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Catania, Italy
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News Article | September 12, 2017
Site: www.eurekalert.org

More than 200 scientists from around the world teamed up to study the genetics of hemoglobin A1c (HbA1c), or "glycated hemoglobin", a measurement used by clinicians to diagnose and monitor diabetes. The authors report that they have identified 60 genetic variants that influence HbA1c measurements, as well as the ability of this test to diagnose diabetes. The gene variants, including one that could lead to African Americans being underdiagnosed with T2D, are described in PLOS Medicine in a paper by James Meigs of Harvard Medical School, USA, and Inês Barroso of the Wellcome Trust Sanger Institute, UK, and colleagues. Levels of HbA1c in a given person depend on both blood glucose levels and characteristics of that person's red blood cells. In the new work, researchers analyzed genetic variants associated with each of these factors, together with HbA1c levels in 160,000 people without diabetes from European, African, and Asian ancestry who had participated in 82 separate studies worldwide. 33,000 people were followed over time to determine whether they were later diagnosed with diabetes. The team identified 60 genetic variants--42 new and 18 previously known--that impact a person's HbA1c levels. People who had more variants that affect HbA1c levels through effects on blood glucose levels were more likely, over time, to develop diabetes (odds ratio 1.05 per HbA1c-raising allele, P=3x10-29). However, people who had more variants that affected HbA1c through effects on red blood cells did not have an increased diabetes risk. The impact of genetic variants on HbA1c levels was largest in those of African ancestry, and the difference could be explained by variation in the gene G6PD (encoding an enzyme related to red blood cell lifespan) which was associated with lower HbA1c levels. 11% of people of African American ancestry carry at least one copy of this genetic variation, which can lower levels of HbA1c despite high blood glucose levels. "HbA1c remains an appropriate diagnostic test for the majority of people of diverse genetic backgrounds," the authors say. "Nevertheless, non-glycemic lowering of measured HbA1c for one in ten African American men who carry this G6PD variant, and one in a hundred African American women homozygous for this variant, could amount to 0.65 million African American adults in the United States with a missed T2D diagnosis using HbA1c as a screening test. We therefore recommend investigation of the possible benefits of screening for the G6PD genotype along with HbA1c." In an accompanying Perspective, Andrew Paterson of the University of Toronto, Canada notes that the impact of the G6PD gene variant on HbA1c results could contribute to the higher risk for long-term diabetes complications in African Americans compared to Americans of European ancestry. "National clinical practice need to be revisited," he writes. "Individuals with this variant should either be screened for T2D using glucose, or sex-and genotype-adjusted thresholds for HbA1c should be used." Please refer to the supporting information file S1 Financial Disclosure for full information with regard to funding and financial support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. I have read the journal's policy and the authors of this manuscript have the following competing interests: AYC is an employee of Merck, however all work for the manuscript was completed before the start of employment. CEE is a current employee of AstraZeneca. CLan receives a stipend as a specialty consulting editor for PLOS Medicine and serves on the journal's editorial board. EI is a scientific advisor for Precision Wellness, Cellink and Olink Proteomics for work unrelated to the present project. GKH declared institution support from Amgen, AstraZeneca, Cerenis, Ionis, Regeneron Pharmaceuticals, Inc. and Sanofi, Synageva. He has served as a consultant and received speaker fees from Aegerion, Amgen, Sanofi, Regeneron Pharmaceuticals, Inc., and Pfizer. IB and spouse own stock in GlaxoSmithKline and Incyte Corporation. JD declared grants from the National Heart, Lung, and Blood Institute (NHLBI) of the National Institute of Health (NIH) during the course of this study. JIR declared funding from NIH grants. MAN consults for Illumina Inc, the Michael J. Fox Foundation and University of California Healthcare among others. MBl receives speaker's honoraria and/or compensation for participation in advisory boards from: Astra Zeneca, Bayer, Boehringer-Ingelheim, Lilly, Novo Nordisk, Novartis, MSD, Pfizer, Riemser and Sanofi. MIM was a member of the editorial board of PLOS Medicine at the time this manuscript was submitted. RAS is an employee and shareholder in GlaxoSmithKline. Wheeler E, Leong A, Liu C-T, Hivert M-F, Strawbridge RJ, Podmore C, et al. (2017) Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis. PLoS Med 14(9): e1002383. https:/ Department of Human Genetics, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, United Kingdom Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America Harvard Medical School, Boston, Massachusetts, United States of America Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, Massachusetts, United States of America Massachusetts General Hospital, Boston, Massachusetts, United States of America Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden Centre for Molecular Medicine, L8:03, Karolinska Universitetsjukhuset, Solna, Sweden MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom Department of Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America Division of Nephrology, University of Utah, Salt Lake City, UT, United States of America Department of Human Genetics, University of Utah, Salt Lake City, UT, United States of America Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, United States of America Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States of America Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, United Kingdom Department of Cardiology, Ealing Hospital NHS Trust, Uxbridge Road, Southall, Middlesex, United Kingdom Life Sciences