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Aleksandrova K, Boeing H, Nöthlings U, Jenab M, Fedirko V, Kaaks R, Lukanova A, Trichopoulou A, Trichopoulos D, Boffetta P, Trepo E, Westhpal S, Duarte-Salles T, Stepien M, Overvad K, Tjønneland A, Halkjær J, Boutron-Ruault MC, Dossus L, Racine A, Lagiou P, Bamia C, Benetou V, Agnoli C, Palli D, Panico S, Tumino R, Vineis P, Bueno-de-Mesquita B, Peeters PH, Gram IT, Lund E, Weiderpass E, Quirós JR, Agudo A, Sánchez MJ, Gavrila D, Barricarte A, Dorronsoro M, Ohlsson B, Lindkvist B, Johansson A, Sund M, Khaw KT, Wareham N, Travis RC, Riboli E, Pischon T. Inflammatory and metabolic biomarkers and risk of liver and biliary tract cancer. Hepatology 2014; 60:858-71. [PMID: 24443059 PMCID: PMC4231978 DOI: 10.1002/hep.27016] [Citation(s) in RCA: 151] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2013] [Revised: 01/09/2014] [Accepted: 01/15/2014] [Indexed: 12/20/2022]
Abstract
UNLABELLED Obesity and associated metabolic disorders have been implicated in liver carcinogenesis; however, there are little data on the role of obesity-related biomarkers on liver cancer risk. We studied prospectively the association of inflammatory and metabolic biomarkers with risks of hepatocellular carcinoma (HCC), intrahepatic bile duct (IBD), and gallbladder and biliary tract cancers outside of the liver (GBTC) in a nested case-control study within the European Prospective Investigation into Cancer and Nutrition. Over an average of 7.7 years, 296 participants developed HCC (n=125), GBTC (n=137), or IBD (n=34). Using risk-set sampling, controls were selected in a 2:1 ratio and matched for recruitment center, age, sex, fasting status, and time of blood collection. Baseline serum concentrations of C-reactive protein (CRP), interleukin-6 (IL-6), C-peptide, total high-molecular-weight (HMW) adiponectin, leptin, fetuin-a, and glutamatdehydrogenase (GLDH) were measured, and incidence rate ratios (IRRs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression. After adjustment for lifestyle factors, diabetes, hepatitis infection, and adiposity measures, higher concentrations of CRP, IL-6, C-peptide, and non-HMW adiponectin were associated with higher risk of HCC (IRR per doubling of concentrations=1.22; 95% CI=1.02-1.46; P=0.03; 1.90; 95% CI=1.30-2.77; P=0.001; 2.25; 95% CI=1.43-3.54; P=0.0005; and 2.09; 95% CI=1.19-3.67; P=0.01, respectively). CRP was associated also with risk of GBTC (IRR=1.22; 95% CI=1.05-1.42; P=0.01). GLDH was associated with risks of HCC (IRR=1.62; 95% CI=1.25-2.11; P=0.0003) and IBD (IRR=10.5; 95% CI=2.20-50.90; P=0.003). The continuous net reclassification index was 0.63 for CRP, IL-6, C-peptide, and non-HMW adiponectin and 0.46 for GLDH, indicating good predictive ability of these biomarkers. CONCLUSION Elevated levels of biomarkers of inflammation and hyperinsulinemia are associated with a higher risk of HCC, independent of obesity and established liver cancer risk factors.
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Affiliation(s)
- Krasimira Aleksandrova
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-RehbrückeNuthetal, Germany
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-RehbrückeNuthetal, Germany
| | - Ute Nöthlings
- Institute of Epidemiology, Christian-Albrechts University of KielKiel, Germany
- Nutritional Epidemiology Unit, Department of Nutritional and Food Science, Institut für Ernährungs- und Lebensmittelwissenschaften, Rheinische Friedrich-Wilhelms-Universität BonnBonn, Germany
| | - Mazda Jenab
- International Agency for Research on Cancer (IARC/World Health Organization [WHO])Lyon, France
| | - Veronika Fedirko
- International Agency for Research on Cancer (IARC/World Health Organization [WHO])Lyon, France
- Department of Epidemiology, Rollins School of Public Health, Emory UniversityAtlanta, GA
- Winship Cancer Institute, Emory UniversityAtlanta, GA
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research CenterHeidelberg, Germany
| | - Annekatrin Lukanova
- Division of Cancer Epidemiology, German Cancer Research CenterHeidelberg, Germany
- Department of Medical Biosciences/Pathology, University of UmeåUmeå, Sweden
| | - Antonia Trichopoulou
- WHO Collaborating Center for Food and Nutrition Policies, Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical SchoolAthens, Greece
- Hellenic Health FoundationAthens, Greece
| | - Dimitrios Trichopoulos
- Hellenic Health FoundationAthens, Greece
- Department of Epidemiology, Harvard School of Public HealthBoston, MA
- Bureau of Epidemiologic Research, Academy of AthensAthens, Greece
| | - Paolo Boffetta
- Institute for Translational Epidemiology, Mount Sinai School of MedicineNew York, NY
| | | | - Sabine Westhpal
- Institute of Clinical Chemistry, Otto-von-Guericke-University MagdeburgMagdeburg, Germany
| | - Talita Duarte-Salles
- International Agency for Research on Cancer (IARC/World Health Organization [WHO])Lyon, France
| | - Magdalena Stepien
- International Agency for Research on Cancer (IARC/World Health Organization [WHO])Lyon, France
| | - Kim Overvad
- Section for Epidemiology, Department of Public Health, Aarhus UniversityAarhus, Denmark
| | - Anne Tjønneland
- Diet, Genes and Environment, Danish Cancer Society Research CenterCopenhagen, Denmark
| | - Jytte Halkjær
- Diet, Genes and Environment, Danish Cancer Society Research CenterCopenhagen, Denmark
| | - Marie-Christine Boutron-Ruault
- Institut National de la Santé et de la Recherche Médicale (INSERM), Center for Research in Epidemiology and Population Health (CESP), U1018, Nutrition, Hormones and Women's Health TeamVillejuif, France
- Université Paris SudUMRS 1018, Villejuif, France
- Institut Gustave RoussyVillejuif, France
| | - Laure Dossus
- Institut National de la Santé et de la Recherche Médicale (INSERM), Center for Research in Epidemiology and Population Health (CESP), U1018, Nutrition, Hormones and Women's Health TeamVillejuif, France
- Université Paris SudUMRS 1018, Villejuif, France
- Institut Gustave RoussyVillejuif, France
| | - Antoine Racine
- Institut National de la Santé et de la Recherche Médicale (INSERM), Center for Research in Epidemiology and Population Health (CESP), U1018, Nutrition, Hormones and Women's Health TeamVillejuif, France
- Université Paris SudUMRS 1018, Villejuif, France
- Institut Gustave RoussyVillejuif, France
| | - Pagona Lagiou
- WHO Collaborating Center for Food and Nutrition Policies, Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical SchoolAthens, Greece
- Department of Epidemiology, Harvard School of Public HealthBoston, MA
- Bureau of Epidemiologic Research, Academy of AthensAthens, Greece
| | - Christina Bamia
- WHO Collaborating Center for Food and Nutrition Policies, Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical SchoolAthens, Greece
- Hellenic Health FoundationAthens, Greece
| | - Vassiliki Benetou
- WHO Collaborating Center for Food and Nutrition Policies, Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical SchoolAthens, Greece
- Hellenic Health FoundationAthens, Greece
| | - Claudia Agnoli
- Nutritional Epidemiology Unit, Fondazione IRCCS Istituto Nazionale TumoriMilano, Italy
| | - Domenico Palli
- Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute (ISPO)Florence, Italy
| | - Salvatore Panico
- Department of Clinical and Experimental Medicine, Federico II UniversityNaples, Italy
| | - Rosario Tumino
- Cancer Registry and Histopathology Unit, “M.P. Arezzo” HospitalRagusa, Italy
| | - Paolo Vineis
- HuGeF FoundationTurin, Italy
- Division of Epidemiology, Public Health and Primary Care, Imperial CollegeLondon, UK
| | - Bas Bueno-de-Mesquita
- National Institute for Public Health and the Environment (RIVM)Bilthoven, the Netherlands
- Department of Gastroenterology and Hepatology, University Medical CenterUtrecht, the Netherlands
| | - Petra H Peeters
- Division of Epidemiology, Public Health and Primary Care, Imperial CollegeLondon, UK
- Julius Center for Health Sciences and Primary Care, University Medical CenterUtrecht, the Netherlands
| | - Inger Torhild Gram
- Department of Community Medicine, Faculty of Health Sciences, University of TromsøTromsø, Norway
| | - Eiliv Lund
- Department of Community Medicine, Faculty of Health Sciences, University of TromsøTromsø, Norway
| | - Elisabete Weiderpass
- Department of Community Medicine, Faculty of Health Sciences, University of TromsøTromsø, Norway
- Department of Research, Cancer Registry of NorwayOslo, Norway
- Department of Medical Epidemiology and Biostatistics, Karolinska InstitutetStockholm, Sweden
- Samfundet FolkhälsanHelsinki, Finland
| | | | - Antonio Agudo
- Unit of Nutrition, Environment and Cancer, Cancer Epidemiology Research Program, Catalan Institute of OncologyBarcelona, Spain
| | - María-José Sánchez
- Andalusian School of Public HealthGranada, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pública-CIBERESP)Madrid, Spain
| | - Diana Gavrila
- Servicio de Epidemiología, Department of Epidemiology, Consejería de Sanidad y Politica SocialMurcia, Spain
- Navarre Public Health InstitutePamplona, Spain
| | - Aurelio Barricarte
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pública-CIBERESP)Madrid, Spain
- Navarre Public Health InstitutePamplona, Spain
| | - Miren Dorronsoro
- Public Health Direction, Basque Regional Health Department and BioDonostia Research Institute-CIBERESPSan Sebastian, Spain
| | - Bodil Ohlsson
- Department of Clinical Sciences, Division of Internal Medicine, Skåne University Hospital, Lund UniversityMalmö, Sweden
| | - Björn Lindkvist
- Institute of Medicine, Sahlgrenska Academy, University of GothenburgGothenburg, Sweden
| | - Anders Johansson
- Department of Odontology/Public Health and Clinical Medicine, Umeå UniversityUmeå, Sweden
| | - Malin Sund
- Department of Surgical and Perioperative Sciences, Surgery and Public Health, Nutrition Research, Umea UniversityUmea, Sweden
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of CambridgeCambridge, UK
| | - Nicholas Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's HospitalCambridge, UK
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Clinical Medicine, University of OxfordOxford, UK
| | - Elio Riboli
- Division of Epidemiology, Public Health and Primary Care, Imperial CollegeLondon, UK
| | - Tobias Pischon
- Molecular Epidemiology Group, Max Delbrück Center for Molecular Medicine Berlin-BuchBerlin-Buch, Germany
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Hamann L, Glaeser C, Schulz S, Gross M, Franke A, Nöthlings U, Schumann RR. A micro RNA-146a polymorphism is associated with coronary restenosis. Int J Immunogenet 2014; 41:393-6. [PMID: 25053223 DOI: 10.1111/iji.12136] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2014] [Revised: 05/09/2014] [Accepted: 06/12/2014] [Indexed: 02/06/2023]
Abstract
The association of the miRNA-146a polymorphism rs2910164 with atherosclerosis and restenosis was investigated. We found no association with atherosclerosis; however, we found a negative association for the G/C (P = 0.007) and a positive association for the C/C genotype with the risk of restenosis, which is the main drawback for cardiac surgery.
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Affiliation(s)
- L Hamann
- Institute of Microbiology and Hygiene, Charité University Medical Center, Berlin, Germany
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103
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Schlesinger S, Siegert S, Koch M, Walter J, Heits N, Hinz S, Jacobs G, Hampe J, Schafmayer C, Nöthlings U. Postdiagnosis body mass index and risk of mortality in colorectal cancer survivors: a prospective study and meta-analysis. Cancer Causes Control 2014; 25:1407-18. [PMID: 25037235 DOI: 10.1007/s10552-014-0435-x] [Citation(s) in RCA: 93] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2014] [Accepted: 07/03/2014] [Indexed: 01/02/2023]
Abstract
PURPOSE Aim of this study was to investigate the association between postdiagnosis body mass index (BMI) and all-cause mortality in colorectal cancer (CRC) survivors in a prospective study and meta-analysis. METHODS We conducted a prospective cohort study on 2,143 CRC survivors in Germany. Participants were recruited to the study on average 4 years after diagnosis, and postdiagnosis BMI was assessed at recruitment using a self-administered questionnaire. CRC survivors were followed up for a mean time of 3.5 years. The association between BMI and all-cause mortality was investigated using multivariable Cox proportional hazards models. Additionally, we performed a meta-analysis of studies on postdiagnosis BMI and all-cause mortality (n = 5, including this study) by applying random-effects models. RESULTS In the prospective analysis, 349 participants died. BMI was not statistically significantly associated with all-cause mortality. Compared to normal weight survivors, the hazard ratios (HRs) [95% confidence interval (CI)] for all-cause mortality in underweight, overweight and obese survivors were 1.65 (0.79-3.45), 0.80 (0.62-1.03) and 0.84 (0.62-1.14), respectively. In the meta-analysis, individuals with underweight were at increased risk for all-cause mortality [HR (95% CI) 1.72 (1.18-2.49)], whereas individuals with overweight had a lower risk [HR (95% CI) 0.79 (0.71-0.88)], compared to normal weight subjects. For obesity, the risk of mortality was also reduced with only borderline significance [HR (95% CI) 0.88 (0.77-1.00)]. CONCLUSIONS While the present study as well as single previously published studies showed that overweight was associated with a non-significant reduced risk for all-cause mortality, our meta-analysis indicated a decreased mortality risk among overweight CRC survivors.
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Affiliation(s)
- Sabrina Schlesinger
- Institute of Epidemiology, Christian-Albrechts University of Kiel, Campus UKSH, Arnold-Heller-Str. 3, Haus 1, 24105, Kiel, Germany,
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104
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Onur S, Niklowitz P, Jacobs G, Nöthlings U, Lieb W, Menke T, Döring F. Ubiquinol reduces gamma glutamyltransferase as a marker of oxidative stress in humans. BMC Res Notes 2014; 7:427. [PMID: 24996614 PMCID: PMC4105833 DOI: 10.1186/1756-0500-7-427] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2014] [Accepted: 06/23/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The reduced form of Coenzyme Q10 (CoQ10), ubiquinol (Q10H2), serves as a potent antioxidant in mitochondria and lipid membranes. There is evidence that Q10H2 protects against oxidative events in lipids, proteins and DNA. Serum gamma-glutamyltransferase (GGT) activity is associated with cardiovascular diseases. In a physiological range, activity of GGT is a potential early and sensitive marker of inflammation and oxidative stress.In this study, we first examined the relationship between CoQ10 status and serum GGT activity in 416 healthy participants between 19 and 62 years of age in a cross-sectional study (cohort I). In the second step, 53 healthy males (21-48 years of age; cohort II) underwent a 14-day Q10H2 supplementation (150 mg/d) to evaluate the effect of Q10H2 supplementation on serum GGT activity and GGT1 gene expression. FINDINGS There was a strong positive association between CoQ10 status and serum GGT activity in cohort I. However, a gender-specific examination revealed differences between male and female volunteers regarding the association between CoQ10 status and serum GGT activity. Q10H2 supplementation (cohort II) caused a significant decrease in serum GGT activity from T0 to T14 (p < 0.001). GGT1 mRNA levels declined 1.49-fold after Q10H2 supplementation. Of note, other liver enzymes (i.e., aspartate aminotransferase, AST) were not affected by Q10H2 supplementation. CONCLUSIONS CoQ10 level is positively associated with serum GGT activity. Supplementation with Q10H2 reduces serum GGT activity. This effect might be caused by gene expression. Overall, we provide preliminary evidence that higher Q10H2 levels improve oxidative stress via reduction of serum GGT activity in humans. TRIAL REGISTRATION Current Controlled Trials ISRCTN26780329.
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Affiliation(s)
| | | | | | | | | | | | - Frank Döring
- Institute of Human Nutrition and Food Science, Division of Molecular Prevention, Christian Albrechts University Kiel, Heinrich-Hecht-Platz 10, 24118 Kiel, Germany.
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Johner SA, Thamm M, Stehle P, Nöthlings U, Kriener E, Völzke H, Gärtner R, Remer T. Interrelations between thyrotropin levels and iodine status in thyroid-healthy children. Thyroid 2014; 24:1071-9. [PMID: 24547873 PMCID: PMC4080866 DOI: 10.1089/thy.2013.0480] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND Worldwide, iodine prophylaxis measures have improved iodine status in populations. Several studies have reported an increase in thyrotropin (TSH) levels coinciding with this prophylaxis. Whether this implies an increased risk for hypothyroidism or simply reflects a physiologic TSH adaptation mechanism is not clear. METHODS Data on iodine and thyroid status of 6-17 year old children and adolescents (n=9175), collected between 2003 and 2006 in the German-wide Health Interview and Examination Survey for Children and Adolescents (KiGGS) Study, provided the basis for the analyses of mutual relationships of urinary iodine status (assessed by iodine/creatinine ratio in spot urines), serum TSH levels, and thyroid volume (determined by ultrasound). For data analyses (multivariable linear regression analysis), only those children were included for whom none of the available parameters (including free triiodothyronine [fT3], free thyroxine [fT4], and thyroperoxidase antibody [TPO-Ab] measurements) indicated a potential pathophysiologic thyroid status (n=6101). RESULTS In this population-based sample of thyroid-healthy children, higher urinary iodine excretion was associated with higher TSH levels (p<0.05), adjusted for sex, age, body surface area, body mass index, fT3/fT4 ratio, and time of blood sampling. Higher TSH levels were not associated with a higher prevalence of TPO-Ab but with lower thyroid volume (p<0.001, fully adjusted). For the present study sample, one-time spot measurements of urinary iodine excretion were not related to thyroid volume, the long-term marker of iodine status. CONCLUSION Our findings show for the first time in thyroid-healthy children that smaller thyroid volume is associated with higher normal TSH levels. A decreased thyroid cell mass and cell amount, as induced by an improved iodine status, does presumably require a higher TSH signal to maintain a constant thyroid hormone production, suggesting an underlying physiologic adaptation. Correspondingly, an increased TSH level should not be used as the single criterion to evaluate the prevalence of hypothyroidism, and the repeatedly observed parallel increases of iodine supply and TSH levels should not readily be interpreted as evidence for an increased hypothyroidism risk. These insights, contradicting conventional interpretations, may contribute to dispel uncertainties about the safety of iodine prophylaxis measures.
