Executive summary of the KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease:an update based on rapidly emerging new evidence

The Kidney Disease: Improving Global Outcomes (KDIGO) 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease (CKD) represents a focused update of the KDIGO 2020 guideline on the topic. The guideline targets a broad audience of clinicians treating people with diabetes and CKD. Topic areas for which recommendations are updated based on new evidence include Chapter 1: Comprehensive care in patients with diabetes and CKD and Chapter 4: Glucose-lowering therapies in patients with type 2 diabetes (T2D) and CKD. The content of previous chapters on Glycemic monitoring and targets in patients with diabetes and CKD (Chapter 2), Lifestyle interventions in patients with diabetes and CKD (Chapter 3), and Approaches to management of patients with diabetes and CKD (Chapter 5) has been deemed current and was not changed. This guideline update was developed according to an explicit process of evidence review and appraisal. Treatment approaches and guideline recommendations are based on systematic reviews of relevant studies and appraisal of the quality of the evidence, and the strength of recommendations followed the “Grading of Recommendations Assessment, Development and Evaluation” (GRADE) approach. Limitations of the evidence are discussed, and areas for which additional research is needed are presented

Executive summary of the 2020 KDIGO Diabetes Management in CKD Guideline:evidence-based advances in monitoring and treatment

THE KIDNEY DISEASE: Improving Global Outcomes (KDIGO) Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease represents the first KDIGO guideline on this subject. The guideline comes at a time when advances in diabetes technology and therapeutics offer new options to manage the large population of patients with diabetes and chronic kidney disease (CKD) at high risk of poor health outcomes. An enlarging base of high-quality evidence from randomized clinical trials is available to evaluate important new treatments offering organ protection, such as sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists. The goal of the new guideline is to provide evidence-based recommendations to optimize the clinical care of people with diabetes and CKD by integrating new options with existing management strategies. In addition, the guideline contains practice points to facilitate implementation when insufficient data are available to make well-justified recommendations or when additional guidance may be useful for clinical application. The guideline covers comprehensive care of patients with diabetes and CKD, glycemic monitoring and targets, lifestyle interventions, antihyperglycemic therapies, and self-management and health systems approaches to management of patients with diabetes and CKD

Discovery of early-stage biomarkers for diabetic kidney disease using ms-based metabolomics (FinnDiane study)

Diabetic kidney disease (DKD) is a devastating complication that affects an estimated third of patients with type 1 diabetes mellitus (DM). There is no cure once the disease is diagnosed, but early treatment at a sub-clinical stage can prevent or at least halt the progression. DKD is clinically diagnosed as abnormally high urinary albumin excretion rate (AER). We hypothesize that subtle changes in the urine metabolome precede the clinically significant rise in AER. To test this, 52 type 1 diabetic patients were recruited by the FinnDiane study that had normal AER (normoalbuminuric). After an average of 5.5 years of follow-up half of the subjects (26) progressed from normal AER to microalbuminuria or DKD (macroalbuminuria), the other half remained normoalbuminuric. The objective of this study is to discover urinary biomarkers that differentiate the progressive form of albuminuria from non-progressive form of albuminuria in humans. Metabolite profiles of baseline 24 h urine samples were obtained by gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS) to detect potential early indicators of pathological changes. Multivariate logistic regression modeling of the metabolomics data resulted in a profile of metabolites that separated those patients that progressed from normoalbuminuric AER to microalbuminuric AER from those patients that maintained normoalbuminuric AER with an accuracy of 75% and a precision of 73%. As this data and samples are from an actual patient population and as such, gathered within a less controlled environment it is striking to see that within this profile a number of metabolites (identified as early indicators) have been associated with DKD already in literature, but also that new candidate biomarkers were found. The discriminating metabolites included acyl-carnitines, acyl-glycines and metabolites related to tryptophan metabolism. We found candidate biomarkers that were univariately significant different. This study demonstrates the potential of multivariate data analysis and metabolomics in the field of diabetic complications, and suggests several metabolic pathways relevant for further biological studies

Genetic Determinants of Glycated Hemoglobin in Type 1 Diabetes

Glycated hemoglobin (HbA(1c)) is an important measure of glycemia in diabetes. HbA(1c) is influenced by environmental and genetic factors both in people with and in people without diabetes. We performed a genome-wide association study (GWAS) for HbA(1c) in a Finnish type 1 diabetes (T1D) cohort, FinnDiane. Top results were examined for replication in T1D cohorts DCCT/EDIC, WESDR, CACTI, EDC, and RASS, and a meta-analysis was performed. Three SNPs in high linkage disequilibrium on chromosome 13 near relaxin family peptide receptor 2 (RXFP2) were associated with HbA(1c) in FinnDiane at genome-wide significance (P <5 x 10(-8)). The minor alleles of rs2085277 and rs1360072 were associated with higher HbA(1c) also in the meta-analysis with RASS (P <5 x 10(-8)), where these variants had minor allele frequencies 1%. Furthermore, these SNPs were associated with HbA(1c) in an East Asian population without diabetes (P 0.013). A weighted genetic risk score created from 55 HbA(1c)-associated variants from the literature was associated with HbA(1c) in FinnDiane but explained only a small amount of variation. Understanding the genetic basis of glycemic control and HbA(1c) may lead to better prevention of diabetes complications.Peer reviewe