Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Seth A, Sharp"'
Autor:
Xiangni Wu, Pin-I Chen, Robert L. Whitener, Matthew S. MacDougall, Vy M. N. Coykendall, Hao Yan, Yong Bin Kim, William Harper, Shiva Pathak, Bettina P. Iliopoulou, Allison Hestor, Diane C. Saunders, Erick Spears, Jean Sévigny, David M. Maahs, Marina Basina, Seth A. Sharp, Anna L. Gloyn, Alvin C. Powers, Seung K. Kim, Kent P. Jensen, Everett H. Meyer
Publikováno v:
Frontiers in Immunology, Vol 15 (2024)
Human regulatory T cells (Treg) suppress other immune cells. Their dysfunction contributes to the pathophysiology of autoimmune diseases, including type 1 diabetes (T1D). Infusion of Tregs is being clinically evaluated as a novel way to prevent or tr
Externí odkaz:
https://doaj.org/article/9eb5d3e2468049a48285a66700ca2b17
Autor:
Kittiya Sukcharoen, Seth A. Sharp, Nicholas J. Thomas, Robert A. Kimmitt, Jamie Harrison, Coralie Bingham, Monika Mozere, Michael N. Weedon, Jessica Tyrrell, Jonathan Barratt, Daniel P. Gale, Richard A. Oram
Publikováno v:
Kidney International Reports, Vol 5, Iss 10, Pp 1643-1650 (2020)
Background: IgA nephropathy (IgAN) is the commonest glomerulonephritis worldwide. Its prevalence is difficult to estimate, as people with mild disease do not commonly receive a biopsy diagnosis. We aimed to generate an IgA nephropathy genetic risk sc
Externí odkaz:
https://doaj.org/article/e9d64aba82b34b94b8572e69f29b77d4
Autor:
Samuel E. Jones, Vincent T. van Hees, Diego R. Mazzotti, Pedro Marques-Vidal, Séverine Sabia, Ashley van der Spek, Hassan S. Dashti, Jorgen Engmann, Desana Kocevska, Jessica Tyrrell, Robin N. Beaumont, Melvyn Hillsdon, Katherine S. Ruth, Marcus A. Tuke, Hanieh Yaghootkar, Seth A. Sharp, Yingjie Ji, Jamie W. Harrison, Rachel M. Freathy, Anna Murray, Annemarie I. Luik, Najaf Amin, Jacqueline M. Lane, Richa Saxena, Martin K. Rutter, Henning Tiemeier, Zoltán Kutalik, Meena Kumari, Timothy M. Frayling, Michael N. Weedon, Philip R. Gehrman, Andrew R. Wood
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-12 (2019)
Quality, quantity and timing of sleep are important factors for overall human health. Here, the authors perform GWAS for sleep traits estimated using wearable accelerometers and identify 47 genetic associations, including 26 novel associations for me
Externí odkaz:
https://doaj.org/article/11003410f8bd43baa3af4a034e6f9454
Autor:
Samuel E. Jones, Jacqueline M. Lane, Andrew R. Wood, Vincent T. van Hees, Jessica Tyrrell, Robin N. Beaumont, Aaron R. Jeffries, Hassan S. Dashti, Melvyn Hillsdon, Katherine S. Ruth, Marcus A. Tuke, Hanieh Yaghootkar, Seth A. Sharp, Yingjie Jie, William D. Thompson, Jamie W. Harrison, Amy Dawes, Enda M. Byrne, Henning Tiemeier, Karla V. Allebrandt, Jack Bowden, David W. Ray, Rachel M. Freathy, Anna Murray, Diego R. Mazzotti, Philip R. Gehrman, Debbie A. Lawlor, Timothy M. Frayling, Martin K. Rutter, David A. Hinds, Richa Saxena, Michael N. Weedon
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-11 (2019)
GWAS have previously found 24 genomic loci associated with chronotype, an individual’s preference for early or late sleep timing. Here, the authors identify 327 additional loci in a sample of 697,828 individuals and further explore the relationship
Externí odkaz:
https://doaj.org/article/7d76303f46514c92adc6ac6228d3ae3f
Autor:
Nicholas J. Thomas, Andrew McGovern, Katherine G. Young, Seth A. Sharp, Michael N. Weedon, Andrew T. Hattersley, John Dennis, Angus G. Jones
Publikováno v:
Journal of Clinical Epidemiology. 153:34-44
We aimed to compare the performance of approaches for classifying insulin treated diabetes within research datasets without measured classification biomarkers, evaluated against two independent biological definitions of diabetes type.We compared accu
Autor:
Helen C. Walkey, Desmond G. Johnston, Nick Oliver, William Hagopian, Akaal Kaur, Seth A. Sharp, John M Dennis, Shivani Misra, Kashyap A. Patel, Michael N. Weedon, Nicholas J. Thomas, Richard A. Oram
Publikováno v:
Diabetologia
Aims/hypothesis Among white European children developing type 1 diabetes, the otherwise common HLA haplotype DR15-DQ6 is rare, and highly protective. Adult-onset type 1 diabetes is now known to represent more overall cases than childhood onset, but i
Autor:
SETH A. SHARP, JONATHAN M. LOCKE, YU XU, DIANE P. FRASER, LAURIC A. FERRAT, MICHAEL N. WEEDON, MICHAEL INOUYE, RICHARD A. ORAM, WILLIAM HAGOPIAN
Publikováno v:
Diabetes. 71
Introduction: Screening for type 1 diabetes (T1D) genetic risk in early life can prevent life threatening complications such as diabetic ketoacidosis and allow cost-effective recruitment into intervention and immunotherapy trials. Celiac disease (CD)
Autor:
Richard A. Oram, Seth A. Sharp, Catherine Pihoker, Lauric Ferrat, Giuseppina Imperatore, Adrienne Williams, Maria J. Redondo, Lynne Wagenknecht, Lawrence M. Dolan, Jean M. Lawrence, Michael N. Weedon, Ralph D’Agostino, William A. Hagopian, Jasmin Divers, Dana Dabelea
Publikováno v:
Diabetes Care
OBJECTIVE Genetic risk scores (GRS) aid classification of diabetes type in White European adult populations. We aimed to assess the utility of GRS in the classification of diabetes type among racially/ethnically diverse youth in the U.S. RESEARCH DES
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13fc817ca5e8e42212ef5dc4b2fee7fc
https://doi.org/10.2337/figshare.19145813
https://doi.org/10.2337/figshare.19145813
Autor:
Anette-G. Ziegler, William Hagopian, Lauric A. Ferrat, Jin-Xiong She, Jeffrey P. Krischer, Beena Akolkar, Michael N. Weedon, Jorma Toppari, Seth A. Sharp, Richard A. Oram, Marian Rewers, Kendra Vehik, Åke Lernmark
Publikováno v:
Nat Med
Type 1 diabetes (T1D)—an autoimmune disease that destroys the pancreatic islets, resulting in insulin deficiency—often begins early in life when islet autoantibody appearance signals high risk1. However, clinical diabetes can follow in weeks or o
Autor:
Nicholas J Thomas, Andrew McGovern, Katherine G Young, Seth A Sharp, Michael N Weedon, Andrew T Hattersley, John Dennis, Angus G Jones
Publikováno v:
SSRN Electronic Journal.
AimsPopulation datasets are increasingly used to study type 1 or 2 diabetes, and inform clinical practice. However, correctly classifying diabetes type, when insulin treated, in population datasets is challenging. Many different approaches have been