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Affiliation(s)
- Simone A. Johner
- DONALD Study Centre at the Research Institute of Child Nutrition, University of Bonn, Dortmund, Germany
- Department of Nutritional Epidemiology, Institute of Nutrition and Food Science (IEL), University of Bonn, Bonn, Germany
| | - Michael Thamm
- Central Epidemiology Laboratory, Department of Epidemiology and Health Reporting, Robert Koch Institute, Berlin, Germany
| | - Peter Stehle
- Department of Nutritional Physiology, Institute of Nutrition and Food Science (IEL), University of Bonn, Bonn, Germany
| | - Ute Nöthlings
- Department of Nutritional Epidemiology, Institute of Nutrition and Food Science (IEL), University of Bonn, Bonn, Germany
| | - Eugen Kriener
- Landratsamt Würzburg, Board of Health, Würzburg, Germany
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Roland Gärtner
- Medizinische Klinik IV, Department of Endocrinology, University of Munich, Munich, Germany
| | - Thomas Remer
- DONALD Study Centre at the Research Institute of Child Nutrition, University of Bonn, Dortmund, Germany
- Department of Nutritional Epidemiology, Institute of Nutrition and Food Science (IEL), University of Bonn, Bonn, Germany
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Koch M, Borggrefe J, Barbaresko J, Groth G, Jacobs G, Siegert S, Lieb W, Müller MJ, Bosy-Westphal A, Heller M, Nöthlings U. Dietary patterns associated with magnetic resonance imaging-determined liver fat content in a general population study. Am J Clin Nutr 2014; 99:369-77. [PMID: 24305680 PMCID: PMC6410901 DOI: 10.3945/ajcn.113.070219] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND The association between diet and fatty liver disease (FLD) has predominantly been analyzed for single nutrients or foods, and findings have been inconsistent. OBJECTIVE We aimed to compare associations of hypothesis-driven and exploratory dietary pattern scores with liver fat content. DESIGN Liver fat was measured by using magnetic resonance imaging as liver signal intensity (LSI) in a population-based, cross-sectional study that included 354 individuals. We applied partial least-squares regression to derive an exploratory dietary pattern score that explained variation in both the intake of 38 food groups, which were assessed by using a food-frequency questionnaire, and LSI. The hypothesis-driven score was calculated on the basis of published studies. Multivariable linear or logistic regression was used to investigate associations between dietary pattern scores and LSI or FLD. RESULTS A higher percentage of LSI variation was explained by the exploratory (12.6%) compared with the hypothesis-driven (2.2%) dietary pattern. Of the 13 most important food groups of the exploratory dietary pattern, intakes of green and black tea, soups, and beer were also individually associated with LSI values. A 1-unit increase in the exploratory dietary pattern score was positively associated with FLD (OR: 1.56; 95% CI: 1.29, 1.88). Furthermore, a 1-unit increase in the hypothesis-driven dietary pattern score, which consisted of alcohol, soft drinks, meat, coffee, and tea, was positively associated with FLD (OR: 1.25; 95% CI: 1.10, 1.43). CONCLUSION We defined a hypothesis-driven dietary pattern and derived an exploratory dietary pattern, both of which included alcohol, meat (poultry), and tea, associated with liver fat content independent from confounders, which should be explored in prospective studies.
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Affiliation(s)
- Manja Koch
- Institutes of Epidemiology (MK and WL), Experimental Medicine (MK, J Barbaresko, SS, and UN), and Human Nutrition and Food Science (MJM), Christian-Albrechts University Kiel, Kiel, Germany; the Department of Radiology, University of Cologne, Cologne, Germany (J Borggrefe); Nutritional Epidemiology, Department of Nutrition and Food Science, Rheinische Friedrich-Wilhelms-University Bonn, Bonn, Germany (J Barbaresko and UN); the Clinic for Diagnostic Radiology (GG and MH) and PopGen Biobank (GJ), University Medical Center Schleswig-Holstein, Kiel, Germany; and the Institute of Nutritional Medicine, University of Hohenheim, Stuttgart, Germany (AB-W)
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Freese J, Feller S, Harttig U, Kleiser C, Linseisen J, Fischer B, Leitzmann MF, Six-Merker J, Michels KB, Nimptsch K, Steinbrecher A, Pischon T, Heuer T, Hoffmann I, Jacobs G, Boeing H, Nöthlings U. Development and evaluation of a short 24-h food list as part of a blended dietary assessment strategy in large-scale cohort studies. Eur J Clin Nutr 2014; 68:324-9. [PMID: 24398637 DOI: 10.1038/ejcn.2013.274] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2013] [Revised: 11/21/2013] [Accepted: 11/21/2013] [Indexed: 11/09/2022]
Abstract
BACKGROUND/OBJECTIVES The validity of dietary assessment in large-scale cohort studies has been questioned. Combining data sources for the estimation of usual intake in a blended approach may enhance the validity of dietary measurement. Our objective was to develop a web-based 24-h food list for Germany to identify foods consumed during the previous 24 h and to evaluate the performance of the new questionnaire in a feasibility study. SUBJECTS/METHODS Available data from the German National Nutrition Survey II were used to develop a finite list of food items. A total of 508 individuals were invited to fill in the 24-h food list via the Internet up to three times during a 3-6-month time period. In addition, participants were asked to evaluate the questionnaire using a brief online evaluation form. RESULTS In total, 246 food items were identified for the 24-h food list, reflecting >75% variation in intake of 27 nutrients and four major food groups. Among the individuals invited, 64% participated in the feasibility study. Of these, 100%, 85% and 68% of participants completed the 24-h food list one, two or three times, respectively. The average time needed to complete the questionnaire was 9 min, and its acceptability by participants was rated as high. CONCLUSIONS The 24-h food list represents a promising new dietary assessment tool that can be used as part of a blended approach combining multiple data sources for valid estimation of usual dietary intake in large-scale cohort studies.
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Affiliation(s)
- J Freese
- 1] Nutritional Epidemiology, Department of Nutrition and Food Sciences, University Bonn, Bonn, Germany [2] Section of Epidemiology, Institute of Experimental Medicine, University Kiel, Kiel, Germany
| | - S Feller
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - U Harttig
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - C Kleiser
- Institute of Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - J Linseisen
- Institute of Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - B Fischer
- Institute of Epidemiology and Preventive Medicine, University Medical Center Regensburg, Regensburg, Germany
| | - M F Leitzmann
- Institute of Epidemiology and Preventive Medicine, University Medical Center Regensburg, Regensburg, Germany
| | - J Six-Merker
- 1] Institute of Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany [2] Institute for Prevention and Cancer Epidemiology, University Medical Center Freiburg, Freiburg, Germany
| | - K B Michels
- 1] Institute for Prevention and Cancer Epidemiology, University Medical Center Freiburg, Freiburg, Germany [2] Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - K Nimptsch
- Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine (MDC) Berlin-Buch, Berlin, Germany
| | - A Steinbrecher
- Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine (MDC) Berlin-Buch, Berlin, Germany
| | - T Pischon
- Molecular Epidemiology Group, Max Delbrueck Center for Molecular Medicine (MDC) Berlin-Buch, Berlin, Germany
| | - T Heuer
- Max Rubner-Institut, Federal Research Institute of Nutrition and Food, Karlsruhe, Germany
| | - I Hoffmann
- Max Rubner-Institut, Federal Research Institute of Nutrition and Food, Karlsruhe, Germany
| | - G Jacobs
- Section of Epidemiology, Institute of Experimental Medicine, University Kiel, Kiel, Germany
| | - H Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - U Nöthlings
- 1] Nutritional Epidemiology, Department of Nutrition and Food Sciences, University Bonn, Bonn, Germany [2] Section of Epidemiology, Institute of Experimental Medicine, University Kiel, Kiel, Germany
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108
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Winkler V, Leitzmann M, Obi N, Ahrens W, Edinger T, Giani G, Linseisen J, Löffler M, Michels K, Nöthlings U, Schipf S, Kluttig A, Wichmann HE, Hoffmann B, Jöckel KH, Becher H. Response in individuals with and without foreign background and application to the National Cohort in Germany: which factors have an effect? Int J Public Health 2014; 59:555-63. [PMID: 24390621 DOI: 10.1007/s00038-013-0539-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2013] [Revised: 12/11/2013] [Accepted: 12/18/2013] [Indexed: 10/25/2022] Open
Abstract
OBJECTIVES Response rates in epidemiologic studies vary widely. This study examines response rates of potential study participants according to foreign versus German background and investigates effects of recruitment strategies. METHODS Response rates and characteristics of recruitment procedures from feasibility studies for a large cohort study conducted in 2011 were analyzed. RESULTS Among 1,235 participants the proportion of recruited individuals with a foreign background was 17.3% (95% confidence interval 15.3-19.5%), significantly lower than in the sampling frame (23.1%). The difference between observed and expected proportion was high among individuals with Turkish background and smaller among ethnic Germans from the Former Soviet Union and other foreign background groups. Common recruitment strategies to increase the response had positive effects in all groups. For the planned recruitment strategy in the forthcoming German National Cohort, we estimate an overall response of approximately 50%. CONCLUSIONS Individuals with Turkish background may need particular efforts to be adequately represented in a population-based cohort in Germany. Other foreign background groups are relatively well represented using standard procedures. An adequate response can be obtained under carefully planned recruitment strategies.
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Affiliation(s)
- Volker Winkler
- Institut für Public Health, Universitätsklinikum Heidelberg, Heidelberg, Germany,
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109
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Sluik D, Boeing H, Li K, Kaaks R, Johnsen NF, Tjønneland A, Arriola L, Barricarte A, Masala G, Grioni S, Tumino R, Ricceri F, Mattiello A, Spijkerman AMW, van der A DL, Sluijs I, Franks PW, Nilsson PM, Orho-Melander M, Fhärm E, Rolandsson O, Riboli E, Romaguera D, Weiderpass E, Sánchez-Cantalejo E, Nöthlings U. Lifestyle factors and mortality risk in individuals with diabetes mellitus: are the associations different from those in individuals without diabetes? Diabetologia 2014; 57:63-72. [PMID: 24132780 DOI: 10.1007/s00125-013-3074-y] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2013] [Accepted: 09/09/2013] [Indexed: 12/25/2022]
Abstract
AIMS/HYPOTHESIS Thus far, it is unclear whether lifestyle recommendations for people with diabetes should be different from those for the general public. We investigated whether the associations between lifestyle factors and mortality risk differ between individuals with and without diabetes. METHODS Within the European Prospective Investigation into Cancer and Nutrition (EPIC), a cohort was formed of 6,384 persons with diabetes and 258,911 EPIC participants without known diabetes. Joint Cox proportional hazard regression models of people with and without diabetes were built for the following lifestyle factors in relation to overall mortality risk: BMI, waist/height ratio, 26 food groups, alcohol consumption, leisure-time physical activity, smoking. Likelihood ratio tests for heterogeneity assessed statistical differences in regression coefficients. RESULTS Multivariable adjusted mortality risk among individuals with diabetes compared with those without was increased, with an HR of 1.62 (95% CI 1.51, 1.75). Intake of fruit, legumes, nuts, seeds, pasta, poultry and vegetable oil was related to a lower mortality risk, and intake of butter and margarine was related to an increased mortality risk. These associations were significantly different in magnitude from those in diabetes-free individuals, but directions were similar. No differences between people with and without diabetes were detected for the other lifestyle factors. CONCLUSIONS/INTERPRETATION Diabetes status did not substantially influence the associations between lifestyle and mortality risk. People with diabetes may benefit more from a healthy diet, but the directions of association were similar. Thus, our study suggests that lifestyle advice with respect to mortality for patients with diabetes should not differ from recommendations for the general population.
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Affiliation(s)
- Diewertje Sluik
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany,
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110
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Lukanova A, Becker S, Hüsing A, Schock H, Fedirko V, Trepo E, Trichopoulou A, Bamia C, Lagiou P, Benetou V, Trichopoulos D, Nöthlings U, Tjønneland A, Overvad K, Dossus L, Teucher B, Boeing H, Aleksandrova K, Palli D, Pala V, Panico S, Tumino R, Ricceri F, Bueno-de-Mesquita HB, Siersema PD, Peeters PHM, Quiros JR, Duell EJ, Molina-Montes E, Chirlaque MD, Gurrea AB, Dorronsoro M, Lindkvist B, Johansen D, Werner M, Sund M, Khaw KT, Wareham N, Key TJ, Travis RC, Rinaldi S, Romieu I, Gunter MJ, Riboli E, Jenab M, Kaaks R. Prediagnostic plasma testosterone, sex hormone-binding globulin, IGF-I and hepatocellular carcinoma: etiological factors or risk markers? Int J Cancer 2014; 134:164-73. [PMID: 23801371 DOI: 10.1002/ijc.28342] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2013] [Revised: 05/05/2013] [Accepted: 05/17/2013] [Indexed: 12/11/2022]
Abstract
Elevated prediagnostic testosterone and insulin-like growth factor I (IGF-I) concentrations have been proposed to increase risk of hepatocellular carcinoma (HCC). However, the metabolism of these hormones is altered as a consequence of liver damage and they may have clinical utility as HCC risk markers. A case-control study was nested within the European Prospective Investigation into Cancer and Nutrition cohort and included 125 incident HCC cases and 247 individually matched controls. Testosterone, sex hormone-binding globulin (SHBG) and IGF-I were analyzed by immunoassays. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated by conditional logistic regression. The area under the receiver operating curves (AUC) was calculated to assess HCC predictive ability of the tested models. After adjustments for epidemiological variables (body mass index, smoking, ethanol intake, hepatitis and diabetes) and liver damage (a score based on albumin, bilirubin, aspartate aminotransaminase, alanine aminotransaminase, gamma-glutamyltransferase and alkaline phosphatase concentrations), only SHBG remained significantly associated with risk [OR for top versus bottom tertile of 3.86 (1.32-11.3), p(trend) = 0.009]. As a single factor SHBG had an AUC of 0.81 (0.75-0.86). A small, but significant increase in AUC was observed when SHBG was added to a model including the liver damage score and epidemiological variables (from 0.89 to 0.91, p = 0.02) and a net reclassification of 0.47% (0.45-0.48). The observed associations of HCC with prediagnostic SHBG, free testosterone and IGF-I concentrations are in directions opposite to that expected under the etiological hypotheses. SHBG has a potential to be tested as prediagnostic risk marker for HCC.
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Affiliation(s)
- Annekatrin Lukanova
- Division of Cancer Epidemiology, German Cancer Research Centre, Heidelberg, Germany; Department of Medical Biosciences, Pathology, University of Umeå, Umeå, Sweden
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111
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Parsa A, Fuchsberger C, Köttgen A, O’Seaghdha CM, Pattaro C, de Andrade M, Chasman DI, Teumer A, Endlich K, Olden M, Chen MH, Tin A, Kim YJ, Taliun D, Li M, Feitosa M, Gorski M, Yang Q, Hundertmark C, Foster MC, Glazer N, Isaacs A, Rao M, Smith AV, O’Connell JR, Struchalin M, Tanaka T, Li G, Hwang SJ, Atkinson EJ, Lohman K, Cornelis MC, Johansson Å, Tönjes A, Dehghan A, Couraki V, Holliday EG, Sorice R, Kutalik Z, Lehtimäki T, Esko T, Deshmukh H, Ulivi S, Chu AY, Murgia F, Trompet S, Imboden M, Kollerits B, Pistis G, Harris TB, Launer LJ, Aspelund T, Eiriksdottir G, Mitchell BD, Boerwinkle E, Schmidt H, Hofer E, Hu F, Demirkan A, Oostra BA, Turner ST, Ding J, Andrews JS, Freedman BI, Giulianini F, Koenig W, Illig T, Döring A, Wichmann HE, Zgaga L, Zemunik T, Boban M, Minelli C, Wheeler HE, Igl W, Zaboli G, Wild SH, Wright AF, Campbell H, Ellinghaus D, Nöthlings U, Jacobs G, Biffar R, Ernst F, Homuth G, Kroemer HK, Nauck M, Stracke S, Völker U, Völzke H, Kovacs P, Stumvoll M, Mägi R, Hofman A, Uitterlinden AG, Rivadeneira F, Aulchenko YS, Polasek O, Hastie N, Vitart V, Helmer C, Wang JJ, Stengel B, Ruggiero D, Bergmann S, Kähönen M, Viikari J, Nikopensius T, Province M, Colhoun H, Doney A, Robino A, Krämer BK, Portas L, Ford I, Buckley BM, Adam M, Thun GA, Paulweber B, Haun M, Sala C, Mitchell P, Ciullo M, Vollenweider P, Raitakari O, Metspalu A, Palmer C, Gasparini P, Pirastu M, Jukema JW, Probst-Hensch NM, Kronenberg F, Toniolo D, Gudnason V, Shuldiner AR, Coresh J, Schmidt R, Ferrucci L, van Duijn CM, Borecki I, Kardia SL, Liu Y, Curhan GC, Rudan I, Gyllensten U, Wilson JF, Franke A, Pramstaller PP, Rettig R, Prokopenko I, Witteman J, Hayward C, Ridker PM, Bochud M, Heid IM, Siscovick DS, Fox CS, Kao WL, Böger CA. Common variants in Mendelian kidney disease genes and their association with renal function. J Am Soc Nephrol 2013; 24:2105-17. [PMID: 24029420 PMCID: PMC3839542 DOI: 10.1681/asn.2012100983] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2012] [Accepted: 07/10/2013] [Indexed: 12/28/2022] Open
Abstract
Many common genetic variants identified by genome-wide association studies for complex traits map to genes previously linked to rare inherited Mendelian disorders. A systematic analysis of common single-nucleotide polymorphisms (SNPs) in genes responsible for Mendelian diseases with kidney phenotypes has not been performed. We thus developed a comprehensive database of genes for Mendelian kidney conditions and evaluated the association between common genetic variants within these genes and kidney function in the general population. Using the Online Mendelian Inheritance in Man database, we identified 731 unique disease entries related to specific renal search terms and confirmed a kidney phenotype in 218 of these entries, corresponding to mutations in 258 genes. We interrogated common SNPs (minor allele frequency >5%) within these genes for association with the estimated GFR in 74,354 European-ancestry participants from the CKDGen Consortium. However, the top four candidate SNPs (rs6433115 at LRP2, rs1050700 at TSC1, rs249942 at PALB2, and rs9827843 at ROBO2) did not achieve significance in a stage 2 meta-analysis performed in 56,246 additional independent individuals, indicating that these common SNPs are not associated with estimated GFR. The effect of less common or rare variants in these genes on kidney function in the general population and disease-specific cohorts requires further research.
