Incretin and glucagon receptor polypharmacology in chronic kidney disease.

Chronic kidney disease is a debilitating condition associated with significant morbidity and mortality. In recent years, the kidney effects of incretin-based therapies, particularly glucagon-like peptide-1 receptor agonists (GLP-1RAs), have garnered substantial interest in the management of type 2 diabetes and obesity. This review delves into the intricate interactions between the kidney, GLP-1RAs, and glucagon, shedding light on their mechanisms of action and potential kidney benefits. Both GLP-1 and glucagon, known for their opposing roles in regulating glucose homeostasis, improve systemic risk factors affecting the kidney, including adiposity, inflammation, oxidative stress, and endothelial function. Additionally, these hormones and their pharmaceutical mimetics may have a direct impact on the kidney. Clinical studies have provided evidence that incretins, including those incorporating glucagon receptor agonism, are likely to exhibit improved kidney outcomes. Although further research is necessary, receptor polypharmacology holds promise for preserving kidney function through eliciting vasodilatory effects, influencing volume and electrolyte handling, and improving systemic risk factors.

Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine.

Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.

Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study.

BACKGROUND AND AIMS: Detecting NASH remains challenging, while at-risk NASH (steatohepatitis and F≥ 2) tends to progress and is of interest for drug development and clinical application. We developed prediction models by supervised machine learning techniques, with clinical data and biomarkers to stage and grade patients with NAFLD. APPROACH AND RESULTS: Learning data were collected in the Liver Investigation: Testing Marker Utility in Steatohepatitis metacohort (966 biopsy-proven NAFLD adults), staged and graded according to NASH CRN. Conditions of interest were the clinical trial definition of NASH (NAS ≥ 4;53%), at-risk NASH (NASH with F ≥ 2;35%), significant (F ≥ 2;47%), and advanced fibrosis (F ≥ 3;28%). Thirty-five predictors were included. Missing data were handled by multiple imputations. Data were randomly split into training/validation (75/25) sets. A gradient boosting machine was applied to develop 2 models for each condition: clinical versus extended (clinical and biomarkers). Two variants of the NASH and at-risk NASH models were constructed: direct and composite models.Clinical gradient boosting machine models for steatosis/inflammation/ballooning had AUCs of 0.94/0.79/0.72. There were no improvements when biomarkers were included. The direct NASH model produced AUCs (clinical/extended) of 0.61/0.65. The composite NASH model performed significantly better (0.71) for both variants. The composite at-risk NASH model had an AUC of 0.83 (clinical and extended), an improvement over the direct model. Significant fibrosis models had AUCs (clinical/extended) of 0.76/0.78. The extended advanced fibrosis model (0.86) performed significantly better than the clinical version (0.82). CONCLUSIONS: Detection of NASH and at-risk NASH can be improved by constructing independent machine learning models for each component, using only clinical predictors. Adding biomarkers only improved the accuracy of fibrosis.

Different roles of protein biomarkers predicting eGFR trajectories in people with chronic kidney disease and diabetes mellitus: a nationwide retrospective cohort study.

BACKGROUND: Chronic kidney disease (CKD) is a common comorbidity in people with diabetes mellitus, and a key risk factor for further life-threatening conditions such as cardiovascular disease. The early prediction of progression of CKD therefore is an important clinical goal, but remains difficult due to the multifaceted nature of the condition. We validated a set of established protein biomarkers for the prediction of trajectories of estimated glomerular filtration rate (eGFR) in people with moderately advanced chronic kidney disease and diabetes mellitus. Our aim was to discern which biomarkers associate with baseline eGFR or are important for the prediction of the future eGFR trajectory. METHODS: We used Bayesian linear mixed models with weakly informative and shrinkage priors for clinical predictors (n = 12) and protein biomarkers (n = 19) to model eGFR trajectories in a retrospective cohort study of people with diabetes mellitus (n = 838) from the nationwide German Chronic Kidney Disease study. We used baseline eGFR to update the models' predictions, thereby assessing the importance of the predictors and improving predictive accuracy computed using repeated cross-validation. RESULTS: The model combining clinical and protein predictors had higher predictive performance than a clinical only model, with an [Formula: see text] of 0.44 (95% credible interval 0.37-0.50) before, and 0.59 (95% credible interval 0.51-0.65) after updating by baseline eGFR, respectively. Only few predictors were sufficient to obtain comparable performance to the main model, with markers such as Tumor Necrosis Factor Receptor 1 and Receptor for Advanced Glycation Endproducts being associated with baseline eGFR, while Kidney Injury Molecule 1 and urine albumin-creatinine-ratio were predictive for future eGFR decline. CONCLUSIONS: Protein biomarkers only modestly improve predictive accuracy compared to clinical predictors alone. The different protein markers serve different roles for the prediction of longitudinal eGFR trajectories potentially reflecting their role in the disease pathway.

