Urinary Ethyl Glucuronide Can Be Used as a Biomarker of Habitual Alcohol Consumption in the General Population

BACKGROUND: Alcohol consumption is a frequently studied risk factor for chronic diseases, but many studies are hampered by self-report of alcohol consumption. The urinary metabolite ethyl glucuronide (EtG), reflecting alcohol consumption during the past 72 h, is a promising objective marker, but population data are lacking. OBJECTIVE: The objective of this study was to assess the reliability of EtG as a marker for habitual alcohol consumption compared with self-report and other biomarkers in the general population. METHODS: Among 6211 participants in the Prevention of Renal and Vascular End-Stage Disease (PREVEND) cohort, EtG concentrations were measured in 24-h urine samples. EtG was considered positive when concentrations were ≥100 ng/mL. Habitual alcohol consumption was self-reported by questionnaire (categories: no/almost never, 1-4 units per month, 2-7 units per week, 1-3 units per day or ≥4 units per day). Plasma HDL cholesterol concentration, erythrocyte mean corpuscular volume (MCV), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and γ-glutamyltransferase (GGT) were determined as indirect biomarkers of alcohol consumption. Sensitivity, specificity, positive and negative predictive value, and proportions of agreement between reported consumption and EtG were calculated. To test the agreement of EtG concentration and alcohol consumption in categories, linear regression analysis was performed. In addition, the association between EtG concentrations and indirect biomarkers was analyzed. RESULTS: Mean age was 53.7 y, and 52.9% of participants men. Of the self-reported abstainers, 92.3% had an EtG concentration <100 ng/mL. Sensitivity was 66.3%, positive predictive value was 96.3%, and negative predictive value was 47.4%. The proportion of positive agreement was 78.5%, and the proportion of negative agreement was 62.7%. EtG concentrations were linearly associated with higher categories of alcohol consumption (P-trend < 0.001), adjusted for age, sex, and renal function. EtG was positively related to MCV, HDL cholesterol, and GGT but not to AST and ALT concentrations. CONCLUSIONS: This study shows that urinary EtG is in reasonable agreement with self-reported alcohol consumption and therefore can be used as an objective marker of habitual alcohol consumption in the general population

Automatic Black-Box Model Order Reduction using Radial Basis Functions

Finite elements methods have long made use of model order reduction (MOR), particularly in the context of fast freqeucny sweeps. In this paper, we discuss a black-box MOR technique, applicable to a many solution methods and not restricted only to spectral responses. We also discuss automated methods for generating a reduced order model that meets a given error tolerance. Numerical examples demonstrate the effectiveness and wide applicability of the method. With the advent of improved computing hardware and numerous fast solution techniques, the field of computational electromagnetics are progressed rapidly in terms of the size and complexity of problems that can be solved. Numerous applications, however, require the solution of a problem for many different configurations, including optimization, parameter exploration, and uncertainly quantification, where the parameters that may be changed include frequency, material properties, geometric dimensions, etc. In such cases, thousands of solutions may be needed, so solve times of even a few minutes can be burdensome. Model order reduction (MOR) may alleviate this difficulty by creating a small model that can be evaluated quickly. Many MOR techniques have been applied to electromagnetic problems over the past few decades, particularly in the context of fast frequency sweeps. Recent works have extended these methods to allow more than one parameter and to allow the parameters to represent material and geometric properties. There are still limitations with these methods, however. First, they almost always assume that the finite element method is used to solve the problem, so that the system matrix is a known function of the parameters. Second, although some authors have presented adaptive methods (e.g., [2]), the order of the model is often determined before the MOR process begins, with little insight about what order is actually needed to reach the desired accuracy. Finally, it not clear how to efficiently extend most methods to the multiparameter case. This paper address the above shortcomings be developing a method that uses a block-box approach to the solution method, is adaptive, and is easily extensible to many parameters