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Affiliation(s)
- Afshin Parsa
- Division of Nephrology, University of Maryland School of Medicine, Baltimore, Maryland
| | - Christian Fuchsberger
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Anna Köttgen
- Renal Division, Freiburg University Clinic, Freiburg, Germany
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Conall M. O’Seaghdha
- National Heart, Lung, and Blood Institute's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts
- Division of Nephrology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Cristian Pattaro
- Centre for Biomedicine, European Academy of Bozen/Bolzano, Bolzano, Italy
| | - Mariza de Andrade
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Daniel I. Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Alexander Teumer
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Karlhans Endlich
- Institute of Anatomy and Cell Biology, University of Greifswald, Greifswald, Germany
| | - Matthias Olden
- Division of Nephrology, Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
- Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Ming-Huei Chen
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Adrienne Tin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Young J. Kim
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland
- Genomics Resource Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Korea
| | - Daniel Taliun
- Centre for Biomedicine, European Academy of Bozen/Bolzano, Bolzano, Italy
| | - Man Li
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Mary Feitosa
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri
| | - Mathias Gorski
- Division of Nephrology, Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
- Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | | | - Meredith C. Foster
- National Heart, Lung, and Blood Institute's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts
| | - Nicole Glazer
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
| | - Aaron Isaacs
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Centre for Medical Systems Biology, Rotterdam, The Netherlands
| | - Madhumathi Rao
- Division of Nephrology, Tufts Evidence Practice Center, Tufts University School of Medicine, Tufts Medical Center, Boston, Massachusetts
| | - Albert V. Smith
- Research Institute, Icelandic Heart Association, Kopavogur, Iceland
- University of Iceland, Reykjavik, Iceland
| | - Jeffrey R. O’Connell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland
| | - Maksim Struchalin
- Departments of Epidemiology and Biostatistics and Forensic Molecular Biology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Toshiko Tanaka
- Clinical Research Branch, National Institute of Aging, Baltimore Maryland
| | - Guo Li
- University of Washington, Seattle, Washington
| | - Shih-Jen Hwang
- National Heart, Lung, and Blood Institute's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts
| | - Elizabeth J. Atkinson
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Kurt Lohman
- Department of Epidemiology and Prevention, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Marilyn C. Cornelis
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts
| | - Åsa Johansson
- Rudbeck Laboratory, Department of Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Anke Tönjes
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Adiposity Diseases Integrated Research and Treatment Center, University of Leipzig, Leipzig, Germany
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | - Elizabeth G. Holliday
- Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, University of Newcastle, Newcastle, Australia
- Centre for Information-Based Medicine, Hunter Medical Research Institute, Newcastle, Australia
| | - Rossella Sorice
- Adriano-Buzzati Traverso-CNR Institute of Genetics and Biophysics, Naples, Italy
| | - Zoltan Kutalik
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Terho Lehtimäki
- Fimlab Laboratories, Department of Clinical Chemistry, School of Medicine, University of Tampere, Tampere, Finland
| | - Tõnu Esko
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, Estonian Biocentre, University of Tartu, Tartu, Estonia
| | - Harshal Deshmukh
- Wellcome Trust Centre for Molecular Medicine, Clinical Research Centre, University of Dundee, Ninewells Hospital, Dundee, United Kingdom
| | - Sheila Ulivi
- IRCCS Burlo Garofolo Institute for Maternal and Child Health, University of Trieste, Trieste, Italy
| | - Audrey Y. Chu
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | | | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Medea Imboden
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Barbara Kollerits
- Division of Genetic Epidemiology, Innsbruck Medical University, Innsbruck, Austria
| | - Giorgio Pistis
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy
| | - Tamara B. Harris
- Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, Maryland
| | - Lenore J. Launer
- Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, Maryland
| | - Thor Aspelund
- Research Institute, Icelandic Heart Association, Kopavogur, Iceland
- University of Iceland, Reykjavik, Iceland
| | | | - Braxton D. Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center, Houston, Texas
| | - Helena Schmidt
- Austrian Stroke Prevention Study, Department of Neurology, Institute of Molecular Biology and Biochemistry, Medical University of Graz, Graz, Austria
| | - Edith Hofer
- Austrian Stroke Prevention Study, Clinical Division of Neurogeriatrics, Department of Neurology, University Clinic of Neurology, Medical University of Graz, Graz, Austria
| | - Frank Hu
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts
| | - Ayse Demirkan
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Ben A. Oostra
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Stephen T. Turner
- Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Jingzhong Ding
- Division of Geriatrics, Department of Internal Medicine, Wake Forest School of Medicine, Wake Forest University, Winston-Salem, North Carolina
| | - Jeanette S. Andrews
- Department of Biostatistical Sciences, Wake Forest School of Medicine, Wake Forest University, Winston-Salem, North Carolina
| | - Barry I. Freedman
- Division of Nephrology, Department of Internal Medicine, Wake Forest School of Medicine, Wake Forest University, Winston-Salem, North Carolina
| | - Franco Giulianini
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Wolfgang Koenig
- Department of Internal Medicine II, Ulm University Clinic, University of Ulm, Ulm, Germany
| | - Thomas Illig
- Hanover Unified Biobank, Hanover Medical School, Hanover, Germany
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, Neuherberg, Germany
| | - Angela Döring
- Institute of Epidemiology I and II, German Research Center for Environmental Health, Neuherberg, Germany
| | - H.-Erich Wichmann
- Institute of Epidemiology I and II, German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Informatics, Biometry, and Epidemiology, Ludwig-Maximilians-University, Munich, Germany
- Grosshadern Clinic, Neuherberg, Germany
| | - Lina Zgaga
- Center for Population Health Sciences, University of Edinburgh Medical School, Edinburgh, Scotland, United Kingdom
| | - Tatijana Zemunik
- Croatian Centre for Global Health, University of Split Medical School, Split, Croatia
| | - Mladen Boban
- Croatian Centre for Global Health, University of Split Medical School, Split, Croatia
| | - Cosetta Minelli
- Centre for Biomedicine, European Academy of Bozen/Bolzano, Bolzano, Italy
| | - Heather E. Wheeler
- Department of Genetics, Stanford University, Stanford, California
- Department of Medicine, University of Chicago, Chicago, Illinois
| | - Wilmar Igl
- Rudbeck Laboratory, Department of Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Ghazal Zaboli
- Rudbeck Laboratory, Department of Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Sarah H. Wild
- Center for Population Health Sciences, University of Edinburgh Medical School, Edinburgh, Scotland, United Kingdom
| | - Alan F. Wright
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Harry Campbell
- Center for Population Health Sciences, University of Edinburgh Medical School, Edinburgh, Scotland, United Kingdom
| | - David Ellinghaus
- Institute of Clinical Molecular Biology, Christian-Albrechts University, Kiel, Germany
| | - Ute Nöthlings
- PopGen Biobank, Schleswig-Holstein University Hospital, Kiel, Germany
- Institute for Epidemiology, University of Kiel, Kiel, Germany
- Department of Nutrition and Food Sciences, University of Bonn, Bonn, Germany
| | - Gunnar Jacobs
- PopGen Biobank, Schleswig-Holstein University Hospital, Kiel, Germany
- Institute for Epidemiology, University of Kiel, Kiel, Germany
| | - Reiner Biffar
- Clinic for Prosthodontic Dentistry, Gerostomatology, and Material Science, University of Greifswald, Greifswald, Germany
| | - Florian Ernst
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Heyo K. Kroemer
- Institute of Pharmacology, University of Greifswald, Greifswald, Germany
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University of Greifswald, Greifswald, Germany
| | - Sylvia Stracke
- Clinic for Internal Medicine A, University of Greifswald, Greifswald, Germany
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Henry Völzke
- Institute for Community Medicine, University of Greifswald, Greifswald, Germany
| | - Peter Kovacs
- Department of Medicine, University of Leipzig, Leipzig, Germany
| | - Michael Stumvoll
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Adiposity Diseases Integrated Research and Treatment Center, University of Leipzig, Leipzig, Germany
| | - Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Yurii S. Aulchenko
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Ozren Polasek
- Croatian Centre for Global Health, University of Split Medical School, Split, Croatia
| | - Nick Hastie
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Veronique Vitart
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Catherine Helmer
- INSERM U897, Institute of Public Health, Victor Segalen Bordeaux II University, Bordeaux, France
- Victor Segalen Bordeaux II University, Bordeaux, France
| | - Jie Jin Wang
- Centre for Vision Research, Westmead Millennium Institute, Westmead Hospital, University of Sydney, Sydney, Australia
- Centre for Eye Research Australia, University of Melbourne, East Melbourne, Australia
| | - Bénédicte Stengel
- INSERM UMRS 1018, Villejuif, France
- UMRS 1018, University of Paris-Sud, Paris, France
| | - Daniela Ruggiero
- Adriano-Buzzati Traverso-CNR Institute of Genetics and Biophysics, Naples, Italy
| | - Sven Bergmann
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, School of Medicine, University of Tampere, Tampere, Finland
| | - Jorma Viikari
- Department of Medicine, Turku University Hospital, University of Turku, Turku, Finland
| | - Tiit Nikopensius
- Institute of Molecular and Cell Biology, Estonian Biocentre, University of Tartu, Tartu, Estonia
| | - Michael Province
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri
| | - Helen Colhoun
- Wellcome Trust Centre for Molecular Medicine, Clinical Research Centre, University of Dundee, Ninewells Hospital, Dundee, United Kingdom
| | - Alex Doney
- National Health Service Tayside, Wellcome Trust Centre for Molecular Medicine, Clinical Research Centre, Ninewells Hospital, Dundee, United Kingdom
| | - Antonietta Robino
- IRCCS Burlo Garofolo Institute for Maternal and Child Health, University of Trieste, Trieste, Italy
| | - Bernhard K. Krämer
- Fifth Department of Medicine, Mannheim University Medical Centre, Mannheim, Germany
| | - Laura Portas
- CNR Institute of Population Genetics, Sassari, Italy
| | - Ian Ford
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, United Kingdom
| | - Brendan M. Buckley
- Department of Pharmacology and Therapeutics, University College Cork, Cork, Ireland
| | - Martin Adam
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Gian-Andri Thun
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Bernhard Paulweber
- First Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Margot Haun
- Division of Genetic Epidemiology, Innsbruck Medical University, Innsbruck, Austria
| | - Cinzia Sala
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy
| | - Paul Mitchell
- Centre for Vision Research, Westmead Millennium Institute, Westmead Hospital, University of Sydney, Sydney, Australia
| | - Marina Ciullo
- Adriano-Buzzati Traverso-CNR Institute of Genetics and Biophysics, Naples, Italy
| | - Peter Vollenweider
- Department of Internal Medicine, Vaudois University Hospital Center, University of Lausanne, Lausanne, Switzerland
| | - Olli Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Andres Metspalu
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, Estonian Biocentre, University of Tartu, Tartu, Estonia
| | - Colin Palmer
- Biomedical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, United Kingdom
| | - Paolo Gasparini
- IRCCS Burlo Garofolo Institute for Maternal and Child Health, University of Trieste, Trieste, Italy
| | - Mario Pirastu
- CNR Institute of Population Genetics, Sassari, Italy
| | - J. Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Interuniversity Cardiology Institute of the Netherlands, Utrecht, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden, The Netherlands
- Durrer Center for Cardiogenetic Research, Amsterdam, The Netherlands
| | - Nicole M. Probst-Hensch
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Florian Kronenberg
- Division of Genetic Epidemiology, Innsbruck Medical University, Innsbruck, Austria
| | - Daniela Toniolo
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy
- CNR Institute of Molecular Genetics, Pavia, Italy
| | - Vilmundur Gudnason
- Research Institute, Icelandic Heart Association, Kopavogur, Iceland
- University of Iceland, Reykjavik, Iceland
| | - Alan R. Shuldiner
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland
- Geriatric Research and Education Clinical Center, Veterans Affairs Medical Center, Baltimore, Maryland
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology, and Clinical Research, Baltimore, Maryland
| | - Reinhold Schmidt
- Austrian Stroke Prevention Study, Clinical Division of Neurogeriatrics, Department of Neurology, University Clinic of Neurology, Medical University of Graz, Graz, Austria
| | - Luigi Ferrucci
- Clinical Research Branch, National Institute of Aging, Baltimore Maryland
| | - Cornelia M. van Duijn
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Centre for Medical Systems Biology, Rotterdam, The Netherlands
- Netherlands Consortium for Healthy Aging, Netherlands Genomics Initiative, Leiden, the Netherlands
| | - Ingrid Borecki
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri
| | - Sharon L.R. Kardia
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Gary C. Curhan
- Channing Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Igor Rudan
- Center for Population Health Sciences, University of Edinburgh Medical School, Edinburgh, Scotland, United Kingdom
| | - Ulf Gyllensten
- Rudbeck Laboratory, Department of Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - James F. Wilson
- Center for Population Health Sciences, University of Edinburgh Medical School, Edinburgh, Scotland, United Kingdom
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts University, Kiel, Germany
| | | | - Rainer Rettig
- Institute of Physiology, University of Greifswald, Karlsburg, Germany
| | - Inga Prokopenko
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Jacqueline Witteman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Paul M. Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Murielle Bochud
- University Institute of Social and Preventive Medicine, Vaudois University Hospital Center, University of Lausanne, Lausanne, Switzerland
| | - Iris M. Heid
- Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany; and
| | | | - Caroline S. Fox
- National Heart, Lung, and Blood Institute's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts
- Division of Endocrinology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - W. Linda Kao
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology, and Clinical Research, Baltimore, Maryland
| | - Carsten A. Böger
- Division of Nephrology, Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
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Hamann L, Koch A, Sur S, Hoefer N, Glaeser C, Schulz S, Gross M, Franke A, Nöthlings U, Zacharowski K, Schumann RR. Association of a common TLR-6 polymorphism with coronary artery disease - implications for healthy ageing? Immun Ageing 2013; 10:43. [PMID: 24498948 PMCID: PMC4028875 DOI: 10.1186/1742-4933-10-43] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2013] [Accepted: 10/23/2013] [Indexed: 12/11/2022]
Abstract
BACKGROUND The pro-inflammatory status of the elderly triggers most of the age-related diseases such as cancer and atherosclerosis. Atherosclerosis, the leading cause world wide of morbidity and death, is an inflammatory disease influenced by life-style and genetic host factors. Stimuli such as oxLDL or microbial ligands have been proposed to trigger inflammation leading to atherosclerosis. It has recently been shown that oxLDL activates immune cells via the Toll-like receptor (TLR) 4/6 complex. Several common single nucleotide polymorphisms (SNPs) of the TLR system have been associated with atherosclerosis. To investigate the role of TLR-6 we analyzed the association of the TLR-6 SNP Pro249Ser with atherogenesis. RESULTS Genotyping of two independent groups with CAD, as well as of healthy controls revealed a significant association of the homozygous genotype with a reduced risk for atherosclerosis (odds ratio: 0.69, 95% CI 0.51-0.95, P = 0.02). In addition, we found a trend towards an association with the risk of restenosis after transluminal coronary angioplasty (odds ratio: 0.53, 95% CI 0.24-1.16, P = 0.12). In addition, first evidence is presented that the frequency of this protective genotype increases in a healthy population with age. Taken together, our results define a role for TLR-6 and its genetic variations in modulating the inflammatory response leading to atherosclerosis. CONCLUSIONS These results may lead to a better risk stratification, and potentially to an improved prophylactic treatment of high-risk populations. Furthermore, the protective effect of this polymorphism may lead to an increase of this genotype in the healthy elderly and may therefore be a novel genetic marker for the well-being during aging.
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Affiliation(s)
- Lutz Hamann
- Institute for Microbiology and Hygiene, Charité University Medical Center, Hindenburgdamm 27, 12003 Berlin, Germany.
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113
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Schlesinger S, Walter J, Hampe J, von Schönfels W, Hinz S, Küchler T, Jacobs G, Schafmayer C, Nöthlings U. Lifestyle factors and health-related quality of life in colorectal cancer survivors. Cancer Causes Control 2013; 25:99-110. [DOI: 10.1007/s10552-013-0313-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2013] [Accepted: 10/12/2013] [Indexed: 11/29/2022]
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114
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Siegert S, Yu Z, Wang-Sattler R, Illig T, Adamski J, Hampe J, Nikolaus S, Schreiber S, Krawczak M, Nothnagel M, Nöthlings U. Diagnosing fatty liver disease: a comparative evaluation of metabolic markers, phenotypes, genotypes and established biomarkers. PLoS One 2013; 8:e76813. [PMID: 24130792 PMCID: PMC3793954 DOI: 10.1371/journal.pone.0076813] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2013] [Accepted: 08/27/2013] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND To date, liver biopsy is the only means of reliable diagnosis for fatty liver disease (FLD). Owing to the inevitable biopsy-associated health risks, however, the development of valid noninvasive diagnostic tools for FLD is well warranted. AIM We evaluated a particular metabolic profile with regard to its ability to diagnose FLD and compared its performance to that of established phenotypes, conventional biomarkers and disease-associated genotypes. METHODS The study population comprised 115 patients with ultrasound-diagnosed FLD and 115 sex- and age-matched controls for whom the serum concentration was measured of 138 different metabolites, including acylcarnitines, amino acids, biogenic amines, hexose, phosphatidylcholines (PCs), lyso-PCs and sphingomyelins. Established phenotypes, biomarkers, disease-associated genotypes and metabolite data were included in diagnostic models for FLD using logistic regression and partial least-squares discriminant analysis. The discriminative power of the ensuing models was compared with respect to area under curve (AUC), integrated discrimination improvement (IDI) and by way of cross-validation (CV). RESULTS Use of metabolic markers for predicting FLD showed the best performance among all considered types of markers, yielding an AUC of 0.8993. Additional information on phenotypes, conventional biomarkers or genotypes did not significantly improve this performance. Phospholipids and branched-chain amino acids were most informative for predicting FLD. CONCLUSION We show that the inclusion of metabolite data may substantially increase the power to diagnose FLD over that of models based solely upon phenotypes and conventional biomarkers.
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Affiliation(s)
- Sabine Siegert
- Cologne Center for Genomics, University of Cologne, Cologne, Germany
- Institute of Experimental Medicine, Section of Epidemiology, Christian-Albrechts University Kiel, Kiel, Germany
- Institute of Epidemiology, Christian-Albrechts University Kiel, Kiel, Germany
- * E-mail:
| | - Zhonghao Yu
- Research Unit of Molecular Epidemiology, Helmholtz-Zentrum München, Neuherberg, Germany
| | - Rui Wang-Sattler
- Research Unit of Molecular Epidemiology, Helmholtz-Zentrum München, Neuherberg, Germany
| | - Thomas Illig
- Research Unit of Molecular Epidemiology, Helmholtz-Zentrum München, Neuherberg, Germany
- Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
| | - Jerzy Adamski
- Genome Analysis Center, Institute of Experimental Genetics, Helmholtz-Zentrum München, Neuherberg, Germany
| | - Jochen Hampe
- Department of General Internal Medicine, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Susanna Nikolaus
- Department of General Internal Medicine, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Stefan Schreiber
- Department of General Internal Medicine, University Hospital Schleswig-Holstein, Kiel, Germany
- Institute of Clinical Molecular Biology, Christian-Albrechts University Kiel, Kiel, Germany
- PopGen Biobank, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Michael Krawczak
- PopGen Biobank, University Hospital Schleswig-Holstein, Kiel, Germany
- Institute of Medical Informatics and Statistics, Christian-Albrechts University Kiel, Kiel, Germany
| | - Michael Nothnagel
- Cologne Center for Genomics, University of Cologne, Cologne, Germany
- Institute of Medical Informatics and Statistics, Christian-Albrechts University Kiel, Kiel, Germany
| | - Ute Nöthlings
- Institute of Experimental Medicine, Section of Epidemiology, Christian-Albrechts University Kiel, Kiel, Germany
- PopGen Biobank, University Hospital Schleswig-Holstein, Kiel, Germany
- Department of Nutrition and Food Sciences, Nutritional Epidemiology, University of Bonn, Bonn, Germany
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115
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Koch M, Jacobs G, Hampe J, Rosenstiel P, Krawczak M, Nöthlings U. Higher fetuin-A level is associated with coexistence of elevated alanine aminotransferase and the metabolic syndrome in the general population. Metab Syndr Relat Disord 2013; 11:377-84. [PMID: 23971757 DOI: 10.1089/met.2013.0078] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Higher fetuin-A levels have been linked to fatty liver disease (FLD), the most common cause of elevated alanine aminotransferase (ALT) levels, but associations between ALT and fetuin-A level have been inconsistent. The presence of the metabolic syndrome in individuals with elevated ALT levels has been shown to characterize more severe FLD. Thus, aim of the study was to investigate the association between fetuin-A level and the coexistence of elevated ALT levels and metabolic syndrome (ALT-MetS). METHODS A population-based cross-sectional study including 728 individuals (age 50-77 years) was conducted. We used multivariable logistic regression analysis to assess the association between serum fetuin-A level and the dichotomous outcome ALT-MetS, defined as coexistence of elevated ALT level (>75th percentile) and metabolic syndrome (any three of the components: abdominal obesity, elevated triglycerides, reduced high-density lipoprotein cholesterol, elevated blood pressure, abnormal glucose metabolism). RESULTS Individuals with a high fetuin-A level had an odds ratio (OR) for ALT-MetS of 2.22 [95% confidence interval (CI) 1.36-3.63; Ptrend=<0.001] comparing extreme tertiles. After excluding individuals with cancer, stroke, or myocardial infarction, individuals with high fetuin-A levels had an OR for ALT-MetS of 2.48 (95% CI 1.38-4.47) comparing extreme tertiles, and we observed statistical interaction between fetuin-A level and age (P=0.048). Fetuin-A level was associated with ALT-MetS in young individuals, defined as <64 years of age (OR 3.30, 95% CI 1.45-7.55; Ptrend=0.004), and not statistically significant in older individuals (OR 1.79, 95% CI 0.74-4.31; Ptrend=0.197). CONCLUSIONS Fetuin-A level was positively associated with ALT-MetS, particularly in younger individuals. Prospective studies in larger populations are warranted.
