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Background Surrogate endpoints, such as those of interest in chronic kidney disease (CKD), are often evaluated using Bayesian meta-regression. Trials used for the analysis can evaluate a variety of interventions for different sub-classifications of disease, which can introduce two additional goals in the analysis. The first is to infer the quality of the surrogate within specific trial subgroups defined by disease or intervention classes. The second is to generate more targeted subgroup-specific predictions of treatment effects on the clinical endpoint. Methods Using real data from a collection of CKD trials and a simulation study, we contrasted surrogate endpoint evaluations under different hierarchical Bayesian approaches. Each approach we considered induces different assumptions regarding the relatedness (exchangeability) of trials within and between subgroups. These include partial-pooling approaches, which allow subgroup-specific meta-regressions and, yet, facilitate data adaptive information sharing across subgroups to potentially improve inferential precision. Because partial-pooling models come with additional parameters relative to a standard approach assuming one meta-regression for the entire set of studies, we performed analyses to understand the impact of the parameterization and priors with the overall goals of comparing precision in estimates of subgroup-specific meta-regression parameters and predictive performance. Results In the analyses considered, partial-pooling approaches to surrogate endpoint evaluation improved accuracy of estimation of subgroup-specific meta-regression parameters relative to fitting separate models within subgroups. A random rather than fixed effects approach led to reduced bias in estimation of meta-regression parameters and in prediction in subgroups where the surrogate was strong. Finally, we found that subgroup-specific meta-regression posteriors were robust to use of constrained priors under the partial-pooling approach, and that use of constrained priors could facilitate more precise prediction for clinical effects in trials of a subgroup not available for the initial surrogacy evaluation. Conclusion Partial-pooling modeling strategies should be considered for surrogate endpoint evaluation on collections of heterogeneous studies. Fitting these models comes with additional complexity related to choosing priors. Constrained priors should be considered when using partial-pooling models when the goal is to predict the treatment effect on the clinical endpoint.

Background This post-hoc analysis of the DELIGHT trial assessed effects of the SGLT2 inhibitor dapagliflozin on iron metabolism and markers of inflammation. Methods Patients with type 2 diabetes and albuminuria were randomized to dapagliflozin, dapagliflozin and saxagliptin, or placebo. We measured hemoglobin, iron markers (serum iron, transferrin saturation, and ferritin), plasma erythropoietin, and inflammatory markers (urinary MCP-1 and urinary/serum IL-6) at baseline and week 24. Results 360/461 (78.1%) participants had available biosamples. Dapagliflozin and dapagliflozin-saxagliptin, compared to placebo, increased hemoglobin by 5.7 g/L (95%CI 4.0, 7.3; p < 0.001) and 4.4 g/L (2.7, 6.0; p < 0.001) and reduced ferritin by 18.6% (8.7, 27.5; p < 0.001) and 18.4% (8.7, 27.1; p < 0.001), respectively. Dapagliflozin reduced urinary MCP-1/Cr by 29.0% (14.6, 41.0; p < 0.001) and urinary IL-6/Cr by 26.6% (9.1, 40.7; p = 0.005) with no changes in other markers. Conclusions Dapagliflozin increased hemoglobin and reduced ferritin and urinary markers of inflammation, suggesting potentially important effects on iron metabolism and inflammation. Trial registration ClinicalTrials.gov NCT02547935.

In this trial, patients with CKD (with or without type 2 diabetes) were randomly assigned to receive dapagliflozin or placebo. The primary composite outcome — a sustained decline in the estimated GFR of at least 50%, end-stage kidney disease, or death from renal or cardiovascular causes — was less frequent with dapagliflozin.

