Improving Patient Decision-Making in Health Care

Outlines regional variations within Minnesota in rates of patients with similar conditions receiving elective surgery, the concept of shared decision making, treatment choices for eight conditions, and steps for ensuring patients make informed decisions

INCORPORATING LOW-DOSE EPIDEMIOLOGY DATA IN A CHLORPYRIFOS RISK ASSESSMENT

USEPA assessed whether epidemiology data suggest that fetal or early-life chlorpyrifos exposure causes neurodevelopmental effects and, if so, whether they occur at exposures below those causing the current most sensitive endpoint, 10% inhibition of blood acetylcholinesterase (AChE). We previously conducted a hypothesis-based weight-of-evidence analysis and found that a proposed causal association between chlorpyrifos exposure and neurodevelopmental effects in the absence of AChE inhibition does not have a substantial basis in existing animal or in vitro studies, and there is no plausible basis for invoking such effects in humans at their far lower exposure levels. The epidemiology studies fail to show consistent patterns; the few associations are likely attributable to alternative explanations. Human data are inappropriate for a dose-response assessment because biomarkers were only measured at one time point, may reflect exposure to other pesticides, and many values are at or below limits of quantification. When considered with pharmacokinetic data, however, these biomarkers provide information on exposure levels relative to those in experimental studies and indicate a margin of exposure of at least 1,000. Because animal data take into account the most sensitive lifestages, the use of AChE inhibition as a regulatory endpoint is protective of adverse effects in sensitive populations

Drinking Behaviors Versus Perceived Norms of Kentucky College Students

Accidents and unintentional injury are the number one cause of death in college-aged adults. More than 1,500 college-aged students die from alcohol-related unintentional injuries per year, including motor-vehicle crashes. Other alcohol-related problems for college students include physical assault, sexual assault, and poor academic performance. In the state of Kentucky 17% of adults report binge drinking habits, and 1 in 4 deaths are due to vehicular accidents involving alcohol consumption. This is in line with the stats for the rest of the country. The purpose of the study is to assess the drinking habits versus perceived drinking norms of students attending a regional Kentucky university. Data will be gathered using a self-reported survey known as the Drinking Behaviors vs. Perceived Norms of Kentucky College Students Survey, consisting of 8 questions asking about their drinking habits versus what they perceive the drinking habits of their peers to be, and 6 demographic questions. The data will be analyzed using frequencies, percentages, and means for demographic results and chi-square analysis comparing self-reported drinking habits and perceived drinking norms. Results will be forthcoming. Keywords: alcohol, norms, drinking behaviors, college student

The Global Burden of Ozone on Respiratory Mortality: No Clear Evidence for Association

The correspondence section is a public forum and, as such, is not peer-reviewed. EHP is not responsible for the accuracy, currency, or reliability of personal opinion expressed herein; it is the sole responsibility of the authors. EHP neither endorses nor disputes their published commentary

Polymorphism Interaction Analysis (PIA): a method for investigating complex gene-gene interactions

<p>Abstract</p> <p>Background</p> <p>The risk of common diseases is likely determined by the complex interplay between environmental and genetic factors, including single nucleotide polymorphisms (SNPs). Traditional methods of data analysis are poorly suited for detecting complex interactions due to sparseness of data in high dimensions, which often occurs when data are available for a large number of SNPs for a relatively small number of samples. Validation of associations observed using multiple methods should be implemented to minimize likelihood of false-positive associations. Moreover, high-throughput genotyping methods allow investigators to genotype thousands of SNPs at one time. Investigating associations for each individual SNP or interactions between SNPs using traditional approaches is inefficient and prone to false positives.</p> <p>Results</p> <p>We developed the Polymorphism Interaction Analysis tool (PIA version 2.0) to include different approaches for ranking and scoring SNP combinations, to account for imbalances between case and control ratios, stratify on particular factors, and examine associations of user-defined pathways (based on SNP or gene) with case status. PIA v. 2.0 detected 2-SNP interactions as the highest ranking model 77% of the time, using simulated data sets of genetic models of interaction (minor allele frequency = 0.2; heritability = 0.01; N = 1600) generated previously [Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM, Moore JH: A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 2007, 31:306–315.]. Interacting SNPs were detected in both balanced (20 SNPs) and imbalanced data (case:control 1:2 and 1:4, 10 SNPs) in the context of non-interacting SNPs.</p> <p>Conclusion</p> <p>PIA v. 2.0 is a useful tool for exploring gene*gene or gene*environment interactions and identifying a small number of putative associations which may be investigated further using other statistical methods and in replication study populations.</p

