Institute of Medical Informatics
Institute of Medical Informatics
News Article | September 12, 2017
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:
Muehlmann M.,Ludwig Maximilians University of Munich |
Koerte I.K.,Ludwig Maximilians University of Munich |
Laubender R.P.,Institute of Medical Informatics |
Steffinger D.,Ludwig Maximilians University of Munich |
And 5 more authors.
Investigative Radiology | Year: 2013
Objectives: The aim of this study was to investigate the relationship between the pressure setting of the ventriculoperitoneal (VP) shunt valve and a magnetic resonance (MR)-based estimate of intracranial pressure (ICP) in children with shunt-treated hydrocephalus without clinical signs of shunt malfunction. Materials and Methods: Institutional review board approval was obtained before the study, and all subjects and/or their legal guardians provided written informed consent. In this prospective study, 15 consecutive patients (median age, 8.25 years; range, 2.2-18.4 years; 6 girls and 9 boys) with shunt-treated hydrocephalus without signs of shunt malfunction were examined with retrospectively gated phase contrast sequences to quantify arterial inflow, venous outflow, and cerebrospinal fluid (CSF) flow to and from the cranial vault. The ratio of the maximal intracranial volume change and the pulse pressure gradient change was used to derive MR-ICP. Spearman ρ was used to test for the association of setting of the shunt valve opening pressure and MR-ICP. Results: Shunt valve opening pressure settings and MR-ICP were positively correlated (Spearman ρ = 0.64, P < 0.01). Median MR-ICP was 8.67 mm Hg (interquartile range [IQR], 1.59 mm Hg) and median setting of the VP-shunt valve was 6.62 mm Hg (IQR, 1.47 mm Hg). The median MR-ICP was 1.9 mm Hg (IQR, 0.73 mm Hg) higher than the setting of the shunt valve. Conclusion: There is a positive correlation between MR-ICP and VP shunt valve opening pressure setting. The systematically higher assessment of MR-ICP is most likely a result of outflow resistance within the shunt tubing system and well within the known fluctuation rates of VP shunt systems. © 2013 by Lippincott Williams & Wilkins.
Crispin A.,Institute of Medical Informatics |
Mansmann U.,Institute of Medical Informatics |
Munte A.,Bavarian Association of Compulsory Health Insurance Physicians |
Op Den Winkel M.,Ludwig Maximilians University of Munich |
And 2 more authors.
Digestion | Year: 2013
Background/Aims: Surveillance colonoscopy is recommended after polypectomy of adenoma and surgery for colorectal cancer. The purpose of this study was to assess the frequency of advanced adenoma and cancer in colonoscopies performed for surveillance compared to screening colonoscopies. Methods: Analysis of relative frequencies of findings in colonoscopies performed for post-adenoma surveillance (post-ad), post-cancer surveillance (post-crc), screening, and follow-up of a positive fecal occult blood test (FOBT). Logistic regression was used to identify the risk for advanced adenoma (adenoma ≥10 mm, containing high-grade dysplasia, or villous histology) and cancer. Results: 324,912 colonoscopies were included in the analysis: 81,877 post-ad, 26,896 post-crc, 178,305 screening, 37,834 positive FOBT. Advanced adenoma (cancer) was diagnosed in 8.0% (0.4%) of post-ad, 5.0% (1.0%) of post-crc, 7.4% (1.1%) of screening, and 11.7% (3.6%) of positive FOBT colonoscopies. Compared to screening, the odds ratios for finding advanced adenoma were 0.93 (95% CI 0.88-0.98) for post-ad, 0.96 (0.86-1.08) for post-crc, and 1.18 (1.09-1.28) for positive FOBT colonoscopies. The odds ratios for the diagnosis of cancer were 0.29 (0.24-0.36) for post-ad, 0.81 (0.61-1.07) for post-crc, and 2.77 (2.43-3.17) for positive FOBT. Conclusion: Colonoscopy for post-ad surveillance but not colonoscopy for post-crc surveillance is associated with a lower risk of diagnosis of advanced adenoma and cancer. Copyright © 2013 S. Karger AG, Basel.