Large trials have shown that sodium glucose co-transporter-2 (SGLT2) inhibitors reduce the risk of adverse kidney and cardiovascular outcomes in patients with heart failure or chronic kidney disease, or with type 2 diabetes and high risk of atherosclerotic cardiovascular disease. None of the trials recruiting patients with and without diabetes were designed to assess outcomes separately in patients without diabetes. We did a systematic review and meta-analysis of SGLT2 inhibitor trials. We searched the MEDLINE and Embase databases for trials published from database inception to Sept 5, 2022. SGLT2 inhibitor trials that were double-blind, placebo-controlled, performed in adults (age ≥18 years), large (≥500 participants per group), and at least 6 months in duration were included. Summary-level data used for analysis were extracted from published reports or provided by trial investigators, and inverse-variance-weighted meta-analyses were conducted to estimate treatment effects. The main efficacy outcomes were kidney disease progression (standardised to a definition of a sustained ≥50% decrease in estimated glomerular filtration rate [eGFR] from randomisation, a sustained low eGFR, end-stage kidney disease, or death from kidney failure), acute kidney injury, and a composite of cardiovascular death or hospitalisation for heart failure. Other outcomes were death from cardiovascular and non-cardiovascular disease considered separately, and the main safety outcomes were ketoacidosis and lower limb amputation. This study is registered with PROSPERO, CRD42022351618. We identified 13 trials involving 90 413 participants. After exclusion of four participants with uncertain diabetes status, we analysed 90 409 participants (74 804 [82·7%] participants with diabetes [>99% with type 2 diabetes] and 15 605 [17·3%] without diabetes; trial-level mean baseline eGFR range 37–85 mL/min per 1·73 m2). Compared with placebo, allocation to an SGLT2 inhibitor reduced the risk of kidney disease progression by 37% (relative risk [RR] 0·63, 95% CI 0·58–0·69) with similar RRs in patients with and without diabetes. In the four chronic kidney disease trials, RRs were similar irrespective of primary kidney diagnosis. SGLT2 inhibitors reduced the risk of acute kidney injury by 23% (0·77, 0·70–0·84) and the risk of cardiovascular death or hospitalisation for heart failure by 23% (0·77, 0·74–0·81), again with similar effects in those with and without diabetes. SGLT2 inhibitors also reduced the risk of cardiovascular death (0·86, 0·81–0·92) but did not significantly reduce the risk of non-cardiovascular death (0·94, 0·88–1·02). For these mortality outcomes, RRs were similar in patients with and without diabetes. For all outcomes, results were broadly similar irrespective of trial mean baseline eGFR. Based on estimates of absolute effects, the absolute benefits of SGLT2 inhibition outweighed any serious hazards of ketoacidosis or amputation. In addition to the established cardiovascular benefits of SGLT2 inhibitors, the randomised data support their use for modifying risk of kidney disease progression and acute kidney injury, not only in patients with type 2 diabetes at high cardiovascular risk, but also in patients with chronic kidney disease or heart failure irrespective of diabetes status, primary kidney disease, or kidney function. UK Medical Research Council and Kidney Research UK.

Abstract Context High-density lipoproteins (HDL) may be protective against type 2 diabetes (T2D) development, but HDL particles vary in size and function, which could lead to differential associations with incident T2D. A newly developed nuclear magnetic resonance (NMR)-derived algorithm provides concentrations for 7 HDL subspecies. Objective We aimed to investigate the association of HDL particle subspecies with incident T2D in the general population. Methods Among 4828 subjects of the Prevention of Renal and Vascular End-Stage Disease (PREVEND) study without T2D at baseline, HDL subspecies with increasing size from H1P to H7P were measured by NMR (LP4 algorithm of the Vantera NMR platform). Results A total of 265 individuals developed T2D (median follow-up of 7.3 years). In Cox regression models, HDL size and H4P (hazard ratio [HR] per 1 SD increase 0.83 [95% CI, 0.69-0.99] and 0.85 [95% CI, 0.75-0.95], respectively) were inversely associated with incident T2D, after adjustment for relevant covariates. In contrast, levels of H2P were positively associated with incident T2D (HR 1.15 [95% CI, 1.01-1.32]). In secondary analyses, associations with large HDL particles and H6P were modified by body mass index (BMI) in such a way that they were particularly associated with a lower risk of incident T2D, in subjects with BMI < 30 kg/m2. Conclusion Greater HDL size and lower levels of H4P were associated with a lower risk, whereas higher levels of H2P were associated with a higher risk of developing T2D. In addition, large HDL particles and H6P were inversely associated with T2D in nonobese subjects.

High concentrations of high-density lipoprotein (HDL) cholesterol are likely associated with a lower risk of posttransplantation diabetes mellitus (PTDM). However, HDL particles vary in size and density with yet unestablished associations with PTDM risk. The aim of our study was to determine the association between different HDL particles and development of PTDM in renal transplant recipients (RTRs). We included 351 stable outpatient adult RTRs without diabetes at baseline evaluation. HDL particle characteristics and size were measured by nuclear magnetic resonance (NMR) spectroscopy. During 5.2 (IQR, 4.1‒5.8) years of follow-up, 39 (11%) RTRs developed PTDM. In multivariable Cox regression analysis, levels of HDL cholesterol (hazard ratio [HR] 0.61, 95% confidence interval [CI] 0.40–0.94 per 1SD increase; p = 0.024) and of large HDL particles (HR 0.68, 95% CI 0.50–0.93 per log 1SD increase; p = 0.017), as well as larger HDL size (HR 0.58, 95% CI 0.36–0.93 per 1SD increase; p = 0.025) were inversely associated with PTDM development, independently of relevant covariates including, age, sex, body mass index, medication use, transplantation-specific parameters, blood pressure, triglycerides, and glucose. In conclusion, higher concentrations of HDL cholesterol and of large HDL particles and greater HDL size were associated with a lower risk of PTDM development in RTRs, independently of established risk factors for PTDM development.

