Validation of a differential diagnostic model of diabetic nephropathy and non-diabetic renal diseases and the establishment of a new diagnostic model

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.

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.

Serum anti-phospholipase A2 receptor antibody in pathological diagnosis of type 2 diabetes mellitus patients with proteinuria

Patients with diabetes mellitus complicated with proteinuria can be diabetic nephropathy (DN), diabetic complicated with non-diabetic kidney disease (NDKD), or DN with NDKD. Among these membranous nephropathy accounted for a large proportion of DN with NDRD. At present, serum anti-phospholipase A2 receptor (PLA2R) antibody is widely used in the diagnosis and evaluation of therapy in idiopathic membranous nephropathy, our study aimed to investigate the diagnostic significance of anti-PLA2R antibody in type 2 diabetes mellitus (T2DM) patients with proteinuria, providing a method for patients with contraindications of kidney biopsy. Eighty-seven T2DM patients with proteinuria who went on kidney biopsy were divided into the DN group, idiopathic membranous nephropathy (IMN) group, and others group according to their pathological results. In our study, 52.87% and 28.74% of patients were found to have IMN and diabetic nephropathy respectively. The levels of anti-PLA2R antibody, total cholesterol, triglyceride, and estimated glomerular filtration rate (eGFR) were higher in the IMN group, while the prevalence of diabetic retinopathy (DR), systolic blood pressure (SBP) and HbA1c were higher in the DN group. For T2DM patients with proteinuria, anti-PLA2R antibody (AUC = 0.904, 95%CI 0.838-0.970) has a high diagnostic value for IMN. The duration of diabetes (OR = 0.798, P = 0.030), eGFR level (OR = 1.030, P = 0.024), and positive anti-PLA2R antibody (OR = 72.727, P < 0.001) favor the diagnosis of IMN, while DR (OR = 50.234, P < 0.001), SBP (OR = 1.041, P = 0.030), and negative anti-PLA2R antibody (OR = 0.008, P  = 0.001) is beneficial to the diagnosis of DN. Our study found that NDKD is not uncommon in patients with T2DM and proteinuria, and IMN was the main pathological type. Positive anti-PLA2R antibody has a strong accuracy in the diagnosis of IMN in patients with T2DM and proteinuria.

Analysis of clinical predictors of kidney diseases in type 2 diabetes patients based on machine learning

BACKGROUND

The heterogeneity of Type 2 Diabetes Mellitus (T2DM) complicated with renal diseases has not been fully understood in clinical practice. The purpose of the study was to propose potential predictive factors to identify diabetic kidney disease (DKD), nondiabetic kidney disease (NDKD), and DKD superimposed on NDKD (DKD + NDKD) in T2DM patients noninvasively and accurately.

METHODS

Two hundred forty-one eligible patients confirmed by renal biopsy were enrolled in this retrospective, analytical study. The features composed of clinical and biochemical data prior to renal biopsy were extracted from patients' electronic medical records. Machine learning algorithms were used to distinguish among different kidney diseases pairwise. Feature variables selected in the developed model were evaluated.

RESULTS

Logistic regression model achieved an accuracy of 0.8306 ± 0.0057 for DKD and NDKD classification. Hematocrit, diabetic retinopathy (DR), hematuria, platelet distribution width and history of hypertension were identified as important risk factors. Then SVM model allowed us to differentiate NDKD from DKD + NDKD with accuracy 0.8686 ± 0.052 where hematuria, diabetes duration, international normalized ratio (INR), D-Dimer, high-density lipoprotein cholesterol were the top risk factors. Finally, the logistic regression model indicated that DD-dimer, hematuria, INR, systolic pressure, DR were likely to be predictive factors to identify DKD with DKD + NDKD.

CONCLUSION

Predictive factors were successfully identified among different renal diseases in type 2 diabetes patients via machine learning methods. More attention should be paid on the coagulation factors in the DKD + NDKD patients, which might indicate a hypercoagulable state and an increased risk of thrombosis.

Development and validation of a novel nomogram to predict diabetic kidney disease in patients with type 2 diabetic mellitus and proteinuric kidney disease

PURPOSE

Differentiating between diabetic kidney disease (DKD) and non-diabetic kidney disease (NDKD) in patients with Type 2 diabetes mellitus (T2DM) is important due to implications on treatment and prognosis. Clinical methods to accurately distinguish DKD from NDKD are lacking. We aimed to develop and validate a novel nomogram to predict DKD in patients with T2DM and proteinuric kidney disease to guide decision for kidney biopsy.

METHODS

A hundred and two patients with Type 2 Diabetes Mellitus (T2DM) who underwent kidney biopsy from 1st January 2007 to 31st December 2016 were analysed. Univariate and multivariate analyses were performed to identify predictive variables and construct a nomogram. The discriminative ability of the nomogram was assessed by calculating the area under the receiver operating characteristic curve (AUROC), while calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration plot. Internal validation of the nomogram was assessed using bootstrap resampling.

RESULTS

Duration of T2DM, HbA1c, absence of hematuria, presence of diabetic retinopathy and absence of positive systemic biomarkers were found to be independent predictors of DKD in multivariate analysis and were represented as a nomogram. The nomogram showed excellent discrimination, with a bootstrap-corrected C statistic of 0.886 (95% CI 0.815-0.956). Both the calibration curve and the Hosmer-Lemeshow goodness-of-fit test (p = 0.242) showed high degree of agreement between the prediction and actual outcome, with the bootstrap bias-corrected curve similarly indicating excellent calibration.

CONCLUSIONS

A novel nomogram incorporating 5 clinical parameters is useful in predicting DKD in type 2 diabetes mellitus patients with proteinuric kidney disease.

Salivary Glycopatterns as Potential Non-Invasive Biomarkers for Diagnosing and Reflecting Severity and Prognosis of Diabetic Nephropathy

Discriminating between diabetic nephropathy (DN) and non-diabetic renal disease (NDRD) can help provide more specific treatments. However, there are no ideal biomarkers for their differentiation. Thus, the aim of this study was to identify biomarkers for diagnosing and predicting the progression of DN by investigating different salivary glycopatterns. Lectin microarrays were used to screen different glycopatterns in patients with DN or NDRD. The results were validated by lectin blotting. Logistic regression and artificial neural network analyses were used to construct diagnostic models and were validated in in another cohort. Pearson's correlation analysis, Cox regression, and Kaplan-Meier survival curves were used to analyse the correlation between lectins, and disease severity and progression. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) and bioinformatics analyses were used to identify corresponding glycoproteins and predict their function. Both the logistic regression model and the artificial neural network model achieved high diagnostic accuracy. The levels of Aleuria aurantia lectin (AAL), Lycopersicon esculentum lectin (LEL), Lens culinaris lectin (LCA), Vicia villosa lectin (VVA), and Narcissus pseudonarcissus lectin (NPA) were significantly correlated with the clinical and pathological parameters related to DN severity. A high level of LCA and a low level of LEL were associated with a higher risk of progression to end-stage renal disease. Glycopatterns in the saliva could be a non-invasive tool for distinguishing between DN and NDRD. The AAL, LEL, LCA, VVA, and NPA levels could reflect the severity of DN, and the LEL and LCA levels could indicate the prognosis of DN.

