Prognostic value of proteinuria and glomerular filtration rate on Taiwanese patients with diabetes mellitus and advanced chronic kidney disease: a single center experience

American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update

OBJECTIVE

The objective of this clinical practice guideline is to provide updated and new evidence-based recommendations for the comprehensive care of persons with diabetes mellitus to clinicians, diabetes-care teams, other health care professionals and stakeholders, and individuals with diabetes and their caregivers.

METHODS

The American Association of Clinical Endocrinology selected a task force of medical experts and staff who updated and assessed clinical questions and recommendations from the prior 2015 version of this guideline and conducted literature searches for relevant scientific papers published from January 1, 2015, through May 15, 2022. Selected studies from results of literature searches composed the evidence base to update 2015 recommendations as well as to develop new recommendations based on review of clinical evidence, current practice, expertise, and consensus, according to established American Association of Clinical Endocrinology protocol for guideline development.

RESULTS

This guideline includes 170 updated and new evidence-based clinical practice recommendations for the comprehensive care of persons with diabetes. Recommendations are divided into four sections: (1) screening, diagnosis, glycemic targets, and glycemic monitoring; (2) comorbidities and complications, including obesity and management with lifestyle, nutrition, and bariatric surgery, hypertension, dyslipidemia, retinopathy, neuropathy, diabetic kidney disease, and cardiovascular disease; (3) management of prediabetes, type 2 diabetes with antihyperglycemic pharmacotherapy and glycemic targets, type 1 diabetes with insulin therapy, hypoglycemia, hospitalized persons, and women with diabetes in pregnancy; (4) education and new topics regarding diabetes and infertility, nutritional supplements, secondary diabetes, social determinants of health, and virtual care, as well as updated recommendations on cancer risk, nonpharmacologic components of pediatric care plans, depression, education and team approach, occupational risk, role of sleep medicine, and vaccinations in persons with diabetes.

CONCLUSIONS

This updated clinical practice guideline provides evidence-based recommendations to assist with person-centered, team-based clinical decision-making to improve the care of persons with diabetes mellitus.

Prognostic Factors of Fatal and Nonfatal Cardiovascular Events in Patients With Type 2 Diabetes: The Role of Renal Function Biomarkers

In this study, 158 patients with different degrees of renal function were followed for 7 years to assess the prognostic value of various risk factors, including carotid intima-media thickness (cIMT) and biomarkers of renal function, for incident cardiovascular morbidity and mortality in patients with type 2 diabetes. The investigators found that estimated glomerular filtration rate, albuminuria, and history of cardiovascular disease (CVD) can be used for prognosis of CVD, whereas cIMT adds little to the accuracy of this prediction.

Development and validation of a predictive model for the progression of diabetic kidney disease to kidney failure

Introduction: A good prediction model plays an important role in determining the progression to diabetic kidney disease. We aimed to create a model to predict progression to kidney failure in patients with diabetic kidney disease.Methods: We retrospectively assessed 641 patients with type 2 diabetic kidney disease as derivation cohort and 280 patients as external out time validation cohort. We used a combination of clinical guidance and univariate logistic regression to select the relevant variables. We calculated the discrimination and calibration of different models. The best model was selected according to the optimal combination of discrimination and calibration.Results: During the 3 years follow up, there were 272 outcomes (42%) in derivation cohort and 138 outcomes (49%) in external validation cohort. The final variables selected in the multivariate logistics regression were age, gender, hemoglobin, NLR, serum cystatin C, eGFR, 24-h urine protein, and the use of oral hypoglycemic drugs. We developed four different models as clinical, laboratory, lab-medication, and full models according to these independent risk factors. Laboratory model performed well in both discrimination and calibration among all the models (C-statistics: external validation 0.863; p value of the Hosmer-Lemeshow, .817). There was no significant difference in NRI among laboratory model, lab-medication model, and full model (p > .05). So, we chose the laboratory model as the optimal model.Conclusion: We constructed a nomogram which contained hemoglobin, NLR, serum cystatin C, eGFR, and 24-h urine protein to predict the risk of patients with diabetic kidney disease initiating renal replacement in 3 years.

Comparative proteinuria management of different angiotensin-converting enzyme inhibitors or angiotensin receptor blockers for normotensive patients with CKD: a Bayesian network meta-analysis

Background

Both angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are blood pressure-lowering agents, but they are also being used to control proteinuria in early chronic kidney disease (CKD) patients. However, clinically, some patients present merely proteinuria without hypertension. No guidelines pointed out how to select treatments for proteinuria in normotensive patients. Thus, we conducted a Bayesian network analysis to evaluate the relative effects of different kinds of ACEI or ARB or their combination on proteinuria and blood pressure reduction.

