Impact of apolipoprotein A1- or lecithin:cholesterol acyltransferase-deficiency on white adipose tissue metabolic activity and glucose homeostasis in mice

High density lipoprotein (HDL) has attracted the attention of biomedical community due to its well-documented role in atheroprotection. HDL has also been recently implicated in the regulation of islets of Langerhans secretory function and in the etiology of peripheral insulin sensitivity. Indeed, data from numerous studies strongly indicate that the functions of pancreatic β-cells, skeletal muscles and adipose tissue could benefit from improved HDL functionality. To better understand how changes in HDL structure may affect diet-induced obesity and type 2 diabetes we aimed at investigating the impact of Apoa1 or Lcat deficiency, two key proteins of peripheral HDL metabolic pathway, on these pathological conditions in mouse models. We report that universal deletion of apoa1 or lcat expression in mice fed western-type diet results in increased sensitivity to body-weight gain compared to control C57BL/6 group. These changes in mouse genome correlate with discrete effects on white adipose tissue (WAT) metabolic activation and plasma glucose homeostasis. Apoa1-deficiency results in reduced WAT mitochondrial non-shivering thermogenesis. Lcat-deficiency causes a concerted reduction in both WAT oxidative phosphorylation and non-shivering thermogenesis, rendering lcat^?/? mice the most sensitive to weight gain out of the three strains tested, followed by apoa1^?/? mice. Nevertheless, only apoa1^?/? mice show disturbed plasma glucose homeostasis due to dysfunctional glucose-stimulated insulin secretion in pancreatic β-islets and insulin resistant skeletal muscles. Our analyses show that both apoa1^?/? and lcat^?/? mice fed high-fat diet have no measurable Apoa1 levels in their plasma, suggesting no direct involvement of Apoa1 in the observed phenotypic differences among groups.     

The Circadian Clock Gene Circuit Controls Protein and Phosphoprotein Rhythms in Arabidopsis thaliana

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Errors-in-variables in joint population pharmacokinetic/pharmacodynamic modeling

Pharmacokinetic (PK) models describe the relationship between the administered dose and the concentration of drug (and/or metabolite) in the blood as a function of time. Pharmacodynamic (PD) models describe the relationship between the concentration in the blood (or the dose) and the biologic response. Population PK/PD studies aim to determine the sources of variability in the observed concentrations/responses across groups of individuals. In this article, we consider the joint modeling of PK/PD data. The natural approach is to specify a joint model in which the concentration and response data are simultaneously modeled. Unfortunately, this approach may not be optimal if, due to sparsity of concentration data, an overly simple PK model is specified. As an alternative, we propose an errors-in-variables approach in which the observed-concentration data are assumed to be measured with error without reference to a specific PK model. We give an example of an analysis of PK/PD data obtained following administration of an anticoagulant drug. The study was originally carried out in order to make dosage recommendations. The prior for the distribution of the true concentrations, which may incorporate an individual's covariate information, is derived as a predictive distribution from an earlier study. The errors-in-variables approach is compared with the joint modeling approach and more naive methods in which the observed concentrations, or the separately modeled concentrations, are substituted into the response model. Throughout, a Bayesian approach is taken with implementation via Markov chain Monte Carlo methods.     

Semiparametric methods for multiple exposure mismeasurement and a bivariate outcome in HIV vaccine trials

Exposure to infection information is important for estimating vaccine efficacy, but it is difficult to collect and prone to missingness and mismeasurement. We discuss study designs that collect detailed exposure information from only a small subset of participants while collecting crude exposure information from all participants and treat estimation of vaccine efficacy in the missing data/measurement error framework. We extend the discordant partner design for HIV vaccine trials of Golm, Halloran, and Longini (1998, Statistics in Medicine, 17, 2335-2352.) to the more complex augmented trial design of Longini, Datta, and Halloran (1996, Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology 13, 440-447) and Datta, Halloran, and Longini (1998, Statistics in Medicine 17, 185-200). The model for this design includes three exposure covariates and both univariate and bivariate outcomes. We adapt recently developed semiparametric missing data methods of Reilly and Pepe (1995, Biometrika 82, 299 314), Carroll and Wand (1991, Journal of the Royal Statistical Society, Series B 53, 573-585), and Pepe and Fleming (1991, Journal of the American Statistical Association 86, 108-113) to the augmented vaccine trial design. We demonstrate with simulated HIV vaccine trial data the improvements in bias and efficiency when combining the different levels of exposure information to estimate vaccine efficacy for reducing both susceptibility and infectiousness. We show that the semiparametric methods estimate both efficacy parameters without bias when the good exposure information is either missing completely at random or missing at random. The pseudolikelihood method of Carroll and Wand (1991) and Pepe and Fleming (1991) was the more efficient of the two semiparametric methods.     