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

BACKGROUND

The disease pathology for diabetes mellitus patients with chronic kidney disease (CKD) may be diabetic nephropathy (DN), non-diabetic renal disease (NDRD), or DN combined with NDRD. Considering that the prognosis and treatment of DN and NDRD differ, their differential diagnosis is of significance. Renal pathological biopsy is the gold standard for diagnosing DN and NDRD. However, it is invasive and cannot be implemented in many patients due to contraindications. This article constructed a new noninvasive evaluation model for differentiating DN and NDRD.

METHODS

We retrospectively screened 1,030 patients with type 2 diabetes who has undergone kidney biopsy from January 2005 to March 2017 in a single center. Variables were ranked according to importance, and the machine learning methods (random forest, RF, and support vector machine, SVM) were then used to construct the model. The final model was validated with an external group (338 patients, April 2017-April 2019).

RESULTS

In total, 929 patients were assigned. Ten variables were selected for model development. The areas under the receiver operating characteristic curves (AUCROCs) for the RF and SVM methods were 0.953 and 0.947, respectively. Additionally, 329 patients were analyzed for external validation. The AUCROCs for the external validation of the RF and SVM methods were 0.920 and 0.911, respectively.

CONCLUSION

We successfully constructed a predictive model for DN and NDRD using machine learning methods, which were better than our regression methods.

CLINICAL TRIAL REGISTRATION

ClinicalTrial.gov, NCT03865914.

Establishment and Validation of a Nomogram Model for Prediction of Diabetic Nephropathy in Type 2 Diabetic Patients with Proteinuria

PURPOSE

To establish and validate the nomogram model for predicting diabetic nephropathy (DN) in type 2 diabetes mellitus (T2DM) patients with proteinuria.

METHODS

A total of 102 patients with T2DM and proteinuria who underwent renal biopsy were included in this study. According to pathological classification of the kidney, the patients were divided into two groups, namely, a DN group (52 cases) and a non-diabetic renal disease (NDRD) group (50 cases). The clinical data were collected, and the factors associated with diabetic nephropathy (DN) were analyzed with multivariate logistic regression. A nomogram model for predicting DN risk was constructed by using R4.1 software. Receiver operator characteristic (ROC) curves were generated, and the K-fold cross-validation method was used for validation. A consistency test was performed by generating the correction curve.

RESULTS

Systolic blood pressure (SBP), diabetic retinopathy (DR), hemoglobin (Hb), fasting plasma glucose (FPG) and triglyceride/cystatin C (TG/Cys-C) ratio were independent factors for DN in T2DM patients with proteinuria (P<0.05). The nomogram model had good prediction efficiency. If the total score of the nomogram exceeds 200, the probability of DN is as high as 95%. The area under the ROC curve was 0.9412 (95% confidence interval (CI) = 0.8981-0.9842). The 10-fold cross-validation showed that the prediction accuracy of the model was 0.8427. The Hosmer-Lemeshow (H-L) test showed that there was no significant difference between the predicted value and the actual observed value (X ² = 6.725, P = 0.567). The calibration curve showed that the fitting degree of the DN nomogram prediction model was good.

CONCLUSION

The nomogram model constructed in the present study improves the diagnostic efficiency of DN in T2DM patients with proteinuria, and it has a high clinical value.

Renal outcomes and prognostic factors in patients with type-2 diabetes and chronic kidney disease confirmed by renal biopsy

Aim

To evaluate the renal outcomes and prognostic factors among patients with type-2 diabetes (T2D) and biopsy-confirmed diabetic nephropathy (DN), non-diabetic renal disease (NDRD) and DN mixed with NDRD (MIX).

Design and Methods

Patients with both T2D and chronic kidney disease (CKD) who underwent renal biopsy between January 2014 and December 2016 were recruited in this prospective observational study. Participants were divided into DN group, NDRD group, or MIX group according to the baseline pathological diagnosis. The primary endpoint was a composite renal event of end-stage renal disease (ESRD) or ⩾ 40% reduction in estimated glomerular filtration rate (eGFR).

Results

Among the 292 participants included, 153 (52.4%) belonged to the DN group, 30 (10.3%) belonged to the NDRD group, and 109 (37.3%) belonged to the MIX group. During the median follow-up of 27 months, the adverse renal events occurred in 132 (44.2%) patients. Compared with NDRD group, the multiple adjusted hazard ratios (HRs) for renal events in patients with DN and MIX groups were 3.900 (95% confidence interval [CI]: 1.103-13.788) and 2.691 (95% CI: 0.662-10.936), respectively. Baseline lower eGFR (HR: 1.159, 95% CI: 1.060-1.266), severe proteinuria (HR: 2.047, 95% CI: 1.227-3.416), lower hemoglobin (HR: 1.170, 95% CI: 1.008-1.267), and a family history of diabetes (HR: 1.138, 95% CI: 1.008-2.285) were independent predictors for adverse renal outcomes in patients with DN.

Conclusion

In patients with T2D and CKD, pure DN and MIX group displayed a worse renal prognosis than NDRD group. Worse renal function, severe proteinuria, lower hemoglobin, and a family history of diabetes may be associated with adverse renal outcomes in patients with DN.

Clinical Predictors of Nondiabetic Kidney Disease in Patients with Diabetes: A Single-Center Study

Background

Although diabetic kidney disease (DKD) could affect up to one-third of patients with diabetes mellitus (DM), these patients can develop kidney diseases different from DKD, or these conditions can superimpose on DKD. Several potential predictors of nondiabetic kidney disease (NDKD) have been proposed, but there are no definitive indications available for kidney biopsy in diabetic patients.

Methods

We designed a single-center, cross-sectional, and retrospective cohort study to identify clinical and laboratory factors associated with a diagnosis of NDKD after native kidney biopsy in diabetic patients and to investigate differences in time to end-stage kidney disease (ESKD) in patients with a diagnosis of DKD and NDKD.

Results

Of 142 patients included in our analysis, 89 (62.68%) had a histopathological diagnosis of NDKD or mixed NDKD + DKD. Patients in the NDKD group had significantly lower HbA1C, lower prevalence of diabetic retinopathy (DR), and less severe proteinuria, and there was a lower proportion of patients with nephrotic syndrome; the DKD group had significantly lower proportion of patients with hematological conditions. In the multivariate binary logistic regression, only absence of DR and presence of a hematological condition significantly predicted NDKD after adjustment for age and sex. Time to ESKD was significantly higher in patients with NDKD or mixed forms than in those with DKD.

Conclusions

After a careful selection, more than half of kidney biopsies performed in diabetic patients can identify NDKD (alone or with concomitant DKD). Absence of DR and coexistence of a hematological condition (especially MGUS) were strong predictors of NDKD in our cohort.

Urinary sediment microRNAs can be used as potential noninvasive biomarkers for diagnosis, reflecting the severity and prognosis of diabetic nephropathy

BACKGROUND

Patients with both diabetes mellitus (DM) and kidney disease could have diabetic nephropathy (DN) or non-diabetic renal disease (NDRD). IgA nephropathy (IgAN) and membranous nephropathy (MN) are the major types of NDRD. No ideal noninvasive diagnostic model exists for differentiating them. Our study sought to construct diagnostic models for these diseases and to identify noninvasive biomarkers that can reflect the severity and prognosis of DN.

METHODS

The diagnostic models were constructed using logistic regression analysis and were validated in an external cohort by receiver operating characteristic curve analysis method. The associations between these microRNAs and disease severity and prognosis were explored using Pearson correlation analysis, Cox regression, Kaplan-Meier survival curves, and log-rank tests.

