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      Social Determinants of Health Factors for Gene–Environment COVID‐19 Research: Challenges and Opportunities


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          The characteristics of a person's health status are often guided by how they live, grow, learn, their genetics, as well as their access to health care. Yet, all too often, studies examining the relationship between social determinants of health (behavioral, sociocultural, and physical environmental factors), the role of demographics, and health outcomes poorly represent these relationships, leading to misinterpretations, limited study reproducibility, and datasets with limited representativeness and secondary research use capacity. This is a profound hurdle in what questions can or cannot be rigorously studied about COVID‐19. In practice, gene–environment interactions studies have paved the way for including these factors into research. Similarly, our understanding of social determinants of health continues to expand with diverse data collection modalities as health systems, patients, and community health engagement aim to fill the knowledge gaps toward promoting health and wellness. Here, a conceptual framework is proposed, adapted from the population health framework, socioecological model, and causal modeling in gene–environment interaction studies to integrate the core constructs from each domain with practical considerations needed for multidisciplinary science.


          Adapted from the population health framework, socioecological model, and causal modeling in gene–environment interactions, this paper proposes a conceptual framework coined as GxSDoH to represent the interactions between social determinants of health (behavioral, sociocultural, and physical environmental factors) and health outcomes. The framework integrates core constructs from each domain with practical and scale considerations for multidisciplinary COVID‐19 studies.

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

            Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.
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              Angiotensin-Converting Enzyme 2: SARS-CoV-2 Receptor and Regulator of the Renin-Angiotensin System

              ACE2 (angiotensin-converting enzyme 2) has a multiplicity of physiological roles that revolve around its trivalent function: a negative regulator of the renin-angiotensin system, facilitator of amino acid transport, and the severe acute respiratory syndrome-coronavirus (SARS-CoV) and SARS-CoV-2 receptor. ACE2 is widely expressed, including, in the lungs, cardiovascular system, gut, kidneys, central nervous system, and adipose tissue. ACE2 has recently been identified as the SARS-CoV-2 receptor, the infective agent responsible for coronavirus disease 2019, providing a critical link between immunity, inflammation, ACE2, and cardiovascular disease. Although sharing a close evolutionary relationship with SARS-CoV, the receptor-binding domain of SARS-CoV-2 differs in several key amino acid residues, allowing for stronger binding affinity with the human ACE2 receptor, which may account for the greater pathogenicity of SARS-CoV-2. The loss of ACE2 function following binding by SARS-CoV-2 is driven by endocytosis and activation of proteolytic cleavage and processing. The ACE2 system is a critical protective pathway against heart failure with reduced and preserved ejection fraction including, myocardial infarction and hypertension, and against lung disease and diabetes mellitus. The control of gut dysbiosis and vascular permeability by ACE2 has emerged as an essential mechanism of pulmonary hypertension and diabetic cardiovascular complications. Recombinant ACE2, gene-delivery of Ace2, Ang 1–7 analogs, and Mas receptor agonists enhance ACE2 action and serve as potential therapies for disease conditions associated with an activated renin-angiotensin system. rhACE2 (recombinant human ACE2) has completed clinical trials and efficiently lowered or increased plasma angiotensin II and angiotensin 1-7 levels, respectively. Our review summarizes the progress over the past 20 years, highlighting the critical role of ACE2 as the novel SARS-CoV-2 receptor and as the negative regulator of the renin-angiotensin system, together with implications for the coronavirus disease 2019 pandemic and associated cardiovascular diseases.

                Author and article information

                Adv Genet (Hoboken)
                Adv Genet (Hoboken)
                Advanced Genetics (Hoboken, N.j.)
                John Wiley and Sons Inc. (Hoboken )
                09 March 2022
                09 March 2022
                : 2100056
                [ 1 ] Division of Biomedical and Health Informatics University of Washington Seattle WA 98195 USA
                [ 2 ] Harborview Injury Prevention Research Center University of Washington Seattle WA 98104 USA
                [ 3 ] Department of Biomedical Informatics University of Utah School of Medicine Salt Lake City UT 84108‐3514 USA
                [ 4 ] Health Informatics and Information Management University of Tennessee Health Science Center Memphis TN 38163 USA
                [ 5 ] National Institute on Minority Health and Health Disparities (NIMHD) National Institutes of Health Bethesda MD 20892‐5465 USA
                [ 6 ] Department of Biomedical Informatics University of Pittsburgh Pittsburgh PA 15206 USA
                [ 7 ] Department of Pediatrics Children's Hospital Los Angeles Los Angeles CA 90015 USA
                [ 8 ] Department of Civil and Environmental Engineering University of California at Berkeley Berkeley CA 94720 USA
                [ 9 ] Division of Biomedical Informatics Department of Internal Medicine University of Kentucky Lexington KY 40506 USA
                [ 10 ] Department of Medical Informatics & Clinical Epidemiology Oregon Health & Science University Portland OR 97239 USA
                [ 11 ] Center for Public Health and Environmental Assessment US Environmental Protection Agency Chapel Hill NC 27514 USA
                [ 12 ] National Human Genome Research Institute (NHGRI) National Institutes of Health Bethesda MD 20892‐2152 USA
                Author notes
                Author information
                © 2022 The Authors. Advanced Genetics published by Wiley Periodicals LLC

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                : 23 October 2021
                Page count
                Figures: 6, Tables: 2, Pages: 16, Words: 12711
                Funded by: Boston University School of Public Health
                Funded by: National Center for Data to Health
                Award ID: NIH/NCATS U24TR002306
                Funded by: National COVID Cohort Collaborative
                Award ID: NIH/NCATS U24TR002306‐04S3
                Funded by: University of Kentucky CTSA funding
                Award ID: UL1TR001998
                Funded by: The Office of Data Science Strategy
                Funded by: NIH , doi 10.13039/100000002;
                Funded by: NCATS , doi 10.13039/100006108;
                Award ID: U24TR002306
                Custom metadata
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.5 mode:remove_FC converted:10.05.2022

                covid‐19,environmental factors,genomics,gxe,medical informatics,social determinant of health


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