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Sun J.,US Toxicology | Schnackenberg L.K.,US Toxicology | Hansen D.K.,Jefferson Nutrition | Beger R.D.,US Toxicology
Bioanalysis | Year: 2010

Background: Valproic acid (VPA; an anticonvulsant drug) therapy is associated with hepatotoxicity as well as renal toxicity. An LC-MS-based metabolomics approach was undertaken in order to detect urinary VPA metabolites and to discover early biomarkers of the adverse effects induced by VPA. Results: CD-1 mice were either subcutaneously injected with 600-mg VPA/kg body weight or vehicle only, and urine samples were collected at 6, 12, 24 and 48 h postinjection. A metabolomics approach combined with principal component analysis was utilized to identify VPA-related metabolites and altered endogenous metabolites in urine. Some VPA metabolites indicated potential liver toxicity caused by VPA administration. Additionally, some altered endogenous metabolites suggested that renal function might be perturbed by VPA dosing. Conclusion: LC-MS-based metabolomics is capable of rapidly profiling VPA drug metabolites and is a powerful tool for the discovery of potential early biomarkers related to perturbations in liver and kidney function. © 2010 Future Science Ltd.

Shankar P.,Georgia Southern University | Ahuja S.,Jefferson Nutrition | Sriram K.,Surgical Nutrition Section and Nutrition Support Team
Nutrition | Year: 2013

Obesity has become an epidemic, not just in the United States, but also across the globe. Obesity is a result of many factors including poor dietary habits, inadequate physical activity, hormonal issues, and sedentary lifestyle, as well as many psychological issues. Direct and indirect costs associated with obesity-related morbidity and mortality have been estimated to be in the billions of dollars. Of the many avenues for treatment, dietary interventions are the most common. Numerous diets have been popularized in the media, with most being fads having little to no scientific evidence to validate their effectiveness. Amidst this rise of weight loss diets, there has been a surge of individual products advertised as assuring quick weight loss; one such product group is non-nutritive sweeteners (NNS). Sugar, a common component of our diet, is also a major contributing factor to a number of health problems, including obesity and increased dental diseases both in adults and children. Most foods marketed towards children are sugar-laden. Obesity-related health issues, such as type 2 diabetes mellitus, cardiovascular diseases, and hypertension, once only commonly seen in older adults, are increasing in youth. Manufacturers of NNS are using this as an opportunity to promote their products, and are marketing them as safe for all ages. A systematic review of several databases and reliable websites on the internet was conducted to identify literature related to NNS. Keywords that were used individually or in combination included, but were not limited to, artificial sweeteners, non-nutritive sweeteners, non-caloric sweeteners, obesity, sugar substitutes, diabetes, and cardiometabolic indicators. The clinical and epidemiologic data available at present are insufficient to make definitive conclusions regarding the benefits of NNS in displacing caloric sweeteners as related to energy balance, maintenance or decrease in body weight, and other cardiometabolic risk factors. Although the FDA and most published (especially industry-funded) studies endorse the safety of these additives, there is a lack of conclusive evidence-based research to discourage or to encourage their use on a regular basis. While moderate use of NNS may be useful as a dietary aid for someone with diabetes or on a weight loss regimen, for optimal health it is recommended that only minimal amounts of both sugar and NNS be consumed. © 2013 Elsevier Inc.

Beaudoin M.A.,Z Technology Corporation | Thorn B.T.,Jefferson Nutrition
Current Neuropharmacology | Year: 2011

