Variation in Alcohol Pharmacokinetics as a Risk Factor for Alcohol Dependence

Background: The significant association between alcohol dehydrogenase (ADH)-2 genotype and alcohol-dependence risk, demonstrated in both Asian and non-Asian populations, suggests a link between the metabolism of alcohol (ethanol) and individual differences in susceptibility to dependence. Methods: We tested this hypothesis by following up on subjects who took part in the Alcohol Challenge Twin Study conducted in 1979-1981 and comparing the blood and breath alcohol results in that study between subjects who subsequently did or did not meet diagnostic criteria for lifetime alcohol dependence in 1992-1993. Results: Subjects who met DSM-III-R criteria for lifetime alcohol dependence at follow-up had higher blood and breath alcohol values after alcohol challenge than never-dependent subjects. Multivariate analysis showed independent effects of susceptibility to alcohol dependence and smoking status on blood alcohol concentrations, whereas habitual alcohol intake at the time of the initial study had marginally significant effects. The risk of alcohol dependence was 2-fold higher in men and 3-fold higher in women with blood or breath alcohol concentrations in the highest quartile than in the lowest quartile. Conclusions: In view of this association and the known genetic influences on both alcohol pharmacokinetics and alcohol dependence, it is probable that part of the heritability of dependence is mediated by genes (other than the known ADH2 and ADH3 polymorphisms) affecting alcohol metabolism.

A zero-augmented gamma mixed model for longitudinal data with many zeros

In many occupational safety interventions, the objective is to reduce the injury incidence as well as the mean claims cost once injury has occurred. The claims cost data within a period typically contain a large proportion of zero observations (no claim). The distribution thus comprises a point mass at 0 mixed with a non-degenerate parametric component. Essentially, the likelihood function can be factorized into two orthogonal components. These two components relate respectively to the effect of covariates on the incidence of claims and the magnitude of claims, given that claims are made. Furthermore, the longitudinal nature of the intervention inherently imposes some correlation among the observations. This paper introduces a zero-augmented gamma random effects model for analysing longitudinal data with many zeros. Adopting the generalized linear mixed model (GLMM) approach reduces the original problem to the fitting of two independent GLMMs. The method is applied to evaluate the effectiveness of a workplace risk assessment teams program, trialled within the cleaning services of a Western Australian public hospital.

Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data

Binning and truncation of data are common in data analysis and machine learning. This paper addresses the problem of fitting mixture densities to multivariate binned and truncated data. The EM approach proposed by McLachlan and Jones (Biometrics, 44: 2, 571-578, 1988) for the univariate case is generalized to multivariate measurements. The multivariate solution requires the evaluation of multidimensional integrals over each bin at each iteration of the EM procedure. Naive implementation of the procedure can lead to computationally inefficient results. To reduce the computational cost a number of straightforward numerical techniques are proposed. Results on simulated data indicate that the proposed methods can achieve significant computational gains with no loss in the accuracy of the final parameter estimates. Furthermore, experimental results suggest that with a sufficient number of bins and data points it is possible to estimate the true underlying density almost as well as if the data were not binned. The paper concludes with a brief description of an application of this approach to diagnosis of iron deficiency anemia, in the context of binned and truncated bivariate measurements of volume and hemoglobin concentration from an individual's red blood cells.

A model of factors affecting an information system's change in state

Management are keen to maximize the life span of an information system because of the high cost, organizational disruption, and risk of failure associated with the re-development or replacement of an information system. This research investigates the effects that various factors have on an information system's life span by understanding how the factors affect an information system's stability. The research builds on a previously developed two-stage model of information system change whereby an information system is either in a stable state of evolution in which the information system's functionality is evolving, or in a state of revolution, in which the information system is being replaced because it is not providing the functionality expected by its users. A case study surveyed a number of systems within one organization. The aim was to test whether a relationship existed between the base value of the volatility index (a measure of the stability of an information system) and certain system characteristics. Data relating to some 3000 user change requests covering 40 systems over a 10-year period were obtained. The following factors were hypothesized to have significant associations with the base value of the volatility index: language level (generation of language of construction), system size, system age, and the timing of changes applied to a system. Significant associations were found in the hypothesized directions except that the timing of user changes was not associated with any change in the value of the volatility index. Copyright (C) 2002 John Wiley Sons, Ltd.

