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

Atypical ADPKD Due to a DNAJB11 Pathogenic Variant: An Educational Case Report

Rationale

Due to next-generation sequencing, variants in new genes such as DNAJB11 are recently being identified as causing atypical autosomal dominant polycystic kidney disease (ADPKD). It is important to describe phenotypes associated with these variants in order to increase awareness among clinicians, especially since genetic variability affects ADPKD severity.

Presenting Concerns of the Patient

We describe a 55-year-old female patient of Haitian origin who presented with slowly deteriorating kidney function, microscopic hematuria, proteinuria, enlarged kidneys with innumerable small cysts, and a family history of chronic kidney disease and cysts. The phenotype was atypical for ADPKD caused by PKD1 or PKD2 variants, since cysts were of small size, kidneys were only moderately enlarged, and the patient had no extra-renal involvement suggestive of typical ADPKD such as liver cysts, pancreatic cysts, cranial aneurysms, or cardiac abnormalities.

Diagnoses

A panel of genes was analyzed by next-generation massive sequencing techniques, including DNAJB11, DZIP1L, GANAB, HNF1B, PKD1, PKD2, and PKHD1. Genetic testing revealed a heterozygous variant in the DNAJB11 gene (c.123 dup), which is predicted to result in premature protein termination (p. Lys42*) and was classified by the laboratory as likely pathogenic.

Interventions

She was treated with candesartan 16 mg once daily to address her proteinuria.

Outcomes

At the time of the most recent follow-up, her proteinuria has increased, and her kidney function continues to slowly deteriorate.

Teaching Points

DNAJB11 variants are a rare cause of atypical ADPKD. It is important to recognize the clinical features that help distinguish DNAJB11 from PKD1 and PKD2 variants. Atypical ADPKD due to DNAJB11 variants is usually characterized by small cysts, normal kidney size, proteinuria, progressive chronic kidney disease, and phenotypic overlap with autosomal dominant tubulointerstitial kidney disease (ADTKD). It may, however, present itself with enlarged kidneys as was seen in our patient. Genetic testing should be offered whenever a patient presents atypical features of ADPKD, which also requires increased awareness among clinicians regarding the various phenotypes of atypical ADPKD.

Multifaceted relationship between diabetes and kidney diseases: Beyond diabetes

Diabetes mellitus is one of the most common causes of chronic kidney disease. Kidney involvement in patients with diabetes has a wide spectrum of clinical presentations ranging from asymptomatic to overt proteinuria and kidney failure. The development of kidney disease in diabetes is associated with structural changes in multiple kidney compartments, such as the vascular system and glomeruli. Glomerular alterations include thickening of the glomerular basement membrane, loss of podocytes, and segmental mesangiolysis, which may lead to microaneurysms and the development of pathognomonic Kimmelstiel-Wilson nodules. Beyond lesions directly related to diabetes, awareness of the possible coexistence of nondiabetic kidney disease in patients with diabetes is increasing. These nondiabetic lesions include focal segmental glomerulosclerosis, IgA nephropathy, and other primary or secondary renal disorders. Differential diagnosis of these conditions is crucial in guiding clinical management and therapeutic approaches. However, the relationship between diabetes and the kidney is bidirectional; thus, new-onset diabetes may also occur as a complication of the treatment in patients with renal diseases. Here, we review the complex and multifaceted correlation between diabetes and kidney diseases and discuss clinical presentation and course, differential diagnosis, and therapeutic oppor-tunities offered by novel drugs.

Presence of Non-Diabetic Kidney Diseases in Biopsy Specimens of Diabetic Patients' Single Center Experience

