Marta Gwinn

Bio: Marta Gwinn is an academic researcher from Centers for Disease Control and Prevention. The author has contributed to research in topics: Population & Public health. The author has an hindex of 53, co-authored 135 publications receiving 10161 citations.

STrengthening the REporting of Genetic Association Studies (STREGA): An Extension of the STROBE Statement

Abstract: Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

A navigator for human genome epidemiology

Abstract: To the Editor: Recent successes in large-scale genetic association studies call for renewed attention to integrating research results, not only among studies, but across disciplines1. At the molecular level, genetic polymorphisms provide a starting point for investigating the functions of complex biological systems. At the population level, epidemiologists can begin to use data on genetic variation, associations and interactions to interpret population attributable fractions and estimate the potential health impact of genetically directed interventions2. Publicly available genetic sequence databases have demonstrated their value in accelerating the Human Genome Project and advancing the field of molecular genetics; newer efforts, such as dbGaP and CGEMS, are now beginning to make genotypephenotype data broadly available to the scientific community3. The published scientific literature also reflects rapid growth in studies of human genetic factors in relation to health and disease. Since 2001, the Human Genome Epidemiology Network (HuGENet) has maintained a database of published, population-based epidemiologic studies of human genes extracted from PubMed4. We recently replaced our PubMed search strategy with a new approach using machine learning, which has reduced manual effort and increased both the sensitivity and specificity of screening. Our curator updates the database weekly with articles newly added to PubMed and assigns to them one or more study types (for example, observational study, meta-analysis or genome-wide association study) and data categories (for example, gene-disease association, gene-environment interaction or pharmacogenomics). Each article is indexed in the database with MeSH terms (using the MeSH hierarchical structure) and gene information from the National Center for Bioinformatics (NCBI) Entrez Gene database. As of November 2007, the database has indexed more than 30,000 articles, referencing more than 3,000 genes and nearly 2,000 disease terms (Table 1). Most articles (80%) describe genetic associations. Approximately 20% of all articles were published in 2007, including 68 of 82 genome-wide association studies. To make this database more accessible and useful to interdisciplinary researchers, we have developed an integrated set of applications known collectively as the HuGE Navigator (http://www.hugenavigator. net). Using PubMed abstracts as the core data source, we have developed data and text mining algorithms to create a knowledge base for exploring genetic associations, candidate gene selection and investigator networks. Genetic information can be displayed whenever needed from major gene-centered databases (for example, Entrez Gene, SwissProt, OMIM and GeneCards), as well as from databases of genetic variation and prevalence (for example, dbSNP and HapMap Project), pathways (for example, CGAP, KEGG and BioCarta), and other aspects (for example, Gene Ontology and Gene Clinics). The HuGE Navigator is constructed according to the principles of open source, standardization, interoperability and extensibility, so that new applications can be easily incorporated5. Currently, the HuGE Navigator allows users to navigate and search the database in an integrated manner by using the six applications discussed below. The HuGE Literature Finder is a search engine for finding published literature on human genome epidemiology, including genetic association studies. The search query can include disease terms, environmental factors, genes, or author names and affiliations. The search results can be further refined by using filtering features, including disease, gene, category, study type, author, year, journal, and country. The filtering process can be performed indefinitely until the desired result is obtained. The results (PubMed IDs) can be exported to the PubMed Web site for further exploration and downloading to bibliographic software. The HuGE Investigator Browser is a search engine for finding investigators or collaborators on the basis of research interests, such as diseases, risk factors, or genes. We extract investigator data by using an accessory utility that automatically parses the affiliation data provided by PubMed6. GeneSelectAssist is a search tool for finding possible candidate genes associated with the subject of interest. Search terms can include diseases and exposures. GeneSelectAssist selects and prioritizes genes on the basis of genetic association studies in the HuGE Navigator database, as well as other PubMed abstracts, and evidence from animal models in the NCBI Entrez Gene database. HuGE Watch is a tool for tracking the evolution of human genome epidemiology research dynamically, on the basis of the literature database. It allows users to view temporal trends in publication by gene, disease, and number of investigators, as well as by the geographic distribution of authors. HuGEpedia is an online encyclopedia that summarizes research on gene-disease associations. We are currently developing a system for extracting data from meta-analyses and published genome-wide associations that will form the basis for a disease-specific synopsis written by domain experts. HuGEpedia can be searched by gene or disease. HuGE Risk Translator is a tool that assesses the validity of genetic variants for predicting health outcomes by calculating epidemiologic measures such as population attributable risk, sensitivity, specificity and positive and negative predictive values. The HuGE Navigator offers a new way to navigate and mine the growing scientific literature on human gene-disease associations and related data in human genome epidemiology. As an interconnected system of applications that users can enter by using genes, diseases, or risk factors as the starting point, HuGE Navigator provides a potential bridge between epidemiologic and genetic research domains. Disease and gene names are mapped to standardized vocabularies, so investigators can use their preferred terms to query the knowledge base. By linking to disease-specific databases, such as AlzGene7, HuGE Navigator aims to be the vehicle for navigating the ‘network of networks’ of investigators now working to

