Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis is an autoimmune disorder which affects small- and, to a lesser degree, medium-sized vessels. ANCA-associated vasculitis encompasses three disease phenotypes: granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), and eosinophilic granulomatosis with polyangiitis (EGPA). This classification is largely based on clinical presentations and has several limitations. Recent research provided evidence that genetic background, risk of relapse, prognosis, and co-morbidities are more closely related to the ANCA serotype, proteinase 3 (PR3)-ANCA and myeloperoxidase (MPO)-ANCA, compared to the disease phenotypes GPA or MPA. This finding has been extended to the investigation of biomarkers predicting disease activity, which again more closely relate to the ANCA serotype. Discoveries related to the immunopathogenesis translated into clinical practice as targeted therapies are on the rise. This review will summarize the current understanding of the immunopathogenesis of ANCA-associated vasculitis and the interplay between ANCA serotype and proposed disease biomarkers and illustrate how the extending knowledge of the immunopathogenesis will likely translate into development of a personalized medicine approach in the management of ANCA-associated vasculitis.

Cardiovascular and renal complications are a major burden for individuals with type 2 diabetes mellitus (T2DM). Besides lifestyle interventions, current guidelines recommend combination drug therapy to prevent or delay the incidence and progression of comorbidities. However, non-adherence to pharmacotherapy is common in chronic conditions such as T2DM and a barrier to successful disease management. Numerous studies have associated medication non-adherence with worse outcome as well as higher health care costs. This narrative review provides (i) an overview on adherence measures used within and outside research settings, (ii) an estimate on the prevalence of non-adherence to antidiabetic and cardiovascular drugs in T2DM, and (iii) specifically focuses on the association of non-adherence to these drugs with renal and cardiovascular outcomes.

Aims/hypothesis: Non-adherence to medication is a frequent barrier in the treatment of patients with type 2 diabetes mellitus, potentially limiting the effectiveness of evidence-based treatments. Previous studies have mostly relied on indirect adherence measures to analyse outcomes based on adherence. The aim of this study was to use LC-MS/MS in urine—a non-invasive, direct and objective measure—to assess non-adherence to cardiometabolic drugs and analyse its association with kidney and cardiovascular outcomes. Methods: This cohort study includes 1125 participants from the PROVALID study, which follows patients with type 2 diabetes mellitus at the primary care level. Baseline urine samples were tested for 79 cardiometabolic drugs and metabolites thereof via LC-MS/MS. An individual was classified as totally adherent if markers for all drugs were detected, partially non-adherent when at least one marker for one drug was detected, and totally non-adherent if no markers for any drugs were detected. Non-adherence was then analysed in the context of cardiovascular (composite of myocardial infarction, stroke and cardiovascular death) and kidney (composite of sustained 40% decline in eGFR, sustained progression of albuminuria, kidney replacement therapy and death from kidney failure) outcomes. Results: Of the participants, 56.3% were totally adherent, 42.0% were partially non-adherent, and 1.7% were totally non-adherent to screened cardiometabolic drugs. Adherence was highest to antiplatelet and glucose-lowering agents and lowest to lipid-lowering agents. Over a median (IQR) follow-up time of 5.10 (4.12–6.12) years, worse cardiovascular outcomes were observed with non-adherence to antiplatelet drugs (HR 10.13 [95% CI 3.06, 33.56]) and worse kidney outcomes were observed with non-adherence to antihypertensive drugs (HR 1.98 [95% CI 1.37, 2.86]). Conclusions/interpretation: This analysis shows that non-adherence to cardiometabolic drug regimens is common in type 2 diabetes mellitus and negatively affects kidney and cardiovascular outcomes. Graphical

Introduction: Diabetic kidney disease (DKD) is a common cause of chronic kidney disease with around 25–40% of patients with diabetes being affected. The course of DKD is variable, and estimated glomerular filtration rate (eGFR) and albuminuria, the currently used clinical markers, are not able to accurately predict the individual disease trajectory, in particular in early stages of the disease. The aim of this study was to assess the association of urine levels of selected protein biomarkers with the progression of DKD at an early stage of disease. Methods: We measured 22 protein biomarkers using the Mesoscale Discovery platform in 461 urine samples of the PROVALID cohort, an observational study of patients with type 2 diabetes mellitus followed at the primary health care level for a minimum of 4 years. Odds ratios (ORs) were estimated for the effect of marker values above median on fast progression using unadjusted and adjusted logistic regression models. RNA expression at the single-cell level in kidney biopsy samples obtained from a cohort of young persons with type 2 diabetes mellitus was in addition determined for markers showing significant associations with disease progression. Results: Increased urinary levels of epidermal growth factor (EGF) were linked to lower odds of fast progression (defined as annual eGFR decline greater than 2.58 mL/min per 1.73 m2) with an OR of 0.60 (95% CI: 0.46, 0.78). The association with outcome was even stronger when adjusting for a set of 14 baseline clinical parameters including age, biological sex, eGFR, body mass index, albuminuria, and HbA1c. Elevated urinary levels of fatty acid-binding protein 3 (FABP3) and vascular cell adhesion molecule 1 (VCAM1) were each significantly associated with fast progression with an OR of 1.44 (95% CI: 1.11, 1.87) and an OR of 1.41 (95% CI: 1.08, 1.83), respectively. Enriched expression of EGF and FABP3 was observed in distal convoluted tubular cells and VCAM1 in parietal epithelial cells at single-cell level from biopsies of patients with early DKD. Conclusion: In summary, we show that lower urinary levels of EGF and higher urinary levels of FABP3 and VCAM1 are significantly associated with DKD progression in early-stage disease.

Fibrinogen-like 2 (FGL2) was recently found to be associated with fibrosis in a mouse model of kidney damage and was proposed as a potential therapeutic target in chronic kidney disease (CKD). We assessed the association of renal FGL2 mRNA expression with the disease outcome in two independent CKD cohorts (NEPTUNE and Innsbruck CKD cohort) using Kaplan Meier survival analysis. The regulation of FGL2 in kidney biopsies of CKD patients as compared to healthy controls was further assessed in 13 human CKD transcriptomics datasets. The FGL2 protein expression in human renal tissue sections was determined via immunohistochemistry. The regulators of FGL2 mRNA expression in renal tissue were identified in the co-expression and upstream regulator analysis of FGL2-positive renal cells via the use of single-cell RNA sequencing data from the kidney precision medicine project (KPMP). Higher renal FGL2 mRNA expression was positively associated with kidney fibrosis and negatively associated with eGFR. Renal FGL2 mRNA expression was upregulated in CKD as compared with healthy controls and associated with CKD progression in the Innsbruck CKD cohort (p-value = 0.0036) and NEPTUNE cohort (p-value = 0.0048). The highest abundance of FGL2 protein in renal tissue was detected in the thick ascending limb of the loop of Henle and macula densa, proximal tubular cells, as well as in glomerular endothelial cells. The upstream regulator analysis identified TNF, IL1B, IFNG, NFKB1, and SP1 as factors potentially inducing FGL2-co-expressed genes, whereas factors counterbalancing FGL2-co-expressed genes included GLI1, HNF1B, or PPARGC1A. In conclusion, renal FGL2 mRNA expression is elevated in human CKD, and higher FGL2 levels are associated with fibrosis and worse outcomes.

Accurate prediction of diabetic kidney disease progression is challenging, but mandatory. Urinary Dickkopf-3 (uDKK3), a tubular, epithelial-derived glycoprotein and marker of tubular injury, is a promising biomarker for kidney function decline. We explored the clinical utility of uDKK3 to predict kidney function decline and adverse cardiovascular events in patients with type 2 diabetes mellitus (T2DM) in a primary health care setting. In this cohort study, 3,232 patients with T2DM were analyzed. The primary end point was a composite of a sustained estimated glomerular filtration rate (eGFR) decline ≥40%; a sustained increase in albuminuria of at least 30%, including a transition in albuminuria class; progression to end-stage kidney disease; and death from kidney failure. After adjustment for confounding variables, uDKK3 values >200 pg/mg creatinine were associated with a higher risk of the composite kidney end point during a median follow-up of 4.26 years. Furthermore, uDKK3 improved the prediction of the 1-year eGFR decline on top of albuminuria. Individuals with high uDKK3 levels also had an increased risk for adverse cardiovascular events and all-cause mortality. uDKK3 identifies patients with T2DM at high risk for kidney function decline on top of established biomarkers (albuminuria and eGFR). In primary care, uDKK3 may help to identify high-risk patients who might benefit from intensified treatment and/or referrals to specialists. Article Highlights: Prediction of kidney function decline is challenging in patients with type 2 diabetes mellitus (T2DM). Urinary Dickkopf-3 (uDKK3), a profibrotic tubular protein, is a promising biomarker for detecting tubular injury and predicting the progression of chronic kidney disease. This study assessed whether uDKK3 measurements improve risk prediction in patients with T2DM treated at the primary care level. Elevated uDKK3 levels were associated with kidney function decline, on top of established biomarkers (estimated glomerular filtration rate and albuminuria). uDKK3 also identified patients at increased risk for cardiovascular events. uDKK3 may help identify high-risk patients in primary care who could benefit from intensified treatment and/or referrals to specialists.