Post-natal heart adaptation in a knock-in mouse model of calsequestrin 2-linked recessive catecholaminergic polymorphic ventricular tachycardia

Cardiac calsequestrin (CASQ2) contributes to intracellular Ca²⁺ homeostasis by virtue of its low-affinity/high-capacity Ca²⁺ binding properties, maintains sarcoplasmic reticulum (SR) architecture and regulates excitation–contraction coupling, especially or exclusively upon β-adrenergic stimulation. Catecholaminergic polymorphic ventricular tachycardia (CPVT) is an inherited arrhythmogenic disease associated with cardiac arrest in children or young adults. Recessive CPVT variants are due to mutations in the CASQ2 gene. Molecular and ultra-structural properties were studied in hearts of CASQ2^R33Q/R33Q and of CASQ2^−/− mice from post-natal day 2 to week 8. The drastic reduction of CASQ2-R33Q is an early developmental event and is accompanied by down-regulation of triadin and junctin, and morphological changes of jSR and of SR-transverse-tubule junctions. Although endoplasmic reticulum stress is activated, no signs of either apoptosis or autophagy are detected. The other model of recessive CPVT, the CASQ2^−/− mouse, does not display the same adaptive pattern. Expression of CASQ2-R33Q influences molecular and ultra-structural heart development; post-natal, adaptive changes appear capable of ensuring until adulthood a new pathophysiological equilibrium.     

A plasma global metabolic profiling approach applied to an exercise study monitoring the effects of glucose, galactose and fructose drinks during post-exercise recovery

A global metabolic profiling methodology based on gas chromatography coupled to time-of-flight mass spectrometry (GC-TOFMS) for human plasma was applied to a human exercise study focused on the effects of beverages containing glucose, galactose, or fructose taken after exercise and throughout a recovery period of 6 h and 45 min. One group of 10 well trained male cyclists performed 3 experimental sessions on separate days (randomized, single center). After performing a standardized depletion protocol on a bicycle, subjects consumed one of three different beverages: maltodextrin (MD)+glucose (2:1 ratio), MD+galactose (2:1), and MD+fructose (2:1), consumed at an average of ～1.25 g of carbohydrate (CHO) ingested per minute. Blood was taken straight after exercise and every 45 min within the recovery phase. With the resulting blood plasma, insulin, free fatty acid (FFA) profile, glucose, and GC-TOFMS global metabolic profiling measurements were performed. The resulting profiling data was able to match the results obtained from the other clinical measurements with the addition of being able to follow many different metabolites throughout the recovery period. The data quality was assessed, with all the labelled internal standards yielding values of <15% CV for all samples (n=335), apart from the labelled sucrose which gave a value of 15.19%. Differences between recovery treatments including the appearance of galactonic acid from the galactose based beverage were also highlighted.     

Regression Calibration in Semiparametric Accelerated Failure Time Models

Summary In large cohort studies, it often happens that some covariates are expensive to measure and hence only measured on a validation set. On the other hand, relatively cheap but error‐prone measurements of the covariates are available for all subjects. Regression calibration (RC) estimation method ( Prentice, 1982 , Biometrika 69 , 331–342) is a popular method for analyzing such data and has been applied to the Cox model by Wang et al. (1997, Biometrics 53 , 131–145) under normal measurement error and rare disease assumptions. In this article, we consider the RC estimation method for the semiparametric accelerated failure time model with covariates subject to measurement error. Asymptotic properties of the proposed method are investigated under a two‐phase sampling scheme for validation data that are selected via stratified random sampling, resulting in neither independent nor identically distributed observations. We show that the estimates converge to some well‐defined parameters. In particular, unbiased estimation is feasible under additive normal measurement error models for normal covariates and under Berkson error models. The proposed method performs well in finite‐sample simulation studies. We also apply the proposed method to a depression mortality study.     

Correction for Covariate Measurement Error in Nonparametric Longitudinal Regression

Summary We introduce a correction for covariate measurement error in nonparametric regression applied to longitudinal binary data arising from a study on human sleep. The data have been surveyed to investigate the association of some hormonal levels and the probability of being asleep. The hormonal effect is modeled flexibly while we account for the error‐prone measurement of its concentration in the blood and the longitudinal character of the data. We present a fully Bayesian treatment utilizing Markov chain Monte Carlo inference techniques, and also introduce block updating to improve sampling and computational performance in the binary case. Our model is partly inspired by the relevance vector machine with radial basis functions, where usually very few basis functions are automatically selected for fitting the data. In the proposed approach, we implement such data‐driven complexity regulation by adopting the idea of Bayesian model averaging. Besides the general theory and the detailed sampling scheme, we also provide a simulation study for the Gaussian and the binary cases by comparing our method to the naive analysis ignoring measurement error. The results demonstrate a clear gain when using the proposed correction method, particularly for the Gaussian case with medium and large measurement error variances, even if the covariate model is misspecified.     

Influence of castration-induced testosterone and estradiol deficiency on obesity and glucose metabolism in male Göttingen minipigs

Low testosterone and estradiol concentrations are predictive for the development of the metabolic syndrome in men and women, respectively. The aim of this study was to investigate the influence of sex hormone deficiency on food intake, body weight, body composition and glucose metabolism in male Göttingen minipigs.Five adult male Göttingen minipigs were studied before castration (pre-cast), 10-18 days (post-cast 1) and 10-11 weeks (post-cast 2) after castration. Parameters of interest were food intake, body weight, body fat percentage and sex hormone concentrations. Furthermore glucose tolerance, glucagon suppression, insulin resistance, beta cell function and disposition index were evaluated by oral and intravenous glucose tolerance tests.Castration led to almost complete disappearance of circulating testosterone and estradiol and secondarily to increased food intake, body weight and body fat percentage. Ten-eighteen days sex hormone deficiency (post-cast 1) did not significantly change any of the investigated metabolic parameters compared to pre-cast levels. Ten weeks after castration (post-cast 2) significant insulin resistance, glucose intolerance and hyperglucagonemia was found, and the beta cell function and the disposition index both were decreased.In conclusion, castration-induced sex hormone deficiency in male Göttingen minipigs results in hyperphagia, obesity and disturbed glucose metabolism, which are some of the features typical for the human metabolic syndrome.     

Competing Risks and Time‐Dependent Covariates

Time‐dependent covariates are frequently encountered in regression analysis for event history data and competing risks. They are often essential predictors, which cannot be substituted by time‐fixed covariates. This study briefly recalls the different types of time‐dependent covariates, as classified by Kalbfleisch and Prentice [The Statistical Analysis of Failure Time Data, Wiley, New York, 2002] with the intent of clarifying their role and emphasizing the limitations in standard survival models and in the competing risks setting. If random (internal) time‐dependent covariates are to be included in the modeling process, then it is still possible to estimate cause‐specific hazards but prediction of the cumulative incidences and survival probabilities based on these is no longer feasible. This article aims at providing some possible strategies for dealing with these prediction problems. In a multi‐state framework, a first approach uses internal covariates to define additional (intermediate) transient states in the competing risks model. Another approach is to apply the landmark analysis as described by van Houwelingen [Scandinavian Journal of Statistics 2007, 34 , 70–85] in order to study cumulative incidences at different subintervals of the entire study period. The final strategy is to extend the competing risks model by considering all the possible combinations between internal covariate levels and cause‐specific events as final states. In all of those proposals, it is possible to estimate the changes/differences of the cumulative risks associated with simple internal covariates. An illustrative example based on bone marrow transplant data is presented in order to compare the different methods.