Real-world data for patients with chronic kidney disease (CKD), specifically pertaining to clinical management, metabolic control, treatment patterns, quality of life (QoL) and dietary patterns, are limited. Understanding these gaps using real-world, routine care data will improve our understanding of the challenges and consequences faced by patients with CKD, and will facilitate the long-term goal of improving their management and prognosis. DISCOVER CKD follows an enriched hybrid study design, with both retrospective and prospective patient cohorts, integrating primary and secondary data from patients with CKD from China, Italy, Japan, Sweden, the UK and the USA. Data will be prospectively captured over a 3-year period from >1000 patients with CKD who will be followed up for at least 1 year via electronic case report form entry during routine clinical visits and also via a mobile/tablet-based application, enabling the capture of patient-reported outcomes (PROs). In-depth interviews will be conducted in a subset of ∼100 patients. Separately, secondary data will be retrospectively captured from >2 000 000 patients with CKD, extracted from existing datasets and registries. The DISCOVER CKD program captures and will report on patient demographics, biomarker and laboratory measurements, medical histories, clinical outcomes, healthcare resource utilization, medications, dietary patterns, physical activity and PROs (including QoL and qualitative interviews). The DISCOVER CKD program will provide contemporary real-world insight to inform clinical practice and improve our understanding of the epidemiology and clinical and economic burden of CKD, as well as determinants of clinical outcomes and PROs from a range of geographical regions in a real-world CKD setting.

Aims/hypothesisThe sodium–glucose cotransporter 2 (SGLT2) inhibitor canagliflozin slows progression of kidney function decline in type 2 diabetes. The aim of this study was to assess the effect of the SGLT2 inhibitor canagliflozin on biomarkers for progression of diabetic kidney disease (DKD).MethodsA canagliflozin mechanism of action (MoA) network model was constructed based on an in vitro transcriptomics experiment in human proximal tubular cells and molecular features linked to SGLT2 inhibitors from scientific literature. This model was mapped onto an established DKD network model that describes molecular processes associated with DKD. Overlapping areas in both networks were subsequently used to select candidate biomarkers that change with canagliflozin therapy. These biomarkers were measured in 296 stored plasma samples from a previously reported 2 year clinical trial comparing canagliflozin with glimepiride.ResultsForty-four proteins present in the canagliflozin MoA molecular model overlapped with proteins in the DKD network model. These proteins were considered candidates for monitoring impact of canagliflozin on DKD pathophysiology. For ten of these proteins, scientific evidence was available suggesting that they are involved in DKD progression. Of these, compared with glimepiride, canagliflozin 300 mg/day decreased plasma levels of TNF receptor 1 (TNFR1; 9.2%; p < 0.001), IL-6 (26.6%; p = 0.010), matrix metalloproteinase 7 (MMP7; 24.9%; p = 0.011) and fibronectin 1 (FN1; 14.9%; p = 0.055) during 2 years of follow-up.Conclusions/interpretationThe observed reduction in TNFR1, IL-6, MMP7 and FN1 suggests that canagliflozin contributes to reversing molecular processes related to inflammation, extracellular matrix turnover and fibrosis.Trial registration ClinicalTrials.gov NCT00968812

SGLT2 inhibitors and GLP-1 receptor agonists are used in patients with type 2 diabetes as glucose lowering therapies, with additional benefits of weight loss and blood pressure reduction. Data from cardiovascular outcome trials have highlighted that these drugs confer protection against major cardiovascular disease in those with established atherosclerotic cardiovascular disease, reduce the risk of admission to hospital for heart failure, and reduce cardiovascular and all-cause mortality. Ongoing research using hard renal endpoints such as end stage kidney disease rather than surrogate markers might clarify the renoprotective benefits of both agents. When used for glucose lowering, SGLT2 inhibitors are most effective if the estimated glomerular filtration rate is more than 60 ml per min per 1·73m2 at initiation and should be avoided where there is a risk of diabetic ketoacidosis. GLP-1 receptor agonists are contraindicated in those with a history of medullary thyroid cancer and used with caution in patients with a history of pancreatitis of a known cause. These drugs are now second-line, or even arguably first-line, glucose lowering therapies in patients with cardiorenal disease, irrespective of glycaemic control. If an SGLT2 inhibitor or GLP-1 receptor agonist is considered suitable in patients with type 2 diabetes, treatment should be prioritised according to existing evidence: GLP-1 receptor agonists should be considered in patients at a high risk of, or with established, cardiovascular disease and SGLT2 inhibitors considered for patients with heart failure (with reduced ejection fraction) or chronic kidney disease (with or without established cardiovascular disease). There is now compelling data on the benefits of these drugs for a range of other clinical indications even without type 2 diabetes, including for GLP-1 receptor agonists in patients with obesity and overweight with weight-related comorbidities.

In this trial in persons with chronic kidney disease and type 2 diabetes, combination therapy with finerenone and empagliflozin led to a greater reduction in the urinary albumin-to-creatinine ratio than either drug alone.