

The lncRNA GAS5/miR-21 diagnostic signature may be a more effective non-invasive biomarker for detecting T2DM. In addition, miR-21 alone may be a more accurate serum biomarker for the early screening of DN patients.

Dyslipidemia and serum cystatin C levels as biomarker of diabetic nephropathy in patients with type 2 diabetes mellitus

Background

Diabetic nephropathy is a leading cause of end-stage renal disease. The diagnostic markers of nephropathy, including the presence of albuminuria and/or a reduced estimated glomerular filtration rate, are not clinically ideal, and most of them are raised after a significant reduction in renal function. Therefore, it is crucial to seek more sensitive and non-invasive biomarkers for the diagnosis of diabetic nephropathy.

Objective of the study

This study aimed to investigate the serum cystatin C levels and dyslipidemia for the detection of diabetic nephropathy in patients with type 2 diabetes mellitus.

Methodology

A hospital-based comparative cross-sectional study was conducted from December 2021 to August 2022 in Tikur, Anbessa specialized teaching hospital with a sample size of 140 patients with type2 diabetes mellitus. Socio-demographic data was collected using a structured questionnaire, and 5 mL of blood was collected from each participant following overnight fasting for biochemical analyses.

Results

In type 2 diabetes patients with nephropathy, we found significant lipoprotein abnormalities and an increase in serum cystatin C (P &lt; 0.001) compared to those without nephropathy. Serum cystatin C, systolic blood pressure, fasting blood glucose, total cholesterol, triglyceride, low density lipoprotein, very low-density lipoprotein, high density lipoprotein, and duration of diabetes were identified as being significantly associated with diabetic nephropathy (P &lt; 0.05) in multivariable logistic regression analysis. The mean values of total cholesterol levels, triglyceride levels, and high-density lipoprotein cholesterol levels were also found to be significantly higher (P &lt; 0.05) in females as compared to male type-2 diabetic patients. The fasting blood glucose levels and lipid profiles of the participants were found to be significantly associated with serum cystatin C levels.

Conclusion

The present study found significant serum cystatin C and lipoprotein abnormalities in T2DM patients with diabetic nephropathy when compared with those without diabetic nephropathy, and these lipoprotein abnormalities were significantly associated with serum cystatin C levels.

Peripheral Blood circRNA Microarray Profiling Identities hsa_circ_0001831 and hsa_circ_0000867 as Two Novel circRNA Biomarkers for Early Type 2 Diabetic Nephropathy

PURPOSE

Type 2 diabetes mellitus (T2DM) increases the incidence of diabetic nephropathy (DN) and eventually progresses to end-stage renal disease. Circular RNAs (circRNAs) are a class of non-coding RNAs that are promising as diagnostic biomarkers and therapeutic targets for human diseases. The aim of this study was to analyze the differential expression of circRNAs (DECs) in peripheral blood from patients with early type 2 diabetic nephropathy (ET2DN), T2DM and controls, which will facilitate to discover some new biomarkers for ET2DN.

PATIENTS AND METHODS

Twenty ET2DN patients, 20 T2DM patients, and 20 normal controls were included in this study. Blood samples from 3 random subjects of age- and sex-matched patients in each group, respectively, were used to detect circRNA expression profiles by circRNA microarray, and the circRNA expression of remaining subjects was validated by real-time quantitative polymerase chain reaction (qRT-PCR). Further functional assessment was performed by bioinformatic tools.

RESULTS

There were 586 DECs in ET2DN vs T2DM group (249 circRNAs were upregulated and 337 circRNAs were downregulated); 176 circRNAs were upregulated and 101 circRNAs were downregulated in T2DM vs control group; 57 circRNAs were upregulated and 5 circRNAs were downregulated in ET2DN vs control group. The functional and pathway enrichment of DECs were analyzed by GO and KEGG. qRT-PCR results revealed that hsa_circ_0001831 and hsa_circ_0000867 were significantly upregulated in ET2DN group compared to both of T2DM and control group. The ROC curve demonstrated that hsa_circ_0001831 and hsa_circ_0000867 have high sensitivity and specificity associated with ET2DN.

CONCLUSION

Our study showed the expression profiles of circRNAs in ET2DN patients and demonstrated that hsa_circ_0001831 and hsa_circ_0000867 can be used as novel diagnostic biomarkers for ET2DN.