RESULTS

Our diagnostic models showed that miR-95-3p, miR-185-5p, miR-1246, and miR-631 could serve as simple and noninvasive tools to distinguish patients with DM, DN, DM with IgAN, and DM with MN. The areas under the curve of the diagnostic models for the four diseases were 0.995, 0.863, 0.859, and 0.792, respectively. The miR-95-3p level was positively correlated with the estimated glomerular filtration rate (p < 0.001) but was negatively correlated with serum creatinine (p < 0.01), classes of glomerular lesions (p < 0.05), and scores of interstitial and vascular lesions (p < 0.05). However, the miR-631 level was positively correlated with proteinuria (p < 0.001). A low miR-95-3p level and a high miR-631 level increased the risk of progression to end-stage renal disease (p = 0.002, p = 0.011).

CONCLUSIONS

These four microRNAs could be noninvasive tools for distinguishing patients with DN and NDRD. The levels of miR-95-3p and miR-631 could reflect the severity and prognosis of DN.

Kidney Biopsy in Type 2 Diabetes: A Multicenter Cross-Sectional Study

INTRODUCTION

Kidney biopsies (KBs) are performed in patients with type 2 diabetes (T2D) to diagnose non-diabetic or hypertensive kidney disease (NDHKD) potentially requiring specific management compared to diabetic and or hypertensive nephropathy (absence of NDHKD). Indications for KB are based on the presence of atypical features compared to the typical course of diabetic nephropathy. In this study, we assessed the association of different patterns of atypical features, or KB indications, with NDHKD.

METHODS

Native KBs performed in patients with T2D were analyzed. Data were collected from the patients' records. KB indications were determined according to the presence of different atypical features considered sequentially: (1) presence of any feature suggesting NDHKD which is not among the following ones, (2) recent onset of nephrotic syndrome, (3) low or rapidly declining estimated glomerular filtration rate (eGFR), (4) rapid increase in proteinuria, (5) short duration of diabetes, (6) presence of hematuria, or (7) normal retinal examination.

RESULTS

Among the 463 KBs analyzed, NDHKD was diagnosed in 40% of the total population and 54, 40, 24, and 7% of the KBs performed for indications 1-4 respectively. Conversely, no patient who underwent KB for indications 5-7 displayed NDHKD. Logistic regression analyses identified eGFRCKD-EPI >15 mL/min/1.73 m2, urinary protein-to-Cr ratio <0.3 g/mmol, hematuria, HbA1c <7%, and diabetes duration <5 years as predictors of NDHKD, independently from the indication group.

CONCLUSION

NDHKD is frequent in T2D. Despite the association of hematuria with NDHKD, our results suggest that presence of hematuria and absence of DR are insufficient to indicate KB in the absence of concurrent atypical features. Conversely, rapid progression of proteinuria and rapid deterioration of eGFR are major signals of NDHKD.

Glomerulonephritis in AKI: From Pathogenesis to Therapeutic Intervention

Acute kidney injury (AKI) is increasingly emerging as a global emergency. Sepsis, major surgery, and nephrotoxic drugs are the main causes of AKI in hospitalized patients. However, glomerulonephritis accounts for about 10% of AKI episodes in adults, mainly related to rapidly progressive glomerulonephritis resulting from granulomatous polyangiitis (GPA, Wegener granulomatosis), microscopic polyangiitis (MPA), and anti-glomerular basement membrane (GBM) disease. Also, diffuse proliferative lupus nephritis, immunoglobulin A nephropathy, post-streptococcal glomerulonephritis, mixed cryoglobulinemia, mesangiocapillary glomerulonephritis, membranous nephropathy, hemolytic uremic syndrome, thrombotic thrombocytopenic purpura, and scleroderma can induce acute renal failure. Early diagnosis of AKI due to glomerulonephritis is crucial for prompt, effective management to improve short- and long-term outcomes. Kidney biopsy is the gold standard for the diagnosis of glomerular disease, but it is not frequently performed in critically ill patients because of their clinical conditions. In this setting, a growing number of diagnostic assays can support the working hypothesis, including antineutrophil cytoplasmic antibodies (ANCAs), anti-double-stranded DNA antibodies, anti-GBM antibodies, antistreptolysin O and anti-DNase B antibodies, cryoglobulins, antiphospholipid antibodies, and complement levels. Therapeutic strategies in AKI patients with glomerulonephritis include high-dose corticosteroids, cyclophosphamide, and plasma exchange. This article reviews the wide spectrum of glomerulopathies associated with AKI, describing the immunological mechanisms underlying glomerular diseases and presenting an overview of the therapeutic options.

Machine learning in nephrology: scratching the surface

Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases. With the development of data preservation and processing, as well as the advancement of machine learning algorithms, machine learning is expected to make remarkable breakthroughs in nephrology. Machine learning models have yielded many preliminaries to moderate and several excellent achievements in the fields, including analysis of renal pathological images, diagnosis and prognosis of chronic kidney diseases and acute kidney injury, as well as management of dialysis treatments. However, it is just scratching the surface of the field; at the same time, machine learning and its applications in renal diseases are facing a number of challenges. In this review, we discuss the application status, challenges and future prospects of machine learning in nephrology to help people further understand and improve the capacity for prediction, detection, and care quality in kidney diseases.

Potential Role of the Renal Arterial Resistance Index in the Differential Diagnosis of Diabetic Kidney Disease

AIMS

To investigate the potential role of renal arterial resistance index (RI) in the differential diagnosis between diabetic kidney disease (DKD) and non-diabetic kidney disease (NDKD) and establish a better-quantified differential diagnostic model.

MATERIALS AND METHODS

We consecutively reviewed 469 type 2 diabetes patients who underwent renal biopsy in our center. According to the renal biopsy results, eligible patients were classified into the DKD group and the NDKD group. The diagnostic significance of RI was evaluated by receiver operating characteristic (ROC) curve analysis. Logistic regression analysis was used to search for independent risk factors associated with DKD. Then a novel diagnostic model was established using multivariate logistic regression analysis.

RESULTS

A total of 332 DKD and 137 NDKD patients were enrolled for analysis. RI was significantly higher in the DKD group compared with those in the NDKD group (0.70 vs. 0.63, p< 0.001). The optimum cutoff value of RI for predicting DKD was 0.66 with sensitivity (69.2%) and specificity (80.9%). Diabetic retinopathy, diabetes duration ≥ 60 months, HbA1c ≥ 7.0(%), RI ≥ 0.66, and body mass index showed statistical significance in the multivariate logistic regression analysis. Then, we constructed a new diagnostic model based on these results. And the validation tests indicated that the new model had good sensitivity (81.5%) and specificity (78.6%).

CONCLUSIONS

RI has a potential role in discriminating DKD from NDKD. The RI-based predicting model can be helpful for differential diagnosis of DKD and NDKD.

Indications for renal biopsy in patients with diabetes. Joint position statement of the Italian Society of Nephrology and the Italian Diabetes Society

AIMS

This joint document of the Italian Society of Nephrology and the Italian Diabetes Society reviews the main indications to perform a renal biopsy in diabetic patients, according to the recommendations of a panel of experts based on all available scientific evidence.

