An interdisciplinary panel of specialists met in Mallorca in the first European Symposium on Morbid Obesity entitled; "Morbid Obesity, an Interdisciplinary Approach". During the two and half days of ...the meeting, the participants discussed several aspects related to pathogenesis, evaluation, and treatment of morbid obesity. The expert panel included basic research scientists, dietitians and nutritionists, exercise physiologists, endocrinologists, psychiatrists, cardiologists, pneumonologists, anesthesiologists, and bariatric surgeons with expertise in the different weight loss surgeries. The symposium was sponsored by the Balearic Islands Health Department; however, this statement is an independent report of the panel and is not a policy statement of any of the sponsors or endorsers of the Symposium. The prevalence of morbid obesity, the most severe state of the disease, has become epidemic. The current recommendations for the therapy of the morbidly obese comes as a result of a National Institutes of Health (NIH) Consensus Conference held in 1991 and subsequently reviewed in 2004 by the American Society for Bariatric Surgery. This document reviews the work-up evaluation of the morbidly obese patient, the current status of the indications for bariatric surgery and which type of procedure should be recommended; it also brings up for discussion some important real-life clinical practice issues, which should be taken into consideration when evaluating and treating morbidly obese patients. Finally, it also goes through current scientific evidence supporting the potential effectiveness of medical therapy as treatment of patients with morbid obesity.
IL6 Gene Promoter Polymorphisms and Type 2 Diabetes
Joint Analysis of Individual Participants’ Data From 21 Studies
Cornelia Huth 1 2 ,
Iris M. Heid 1 ,
Caren Vollmert 1 ,
Christian Gieger 1 2 ,
...Harald Grallert 1 ,
Johanna K. Wolford 3 ,
Birgit Langer 1 ,
Barbara Thorand 1 ,
Norman Klopp 1 ,
Yasmin H. Hamid 4 ,
Oluf Pedersen 4 ,
Torben Hansen 4 ,
Valeriya Lyssenko 5 ,
Leif Groop 5 ,
Christa Meisinger 1 ,
Angela Döring 1 ,
Hannelore Löwel 1 ,
Wolfgang Lieb 6 ,
Christian Hengstenberg 7 ,
Wolfgang Rathmann 8 ,
Stephan Martin 8 ,
Jeffrey W. Stephens 9 ,
Helen Ireland 10 ,
Hugh Mather 11 ,
George J. Miller 12 ,
Heather M. Stringham 13 ,
Michael Boehnke 13 ,
Jaakko Tuomilehto 14 15 16 ,
Heiner Boeing 17 ,
Matthias Möhlig 18 ,
Joachim Spranger 18 ,
Andreas Pfeiffer 18 ,
Ingrid Wernstedt 19 ,
Anders Niklason 20 ,
Abel López-Bermejo 21 ,
José-Manuel Fernández-Real 21 ,
Robert L. Hanson 22 ,
Luis Gallart 23 ,
Joan Vendrell 23 ,
Anastasia Tsiavou 24 ,
Erifili Hatziagelaki 25 ,
Steve E. Humphries 10 ,
H.-Erich Wichmann 1 2 ,
Christian Herder 8 and
Thomas Illig 1
1 GSF-Institute of Epidemiology, Neuherberg, Germany
2 Institute of Biometry and Epidemiology, University of Munich, Munich, Germany
3 Translational Genomics Research Institute, Phoenix, Arizona
4 Steno Diabetes Center, Copenhagen, Denmark
5 Department of Clinical Sciences, University Hospital Malmö, Malmö, Sweden
6 Clinic and Policlinic for Internal Medicine II and Institute of Human Genetics, University of Lübeck, Lübeck, Germany
7 Clinic and Policlinic for Internal Medicine II, University of Regensburg, Regensburg, Germany
8 German Diabetes Center, Leibniz Institute at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
9 Medical School, University of Wales, Swansea, U.K
10 Centre for Cardiovascular Genetics, Royal Free and University College Medical School, London, U.K
11 Ealing Hospital, London, U.K
12 Medical Research Council Cardiovascular Research Group, Wolfson Institute of Preventive Medicine, London, U.K
13 Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
14 Diabetes and Genetic Epidemiology Unit, National Public Health Institute, Helsinki, Finland
15 Department of Public Health, University of Helsinki, Helsinki, Finland
16 South Ostrobothnia Central Hospital, Seinäjoki, Finland
17 Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke, Germany
18 Department of Clinical Nutrition, German Institute of Human Nutrition, Potsdam-Rehbruecke, Germany
19 Institute of Neuroscience and Physiology, Sahlgrenska Academy at Gothenborg University, Gothenborg, Sweden
20 Department of Clinical Pharmacology, Sahlgrenska University Hospital, Gothenburg, Sweden
21 Diabetes, Endocrinology and Nutrition Unit, University Hospital Josep Trueta, Girona, Spain
22 National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona
23 Research Unit, University Hospital Joan XXIII, Tarragona, Spain
24 Onassis Cardiac Surgery Center, Molecular Immunopathology and Histocompatibility Laboratory, Athens, Greece
25 2nd Department of Internal Medicine, University Hospital Attikon, Athens, Greece
Address correspondence and reprint requests to Dr. Thomas Illig, Institute of Epidemiology, GSF-National Research Center for
Environment and Health, Ingolstaedter Landstrasse 1, 85764 Neuherberg, Germany. E-mail: illig{at}gsf.de
Abstract
Several lines of evidence indicate a causal role of the cytokine interleukin (IL)-6 in the development of type 2 diabetes
in humans. Two common polymorphisms in the promoter of the IL-6 encoding gene IL6 , −174G>C (rs1800795) and −573G>C (rs1800796), have been investigated for association with type 2 diabetes in numerous studies
but with results that have been largely equivocal. To clarify the relationship between the two IL6 variants and type 2 diabetes, we analyzed individual data on >20,000 participants from 21 published and unpublished studies.