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Affiliation(s)
- Manja Koch
- 1 Institute of Epidemiology, Christian-Albrechts University Kiel , Kiel, Germany
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Barbaresko J, Koch M, Schulze MB, Nöthlings U. Dietary pattern analysis and biomarkers of low-grade inflammation: a systematic literature review. Nutr Rev 2013; 71:511-27. [PMID: 23865797 DOI: 10.1111/nure.12035] [Citation(s) in RCA: 387] [Impact Index Per Article: 35.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
The purpose of the present literature review was to investigate and summarize the current evidence on associations between dietary patterns and biomarkers of inflammation, as derived from epidemiological studies. A systematic literature search was conducted using PubMed, Web of Science, and EMBASE, and a total of 46 studies were included in the review. These studies predominantly applied principal component analysis, factor analysis, reduced rank regression analysis, the Healthy Eating Index, or the Mediterranean Diet Score. No prospective observational study was found. Patterns identified by reduced rank regression as being statistically significantly associated with biomarkers of inflammation were almost all meat-based or "Western" patterns. Studies using principal component analysis or a priori-defined diet scores found that meat-based or "Western-like" patterns tended to be positively associated with biomarkers of inflammation, predominantly C-reactive protein, while vegetable- and fruit-based or "healthy" patterns tended to be inversely associated. While results of the studies were inconsistent, interventions with presumed healthy diets resulted in reductions of almost all investigated inflammatory biomarkers. In conclusion, prospective studies are warranted to confirm the reported findings and further analyze associations, particularly by investigating dietary patterns as risk factors for changes in inflammatory markers over time.
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Affiliation(s)
- Janett Barbaresko
- Department of Nutrition and Food Sciences, Nutritional Epidemiology, University of Bonn, Bonn, Germany.
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Zamora-Ros R, Fedirko V, Trichopoulou A, González CA, Bamia C, Trepo E, Nöthlings U, Duarte-Salles T, Serafini M, Bredsdorff L, Overvad K, Tjønneland A, Halkjaer J, Fagherazzi G, Perquier F, Boutron-Ruault MC, Katzke V, Lukanova A, Floegel A, Boeing H, Lagiou P, Trichopoulos D, Saieva C, Agnoli C, Mattiello A, Tumino R, Sacerdote C, Bueno-de-Mesquita HB, Peeters PHM, Weiderpass E, Engeset D, Skeie G, Argüelles MV, Molina-Montes E, Dorronsoro M, Tormo MJ, Ardanaz E, Ericson U, Sonestedt E, Sund M, Landberg R, Khaw KT, Wareham NJ, Crowe FL, Riboli E, Jenab M. Dietary flavonoid, lignan and antioxidant capacity and risk of hepatocellular carcinoma in the European prospective investigation into cancer and nutrition study. Int J Cancer 2013; 133:2429-43. [PMID: 23649669 DOI: 10.1002/ijc.28257] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2013] [Accepted: 04/16/2013] [Indexed: 12/15/2022]
Abstract
Limited epidemiological evidence suggests a protective role for plant foods rich in flavonoids and antioxidants in hepatocellular cancer (HCC) etiology. Our aim was to prospectively investigate the association between dietary intake of flavonoids, lignans and nonenzymatic antioxidant capacity (NEAC) and HCC risk. Data from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort including 477,206 subjects (29.8% male) recruited from ten Western European countries, was analyzed. Flavonoid, lignan and NEAC intakes were calculated using a compilation of existing food composition databases linked to dietary information from validated dietary questionnaires. Dietary NEAC was based on ferric reducing antioxidant capacity (FRAP) and total radical-trapping antioxidant parameter (TRAP). Hepatitis B/C status was measured in a nested case-control subset. During a mean follow-up of 11-years, 191 incident HCC cases (66.5% men) were identified. Using Cox regression, multivariable adjusted models showed a borderline nonsignificant association of HCC with total flavonoid intake (highest versus lowest tertile, HR = 0.65, 95% CI: 0.40-1.04; p(trend) = 0.065), but not with lignans. Among flavonoid subclasses, flavanols were inversely associated with HCC risk (HR = 0.62, 95% CI: 0.39-0.99; p(trend) = 0.06). Dietary NEAC was inversely associated with HCC (FRAP: HR 0.50, 95% CI: 0.31-0.81; p(trend) = 0.001; TRAP: HR 0.49, 95% CI: 0.31-0.79; p(trend) = 0.002), but statistical significance was lost after exclusion of the first 2 years of follow-up. This study suggests that higher intake of dietary flavanols and antioxidants may be associated with a reduced HCC risk.
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Affiliation(s)
- Raul Zamora-Ros
- Unit of Nutrition, Environment and Cancer, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain; Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC), Lyon, France
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118
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Schlesinger S, Aleksandrova K, Pischon T, Jenab M, Fedirko V, Trepo E, Overvad K, Roswall N, Tjønneland A, Boutron-Ruault MC, Fagherazzi G, Racine A, Kaaks R, Grote VA, Boeing H, Trichopoulou A, Pantzalis M, Kritikou M, Mattiello A, Sieri S, Sacerdote C, Palli D, Tumino R, Peeters PH, Bueno-de-Mesquita HB, Weiderpass E, Quirós JR, Zamora-Ros R, Sánchez MJ, Arriola L, Ardanaz E, Tormo MJ, Nilsson P, Lindkvist B, Sund M, Rolandsson O, Khaw KT, Wareham N, Travis RC, Riboli E, Nöthlings U. Diabetes mellitus, insulin treatment, diabetes duration, and risk of biliary tract cancer and hepatocellular carcinoma in a European cohort. Ann Oncol 2013; 24:2449-55. [PMID: 23720454 DOI: 10.1093/annonc/mdt204] [Citation(s) in RCA: 100] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Evidence on associations between self-reported diabetes mellitus, diabetes duration, age at diabetes diagnosis, insulin treatment, and risk of biliary tract cancer (BTC) and hepatocellular carcinoma (HCC), independent of general and abdominal obesity is scarce. PATIENTS AND METHODS We conducted a prospective analysis in the EPIC-cohort study among 363 426 participants with self-reported diabetes data. Multivariable adjusted relative risks and 95% confidence intervals were estimated from Cox regression models. In a nested case-control subset, analyses were carried out in HCV/HBV-negative individuals. RESULTS During 8.5 years of follow-up, 204 BTC cases [including 75 gallbladder cancer (GBC) cases], and 176 HCC cases were identified. Independent of body mass index and waist-to-height ratio diabetes status was associated with higher risk of BTC and HCC [1.77 (1.00-3.13) and 2.17 (1.36-3.47)]. For BTC, the risk seemed to be higher in participants with shorter diabetes duration and those not treated with insulin. Regarding cancer subsites, diabetes was only associated with GBC [2.72 (1.17-6.31)]. The risk for HCC was particularly higher in participants treated with insulin. The results were not appreciably different in HCV/HBV-negative individuals. CONCLUSION(S) This study supports the hypothesis that diabetes is a risk factor for BTC (particularly GBC) and HCC. Further research is required to establish whether diabetes treatment or duration is associated with these cancers.
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Affiliation(s)
- S Schlesinger
- Institute of Epidemiology, Christian-Albrechts University of Kiel, Kiel, Germany.
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Fedirko V, Trichopolou A, Bamia C, Duarte-Salles T, Trepo E, Aleksandrova K, Nöthlings U, Lukanova A, Lagiou P, Boffetta P, Trichopoulos D, Katzke VA, Overvad K, Tjønneland A, Hansen L, Boutron-Ruault MC, Fagherazzi G, Bastide N, Panico S, Grioni S, Vineis P, Palli D, Tumino R, Bueno-de-Mesquita HB, Peeters PH, Skeie G, Engeset D, Parr CL, Jakszyn P, Sánchez MJ, Barricarte A, Amiano P, Chirlaque M, Quirós JR, Sund M, Werner M, Sonestedt E, Ericson U, Key TJ, Khaw KT, Ferrari P, Romieu I, Riboli E, Jenab M. Consumption of fish and meats and risk of hepatocellular carcinoma: the European Prospective Investigation into Cancer and Nutrition (EPIC). Ann Oncol 2013; 24:2166-73. [PMID: 23670094 DOI: 10.1093/annonc/mdt168] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND While higher intake of fish and lower consumption of red/processed meats have been suggested to play a protective role in the etiology of several cancers, prospective evidence for hepatocellular carcinoma (HCC) is limited, particularly in Western European populations. METHODS The associations of fish and meats with HCC risk were analyzed in the EPIC cohort. Between 1992 and 2010, 191 incident HCC were identified among 477 206 participants. Baseline diet was assessed using validated dietary questionnaires. A single 24-h diet recall from a cohort subsample was used for calibration. Multivariable proportional hazard regression was utilized to estimate hazard ratios (HR) and 95% confidence intervals (CI). In a nested case-control subset (HCC = 122), HBV/HCV status and liver function biomarkers were measured. RESULTS HCC risk was inversely associated with intake of total fish (per 20 g/day increase, HR = 0.83, 95% CI 0.74-0.95 and HR = 0.80, 95% CI 0.69-0.97 before and after calibration, respectively). This inverse association was also suggested after adjusting for HBV/HCV status and liver function score (per 20-g/day increase, RR = 0.86, 95% CI 0.66-1.11 and RR = 0.74, 95% CI 0.50-1.09, respectively) in a nested case-control subset. Intakes of total meats or subgroups of red/processed meats, and poultry were not associated with HCC risk. CONCLUSIONS In this large European cohort, total fish intake is associated with lower HCC risk.
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Affiliation(s)
- V Fedirko
- Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC-WHO), Lyon, France.
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Chan K, Patel RS, Newcombe P, Nelson CP, Qasim A, Epstein SE, Burnett S, Vaccarino VL, Zafari AM, Shah SH, Anderson JL, Carlquist JF, Hartiala J, Allayee H, Hinohara K, Lee BS, Erl A, Ellis KL, Goel A, Schaefer AS, Mokhtari NE, Goldstein BA, Hlatky MA, Go AS, Shen GQ, Gong Y, Pepine C, Laxton RC, Wittaker JC, Tang WHW, Johnson JA, Wang QK, Assimes TL, Nöthlings U, Farrall M, Watkins H, Richards AM, Cameron VA, Muendlein A, Drexel H, Koch W, Park JE, Kimura A, Shen WF, Simpson IA, Hazen SL, Horne BD, Hauser ER, Quyyumi AA, Reilly MP, Samani NJ, Ye S. 126 CHROMOSOME 9P21 LOCUS AND ANGIOGRAPHIC CORONARY ARTERY DISEASE BURDEN: A COLLABORATIVE META-ANALYSIS. Heart 2013. [DOI: 10.1136/heartjnl-2013-304019.126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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121
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Weikert C, Drogan D, di Giuseppe R, Fritsche A, Buijsse B, Nöthlings U, Willich SN, Berger K, Boeing H. Liver enzymes and stroke risk in middle-aged German adults. Atherosclerosis 2013; 228:508-14. [PMID: 23608248 DOI: 10.1016/j.atherosclerosis.2013.03.026] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2012] [Revised: 03/13/2013] [Accepted: 03/25/2013] [Indexed: 12/29/2022]
Abstract
OBJECTIVE To investigate the association between the liver enzymes γ-glutamyltransferase (GGT) and (alanine aminotransferase) ALT and risk of stroke, its subtypes including TIA as well as fatal and non-fatal events. METHODS A case-cohort study within the European Prospective Investigation into Cancer and Nutrition-Potsdam Study comprising 27548 middle-aged subjects was designed. GGT and ALT were measured in plasma of 353 individuals who developed a stroke and in 2110 individuals who remained free of cardiovascular events during a mean follow-up of 8.2 ± 2.2 years. Cox proportional-hazard models were applied to evaluate the association between liver enzymes and stroke risk. RESULTS After adjustment for established clinical and lifestyle factors, a 1 unit change in naturally logged GGT was related to a 1.20 (95%CI: 1.03-1.40) increased stroke risk. Risk estimates did not significantly differ between fatal (Relative Risk (RR) = 1.35, 95%CI: 1.14-1.61) and non-fatal events (RR = 1.15; 95%CI: 0.97-1.36). ALT was not associated with overall stroke risk (RR = 0.95; 95%CI: 0.71-1.26). However, in subtype analyses we observed in multivariable adjusted models a significant increased risk of hemorrhagic stroke (RR = 2.00; 95% CI: 1.01-3.96), but decreased risk of ischemic stroke (RR = 0.66; 95%CI: 0.44-0.998). CONCLUSIONS Our data provide further evidence for a link between GGT, but not ALT and overall stroke suggesting that these biomarkers are involved in different pathways of disease development. Further studies are needed to clarify the putative relationships between ALT and subtypes of stroke.
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Affiliation(s)
- Cornelia Weikert
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
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Johner SA, Thamm M, Kriener E, Völzke H, Gärtner R, Nöthlings U, Remer T. Iodine status dependent changes of TSH serum levels - a deeper analysis of the representative KiGGS sample of German children and adolescents. Exp Clin Endocrinol Diabetes 2013. [DOI: 10.1055/s-0033-1336648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Sluik D, Buijsse B, Muckelbauer R, Kaaks R, Teucher B, Johnsen NF, Tjønneland A, Overvad K, Ostergaard JN, Amiano P, Ardanaz E, Bendinelli B, Pala V, Tumino R, Ricceri F, Mattiello A, Spijkerman AMW, Monninkhof EM, May AM, Franks PW, Nilsson PM, Wennberg P, Rolandsson O, Fagherazzi G, Boutron-Ruault MC, Clavel-Chapelon F, Castaño JMH, Gallo V, Boeing H, Nöthlings U. Physical Activity and Mortality in Individuals With Diabetes Mellitus: A Prospective Study and Meta-analysis. ACTA ACUST UNITED AC 2013; 172:1285-95. [PMID: 22868663 DOI: 10.1001/archinternmed.2012.3130] [Citation(s) in RCA: 174] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND Physical activity (PA) is considered a cornerstone of diabetes mellitus management to prevent complications, but conclusive evidence is lacking. METHODS This prospective cohort study and meta-analysis of existing studies investigated the association between PA and mortality in individuals with diabetes. In the EPIC study (European Prospective Investigation Into Cancer and Nutrition), a cohort was defined of 5859 individuals with diabetes at baseline. Associations of leisure-time and total PA and walking with cardiovascular disease (CVD) and total mortality were studied using multivariable Cox proportional hazards regression models. Fixed- and random-effects meta-analyses of prospective studies published up to December 2010 were pooled with inverse variance weighting. RESULTS In the prospective analysis, total PA was associated with lower risk of CVD and total mortality. Compared with physically inactive persons, the lowest mortality risk was observed in moderately active persons: hazard ratios were 0.62 (95% CI, 0.49-0.78) for total mortality and 0.51 (95% CI, 0.32-0.81) for CVD mortality. Leisure-time PA was associated with lower total mortality risk, and walking was associated with lower CVD mortality risk. In the meta-analysis, the pooled random-effects hazard ratio from 5 studies for high vs low total PA and all-cause mortality was 0.60 (95% CI, 0.49-0.73). CONCLUSIONS Higher levels of PA were associated with lower mortality risk in individuals with diabetes. Even those undertaking moderate amounts of activity were at appreciably lower risk for early death compared with inactive persons. These findings provide empirical evidence supporting the widely shared view that persons with diabetes should engage in regular PA.
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Fedirko V, Lukanova A, Bamia C, Trichopolou A, Trepo E, Nöthlings U, Schlesinger S, Aleksandrova K, Boffetta P, Tjønneland A, Johnsen NF, Overvad K, Fagherazzi G, Racine A, Boutron-Ruault MC, Grote V, Kaaks R, Boeing H, Naska A, Adarakis G, Valanou E, Palli D, Sieri S, Tumino R, Vineis P, Panico S, Bueno-de-Mesquita HBA, Siersema PD, Peeters PH, Weiderpass E, Skeie G, Engeset D, Quirós JR, Zamora-Ros R, Sánchez MJ, Amiano P, Huerta JM, Barricarte A, Johansen D, Lindkvist B, Sund M, Werner M, Crowe F, Khaw KT, Ferrari P, Romieu I, Chuang SC, Riboli E, Jenab M. Glycemic index, glycemic load, dietary carbohydrate, and dietary fiber intake and risk of liver and biliary tract cancers in Western Europeans. Ann Oncol 2013; 24:543-553. [PMID: 23123507 PMCID: PMC3551485 DOI: 10.1093/annonc/mds434] [Citation(s) in RCA: 84] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2012] [Revised: 07/20/2012] [Accepted: 07/24/2012] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The type and quantity of dietary carbohydrate as quantified by glycemic index (GI) and glycemic load (GL), and dietary fiber may influence the risk of liver and biliary tract cancers, but convincing evidence is lacking. PATIENTS AND METHODS The association between dietary GI/GL and carbohydrate intake with hepatocellular carcinoma (HCC; N = 191), intrahepatic bile duct (IBD; N = 66), and biliary tract (N = 236) cancer risk was investigated in 477 206 participants of the European Prospective Investigation into Cancer and Nutrition cohort. Dietary intake was assessed by country-specific, validated dietary questionnaires. Hazard ratios and 95% confidence intervals were estimated from proportional hazard models. HBV/HCV status was measured in a nested case-control subset. RESULTS Higher dietary GI, GL, or increased intake of total carbohydrate was not associated with liver or biliary tract cancer risk. For HCC, divergent risk estimates were observed for total sugar = 1.43 (1.17-1.74) per 50 g/day, total starch = 0.70 (0.55-0.90) per 50 g/day, and total dietary fiber = 0.70 (0.52-0.93) per 10 g/day. The findings for dietary fiber were confirmed among HBV/HCV-free participants [0.48 (0.23-1.01)]. Similar associations were observed for IBD [dietary fiber = 0.59 (0.37-0.99) per 10 g/day], but not biliary tract cancer. CONCLUSIONS Findings suggest that higher consumption of dietary fiber and lower consumption of total sugars are associated with lower HCC risk. In addition, high dietary fiber intake could be associated with lower IBD cancer risk.