Indicators of Kidney Fibrosis in Patients with Type 2 Diabetes and Chronic Kidney Disease Treated with Dulaglutide.

INTRODUCTION: In the AWARD-7 study in patients with type 2 diabetes and moderate-to-severe chronic kidney disease, once-weekly dulaglutide slowed the decline in estimated glomerular filtration rate (eGFR) and decreased the urine albumin/creatinine ratio compared to insulin glargine at the end of 52 weeks of treatment. In this exploratory post hoc analysis, changes in two fibrosis biomarkers, serum PRO-C6 (type VI collagen formation) and urine C3M (type III collagen degradation), were evaluated. METHODS: In the groups treated with dulaglutide 1.5 mg or insulin glargine (N = 330), serum PRO-C6 and urine C3M were measured using competitive enzyme-linked immunosorbent assays. Biomarker changes were assessed by a mixed-effects model for repeated measures. Pearson correlation analyses were conducted to determine associations between changes in kidney fibrosis biomarkers and eGFR measures at 52 weeks. RESULTS: At weeks 26 and 52 of treatment in the overall population, serum PRO-C6 levels were significantly lower in the dulaglutide group versus insulin glargine group with percent change from baseline of (least squares mean ± standard error) -4.6% ± 1.9 and -0.2% ± 2.2 versus 5.7% ± 2.0 and 8.0% ± 2.3 (p < 0.01), respectively, and urine C3M levels were significantly higher in the dulaglutide group versus insulin glargine group with percent change from baseline of 10.9% ± 8.2 and 20.7% ± 8.8 versus -10.0% ± 6.5 and -16.9% ± 6.4 (p < 0.05), respectively. These findings appeared greater in the subgroup with macroalbuminuria. Serum PRO-C6 negatively correlated with eGFR, while urine C3M positively correlated with eGFR. CONCLUSION: Dulaglutide treatment was associated with biomarker changes that indicated lower type VI collagen formation and higher type III collagen degradation compared to treatment with insulin glargine, suggesting a potential drug effect to reduce kidney fibrosis.

Results of untargeted analysis using the SOMAscan proteomics platform indicates novel associations of circulating proteins with risk of progression to kidney failure in diabetes.

This study applies a large proteomics panel to search for new circulating biomarkers associated with progression to kidney failure in individuals with diabetic kidney disease. Four independent cohorts encompassing 754 individuals with type 1 and type 2 diabetes and early and late diabetic kidney disease were followed to ascertain progression to kidney failure. During ten years of follow-up, 227 of 754 individuals progressed to kidney failure. Using the SOMAscan proteomics platform, we measured baseline concentration of 1129 circulating proteins. In our previous publications, we analyzed 334 of these proteins that were members of specific candidate pathways involved in diabetic kidney disease and found 35 proteins strongly associated with risk of progression to kidney failure. Here, we examined the remaining 795 proteins using an untargeted approach. Of these remaining proteins, 11 were significantly associated with progression to kidney failure. Biological processes previously reported for these proteins were related to neuron development (DLL1, MATN2, NRX1B, KLK8, RTN4R and ROR1) and were implicated in the development of kidney fibrosis (LAYN, DLL1, MAPK11, MATN2, endostatin, and ROR1) in cellular and animal studies. Specific mechanisms that underlie involvement of these proteins in progression of diabetic kidney disease must be further investigated to assess their value as targets for kidney-protective therapies. Using multivariable LASSO regression analysis, five proteins (LAYN, ESAM, DLL1, MAPK11 and endostatin) were found independently associated with risk of progression to kidney failure. Thus, our study identified proteins that may be considered as new candidate prognostic biomarkers to predict risk of progression to kidney failure in diabetic kidney disease. Furthermore, three of these proteins (DLL1, ESAM, and MAPK11) were selected as candidate biomarkers when all SOMAscan results were evaluated.