Modification of sample size in group sequential clinical trials

In group sequential clinical trials, sample size reestimation can be a complicated issue when it allows for change of sample size to be influenced by an observed sample path. Our simulation studies show that increasing sample size based on an interim estimate of the treatment difference can substantially inflate the probability of type I error in most practical situations. A new group sequential test procedure is developed by modifying the weights used in the traditional repeated significance two-sample mean test. The new test has the type I error probability preserved at the target level and can provide a substantial gain in power with the increase of sample size. Generalization of the new procedure is discussed.     

Approximate standard errors in semiparametric models

We consider semiparametric models with p regressor terms and q smooth terms. We obtain an explicit expression for the estimate of the regression coefficients given by the back-fitting algorithm. The calculation of the standard errors of these estimates based on this expression is a considerable computational exercise. We present an alternative, approximate method of calculation that is less demanding. With smoothing splines, the method is exact, while with loess, it gives good estimates of standard errors. We assess the adequacy of our approximation and of another approximation with the help of two examples.     

Codon Usage Decreases the Error Minimization Within the Genetic Code

The genetic code is not random but instead is organized in such a way that single nucleotide substitutions are more likely to result in changes between similar amino acids. This fidelity, or error minimization, has been proposed to be an adaptation within the genetic code. Many models have been proposed to measure this adaptation within the genetic code. However, we find that none of these consider codon usage differences between species. Furthermore, use of different indices of amino acid physicochemical characteristics leads to different estimations of this adaptation within the code. In this study, we try to establish a more accurate model to address this problem. In our model, a weighting scheme is established for mistranslation biases of the three different codon positions, transition/transversion biases, and codon usage. Different indices of amino acids physicochemical characteristics are also considered. In contrast to pervious work, our results show that the natural genetic code is not fully optimized for error minimization. The genetic code, therefore, is not the most optimized one for error minimization, but one that balances between flexibility and fidelity for different species.     

How much variance is explained by ecologists? Additional perspectives

Oecologia - A recent meta-analysis of meta-analyses by Møller and Jennions (2002, Oecologia 132:492–500) suggested that ecologists using statistical models are explaining between 2.5%...     

Shrinkage estimation for functional principal component scores with application to the population kinetics of plasma folate

We present the application of a nonparametric method to performing functional principal component analysis for functional curve data that consist of measurements of a random trajectory for a sample of subjects. This design typically consists of an irregular grid of time points on which repeated measurements are taken for a number of subjects. We introduce shrinkage estimates for the functional principal component scores that serve as the random effects in the model. Scatterplot smoothing methods are used to estimate the mean function and covariance surface of this model. We propose improved estimation in the neighborhood of and at the diagonal of the covariance surface, where the measurement errors are reflected. The presence of additive measurement errors motivates shrinkage estimates for the functional principal component scores. Shrinkage estimates are developed through best linear prediction and in a generalized version, aiming at minimizing one-curve-leave-out prediction error. The estimation of individual trajectories combines data obtained from that individual as well as all other individuals. We apply our methods to new data regarding the analysis of the level of 14C-folate in plasma as a function of time since dosing of healthy adults with a small tracer dose of 14C-folic acid. A time transformation was incorporated to handle design irregularity concerning the time points on which the measurements were taken. The proposed methodology, incorporating shrinkage and data-adaptive features, is seen to be well suited for describing population kinetics of 14C-folate-specific activity and random effects, and can also be applied to other functional data analysis problems.     

Bayesian latent variable models for median regression on multiple outcomes

Often a response of interest cannot be measured directly and it is necessary to rely on multiple surrogates, which can be assumed to be conditionally independent given the latent response and observed covariates. Latent response models typically assume that residual densities are Gaussian. This article proposes a Bayesian median regression modeling approach, which avoids parametric assumptions about residual densities by relying on an approximation based on quantiles. To accommodate within-subject dependency, the quantile response categories of the surrogate outcomes are related to underlying normal variables, which depend on a latent normal response. This underlying Gaussian covariance structure simplifies interpretation and model fitting, without restricting the marginal densities of the surrogate outcomes. A Markov chain Monte Carlo algorithm is proposed for posterior computation, and the methods are applied to single-cell electrophoresis (comet assay) data from a genetic toxicology study.