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Your search keyword '"Joseph B. Leader"' showing total 6 results
Start Over Author "Joseph B. Leader" Publication Type Academic Journals
6 results on '"Joseph B. Leader"'

1. Genetic inactivation of ANGPTL4 improves glucose homeostasis and is associated with reduced risk of diabetes

Author

Viktoria Gusarova, Colm O’Dushlaine, Tanya M. Teslovich, Peter N. Benotti, Tooraj Mirshahi, Omri Gottesman, Cristopher V. Van Hout, Michael F. Murray, Anubha Mahajan, Jonas B. Nielsen, Lars Fritsche, Anders Berg Wulff, Daniel F. Gudbjartsson, Marketa Sjögren, Connor A. Emdin, Robert A. Scott, Wen-Jane Lee, Aeron Small, Lydia C. Kwee, Om Prakash Dwivedi, Rashmi B. Prasad, Shannon Bruse, Alexander E. Lopez, John Penn, Anthony Marcketta, Joseph B. Leader, Christopher D. Still, H. Lester Kirchner, Uyenlinh L. Mirshahi, Amr H. Wardeh, Cassandra M. Hartle, Lukas Habegger, Samantha N. Fetterolf, Teresa Tusie-Luna, Andrew P. Morris, Hilma Holm, Valgerdur Steinthorsdottir, Patrick Sulem, Unnur Thorsteinsdottir, Jerome I. Rotter, Lee-Ming Chuang, Scott Damrauer, David Birtwell, Chad M. Brummett, Amit V. Khera, Pradeep Natarajan, Marju Orho-Melander, Jason Flannick, Luca A. Lotta, Cristen J. Willer, Oddgeir L. Holmen, Marylyn D. Ritchie, David H. Ledbetter, Andrew J. Murphy, Ingrid B. Borecki, Jeffrey G. Reid, John D. Overton, Ola Hansson, Leif Groop, Svati H. Shah, William E. Kraus, Daniel J. Rader, Yii-Der I. Chen, Kristian Hveem, Nicholas J. Wareham, Sekar Kathiresan, Olle Melander, Kari Stefansson, Børge G. Nordestgaard, Anne Tybjærg-Hansen, Goncalo R. Abecasis, David Altshuler, Jose C. Florez, Michael Boehnke, Mark I. McCarthy, George D. Yancopoulos, David J. Carey, Alan R. Shuldiner, Aris Baras, Frederick E. Dewey, and Jesper Gromada

Subjects
Science
Abstract

Genetic variation in ANGPTL4 is associated with lipid traits. Here, the authors find that predicted loss-of-function variants in ANGPTL4 are associated with glucose homeostasis and reduced risk of type 2 diabetes and that Angptl4 −/− mice on a high-fat diet show improved insulin sensitivity.

Published
2018
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5. Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patients.

Author

Matthew T Oetjens, Jonathan Z Luo, Alexander Chang, Joseph B Leader, Dustin N Hartzel, Bryn S Moore, Natasha T Strande, H Lester Kirchner, David H Ledbetter, Anne E Justice, David J Carey, and Tooraj Mirshahi

Subjects
Medicine, Science
Abstract

BackgroundEmpirical data on conditions that increase risk of coronavirus disease 2019 (COVID-19) progression are needed to identify high risk individuals. We performed a comprehensive quantitative assessment of pre-existing clinical phenotypes associated with COVID-19-related hospitalization.MethodsPhenome-wide association study (PheWAS) of SARS-CoV-2-positive patients from an integrated health system (Geisinger) with system-level outpatient/inpatient COVID-19 testing capacity and retrospective electronic health record (EHR) data to assess pre-COVID-19 pandemic clinical phenotypes associated with hospital admission (hospitalization).ResultsOf 12,971 individuals tested for SARS-CoV-2 with sufficient pre-COVID-19 pandemic EHR data at Geisinger, 1604 were SARS-CoV-2 positive and 354 required hospitalization. We identified 21 clinical phenotypes in 5 disease categories meeting phenome-wide significance (PConclusionsThis study provides quantitative estimates of the contribution of pre-existing clinical phenotypes to COVID-19 hospitalization and highlights kidney disorders as the strongest factors associated with hospitalization in an integrated US healthcare system.

Published
2020
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6. Patient-Centered Precision Health In A Learning Health Care System: Geisinger's Genomic Medicine Experience.

Author

Williams MS, Buchanan AH, Davis FD, Faucett WA, Hallquist MLG, Leader JB, Martin CL, McCormick CZ, Meyer MN, Murray MF, Rahm AK, Schwartz MLB, Sturm AC, Wagner JK, Williams JL, Willard HF, and Ledbetter DH

Subjects
Female, Humans, Learning, Male, Program Development, Program Evaluation, United States, Delivery of Health Care, Integrated organization & administration, Genomics, Patient-Centered Care organization & administration, Precision Medicine
Abstract

Health care delivery is increasingly influenced by the emerging concepts of precision health and the learning health care system. Although not synonymous with precision health, genomics is a key enabler of individualized care. Delivering patient-centered, genomics-informed care based on individual-level data in the current national landscape of health care delivery is a daunting challenge. Problems to overcome include data generation, analysis, storage, and transfer; knowledge management and representation for patients and providers at the point of care; process management; and outcomes definition, collection, and analysis. Development, testing, and implementation of a genomics-informed program requires multidisciplinary collaboration and building the concepts of precision health into a multilevel implementation framework. Using the principles of a learning health care system provides a promising solution. This article describes the implementation of population-based genomic medicine in an integrated learning health care system-a working example of a precision health program.

Published
2018
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