The dual glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonist tirzepatide improves cardiovascular risk biomarkers in patients with type 2 diabetes: A post hoc analysis.

In a phase 2 trial of once-weekly tirzepatide (1, 5, 10, or 15 mg), dulaglutide (1.5 mg), or placebo, the dual glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonist tirzepatide dose-dependently reduced HbA1c and body weight in patients with type 2 diabetes. In this post hoc analysis, inflammation, endothelial dysfunction, and cellular stress biomarkers were measured at baseline, 4, 12, and 26 weeks to evaluate the additional effects of tirzepatide on cardiovascular risk factors. At 26 weeks, tirzepatide 10 and 15 mg decreased YKL-40 (also known as chitinase-3 like-protein-1), intercellular adhesion molecule 1 (ICAM-1), leptin, and growth differentiation factor 15 levels versus baseline, and YKL-40 and leptin levels versus placebo and dulaglutide. Tirzepatide 15 mg also decreased ICAM-1 levels versus placebo and dulaglutide, and high-sensitivity C-reactive protein (hsCRP) levels versus baseline and placebo, but not dulaglutide. GlycA, interleukin 6, vascular cell adhesion molecule 1, and N-terminal-pro hormone B-type natriuretic peptide levels were not significantly changed in any group. YKL-40, hsCRP, and ICAM-1 levels rapidly decreased within 4 weeks of treatment with tirzepatide 10 and 15 mg, whereas the decrease in leptin levels was more gradual and did not plateau by 26 weeks. In this hypothesis-generating exploratory analysis, tirzepatide decreased several biomarkers that have been associated with cardiovascular risk.

Comprehensive Search for Novel Circulating miRNAs and Axon Guidance Pathway Proteins Associated with Risk of ESKD in Diabetes.

BACKGROUND: Mechanisms underlying the pro gression of diabetic kidney disease to ESKD are not fully understood. METHODS: We performed global microRNA (miRNA) analysis on plasma from two cohorts consisting of 375 individuals with type 1 and type 2 diabetes with late diabetic kidney disease, and targeted proteomics analysis on plasma from four cohorts consisting of 746 individuals with late and early diabetic kidney disease. We examined structural lesions in kidney biopsy specimens from the 105 individuals with early diabetic kidney disease. Human umbilical vein endothelial cells were used to assess the effects of miRNA mimics or inhibitors on regulation of candidate proteins. RESULTS: In the late diabetic kidney disease cohorts, we identified 17 circulating miRNAs, represented by four exemplars (miR-1287-5p, miR-197-5p, miR-339-5p, and miR-328-3p), that were strongly associated with 10-year risk of ESKD. These miRNAs targeted proteins in the axon guidance pathway. Circulating levels of six of these proteins-most notably, EFNA4 and EPHA2-were strongly associated with 10-year risk of ESKD in all cohorts. Furthermore, circulating levels of these proteins correlated with severity of structural lesions in kidney biopsy specimens. In contrast, expression levels of genes encoding these proteins had no apparent effects on the lesions. In in vitro experiments, mimics of miR-1287-5p and miR-197-5p and inhibitors of miR-339-5p and miR-328-3p upregulated concentrations of EPHA2 in either cell lysate, supernatant, or both. CONCLUSIONS: This study reveals novel mechanisms involved in progression to ESKD and points to the importance of systemic factors in the development of diabetic kidney disease. Some circulating miRNAs and axon guidance pathway proteins represent potential targets for new therapies to prevent and treat this condition.

A Sequential Algorithm Combining ADAPT and Liver Stiffness Can Stage Metabolic-Associated Fatty Liver Disease in Hospital-Based and Primary Care Patients.