PubMed | Martin Luther University of Halle Wittenberg, University of Bonn, Institute of Medical Informatics, Life and Brain Center and University of Heidelberg
Type: Journal Article | Journal: American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics | Year: 2015
Transcription factor 4 (TCF4) is one of the most robust of all reported schizophrenia risk loci and is supported by several genetic and functional lines of evidence. While numerous studies have implicated common genetic variation at TCF4 in schizophrenia risk, the role of rare, small-sized variants at this locus-such as single nucleotide variants and short indels which are below the resolution of chip-based arrays requires further exploration. The aim of the present study was to investigate the association between rare TCF4 sequence variants and schizophrenia. Exon-targeted resequencing was performed in 190 German schizophrenia patients. Six rare variants at the coding exons and flanking sequences of the TCF4 gene were identified, including two missense variants and one splice site variant. These six variants were then pooled with nine additional rare variants identified in 379 European participants of the 1000 Genomes Project, and all 15 variants were genotyped in an independent German sample (n=1,808 patients; n=2,261 controls). These data were then analyzed using six statistical methods developed for the association analysis of rare variants. No significant association (P<0.05) was found. However, the results from our association and power analyses suggest that further research into the possible involvement of rare TCF4 sequence variants in schizophrenia risk is warranted by the assessment of larger cohorts with higher statistical power to identify rare variant associations.
Reulen H.-J.,Ludwig Maximilians University of Munich |
Poepperl G.,Nuclear Medicine |
Goetz C.,Ludwig Maximilians University of Munich |
Gildehaus F.J.,Nuclear Medicine |
And 5 more authors.
Journal of Neurosurgery | Year: 2015
Object The aim in this study was to present long-term results regarDing overall survival (OS), adverse effects, and toxicity following fractionated intracavitary radioimmunotherapy (RIT) with iodine-131? or yttrium-90?labeled anti-tenascin monoclonal antibody (131I-mAB or 90Y-mAB) for the treatment of patients with malignant glioma. Methods In 55 patients (15 patients with WHO Grade III anaplastic astrocytoma [AA] and 40 patients with WHO Grade IV glioblastoma multiforme [GBM]) following tumor resection and conventional radiotherapy, radioimmunoconjugate was introduced into the postoperative resection cavity. Patients received 5 cycles of 90Y-mAB (Group A, average dose 18 mCi/ cycle), 5 cycles of 131I-mAB (Group B, average dose 30 mCi/cycle), or 3 cycles of 131I-mAB (Group C, 50, 40, and 30 mCi). Results Median OS of patients with AA was 77.2 months (95% CI 30.8 to > 120). Five AA patients (33%) are currently alive, with a median observation time of 162.2 months. Median OS of all 40 patients with GBM was 18.9 months (95% CI 15.8-25.3), and median OS was 25.3 months (95% CI 18-30) for those patients treated with the 131I-mAB. Three GBM patients are currently alive. One-, 2-, and 3-year survival probabilities were 100%, 93.3%, and 66.7%, respectively, for AA patients and 82.5%, 42.5%, and 15.9%, respectively, for GBM patients. Restratification of GBM patients by recursive partitioning analysis (RPA) Classes III, IV, and V produced median OSs of 31.1, 18.9, and 14.5 months, respectively (p = 0.004), which was higher than expected. Multivariate analysis confirmed the role of RPA class, age, and treatment in predicting survival. No Grade 3 or 4 hematological, nephrologic, or hepatic toxic effects were observed; 4 patients developed Grade 3 neurological deficits. Radiological signs of radionecrosis were observed in 6 patients, who were all responDing well to steroids. Conclusions Median OS of GBM and AA patients treated with 131I-mABs reached 25.3 and 77.2 months, respectively, thus markedly exceeDing that of historical controls. Adverse events remained well controllable with the fractionated dosage regimen. © AANS, 2015.