DATA SYNTHESIS

Renal biopsy has a pivotal role in assessing the nature and severity of renal injury in patients with diabetic kidney disease (DKD). The procedure is mandatory in the presence of one of more of the following features: rapid onset or progression of albuminuria or sudden onset of nephrotic syndrome, rapid GFR decline with or without albuminuria, hematuria, active urine sediment, clinical and/or laboratory suspicion of other systemic diseases, and, in patients with type 1 diabetes, short diabetes duration and absence of retinopathy. Indeed, ~40% of diabetic individuals with kidney injury undergoing renal biopsy are affected by a non-diabetic renal disease (NDRD). Furthermore, the histological evaluation of patients with suspected classical diabetic nephropathy allows to define the extent of glomerular, tubulo-interstitial and vascular lesions, thus providing important prognostic (and potentially therapeutic) data. In the future, the indications for renal biopsy might be extended to the definition of the histological lesions underlying the "nonalbuminuric" DKD phenotypes, as well as to the evaluation of the response to treatment with the new anti-hyperglycemic drugs that provide cardiorenal protection.

CONCLUSIONS

In view of the heterogeneous clinical presentation and course of DKD and of the related heterogeneous histopathological patterns, a more extensive use of renal biopsy may be crucial to provide valuable information with important pathogenic, diagnostic, prognostic, and therapeutic implications.

Non-diabetic glomerular lesions in diabetic kidney disease: clinical predictors and outcome in an Eastern European cohort

PURPOSE

Since patients' prognosis depends on the lesions identified by kidney biopsy (KB), we aimed to evaluate predictors of non-diabetic kidney disease (NDKD) in diabetic subjects and to assess their kidney outcome as compared to diabetic nephropathy (DN).

METHODS

180 adults diagnosed by KB with DN (n = 120) or NDKD (n = 60), over a 10 year time-span, were retrospectively included and followed for a mean of 48.1 (95% CI 43.1-53.1) months. Patients with superimposed specific lesions over DN and with steroid-induced diabetes were excluded. The primary endpoint was renal replacement therapy (RRT) initiation. Only subjects who were alive at the end of follow-up (73 with DN and 38 with NDKD) entered the kidney survival analysis.

RESULTS

Membranous nephropathy (9%) was the most common NDKD. Predictors for NDKD were shorter duration of diabetes (OR 0.88; 95% CI 0.81-0.96, p = 0.004), absence of diabetic retinopathy (OR 0.08; 95% CI 0.01-0.44, p = 0.003), and nephrotic syndrome at presentation (OR 3.55; 95% CI 1.39-9.04, p = 0.008). Subjects with NDKD needed RRT later as those with DN [82 (95% CI 67-97.1) vs. 45 (95% CI 34-56.5) months, p = 0.001]. In an adjusted Cox model, biopsy diagnosed DN independently predicted RRT (OR 4.43; 95% CI 1.54-12.7, p = 0.006). Other predictors were lower eGFR, higher proteinuria, and absence of renin-angiotensin inhibitor therapy.

CONCLUSION

As one-third of the investigated subjects had NDKD, and NDKD was associated with a better kidney survival, independently predicted by the type of glomerular lesion, KB appears the most reliable tool to guide therapy and to assess outcome in patients with diabetic kidney disease.

The association of hematuria on kidney clinicopathologic features and renal outcome in patients with diabetic nephropathy: a biopsy-based study

PURPOSE

To investigate the relationships between hematuria, clinicopathological features and renal outcomes in patients with biopsy-proven diabetic nephropathy (DN).

METHODS

This cohort study included 261 patients with DN. Participants were divided into two groups according to number of red blood cells per high-power field (RBC/hpf) in urine sediment: the hematuria (-) group (≤ 3 RBC/hpf) and the hematuria (+) group (> 3 RBC/hpf). Basic clinical parameters were measured at the time of renal biopsy; relationships between hematuria and clinicopathological features and renal outcomes were analyzed.

RESULTS

Patients in the hematuria (+) group often had overt proteinuria. Interstitial inflammation was more severe in the hematuria (+) group than in the hematuria (-) group. Glomerular arteriolar hyalinosis, interstitial fibrosis and tubular atrophy were comparable between groups. For patients with early DN (eGFR ≥ 60 ml/min/1.73 m²), urinary RBC/hpf at baseline was positively correlated with glomerular classification, interstitial fibrosis/tubular atrophy scores and interstitial inflammation scores. In prognostic analysis, hematuria was associated with a higher risk of progression to end-stage renal disease. Hematuria remained an independent predictor after adjustment for confounding factors such as sex, age, duration of diabetes, serum glucose level, hypertension, cholesterol, eGFR and urine protein excretion, especially in patients with early DN and in male patients.

CONCLUSION

In this study, hematuria was associated with more severe renal pathologic lesions in patients with DN. The presence of hematuria could be an independent predictor of renal outcome in patients with early DN.

Current findings of kidney biopsy including nephropathy associated with hypertension and diabetes mellitus in Korea

BACKGROUND/AIMS

This study aimed to investigate long-term temporal trends and outcomes of biopsy-proven kidney diseases in a multicenter kidney biopsy cohort, focusing on hypertension and diabetes, the leading causes of end-stage kidney disease (ESKD).

METHODS

The study included a total of 21,426 patients who underwent kidney biopsy from 1979 to 2018 in 18 hospitals in Korea. We selected subgroups of adults with diabetes (n = 2,813) or clinically presumed hypertensive nephrosclerosis (HT-N, n = 2,917). Clinical, demographic, and laboratory data were collected in conjunction with pathologic findings. The prevalence of pathologically confirmed kidney diseases over time and their associations with clinical outcomes were evaluated.

RESULTS

The prevalence of biopsy-proven diabetic nephropathy (DN) has increased significantly from 2.5% to 6.0% in the total cohort in the recent 30 years with an increase in the prevalence of diabetes. Approximately 68% of total diabetic patients had non-diabetic renal disease (NDRD); the proportion was retained since 2000s. DN showed a significantly higher risk of ESKD than NDRD (hazard ratio [HR], 1.59; 95% confidence interval [CI], 1.35 to 1.88). The prevalence of biopsy-proven HT-N remained < 2% in the total cohort for several decades. There was no difference in risks of ESKD between patients with or without biopsy-proven HT-N (HR, 0.93; 95% CI, 0.54 to 1.59).

CONCLUSION

In recent decades, the prevalence of diabetes and DN has significantly increased in the kidney biopsy cohort, showing an increased risk of ESKD. Despite the large numbers of patients meeting the clinical criteria of HT-N, most of those were diagnosed with pathologic diagnoses other than HT-N.

Red Blood Cell Distribution Width is Associated with Glomerulonephritis in Diabetic Patients with Albuminuria

Background

The aim of this study was to explore predictive factors to inform accurate diagnosis of glomerulonephritis (GNs) in patients with diabetes.

Material/Methods

Clinical characteristics and laboratory data were retrospectively analyzed from 200 patients with diabetes including 115 patients who had undergone a renal biopsy. Eligible patients were categorized into three groups: pure type 2 diabetes mellitus (T2DM), isolated diabetic nephropathy (DN), and GN. Odds ratios (ORs) were calculated to evaluate the contributions of predictive factors for GN. A receiver operating characteristic curve (ROC) was created to obtain cut-off values for predictive factors for GNs and investigate their corresponding predictive accuracy.