INTRODUCTION: Metabolic-associated fatty liver disease is common, with fibrosis the major determinant of adverse outcomes. Population-based screening tools with high diagnostic accuracy for the staging of fibrosis are lacking. METHODS: Three independent cohorts, 2 with both liver biopsy and liver stiffness measurements (LSMs, n = 254 and 65) and a population sample (n = 713), were studied. The performance of a recently developed noninvasive algorithm (ADAPT [age, diabetes, PRO-C3 and platelets panel]) as well as aspartate aminotransferase-to-platelet ratio index, fibrosis-4, nonalcoholic fatty liver disease fibrosis score, and LSM was used to stage patients for significant (≥F2) and advanced (≥F3) fibrosis. RESULTS: In the hospital-based cohorts, the N-terminal propeptide of type 3 collagen (Pro-C3) increased with fibrosis stage (P < 0.0001) and independently associated with advanced fibrosis (odds ratio = 1.091, 95% confidence interval [CI]: 1.053-1.113, P = 0.0001). ADAPT showed areas under the receiver operating characteristics curve of 0.831 (95% CI: 0.779-0.875) in the derivation and 0.879 (95% CI: 0.774-0.946) in the validation cohort for advanced fibrosis. This was superior to the existing fibrosis scores, aspartate aminotransferase-to-platelet ratio index, fibrosis-4, BARD (BMI, aspartate aminotransferase to alanine aminotransferase ratio [AAR], diabetes), and nonalcoholic fatty liver disease fibrosis score in most comparisons and comparable with LSM. Serial use of ADAPT and LSM had diagnostic accuracy of 92.5%, with 98% and 100% negative predictive value in the derivation and validation cohorts, respectively. In the population cohort, PRO-C3 associated with advanced fibrosis (P = 0.04), while ADAPT had a negative predictive value of 98% for excluding advanced fibrosis. DISCUSSION: PRO-C3 and ADAPT reliably exclude advanced fibrosis in low-risk populations. The serial combination of ADAPT with LSM has high diagnostic accuracy with a low requirement for liver biopsy. The proposed algorithm would help stratify those who need biopsies and narrow down those patients who would need to be referred to specialty clinics.

The dual glucose-dependent insulinotropic peptide and glucagon-like peptide-1 receptor agonist, tirzepatide, improves lipoprotein biomarkers associated with insulin resistance and cardiovascular risk in patients with type 2 diabetes.

AIM: To better understand the marked decrease in serum triglycerides observed with tirzepatide in patients with type 2 diabetes, additional lipoprotein-related biomarkers were measured post hoc in available samples from the same study. MATERIALS AND METHODS: Patients were randomized to receive once-weekly subcutaneous tirzepatide (1, 5, 10 or 15 mg), dulaglutide (1.5 mg) or placebo. Serum lipoprotein profile, apolipoprotein (apo) A-I, B and C-III and preheparin lipoprotein lipase (LPL) were measured at baseline and at 4, 12 and 26 weeks. Lipoprotein particle profile by nuclear magnetic resonance was assessed at baseline and 26 weeks. The lipoprotein insulin resistance (LPIR) score was calculated. RESULTS: At 26 weeks, tirzepatide dose-dependently decreased apoB and apoC-III levels, and increased serum preheparin LPL compared with placebo. Tirzepatide 10 and 15 mg decreased large triglyceride-rich lipoprotein particles (TRLP), small low-density lipoprotein particles (LDLP) and LPIR score compared with both placebo and dulaglutide. Treatment with dulaglutide also reduced apoB and apoC-III levels but had no effect on either serum LPL or large TRLP, small LDLP and LPIR score. The number of total LDLP was also decreased with tirzepatide 10 and 15 mg compared with placebo. A greater reduction in apoC-III with tirzepatide was observed in patients with high compared with normal baseline triglycerides. At 26 weeks, change in apoC-III, but not body weight, was the best predictor of changes in triglycerides with tirzepatide, explaining up to 22.9% of their variability. CONCLUSIONS: Tirzepatide treatment dose-dependently decreased levels of apoC-III and apoB and the number of large TRLP and small LDLP, suggesting a net improvement in atherogenic lipoprotein profile.

Associations of osteopontin and NT-proBNP with circulating miRNA levels in acute coronary syndrome.