The complications of type 2 diabetes mellitus (T2DM) are well known and one of them is diabetic chronic kidney disease (DCKD). Over time, it has become clear that patients with T2DM can have nondiabetic chronic kidney diseases (NDCKD), especially those that affect the glomeruli. Clinical indicators for identifying DCKD from NDCKD with high sensitivity and specificity have not yet been identified. Therefore, kidney biopsy remains the golden standard for DCKD diagnosis in patients with T2DM. Despite some indications for kidney biopsy, criteria for a biopsy differ between countries, regions, and doctors. The aim of the study was to analyze the biopsy findings in our T2DM population and the justification of the biopsy according to widely accepted criteria. This single center retrospective study analyzed data from 74 patients with T2DM who underwent kidney biopsy from January 2014 to January 2021. According to the biopsy data, we categorized31 patients in the DN group, patients with typical diabetic glomerulopathy, 11 patients in the mixed group, patients who had pathohistological elements for both DN and non-DN glomerulopathy, and 32 patients in the non-DN group, patients with primary glomerulopathy not linked with DM. In the non-DN and mixed groups, the most frequent glomerulopathy was mesangioproliferative glomerulonephritis, including IgA and non-IgA forms, found in 10 patients, and membranous nephropathy (MN) in 10 patients. We analyzed several parameters and only the amount of proteinuria was found to be significantly linked to biopsy findings related to DN. With the existing criteria for kidney biopsy, we managed to detect changes in the kidneys in about half of our patients with T2DM. These patients required specific treatment, different from that which we use for DCKD patients.

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.

Clinical features and predictive biomarkers for bladder cancer in patients with type 2 diabetes presenting with haematuria

AIMS

To identify clinical features and protein biomarkers associated with bladder cancer (BC) in individuals with type 2 diabetes mellitus presenting with haematuria.

MATERIALS AND METHODS

Data collected from the Haematuria Biomarker (HaBio) study was used in this analysis. A matched sub-cohort of patients with type 2 diabetes and patients without diabetes was created based on age, sex, and BC diagnosis, using approximately a 1:2 fixed ratio. Randox Biochip Array Technology and ELISA were applied for measurement of 66 candidate serum and urine protein biomarkers. Hazard ratios and 95% confidence intervals were estimated by chi-squared and Wilcoxon rank sum test for clinical features and candidate protein biomarkers. Diagnostic protein biomarker models were identified using Lasso-based binominal regression analysis.

RESULTS

There was no difference in BC grade, stage, and severity between individuals with type 2 diabetes and matched controls. Incidence of chronic kidney disease (CKD) was significantly higher in patients with type 2 diabetes (p = 0.008), and CKD was significantly associated with BC in patients with type 2 diabetes (p = 0.032). A biomarker model, incorporating two serum (monocyte chemoattractant protein 1 and vascular endothelial growth factor) and three urine (interleukin 6, cytokeratin 18, and cytokeratin 8) proteins, predicted incidence of BC with an Area Under the Curve (AUC) of 0.84 in individuals with type 2 diabetes. In people without diabetes, the AUC was 0.66.

CONCLUSIONS

We demonstrate the potential clinical utility of a biomarker panel, which includes proteins related to BC pathogenesis and type 2 diabetes, for monitoring risk of BC in patients with type 2 diabetes. Earlier urology referral of patients with type 2 diabetes will improve outcomes for these patients.

TRIAL REGISTRATION

http://www.isrctn.com/ISRCTN25823942.

Development and Validation of a Model That Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Cross-Sectional Study

Purpose

To develop a nomogram model that predicts the risk of diabetic nephropathy (DN) incidence in type 2 diabetes mellitus (T2DM) patients.

Methods

We collect information from electronic medical record systems. The data were split into a training set (n=521) containing 73.8% of patients and a validation set (n=185) holding the remaining 26.2% of patients based on the date of data collection. Stepwise and multivariable logistic regression analyses were used to screen out DN risk factors. A predictive model including selected risk factors was developed by logistic regression analysis. The results of binary logistic regression are presented through forest plots and nomogram. Lastly, the c-index, calibration plots, and receiver operating characteristic (ROC) curves were used to assess the accuracy of the nomogram in internal and external validation. The clinical benefit of the model was evaluated by decision curve analysis.

Results

Predictors included serum creatinine (Scr), hypertension, glycosylated hemoglobin A1c (HbA1c), blood urea nitrogen (BUN), body mass index (BMI), triglycerides (TG), and Diabetic peripheral neuropathy (DPN). Harrell's C-indexes were 0.773 (95% CI:0.726-0.821) and 0.758 (95% CI:0.679-0.837) in the training and validation sets, respectively. Decision curve analysis (DCA) demonstrated that the novel nomogram was clinically valuable.

Conclusion

Our simple nomogram with seven factors may help clinicians predict the risk of DN incidence in patients with T2DM.