Results

Red cell distribution width (RDW) was significantly higher in the GN group than in the DN group. Multivariate regression analysis revealed that baseline RDW level (OR=1.988, 95% CI=1.237~3.194, P=0.005) was an independent predictive factor for development of GNs.

Conclusions

Increased RDW levels are independently associated with a greater risk of GN in patients with diabetes who have albuminuria, and may be an additional valuable and noninvasive predictive tool for differentiating GNs and DN.

Timing of kidney biopsy in type 2 diabetic patients: a stepwise approach

BACKGROUND

Diabetic nephropathy (DN) is the most prevalent cause of renal disease in type 2 diabetic patients and is usually diagnosed clinically. A kidney biopsy is considered when non-diabetic renal disease (NDRD) is suspected, such as rapid progression in renal function impairment and severe proteinuria. Still, there is yet no consensus on the timing of kidney biopsy in type 2 diabetic patients. This study aims to identify markers that can help differentiate between DN and NDRD and guide the decision of kidney biopsy.

METHODS

We retrospectively reviewed patients with type 2 diabetes who received kidney biopsy from 2008 to 2017 at Taipei Veterans General Hospital. Ophthalmologist consultation and outpatient records, diagnosis of kidney biopsy, laboratory data, and clinical characteristics were collected.

RESULTS

This study enrolled 160 type 2 diabetic patients, among which 120 (75%) had isolated DN and 40 (25%) had NDRD ± DN (26 had isolated NDRD, and 14 had NDRD superimposed on DN). In multivariate logistic regression analysis, DM duration (odds ratio [OR]: 0.907; 95% confidence interval [CI]: 0.842-0.977; P = 0.01), diabetic retinopathy (OR: 0.196; 95% CI: 0.061-0.627; P = 0.006), and urinary RBC (OR: 1.068; 95% CI: 1.024-1.115; P = 0.002) were independent predictors of NDRD. In patients with diabetic retinopathy (n = 112, 70%), the presence of proliferative diabetic retinopathy, pan-retinal photocoagulation, and hematuria were factors predicting NDRD; and in patients without diabetic retinopathy (n = 48, 30%), short DM duration and hematuria were factors predicting NDRD.

CONCLUSIONS

Using diabetic retinopathy, DM duration, and hematuria, we developed a 3-step approach to stratify patients into three categories with the different likelihoods of having NDRD. Then different strategies could be taken accordingly. Our stepwise approach is easy to follow and may serve as an appropriate and useful tool to help clinicians in making decisions of kidney biopsy in type 2 DM patients presenting with kidney diseases.

Design and validation of a scoring model for differential diagnosis of diabetic nephropathy and nondiabetic renal diseases in type 2 diabetic patients

BACKGROUND

We aim to design a scoring model for differential diagnosis between diabetic nephropathy (DN) and nondiabetic renal disease (NDRD) in type 2 diabetic patients through a combination of clinical variables.

METHODS

A total of 170 patients with type 2 diabetes who underwent kidney biopsies were included and divided into three groups according to pathological findings: DN group (n = 46), MIX group (DN + NDRD, n = 54), NDRD group (n = 70). Clinical characteristics and laboratory data were collected and compared among groups. Variables with a significant statistical difference between DN and NDRD patients were analyzed by logistic regression to predict the presence of NDRD; then a scoring model was established based on the regression coefficient and further validated in an independent cohort of 67 patients prospectively.

RESULTS

On biopsy, 72.9% of patients had NDRD, and the most common pathological type was membranous nephropathy. The established scoring model for predicting NDRD included five predictors: age, systolic blood pressure, hemoglobin, duration of diabetes, and absence of diabetic retinopathy. The model demonstrated good discrimination and calibration (area under curve [AUC] 0.863, 95% CI, 0.800-0.925; Hosmer-Lemeshow [H-L] P = .062). Furthermore, high prediction accuracy (AUC = 0.900; 95% CI, 0.815-0.985) in the validation cohort proved the stability of the model.

CONCLUSIONS

We present a simple, robust scoring model for predicting the presence of NDRD with high accuracy (0.85) for the first time. This decision support tool provides a noninvasive method for differential diagnosis of DN and NDRD, which may help clinicians assess the risk-benefit ratio of kidney biopsy for type 2 diabetic patients with renal impairment.

Risk factors for non-diabetic renal disease in diabetic patients

Background

Diabetic patients with kidney disease have a high prevalence of non-diabetic renal disease (NDRD). Renal and patient survival regarding the diagnosis of diabetic nephropathy (DN) or NDRD have not been widely studied. The aim of our study is to evaluate the prevalence of NDRD in patients with diabetes and to determine the capacity of clinical and analytical data in the prediction of NDRD. In addition, we will study renal and patient prognosis according to the renal biopsy findings in patients with diabetes.

Methods

Retrospective multicentre observational study of renal biopsies performed in patients with diabetes from 2002 to 2014.

Results

In total, 832 patients were included: 621 men (74.6%), mean age of 61.7 ± 12.8 years, creatinine was 2.8 ± 2.2 mg/dL and proteinuria 2.7 (interquartile range: 1.2–5.4) g/24 h. About 39.5% (n = 329) of patients had DN, 49.6% (n = 413) NDRD and 10.8% (n = 90) mixed forms. The most frequent NDRD was nephroangiosclerosis (NAS) (n = 87, 9.3%). In the multivariate logistic regression analysis, older age [odds ratio (OR) = 1.03, 95% CI: 1.02–1.05, P < 0.001], microhaematuria (OR = 1.51, 95% CI: 1.03–2.21, P = 0.033) and absence of diabetic retinopathy (DR) (OR = 0.28, 95% CI: 0.19–0.42, P < 0.001) were independently associated with NDRD. Kaplan–Meier analysis showed that patients with DN or mixed forms presented worse renal prognosis than NDRD (P < 0.001) and higher mortality (P = 0.029). In multivariate Cox analyses, older age (P < 0.001), higher serum creatinine (P < 0.001), higher proteinuria (P < 0.001), DR (P = 0.007) and DN (P < 0.001) were independent risk factors for renal replacement therapy. In addition, older age (P < 0.001), peripheral vascular disease (P = 0.002), higher creatinine (P = 0.01) and DN (P = 0.015) were independent risk factors for mortality.

Conclusions

The most frequent cause of NDRD is NAS. Elderly patients with microhaematuria and the absence of DR are the ones at risk for NDRD. Patients with DN presented worse renal prognosis and higher mortality than those with NDRD. These results suggest that in some patients with diabetes, kidney biopsy may be useful for an accurate renal diagnosis and subsequently treatment and prognosis.

When to perform renal biopsy in patients with type2 diabetes mellitus? Predictive model of non-diabetic renal disease

INTRODUCTION

Diabetic nephropathy (DN) is one of the most frequent complications in patients with diabetes mellitus (DM) and its diagnosis is usually established on clinical grounds. However, kidney involvement in some diabetic patients can be due to other causes, and renal biopsy might be needed to exclude them. The aim of our study was to establish the clinical and analytical data that predict DN and no-diabetic renal disease (NDRD), and to develop a predictive model (score) to confirm or dismiss DN.

MATERIAL AND METHODS

We conducted a transversal, observational and retrospective study, including renal biopsies performed in type2 DM patients, between 2000 and 2018.

