Background Numerous studies report that bariatric surgery patients report more physical activity (PA) after surgery than before, but the quality of PA assessment has been questionable. Methods The Longitudinal Assessment of Bariatric Surgery-2 is a 10-center longitudinal study of adults undergoing bariatric surgery. Of 2458 participants, 455 were given an activity monitor, which records steps/minute, and an exercise diary before and 1 year after surgery. Mean step/day, active minutes/day, and high-cadence minutes/week were calculated for 310 participants who wore the monitor at least 10 hours/day for at least 3 days at both time points. Pre- and post-surgery PA were compared for differences using the Wilcoxon signed-rank test. Generalized Estimating Equations identified independent pre-operative predictors of post-operative PA. Results PA increased significantly (p<.0001) pre- to post-operative for all PA measures. Median values pre- and post-operative were: 7563 and 8788 steps/day; 309 and 340 active minutes/day; and 72 and 112 high-cadence minutes/week, respectively. However, depending on the PA measure, 24–29% of participants were at least 5% less active post-operative than pre-operative. Controlling for surgical procedure, sex, age and BMI, higher PA preoperative independently predicted higher PA post-operative (p<.0001, all PA measures). Less pain, not having asthma and self-report of increasing PA as a weight loss strategy pre-operative also independently predicted more high-cadence minutes/week post-operative (p<.05). Conclusion The majority of adults increase their PA level following bariatric surgery. However, most remain insufficiently active and some become less active. Increasing PA, addressing pain and treating asthma prior to surgery may have a positive impact on post-operative PA.

No abstract

C ardiovascular disease underlies the majority of deaths in patients with dialysis-dependent end-stage renal disease (ESRD). 1 Standard cardiovascular therapies have rarely been tested in this population and with disappointing results. For example, 3 trials of HMG-CoA reductase inhibitors found that neither overall nor cardiovascular mortality was reduced in hemodialysis (HD) patients treated with statins compared with placebo. 2-4 These and other findings highlight the need to evaluate therapies to reduce cardiovascular morbidity and mortality specifically in patients receiving maintenance dialysis. 5 Several lines of evidence suggest that in ESRD, the heart undergoes progressive fibrosis and rarefaction of the microvasculature, 6-8 structural changes that predispose to arrhythmias and contribute to heart failure by reducing

Poor metabolic control and host genetic predisposition are critical for diabetic kidney disease (DKD) development. The epigenome integrates information from sequence variations and metabolic alterations. Here, we performed a genome-wide methylome association analysis in 500 subjects with DKD from the Chronic Renal Insufficiency Cohort for DKD phenotypes, including glycemic control, albuminuria, kidney function, and kidney function decline. We show distinct methylation patterns associated with each phenotype. We define methylation variations that are associated with underlying nucleotide variations (methylation quantitative trait loci) and show that underlying genetic variations are important drivers of methylation changes. We implemented Bayesian multitrait colocalization analysis (moloc) and summary data-based Mendelian randomization to systematically annotate genomic regions that show association with kidney function, methylation, and gene expression. We prioritized 40 loci, where methylation and gene-expression changes likely mediate the genotype effect on kidney disease development. Functional annotation suggested the role of inflammation, specifically, apoptotic cell clearance and complement activation in kidney disease development. Our study defines methylation changes associated with DKD phenotypes, the key role of underlying genetic variations driving methylation variations, and prioritizes methylome and gene-expression changes that likely mediate the genotype effect on kidney disease pathogenesis.

Repeated measures of various biomarkers provide opportunities for us to enhance understanding of many important clinical aspects of CKD, including patterns of disease progression, rates of kidney function decline under different risk factors, and the degree of heterogeneity in disease manifestations across patients. However, because of unique features, such as correlations across visits and time dependency, these data must be appropriately handled using longitudinal data analysis methods. We provide a general overview of the characteristics of data collected in cohort studies and compare appropriate statistical methods for the analysis of longitudinal exposures and outcomes. We use examples from the Chronic Renal Insufficiency Cohort Study to illustrate these methods. More specifically, we model longitudinal kidney outcomes over annual clinical visits and assess the association with both baseline and longitudinal risk factors.

An effect modifier is a pretreatment covariate that affects the magnitude of the treatment effect or its stability. When there is effect modification, an overall test that ignores an effect modifier may be more sensitive to unmeasured bias than a test that combines results from subgroups defined by the effect modifier. If there is effect modification, one would like to identify specific subgroups for which there is evidence of effect that is insensitive to small or moderate biases. In this paper, we propose an exploratory method for discovering effect modification, and combine it with a confirmatory method of simultaneous inference that strongly controls the familywise error rate in a sensitivity analysis, despite the fact that the groups being compared are defined empirically. A new form of matching, strength-k matching, permits a search through more than k covariates for effect modifiers, in such a way that no pairs are lost, provided that at most k covariates are selected to group the pairs. In a strength-k match, each set of k covariates is exactly balanced, although a set of more than k covariates may exhibit imbalance. We apply the proposed method to study the effects of the earthquake that struck Chile in 2010.

In medical sciences, statistical analyses based on observational studies are common phenomena. One peril of drawing inferences about the effect of a treatment on subjects using observational studies is the lack of randomized assignment of subjects to the treatment. After adjusting for measured pretreatment covariates, perhaps by matching, a sensitivity analysis examines the impact of an unobserved covariate, u, in an observational study. One type of sensitivity analysis uses two sensitivity parameters to measure the degree of departure of an observational study from randomized assignment. One sensitivity parameter relates u to treatment and the other relates u to response. For subject matter experts, it may be difficult to specify plausible ranges of values for the sensitivity parameters on their absolute scales. We propose an approach that calibrates the values of the sensitivity parameters to the observed covariates and is more interpretable to subject matter experts. We will illustrate our method using data from the U.S. National Health and Nutrition Examination Survey regarding the relationship between cigarette smoking and blood lead levels.

No abstract