Institute, National University of Singapore, Singapore, Singapore Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, United States of America Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun, Jilin, China College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China Division of General Internal Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, United States of America Welch Center for Prevention, Epidemiology and Clinical Research, The Johns Hopkins University, Baltimore, MD, United States of America Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan University of Virginia Center for Public Health Genomics, Charlottesville, VA, United States of America Personalised Healthcare & Biomarkers, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Cambridge, United Kingdom California Pacific Medical Center Research Institute, San Francisco, California, United States of America Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 1, Amsterdam, Netherlands The Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, People's Republic of China Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, United States of America William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom Vth Department of Medicine, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden Lund University Diabetes Centre, Lund University, Lund, Sweden University of Lille, CNRS, Institut Pasteur of Lille, UMR 8199ÐEGID, Lille, France Singapore Eye Research Institute, The Academia Level 6, Discovery Tower, Singapore, Singapore The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, NY, United States of America The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, NY, United States of America Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom Institute of Epidemiology II, Research Unit of Molecular Epidemiology, Helmholtz Zentrum MuÈnchen, German Research Center for Environmental Health, Neuherberg, Germany German Center for Diabetes Research (DZD e.V.), Partner Munich, Munich, Germany Data Tecnica International, Glen Echo, MD, United States of America Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, United States of America MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland Department of Epidemiology, University of Groningen, University Medical Center Groningen, RB, Groningen, The Netherlands Data Coordinating Center, Boston University School of Public Health, Boston, MA, United States of America Istituto di Ricerca Genetica e Biomedica (IRGB), CNR, Monserrato, Italy Department of Preventive Medicine, University of Southern California, Los Angeles, CA, United States of America Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands MRC Unit for Lifelong Health & Ageing, London, United Kingdom Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DU, United Kingdom Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Instituto de InvestigacioÂn Sanitaria del Hospital ClõÂnico San Carlos (IdISSC), Madrid, Spain Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany National Institute for Health and Welfare (THL), Helsinki, Finland University of Helsinki, Institute for Molecular Medicine, Finland (FIMM) and Diabetes and Obesity Research Program, Helsinki, Finland Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, United States of America Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Amsterdam, DE, Netherlands Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, DG, Netherlands Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem NC, United States of America Integrated Research and Treatment (IFB) Center Adiposity Diseases, University of Leipzig, Liebigstrasse Leipzig, Germany Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh, Scotland Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, United States of America School of Chinese Medicine, China Medical University, North Dist., Taichung City, Taiwan Department of Biomedical Science, Hallym University, Chuncheon, Gangwon-do, South Korea Department of Nutrition Sciences, University of Alabama at Birmingham and the Birmingham Veterans Affairs Medical Center, Birmingham, AL, United States of America Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America Department of Psychiatry, University of Groningen, University Medical Center Groningen, AB, Groningen, Netherlands Institute for Clinical Diabetology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University Dusseldorf, Dusseldorf, Germany German Center for Diabetes Research (DZD), Munchen-Neuherberg, Germany Division of Endocrinology, Diabetes, Metabolism, Department of Internal Medicine, Wexner Medical Center, The Ohio State University, Columbus, OH, United States of America Boston VA Research Institute, Inc., Boston, MA, United States of America Department of Geriatric Medicine, Ehime University Graduate School of Medicine, Ehime, Japan Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan Department of Geriatric Medicine and Nephrology, Osaka University Graduate School of Medicine, Suita, Japan Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore Center of Pediatric Research, University Hospital for Children & Adolescents, Dept. of Women's & Child Health, University of Leipzig, Leipzig, Germany LIFE Child, LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany Faculty of Collaborative Regional Innovation, Ehime University, Ehime, Japan Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan Institute of Human Genetics, Technische Universitèt Muènchen, Munich, Germany Institute of Human Genetics, Helmholtz Zentrum MuÈnchen, Neuherberg, Germany Munich Cluster for Systems Neurology (SyNergy), Munich, Germany Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America Center for Evidence-based Healthcare, University Hospital and Medical Faculty Carl Gustav Carus, TU Dresden Fetscherstrasse 74, Dresden, Germany Institute of Genetic Epidemiology, Helmholtz Zentrum MuènchenÐ German Research Center for Environmental Health, Neuherberg, Germany Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universitèt, Munich, Germany DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany Laboratory of Genetics, National Institute on Aging, Baltimore, MD, United States of America Institute for Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, United