RESULTS

Two hundred seven DM patients were included in our study. The mean age was 64.5±10.6 years and 74% were male. DN was found in 126 (61%) of the biopsies and NDRD in 81 (39%). Diabetic retinopathy was presented in 58% of DN patients, but only in 6% of NDRD patients (P<.001). Patients with NDRD were diagnosed of primary glomerulopathies (52%), nephroangiosclerosis (16%), inmunoallergic interstitial nephritis (15%) and vasculitis (8.5%). In the multivariate analysis, retinopathy (OR26.7; 95%CI: 6.8-104.5), chronic ischaemia of lower limbs (OR4,37; 95%CI: 1.33-14.3), insulin therapy (OR3.05; 95%CI: 1.13-8.25), time course of DM ≥10years (OR2.71; 95%CI: 1.1-6.62) and nephrotic range proteinuria (OR2.91; 95%CI: 1.2-7.1) were independent predictors for DN. Microhaematuria defined as ≥10 red blood cells per high-power field (OR0.032; 95%CI: 0.01-0.11) and overweight (OR0.21; 95%CI: 0.08-0.5) were independent predictors of NDRD. According to the predictive model based on the multivariate analysis, all patients with a score >3 had DN and 94% of cases with a score ≤1 had NDRD (score ranked from -6 to 8points).

CONCLUSIONS

NDRD is common in DM patients (39%), being primary glomerulonephritis the most frequent ethology. The absence of retinopathy and the presence of microhematuria are highly suggestive of NDRD. The use of our predictive model could facilitate the indication of performing a renal biopsy in DM patients.

Diagnostic Performance of Retinopathy in the Detection of Diabetic Nephropathy in Type 2 Diabetes: A Systematic Review and Meta-Analysis of 45 Studies

AIMS

To conduct an evidence-based evaluation of diabetic retinopathy (DR) for the diagnosis of diabetic nephropathy (DN) in type 2 diabetics with kidney disease.

METHODS

We systematically searched PubMed, EMBASE, and the Cochrane Library from inception to June 27, 2018, including the reference lists of identified primary studies. A study was included if it (1) used DR as a diagnostic test for DN; and (2) used histological evaluation of renal tissues as the reference standard.

RESULTS

The analysis included 45 studies (4,561 patients). A bivariate analysis yielded a sensitivity of 0.67 (95% CI 0.61-0.74) and a specificity of 0.78 (95% CI 0.73-0.82). The summary receiver operating characteristic curve analysis provided an area under the curve (AUC) of 0.79 (95% CI 0.76-0.83). In a setting of 41% prevalence of DN, the probability of DN would be 68% if the test of DR was positive, and the probability of DN would be 23% if it was negative. In addition, although the mean specificity of proliferative DR for the detection of DN was 0.99 (95% CI 0.45-1.00), the mean sensitivity was 0.34 (95% CI 0.24-0.44), and the AUC was 0.58 (95% CI 0.53-0.62).

CONCLUSIONS

DR is helpful in diagnosing DN in persons with type 2 diabetes and kidney disease, but the severity of DR may not parallel the presence of DN.

Validation of the 2007 kidney disease outcomes quality initiative clinical practice guideline for the diagnosis of diabetic nephropathy and nondiabetic renal disease in Chinese patients

AIMS

Diabetes mellitus (DM) has overtaken infection and immunological factors as the most common cause of end-stage renal disease. The 2007 Kidney Disease Outcomes Quality Initiative (KDOQI) guideline is a widely accepted guideline for the clinical diagnosis of diabetic nephropathy (DN) and non-diabetic renal disease (NDRD). Our study sought to verify its diagnostic ability in the Chinese population.

METHODS

We included 773 patients with DM who underwent a renal biopsy at the Chinese PLA General Hospital from 2007 to 2016. All patients were divided into three groups according to their pathological findings: isolated DN, isolated NDRD, and DN combined with NDRD.

RESULTS

Good sensitivity and poor specificity were found for the prediction of NDRD in the Chinese population. Rapidly decreasing estimated glomerular filtration rate, systemic disease, refractory hypertension, and the existence of "grey area" patients may have contributed to the poor diagnostic ability.

CONCLUSIONS

The diagnostic ability of the 2007 KDOQI guideline for DN and NDRD was unsatisfactory. The high sensitivity and low specificity of the guideline made it more suitable as screening criteria rather than as diagnostic criteria.

Alteration of glycosylation in serum proteins: a new potential indicator to distinguish non-diabetic renal diseases from diabetic nephropathy

Diabetic nephropathy (DN) and nondiabetic renal disease (NDRD) are two major categories of renal diseases in diabetes mellitus patients. The clinical differentiation among them is usually not so clear and effective. In this study, sera from DN and NDRD patients were collected, and glycan profiles of serum proteins from DN and NDRD patients were investigated and compared by using lectin microarray and lectin blot. Then, altered glycoproteins were enriched by lectin coupled magnetic particle conjugate and characterized by LC-MS/MS. We found significant change in glycan patterns between DN and NDRD patients. In particular, the relative abundance of the glycopattern of Galβ1-3GalNAc which was identified by BPL (Bauhinia purpurea lectin) was significantly decreased in DN patients compared to four types of NDRD patients (p < 0.05). Moreover, BPL blotting indicated that the proteins with a molecular weight of about 53 kDa exhibited low staining signal in DN compared to all NDRD groups, which was consistent with results of lectin microarrays. After enriching by BPL and identification by LC-MS/MS, a total of 235 and 258 proteins were characterized from NDRD and DN respectively. Among these, the relative abundance of 12 isolated serum proteins exhibited significantly alteration between DN and NDRD (p < 0.05). Our findings indicated not only the relative abundance of Galβ1-3GalNAc on serum proteins but also certain glycoproteins modified with this glycopattern showed a difference between DN and NDRD patients. This suggested that the analysis of this alteration by using urine specimens may constitute an additional valuable diagnostic tool for differentiating DN and NDRD with a non-invasive method.

Accuracy of hematuria for predicting non-diabetic renal disease in patients with diabetes and kidney disease: A systematic review and meta-analysis

AIMS

To clarify the predictive value of hematuria in patients with diabetes and non-diabetic renal disease (NDRD).

METHODS

The databases of Medline, Embase and the Cochrane Library were searched up to November 22, 2017. The pooled sensitivity, specificity, positive and negative likelihood ratio (PLR, NLR), diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic (SROC) curve (AUC) were calculated using a bivariate mixed-effects regression model.

RESULTS

Thirty-eight articles were eligible, of which 35 articles with 4005 patients investigated hematuria. The pooled sensitivity and specificity of hematuria to predict NDRD were 0.42 (95% CI 0.35-0.49) and 0.72 (95% CI 0.64-0.79), respectively. The pooled PLR and NLR were 1.49 (95% CI 1.28-1.75) and 0.81 (95% CI 0.75-0.87), respectively. The DOR was 1.85 (95% CI 1.49-2.30). The pooled AUC was 0.59 (95% CI 0.54-0.63). For dysmorphic erythrocytes, the pooled sensitivity was 0.27 (95% CI 0.23-0.32), while the specificity was 0.94 (95% CI 0.91-0.97). There was heterogeneity among studies (p < 0.001), and no publication bias was identified.

CONCLUSIONS

Type 2 diabetes patients presenting with hematuria are slightly more likely to develop NDRD. Dysmorphic erythrocytes may be more useful than microhematuria in diagnosing for NDRD in type 2 diabetes with proteinuria.