Wild Buckwheat Is Unlikely to Pose a Risk to Buckwheat-Allergic Individuals

Buckwheat (Fagopyrum esculentum) is a commonly allergenic food especially in Asia where buckwheat is more commonly consumed. Wild buckwheat (Polygonum convolvulus, recently changed to Fallopia convolvulus) is an annual weed prevalent in grain-growing areas of the United States. Wild buckwheat is not closely related to edible buckwheat although the seeds do have some physical resemblance. A large shipment of wheat into Japan was halted by the discovery of the adventitious presence of wild buckwheat seeds over possible concerns for buckwheat-allergic consumers. However, IgE-binding was not observed to an extract of wild buckwheat using sera from 3 buckwheat-allergic individuals either by radio-allergosorbent test inhibition or by immunoblotting after protein separation by sodium dodecyl sulfate–polyacrylamide gel electrophoresis. Furthermore, the extract of wild buckwheat was not detected in a buckwheat enzyme-linked immunosorbent assay developed with antisera against common buckwheat. Thus, wild buckwheat is highly unlikely to pose any risk to buckwheat-allergic individuals. The common names of plants should not be a factor in the risk assessment for possible cross-allergenicity

BRCA2 polymorphic stop codon K3326X and the risk of breast, prostate, and ovarian cancers

Background: The K3326X variant in BRCA2 (BRCA2*c.9976A&gt;T; p.Lys3326*; rs11571833) has been found to be associated with small increased risks of breast cancer. However, it is not clear to what extent linkage disequilibrium with fully pathogenic mutations might account for this association. There is scant information about the effect of K3326X in other hormone-related cancers. Methods: Using weighted logistic regression, we analyzed data from the large iCOGS study including 76 637 cancer case patients and 83 796 control patients to estimate odds ratios (ORw) and 95% confidence intervals (CIs) for K3326X variant carriers in relation to breast, ovarian, and prostate cancer risks, with weights defined as probability of not having a pathogenic BRCA2 variant. Using Cox proportional hazards modeling, we also examined the associations of K3326X with breast and ovarian cancer risks among 7183 BRCA1 variant carriers. All statistical tests were two-sided. Results: The K3326X variant was associated with breast (ORw = 1.28, 95% CI = 1.17 to 1.40, P = 5.9x10- 6) and invasive ovarian cancer (ORw = 1.26, 95% CI = 1.10 to 1.43, P = 3.8x10-3). These associations were stronger for serous ovarian cancer and for estrogen receptor–negative breast cancer (ORw = 1.46, 95% CI = 1.2 to 1.70, P = 3.4x10-5 and ORw = 1.50, 95% CI = 1.28 to 1.76, P = 4.1x10-5, respectively). For BRCA1 mutation carriers, there was a statistically significant inverse association of the K3326X variant with risk of ovarian cancer (HR = 0.43, 95% CI = 0.22 to 0.84, P = .013) but no association with breast cancer. No association with prostate cancer was observed. Conclusions: Our study provides evidence that the K3326X variant is associated with risk of developing breast and ovarian cancers independent of other pathogenic variants in BRCA2. Further studies are needed to determine the biological mechanism of action responsible for these associations

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment