Institution
Glostrup Hospital
Healthcare•Glostrup Municipality, Denmark•
About: Glostrup Hospital is a healthcare organization based out in Glostrup Municipality, Denmark. It is known for research contribution in the topics: Population & Migraine. The organization has 1844 authors who have published 2237 publications receiving 105232 citations.
Topics: Population, Migraine, Blood pressure, Diabetes mellitus, Type 2 diabetes
Papers published on a yearly basis
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5,847 citations
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University of Copenhagen1, Institut national de la recherche agronomique2, Vrije Universiteit Brussel3, South China University of Technology4, Glostrup Hospital5, Aalborg University6, University of Southern Denmark7, Technical University of Denmark8, Wageningen University and Research Centre9, Pierre-and-Marie-Curie University10, French Institute of Health and Medical Research11, University of Helsinki12, Institut de recherche pour le développement13
TL;DR: The authors' classifications based on variation in the gut microbiome identify subsets of individuals in the general white adult population who may be at increased risk of progressing to adiposity-associated co-morbidities.
Abstract: We are facing a global metabolic health crisis provoked by an obesity epidemic. Here we report the human gut microbial composition in a population sample of 123 non-obese and 169 obese Danish individuals. We find two groups of individuals that differ by the number of gut microbial genes and thus gut bacterial richness. They contain known and previously unknown bacterial species at different proportions; individuals with a low bacterial richness (23% of the population) are characterized by more marked overall adiposity, insulin resistance and dyslipidaemia and a more pronounced inflammatory phenotype when compared with high bacterial richness individuals. The obese individuals among the lower bacterial richness group also gain more weight over time. Only a few bacterial species are sufficient to distinguish between individuals with high and low bacterial richness, and even between lean and obese participants. Our classifications based on variation in the gut microbiome identify subsets of individuals in the general white adult population who may be at increased risk of progressing to adiposity-associated co-morbidities.
3,448 citations
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TL;DR: An artificial neural network method is presented that predicts phosphorylation sites in independent sequences with a sensitivity in the range from 69 % to 96 % and predicts novel phosphorylated sites in the p300/CBP protein that may regulate interaction with transcription factors and histone acetyltransferase activity.
2,984 citations
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Wellcome Trust Centre for Human Genetics1, University of Michigan2, University of Oxford3, Massachusetts Institute of Technology4, Brigham and Women's Hospital5, Harvard University6, Lund University7, Steno Diabetes Center8, University of Southern California9, National Institutes of Health10, Health Science University11, Novartis12, Ninewells Hospital13, University of Exeter14, University of Düsseldorf15, Queen Mary University of London16, Glostrup Hospital17, deCODE genetics18, University of Eastern Finland19, University of Cambridge20, Aarhus University21, University of North Carolina at Chapel Hill22, Norwegian University of Science and Technology23, Wellcome Trust Sanger Institute24, University of Bristol25, University of Helsinki26, Newcastle University27
TL;DR: The results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D, and detect at least six previously unknown loci with robust evidence for association.
Abstract: Genome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D). Established associations to common and rare variants explain only a small proportion of the heritability of T2D. As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and approximately 2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975. We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P = 5.0 x 10(-14)), CDC123-CAMK1D (P = 1.2 x 10(-10)), TSPAN8-LGR5 (P = 1.1 x 10(-9)), THADA (P = 1.1 x 10(-9)), ADAMTS9 (P = 1.2 x 10(-8)) and NOTCH2 (P = 4.1 x 10(-8)) gene regions. Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D.
1,872 citations
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TL;DR: A new method for kinase‐specific prediction of phosphorylation sites, NetPhosK, is presented, which extends the earlier and more general tool, netPhos, and the issues of underestimation, over‐prediction and strategies for improving prediction specificity are discussed.
Abstract: Post-translational modifications (PTMs) occur on almost all proteins analyzed to date. The function of a modified protein is often strongly affected by these modifications and therefore increased knowledge about the potential PTMs of a target protein may increase our understanding of the molecular processes in which it takes part. High-throughput methods for the identification of PTMs are being developed, in particular within the fields of proteomics and mass spectrometry. However, these methods are still in their early stages, and it is indeed advantageous to cut down on the number of experimental steps by integrating computational approaches into the validation procedures. Many advanced methods for the prediction of PTMs exist and many are made publicly available. We describe our experiences with the development of prediction methods for phosphorylation and glycosylation sites and the development of PTM-specific databases. In addition, we discuss novel ideas for PTM visualization (exemplified by kinase landscapes) and improvements for prediction specificity (by using ESS--evolutionary stable sites). As an example, we present a new method for kinase-specific prediction of phosphorylation sites, NetPhosK, which extends our earlier and more general tool, NetPhos. The new server, NetPhosK, is made publicly available at the URL http://www.cbs.dtu.dk/services/NetPhosK/. The issues of underestimation, over-prediction and strategies for improving prediction specificity are also discussed.
1,838 citations
Authors
Showing all 1849 results
Name | H-index | Papers | Citations |
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Leif Groop | 158 | 919 | 136056 |
Torben Jørgensen | 135 | 883 | 86822 |
Jes Olesen | 124 | 808 | 79687 |
Peter J. Goadsby | 123 | 946 | 73783 |
Lars Køber | 114 | 1155 | 77298 |
Olle Melander | 109 | 783 | 75911 |
Louis M. Kunkel | 104 | 352 | 44428 |
Lars Edvinsson | 97 | 1003 | 43544 |
Baha M. Sibai | 93 | 470 | 30922 |
Knut Borch-Johnsen | 88 | 333 | 40123 |
Mikkel Østergaard | 87 | 583 | 27162 |
Sten Madsbad | 87 | 532 | 28980 |
Allan Linneberg | 85 | 577 | 45508 |
Olaf B. Paulson | 85 | 455 | 22268 |
Alan H. Beggs | 83 | 336 | 26523 |