Advances in computer technology have allowed quantification of the electroencephalogram (EEG) and expansion of quantitative EEG (qEEG) analysis in neurophysiology, as well as clinical neurology, with great success. Among the variety of techniques in this field, frequency (spectral) analysis using Fast Fourier Transforms (FFT) provides a sensitive tool for time-course studies of different compounds acting on particular neurotransmitter systems. Studies presented here include Electrocorticogram (ECoG) analysis following exposure to a glutamic acid analogue - domoic acid (DOM), psychoactive indole alkaloid - ibogaine, as well as cocaine and gamma-hydroxybutyrate (GHB). The ECoG was recorded in conscious rats via a tether and swivel system. The EEG signal frequency analysis revealed an association between slow-wave EEG activity delta and theta and the type of behavioral seizures following DOM administration. Analyses of power spectra obtained in rats exposed to cocaine alone or after pretreatment with ibogaine indicated the contribution of the serotonergic system in ibogaine mediated response to cocaine (increased power in alpha1 band). Ibogaine also lowered the threshold for cocaine-induced electrographic seizures (increased power in the low-frequency bands, delta and theta). Daily intraperitoneal administration of cocaine for two weeks was associated with a reduction in slow-wave ECoG activity 24 hrs following the last injection when compared with controls. Similar decreased cortical activity in low-frequency bands observed in chronic cocaine users has been associated with reduced metabolic activity in the frontal cortex. The FFT analyses of power spectra relative to baseline indicated a significant energy increase over all except beta2 frequency bands following exposure to 400 and 800 mg/kg GHB. The EEG alterations detected in rats following exposure to GHB resemble absence seizures observed in human petit mal epilepsy. Spectral analysis of the EEG signals combined with behavioral observations may prove to be a useful approach in studying chronic exposure to drugs of abuse and treatment of drug dependence. ©2011 Bentham Science Publishers Ltd.

Lin W.-J.,Jefferson Nutrition | Chen J.J.,Jefferson Nutrition | Chen J.J.,China Medical University at Taichung
Pharmacogenomics | Year: 2011

Aim: Drug-induced toxicity that leads to termination of candidate drugs or postmarketing removal of approved drugs can potentially be explained by the existence of susceptible subpopulations. If the susceptible subpopulations are identified in advance, the drug’s benefits could be maximized by optimal treatment decisions. This article presents a statistical model and an approach for identifying pharmacogenomic biomarkers of susceptibility to drug-induced toxicity for detecting the susceptible subpopulations. Materials & methods: Biomarkers are categorized into three disjoint sets: biomarkers of both susceptibility and exposure (A); biomarkers of susceptibility only (B); and biomarkers of exposure only (C). Set B contains the most useful biomarkers to identify susceptible subpopulations prior to drug exposure; these markers demonstrate no change in response before and after drug exposure. We present a sample size analysis to illustrate the issues and challenges facing identifying biomarker set B. Results: The required sample size increases as the proportion of the susceptible subpopulation decreases. The examples demonstrated that at least 75 subjects per group are needed for a population with a 5% susceptible subpopulation and more than 1000 are often necessary. Conclusion: This study demonstrates that the biomarkers identified by common methods are a mixture of biomarkers of exposure and susceptibility (A and C), it further demonstrates that the proposed approach could be used to identify biomarkers of susceptibility (B), where a large sample size may be required for adequate power and low false-positive rate. © 2011 Future Medicine Ltd.

Lin W.-J.,Jefferson Nutrition | Hsueh H.-M.,National Chengchi University | Chen J.J.,Jefferson Nutrition | Chen J.J.,China Medical University at Taichung
BMC Bioinformatics | Year: 2010

Background: Before conducting a microarray experiment, one important issue that needs to be determined is the number of arrays required in order to have adequate power to identify differentially expressed genes. This paper discusses some crucial issues in the problem formulation, parameter specifications, and approaches that are commonly proposed for sample size estimation in microarray experiments. Common methods for sample size estimation are formulated as the minimum sample size necessary to achieve a specified sensitivity (proportion of detected truly differentially expressed genes) on average at a specified false discovery rate (FDR) level and specified expected proportion (π1) of the true differentially expression genes in the array. Unfortunately, the probability of detecting the specified sensitivity in such a formulation can be low. We formulate the sample size problem as the number of arrays needed to achieve a specified sensitivity with 95% probability at the specified significance level. A permutation method using a small pilot dataset to estimate sample size is proposed. This method accounts for correlation and effect size heterogeneity among genes.Results: A sample size estimate based on the common formulation, to achieve the desired sensitivity on average, can be calculated using a univariate method without taking the correlation among genes into consideration. This formulation of sample size problem is inadequate because the probability of detecting the specified sensitivity can be lower than 50%. On the other hand, the needed sample size calculated by the proposed permutation method will ensure detecting at least the desired sensitivity with 95% probability. The method is shown to perform well for a real example dataset using a small pilot dataset with 4-6 samples per group.Conclusions: We recommend that the sample size problem should be formulated to detect a specified proportion of differentially expressed genes with 95% probability. This formulation ensures finding the desired proportion of true positives with high probability. The proposed permutation method takes the correlation structure and effect size heterogeneity into consideration and works well using only a small pilot dataset. © 2010 Lin et al; licensee BioMed Central Ltd.

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