Kidney outcomes and all-cause mortality in people with type 2 diabetes exhibiting non-albuminuric kidney insufficiency

AIM/HYPOTHESIS

It remains unclear whether people with diabetes exhibiting non-albuminuric kidney insufficiency have higher risk of kidney function decline and mortality compared with those exhibiting preserved kidney function without albuminuria. Furthermore, information regarding the incidence of albuminuria in people with this unique phenotype is sparse. Here, we aimed to elucidate the risk of the kidney outcomes and all-cause mortality in people with diabetes exhibiting non-albuminuric kidney insufficiency.

METHODS

In this retrospective cohort study, 8320 Japanese adults with type 2 diabetes were classified into four groups based on the presence of albuminuria and kidney insufficiency at baseline, defined as urinary albumin/creatinine ratio of equal to or above 30 mg/g and eGFR of less than 60 ml min^-1 1.73 m^-2, respectively. The primary composite kidney endpoint was a 50% decrease in eGFR from baseline or the initiation of kidney replacement therapy. The annual percentage change in eGFR slope and progression of albuminuria category were evaluated as the secondary and tertiary kidney endpoints, respectively. All-cause death was also set as the endpoint.

RESULTS

Compared with people exhibiting non-albuminuric preserved kidney function, those with non-albuminuric kidney insufficiency had the higher risk for the primary kidney endpoint (HR 4.1; 95% CI 2.5, 6.7; p < 0.001), steep percentage change in eGFR slope (-1.96%/year vs -1.36%/year, p < 0.001), incidence of albuminuria (HR 2.1; 1.7, 2.6; p < 0.001) and all-cause mortality (HR 1.5; 1.2, 2.0; p = 0.003). In the sensitivity analyses treating the incidence of albuminuria as a competing risk, people with non-albuminuric kidney insufficiency still had higher risk for the primary kidney endpoint and all-cause mortality than those with non-albuminuric preserved kidney function (subdistribution HR 2.8; 1.4, 5.6; p = 0.004; and 1.6; 1.1, 2.2; p = 0.014, respectively).

CONCLUSIONS/INTERPRETATION

People with type 2 diabetes exhibiting non-albuminuric kidney insufficiency had poorer kidney outcomes and life prognosis than those exhibiting non-albuminuric preserved kidney function.

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.

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.

Kidney Biopsy in Type 2 Diabetic Patients: Critical Reflections on Present Indications and Diagnostic Alternatives

Roughly 3% of patients worldwide with a new diagnosis of type 2 diabetes mellitus (T2DM) already have an overt nephropathy at diagnosis and about 20-30% of the remaining ones develop a complication of this kind later in life. The early identification of kidney disease in diabetic patients is important as it slows its progression, which is important not only because this reduces the need for renal replacement therapy, but also because it decreases the high rate of mortality and morbidity associated with a reduction in kidney function. The increasing prevalence of type 2 diabetes and the consequent greater probability of finding different types of kidney diseases in diabetic patients frequently gives rise to overlapping diagnoses, a definition encompassing the differential diagnosis between diabetic and non-diabetic kidney disease. The issue is made more complex by the acknowledgement of the increasing frequency of presentations of what is termed "diabetic kidney disease" without relevant proteinuria, in particular in T2DM patients. Distinguishing between diabetes related and non-diabetes related forms of kidney disease in diabetic patients is not only a semantic question, as different diseases require different clinical management. However, while the urologic and macrovascular complications of diabetes, as well as overlapping parenchymal damage, can be diagnosed by means of imaging studies, often only a kidney biopsy will make a differential diagnosis possible. In fact, the coexistence of typical diabetic lesions, such as nodular glomerulopathy or glomerulosclerosis, with different glomerular, vascular and tubulo-interstitial alterations has been extensively described, and an analysis of the dominant histological pattern can contribute to determining what therapeutic approach should be adopted. However, due to the high frequency of kidney diseases, and to the fact that T2DM patients are often affected by multiple comorbidities, a kidney biopsy is not generally performed in T2DM patients. What follows is a review aiming to discuss the diagnostic work-up, on the base of clinical, laboratory and imaging criteria, and evaluate the present indications and alternatives to renal biopsy.

Cross-sectional and longitudinal associations between dipstick hematuria and chronic kidney disease in patients with type 2 diabetes

AIMS

To examine the association of dipstick hematuria with kidney function and albuminuria in patients with type 2 diabetes (T2D).