Clinical and pathological factors associated with progression of diabetic nephropathy

As an important complication of diabetic mellitus, diabetic kidney disease (DKD) is becoming a major cause of morbidity and mortality worldwide. Given the diminished quality of life and the economic cost, identification of the risk factors for DKD and initiation of in-time therapies are of great importance. The initiators of DKD include hyperglycemia and predisposing genes, while the promotors of DKD include hyperglycemia, albuminuria, hypertension, dyslipdemia, insulin resistance, anemia, long duration of diabetes, smoking, ethnicity, westernization, sex, age, and so on. This article summarizes the values and limitations of some factors in the development and progression of DKD. Further study is needed to establish whether modulating these factors can improve the outcomes of DKD.

Hematuria and Renal Outcomes in Patients With Diabetic Chronic KidneyDisease

BACKGROUND

Hematuria may indicate nondiabetic renal disease in diabetic chronic kidney disease (CKD). However, some studies have reported that hematuria is noted in diabetic nephropathy and is associated with albuminuria. Hematuria is a risk factor for end-stage renal disease in glomerulonephritis, but its prognostic value in diabetic CKD is unknown. We investigated the factors associated with hematuria and the prognostic value of hematuria in patients with diabetic CKD.

MATERIAL AND METHODS

We included 1958 patients with type 2 diabetes and CKD stages 1-5, and 111 patients underwent renal biopsy. Patients in the biopsied cohort were younger and had more severe proteinuria, compared with those in the total cohort; hematuria was associated with nondiabetic renal disease.

RESULTS

In the total cohort, hematuria was observed in 15.0% of the patients and was associated with young age, a lower estimated glomerular filtration rate, proteinuria, high blood pressure and short diabetes duration. Hematuria was significantly associated with an increased risk (hazard ratio 1.39, 95% CI: 1.10-1.76, P < 0.001) of end-stage renal disease, particularly in patients with CKD stages 1-3 or a urine protein-to-creatinine ratio of <1,500mg/g (P for interaction < 0.05). The odds ratio of hematuria for rapid renal progression was 1.81 (95% CI: 1.29-2.53, P < 0.001).

CONCLUSIONS

Hematuria is associated with nondiabetic renal disease in biopsied patients with diabetic CKD and is associated with an increased risk of end-stage renal disease in patients with early diabetic CKD.

The modern spectrum of biopsy-proven renal disease in Chinese diabetic patients-a retrospective descriptive study

Background

Renal biopsies performed in diabetic patients are increasing and becoming more complex. Comprehensive data on modern spectrum of biopsy-proven renal disease in Chinese diabetic patients are lacking.

Methods

In a nationwide renal biopsy survey including 71,151 native biopsies from 2004 to 2014, diabetic patients were identified according to the clinical diagnosis from referral records. The clinical data were extracted from referral records and pathological reports.

Results

A total of 1,604 diabetic patients, including 61 patients with T1DM, were analyzed in this study. The median age is 51.39 ± 11.37 years. Male patients accounted for 58% of the population. We found that only 44.7% of diabetic patients had the isolated pathological diagnosis of diabetic nephropathy (DN), while 49.1% had non-diabetic renal disease (NDRD) alone, and 6.2% had NDRD superimposed on DN. Nephrotic syndrome (n = 824, 51.4%) was the most common clinical indication for renal biopsy. Among 887 patients with NDRD, membranous nephropathy (n = 357) was the leading diagnosis, followed by IgA nephropathy (n = 179). Hypertensive renal disease (n = 32), tubulointerstitial nephropathy (n = 27) and acute tubular necrosis (n = 16) accounted for 3.5%, 2.9%, 1.7% of the NDRD cases respectively. Nearly a half (49.2%) of patients with T1DM had NDRD.

Discussion

Over 55% diabetic patients with kidney disease were diagnosed as non-diabetic renal disease, among which MN and IgAN were the most common two pathological types.

Clinicopathological features of diabetic and nondiabetic renal diseases in type 2 diabetic patients with nephrotic-range proteinuria

Heavy proteinuria with or without features of nephrotic syndrome is associated with many primary and systemic diseases. For diabetic patients, distinguishing nondiabetic renal disease (NDRD) from diabetic nephropathy (DN) is important in choosing treatment modalities and determining renal prognosis. However, clinical relevance of heavy proteinuria is inconsistent with clinical DN assessments. This study investigated the clinicopathological features and renal outcomes of DN and NDRD in type 2 diabetic patients with nephrotic-range proteinuria.We enrolled 220 cases of type 2 diabetic patients who underwent renal biopsy. They were grouped according to the presence of nephritic-range proteinuria and pathological features. Baseline characteristics, laboratory findings, types of pathological diagnosis, and renal outcomes were analyzed in patients with heavy proteinuria.Upon kidney biopsy, 129 patients (58.6%) showed nephritic-range proteinuria. Patients with heavy proteinuria (an average urine protein-to-creatinine ratio of 10,008 ± 7307 mg/gCr) showed lower serum albumin levels and higher total cholesterol levels, but did not show any difference in age, duration of diabetes, renal function, or the presence of retinopathy compared with those with mild-to-moderate proteinuria (an average urine protein-to-creatinine ratio of 1581 ± 979 mg/gCr). Renal biopsy revealed that the prevalence of NDRD was 37.2% in patients with heavy proteinuria, which was significantly lower than that in patients with mild-to-moderate proteinuria (63.7%). The most common pathological types of NDRD were membranous nephropathy (41.7%), IgA nephropathy (14.6%), and minimal change disease (10.4%). NDRD patients showed lower prevalence of diabetic retinopathy and better kidney function irrespective of proteinuria. Immunosuppressive treatment was administered more frequently in patients with heavy proteinuria (56.3%) compared with patients with mild-to-moderate proteinuria (20%) because of the pathological differences according to the amount of proteinuria. Renal outcomes were significantly worse in patients with DN than in patients with NDRD.DN patients with heavy proteinuria exhibited different prevalence of NDRD and worse prognosis. Renal biopsy in type 2 diabetic patients should be more extensively considered to accurately diagnose NDRD, guide further management, and predict renal outcomes, especially in patients with nephrotic-range proteinuria.

Diabetic Retinopathy and Clinical Parameters Favoring the Presence of Diabetic Nephropathy could Predict Renal Outcome in Patients with Diabetic Kidney Disease

Diabetes duration, diabetic retinopathy (DR), and a diagnostic model have been proposed as clinical parameters favoring the presence of diabetic nephropathy (DN) in biopsied patients with diabetic kidney disease. DN, compared with non-diabetic renal disease, had poorer renal outcomes. We tested whether these clinical parameters favoring DN are associated with poorer renal outcomes in non-biopsied patients. In this study, 1330 patients with type 2 diabetes and chronic kidney disease stages 1-4 were included and divided according to diabetes mellitus (DM) duration >8 years, DR, or a diagnostic model for DN. These clinical parameters favoring DN were found in 62-77% of patients and associated with higher levels of proteinuria. In a Cox survival analysis, DR and the diagnostic model favoring DN were associated with an increased risk for end-stage renal disease with adjusted hazard ratios of 1.69 (95% CI: 1.16-2.45, P = 0.006) and 1.66 (95% CI: 1.05-2.61, P = 0.029), respectively. DR was associated with an increased risk for rapid renal disease progression. DM >8 years was not associated with renal outcome. Propensity score-matched analyses also showed similar results. In conclusion, DR and the diagnostic model favoring DN were associated with poorer renal outcomes.

