The Effects of Vitamin E, Silymarin and Carnitine on the Metabolic Abnormalities Associated with Nonalcoholic Liver Disease.

The obesity epidemic has resulted in an increase in the incidence of metabolic syndrome, and liver disease. Studies indicate that antioxidant supplementation may improve abnormal liver chemistries, glucose control, and hyperlipidemia, in patients with nonalcoholic fatty liver disease (NAFLD). The primary objective of the study was to determine the normalization of abnormalities in hepatic function testing in patients with NAFLD when treated with vitamin E 200 IU, Silymarin 750 mg, and l-carnitine 1 gram (VSC) for 18 weeks in comparison to a placebo-controlled group. Secondary objectives were to evaluate changes in blood glucose level, insulin, total cholesterol, triglycerides, high-density lipoproteins (HDL), low-density lipoproteins (LDL), C-reactive protein (CRP), hemoglobin A1C (HgA1c), and homeostatic models assessment (HOMA) in patients treated with VSC vs placebo. Findings showed that VSC caused a significant reduction in serum glucose, insulin, and HOMA levels. While there were downtrends in the other measured values these were not statistically significant. In this 18-week study, the ability of this supplement in reducing markers of liver inflammation, glucose, insulin, and triglycerides indicate that this supplement could play an important role in the treatment of nonalcoholic fatty liver disease, diabetes, and the metabolic syndrome.

Multistage designs in the genomic era: providing balance in complex disease studies.

In this summary paper, we describe the contributions included in the Multistage Design group (Group 14) at the Genetic Analysis Workshop 15, which was held during November 12-14, 2006. Our group contrasted and compared different approaches to reducing complexity in a genetic study through implementation of staged designs. Most groups used the simulated dataset (problem 3), which provided ample opportunities for evaluating various staged designs. A wide range of multistage designs that targeted different aspects of complexity were explored. We categorized these approaches as reducing phenotypic complexity, model complexity, analytic complexity or genetic complexity. In general we learned that: (1) when staged designs are carefully planned and implemented, the power loss compared to a single-stage analysis can be minimized and study cost is greatly reduced; (2) a joint analysis of the results from each stage is generally more powerful than treating the second stage as a replication analysis.

Logistic regression trees for initial selection of interesting loci in case-control studies.

Modern genetic epidemiology faces the challenge of dealing with hundreds of thousands of genetic markers. The selection of a small initial subset of interesting markers for further investigation can greatly facilitate genetic studies. In this contribution we suggest the use of a logistic regression tree algorithm known as logistic tree with unbiased selection. Using the simulated data provided for Genetic Analysis Workshop 15, we show how this algorithm, with incorporation of multifactor dimensionality reduction method, can reduce an initial large pool of markers to a small set that includes the interesting markers with high probability.