Collected data represent eight different countries, making this the largest association analysis for type 2 diabetes reported
to date. The GC and CC genotypes of IL6 −174G>C were associated with a decreased risk of type 2 diabetes (odds ratio 0.91, P = 0.037), corresponding to a risk modification of nearly 9%. No evidence for association was found between IL6 −573G>C and type 2 diabetes. The observed association of the IL6 −174 C-allele with a reduced risk of type 2 diabetes provides further evidence for the hypothesis that immune mediators are
causally related to type 2 diabetes; however, because the association is borderline significant, additional data are still
needed to confirm this finding.
FHS, Framingham Heart Study
HWE, Hardy-Weinberg equilibrium
IL, interleukin
IPD, individual participants’ data
Footnotes
Additional information for this article can be found in an online appendix at http://diabetes.diabetesjournals.org .
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore
be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Accepted July 12, 2006.
Received May 3, 2006.
DIABETES
As the developments in high throughput technologies have become more common and accessible it is becoming usual to take several distinct simultaneous approaches to study the same problem. In ...practice, this means that data of different types (expression, proteins, metabolites...) may be available for the same study, highlighting the need for methods and tools to analyze them in a combined way. In recent years there have been developed many methods that allow for the integrated analysis of different types of data. Corresponding to a certain tradition in bioinformatics many methodologies are rooted in machine learning such as bayesian networks, support vector machines or graph-based methods. In contrast with the high number of applications from these fields, another that seems to have contributed less to “omic” data integration is multivariate statistics, which has however a long tradition in being used to combine and visualize multidimensional data. In this work, we discuss the application of multivariate statistical approaches to integrate bio-molecular information by using multiple factorial analysis. The techniques are applied to a real unpublished data set consisting of three different data types: clinical variables, expression microarrays and DNA Gel Electrophoretic bands. We show how these statistical techniques can be used to perform reduction dimension and then visualize data of one type useful to explain those from other types. Whereas this is more or less straightforward when we deal with two types of data it turns to be more complicated when the goal is to visualize simultaneously more than two types. Comparison between the approaches shows that the information they provide is complementary suggesting their combined use yields more information than simply using one of them.
Background. Several studies have investigated associations between the -174GC single nucleotide polymorphism (rs1800795) of the IL6 gene and phenotypes related to type 2 diabetes mellitus (T2DM) but ...presented inconsistent results. Aims. This joint analysis aimed to clarify whether IL6 -174GC was associated with glucose and circulating interleukin-6 concentrations as well as body mass index (BMI). Methods. Individual-level data from all studies of the IL6-T2DM consortium on Caucasian subjects with available BMI were collected. As study-specific estimates did not show heterogeneity (P0.1), they were combined by using the inverse-variance fixed-effect model. Results. The main analysis included 9440, 7398, 24,117, or 5659 non-diabetic and manifest T2DM subjects for fasting glucose, 2-hour glucose, BMI, or circulating interleukin-6 levels, respectively. IL6 -174 C-allele carriers had significantly lower fasting glucose (-0.091 mmol/L, P=0.014). There was no evidence for association between IL6 -174GC and BMI or interleukin-6 levels, except in some subgroups. Conclusions. Our data suggest that C-allele carriers of the IL6 -174GC polymorphism have lower fasting glucose levels on average, which substantiates previous findings of decreased T2DM risk of these subjects.
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