The genomic regulatory networks underlying the pathogenesis of non-ST-segment elevation acute coronary syndrome (NSTE-ACS) are incompletely understood. As intermediate traits, protein biomarkers report on underlying disease severity and prognosis in NSTE-ACS. We hypothesized that integration of dense microRNA (miRNA) profiling with biomarker measurements would highlight potential regulatory pathways that underlie the relationships between prognostic biomarkers, miRNAs, and cardiovascular phenotypes. We performed miRNA sequencing using whole blood from 186 patients from the TRILOGY-ACS trial. Seven circulating prognostic biomarkers were measured: NH2-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein, osteopontin (OPN), myeloperoxidase, growth differentiation factor 15, monocyte chemoattractant protein, and neopterin. We tested miRNAs for association with each biomarker with generalized linear models and controlled the false discovery rate at 0.05. Ten miRNAs, including known cardiac-related miRNAs 25-3p and 423-3p, were associated with NT-proBNP levels (min. P = 7.5 × 10-4) and 48 miRNAs, including cardiac-related miRNAs 378a-3p, 20b-5p and 320a, -b, and -d, were associated with OPN levels (min. P = 1.6 × 10-6). NT-proBNP and OPN were also associated with time to cardiovascular death, myocardial infarction (MI), or stroke in the sample. By integrating large-scale miRNA profiling with circulating biomarkers as intermediate traits, we identified associations of known cardiac-related and novel miRNAs with two prognostic biomarkers and identified potential genomic networks regulating these biomarkers. These results, highlighting plausible biological pathways connecting miRNAs with biomarkers and outcomes, may inform future studies seeking to delineate genomic pathways underlying NSTE-ACS outcomes.

Integrative analysis of prognostic biomarkers derived from multiomics panels helps discrimination of chronic kidney disease trajectories in people with type 2 diabetes.

Clinical risk factors explain only a fraction of the variability of estimated glomerular filtration rate (eGFR) decline in people with type 2 diabetes. Cross-omics technologies by virtue of a wide spectrum screening of plasma samples have the potential to identify biomarkers for the refinement of prognosis in addition to clinical variables. Here we utilized proteomics, metabolomics and lipidomics panel assay measurements in baseline plasma samples from the multinational PROVALID study (PROspective cohort study in patients with type 2 diabetes mellitus for VALIDation of biomarkers) of patients with incident or early chronic kidney disease (median follow-up 35 months, median baseline eGFR 84 mL/min/1.73 m2, urine albumin-to-creatinine ratio 8.1 mg/g). In an accelerated case-control study, 258 individuals with a stable eGFR course (median eGFR change 0.1 mL/min/year) were compared to 223 individuals with a rapid eGFR decline (median eGFR decline -6.75 mL/min/year) using Bayesian multivariable logistic regression models to assess the discrimination of eGFR trajectories. The analysis included 402 candidate predictors and showed two protein markers (KIM-1, NTproBNP) to be relevant predictors of the eGFR trajectory with baseline eGFR being an important clinical covariate. The inclusion of metabolomic and lipidomic platforms did not improve discrimination substantially. Predictions using all available variables were statistically indistinguishable from predictions using only KIM-1 and baseline eGFR (area under the receiver operating characteristic curve 0.63). Thus, the discrimination of eGFR trajectories in patients with incident or early diabetic kidney disease and maintained baseline eGFR was modest and the protein marker KIM-1 was the most important predictor.

Markers of early progressive renal decline in type 2 diabetes suggest different implications for etiological studies and prognostic tests development.

To identify determinants of early progressive renal decline in type 2 diabetes a range of markers was studied in 1032 patients enrolled into the 2nd Joslin Kidney Study. eGFR slopes estimated from serial measurements of serum creatinine during 5-12 years of follow-up were used to define early renal decline. At enrollment, all patients had normal eGFR, 58% had normoalbuminuria and 42% had albuminuria. Early renal decline developed in 6% and in 18% patients, respectively. As determinants, we examined baseline values of clinical characteristics, circulating markers: TNFR1, KIM-1, and FGF23, and urinary markers: albumin, KIM-1, NGAL, MCP-1, EGF (all normalized to urinary creatinine) and the ratio of EGF to MCP-1. In univariate analysis, all plasma and urinary markers were significantly associated with risk of early renal decline. When analyzed together, systolic blood pressure, TNFR1, KIM-1, the albumin to creatinine ratio, and the EGF/MCP-1 ratio remained significant with the latter having the strongest effect. Integration of these markers into a multi-marker prognostic test resulted in a significant improvement of discriminatory performance of risk prediction of early renal decline, compared with the albumin to creatinine ratio and systolic blood pressure alone. However, the positive predictive value was only 50% in albuminuric patients. Thus, markers in plasma and urine indicate that the early progressive renal decline in Type 2 diabetes has multiple determinants with strong evidence for involvement of tubular damage. However, new, more informative markers are needed to develop a better prognostic test for such decline that can be used in a clinical setting.