States of America University of Split, Split, Croatia Centre for Population Health Sciences, University of Edinburgh, Edinburgh, United Kingdom Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, United Kingdom Department of Medicine, Medical University of South Carolina, Charleston, SC, United States of America Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, United States of America Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom Department of Endocrinology and Diabetology, University Hospital Dusseldorf, Dusseldorf, Germany INSERM, UMR S 1138, Centre de Recherche des Cordelier, 15 rue de l'Ecole de MeÂdecine, Paris, France Universite Paris Diderot, Sorbonne Paris Cite, UFR de Medecine, 16 rue Henri Huchard, Paris, France Assistance Publique Hopitaux de Paris, Bichat Hospital, DHU FIRE, Department of Diabetology, Endocrinology and Nutrition, 46 rue Henri Huchard, Paris, France University of Edinburgh, Edinburgh, United Kingdom Science for life laboratory, Karolinska Institutet, Tomtebodavagen 23A, Solna, Sweden The New York Academy of Medicine, New York, City, NY, United States of America Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universitat, Munich, Germany Department of Genetics, University of Groningen, University Medical Center Groningen, RB, Groningen, Netherlands Health Disparities Unit, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, Singapore Department of Medicine; University of Leipzig, Liebigstrasse 18, Leipzig, Germany Dept of Community Medicine, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway Institute of Cardiovascular Science, University College London, London, United Kingdom Department of Vascular Medicine, Academic Medical Center, Amsterdam, The Netherlands Department of Epidemiology and Public Health, University College London, London, United Kingdom Institute for Social and Economic Research, University of Essex, Colchester, United Kingdom Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, Medical Research Institute, Ninewells Hospital and Medical School, Dundee, United Kingdom Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore Singapore National Eye Centre, Singapore, Singapore Dept of Medicine III, University of Dresden, Medical Faculty Carl Gustav Carus, Fetscherstrasse 74, Dresden, Germany Imperial College Healthcare NHS Trust, London, United Kingdom Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America Broad Institute of MIT and Harvard, Cambridge, MA, United States of America Dipartimento di Scienze Biomediche, Università di Sassari, SS, Italy Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia Brown Foundation Institute of Molecular Medicine, Division of Epidemiology, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, United States of America Braun School of Public Health, Hebrew University-Hadassah Medical Center, Jerusalem, Israel CNRS 8199-Lille University, France Finnish Institute for Molecular Medicine (FIMM), Helsinki, Finland Department of Laboratory Medicine and Pathology, University of Minnesota, 420 Delaware Street Minneapolis, MN, United States of America National Institute on Aging, Bethesda, Maryland, United States of America Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore, Singapore Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, United States of America Duke-NUS Medical School Singapore, Singapore National Heart and Lung Institute, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University, Winston-Salem, North Carolina, United States of America The Mindich Child Health Development Institute, The Icahn School of Medicine at Mount Sinai, New York, NY, United States of America Department of Medical Epidemiology and Biostatistics, Karolinska Insitutet, Novels vag 12a, Stockholm, Sweden Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, 8036, Austria Synlab Academy, Synlab Services GmbH, Mannheim, Germany Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, United Kingdom Division of Cardiovascular Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, United States of America Center for Non-Communicable Diseases, Karachi, Pakistan Department of Medicine, Central Hospital, Central Finland, Jyvaskyla, Finland Division of Endocrine and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan School of Medicine, National Yang-Ming University, Taipei, Taiwan School of Medicine, National defense Medical Center, Taipei, Taiwan Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland Dasman Diabetes Institute, Dasman, Kuwait Centre for Vascular Prevention, Danube-University Krems, Krems, Austria Saudi Diabetes Research Group, King Abdulaziz University, Fahd Medical Research Center, Jeddah, Saudi Arabia Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, United States of America Division of Cancer Control and Population Sciences, University of Pittsburgh Cancer Institute, Pittsburgh, PA, United States of America Laboratory of Epidemiology & Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America Department of Haematology, University of Cambridge, Hills Rd, Cambridge, United Kingdom The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge, United Kingdom Biomedical Research Centre Oxford Haematology Theme and Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Headington, Oxford, United Kingdom NHS Blood and Transplant, Headington, Oxford, United Kingdom Diabetes Unit and Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, United States of America Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, United States of America Department of Medicine, McGill University, Montreal, Quebec, Canada Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom IN YOUR COVERAGE PLEASE USE THIS URL TO PROVIDE ACCESS TO THE FREELY AVAILABLE PAPER: The author received no funding for this work. The author has declared that no competing interests exist. Paterson AD (2017) HbA1c for type 2 diabetes diagnosis in Africans and African Americans: Personalized medicine NOW! PLoS Med 14(9): e1002384. https:/ Genetics and Genome Biology, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada Divisions of Epidemiology and Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada IN YOUR COVERAGE PLEASE USE THIS URL TO PROVIDE ACCESS TO THE FREELY AVAILABLE PAPER:


News Article | December 20, 2016
Site: www.eurekalert.org

The bionic pancreas system developed by Boston University (BU) investigators proved better than either conventional or sensor-augmented insulin pump therapy at managing blood sugar levels in patients with type 1 diabetes living at home, with no restrictions, over 11 days. The report of a clinical trial led by a Massachusetts General Hospital (MGH) physician is receiving advance online publication in The Lancet. "For study participants living at home without limitations on their activity and diet, the bionic pancreas successfully reduced average blood glucose, while at the same time decreasing the risk of hypoglycemia," says Steven Russell, MD, PhD, of the MGH Diabetes Unit. "This system requires no information other than the patient's body weight to start, so it will require much less time and effort by health care providers to initiate treatment. And since no carbohydrate counting is required, it significantly reduces the burden on patients associated with diabetes management." Developed by Edward Damiano, PhD, and Firas El-Khatib, PhD, of the BU Department of Biomedical Engineering, the bionic pancreas controls patients' blood sugar with both insulin and glucagon, a hormone that increases glucose levels. After a 2010 clinical trial confirmed that the original version of the device could maintain near-normal blood sugar levels for more than 24 hours in adult patients, two follow-up trials - reported in a 2014 New England Journal of Medicine paper - showed that an updated version of the system successfully controlled blood sugar levels in adults and adolescents for five days. Another follow-up trial published in The Lancet Diabetes and Endocrinology in 2016 showed it could do the same for children as young as 6 years of age. While minimal restrictions were placed on participants in the 2014 trials, participants in both spent nights in controlled settings and were accompanied at all times by either a nurse for the adult trial or remained in a diabetes camp for the adolescent and pre-adolescent trials. Participants in the current trial had no such restrictions placed upon them, as they were able to pursue normal activities at home or at work with no imposed limitations on diet or exercise. Patients needed to live within a 30-minute drive of one of the trial sites - MGH, the University of Massachusetts Medical Center, Stanford University, and the University of North Carolina at Chapel Hill - and needed to designate a contact person who lived with them and could be contacted by study staff, if necessary. The bionic pancreas system - the same as that used in the 2014 studies - consisted of a smartphone (iPhone 4S) that could wirelessly communicate with two pumps delivering either insulin or glucagon. Every five minutes the smartphone received a reading from an attached continuous glucose monitor, which was used to calculate and administer a dose of either insulin or glucagon. The algorighms controlling the system were updated for the current trial to better respond to blood sugar variations. While the device allows participants to enter information about each upcoming meal into a smartphone app, allowing the system to deliver an anticipatory insulin dose, such entries were optional in the current trial. If participants' blood sugar dropped to dangerous levels or if the monitor or one of the pumps was disconnected for more than 15 minutes, the system would alerted study staff, allowing them to check with the participants or their contact persons. Study participants were adults who had been diagnosed with type 1 diabetes for a year or more and had used an insulin pump to manage their care for at least six months. Each of 39 participants that finished the study completed two 11-day study periods, one using the bionic pancreas and one using their usual insulin pump and any continous glucose monitor they had been using. In addition to the automated monitoring of glucose levels and administered doses of insulin or glucagon, participants completed daily surveys regarding any episodes of symptomatic hypoglycemia, carbohydrates consumed to treat those episodes, and any episodes of nausea. On days when