The large spectrum of renal disease in diabetic patients

The prevalence of diabetic nephropathy (DN) among diabetic patients seems to be overestimated. Recent studies with renal biopsies show that the incidence of non-diabetic nephropathy (NDN) among diabetic patients is higher than expected. Renal impairment of diabetic patients is frequently attributed to DN without meeting the KDOQI criteria or performing renal biopsy to exclude NDN. In this editorial, we update the spectrum of renal disease in diabetic patients and the impact on diagnosis, prognosis and therapy.

Glycopatterns of Urinary Protein as New Potential Diagnosis Indicators for Diabetic Nephropathy

Diabetic nephropathy is a major cause of chronic kidney disease and end-stage kidney disease. However, so little is known about alterations of the glycopatterns in urine with the development of diabetic nephropathy. Presently, we interrogated glycopatterns in urine specimens using a lectin microarray. The results showed that expression levels of Siaα2-6Gal/GalNAc recognized by SNA exhibited significantly increased tendency with the development of diabetic nephropathy; moreover, SNA blotting indicated glycoproteins (90 kDa, 70 kDa, and 40 kDa) in urine may contribute to this alteration. Furthermore, the glycopatterns of (GlcNAc)_2-4 recognized by STL exhibited difference between diabetic and nondiabetic nephropathy. The results of urinary protein microarray fabricated by another 48 urine specimens also indicated (GlcNAc)_2-4 is a potential indictor to differentiate the patients with diabetic nephropathy from nondiabetic nephropathy. Furtherly, STL blotting showed that the 50 kDa glycoproteins were correlated with this alteration. In conclusion, our data provide pivotal information to monitor the development of diabetic nephropathy and distinguish between diabetic nephropathy and nondiabetic renal disease based on precise alterations of glycopatterns in urinary proteins, but further studies are needed in this regard.

Predictive factors for non-diabetic nephropathy in diabetic patients. The utility of renal biopsy

BACKGROUND AND OBJECTIVES

Diabetic renal lesions can only be diagnosed by kidney biopsy. These biopsies have a high prevalence of non-diabetic lesions. The aims of the study were to determine the predictability of non-diabetic nephropathy (NDN) in diabetics and study differences in survival and renal prognosis. In addition, we evaluated histological lesions and the effect of proteinuria on survival and renal prognosis in patients with diabetic nephropathy (DN).

MATERIAL AND METHODS

A descriptive, retrospective study of kidney biopsies of diabetics between 1990 and 2013 in our centre.

RESULTS

110 patients were included in the study: 87 men (79%), mean age 62 years (50-74), mean serum creatinine 2.6mg/dl (0.9-4.3) and proteinuria 3.5g/24hours (0.5-6.5). 61.8% showed NDN, 34.5% showed DN and 3,6% showed DN+NDN. The most common NDN was IgA nephropathy (13,2%). In the multivariate analysis, creatinine (OR: 1.48, 1.011-2.172, p=0.044), proteinuria/24hours (OR: 0.813, 0.679-0.974, p=0.025), duration of diabetes (OR: 0.992, 0.987-0.998, p=0.004), age (OR: 1.068, 95% CI: 1.010-1.129, p=0.022), and diabetic retinopathy (OR: 0.23, 0.066-0.808, p=0.022) were independently associated with NDN. We did not find any differences in survival or renal prognosis. Concerning patients with DN, increased nodular mesangial expansion (p=0.02) and worse renal prognosis (p=0.004) were observed in nephrotic proteinuria as compared to non-nephrotic proteinuria. We did not find differences in patient survival.

CONCLUSIONS

The most common cause of NDN was IgA nephropathy. Higher creatinine levels, shorter duration of diabetes, absence of diabetic retinopathy, lower proteinuria, and older age were risk factors for NDN. Patients with DN and nephrotic-range proteinuria had worse renal prognosis.

Clinicopathological characteristics of non-diabetic renal disease in patients with type 2 diabetes mellitus in a northeastern Chinese medical center: a retrospective analysis of 273 cases

OBJECTIVE

To investigate the clinical and histopathological features of non-diabetic renal disease (NDRD) superimposed on diabetic nephropathy (DN) in northeastern Chinese patients with type 2 diabetes mellitus (T2D), and compare the changes with those of pure DN and isolated NDRD.

METHODS

Single-center retrospective analysis based on medical records of 273 patients (172 men, mean age: 51.1 ± 12.4 years) with T2D who underwent renal biopsy between February 2000 and October 2015. All patients were diagnosed as cases of pure DN, isolated NDRD or NDRD superimposed on DN.

RESULTS

Out of the 273 T2D patients, 68 (24.9 %) had DN, 175 (64.1 %) had NDRD, and 30 (11.0 %) had NDRD superimposed on DN. Idiopathic membranous nephropathy (IMN, 29.7 %) was the most common NDRD followed by IgA nephropathy (IgAN, 22.9 %), and hypertensive renal arteriolar sclerosis was the most common lesion in patients diagnosed as NDRD superimposed on DN. Patients with NDRD had a shorter duration of diabetes and lower frequencies of diabetic retinopathy (DR, 6.9 %) and renal failure (28.0 %), which is consistent with higher estimated glomerular filtration rates (eGFR) and lower systolic blood pressure (SBP). No significant between-group differences were observed with respect to proteinuria and hematuria.

CONCLUSION

Renal biopsy is strongly recommended for T2D patients to distinguish DN, NDRD and NDRD superimposed on DN, especially in patients with no signs of DR. This approach may help in early diagnosis and treatment of NDRD and improve renal outcomes in northeastern Chinese T2D patients.

Dysmorphic erythrocytes are superior to hematuria for indicating non-diabetic renal disease in type 2 diabetics

AIMS/INTRODUCTION

There are sparse and limited studies on erythrocyte morphology in renal biopsy identifying nephropathic patients among type 2 diabetics. The present study sought to clarify the predictive value of dysmorphic erythrocytes in type 2 diabetics with non-diabetic renal disease and influences on hematuria.

MATERIALS AND METHODS

We examined 198 patients with type 2 diabetes who underwent kidney biopsies between 2012 and 2013. Hematuria was defined as >3 or >10 red blood cells per high-power field (RBCs/hpf) in urine sediment. If >80% of the erythrocytes were dysmorphic, glomerular hematuria was diagnosed. Clinical findings and predictive value of dysmorphic erythrocytes were compared between patients with hematuria (n = 19) and those without (n = 61). The potential risk factors for hematuria among diabetic nephropathy patients were also screened.

RESULTS

There was a statistically significant difference between the diabetic nephropathy group and the non-diabetic renal disease group (6.6 vs 16.8%; P = 0.04) when the demarcation point of hematuria was 10 RBCs/hpf. When the definition of hematuria was based on an examination of urinary erythrocyte morphology, a marked difference was seen (3.3 vs 24.8%; P < 0.001). Glomerular hematuria showed high specificity and a positive predictive value (0.97 and 0.94, respectively) in non-diabetic renal disease. A multivariate analysis showed that nephrotic syndrome was significantly associated with hematuria (odds ratio 3.636; P = 0.034).

CONCLUSIONS

Dysmorphic erythrocytes were superior to hematuria for indicating non-diabetic renal disease in type 2 diabetics. Nephrotic syndrome was an independent risk factor for hematuria.