Liu Y.-C.,National Cheng Kung University |
Chen L.-C.,Academia Sinica, Taiwan |
Liu C.-W.,National Cheng Kung University |
Tseng V.S.,National Cheng Kung University |
Tseng V.S.,Institute of Medical Informatics
International Journal of Data Mining and Bioinformatics | Year: 2014
In recent years, mass spectrometry data analysis has become an important protein identifi cation technique. The mass spectrometry technologies emerge as useful tools for biomarker discovery through studying protein profi les in various biological specimens. In mining mass spectrometry datasets, peak alignment is a critical issue among the preprocessing steps that affect the quality of analysis results. However, the existing peak alignment methods are sensitive to noise peaks across various mass spectrometry samples. In this paper, we proposed a novel algorithm named Two-Phase Clustering for peak Alignment (TPC-Align) to align mass spectrometry peaks across samples in the pre-processing phase. The TPC-Align algorithm sequentially considers the distribution of intensity values and the locations of mass-to-charge ratio values of peaks between samples. Moreover, TPC-Align algorithm can also report a list of signifi cantly differential peaks between samples, which serve as the candidate biomarkers for further biological study. The proposed peak alignment method was compared to the current peak alignment approach based on one-dimension hierarchical clustering through experimental evaluations and the results show that TPC-Align outperforms the traditional method on the real dataset. © 2014 Inderscience Enterprises Ltd.
Stang A.,German Cancer Research Center |
Jockel K.-H.,Institute of Medical Informatics
Cancer | Year: 2015
BACKGROUND: After a pilot study on skin cancer screening was performed between 2003 and 2004 in Schleswig-Holstein, Germany, the country implemented what to the authors' knowledge is the first nationwide skin cancer screening program in the world in 2008. The objective of the current study was to provide details regarding mortality trends in Schleswig-Holstein and Germany in relation to the screening. METHODS: Annual age-standardized mortality rates for skin melanoma (using the 10th Revision of the International Statistical Classification of Diseases and Related Health Problems [ICD-10] code C43) and malignant neoplasms of ill-defined, secondary, and unspecified sites (ICD-10 code C76-C80) were analyzed. The European Standard population was used for age standardization. A bias analysis was performed to estimate the number of skin melanoma deaths that may have been incorrectly counted as ICD-10 code C76-C80 when the skin melanoma mortality declined in Schleswig-Holstein. RESULTS: The observed mortality decline in Schleswig-Holstein 5 years after the pilot study was accompanied by a considerable increase in the number of deaths due to malignant neoplasms of ill-defined, secondary, and unspecified sites (ICD-10 code C76-C80) that is not explainable by an increase in the incidence of these neoplasms. Incorrect assignment of 8 to 35 and 12 to 23 skin melanoma deaths per year among men and women, respectively, as ICD-10 code C76-C80 during 2007 through 2010 could explain the transient skin melanoma mortality decline observed in Schleswig-Holstein. Five years after implementation of the program, the nationwide skin melanoma mortality increased (age-standardized rate change of +0.4 per 100,000 person-years [95% confidence interval, 0.2-0.6] in men and +0.1 per 100,000 person-years [95% confidence interval, -0.1 to 0.2] in women). CONCLUSIONS: Although the current analyses raise doubts that the skin cancer screening program in Germany can reduce the skin cancer mortality rate, the authors do not believe the program should be immediately stopped. Further in-depth evaluations are required. © 2015 American Cancer Society.
Varsier N.,Orange Group |
Dahdouh S.,Orange Group |
Dahdouh S.,Telecom ParisTech |
Serrurier A.,Telecom ParisTech |
And 9 more authors.
Physics in Medicine and Biology | Year: 2014
This paper analyzes the influence of pregnancy stage and fetus position on the whole-body and brain exposure of the fetus to radiofrequency electromagnetic fields. Our analysis is performed using semi-homogeneous pregnant woman models between 8 and 32 weeks of amenorrhea. By analyzing the influence of the pregnancy stage on the environmental whole-body and local exposure of a fetus in vertical position, head down or head up, in the 2100 MHz frequency band, we concluded that both whole-body and average brain exposures of the fetus decrease during the first pregnancy trimester, while they advance during the pregnancy due to the rapid weight gain of the fetus in these first stages. From the beginning of the second trimester, the whole-body and the average brain exposures are quite stable because the weight gains are quasi proportional to the absorbed power increases. The behavior of the fetus whole-body and local exposures during pregnancy for a fetus in the vertical position with the head up were found to be of a similar level, when compared to the position with the head down they were slightly higher, especially in the brain. © 2014 Institute of Physics and Engineering in Medicine.
