Higher urine 1-hydroxy pyrene glucuronide (1-OHPG) is associated with tobacco smoke exposure and drinking maté in healthy subjects from Rio Grande do Sul, Brazil

BACKGROUND: The highest rates of esophageal squamous cell carcinoma (ESCC) in Brazil occur in Rio Grande do Sul, the most southern state, which has incidence rates of 20.4/100,000/year for men and 6.5/100,000/year for women. Exposure to carcinogenic polycyclic aromatic hydrocarbons (PAHs) through tobacco smoke and other sources may increase the risk of ESCC. The aims of the current study were to investigate the degree and sources of PAH exposure of the inhabitants of this region of southern Brazil. METHODS: Two hundred healthy adults (half smokers, half non smokers, half male and half female) were recruited, given a standardized questionnaire, and asked to provide a urine sample for measurement of 1-hydroxypyrene glucuronide (1-OHPG), a PAH metabolite). Urine 1-OHPG concentrations were measured using immunoaffinity chromatography and synchronous fluorescence spectroscopy and urine cotinine was measured using a dipstick test. We examined factors associated with 1-OHPG concentration using Wilcoxon tests and multiple linear regression. RESULTS: Urine 1-hydroxypyrene glucuronide (1-OHPG) was successfully measured on 199 subjects. The median (interquartile range) of urine 1-OHPG in the 199 participants was 2.09 pmol/mL (0.51, 5.84). Tobacco smoke exposure and maté drinking were statistically significantly associated with higher urine 1-OHPG concentrations in the multivariate linear regression model. CONCLUSION: Tobacco smoke and maté both contribute to high levels of benzo[a]pyrene exposure in the people of southern Brazil. This high PAH exposure may contribute to the high rates of ESCC observed in this population. The increased urine 1-OHPG concentrations associated with maté suggest that contaminants, not just thermal injury, may help explain the increased risk of ESCC previously reported for maté consumption

Early parenting intervention: Family risk and first-time parenting related to intervention effectiveness

The effects of cumulative risk and parity on the effectiveness of a home based parenting intervention were tested in a randomized controlled trial with 237 families with 1- to 3-year-old children screened for high levels of externalizing behavior. The intervention was aimed at enhancing positive parenting and decreasing externalizing behaviors. The results showed that cumulative risk was not associated with either change in child externalizing behaviors or change in positive parenting. When intervention effectiveness was compared for primiparas (i.e., first-time mothers) versus multiparas (i.e., mothers with more than one child), we found that intervention mothers of first-born children displayed an increase in their use of positive discipline strategies as compared to first-time mothers in the control group, whereas a similar effect for multiparas was absent. Among multiparas we found an intervention effect on sensitivity, with control group mothers showing an increase in sensitivity, whereas the intervention group showed a constant level of sensitivity over time. These results suggest that parity may be a moderator of intervention effectiveness. Implications for investigating moderators of intervention effectiveness are discussed. © 2007 Springer Science+Business Media, LLC

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age  6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score  652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

Multi-view learning with additive models on graphs.

This thesis provides a methodology for approaching multi-view learning, a broad range of problems that share a common challenge: incorporating several distinct domains of data together with a response for the purpose of prediction. Learning in this context is either classification or regression, and, simply stated, a domain is a set of variables that summarizes one particular source of data. One example of a multiview learning problem is predicting whether a departmental web page is personal or related to an academic course by using one domain of page text information and another domain of hyperlink information from the set of available web pages. Another example from pharmacology combines binary chemical fingerprints and continuous biological assays to predict the side-effect status of a compound. In this thesis, we study the problem of learning with proximity graphs constructed from the individual domains, where each graph represents one domain. The main idea is to model these graphs in an informative way about the response. During graph construction, we notice that unlabeled observations (observed with the response missing) affect the underlying topology of the graph. To account for this, a semi-supervised framework is necessary, using both the labeled and unlabeled observations during training. To address this challenge, a sequential graph-based nearest neighbor classifier is proposed, which utilizes the topology of the graph to produces estimates for the observations. Connecting graph-based nearest neighbors to loss function optimization leads to the more general Fitting The Fits (FTF) algorithm. The algorithm iteratively obtains predicted response values for unlabeled observations and then uses these predictions as sources (pseudo-labeled observations) at subsequent stages. The properties of this procedure are studied both theoretically and empirically on several applications. In the multi-view learning setting, we provide a framework for using these graphs and interactions between these graphs as terms in an additive model. The FTF procedure provides the basis for fitting this model. Using this model, we provide methods for domain selection and for assessing variable importance. The technique is demonstrated on real and simulated data.Ph.D.Pure SciencesStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/126660/2/3276129.pd

On Data Integration Problems With Manifolds

<p>This article focuses on data integration problems where the predictor variables for some response variable partition into known subsets. This type of data is often referred to as multi-view data, and each subset of the predictors is called a data view. Accounting for data views can add practical value in terms of both interpretation and predictive performance. Many existing approaches for multi-view data rely on view-agreement principles, strong smoothness assumptions, or regularization penalties. The former approaches can be sensitive to modest noise in the response or predictor variables, while the latter approach is linear and can usually be out-performed. We develop semiparametric data integration methods to span key tradeoffs including the bias-variance tradeoff on prediction error, the possibility that the data may be fully viewed with no appreciable view relationships, and the use of sparse anchor point methods to detect and use manifolds (i.e., possibly nonelliptical structures) within views if they enhance performance. Theoretical results help justify the new technique, and its effectiveness and computational feasibility are demonstrated empirically. This new semiparametric methodology is available for public use through the supplemental R package mvltools. Additional supplementary material for this article is also available online.</p