participants were on the bionic pancreas, their average blood glucose levels were significantly lower - 141 mg/dl versus 162 mg/dl - than when on their standard treatment. Blood sugar levels were at levels indicating hypoglycemia (less than 60 mg/dl) for 0.6 percent of the time when participants were on the bionic pancreas, versus 1.9 percent of the time on standard treatment. Participants reported fewer episodes of symptomatic hypoglycemia while on the bionic pancreas, and no episodes of severe hypoglycemia were associated with the system. The system performed even better during the overnight period, when the risk of hypoglycemia is particularly concerning. "Patients with type 1 diabetes worry about developing hypoglycemia when they are sleeping and tend to let their blood sugar run high at night to reduce that risk," explains Russell, an assistant professor of Medicine at Harvard Medical School. "Our study showed that the bionic pancreas reduced the risk of overnight hypoglycemia to almost nothing without raising the average glucose level. In fact the improvement in average overnight glucose was greater than the improvement in average glucose over the full 24-hour period." Damiano, whose work on this project is inspired by his own 17-year-old son's type 1 diabetes, adds, "The availability of the bionic pancreas would dramatically change the life of people with diabetes by reducing average glucose levels - thereby reducing the risk of diabetes complications - reducing the risk of hypoglycemia, which is a constant fear of patients and their families, and reducing the emotional burden of managing type 1 diabetes." A co-author of the Lancet report, Damiano is a professor of Biomedical Engineering at Boston University. The BU patents covering the bionic pancreas have been licensed to Beta Bionics, a startup company co-founded by Damiano and El-Khatib. The company's latest version of the bionic pancreas, called the iLet, integrates all components into a single unit, which will be tested in future clinical trials. People interested in participating in upcoming trials may contact Russell's team at the MGH Diabetes Research Center in care of Llazar Cuko. El-Khatib is the lead author of the Lancet paper, and additional co-authors include David Harlan, MD, of the University of Massachusetts Medical Center, Bruce Buckingham,MD, of Stanford University, and John Buse, MD, of the University of North Carolina at Chapel Hill. Support for the study includes National Institute of Health grants R01DK097657 and DP3DK101084 and National Center for Advancing Translational Sciences awards UL1TR001453, UL1TR001085 and UL1TR001111. Founded in 1839, Boston University is an internationally recognized institution of higher education and research. With more than 33,000 students, it is the fourth-largest independent university in the United States. BU consists of 16 schools and colleges, along with a number of multi-disciplinary centers and institutes integral to the University's research and teaching mission. In 2012, BU joined the Association of American Universities, a consortium of 62 leading research universities in the United States and Canada. Massachusetts General Hospital, founded in 1811, is the original and largest teaching hospital of Harvard Medical School. The MGH Research Institute conducts the largest hospital-based research program in the nation, with an annual research budget of more than $800 million and major research centers in HIV/AIDS, cardiovascular research, cancer, computational and integrative biology, cutaneous biology, human genetics, medical imaging, neurodegenerative disorders, regenerative medicine, reproductive biology, systems biology, photomedicine and transplantation biology. The MGH topped the 2015 Nature Index list of health care organizations publishing in leading scientific journals and earned the prestigious 2015 Foster G. McGaw Prize for Excellence in Community Service. In August 2016 the MGH was once again named to the Honor Roll in the U.S. News & World Report list of "America's Best Hospitals."


Harrison C.L.,Monash University | Lombard C.B.,Monash University | Strauss B.J.,Monash University | Teede H.J.,Diabetes Unit
Obesity | Year: 2013