Sodium and Potassium Intake and Mortality Among US Adults: Prospective Data From the Third National Health and Nutrition Examination Survey

Abstract: confidenceinterval[CI],1.03-1.41per1000mg/d),whereas higher potassium intake was associated with lower mortality risk (HR, 0.80; 95% CI, 0.67-0.94 per 1000 mg/d). For sodium-potassium ratio, the adjusted HRs comparing thehighestquartilewiththelowestquartilewereHR,1.46 (95%CI,1.27-1.67)forall-causemortality;HR,1.46(95% CI, 1.11-1.92) for CVD mortality; and HR, 2.15 (95% CI, 1.48-3.12) for IHD mortality. These findings did not differsignificantlybysex,race/ethnicity,bodymassindex,hypertension status, education levels, or physical activity. Conclusion:Our findings suggest that a higher sodiumpotassium ratio is associated with significantly increased risk of CVD and all-cause mortality, and higher sodium intake is associated with increased total mortality in the general US population.

Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes

Abstract: Background: We present a potentially useful alternative approach based on support vector machine (SVM) techniques to classify persons with and without common diseases. We illustrate the method to detect persons with diabetes and pre-diabetes in a cross-sectional representative sample of the U.S. population. Methods: We used data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES) to develop and validate SVM models for two classification schemes: Classification Scheme I (diagnosed or undiagnosed diabetes vs. pre-diabetes or no diabetes) and Classification Scheme II (undiagnosed diabetes or prediabetes vs. no diabetes). The SVM models were used to select sets of variables that would yield the best classification of individuals into these diabetes categories. Results: For Classification Scheme I, the set of diabetes-related variables with the best classification performance included family history, age, race and ethnicity, weight, height, waist circumference, body mass index (BMI), and hypertension. For Classification Scheme II, two additional variables–sex and physical activity–were included. The discriminative abilities of the SVM models for Classification Schemes I and II, according to the area under the receiver operating characteristic (ROC) curve, were 83.5% and 73.2%, respectively. The web-based tool-Diabetes Classifier was developed to demonstrate a user-friendly application that allows for individual or group assessment with a configurable, user-defined threshold. Conclusions: Support vector machine modeling is a promising classification approach for detecting persons with common diseases such as diabetes and pre-diabetes in the population. This approach should be further explored in other complex diseases using common variables.

A Novel Coronavirus from Patients with Pneumonia in China, 2019.

Abstract: In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A previously unknown betacoronavirus was discovered through the use of unbiased sequencing in samples from patients with pneumonia. Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily. Different from both MERS-CoV and SARS-CoV, 2019-nCoV is the seventh member of the family of coronaviruses that infect humans. Enhanced surveillance and further investigation are ongoing. (Funded by the National Key Research and Development Program of China and the National Major Project for Control and Prevention of Infectious Disease in China.).

The Strengthening the Reporting of Observational Studies in Epidemiology [STROBE] statement: guidelines for reporting observational studies

Abstract: Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalisability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. 18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the Web sites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.

The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies

Abstract: Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study’s generalizability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control and cross-sectional studies. We convened a two-day workshop, in September 2004, with methodologists, researchers and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the web sites of PLoS Medicine, Annals of Internal Medicine and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.

Standards of Medical Care in Diabetes

Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.