Methods

The protocol was registered in PROSPERO with ID CRD42017073721. A comprehensive literature database query was carried out systematically according to PICOS strategies. The primary outcome was reduction in proteinuria, and the secondary outcomes were eGFR reduction and blood pressure reduction. Random-effects pairwise and Bayesian network meta-analyses were used to estimate the effect of different regimens.

Results

A total of 14 RCTs with 1,098 patients were included in the analysis. All treatment strategies of ACEI, ARB or their combination had significantly greater efficacy in reducing proteinuria than placebo in normotensive CKD patients. The combination therapy of olmesartan+temocapril had the highest probability (22%) of being the most effective treatment to reduce proteinuria in normotensive CKD patients. Olmesartan and lisinopril ranked second (12%), and temocapril ranked third (15%) but reduced blood pressure less than placebo. For IgA nephropathy, the combination therapy of olmesartan+temocapril also had the highest probability (43%) of being the best antiproteinuric treatment, while enalapril had the highest probability (58%) of being the best antiproteinuric therapy for diabetic nephropathy.

Conclusions

The combination therapy of olmesartan plus temocapril appeared to be the most efficacious for reducing proteinuria in normotensive CKD patients and IgA nephropathy, but the clinical application should be balanced against potential harms. Temocapril can be an option when practitioners are searching for more proteinuria reduction but less blood pressure variation. In normotensive diabetic nephropathy, monotherapy with the ACEI enalapril seems to be the most efficacious intervention for reducing albuminuria. Future studies are required to give a more definitive recommendation.

Development and validation of a predictive model for end-stage renal disease risk in patients with diabetic nephropathy confirmed by renal biopsy

This study was performed to develop and validate a predictive model for the risk of end-stage renal disease (ESRD) inpatients with diabetic nephropathy (DN) confirmed by renal biopsy. We conducted a retrospective study with 968 patients with T2DM who underwentrenal biopsy for the pathological confirmation of DNat the First Affiliated Hospital of Zhengzhou University from February 2012 to January 2015; the patients were followed until December 2018. The outcome was defined as a fatal or nonfatal ESRD event (peritoneal dialysis or hemodialysis for ESRD, renal transplantation, or death due to chronic renal failure or ESRD). The dataset was randomly split into development (75%) and validation (25%) cohorts. We used stepwise multivariablelogistic regression to identify baseline predictors for model development. The model’s performance in the two cohorts, including discrimination and calibration, was evaluated by the C-statistic and the P value of the Hosmer-Lemeshow test. During the 3-year follow-up period, there were 225 outcome events (47.1%) during follow-up. Outcomes occurred in 187 patients (52.2%) in the derivation cohort and 38 patients (31.7%) in the validation cohort. The variables selected in the final multivariable logistic regression after backward selection were pathological grade, Log Urinary Albumin-to-creatinine ratio (Log ACR), cystatin C, estimated glomerular filtration rate (eGFR) and B-type natriuretic peptide (BNP). 4 prediction models were created in a derivation cohort of 478 patients: a clinical model that included cystatin C, eGFR, BNP, Log ACR; a clinical-pathological model and a clinical-medication model, respectively, also contained pathological grade and renin-angiotensin system blocker (RASB) use; and a full model that also contained the pathological grade, RASB use and age. Compared with the clinical model, the clinical-pathological model and the full model had better C statistics (0.865 and 0.866, respectively, vs. 0.864) in the derivation cohort and better C statistics (0.876 and 0.875, respectively, vs. 0.870) in the validation cohort. Among the four models, the clinical-pathological model had the lowest AIC of 332.53 and the best P value of 0.909 of the Hosmer-Lemeshow test. We constructed a nomogram which was a simple calculator to predict the risk ratio of progression to ESRD for patients with DN within 3 years. The clinical-pathological model using routinely available clinical measurements was shown to be accurate and validated method for predicting disease progression in patients with DN. The risk model can be used in clinical practice to improve the quality of risk management and early intervention.

Clinicopathological features and outcomes of diabetic kidney disease with extracapillary hypercellularity: a Japanese single-center experience

BACKGROUND

The prognostic significance of glomerular extracapillary hypercellularity (EXHC) in diabetic kidney disease (DKD) is unclear. The aim of this study was to investigate the clinicopathological features and outcomes of DKD patients with EXHC.

METHODS

We studied 70 cases of renal biopsy-confirmed type 2 DKD that were diagnosed between 2004 and 2014 and compared the clinicopathological features and outcomes of 22 patients with EXHC (EXHC group) with those of 48 patients without EXHC (control group). All of the patients were Japanese. We assessed the renal biopsy specimens based on the Renal Pathology Society classification system. Clinical and laboratory data were collected at the time of the renal biopsy, and renal outcomes were assessed based on progression to end-stage renal disease (ESRD) requiring renal replacement therapy. The median duration of the observation period was 3 years.

RESULTS

In pathological features, nodular sclerosis (Kimmelstiel-Wilson lesions) was observed more frequently in the EXHC group than in the control group (63.6% vs. 35.4%, P = 0.027). There were no significant intergroup differences in clinical features or renal outcomes. Univariate and multivariate Cox regression analyses of all patients showed that a high level of proteinuria, a low initial eGFR, and severe interstitial inflammation were poor prognostic factors.

CONCLUSIONS

EXHC is related to nodular sclerosis, which is a known risk factor for ESRD. Careful observation is needed during the follow-up of DKD patients with EXHC, although there were no significant differences in renal outcomes between the EXHC and control groups.

Identifying Synergistic Mechanisms of Multiple Ingredients in Shuangbai Tablets against Proteinuria by Virtual Screening and a Network Pharmacology Approach

Shuangbai Tablets (SBT), a traditional herbal mixture, has shown substantial clinical efficacy. However, a systematic mechanism of its active ingredients and pharmacological mechanisms of action against proteinuria continues being lacking. A network pharmacology approach was effectual in discovering the relationship of multiple ingredients and targets of the herbal mixture. This study aimed to identify key targets, major active ingredients, and pathways of SBT against proteinuria by network pharmacology approach combined with thin layer chromatography (TLC). Human phenotype (HP) disease analysis, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and molecular docking were used in this study. To this end, a total of 48 candidate targets of 118 active ingredients of SBT were identified. Network analysis showed PTGS2, ESR1, and NOS2 to be the three key targets, and beta-sitosterol, quercetin, and berberine were the three major active ingredients; among them one of the major active ingredients, quercetin, was discriminated by TLC. These results of the functional enrichment analysis indicated that the most relevant disease including these 48 candidate proteins is proteinuria, SBT treated proteinuria by sympathetically regulating multiple biological pathways, such as the HIF-1, RAS, AGE-RAGE, and VEGF signaling pathways. Additionally, molecular docking validation suggested that major active ingredients of SBT were capable of binding to HIF-1A and VEGFA of the main pathways. Consequently, key targets, major active ingredients, and pathways based on data analysis of SBT against proteinuria were systematically identified confirming its utility and providing a new drug against proteinuria.

Value of adding the renal pathological score to the kidney failure risk equation in advanced diabetic nephropathy

BACKGROUND

There have been a limited number of biopsy-based studies on diabetic nephropathy, and therefore the clinical importance of renal biopsy in patients with diabetes in late-stage chronic kidney disease (CKD) is still debated. We aimed to clarify the renal prognostic value of pathological information to clinical information in patients with diabetes and advanced CKD.

METHODS

We retrospectively assessed 493 type 2 diabetics with biopsy-proven diabetic nephropathy in four centers in Japan. 296 patients with stage 3-5 CKD at the time of biopsy were identified and assigned two risk prediction scores for end-stage renal disease (ESRD): the Kidney Failure Risk Equation (KFRE, a score composed of clinical parameters) and the Diabetic Nephropathy Score (D-score, a score integrated pathological parameters of the Diabetic Nephropathy Classification by the Renal Pathology Society (RPS DN Classification)). They were randomized 2:1 to development and validation cohort. Hazard Ratios (HR) of incident ESRD were reported with 95% confidence interval (CI) of the KFRE, D-score and KFRE+D-score in Cox regression model. Improvement of risk prediction with the addition of D-score to the KFRE was assessed using c-statistics, continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

RESULTS

During median follow-up of 1.9 years, 194 patients developed ESRD. The cox regression analysis showed that the KFRE,D-score and KFRE+D-score were significant predictors of ESRD both in the development cohort and in the validation cohort. The c-statistics of the D-score was 0.67. The c-statistics of the KFRE was good, but its predictive value was weaker than that in the miscellaneous CKD cohort originally reported (c-statistics, 0.78 vs. 0.90) and was not significantly improved by adding the D-score (0.78 vs. 0.79, p = 0.83). Only continuous NRI was positive after adding the D-score to the KFRE (0.4%; CI: 0.0-0.8%).

CONCLUSIONS

We found that the predict values of the KFRE and the D-score were not as good as reported, and combining the D-score with the KFRE did not significantly improve prediction of the risk of ESRD in advanced diabetic nephropathy. To improve prediction of renal prognosis for advanced diabetic nephropathy may require different approaches with combining clinical and pathological parameters that were not measured in the KFRE and the RPS DN Classification.