METHODS

A total of 7,945 patients with T2D were studied. In the cross-sectional study, patients were classified into 6 groups based on the stage of albuminuria and estimated glomerular filtration rate (eGFR) of 60 mL/min/1.73 m² at baseline. In the longitudinal study where patients were classified by the presence of hematuria, the primary composite endpoint was a 30% decrease in eGFR from baseline or the initiation of kidney replacement therapy. Other outcomes included eGFR slope and stage progression of albuminuria.

RESULTS

Cross-sectionally, hematuria was more prevalent in patients with more advanced stages of albuminuria and with lower eGFR, independently of each other. In the longitudinal study, patients with hematuria experienced 50% increased incidence of the primary endpoint (p < 0.001). The eGFR slope was steeper in patients with hematuria than in those without hematuria (p < 0.001). On the other hand, hematuria was unlikely to be associated with the progression of albuminuria.

CONCLUSIONS

In patients with T2D, dipstick hematuria was associated with prevalent albuminuria and reduced eGFR, as well as faster decline in kidney function but not higher risk of development or progression of albuminuria.

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.

Insights into the Role of Renal Biopsy in Patients with T2DM: A Literature Review of Global Renal Biopsy Results

INTRODUCTION

Renal biopsy performed in patients with type 2 diabetes mellitus (T2DM) for atypical or suspected diabetic kidney disease (DKD) reveals one of three possibilities: diabetic nephropathy (DN, pathological diagnosis of DKD), nondiabetic kidney disease (NDKD) and DN plus NDKD (mixed form). NDKD (including the mixed form) is increasingly being recognized worldwide. With the emerging concept of DKD and the complexity of routine application of renal biopsy, the identification of "clinical indicators" to differentiate DKD from NDKD has been an area of active research.

METHODS

The PubMed database was searched for relevant articles mainly according to the keyword search method. We reviewed prevalence of the three types of DKD and different pathological lesions of NDKD. We also reviewed the clinical indicators used to identify DKD and NDKD.

RESULTS

The literature search identified 40 studies (5304 data) worldwide between 1977 and 2019 that looked at global renal biopsy and pathological NDKD lesions. The overall prevalence rate of DN, NDKD and DN plus NDKD is reported to be 41.3, 40.6 and 18.1%, respectively. In Asia, Africa (specifically Morocco and Tunisia) and Europe, the most common isolated NDKD pathological type is membranous nephropathy, representing 24.1, 15.1 and 22.6% of cases, respectively. In contrast, focal segmental glomerulosclerosis is reported to be the primary pathological type in North America (specifically the USA) and Oceania (specifically New Zealand), representing 22% and 63.9% of cases, respectively. Tubulointerstitial disease accounts for a high rate in the mixed group (21.7%), with acute interstitial nephritis being the most prevalent (9.3%), followed by acute tubular necrosis (9.0%). Regarding clinical indicators to differentiate DKD from NDKD, a total of 14 indicators were identified included in 42 studies. Among these, the most commonly studied indicators included diabetic retinopathy, duration of diabetes, proteinuria and hematuria. Regrettably, indicators with high sensitivity and specificity have not yet been identified.

CONCLUSION

To date, renal biopsy is still the gold standard to diagnose diabetes complicated with renal disease, especially when T2DM patients present atypical DKD symptoms (e.g. absence of diabetic retinopathy, shorter duration of diabetes, microscopic hematuria, sub-nephrotic range proteinuria, lower glycated hemoglobin, lower fasting blood glucose). We conclude that renal biopsy as early as possible is of great significance to enable personalized treatment to T2DM patients.

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.

An updated overview of diabetic nephropathy: Diagnosis, prognosis, treatment goals and latest guidelines

Diabetic nephropathy (DN) is a major healthcare challenge. It occurs in up to 50% of those living with diabetes, is a major cause of end-stage kidney disease (ESKD) that requires treatment with dialysis or renal transplantation, and is associated with significantly increased cardiovascular morbidity and mortality. DN is a clinical syndrome characterized by persistent albuminuria and a progressive decline in renal function, but it is increasingly recognized that the presentation and clinical course of kidney disease in diabetes is heterogeneous. The term diabetic kidney disease (DKD) is now commonly used to encompass the spectrum of people with diabetes who have either albuminuria or reductions in renal function. In this article, the clinical presentation and approach to diagnosis of DKD will be discussed, as will its prognosis. The general principles of management of DKD will also be reviewed with reference to current international guidelines.

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.