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Affiliation(s)
- V Fedirko
- Nutritional Epidemiology Group, Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC-WHO), Lyon, France.
| | - A Lukanova
- Division of Cancer Epidemiology, German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | - C Bamia
- WHO Collaborating Center for Food and Nutrition Policies, Department of Hygiene, Epidemiology, Medical Statistics, University of Athens Medical School, Athens
| | - A Trichopolou
- WHO Collaborating Center for Food and Nutrition Policies, Department of Hygiene, Epidemiology, Medical Statistics, University of Athens Medical School, Athens; Hellenic Health Foundation, Athens, Greece
| | - E Trepo
- Centre de Bioloqie Republique, Lyon, France
| | - U Nöthlings
- Section of Epidemiology, Institute for Experimental Medicine, Christian-Albrechts University of Kiel, Kiel
| | - S Schlesinger
- Section of Epidemiology, Institute for Experimental Medicine, Christian-Albrechts University of Kiel, Kiel
| | - K Aleksandrova
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - P Boffetta
- Institute for Translational Epidemiology, Mount Sinai School of Medicine, The Tisch Cancer Institute, New York, USA
| | - A Tjønneland
- Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen
| | - N F Johnsen
- Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen
| | - K Overvad
- Department of Epidemiology, School of Public Health, Aarhus University, Aarhus, Denmark
| | - G Fagherazzi
- Centre for Research in Epidemiology and Population Health, Inserm (Institut National de la Santé et de la Recherche Médicale), Institut Gustave Roussy Villejuif; Paris South University, UMRS 1018 Villejuif, France
| | - A Racine
- Centre for Research in Epidemiology and Population Health, Inserm (Institut National de la Santé et de la Recherche Médicale), Institut Gustave Roussy Villejuif; Paris South University, UMRS 1018 Villejuif, France
| | - M C Boutron-Ruault
- Centre for Research in Epidemiology and Population Health, Inserm (Institut National de la Santé et de la Recherche Médicale), Institut Gustave Roussy Villejuif; Paris South University, UMRS 1018 Villejuif, France
| | - V Grote
- Division of Cancer Epidemiology, German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | - R Kaaks
- Division of Cancer Epidemiology, German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | - H Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - A Naska
- WHO Collaborating Center for Food and Nutrition Policies, Department of Hygiene, Epidemiology, Medical Statistics, University of Athens Medical School, Athens
| | - G Adarakis
- Hellenic Health Foundation, Athens, Greece
| | - E Valanou
- Hellenic Health Foundation, Athens, Greece
| | - D Palli
- Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute - ISPO, Florence
| | - S Sieri
- Nutritional Epidemiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan
| | - R Tumino
- Cancer Registry and Histopathology Unit, "Civile M.P.Arezzo" Hospital, Ragusa, Italy
| | - P Vineis
- School of Public Health, Imperial College, London, UK; HuGeF Foundation, Turin
| | - S Panico
- Department of Clinical and Experimental Medicine, Federico II University, Naples, Italy
| | - H B As Bueno-de-Mesquita
- Centre for Nutrition and Health, National Institute for Public Health and the Environment (RIVM), Bilthoven; Department of Gastroenterology and Hepatology, University Medical Centre Utrecht (UMCU), Utrecht
| | - P D Siersema
- Department of Gastroenterology and Hepatology, University Medical Centre Utrecht (UMCU), Utrecht
| | - P H Peeters
- Department of Epidemiology Julius Centre for Health Sciences and Primary Care, University Medical Centre, Utrecht, the Netherlands; School of Public Health, Imperial College, London, UK
| | - E Weiderpass
- Department of Community Medicine, University of Tromsø, Tromsø; Cancer Registry of Norway, Oslo, Norway; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Samfundet Folkhälsan, Genetic Epidemiology Group, Folkhälsan Research Center, University of Helsinki, Helsinki, Finland
| | - G Skeie
- Department of Community Medicine, University of Tromsø, Tromsø
| | - D Engeset
- Department of Community Medicine, University of Tromsø, Tromsø
| | - J R Quirós
- Public Health Directorate, Health and Health Care Services Council, Asturias
| | - R Zamora-Ros
- Unit of Nutrition, Environment and Cancer, Catalan Institute of Oncology (ICO-IDIBELL), Barcelona
| | - M J Sánchez
- Andalusian School of Public Health, Granada; Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pública-CIBERESP) Granada
| | - P Amiano
- Public Health Division of Gipuzkoa, BIODonostia Research Institute, Department ofHealth of the regional Government of the Basque Country, San Sebastian; Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pública-CIBERESP) Granada
| | - J M Huerta
- Department of Epidemiology, Murcia Regional Health Council, Murcia; Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pública-CIBERESP) Granada
| | - A Barricarte
- Navarre Public Health Institute, Pamplona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pública-CIBERESP) Granada
| | | | - B Lindkvist
- Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg
| | - M Sund
- Department of Surgical and Perioperative Sciences, Umea University
| | - M Werner
- Department of Public Health and Clinical Medicine, Umea University, Sweden
| | - F Crowe
- Cancer Epidemiology Unit, Nuffield Department of Clinical Medicine, University of Oxford, Oxford
| | - K T Khaw
- Clinical Gerontology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - P Ferrari
- Nutritional Epidemiology Group, Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - I Romieu
- Nutritional Epidemiology Group, Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - S C Chuang
- School of Public Health, Imperial College, London, UK
| | - E Riboli
- School of Public Health, Imperial College, London, UK
| | - M Jenab
- Nutritional Epidemiology Group, Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC-WHO), Lyon, France
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Nöthlings U. Introduction of the new associate editor of Annals of Nutrition and Metabolism. Ann Nutr Metab 2013; 62:I. [PMID: 23392209 DOI: 10.1159/000346635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
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Chan K, Patel RS, Newcombe P, Nelson CP, Qasim A, Epstein SE, Burnett S, Vaccarino VL, Zafari AM, Shah SH, Anderson JL, Carlquist JF, Hartiala J, Allayee H, Hinohara K, Lee BS, Erl A, Ellis KL, Goel A, Schaefer AS, El Mokhtari NE, Goldstein BA, Hlatky MA, Go AS, Shen GQ, Gong Y, Pepine C, Laxton RC, Whittaker JC, Tang WHW, Johnson JA, Wang QK, Assimes TL, Nöthlings U, Farrall M, Watkins H, Richards AM, Cameron VA, Muendlein A, Drexel H, Koch W, Park JE, Kimura A, Shen WF, Simpson IA, Hazen SL, Horne BD, Hauser ER, Quyyumi AA, Reilly MP, Samani NJ, Ye S. Association between the chromosome 9p21 locus and angiographic coronary artery disease burden: a collaborative meta-analysis. J Am Coll Cardiol 2013; 61:957-70. [PMID: 23352782 DOI: 10.1016/j.jacc.2012.10.051] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2012] [Accepted: 10/30/2012] [Indexed: 01/21/2023]
Abstract
OBJECTIVES This study sought to ascertain the relationship of 9p21 locus with: 1) angiographic coronary artery disease (CAD) burden; and 2) myocardial infarction (MI) in individuals with underlying CAD. BACKGROUND Chromosome 9p21 variants have been robustly associated with coronary heart disease, but questions remain on the mechanism of risk, specifically whether the locus contributes to coronary atheroma burden or plaque instability. METHODS We established a collaboration of 21 studies consisting of 33,673 subjects with information on both CAD (clinical or angiographic) and MI status along with 9p21 genotype. Tabular data are provided for each cohort on the presence and burden of angiographic CAD, MI cases with underlying CAD, and the diabetic status of all subjects. RESULTS We first confirmed an association between 9p21 and CAD with angiographically defined cases and control subjects (pooled odds ratio [OR]: 1.31, 95% confidence interval [CI]: 1.20 to 1.43). Among subjects with angiographic CAD (n = 20,987), random-effects model identified an association with multivessel CAD, compared with those with single-vessel disease (OR: 1.10, 95% CI: 1.04 to 1.17)/copy of risk allele). Genotypic models showed an OR of 1.15, 95% CI: 1.04 to 1.26 for heterozygous carrier and OR: 1.23, 95% CI: 1.08 to 1.39 for homozygous carrier. Finally, there was no significant association between 9p21 and prevalent MI when both cases (n = 17,791) and control subjects (n = 15,882) had underlying CAD (OR: 0.99, 95% CI: 0.95 to 1.03)/risk allele. CONCLUSIONS The 9p21 locus shows convincing association with greater burden of CAD but not with MI in the presence of underlying CAD. This adds further weight to the hypothesis that 9p21 locus primarily mediates an atherosclerotic phenotype.
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Affiliation(s)
- Kenneth Chan
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University London, London, United Kingdom
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Johner SA, Thamm M, Nöthlings U, Remer T. Iodine status in preschool children and evaluation of major dietary iodine sources: a German experience. Eur J Nutr 2012; 52:1711-9. [PMID: 23212532 DOI: 10.1007/s00394-012-0474-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2012] [Accepted: 11/22/2012] [Indexed: 11/30/2022]
Abstract
PURPOSE Even mild iodine deficiency may negatively affect cognitive performance, especially at a young age. Our aim was to investigate iodine status in very young children and to assess the importance of iodized salt in processed foods of which the use has decreased during the last years in Germany. METHODS Twenty-four hours urinary iodine excretion (UIE) as a marker of iodine intake was measured in 378 24 h urine samples collected 2003-2010 by 221 3 to <6 years old participants of the DONALD Study. Parallel 3-d weighed dietary records and measurements of urinary sodium excretion provided data on the daily consumption of the most important iodine sources in the children's diet (iodized salt, milk, fish, meat and eggs). Time trends of UIE (2003-2010) and contributions of the different food groups were analyzed by using linear mixed-effects regression models. RESULTS Median UIE of 71 μg/d in boys and 65 μg/d in girls (P = 0.03), corresponding to an iodine intake of 82 and 75 μg/d, respectively (assumption: 15% non-renal iodine losses), was below the recommended dietary allowance (RDA) of 90 μg/d. Milk, salt and egg intake were significant predictors of UIE; milk and salt together accounted for >80% of iodine supply. Between 2003 and 2010, UIE decreased significantly by approximately 1 μg/d per year. The contribution of salt intake to UIE decreased from 2003-2006 to 2007-2010. CONCLUSION In countries where salt is a major iodine source, already modest decreases in the iodized proportion of salt used in processed foods may relevantly impair iodine status even in preschool children.
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Affiliation(s)
- Simone A Johner
- IEL-Nutritional Epidemiology, DONALD Study at the Research Institute of Child Nutrition, University of Bonn, Heinstück 11, 44225, Dortmund, Germany,
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Siegert S, Hampe J, Schafmayer C, von Schönfels W, Egberts JH, Försti A, Chen B, Lascorz J, Hemminki K, Franke A, Nothnagel M, Nöthlings U, Krawczak M. Genome-wide investigation of gene-environment interactions in colorectal cancer. Hum Genet 2012; 132:219-31. [PMID: 23114982 DOI: 10.1007/s00439-012-1239-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2012] [Accepted: 10/08/2012] [Indexed: 01/20/2023]
Abstract
Colorectal cancer (CRC), one of the most frequent neoplasias worldwide, has both genetic and environmental causes. As yet, however, gene-environment (G × E) interactions in CRC have been studied mostly for a small number of candidate genes only. Therefore, we investigated the possible interaction, in CRC etiology, between single-nucleotide polymorphisms (SNPs) on the one hand, and overweight, smoking and alcohol consumption on the other, at a genome-wide level. To this end, we adopted a two-tiered approach comprising a case-only screening stage I (314 cases) and a case-control validation stage II (259 cases, 1,002 controls). Interactions with the smallest p value in stage I were verified in stage II using multiple logistic regression analysis adjusted for sex and age. In addition, we specifically studied known CRC-associated SNPs for possible G × E interactions. Upon adjustment for sex and age, and after allowing for multiple testing, however, only a single SNP (rs1944511) was found to be involved in a statistically significant interaction, namely with overweight (multiplicity-corrected p = 0.042 in stage II). Several other G × E interactions were nominally significant but failed correction for multiple testing, including a previously reported interaction between rs9929218 and alcohol consumption that also emerged in our candidate SNP study (nominal p = 0.008). Notably, none of the interactions identified in our genome-wide analysis was with a previously reported CRC-associated SNP. Our study therefore highlights the potential of an "agnostic" genome-wide approach to G × E analysis.
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Affiliation(s)
- Sabine Siegert
- Section of Epidemiology, Institute of Experimental Medicine, Christian-Albrechts University Kiel, Kiel, Germany
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Chasman DI, Fuchsberger C, Pattaro C, Teumer A, Böger CA, Endlich K, Olden M, Chen MH, Tin A, Taliun D, Li M, Gao X, Gorski M, Yang Q, Hundertmark C, Foster MC, O'Seaghdha CM, Glazer N, Isaacs A, Liu CT, Smith AV, O'Connell JR, Struchalin M, Tanaka T, Li G, Johnson AD, Gierman HJ, Feitosa MF, Hwang SJ, Atkinson EJ, Lohman K, Cornelis MC, Johansson A, Tönjes A, Dehghan A, Lambert JC, Holliday EG, Sorice R, Kutalik Z, Lehtimäki T, Esko T, Deshmukh H, Ulivi S, Chu AY, Murgia F, Trompet S, Imboden M, Coassin S, Pistis G, Harris TB, Launer LJ, Aspelund T, Eiriksdottir G, Mitchell BD, Boerwinkle E, Schmidt H, Cavalieri M, Rao M, Hu F, Demirkan A, Oostra BA, de Andrade M, Turner ST, Ding J, Andrews JS, Freedman BI, Giulianini F, Koenig W, Illig T, Meisinger C, Gieger C, Zgaga L, Zemunik T, Boban M, Minelli C, Wheeler HE, Igl W, Zaboli G, Wild SH, Wright AF, Campbell H, Ellinghaus D, Nöthlings U, Jacobs G, Biffar R, Ernst F, Homuth G, Kroemer HK, Nauck M, Stracke S, Völker U, Völzke H, Kovacs P, Stumvoll M, Mägi R, Hofman A, Uitterlinden AG, Rivadeneira F, Aulchenko YS, Polasek O, Hastie N, Vitart V, Helmer C, Wang JJ, Stengel B, Ruggiero D, Bergmann S, Kähönen M, Viikari J, Nikopensius T, Province M, Ketkar S, Colhoun H, Doney A, Robino A, Krämer BK, Portas L, Ford I, Buckley BM, Adam M, Thun GA, Paulweber B, Haun M, Sala C, Mitchell P, Ciullo M, Kim SK, Vollenweider P, Raitakari O, Metspalu A, Palmer C, Gasparini P, Pirastu M, Jukema JW, Probst-Hensch NM, Kronenberg F, Toniolo D, Gudnason V, Shuldiner AR, Coresh J, Schmidt R, Ferrucci L, Siscovick DS, van Duijn CM, Borecki IB, Kardia SLR, Liu Y, Curhan GC, Rudan I, Gyllensten U, Wilson JF, Franke A, Pramstaller PP, Rettig R, Prokopenko I, Witteman J, Hayward C, Ridker PM, Parsa A, Bochud M, Heid IM, Kao WHL, Fox CS, Köttgen A. Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function. Hum Mol Genet 2012; 21:5329-43. [PMID: 22962313 DOI: 10.1093/hmg/dds369] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
In conducting genome-wide association studies (GWAS), analytical approaches leveraging biological information may further understanding of the pathophysiology of clinical traits. To discover novel associations with estimated glomerular filtration rate (eGFR), a measure of kidney function, we developed a strategy for integrating prior biological knowledge into the existing GWAS data for eGFR from the CKDGen Consortium. Our strategy focuses on single nucleotide polymorphism (SNPs) in genes that are connected by functional evidence, determined by literature mining and gene ontology (GO) hierarchies, to genes near previously validated eGFR associations. It then requires association thresholds consistent with multiple testing, and finally evaluates novel candidates by independent replication. Among the samples of European ancestry, we identified a genome-wide significant SNP in FBXL20 (P = 5.6 × 10(-9)) in meta-analysis of all available data, and additional SNPs at the INHBC, LRP2, PLEKHA1, SLC3A2 and SLC7A6 genes meeting multiple-testing corrected significance for replication and overall P-values of 4.5 × 10(-4)-2.2 × 10(-7). Neither the novel PLEKHA1 nor FBXL20 associations, both further supported by association with eGFR among African Americans and with transcript abundance, would have been implicated by eGFR candidate gene approaches. LRP2, encoding the megalin receptor, was identified through connection with the previously known eGFR gene DAB2 and extends understanding of the megalin system in kidney function. These findings highlight integration of existing genome-wide association data with independent biological knowledge to uncover novel candidate eGFR associations, including candidates lacking known connections to kidney-specific pathways. The strategy may also be applicable to other clinical phenotypes, although more testing will be needed to assess its potential for discovery in general.
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Affiliation(s)
- Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA 02215, USA
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Schlesinger S, Nöthlings U. Vitamin intake and risk of liver cancer: potential for prevention? Chin Clin Oncol 2012; 1:7. [PMID: 25842065 DOI: 10.3978/j.issn.2304-3865.2012.08.04] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2012] [Accepted: 08/27/2012] [Indexed: 06/04/2023]
Affiliation(s)
- Sabrina Schlesinger
- Institute for Experimental Medicine, Section of Epidemiology, Christian-Albrechts University of Kiel, Kiel, Germany.
| | - Ute Nöthlings
- Department of Nutrition and Food Sciences, Nutritional Epidemiology, University of Bonn, Bonn, Germany
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Burger KNJ, Beulens JWJ, van der Schouw YT, Sluijs I, Spijkerman AMW, Sluik D, Boeing H, Kaaks R, Teucher B, Dethlefsen C, Overvad K, Tjønneland A, Kyrø C, Barricarte A, Bendinelli B, Krogh V, Tumino R, Sacerdote C, Mattiello A, Nilsson PM, Orho-Melander M, Rolandsson O, Huerta JM, Crowe F, Allen N, Nöthlings U. Dietary fiber, carbohydrate quality and quantity, and mortality risk of individuals with diabetes mellitus. PLoS One 2012; 7:e43127. [PMID: 22927948 PMCID: PMC3426551 DOI: 10.1371/journal.pone.0043127] [Citation(s) in RCA: 68] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2012] [Accepted: 07/16/2012] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Dietary fiber, carbohydrate quality and quantity are associated with mortality risk in the general population. Whether this is also the case among diabetes patients is unknown. OBJECTIVE To assess the associations of dietary fiber, glycemic load, glycemic index, carbohydrate, sugar, and starch intake with mortality risk in individuals with diabetes. METHODS This study was a prospective cohort study among 6,192 individuals with confirmed diabetes mellitus (mean age of 57.4 years, and median diabetes duration of 4.4 years at baseline) from the European Prospective Investigation into Cancer and Nutrition (EPIC). Dietary intake was assessed at baseline (1992-2000) with validated dietary questionnaires. Cox proportional hazards analysis was performed to estimate hazard ratios (HRs) for all-cause and cardiovascular mortality, while adjusting for CVD-related, diabetes-related, and nutritional factors. RESULTS During a median follow-up of 9.2 y, 791 deaths were recorded, 306 due to CVD. Dietary fiber was inversely associated with all-cause mortality risk (adjusted HR per SD increase, 0.83 [95% CI, 0.75-0.91]) and CVD mortality risk (0.76[0.64-0.89]). No significant associations were observed for glycemic load, glycemic index, carbohydrate, sugar, or starch. Glycemic load (1.42[1.07-1.88]), carbohydrate (1.67[1.18-2.37]) and sugar intake (1.53[1.12-2.09]) were associated with an increased total mortality risk among normal weight individuals (BMI≤25 kg/m(2); 22% of study population) but not among overweight individuals (P interaction≤0.04). These associations became stronger after exclusion of energy misreporters. CONCLUSIONS High fiber intake was associated with a decreased mortality risk. High glycemic load, carbohydrate and sugar intake were associated with an increased mortality risk in normal weight individuals with diabetes.