Objective: Optimizing gestational weight gain (GWG) in early pregnancy is of clinical and public health importance, especially in higher risk pregnancies. Design and Methods: In a robustly designed, randomized controlled trial, 228 pregnant women at risk of developing gestational diabetes mellitus (GDM) were allocated to either control (written health information only) or intervention (four-session lifestyle program). All women received standard maternal care. Measures were completed at 12-15 and 26-28 weeks gestation. Measures included anthropometrics (weight and height), physical activity (pedometer and International Physical Activity Questionnaire), questionnaires (risk perception), and GDM screening. Results: The mean (SD) age [31.7 (4.5) and 32.4 (4.7) years] and body mass index [BMI; 30.3 (5.9) and 30.4 (5.6) kg/m 2] were similar between control and intervention groups, respectively. By 28 weeks, GWG was significantly different between control and intervention groups [6.9 (3.3) vs. 6.0 (2.8) kg, P < 0.05]. When stratified according to baseline BMI, overweight women in the control group gained significantly more weight compared to overweight women in the intervention group [7.8 (3.4) vs. 6.0 (2.2) kg, P < 0.05], yet in obese women, GWG was similar in both groups. Physical activity levels declined by 28 weeks gestation overall (P < 0.01); however, the intervention group retained a 20% higher step count compared to controls [5,203 (3,368) vs. 4,140 (2,420) steps/day, P < 0.05]. Overall, GDM prevalence was 22%, with a trend toward less cases in the intervention group (P = 0.1). Conclusions: Results indicate that a low-intensity lifestyle intervention, integrated with antenatal care, optimizes healthy GWG and attenuates physical activity decline in early pregnancy. Efficacy in limiting weight gain was greatest in overweight women and in high-risk ethnically diverse women. Copyright © 2013 The Obesity Society.


Harrison C.L.,Monash University | Thompson R.G.,Monash University | Teede H.J.,Monash University | Teede H.J.,Diabetes Unit | Lombard C.B.,Monash University
International Journal of Behavioral Nutrition and Physical Activity | Year: 2011

Background: Currently, little is known about physical activity patterns in pregnancy with prior estimates predominantly based on subjective assessment measures that are prone to error. Given the increasing obesity rates and the importance of physical activity in pregnancy, we evaluated the relationship and agreement between subjective and objective physical activity assessment tools to inform researchers and clinicians on optimal assessment of physical activity in pregnancy.Methods: 48 pregnant women between 26-28 weeks gestation were recruited. The Yamax pedometer and Actigraph accelerometer were worn for 5-7 days under free living conditions and thereafter the International Physical Activity Questionnaire (IPAQ) was completed. IPAQ and pedometer estimates of activity were compared to the more robust and accurate accelerometer data.Results: Of 48 women recruited, 30 women completed the study (mean age: 33.6 ± 4.7 years; mean BMI: 31.2 ± 5.1 kg/m2) and 18 were excluded (failure to wear [n = 8] and incomplete data [n = 10]). The accelerometer and pedometer correlated significantly on estimation of daily steps (ρ = 0.69, p < 0.01) and had good absolute agreement with low systematic error (mean difference: 505 ± 1498 steps/day). Accelerometer and IPAQ estimates of total, light and moderate Metabolic Equivalent minutes/day (MET min-1day-1) were not significantly correlated and there was poor absolute agreement. Relative to the accelerometer, the IPAQ under predicted daily total METs (105.76 ± 259.13 min-1day-1) and light METs (255.55 ± 128.41 min-1day-1) and over predicted moderate METs (-112.25 ± 166.41 min-1day-1).Conclusion: Compared with the accelerometer, the pedometer appears to provide a reliable estimate of physical activity in pregnancy, whereas the subjective IPAQ measure performed less accurately in this setting. Future research measuring activity in pregnancy should optimally encompass objective measures of physical activity.Trial Registration: Australian New Zealand Clinical Trial Registry Number: ACTRN12608000233325. Registered 7/5/2008. © 2011 Harrison et al; licensee BioMed Central Ltd.


Teede H.,Monash University | Teede H.,Diabetes Unit | Deeks A.,Monash University | Moran L.,Monash University
BMC Medicine | Year: 2010