JAK1/JAK2 inhibition by baricitinib in diabetic kidney disease: results from a Phase 2 randomized controlled clinical trial.

Background: Inflammation signaled by Janus kinases (JAKs) promotes progression of diabetic kidney disease (DKD). Baricitinib is an oral, reversible, selective inhibitor of JAK1 and JAK2. This study tested the efficacy of baricitinib versus placebo on albuminuria in adults with Type 2 diabetes at high risk for progressive DKD. Methods: In this Phase 2, double-blind, dose-ranging study, participants were randomized 1:1:1:1:1 to receive placebo or baricitinib (0.75 mg daily; 0.75 mg twice daily; 1.5 mg daily; or 4 mg daily), for 24 weeks followed by 4-8 weeks of washout. Results: Participants (N = 129) were 63±9.1 (mean±standard deviation) years of age, 27.1% (35/129) women and 11.6% (15/129) African-American race. Baseline hemoglobin A1c (HbA1c) was 7.3±1% and estimated glomerular filtration rate was 45.0±12.1 mL/min/1.73 m2 with first morning urine albumin-creatinine ratio (UACR) of 820 (407-1632) (median; interquartile range) mg/g. Baricitinib, 4 mg daily, decreased morning UACR by 41% at Week 24 compared with placebo (ratio to baseline 0.59, 95% confidence interval 0.38-0.93, P = 0.022). UACR was decreased at Weeks 12 and 24 and after 4-8 weeks of washout. Baricitinib 4 mg decreased inflammatory biomarkers over 24 weeks (urine C-X-C motif chemokine 10 and urine C-C motif ligand 2, plasma soluble tumor necrosis factor receptors 1 and 2, intercellular adhesion molecule 1 and serum amyloid A). The only adverse event rate that differed between groups was anemia at 32.0% (8/25) for baricitinib 4 mg daily versus 3.7% (1/27) for placebo. Conclusions: Baricitinib decreased albuminuria in participants with Type 2 diabetes and DKD. Further research is required to determine if baricitinib reduces DKD progression.

Anti-TGF-ß1 Antibody Therapy in Patients with Diabetic Nephropathy.

TGF-ß has been implicated as a major pathogenic factor in diabetic nephropathy. This randomized, double-blind, phase 2 study assessed whether modulating TGF-ß1 activity with a TGF-ß1-specific, humanized, neutralizing monoclonal antibody (TGF-ß1 mAb) is safe and more effective than placebo in slowing renal function loss in patients with diabetic nephropathy on chronic stable renin-angiotensin system inhibitor treatment. We randomized 416 patients aged ≥25 years with type 1 or type 2 diabetes, a serum creatinine (SCr) level of 1.3-3.3 mg/dl for women and 1.5-3.5 mg/dl for men (or eGFR of 20-60 ml/min per 1.73 m2), and a 24-hour urine protein-to-creatinine ratio ≥800 mg/g to TGF-ß1 mAb (2-, 10-, or 50-mg monthly subcutaneous dosing for 12 months) or placebo. We assessed a change in SCr from baseline to 12 months as the primary efficacy variable. Although the Data Monitoring Committee did not identify safety issues, we terminated the trial 4 months early for futility on the basis of their recommendation. The placebo group had a mean±SD change in SCr from baseline to end of treatment of 0.33±0.67 mg/dl. Least squares mean percentage change in SCr from baseline to end of treatment did not differ between placebo (14%; 95% confidence interval [95% CI], 9.7% to 18.2%) and TGF-ß1 mAb treatments (20% [95% CI, 15.3% to 24.3%], 19% [95% CI, 14.2% to 23.0%], and 19% [95% CI, 14.0% to 23.3%] for 2-, 10-, and 50-mg doses, respectively). Thus, TGF-ß1 mAb added to renin-angiotensin system inhibitors did not slow progression of diabetic nephropathy.