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Affiliation(s)
- Koert N. J. Burger
- Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands
| | - Joline W. J. Beulens
- Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands
| | - Yvonne T. van der Schouw
- Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands
| | - Ivonne Sluijs
- Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands
| | - Annemieke M. W. Spijkerman
- Center for Prevention and Health Services Research, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Diewertje Sluik
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany
| | - Rudolf Kaaks
- German Cancer Research Center, Heidelberg, Germany
| | | | - Claus Dethlefsen
- Department of Cardiology, Center for Cardiovascular Research, Aalborg Hospital, Aarhus University Hospital, Aalborg, Denmark
| | - Kim Overvad
- Department of Cardiology, Center for Cardiovascular Research, Aalborg Hospital, Aarhus University Hospital, Aalborg, Denmark
- Department of Epidemiology, School of Public Health, Aarhus University, Aarhus, Denmark
| | | | - Cecilie Kyrø
- Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Aurelio Barricarte
- Navarre Public Health Institute, Pamplona, and Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pública-CIBERESP),Pamplona, Spain
| | - Benedetta Bendinelli
- Molecular and Nutritional Epidemiology Unit, Istituto per lo Studio e la Prevenzione Oncologica, Florence, Italy
| | - Vittorio Krogh
- Nutritional Epidemiology Unit, IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Rosario Tumino
- Cancer Registry and Histopathology Unit, “Civile M. P. Arezzo” Hospital, Ragusa, Italy
| | - Carlotta Sacerdote
- Center for Cancer Prevention (Piedmont), and Human Genetic Foundation, Turin, Italy
| | - Amalia Mattiello
- Department of Clinical and Experimental Medicine, Federico II University, Naples, Italy
| | - Peter M. Nilsson
- Department of Clinical Sciences, Lund University Hospital, Malmö, Sweden
| | | | - Olov Rolandsson
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - José María Huerta
- Department of Epidemiology, Murcia Regional Health Authority, and CIBER Epidemiología y Salud Pública (CIBERESP), Murcia, Spain
| | - Francesca Crowe
- Cancer Epidemiology Unit, University of Oxford, Oxford, United Kingdom
| | - Naomi Allen
- Cancer Epidemiology Unit, University of Oxford, Oxford, United Kingdom
| | - Ute Nöthlings
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany
- Epidemiology Section, Institute for Experimental Medicine, Christian-Albrechts-University of Kiel, Kiel, Germany
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132
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Schlesinger S, Aleksandrova K, Pischon T, Fedirko V, Jenab M, Trepo E, Boffetta P, Dahm CC, Overvad K, Tjønneland A, Halkjaer J, Fagherazzi G, Boutron-Ruault MC, Carbonnel F, Kaaks R, Lukanova A, Boeing H, Trichopoulou A, Bamia C, Lagiou P, Palli D, Grioni S, Panico S, Tumino R, Vineis P, HB BDM, van den Berg S, Peeters PH, Braaten T, Weiderpass E, Quirós JR, Travier N, Sánchez MJ, Navarro C, Barricarte A, Dorronsoro M, Lindkvist B, Regner S, Werner M, Sund M, Khaw KT, Wareham N, Travis RC, Norat T, Wark PA, Riboli E, Nöthlings U. Abdominal obesity, weight gain during adulthood and risk of liver and biliary tract cancer in a European cohort. Int J Cancer 2012; 132:645-57. [DOI: 10.1002/ijc.27645] [Citation(s) in RCA: 136] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2012] [Accepted: 04/18/2012] [Indexed: 12/15/2022]
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133
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Sluik D, Boeing H, Montonen J, Kaaks R, Lukanova A, Sandbaek A, Overvad K, Arriola L, Ardanaz E, Saieva C, Grioni S, Tumino R, Sacerdote C, Mattiello A, Spijkerman AMW, van der A DL, Beulens JWJ, van Dieren S, Nilsson PM, Groop LC, Franks PW, Rolandsson O, Bueno-de-Mesquita B, Nöthlings U. HbA1c measured in stored erythrocytes is positively linearly associated with mortality in individuals with diabetes mellitus. PLoS One 2012; 7:e38877. [PMID: 22719972 PMCID: PMC3374773 DOI: 10.1371/journal.pone.0038877] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2012] [Accepted: 05/13/2012] [Indexed: 01/12/2023] Open
Abstract
Introduction Observational studies have shown that glycated haemoglobin (HbA1c) is related to mortality, but the shape of the association is less clear. Furthermore, disease duration and medication may modify this association. This observational study explored the association between HbA1c measured in stored erythrocytes and mortality. Secondly, it was assessed whether disease duration and medication use influenced the estimates or were independently associated with mortality. Methods Within the European Prospective Investigation into Cancer and Nutrition a cohort was analysed of 4,345 individuals with a confirmed diagnosis of diabetes at enrolment. HbA1c was measured in blood samples stored up to 19 years. Multivariable Cox proportional hazard regression models for all-cause mortality investigated HbA1c in quartiles as well as per 1% increment, diabetes medication in seven categories of insulin and oral hypoglycaemic agents, and disease duration in quartiles. Results After a median follow-up of 9.3 years, 460 participants died. Higher HbA1c was associated with higher mortality: Hazard Ratio for 1%-increase was 1.11 (95% CI 1.06, 1.17). This association was linear (P-nonlinearity =0.15) and persistent across categories of medication use, disease duration, and co-morbidities. Compared with metformin, other medication types were not associated with mortality. Longer disease duration was associated with mortality, but not after adjustment for HbA1c and medication. Conclusion This prospective study showed that persons with lower HbA1c had better survival than those with higher HbA1c. The association was linear and independent of disease duration, type of medication use, and presence of co-morbidities. Any improvement of HbA1c appears to be associated with reduced mortality risk.
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Affiliation(s)
- Diewertje Sluik
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
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134
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Rohrmann S, Linseisen J, Nöthlings U, Overvad K, Egeberg R, Tjønneland A, Boutron-Ruault MC, Clavel-Chapelon F, Cottet V, Pala V, Tumino R, Palli D, Panico S, Vineis P, Boeing H, Pischon T, Grote V, Teucher B, Khaw KT, Wareham NJ, Crowe FL, Goufa I, Orfanos P, Trichopoulou A, Jeurnink SM, Siersema PD, Peeters PHM, Brustad M, Engeset D, Skeie G, Duell EJ, Amiano P, Barricarte A, Molina-Montes E, Rodríguez L, Tormo MJ, Sund M, Ye W, Lindkvist B, Johansen D, Ferrari P, Jenab M, Slimani N, Ward H, Riboli E, Norat T, Bueno-de-Mesquita HB. Meat and fish consumption and risk of pancreatic cancer: results from the European Prospective Investigation into Cancer and Nutrition. Int J Cancer 2012; 132:617-24. [PMID: 22610753 DOI: 10.1002/ijc.27637] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2012] [Accepted: 04/10/2012] [Indexed: 12/13/2022]
Abstract
Pancreatic cancer is the fourth most common cause of cancer death worldwide with large geographical variation, which implies the contribution of diet and lifestyle in its etiology. We examined the association of meat and fish consumption with risk of pancreatic cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC). A total of 477,202 EPIC participants from 10 European countries recruited between 1992 and 2000 were included in our analysis. Until 2008, 865 nonendocrine pancreatic cancer cases have been observed. Calibrated relative risks (RRs) and 95% confidence intervals (CIs) were computed using multivariable-adjusted Cox hazard regression models. The consumption of red meat (RR per 50 g increase per day = 1.03, 95% CI = 0.93-1.14) and processed meat (RR per 50 g increase per day = 0.93, 95% CI = 0.71-1.23) were not associated with an increased pancreatic cancer risk. Poultry consumption tended to be associated with an increased pancreatic cancer risk (RR per 50 g increase per day = 1.72, 95% CI = 1.04-2.84); however, there was no association with fish consumption (RR per 50 g increase per day = 1.22, 95% CI = 0.92-1.62). Our results do not support the conclusion of the World Cancer Research Fund that red or processed meat consumption may possibly increase the risk of pancreatic cancer. The positive association of poultry consumption with pancreatic cancer might be a chance finding as it contradicts most previous findings.
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Affiliation(s)
- Sabine Rohrmann
- Division of Cancer Epidemiology and Prevention, Institute of Social and Preventive Medicine, University of Zurich, Zurich, Switzerland.
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135
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Pattaro C, Köttgen A, Teumer A, Garnaas M, Böger CA, Fuchsberger C, Olden M, Chen MH, Tin A, Taliun D, Li M, Gao X, Gorski M, Yang Q, Hundertmark C, Foster MC, O'Seaghdha CM, Glazer N, Isaacs A, Liu CT, Smith AV, O'Connell JR, Struchalin M, Tanaka T, Li G, Johnson AD, Gierman HJ, Feitosa M, Hwang SJ, Atkinson EJ, Lohman K, Cornelis MC, Johansson Å, Tönjes A, Dehghan A, Chouraki V, Holliday EG, Sorice R, Kutalik Z, Lehtimäki T, Esko T, Deshmukh H, Ulivi S, Chu AY, Murgia F, Trompet S, Imboden M, Kollerits B, Pistis G, Harris TB, Launer LJ, Aspelund T, Eiriksdottir G, Mitchell BD, Boerwinkle E, Schmidt H, Cavalieri M, Rao M, Hu FB, Demirkan A, Oostra BA, de Andrade M, Turner ST, Ding J, Andrews JS, Freedman BI, Koenig W, Illig T, Döring A, Wichmann HE, Kolcic I, Zemunik T, Boban M, Minelli C, Wheeler HE, Igl W, Zaboli G, Wild SH, Wright AF, Campbell H, Ellinghaus D, Nöthlings U, Jacobs G, Biffar R, Endlich K, Ernst F, Homuth G, Kroemer HK, Nauck M, Stracke S, Völker U, Völzke H, Kovacs P, Stumvoll M, Mägi R, Hofman A, Uitterlinden AG, Rivadeneira F, Aulchenko YS, Polasek O, Hastie N, Vitart V, Helmer C, Wang JJ, Ruggiero D, Bergmann S, Kähönen M, Viikari J, Nikopensius T, Province M, Ketkar S, Colhoun H, Doney A, Robino A, Giulianini F, Krämer BK, Portas L, Ford I, Buckley BM, Adam M, Thun GA, Paulweber B, Haun M, Sala C, Metzger M, Mitchell P, Ciullo M, Kim SK, Vollenweider P, Raitakari O, Metspalu A, Palmer C, Gasparini P, Pirastu M, Jukema JW, Probst-Hensch NM, Kronenberg F, Toniolo D, Gudnason V, Shuldiner AR, Coresh J, Schmidt R, Ferrucci L, Siscovick DS, van Duijn CM, Borecki I, Kardia SLR, Liu Y, Curhan GC, Rudan I, Gyllensten U, Wilson JF, Franke A, Pramstaller PP, Rettig R, Prokopenko I, Witteman JCM, Hayward C, Ridker P, Parsa A, Bochud M, Heid IM, Goessling W, Chasman DI, Kao WHL, Fox CS. Genome-wide association and functional follow-up reveals new loci for kidney function. PLoS Genet 2012; 8:e1002584. [PMID: 22479191 PMCID: PMC3315455 DOI: 10.1371/journal.pgen.1002584] [Citation(s) in RCA: 152] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2011] [Accepted: 01/22/2012] [Indexed: 01/06/2023] Open
Abstract
Chronic kidney disease (CKD) is an important public health problem with a genetic component. We performed genome-wide association studies in up to 130,600 European ancestry participants overall, and stratified for key CKD risk factors. We uncovered 6 new loci in association with estimated glomerular filtration rate (eGFR), the primary clinical measure of CKD, in or near MPPED2, DDX1, SLC47A1, CDK12, CASP9, and INO80. Morpholino knockdown of mpped2 and casp9 in zebrafish embryos revealed podocyte and tubular abnormalities with altered dextran clearance, suggesting a role for these genes in renal function. By providing new insights into genes that regulate renal function, these results could further our understanding of the pathogenesis of CKD.
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Affiliation(s)
- Cristian Pattaro
- Institute of Genetic Medicine, European Academy of Bozen/Bolzano (EURAC) and Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- Renal Division, Freiburg University Clinic, Freiburg, Germany
| | - Alexander Teumer
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Maija Garnaas
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Carsten A. Böger
- Department of Internal Medicine II, University Medical Center Regensburg, Regensburg, Germany
| | - Christian Fuchsberger
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Matthias Olden
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
- Department of Epidemiology and Preventive Medicine, Regensburg University Medical Center, Regensburg, Germany
| | - Ming-Huei Chen
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, United States of America
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Adrienne Tin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Daniel Taliun
- Institute of Genetic Medicine, European Academy of Bozen/Bolzano (EURAC) and Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Man Li
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Xiaoyi Gao
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Mathias Gorski
- Department of Epidemiology and Preventive Medicine, University Hospital Regensburg, Regensburg, Germany
- Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | | | - Meredith C. Foster
- National Heart, Lung, and Blood Institute's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts, United States of America
| | - Conall M. O'Seaghdha
- National Heart, Lung, and Blood Institute's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts, United States of America
- Division of Nephrology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Nicole Glazer
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, United States of America
| | - Aaron Isaacs
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Centre for Medical Systems Biology, Leiden, The Netherlands
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Albert V. Smith
- Icelandic Heart Association, Research Institute, Kopavogur, Iceland
- University of Iceland, Reykjavik, Iceland
| | - Jeffrey R. O'Connell
- Department of Medicine, University of Maryland Medical School, Baltimore, Maryland, United States of America
| | - Maksim Struchalin
- Department of Epidemiology and Biostatistics and Department of Forensic Molecular Biology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Toshiko Tanaka
- Clinical Research Branch, National Institute of Aging, Baltimore, Maryland, United States of America
| | - Guo Li
- University of Washington, Seattle, Washington, United States of America
| | - Andrew D. Johnson
- National Heart, Lung, and Blood Institute's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts, United States of America
| | - Hinco J. Gierman
- Department of Developmental Biology, Stanford University, Stanford, California, United States of America
| | - Mary Feitosa
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Shih-Jen Hwang
- National Heart, Lung, and Blood Institute's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts, United States of America
| | - Elizabeth J. Atkinson
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Kurt Lohman
- Department of Epidemiology and Prevention, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Marilyn C. Cornelis
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Åsa Johansson
- Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Anke Tönjes
- Department of Medicine, University of Leipzig, Leipzig, Germany
- IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Elizabeth G. Holliday
- Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, University of Newcastle, Newcastle, Australia
- Centre for Information-based Medicine, Hunter Medical Research Institute, Newcastle, Australia
| | - Rossella Sorice
- Institute of Genetics and Biophysics “Adriano-Buzzati Traverso”–CNR, Napoli, Italy
| | - Zoltan Kutalik
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, University of Tampere and Tampere University Hospital, Centre for Laboratory Medicine Tampere Finn-Medi 2, Tampere, Finland
| | - Tõnu Esko
- Estonian Genome Center of University of Tartu (EGCUT), Tartu, Estonia
- Estonian Biocenter and Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Harshal Deshmukh
- Wellcome Trust Centre for Molecular Medicine, Clinical Research Centre, Ninewells Hospital, University of Dundee, Dundee, United Kingdom
| | - Sheila Ulivi
- Institute for Maternal and Child Health – IRCCS “Burlo Garofolo”, Trieste, Italy
| | - Audrey Y. Chu
- Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | | | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Medea Imboden
- Unit of Chronic Disease Epidemiology, Swiss Tropical and Public Health Institute, Basel, Switzerland
| | - Barbara Kollerits
- Division of Genetic Epidemiology, Innsbruck Medical University, Innsbruck, Austria
| | - Giorgio Pistis
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milano, Italy
| | | | | | | | | | - Tamara B. Harris
- Laboratory of Epidemiology, Demography, and Biometry, NIA, Bethesda, Maryland, United States of America
| | - Lenore J. Launer
- Laboratory of Epidemiology, Demography, and Biometry, NIA, Bethesda, Maryland, United States of America
| | - Thor Aspelund
- Icelandic Heart Association, Research Institute, Kopavogur, Iceland
- University of Iceland, Reykjavik, Iceland
| | | | - Braxton D. Mitchell
- Department of Medicine, University of Maryland Medical School, Baltimore, Maryland, United States of America
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center, Houston, Texas, United States of America
| | - Helena Schmidt
- Austrian Stroke Prevention Study, Institute of Molecular Biology and Biochemistry and Department of Neurology, Medical University Graz, Graz, Austria
| | - Margherita Cavalieri
- Austrian Stroke Prevention Study, University Clinic of Neurology, Department of Special Neurology, Medical University Graz, Graz, Austria
| | - Madhumathi Rao
- Division of Nephrology/Tufts Evidence Practice Center, Tufts University School of Medicine, Tufts Medical Center, Boston, Massachusetts, United States of America
| | - Frank B. Hu
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Ayse Demirkan
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Ben A. Oostra
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Mariza de Andrade
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Stephen T. Turner
- Department of Internal Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Jingzhong Ding
- Department of Internal Medicine/Geriatrics, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Jeanette S. Andrews
- Department of Biostatistical Sciences, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Barry I. Freedman
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | | | - Thomas Illig
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Angela Döring
- Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - H.-Erich Wichmann
- Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Informatics, Biometry, and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
- Klinikum Grosshadern, Neuherberg, Germany
| | - Ivana Kolcic
- Croatian Centre for Global Health, University of Split Medical School, Split, Croatia
| | - Tatijana Zemunik
- Croatian Centre for Global Health, University of Split Medical School, Split, Croatia
| | - Mladen Boban
- Croatian Centre for Global Health, University of Split Medical School, Split, Croatia
| | - Cosetta Minelli
- Institute of Genetic Medicine, European Academy of Bozen/Bolzano (EURAC) and Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Heather E. Wheeler
- Department of Genetics, Stanford University, Stanford, California, United States of America
- Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Wilmar Igl
- Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Ghazal Zaboli
- Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Sarah H. Wild
