Kidney Injury Molecule-1 and Periostin Urinary Excretion and Tissue Expression Levels and Association with Glomerular Disease Outcomes.

INTRODUCTIONS: Kidney injury molecule-1 (KIM-1) and periostin (POSTN) are proximal and distal tubule injury biomarkers. We tested whether baseline urine KIM-1/creatinine (uKIM-1/cr) and/or uPOSTN/cr correlated with disease severity or improved a remission prediction model. METHODS: Baseline uKIM1/cr and uPOSTN/cr were measured on spot urine samples from immunosuppression-free patients enrolled in Nephrotic Syndrome Study Network until December 15, 2014. Urine protein/creatinine (UPCR) and albumin/creatinine (UACR) were measured at baseline, 4 months, and until last follow-up. Glomerular and tubulointerstitial (TI) expression arrays were analyzed from a baseline research renal biopsy core collected during a clinically indicated biopsy.Renal diagnoses were centrally confirmed, sections scanned, and measured morphometrically. Correlations between baseline uKIM-1/cr and uPOSTN/cr and UPCR, UACR, histopathologic features, glomerular and TI KIM-1 and POSTN expression levels, and renal outcomes were assessed. RESULTS: Baseline uKIM-1/cr correlated with UPCR and UACR, and were associated with complete remission after adjustment for proteinuria, histopathologic diagnosis, and treatment. Baseline uKIM-1/cr also correlated with degree of foot process effacement and acute tubular injury. Glomerular and TI KIM-1 expression levels correlated with UPCR and UACR. Higher TI KIM-1 expression levels correlated with interstitial fibrosis, tubular atrophy, and global glomerulosclerosis, while glomerular KIM-1 expression correlated with time to remission. Findings for POSTN were of lesser statistical strength. DISCUSSION/CONCLUSION: Lower baseline uKIM-1/cr values were associated with more rapid time to complete remission after adjusting for proteinuria, histopathologic diagnosis, and treatment. Increased TI KIM-1 expression levels in proteinuric states were associated with chronic morphological injury; lower glomerular expression levels were associated with a greater potential for proteinuria reversibility.

Sample size determination for Bayesian analysis of small n sequential, multiple assignment, randomized trials (snSMARTs) with three agents.

The small n, Sequential, Multiple Assignment, Randomized Trial (snSMART) is a two-stage clinical trial design for rare diseases motivated by the comparison of three active treatments for isolated skin vasculitis in the ongoing clinical trial ARAMIS (a randomized multicenter study for isolated skin vasculitis, NCT09239573). In Stage 1, all patients are randomized to one of three treatments. In Stage 2, patients who respond to their initial treatment receive the same treatment again, while those who fail to respond are re-randomized to one of the two remaining treatments. A Bayesian method for estimating the response rate of each individual treatment in a three-arm snSMART demonstrated efficiency gains for a given sample size relative to other existing frequentist approaches. However, these efficiency gains are dependent upon knowing how many subjects are required to determine a specific difference in the treatment response rates. Because few sample size calculation methods for snSMARTs exist, we propose a Bayesian sample size calculation for an snSMART designed to distinguish the best treatment from the second-best treatment. Although our methods are based on asymptotic approximations, we demonstrate via simulations that our proposed sample size calculation approach produces the desired statistical power, even in small samples. Moreover, our methods and applet produce sample sizes quickly, thereby saving time relative to using simulations to determine the appropriate sample size. We compare our proposed sample size to an existing frequentist method based upon a weighted Z-statistic and demonstrate that the Bayesian method requires far fewer patients than the frequentist method for a study with the same design parameters.

Long-term outcomes following completion of a structured nutrition and exercise lifestyle intervention program for patients with metabolic syndrome.

INTRODUCTION: Metabolic syndrome is associated with an increased risk of cardiovascular disease and multiple other chronic health conditions. Studies have demonstrated the effectiveness of structured diet and exercise programs to improve the components of metabolic syndrome. The durability of these benefits after program completion is unclear. The aim of this study was to evaluate trends in cardiovascular risk factors 12 months post completion of a 12- or 24-week structured lifestyle intervention program. METHODS: Individuals with metabolic syndrome were referred to the Metabolic Fitness program, a 12- or 24-week lifestyle intervention program consisting of weekly exercise and nutrition education sessions. Patients were assessed at baseline, 12 weeks, and 24 weeks for those in the 24-week program. Data collection included weight, body mass index, waist circumference, body composition percentage, sBP, dBP, fasting blood glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. Unstructured follow-up data were obtained by retrospective chart review for up to 12 months post program completion. RESULTS: Two-hundred twenty-five patients were enrolled in the 12-week program and 121 in the 24-week program. At the conclusion of the 12-week program, patients showed significant improvement in sBP and dBP. At the conclusion of the 24-week program, patients showed significant improvement in body mass index, weight, sBP, dBP, fasting blood glucose, total cholesterol, and triglycerides. However, 12 months after program completion, while the majority of parameters were still improved compared with baseline, only change in low-density lipoprotein cholesterol remained significantly improved compared with the end of 12-week program, and sBP had increased back above baseline in both programs. CONCLUSION: Patients with metabolic syndrome participating in a structured lifestyle intervention program show significant improvement in their cardiovascular risk and metabolic profile at program completion. The durability of these improvements appears to wane over time, however, stressing the need for programs that can facilitate maintenance of long-term behavior change.

A Bayesian analysis of small n sequential multiple assignment randomized trials (snSMARTs).

Designing clinical trials to study treatments for rare diseases is challenging because of the limited number of available patients. A suggested design is known as the small n sequential multiple assignment randomized trial (snSMART), in which patients are first randomized to one of multiple treatments (stage 1). Patients who respond to their initial treatment continue the same treatment for another stage, while those who fail to respond are rerandomized to one of the remaining treatments (stage 2). The data from both stages are used to compare the efficacy between treatments. Analysis approaches for snSMARTs are limited, and we propose a Bayesian approach that allows for borrowing of information across both stages. Through simulation, we compare the bias, root-mean-square error, width, and coverage rate of 95% confidence/credible interval of estimators from of our approach to estimators produced from (i) standard approaches that only use the data from stage 1, and (ii) a log-Poisson model using data from both stages whose parameters are estimated via generalized estimating equations. We demonstrate the root-mean-square error and width of 95% confidence/credible intervals of our estimators are smaller than the other approaches in realistic settings, so that the collection and use of stage 2 data in snSMARTs provide improved inference for treatments of rare diseases.