Polycystic ovary syndrome (PCOS) is of clinical and public health importance as it is very common, affecting up to one in five women of reproductive age. It has significant and diverse clinical implications including reproductive (infertility, hyperandrogenism, hirsutism), metabolic (insulin resistance, impaired glucose tolerance, type 2 diabetes mellitus, adverse cardiovascular risk profiles) and psychological features (increased anxiety, depression and worsened quality of life). Polycystic ovary syndrome is a heterogeneous condition and, as such, clinical and research agendas are broad and involve many disciplines. The phenotype varies widely depending on life stage, genotype, ethnicity and environmental factors including lifestyle and bodyweight. Importantly, PCOS has unique interactions with the ever increasing obesity prevalence worldwide as obesity-induced insulin resistance significantly exacerbates all the features of PCOS. Furthermore, it has clinical implications across the lifespan and is relevant to related family members with an increased risk for metabolic conditions reported in first-degree relatives. Therapy should focus on both the short and long-term reproductive, metabolic and psychological features. Given the aetiological role of insulin resistance and the impact of obesity on both hyperinsulinaemia and hyperandrogenism, multidisciplinary lifestyle improvement aimed at normalising insulin resistance, improving androgen status and aiding weight management is recognised as a crucial initial treatment strategy. Modest weight loss of 5% to 10% of initial body weight has been demonstrated to improve many of the features of PCOS. Management should focus on support, education, addressing psychological factors and strongly emphasising healthy lifestyle with targeted medical therapy as required. Monitoring and management of long-term metabolic complications is also an important part of routine clinical care. Comprehensive evidence-based guidelines are needed to aid early diagnosis, appropriate investigation, regular screening and treatment of this common condition. Whilst reproductive features of PCOS are well recognised and are covered here, this review focuses primarily on the less appreciated cardiometabolic and psychological features of PCOS. © 2010 Teede et al; licensee BioMed Central Ltd.


Harrison C.L.,Monash University | Lombard C.B.,Monash University | Lombard C.B.,Diabetes Unit | Moran L.J.,Monash University | And 2 more authors.
Human Reproduction Update | Year: 2011

Background: Polycystic ovary syndrome (PCOS) is a common endocrine disorder, affecting 8-12% of women. Lifestyle modification, including increased physical activity, is the first-line approach in managing PCOS. A systematic review was performed to identify and describe the effect of exercise as an independent intervention on clinical outcomes in PCOS. Methods: Five databases were searched with no time limit. A pre-specified definition of PCOS was not used. Studies were included if exercise therapy (aerobic and/or resistance) could be evaluated as an independent treatment against a comparison group. Outcomes measured included cardiovascular risk factors [insulin resistance (IR), lipid profiles, blood pressure and weight] and reproductive measures (ovulation, menstrual regularity and fertility outcomes). Quality analysis was performed based on the Cochrane Handbook of Systematic Reviews and the Quality of Reporting of Meta-Analyses checklist. Results: Eight manuscripts were identified (five randomized controlled trials and three cohort studies). All studies involved moderate intensity physical activity and most were of either 12 or 24 weeks duration with frequency and duration of exercise sessions ranging between studies. The most consistent improvements included improved ovulation, reduced IR (9-30%) and weight loss (4.5-10%). Improvements were not dependant on the type of exercise, frequency or length of exercise sessions. Conclusions: Exercise-specific interventions in PCOS are limited. Studies vary considerably in design, intensity and outcome measures; therefore conclusive results remain elusive. Larger, optimally designed studies are needed to both gain insights into the mechanisms of exercise action and to evaluate the public health impact of exercise of PCOS. © The Author 2010. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved.


Francia P.,University of Florence | Gulisano M.,University of Florence | Anichini R.,Diabetes Unit | Seghieri G.,Tuscany Regional Health Agency ARS
Current Diabetes Reviews | Year: 2014

Lower extremity ulcers represent a serious and costly complication of diabetes mellitus. Many factors contribute to the development of diabetic foot. Peripheral neuropathy and peripheral vascular disease are the main causes of foot ulceration and contribute in turn to the growth of additional risk factors such as limited joint mobility, muscular alterations and foot deformities. Moreover, a deficit of balance, posture and biomechanics can be present, in particular in patients at high risk for ulceration. The result of this process may be the development of a vicious cycle which leads to abnormal distribution of the foot's plantar pressures in static and dynamic postural conditions. This review shows that some of these risk factors significantly improve after a few weeks of exercise therapy (ET) intervention. Accordingly it has been suggested that ET can be an important weapon in the prevention of foot ulcer. The aim of ET can relate to one or more alterations typically found in diabetic patients, although greater attention should be paid to the evaluation and possible correction of body balance, rigid posture and biomechanics. Some of the most important limitations of ET are difficult access to therapy, patient compliance and the transitoriness of the results if the training stops. Many proposals have been made to overcome such limitations. In particular, it is important that specialized centers offer the opportunity to participate in ET and during the treatment the team should work to change the patient’s lifestyle by improving the execution of appropriate daily physical activity. © 2014 Bentham Science Publishers.