- Center for Population Health Sciences, University of Edinburgh Medical School, Edinburgh, United Kingdom
| | - Alan F. Wright
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Harry Campbell
- Center for Population Health Sciences, University of Edinburgh Medical School, Edinburgh, United Kingdom
| | - David Ellinghaus
- Institute of Clinical Molecular Biology, Christian-Albrechts University, Kiel, Germany
| | - Ute Nöthlings
- Institute of Clinical Molecular Biology, Christian-Albrechts University, Kiel, Germany
- popgen Biobank, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Gunnar Jacobs
- Institute of Clinical Molecular Biology, Christian-Albrechts University, Kiel, Germany
- popgen Biobank, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Reiner Biffar
- Clinic for Prosthodontic Dentistry, Gerostomatology, and Material Science, University of Greifswald, Greifswald, Germany
| | - Karlhans Endlich
- Institute of Anatomy and Cell Biology, University of Greifswald, Greifswald, Germany
| | - Florian Ernst
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Heyo K. Kroemer
- Institute of Pharmacology, University of Greifswald, Greifswald, Germany
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, Ernst-Moritz-Arndt-University Greifswald, Greifswald, Germany
| | - Sylvia Stracke
- Clinic for Internal Medicine A, University of Greifswald, Greifswald, Germany
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Henry Völzke
- Institute for Community Medicine, University of Greifswald, Greifswald, Germany
| | - Peter Kovacs
- Department of Medicine, University of Leipzig, Leipzig, Germany
| | - Michael Stumvoll
- Department of Medicine, University of Leipzig, Leipzig, Germany
- IFB Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - Reedik Mägi
- Estonian Genome Center of University of Tartu (EGCUT), Tartu, Estonia
- Wellcome Trust Centre for Human Genetics and Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Albert Hofman
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Andre G. Uitterlinden
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Yurii S. Aulchenko
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Ozren Polasek
- Croatian Centre for Global Health, Faculty of Medicine, University of Split, Split, Croatia
| | - Nick Hastie
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Catherine Helmer
- INSERM U897, Université Victor Ségalen Bordeaux 2, ISPED, Bordeaux, France
- Université Bordeaux 2 Victor Segalen, Bordeaux, France
| | - Jie Jin Wang
- Centre for Vision Research, Westmead Millennium Institute, Westmead Hospital, University of Sydney, Sydney, Australia
- Centre for Eye Research Australia (CERA), University of Melbourne, Melbourne, Australia
| | - Daniela Ruggiero
- Institute of Genetics and Biophysics “Adriano-Buzzati Traverso”–CNR, Napoli, Italy
| | - Sven Bergmann
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Mika Kähönen
- Department of Clinical Physiology, University of Tampere and Tampere University Hospital, Tampere, Finland
| | - Jorma Viikari
- Department of Medicine, University of Turku and Turku University Hospital, Turku, Finland
| | - Tiit Nikopensius
- Estonian Biocenter and Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Michael Province
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Shamika Ketkar
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Helen Colhoun
- Wellcome Trust Centre for Molecular Medicine, Clinical Research Centre, Ninewells Hospital, University of Dundee, Dundee, United Kingdom
| | - Alex Doney
- NHS Tayside, Wellcome Trust Centre for Molecular Medicine, Clinical Research Centre, Ninewells Hospital, University of Dundee, Dundee, United Kingdom
| | - Antonietta Robino
- Institute for Maternal and Child Health, IRCCS “Burlo Garofolo,” University of Trieste, Trieste, Italy
| | - Franco Giulianini
- Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Bernhard K. Krämer
- University Medical Centre Mannheim, 5th Department of Medicine, Mannheim, Germany
| | - Laura Portas
- Institute of Population Genetics – CNR, Sassari, Italy
| | - Ian Ford
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, United Kingdom
| | - Brendan M. Buckley
- Department of Pharmacology and Therapeutics, University College Cork, Cork, Ireland
| | - Martin Adam
- Unit of Chronic Disease Epidemiology, Swiss Tropical and Public Health Institute, Basel, Switzerland
| | - Gian-Andri Thun
- Unit of Chronic Disease Epidemiology, Swiss Tropical and Public Health Institute, Basel, Switzerland
| | - Bernhard Paulweber
- First Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Margot Haun
- Division of Genetic Epidemiology, Innsbruck Medical University, Innsbruck, Austria
| | - Cinzia Sala
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milano, Italy
| | - Marie Metzger
- Inserm UMRS 1018, CESP Team 10, Université Paris Sud, Villejuif, France
| | - Paul Mitchell
- Centre for Vision Research, Westmead Millennium Institute, Westmead Hospital, University of Sydney, Sydney, Australia
| | - Marina Ciullo
- Institute of Genetics and Biophysics “Adriano-Buzzati Traverso”–CNR, Napoli, Italy
| | - Stuart K. Kim
- Department of Developmental Biology, Stanford University, Stanford, California, United States of America
- Department of Genetics, Stanford University, Stanford, California, United States of America
| | - Peter Vollenweider
- Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Olli Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, Department of Clinical Physiology, Turku University Hospital, University of Turku, Turku, Finland
| | - Andres Metspalu
- Estonian Genome Center of University of Tartu (EGCUT), Tartu, Estonia
- Estonian Biocenter and Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Colin Palmer
- Biomedical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, United Kingdom
| | - Paolo Gasparini
- Institute for Maternal and Child Health, IRCCS “Burlo Garofolo,” University of Trieste, Trieste, Italy
| | - Mario Pirastu
- Institute of Population Genetics – CNR, Sassari, Italy
| | - J. Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Interuniversity Cardiology Institute of the Netherlands (ICIN), Utrecht, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden, The Netherlands
- Durrer Center for Cardiogenetic Research, Amsterdam, The Netherlands
| | - Nicole M. Probst-Hensch
- Unit of Chronic Disease Epidemiology, Swiss Tropical and Public Health Institute, Basel, Switzerland
| | - Florian Kronenberg
- Division of Genetic Epidemiology, Innsbruck Medical University, Innsbruck, Austria
| | - Daniela Toniolo
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milano, Italy
| | - Vilmundur Gudnason
- Icelandic Heart Association, Research Institute, Kopavogur, Iceland
- University of Iceland, Reykjavik, Iceland
| | - Alan R. Shuldiner
- Department of Medicine, University of Maryland Medical School, Baltimore, Maryland, United States of America
- Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, Maryland, United States of America
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- Welch Center for Prevention, Epidemiology, and Clinical Research, Baltimore, Maryland, United States of America
| | - Reinhold Schmidt
- Austrian Stroke Prevention Study, University Clinic of Neurology, Department of Special Neurology, Medical University Graz, Graz, Austria
| | - Luigi Ferrucci
- Clinical Research Branch, National Institute of Aging, Baltimore, Maryland, United States of America
| | | | - Cornelia M. van Duijn
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Ingrid Borecki
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Gary C. Curhan
- Brigham and Women's Hospital and Channing Laboratory, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Igor Rudan
- Center for Population Health Sciences, University of Edinburgh Medical School, Edinburgh, United Kingdom
| | - Ulf Gyllensten
- Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - James F. Wilson
- Center for Population Health Sciences, University of Edinburgh Medical School, Edinburgh, United Kingdom
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts University, Kiel, Germany
| | - Peter P. Pramstaller
- Institute of Genetic Medicine, European Academy of Bozen/Bolzano (EURAC) and Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Rainer Rettig
- Institute of Physiology, University of Greifswald, Greifswald, Germany
| | - Inga Prokopenko
- Wellcome Trust Centre for Human Genetics and Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, United Kingdom
| | | | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Paul Ridker
- Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Afshin Parsa
- Division of Nephrology, University of Maryland Medical School, Baltimore, Maryland, United States of America
| | - Murielle Bochud
- University Institute of Social and Preventive Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Epalinges, Switzerland
| | - Iris M. Heid
- Department of Epidemiology and Preventive Medicine, University Hospital Regensburg, Regensburg, Germany
- Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Wolfram Goessling
- Divisions of Genetics and Gastroenterology, Department of Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Harvard Stem Cell Institute, Harvard University, Cambridge, Massachusetts, United States of America
| | - Daniel I. Chasman
- Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - W. H. Linda Kao
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
- Welch Center for Prevention, Epidemiology, and Clinical Research, Baltimore, Maryland, United States of America
| | - Caroline S. Fox
- National Heart, Lung, and Blood Institute's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts, United States of America
- Division of Endocrinology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
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Helbig KL, Nothnagel M, Hampe J, Balschun T, Nikolaus S, Schreiber S, Franke A, Nöthlings U. A case-only study of gene-environment interaction between genetic susceptibility variants in NOD2 and cigarette smoking in Crohn's disease aetiology. BMC Med Genet 2012; 13:14. [PMID: 22416979 PMCID: PMC3314543 DOI: 10.1186/1471-2350-13-14] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/03/2011] [Accepted: 03/14/2012] [Indexed: 12/19/2022]
Abstract
Background Genetic variation in NOD2 and cigarette smoking are well-established risk factors for the development of Crohn's disease (CD). However, little is known about a potential interaction between these risk factors. We investigated gene-environment interactions between CD-associated NOD2 alleles and cigarette smoking in a large sample of patients with CD. Methods Three previously reported CD-associated variants in NOD2 (R702W, G908R, 1007fs) were genotyped in 1636 patients with CD continuously recruited between 1995 and 2010 based on physician referral. Data on history of smoking behaviour was obtained for all participants through a written questionnaire. Using a case-only design, we performed logistic regression analyses to investigate statistical interactions between NOD2 risk alleles and smoking status. Results We detected a significant negative interaction between carriership of at least one of the NOD2 risk alleles and history of ever having smoked (OR = 0.71; p = 0.005) as well as smoking at the time of CD diagnosis (OR = 0.68; p = 0.005). Subsequent separate analyses of the three variants revealed a significant negative interaction between the 1007fs variant and history of ever having smoked (OR = 0.64; p = 9 × 10-4) and smoking at the time of CD diagnosis (OR = 0.53; p = 7 × 10-5). Conclusions The observed significant negative gene-environment interaction suggests that the risk increase for CD conferred simultaneously by cigarette smoking and the 1007fs NOD2 polymorphism is smaller than expected and may point to a biological interaction. Our findings warrant further investigation in epidemiological and functional studies to elucidate pathophysiology as well as to aid in the development of recommendations for disease prevention.
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Affiliation(s)
- Katherine L Helbig
- Institute for Experimental Medicine, Section of Epidemiology, Christian-Albrechts-University of Kiel, Arnold-Heller-Strasse 3, Haus 3, 24105 Kiel, Germany.
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137
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Sluik D, Beulens JWJ, Weikert C, van Dieren S, Spijkerman AMW, van der A DL, Fritsche A, Joost HG, Boeing H, Nöthlings U. Gamma-glutamyltransferase, cardiovascular disease and mortality in individuals with diabetes mellitus. Diabetes Metab Res Rev 2012; 28:284-8. [PMID: 22144398 DOI: 10.1002/dmrr.2261] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
BACKGROUND Increased plasma activity of gamma-glutamyltransferase (GGT) is associated with cardiovascular diseases (CVD) and mortality in the general population. We investigated the association between GGT, CVD and mortality in individuals with diabetes mellitus. METHODS Data used were from 1280 participants, aged 35-70 years, with a confirmed diagnosis of diabetes mellitus in the European Prospective Investigation into Cancer and Nutrition in Potsdam (Germany), Bilthoven and Utrecht (the Netherlands). Multivariate hazard ratios (HR) and 95% confidence intervals (CI) for CVD (non-fatal and fatal events) and overall mortality were estimated using sex-specific quartiles of GGT. RESULTS After 8.2 years follow-up, 108 incident CVD cases and 84 deaths were observed. Participants with high GGT activity had an increased mortality risk: HR in the highest quartile was 3.96 (95% CI 1.74, 9.00). This association was in particular present in former and current smokers, younger persons and those with a higher waist-height ratio and alcohol consumption. No associations were observed for non-fatal CVD and non-fatal and fatal CVD events combined. CONCLUSIONS Higher GGT plasma activity is associated with increased all-cause mortality in individuals with diabetes.
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Affiliation(s)
- Diewertje Sluik
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
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Wennberg P, Rolandsson O, Jerdén L, Boeing H, Sluik D, Kaaks R, Teucher B, Spijkerman A, Bueno de Mesquita B, Dethlefsen C, Nilsson P, Nöthlings U. Self-rated health and mortality in individuals with diabetes mellitus: prospective cohort study. BMJ Open 2012; 2:e000760. [PMID: 22337818 PMCID: PMC3282291 DOI: 10.1136/bmjopen-2011-000760] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
OBJECTIVES To investigate whether low self-rated health (SRH) is associated with increased mortality in individuals with diabetes. DESIGN Population-based prospective cohort study. SETTING Enrolment took place between 1992 and 2000 in four centres (Bilthoven, Heidelberg, Potsdam, Umeå) in a subcohort nested in the European Prospective Investigation into Cancer and Nutrition. PARTICIPANTS 3257 individuals (mean ± SD age was 55.8±7.6 years and 42% women) with confirmed diagnosis of diabetes mellitus. PRIMARY OUTCOME MEASURE The authors used Cox proportional hazards modelling to estimate HRs for total mortality controlling for age, centre, sex, educational level, body mass index, physical inactivity, smoking, insulin treatment, hypertension, hyperlipidaemia and history of myocardial infarction, stroke or cancer. RESULTS During follow-up (mean follow-up ± SD was 8.6±2.3 years), 344 deaths (241 men/103 women) occurred. In a multivariate model, individuals with low SRH were at higher risk of mortality (HR 1.38, 95% CI 1.10 to 1.73) than those with high SRH. The association was mainly driven by increased 5-year mortality and was stronger among individuals with body mass index of <25 kg/m(2) than among obese individuals. In sex-specific analyses, the association was statistically significant in men only. There was no indication of heterogeneity across centres. CONCLUSIONS Low SRH was associated with increased mortality in individuals with diabetes after controlling for established risk factors. In patients with diabetes with low SRH, the physician should consider a more detailed consultation and intensified support.
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Affiliation(s)
- Patrik Wennberg
- Department of Public Health and Clinical Medicine, Family Medicine, Umeå University, Umeå, Sweden.
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Gieger C, Radhakrishnan A, Cvejic A, Tang W, Porcu E, Pistis G, Serbanovic-Canic J, Elling U, Goodall AH, Labrune Y, Lopez LM, Mägi R, Meacham S, Okada Y, Pirastu N, Sorice R, Teumer A, Voss K, Zhang W, Ramirez-Solis R, Bis JC, Ellinghaus D, Gögele M, Hottenga JJ, Langenberg C, Kovacs P, O'Reilly PF, Shin SY, Esko T, Hartiala J, Kanoni S, Murgia F, Parsa A, Stephens J, van der Harst P, Ellen van der Schoot C, Allayee H, Attwood A, Balkau B, Bastardot F, Basu S, Baumeister SE, Biino G, Bomba L, Bonnefond A, Cambien F, Chambers JC, Cucca F, D'Adamo P, Davies G, de Boer RA, de Geus EJC, Döring A, Elliott P, Erdmann J, Evans DM, Falchi M, Feng W, Folsom AR, Frazer IH, Gibson QD, Glazer NL, Hammond C, Hartikainen AL, Heckbert SR, Hengstenberg C, Hersch M, Illig T, Loos RJF, Jolley J, Khaw KT, Kühnel B, Kyrtsonis MC, Lagou V, Lloyd-Jones H, Lumley T, Mangino M, Maschio A, Mateo Leach I, McKnight B, Memari Y, Mitchell BD, Montgomery GW, Nakamura Y, Nauck M, Navis G, Nöthlings U, Nolte IM, Porteous DJ, Pouta A, Pramstaller PP, Pullat J, Ring SM, Rotter JI, Ruggiero D, Ruokonen A, Sala C, Samani NJ, Sambrook J, Schlessinger D, Schreiber S, Schunkert H, Scott J, Smith NL, Snieder H, Starr JM, Stumvoll M, Takahashi A, Tang WHW, Taylor K, Tenesa A, Lay Thein S, Tönjes A, Uda M, Ulivi S, van Veldhuisen DJ, Visscher PM, Völker U, Wichmann HE, Wiggins KL, Willemsen G, Yang TP, Hua Zhao J, Zitting P, Bradley JR, Dedoussis GV, Gasparini P, Hazen SL, Metspalu A, Pirastu M, Shuldiner AR, Joost van Pelt L, Zwaginga JJ, Boomsma DI, Deary IJ, Franke A, Froguel P, Ganesh SK, Jarvelin MR, Martin NG, Meisinger C, Psaty BM, Spector TD, Wareham NJ, Akkerman JWN, Ciullo M, Deloukas P, Greinacher A, Jupe S, Kamatani N, Khadake J, Kooner JS, Penninger J, Prokopenko I, Stemple D, Toniolo D, Wernisch L, Sanna S, Hicks AA, Rendon A, Ferreira MA, Ouwehand WH, Soranzo N. New gene functions in megakaryopoiesis and platelet formation. Nature 2011; 480:201-8. [PMID: 22139419 PMCID: PMC3335296 DOI: 10.1038/nature10659] [Citation(s) in RCA: 309] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2010] [Accepted: 10/21/2011] [Indexed: 12/23/2022]
Abstract
Platelets are the second most abundant cell type in blood and are essential for maintaining haemostasis. Their count and volume are tightly controlled within narrow physiological ranges, but there is only limited understanding of the molecular processes controlling both traits. Here we carried out a high-powered meta-analysis of genome-wide association studies (GWAS) in up to 66,867 individuals of European ancestry, followed by extensive biological and functional assessment. We identified 68 genomic loci reliably associated with platelet count and volume mapping to established and putative novel regulators of megakaryopoiesis and platelet formation. These genes show megakaryocyte-specific gene expression patterns and extensive network connectivity. Using gene silencing in Danio rerio and Drosophila melanogaster, we identified 11 of the genes as novel regulators of blood cell formation. Taken together, our findings advance understanding of novel gene functions controlling fate-determining events during megakaryopoiesis and platelet formation, providing a new example of successful translation of GWAS to function.
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Affiliation(s)
- Christian Gieger
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr 1, 85764 Neuherberg, Germany.