Genetic Covariation between Serum {gamma}-Glutamyltransferase Activity and Cardiovascular Risk Factors

Background: Several studies have shown that variation in serum gamma-glutamyltransferase (GGT) in the population is associated with risk of death or development of cardiovascular disease, type 2 diabetes, stroke, or hypertension. This association is only partly explained by associations between GGT and recognized risk factors. Our aim was to estimate the relative importance of genetic and environmental sources of variation in GGT as well as genetic and environmental sources of covariation between GGT and other liver enzymes and markers of cardiovascular risk in adult twin pairs. Methods: We recruited 1134 men and 2241 women through the Australian Twin Registry. Data were collected through mailed questionnaires, telephone interviews, and by analysis of blood samples. Sources of variation in GGT, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) and of covariation between GGT and cardiovascular risk factors were assessed by maximum-likelihood model-fitting. Results: Serum GGT, ALT, and AST were affected by additive genetic and nonshared environmental factors, with heritabilities estimated at 0.52, 0.48, and 0.32, respectively. One-half of the genetic variance in GGT was shared with ALT, AST, or both. There were highly significant correlations between GGT and body mass index; serum lipids, lipoproteins, glucose, and insulin; and blood pressure. These correlations were more attributable to genes that affect both GGT and known cardiovascular risk factors than to environmental factors. Conclusions: Variation in serum enzymes that reflect liver function showed significant genetic effects, and there was evidence that both genetic and environmental factors that affect these enzymes can also affect cardiovascular risk. (C) 2002 American Association for Clinical Chemistry.

Risk factors for non-haemorrhagic stroke in patients with coronary heart disease and the effect of lipid-modifying therapy with pravastatin

Objective To determine the relative importance of recognised risk factors for non-haemorrhagic stroke, including serum cholesterol and the effect of cholesterol-lowering therapy, on the occurrence of non-haemorrhagic stroke in patients enrolled in the LIPID (Long-term Intervention with Pravastatin in Ischaemic Disease) study. Design The LIPID study was a placebo-controlled, double-blind trial of the efficacy on coronary heart disease mortality of pravastatin therapy over 6 years in 9014 patients with previous acute coronary syndromes and baseline total cholesterol of 4-7 mmol/l. Following identification of patients who had suffered non-haemorrhagic stroke, a pre-specified secondary end point, multivariate Cox regression was used to determine risk in the total population. Time-to-event analysis was used to determine the effect of pravastatin therapy on the rate of non-haemorrhagic stroke. Results There were 388 non-haemorrhagic strokes in 350 patients. Factors conferring risk of future non-haemorrhagic stroke were age, atrial fibrillation, prior stroke, diabetes, hypertension, systolic blood pressure, cigarette smoking, body mass index, male sex and creatinine clearance. Baseline lipids did not predict non-haemorrhagic stroke. Treatment with pravastatin reduced non-haemorrhagic stroke by 23% (P= 0.016) when considered alone, and 21% (P= 0.024) after adjustment for other risk factors. Conclusions The study confirmed the variety of risk factors for non-haemorrhagic stroke. From the risk predictors, a simple prognostic index was created for nonhaemorrhagic stroke to identify a group of patients at high risk. Treatment with pravastatin resulted in significant additional benefit after allowance for risk factors. (C) 2002 Lippincott Williams Wilkins.

An overview of APSIM, a model designed for farming systems simulation

The Agricultural Production Systems Simulator (APSIM) is a modular modelling framework that has been developed by the Agricultural Production Systems Research Unit in Australia. APSIM was developed to simulate biophysical process in farming systems, in particular where there is interest in the economic and ecological outcomes of management practice in the face of climatic risk. The paper outlines APSIM's structure and provides details of the concepts behind the different plant, soil and management modules. These modules include a diverse range of crops, pastures and trees, soil processes including water balance, N and P transformations, soil pH, erosion and a full range of management controls. Reports of APSIM testing in a diverse range of systems and environments are summarised. An example of model performance in a long-term cropping systems trial is provided. APSIM has been used in a broad range of applications, including support for on-farm decision making, farming systems design for production or resource management objectives, assessment of the value of seasonal climate forecasting, analysis of supply chain issues in agribusiness activities, development of waste management guidelines, risk assessment for government policy making and as a guide to research and education activity. An extensive citation list for these model testing and application studies is provided. Crown Copyright (C) 2002 Published by Elsevier Science B.V. All rights reserved.