Toperoff G.,Hebrew University of Jerusalem | Aran D.,Hebrew University of Jerusalem | Kark J.D.,Hebrew University of Jerusalem | Rosenberg M.,Hebrew University of Jerusalem | And 7 more authors.
Human Molecular Genetics | Year: 2012

Inter-individual DNA methylation variations were frequently hypothesized to alter individual susceptibility to Type 2 Diabetes Mellitus (T2DM). Sequence-influenced methylations were described in T2DM-associated genomic regions, but evidence for direct, sequence-independent association with disease risk is missing. Here, we explore disease-contributing DNA methylation through a stepwise study design: first, a pool-based, genome-scale screen among 1169 case and control individuals revealed an excess of differentially methylated sites in genomic regions that were previously associated with T2DM through genetic studies. Next, in-depth analyses were performed at selected top-ranking regions. A CpG site in the first intron of the FTO gene showed small (3.35%) but significant (P = 0.000021) hypomethylation of cases relative to controls. The effect was independent of the sequence polymorphism in the region and persists among individuals carrying the sequence-risk alleles. The odds of belonging to the T2DM group increased by 6.1% for every 1% decrease in methylation (OR = 1.061, 95% CI: 1.032-1.090), the odds ratio for decrease of 1 standard deviation of methylation (adjusted to gender) was 1.5856 (95% CI: 1.2824-1.9606) and the sensitivity (area under the curve = 0.638, 95% CI: 0.586-0.690; males = 0.675, females = 0.609) was better than that of the strongest known sequence variant. Furthermore, a prospective study in an independent population cohort revealed significant hypomethylation of young individuals that later progressed to T2DM, relative to the individuals who stayed healthy. Further genomic analysis revealed co-localization with gene enhancers and with binding sites for methylation-sensitive transcriptional regulators. The data showed that low methylation level at the analyzed sites is an early marker of T2DM and suggests a novel mechanism by which early-onset, inter-individual methylation variation at isolated non-promoter genomic sites predisposes to T2DM. © The Author 2011. Published by Oxford University Press. All rights reserved.


Zenari L.,Diabetes Unit | Marangoni A.,Ospedale San Bassiano
Diabetes, Obesity and Metabolism | Year: 2013

The aim of therapy in type 2 diabetes in terms of blood glucose control is to reduce to target levels HbA1c and to reduce glycaemic variability in order to avoid both hypoglycaemia and wide excursions of postprandial glucose. The first approach to reduce glycaemic variability should consider a dietary and behavioural approach aiming to limit the glycaemic index and the glycaemic load of food and the prescription and implementation of a physical activity plan appropriate for the subject. From the pharmacological point of view, the diabetes specialist has now a much richer therapeutic armamentarium. The therapeutic algorithms can help the physician to choose the most appropriate drug. The traditional approach involves: i) metformin, acting mainly on fasting blood glucose; ii) sulphonylureas, that have shown a number of drawbacks, including the high risk of hypoglycemia; iii) pioglitazone, with a substantial effect on fasting and postprandial glucose and a low risk of hypoglycaemia; iv) insulin, that can be utilized with the basal or prandial approach. The new drugs belonging to the class of dipeptidyl peptidase-4 inhibitors have shown the reduction of postprandial glucose, a neutral effect on weight increase, a good safety profile and preliminary positive cardiovascular effects. When excess weight prevails, the glucagon-like peptide-1 agonists may be the preferred choice for their effect on weight reduction, reduction of hyperinsulinism and glycaemic variability. © 2013 John Wiley & Sons Ltd.


Deeks A.A.,Monash Institute of Medical Research | Gibson-Helm M.E.,Monash Institute of Medical Research | Teede H.J.,Monash Institute of Medical Research | Teede H.J.,Diabetes Unit
Fertility and Sterility | Year: 2010

Polycystic ovary syndrome (PCOS) is associated with high levels of depression, which impact quality of life and limit self-efficacy, yet less is known about prevalence of anxiety. This cross-sectional, observational study of community-based women with PCOS comprehensively examined mood and found that anxiety existed at higher levels than depression, anxiety was underdiagnosed, and more women with PCOS who reported infertility were depressed. © 2010 by American Society for Reproductive Medicine.

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