Herbst A.,Ludwig Maximilians University of Munich |
Rahmig K.,Ludwig Maximilians University of Munich |
Stieber P.,Institute of Clinical Chemistry |
Philipp A.,Ludwig Maximilians University of Munich |
And 6 more authors.
American Journal of Gastroenterology | Year: 2011
Objectives: Colorectal cancer is the third most common cancer and a major cause of cancer-related deaths. Early detection of colonic lesions can reduce the incidence and mortality of colorectal cancer. Colonoscopy is the screening test for colorectal cancer with the highest efficacy, but its acceptance in the general public is rather low. To identify suitable tumor-derived markers that could detect colorectal cancer in blood samples, we analyzed the methylation status of a panel of genes in sera of affected patients. Methods: Using methylation-specific quantitative PCR, we analyzed the methylation of ten marker genes in sera of healthy individuals and patients with colorectal cancer. Results: Only HLTF, HPP1/TPEF, and NEUROG1 DNA methylation was detectable in at least 50% of patients with colorectal cancers. Whereas HLTF and HPP1/TPEF preferentially detected advanced and metastasized colorectal cancers, NEUROG1 methylation was detectable in UICC stages I-IV at a similar rate. Compared with other methylation markers, such as ALX4, SEPT9, and vimentin, NEUROG1 shows a higher sensitivity for colorectal cancer at UICC stages I and II. At a specificity of 91%, NEUROG1 reached a sensitivity of 61% (confidence interval, 50.4-70.6%) for the detection of colorectal cancers. Furthermore, detection of NEUROG1 methylation was independent of age and gender. Conclusions: Methylation of the NEUROG1 gene is frequently found in sera of patients with colorectal cancers independent of tumor stage. The quantitative detection of NEUROG1 DNA methylation in serum is a suitable approach for the non-invasive screening for asymptomatic colorectal cancer. © 2011 by the American College of Gastroenterology.
Lippert J.,University of Munster |
Halfter H.,University of Munster |
Heidbreder A.,University of Munster |
Rohr D.,University of Munster |
And 4 more authors.
PLoS ONE | Year: 2014
From single cell organisms to the most complex life forms, the 24-hour circadian rhythm is important for numerous aspects of physiology and behavior such as daily periodic fluctuations in body temperature and sleep-wake cycles. Influenced by environmental cues - mainly by light input -, the central pacemaker in the thalamic suprachiasmatic nuclei (SCN) controls and regulates the internal clock mechanisms which are present in peripheral tissues. In order to correlate modifications in the molecular mechanisms of circadian rhythm with the pathophysiology of idiopathic hypersomnia, this study aimed to investigate the dynamics of the expression of circadian clock genes in dermal fibroblasts of idiopathic hypersomniacs (IH) in comparison to those of healthy controls (HC). Ten clinically and polysomnographically proven IH patients were recruited from the department of sleep medicine of the University Hospital of Muenster. Clinical diagnosis was done by two consecutive polysomnographies (PSG) and Multiple Sleep Latency Test (MSLT). Fourteen clinical healthy volunteers served as control group. Dermal fibroblasts were obtained via punch biopsy and grown in cell culture. The expression of circadian clock genes was investigated by semiquantitative Reverse Transcriptase-PCR qRT-PCR analysis, confirming periodical oscillation of expression of the core circadian clock genes BMAL1, PER1/2 and CRY1/2. The amplitude of the rhythmically expressed BMAL1, PER1 and PER2 was significantly dampened in dermal fibroblasts of IH compared to HC over two circadian periods whereas the overall expression of only the key transcriptional factor BMAL1 was significantly reduced in IH. Our study suggests for the first time an aberrant dynamics in the circadian clock in IH. These findings may serve to better understand some clinical features of the pathophysiology in sleep - wake rhythms in IH. © 2014 Lippert et al.