Improved clinical trial enrollment criterion to identify patients with diabetes at risk of end-stage renal disease.

Design of Phase III trials for diabetic nephropathy currently requires patients at a high risk of progression defined as within three years of a hard end point (end-stage renal disease, 40% loss of estimated glomerular filtration rate, or death). To improve the design of these trials, we used natural history data from the Joslin Kidney Studies of chronic kidney disease in patients with diabetes to develop an improved criterion to identify such patients. This included a training cohort of 279 patients with type 1 diabetes and 134 end points within three years, and a validation cohort of 221 patients with type 2 diabetes and 88 end points. Previous trials selected patients using clinical criteria for baseline urinary albumin-to-creatinine ratio and estimated glomerular filtration rate. Application of these criteria to our cohort data yielded sensitivities (detection of patients at risk) of 70-80% and prognostic values of only 52-63%. We applied classification and regression trees analysis to select from among all clinical characteristics and markers the optimal prognostic criterion that divided patients with type 1 diabetes according to risk. The optimal criterion was a serum tumor necrosis factor receptor 1 level over 4.3 ng/ml alone or 2.9-4.3 ng/ml with an albumin-to-creatinine ratio over 1900 mg/g. Remarkably, this criterion produced similar results in both type 1 and type 2 diabetic patients. Overall, sensitivity and prognostic value were high (72% and 81%, respectively). Thus, application of this criterion to enrollment in future clinical trials could reduce the sample size required to achieve adequate statistical power for detection of treatment benefits.

A prospective study of multiple protein biomarkers to predict progression in diabetic chronic kidney disease.

BACKGROUND: Diabetic nephropathy imposes a substantial cardiovascular and renal burden contributing to both morbidity and excess mortality. Progression of chronic kidney disease (CKD) in diabetes mellitus is variable, and few biomarkers are available to predict progression accurately. Identification of novel predictive biomarkers may inform clinical care and assist in the design of clinical trials. We hypothesized that urinary and plasma protein biomarkers predict CKD progression independently of the known clinical markers such as albuminuria and estimated glomerular filtration rate (eGFR) in diabetic nephropathy. METHODS: We studied 67 US veterans with CKD due to type 2 diabetes mellitus and 20 age-matched controls (no CKD, hypertension or cardiovascular disease). After clinical evaluation and the collection of blood and urine specimens for 24 biomarkers, we followed subjects prospectively for the next 2-6 years. CKD progression was defined in three ways: (i) clinically by examining eGFR versus time plots for each individual (slope progression), (ii) progression to end-stage renal disease (ESRD) and (iii) a composite outcome of ESRD or death. RESULTS: Among 17 urinary and 7 plasma biomarkers evaluated, the relationship of the biomarkers with outcome was as follows: (i) for progression identified by eGFR plots, urinary C-terminal fibroblast growth factor (FGF)-23 emerged to have the strongest primary association (adjusted odds ratio [aOR] 2.08, P = 0.008); (ii) for ESRD, plasma vascular endothelial growth factor (VEGF) had an association (aOR: 1.44, P = 0.027) and (iii) for the composite outcome of death and ESRD, plasma C-terminal FGF-23 also had a robust direct association (aOR: 3.07, P = 0.008). CONCLUSION: The relationship of biomarkers with future progression of CKD is complex and depends in part on how CKD progression is defined. Biomarkers in the FGF-23 and VEGF-A pathways predicted patient progression independently of albuminuria levels in this patient cohort. Additional studies in other cohorts will help further validate this pilot study.

Preanalytical considerations in quantifying circulating miRNAs that predict end-stage kidney disease in diabetes.

