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Sebastopol, CA, United States

Briggs F.B.S.,University of California at Berkeley | Acuna B.,Kaiser Permanente | Shen L.,Kaiser Permanente | Ramsay P.,University of California at Berkeley | And 10 more authors.
Epidemiology | Year: 2014

BACKGROUND: Tobacco smoke is an established risk factor for multiple sclerosis (MS). We hypothesized that variation in genes involved in metabolism of tobacco smoke constituents may modify MS risk in smokers. METHODS: A three-stage gene-environment investigation was conducted for NAT1, NAT2, and GSTP1 variants. The discovery analysis was conducted among 1588 white MS cases and controls from the Kaiser Permanente Northern California Region HealthPlan (Kaiser). The replication analysis was carried out in 988 white MS cases and controls from Sweden. RESULTS: Tobacco smoke exposure at the age of 20 years was associated with greater MS risk in both data sets (in Kaiser, odds ratio [OR] = 1.51 [95% confidence interval (CI) = 1.17-1.93]; in Sweden, OR = 1.35 [1.04-1.74]). A total of 42 NAT1 variants showed evidence for interaction with tobacco smoke exposure (Pcorrected < 0.05). Genotypes for 41 NAT1 single nucleotide polymorphisms (SNPs) were studied in the replication data set. A variant (rs7388368C>A) within a dense transcription factor-binding region showed evidence for interaction (Kaiser, OR for interaction = 1.75 [95% CI = 1.19-2.56]; Sweden, OR = 1.62 [1.05-2.49]). Tobacco smoke exposure was associated with MS risk among rs7388368A carriers only; homozygote individuals had the highest risk (A/A, OR = 5.17 [95% CI = 2.17-12.33]). CONCLUSIONS: We conducted a three-stage analysis using two population-based case-control datasets that consisted of a discovery population, a replication population, and a pooled analysis. NAT1 emerged as a genetic effect modifier of tobacco smoke exposure in MS susceptibility. Copyright © 2014 by Lippincott Williams & Wilkins.

Winkler C.,Fairfield University | Funk M.,Yale University | Schindler D.M.,Palm Drive Hospital | Hemsey J.Z.,University of California at San Francisco | And 2 more authors.
Heart and Lung: Journal of Acute and Critical Care | Year: 2013

Objectives: In patients with acute coronary syndrome (ACS), we sought to: 1) describe arrhythmias during hospitalization, 2) explore the association between arrhythmias and patient outcomes, and 3) explore predictors of the occurrence of arrhythmias. Methods: In a prospective sub-study of the IMMEDIATE AIM study, we analyzed electrocardiographic (ECG) data from 278 patients with ACS. On emergency department admission, a Holter recorder was attached for continuous 12-lead ECG monitoring. Results: Approximately 22% of patients had more than 50 premature ventricular contractions (PVCs) per hour. Non-sustained ventricular tachycardia (VT) occurred in 15% of patients. Very few patients (≤1%) had a malignant arrhythmia (sustained VT, asystole, torsade de pointes, or ventricular fibrillation). Only more than 50PVCs/hour independently predicted an increased length of stay ( p<.0001). No arrhythmias predicted mortality. Age greater than 65 years and a final diagnosis of acute myocardial infarction independently predicted more than 50PVCs per hour ( p=.0004). Conclusions: Patients with ACS seem to have fewer serious arrhythmias today, which may have implications for the appropriate use of continuous ECG monitoring. © 2013 Elsevier Inc.

Ghorbanian P.,Villanova University | Devilbiss D.M.,NexStep Biomarkers LLC | Hess T.,Palm Drive Hospital | Bernstein A.,Palm Drive Hospital | And 2 more authors.
Medical and Biological Engineering and Computing | Year: 2015

We have developed a novel approach to elucidate several discriminating EEG features of Alzheimer’s disease. The approach is based on the use of a variety of continuous wavelet transforms, pairwise statistical tests with multiple comparison correction, and several decision tree algorithms, in order to choose the most prominent EEG features from a single sensor. A pilot study was conducted to record EEG signals from Alzheimer’s disease (AD) patients and healthy age-matched control (CTL) subjects using a single dry electrode device during several eyes-closed (EC) and eyes-open (EO) resting conditions. We computed the power spectrum distribution properties and wavelet and sample entropy of the wavelet coefficients time series at scale ranges approximately corresponding to the major brain frequency bands. A predictive index was developed using the results from statistical tests and decision tree algorithms to identify the most reliable significant features of the AD patients when compared to healthy controls. The three most dominant features were identified as larger absolute mean power and larger standard deviation of the wavelet scales corresponding to 4–8 Hz ($$\theta$$θ) during EO and lower wavelet entropy of the wavelet scales corresponding to 8–12 Hz ($$\alpha$$α) during EC, respectively. The fourth reliable set of distinguishing features of AD patients was lower relative power of the wavelet scales corresponding to 12–30 Hz ($$\beta$$β) followed by lower skewness of the wavelet scales corresponding to 2–4 Hz (upper $$\delta$$δ), both during EO. In general, the results indicate slowing and lower complexity of EEG signal in AD patients using a very easy-to-use and convenient single dry electrode device. © 2015, International Federation for Medical and Biological Engineering.

Ghorbanian P.,Villanova University | Devilbiss D.M.,NexStep Biomarkers | Simon A.J.,Portable On demand Diagnostics Inc. | Bernstein A.,Palm Drive Hospital | And 2 more authors.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2012