Biological determinants of extinction risk: why are smaller species less vulnerable?

It is becoming increasingly clear that species of smaller body size tend to be less vulnerable to contemporary extinction threats than larger species, but few studies have examined the mechanisms underlying this pattern. In this paper, data for the Australian terrestrial mammal fauna are used to ask whether higher reproductive output or smaller home ranges can explain the reduced extinction risk of smaller species. Extinct and endangered species do indeed have smaller litters and larger home ranges for their body size than expected under a null model. In multiple regressions, however, only litter size is a significant predictor of extinction risk once body size and phylogeny are controlled for. Larger litters contribute to fast population growth, and are probably part of the reason that smaller species are less extinction-prone. The effect of litter size varies between the mesic coastal regions and the and interior of Australia, indicating that the environment a species inhabits mediates the effect of biology on extinction risk. These results suggest that predicting extinction risk from biological traits is likely to be a complex task which must consider explicitly interactions between biology and environment.

Bridging the gap between different statistical approaches: An integrated framework for modelling

This paper proposes a template for modelling complex datasets that integrates traditional statistical modelling approaches with more recent advances in statistics and modelling through an exploratory framework. Our approach builds on the well-known and long standing traditional idea of 'good practice in statistics' by establishing a comprehensive framework for modelling that focuses on exploration, prediction, interpretation and reliability assessment, a relatively new idea that allows individual assessment of predictions. The integrated framework we present comprises two stages. The first involves the use of exploratory methods to help visually understand the data and identify a parsimonious set of explanatory variables. The second encompasses a two step modelling process, where the use of non-parametric methods such as decision trees and generalized additive models are promoted to identify important variables and their modelling relationship with the response before a final predictive model is considered. We focus on fitting the predictive model using parametric, non-parametric and Bayesian approaches. This paper is motivated by a medical problem where interest focuses on developing a risk stratification system for morbidity of 1,710 cardiac patients given a suite of demographic, clinical and preoperative variables. Although the methods we use are applied specifically to this case study, these methods can be applied across any field, irrespective of the type of response.

The role of melanocortin-1 receptor polymorphism in skin cancer risk phenotypes

We have examined melanocortin-1 receptor (MC1R) variant allele frequencies in the general population and in a collection of adolescent dizygotic and monozygotic twins to determine statistical associations of pigmentation phenotypes with increased skin cancer risk. This included hair and skin color, freckling, mole count and sun exposed skin reflectance. Nine variants were studied and designated as either strong R (OR = 63; 95% CI 32-140) or weak r (OR = 5; 95% CI 3-11) red hair alleles. Penetrance of each MC1R variant allele was consistent with an allelic model where effects were multiplicative for red hair but additive for skin reflectance. To assess the interaction of the brown eye color gene BEY2/OCA2 on the phenotypic effects of variant MC1R alleles we imputed OCA2 genotype in the twin collection. A modifying effect of OCA2 on MC1R variant alleles was seen on constitutive skin color, freckling and mole count. In order to study the individual effects of these variants on pigmentation phenotype we have established a series of human primary melanocyte strains genotyped for the MC1R receptor. These include strains which are MC1R wild-type consensus, variant heterozygotes, and homozygotes for strong R alleles Arg151Cys and Arg160Trp. Ultrastructural analysis demonstrated that only consensus strains contained stage III and IV melanosomes in their terminal dendrites whereas Arg151Cys and Arg160Trp homozygous strains contained only immature stage I and II melanosomes. Such genetic association studies combined with the functional analysis of MC1R variant alleles in melanocytic cells should provide a link in understanding the association between pigmentary phototypes and skin cancer risk.