Our previous study identified 8 risk and 9 protective plasma miRNAs associated with progression to end-stage kidney disease (ESKD) in diabetes. This study aimed to elucidate preanalytical factors that influence the quantification of circulating miRNAs. Using the EdgeSeq platform, which quantifies 2,002 miRNAs in plasma, including ESKD-associated miRNAs, we compared miRNA profiles in whole plasma versus miRNA profiles in RNA extracted from the same plasma specimens. Less than half of the miRNAs were detected in standard RNA extraction from plasma. Detection of individual and concentrations of miRNAs were much lower when RNA extracted from plasma was quantified by RNA sequencing (RNA-Seq) or quantitative reverse transcription PCR (qRT-PCR) platforms compared with EdgeSeq. Plasma profiles of miRNAs determined by the EdgeSeq platform had excellent reproducibility in assessment and had no variation with age, sex, hemoglobin A1c, BMI, and cryostorage time. The risk ESKD-associated miRNAs were detected and measured accurately only in whole plasma and using the EdgeSeq platform. Protective ESKD-associated miRNAs were detected by all platforms except qRT-PCR; however, correlations among concentrations obtained with different platforms were weak or nonexistent. In conclusion, preanalytical factors have a profound effect on detection and quantification of circulating miRNAs in ESKD in diabetes. Quantification of miRNAs in whole plasma and using the EdgeSeq platform may be the preferable method to study profiles of circulating cell-free miRNAs associated with ESKD and possibly other diseases.

Postoperative optimization of cochlear implantation for single sided deafness and asymmetric hearing loss: a systematic review.

OBJECTIVE: Identify and evaluate the effectiveness of methods for improving postoperative cochlear implant (CI) hearing performance in subjects with single-sided deafness (SSD) and asymmetric hearing loss (AHL). DATA SOURCES: Embase, PubMed, Scopus. REVIEW METHODS: Systematic review and narrative synthesis. English language studies of adult CI recipients with SSD and AHL reporting a postoperative intervention and comparative audiometric data pertaining to speech in noise, speech in quiet and sound localization were included. RESULTS: 32 studies met criteria for full text review and 6 (n = 81) met final inclusion criteria. Interventions were categorized as: formal auditory training, programming techniques, or hardware optimization. Formal auditory training (n = 10) found no objective improvement in hearing outcomes. Experimental CI maps did not improve audiologic outcomes (n = 9). Programed CI signal delays to improve synchronization demonstrated improved sound localization (n = 12). Hardware optimization, including multidirectional (n = 29) and remote (n = 11) microphones, improved sound localization and speech in noise, respectively. CONCLUSION: Few studies meeting inclusion criteria and small sample sizes highlight the need for further study. Formal auditory training did not appear to improve hearing outcomes. Programming techniques, such as CI signal delay, and hardware optimization, such as multidirectional and remote microphones, show promise to improve outcomes for SSD and AHL CI users.

Biomarker Changes Associated With Both Dulaglutide and Cardiovascular Events in the REWIND Randomized Controlled Trial: A Nested Case-Control Post Hoc Analysis.

OBJECTIVE: The glucagon-like peptide-1 receptor agonist dulaglutide reduced MACE in the Researching Cardiovascular Events with a Weekly Incretin in Diabetes (REWIND) trial. This article expores the relationship of selected biomarkers to both dulaglutide and major adverse cardiovascular events (MACE). RESEARCH DESIGN AND METHODS: In this post hoc analysis, stored fasting baseline and 2-year plasma samples from 824 REWIND participants with MACE during follow-up and 845 matched non-MACE participants were analyzed for 2-year changes in 19 protein biomarkers. Two-year changes in 135 metabolites were also analyzed in 600 participants with MACE during follow-up and in 601 matched non-MACE participants. Linear and logistic regression models were used to identify proteins that were associated with both dulaglutide treatment and MACE. Similar models were used to identify metabolites that were associated with both dulaglutide treatment and MACE. RESULTS: Compared with placebo, dulaglutide was associated with a greater reduction or lesser 2-year rise from baseline in N-terminal prohormone of brain natriuretic peptide (NT-proBNP), growth differentiation factor 15 (GDF-15), high-sensitivity C-reactive protein, and a greater 2-year rise in C-peptide. Compared with placebo, dulaglutide was also associated with a greater fall from baseline in 2-hydroxybutyric acid and a greater rise in threonine (P < 0.001). Increases from baseline in two of the proteins (but neither metabolite) were associated with MACE, including NT-proBNP (OR 1.267; 95% CI 1.119, 1.435; P < 0.001) and GDF-15 (OR 1.937; 95% CI 1.424, 2.634; P < 0.001). CONCLUSIONS: Dulaglutide was associated with a reduced 2-year rise from baseline of NT-proBNP and GDF-15. Higher rises of these biomarkers were also associated with MACE.