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Trichopoulos D, Bamia C, Lagiou P, Fedirko V, Trepo E, Jenab M, Pischon T, Nöthlings U, Overved K, Tjønneland A, Outzen M, Clavel-Chapelon F, Kaaks R, Lukanova A, Boeing H, Aleksandrova K, Benetou V, Zylis D, Palli D, Pala V, Panico S, Tumino R, Sacerdote C, Bueno-De-Mesquita HB, Van Kranen HJ, Peeters PHM, Lund E, Quirós JR, González CA, Sanchez Perez MJ, Navarro C, Dorronsoro M, Barricarte A, Lindkvist B, Regnér S, Werner M, Hallmans G, Khaw KT, Wareham N, Key T, Romieu I, Chuang SC, Murphy N, Boffetta P, Trichopoulou A, Riboli E. Hepatocellular carcinoma risk factors and disease burden in a European cohort: a nested case-control study. J Natl Cancer Inst 2011; 103:1686-95. [PMID: 22021666 DOI: 10.1093/jnci/djr395] [Citation(s) in RCA: 175] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND To date, no attempt has been made to systematically determine the apportionment of the hepatocellular carcinoma burden in Europe or North America among established risk factors. METHODS Using data collected from 1992 to 2006, which included 4,409,809 person-years in the European Prospective Investigation into Cancer and nutrition (EPIC), we identified 125 case patients with hepatocellular carcinoma, of whom 115 were matched to 229 control subjects. We calculated odds ratios (ORs) for the association of documented risk factors for hepatocellular carcinoma with incidence of this disease and estimated their importance in this European cohort. RESULTS Chronic hepatitis B virus (HBV) or hepatitis C virus (HCV) infection (OR = 9.10, 95% confidence interval [CI] = 2.10 to 39.50 and OR = 13.36, 95% CI = 4.11 to 43.45, respectively), obesity (OR = 2.13, 95% CI = 1.06 to 4.29), former or current smoking (OR = 1.98, 95% CI = 0.90 to 4.39 and OR = 4.55, 95% CI = 1.90 to 10.91, respectively), and heavy alcohol intake (OR = 1.77, 95% CI = 0.73 to 4.27) were associated with hepatocellular carcinoma. Smoking contributed to almost half of all hepatocellular carcinomas (47.6%), whereas 13.2% and 20.9% were attributable to chronic HBV and HCV infection, respectively. Obesity and heavy alcohol intake contributed 16.1% and 10.2%, respectively. Almost two-thirds (65.7%, 95% CI = 50.6% to 79.3%) of hepatocellular carcinomas can be accounted for by exposure to at least one of these documented risk factors. CONCLUSIONS Smoking contributed to more hepatocellular carcinomas in this Europe-wide cohort than chronic HBV and HCV infections. Heavy alcohol consumption and obesity also contributed to sizeable fractions of this disease burden. These contributions may be underestimates because EPIC volunteers are likely to be more health conscious than the general population.
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Affiliation(s)
- Dimitrios Trichopoulos
- Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA.
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Sluik D, Boeing H, Montonen J, Pischon T, Kaaks R, Teucher B, Tjønneland A, Halkjaer J, Berentzen TL, Overvad K, Arriola L, Ardanaz E, Bendinelli B, Grioni S, Tumino R, Sacerdote C, Mattiello A, Spijkerman AMW, van der A DL, Beulens JW, van der Schouw YT, Nilsson PM, Hedblad B, Rolandsson O, Franks PW, Nöthlings U. Associations between general and abdominal adiposity and mortality in individuals with diabetes mellitus. Am J Epidemiol 2011; 174:22-34. [PMID: 21616928 DOI: 10.1093/aje/kwr048] [Citation(s) in RCA: 67] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Individuals with diabetes mellitus are advised to achieve a healthy weight to prevent complications. However, fat mass distribution has hardly been investigated as a risk factor for diabetes complications. The authors studied associations between body mass index, waist circumference, waist/hip ratio, and waist/height ratio and mortality among individuals with diabetes mellitus. Within the European Prospective Investigation into Cancer and Nutrition, a subcohort was defined as 5,435 individuals with a confirmed self-report of diabetes mellitus at baseline in 1992-2000. Participants were aged 57.3 (standard deviation, 6.3) years, 54% were men, the median diabetes duration was 4.6 (interquartile range, 2.0-9.8) years, and 22% of the participants used insulin. Body mass index, as indicator of general obesity, was not associated with higher mortality, whereas all measurements of abdominal obesity showed a positive association. Associations generally were slightly weaker in women. The strongest association was observed for waist/height ratio: In the fifth quintile, the hazard rate ratio was 1.88 (95% confidence interval: 1.33, 2.65) for men and 2.46 (95% confidence interval: 1.46, 4.14) for women. Measurements of abdominal, but not general, adiposity were associated with higher mortality in diabetic individuals. The waist/height ratio showed the strongest association. Respective indicators might be investigated in risk prediction models.
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Affiliation(s)
- Diewertje Sluik
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrucke, Arthur-Scheunert-Allee 114–116, 14558 Nuthetal, Germany.
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Huebbe P, Nebel A, Siegert S, Moehring J, Boesch-Saadatmandi C, Most E, Pallauf J, Egert S, Müller MJ, Schreiber S, Nöthlings U, Rimbach G. APOE ε4 is associated with higher vitamin D levels in targeted replacement mice and humans. FASEB J 2011; 25:3262-70. [PMID: 21659554 DOI: 10.1096/fj.11-180935] [Citation(s) in RCA: 61] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The allele ε4 of apolipoprotein E (APOE), which is a key regulator of lipid metabolism, represents a risk factor for cardiovascular diseases and Alzheimer's disease. Despite its adverse effects, the allele is common and shows a nonrandom global distribution that is thought to be the result of evolutionary adaptation. One hypothesis proposes that the APOE ε4 allele protects against vitamin D deficiency. Here we present, for the first time, experimental and epidemiological evidence that the APOE ε4 allele is indeed associated with higher serum vitamin D [25(OH)D] levels. In APOE4 targeted replacement mice, significantly higher 25(OH)D levels were found compared with those in APOE2 and APOE3 mice (70.9 vs. 41.8 and 27.8 nM, P<0.05). Furthermore, multivariate adjusted models show a positive association of the APOE ε4 allele with 25(OH)D levels in a small collective of human subjects (n=93; P=0.072) and a general population sample (n=699; P=0.003). The novel link suggests ε4 as a modulator of vitamin D status. Although this result agrees well with evolutionary aspects, it appears contradictory with regard to chronic diseases, especially cardiovascular disease. Large prospective cohort studies are now needed to investigate the potential implications of this finding for chronic disease risks.
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Affiliation(s)
- Patricia Huebbe
- Institute of Human Nutrition and Food Science, Christian-Albrechts-University, Kiel, Germany
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143
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Haubrock J, Nöthlings U, Volatier JL, Dekkers A, Ocké M, Harttig U, Illner AK, Knüppel S, Andersen LF, Boeing H. Estimating usual food intake distributions by using the multiple source method in the EPIC-Potsdam Calibration Study. J Nutr 2011; 141:914-20. [PMID: 21430241 DOI: 10.3945/jn.109.120394] [Citation(s) in RCA: 191] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Estimating usual food intake distributions from short-term quantitative measurements is critical when occasionally or rarely eaten food groups are considered. To overcome this challenge by statistical modeling, the Multiple Source Method (MSM) was developed in 2006. The MSM provides usual food intake distributions from individual short-term estimates by combining the probability and the amount of consumption with incorporation of covariates into the modeling part. Habitual consumption frequency information may be used in 2 ways: first, to distinguish true nonconsumers from occasional nonconsumers in short-term measurements and second, as a covariate in the statistical model. The MSM is therefore able to calculate estimates for occasional nonconsumers. External information on the proportion of nonconsumers of a food can also be handled by the MSM. As a proof-of-concept, we applied the MSM to a data set from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Calibration Study (2004) comprising 393 participants who completed two 24-h dietary recalls and one FFQ. Usual intake distributions were estimated for 38 food groups with a proportion of nonconsumers > 70% in the 24-h dietary recalls. The intake estimates derived by the MSM corresponded with the observed values such as the group mean. This study shows that the MSM is a useful and applicable statistical technique to estimate usual food intake distributions, if at least 2 repeated measurements per participant are available, even for food groups with a sizeable percentage of nonconsumers.
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Affiliation(s)
- Jennifer Haubrock
- Department of Epidemiology, German Institute of Human Nutrition, Nuthetal 14558, Germany.
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144
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Nöthlings U, Boeing H, Maskarinec G, Sluik D, Teucher B, Kaaks R, Tjønneland A, Halkjaer J, Dethlefsen C, Overvad K, Amiano P, Toledo E, Bendinelli B, Grioni S, Tumino R, Sacerdote C, Mattiello A, Beulens JWJ, Iestra JA, Spijkerman AMW, van der A DL, Nilsson P, Sonestedt E, Rolandsson O, Franks PW, Vergnaud AC, Romaguera D, Norat T, Kolonel LN. Food intake of individuals with and without diabetes across different countries and ethnic groups. Eur J Clin Nutr 2011; 65:635-41. [PMID: 21346715 DOI: 10.1038/ejcn.2011.11] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
BACKGROUND/OBJECTIVES Given the importance of nutrition therapy in diabetes management, we hypothesized that food intake differs between individuals with and without diabetes. We investigated this hypothesis in two large prospective studies including different countries and ethnic groups. SUBJECTS/METHODS Study populations were the European Prospective Investigation into Cancer and Nutrition Study (EPIC) and the Multiethnic Cohort Study (MEC). Dietary intake was assessed by food frequency questionnaires, and calibrated using 24h-recall information for the EPIC Study. Only confirmed self-reports of diabetes at cohort entry were included: 6192 diabetes patients in EPIC and 13 776 in the MEC. For the cross-sectional comparison of food intake and lifestyle variables at baseline, individuals with and without diabetes were matched 1:1 on sex, age in 5-year categories, body mass index in 2.5 kg/m(2) categories and country. RESULTS Higher intake of soft drinks (by 13 and 44% in the EPIC and MEC), and lower consumption of sweets, juice, wine and beer (>10% difference) were observed in participants with diabetes compared with those without. Consumption of vegetables, fish and meat was slightly higher in individuals with diabetes in both studies, but the differences were <10%. Findings were more consistent across different ethnic groups than countries, but generally showed largely similar patterns. CONCLUSIONS Although diabetes patients are expected to undergo nutritional education, we found only small differences in dietary behavior in comparison with cohort members without diabetes. These findings suggest that emphasis on education is needed to improve the current behaviors to assist in the prevention of complications.
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Affiliation(s)
- U Nöthlings
- Epidemiology Section, Institute for Experimental Medicine, Christian-Albrechts-University of Kiel, Kiel, Germany.
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145
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van Dieren S, Peelen LM, Nöthlings U, van der Schouw YT, Rutten GEHM, Spijkerman AMW, van der A DL, Sluik D, Boeing H, Moons KGM, Beulens JWJ. External validation of the UK Prospective Diabetes Study (UKPDS) risk engine in patients with type 2 diabetes. Diabetologia 2011; 54:264-70. [PMID: 21076956 PMCID: PMC3017299 DOI: 10.1007/s00125-010-1960-0] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2010] [Accepted: 10/08/2010] [Indexed: 11/25/2022]
Abstract
AIMS/HYPOTHESIS Treatment guidelines recommend the UK Prospective Diabetes Study (UKPDS) risk engine for predicting cardiovascular risk in patients with type 2 diabetes, although validation studies showed moderate performance. The methods used in these validation studies were diverse, however, and sometimes insufficient. Hence, we assessed the discrimination and calibration of the UKPDS risk engine to predict 4, 5, 6 and 8 year cardiovascular risk in patients with type 2 diabetes. METHODS The cohort included 1,622 patients with type 2 diabetes. During a mean follow-up of 8 years, patients were followed for incidence of CHD and cardiovascular disease (CVD). Discrimination and calibration were assessed for 4, 5, 6 and 8 year risk. Discrimination was examined using the c-statistic and calibration by visually inspecting calibration plots and calculating the Hosmer-Lemeshow χ(2) statistic. RESULTS The UKPDS risk engine showed moderate to poor discrimination for both CHD and CVD (c-statistic of 0.66 for both 5 year CHD and CVD risks), and an overestimation of the risk (224% and 112%). The calibration of the UKPDS risk engine was slightly better for patients with type 2 diabetes who had been diagnosed with diabetes more than 10 years ago compared with patients diagnosed more recently, particularly for 4 and 5 year predicted CVD and CHD risks. Discrimination for these periods was still moderate to poor. CONCLUSIONS/INTERPRETATION We observed that the UKPDS risk engine overestimates CHD and CVD risk. The discriminative ability of this model is moderate, irrespective of various subgroup analyses. To enhance the prediction of CVD in patients with type 2 diabetes, this model should be updated.
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Affiliation(s)
- S van Dieren
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Str 6.131, PO Box 85500, 3508 GA Utrecht, The Netherlands.
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146
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van Dieren S, Nöthlings U, van der Schouw YT, Spijkerman AMW, Rutten GEHM, van der A DL, Sluik D, Weikert C, Joost HG, Boeing H, Beulens JWJ. Non-fasting lipids and risk of cardiovascular disease in patients with diabetes mellitus. Diabetologia 2011; 54:73-7. [PMID: 20959955 PMCID: PMC2995865 DOI: 10.1007/s00125-010-1945-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2010] [Accepted: 09/20/2010] [Indexed: 11/30/2022]
Abstract
AIMS/HYPOTHESIS The aim of this study was to examine the effect of postprandial time on the associations and predictive value of non-fasting lipid levels and cardiovascular disease risk in participants with diabetes. METHODS This study was conducted among 1,337 participants with diabetes from the Dutch and German (Potsdam) contributions to the European Prospective Investigation into Cancer and Nutrition. At baseline, total cholesterol, LDL- and HDL-cholesterol and triacylglycerol concentrations were measured and the ratio of total cholesterol/HDL-cholesterol was calculated. Participants were followed for incidence of cardiovascular disease. RESULTS Lipid concentrations changed minimally with increasing postprandial time, except for triacylglycerol which was elevated just after a meal and declined over time (1.86 at 0.1 h to 1.33 at >6 h, p for trend <0.001). During a mean follow-up of 8 years, 116 cardiovascular events were documented. After adjustment for potential confounders, triacylglycerol (HR for third tertile compared with first tertile (HR(t)₃(to)₁), 1.73 [95% CI 1.04, 2.87]), HDL-cholesterol (HR(t)₃(to)₁, 0.41 [95% CI 0.23, 0.72]) and total cholesterol/HDL-cholesterol ratio (HR(t)₃(to)₁, 1.65 [95% CI 0.95, 2.85]) were associated with cardiovascular disease, independent of postprandial time. Cardiovascular disease risk prediction using the UK Prospective Diabetes Study risk engine was not affected by postprandial time. CONCLUSIONS/INTERPRETATION Postprandial time did not affect associations between lipid concentrations and cardiovascular disease risk in patients with diabetes, nor did it influence prediction of cardiovascular disease. Therefore, it may not be necessary to use fasting blood samples to determine lipid concentrations for cardiovascular disease risk prediction in patients with diabetes.
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Affiliation(s)
- S van Dieren
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, the Netherlands.
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147
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Nöthlings U, Maskarinec G, Boeing H, Kolonel L. Food intake of individuals with and without diabetes across different countries and ethnic groups. Gesundheitswesen 2010. [DOI: 10.1055/s-0030-1266325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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148
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Büchner FL, Bueno-de-Mesquita HB, Ros MM, Overvad K, Dahm CC, Hansen L, Tjønneland A, Clavel-Chapelon F, Boutron-Ruault MC, Touillaud M, Kaaks R, Rohrmann S, Boeing H, Nöthlings U, Trichopoulou A, Zylis D, Dilis V, Palli D, Sieri S, Vineis P, Tumino R, Panico S, Peeters PHM, van Gils CH, Lund E, Gram IT, Braaten T, Sánchez MJ, Agudo A, Larrañaga N, Ardanaz E, Navarro C, Argüelles MV, Manjer J, Wirfält E, Hallmans G, Rasmuson T, Key TJ, Khaw KT, Wareham N, Slimani N, Vergnaud AC, Xun WW, Kiemeney LALM, Riboli E. Variety in fruit and vegetable consumption and the risk of lung cancer in the European prospective investigation into cancer and nutrition. Cancer Epidemiol Biomarkers Prev 2010; 19:2278-86. [PMID: 20807832 DOI: 10.1158/1055-9965.epi-10-0489] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND We investigated whether a varied consumption of vegetables and fruits is associated with lower lung cancer risk in the European Prospective Investigation into Cancer and Nutrition study. METHODS After a mean follow-up of 8.7 years, 1,613 of 452,187 participants with complete information were diagnosed with lung cancer. Diet diversity scores (DDS) were used to quantify the variety in fruit and vegetable consumption. Multivariable proportional hazards models were used to assess the associations between DDS and lung cancer risk. All models were adjusted for smoking behavior and the total consumption of fruit and vegetables. RESULTS With increasing variety in vegetable subgroups, risk of lung cancer decreases [hazard ratios (HR), 0.77; 95% confidence interval (CI), 0.64-0.94 highest versus lowest quartile; P trend = 0.02]. This inverse association is restricted to current smokers (HR, 0.73; 95% CI, 0.57-0.93 highest versus lowest quartile; P trend = 0.03). In continuous analyses, in current smokers, lower risks were observed for squamous cell carcinomas with more variety in fruit and vegetable products combined (HR/two products, 0.88; 95% CI, 0.82-0.95), vegetable subgroups (HR/subgroup, 0.88; 95% CI, 0.79-0.97), vegetable products (HR/two products, 0.87; 95% CI, 0.79-0.96), and fruit products (HR/two products, 0.84; 95% CI, 0.72-0.97). CONCLUSION Variety in vegetable consumption was inversely associated with lung cancer risk among current smokers. Risk of squamous cell carcinomas was reduced with increasing variety in fruit and/or vegetable consumption, which was mainly driven by the effect in current smokers. IMPACT Independent from quantity of consumption, variety in fruit and vegetable consumption may decrease lung cancer risk.
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Affiliation(s)
- Frederike L Büchner
- National Institute ofPublicHealth and the Environment, Bilthoven, The Netherlands.
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149
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Nöthlings U, Ford ES, Kröger J, Boeing H. Lifestyle factors and mortality among adults with diabetes: findings from the European Prospective Investigation into Cancer and Nutrition-Potsdam study*. J Diabetes 2010; 2:112-7. [PMID: 20923493 DOI: 10.1111/j.1753-0407.2010.00069.x] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Healthy lifestyle behaviors are among the cornerstones of diabetes self-management, but the extent to which healthy lifestyle factors could potentially prevent premature mortality among people with diabetes remains unknown. The aim of the present study was to estimate the reduction in mortality that could be achieved if people with diabetes did not smoke, had a body mass index <30 kg/m(2) , performed physical activity for ≥3.5 h/week, reported better dietary habits, and consumed alcohol moderately. METHODS A prospective cohort study of 1263 German men and women with diabetes aged 35-65 years who were followed for an average of 7.8 years was used and multivariate Cox regression models for all-cause and cause-specific mortality were calculated. RESULTS Approximately 7% of study participants had no favorable factors, 24% had one, 35% had two, and 34% had three or more. Compared with participants who had no favorable factors, the reduction in risk was 34% [95% confidence interval (CI) 19%, 63%] for those with one favorable factor, 49% (95% CI 9%, 71%) for those with two, and 63% (95% CI 31%, 80%) for those with three or more. Furthermore, a competing risk analysis did not show any difference in the inverse associations with mortality due to cardiovascular disease, cancer, or other causes. CONCLUSIONS Favorable lifestyle factors can potentially achieve substantial reductions in premature mortality among people with diabetes. Our results emphasize the importance of helping people with diabetes optimize their lifestyle behaviors.
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Affiliation(s)
- Ute Nöthlings
- Department of Epidemiology, German Institute for Human Nutrition (DIfE), Potsdam-Rehbrücke, Nuthetal, Germany
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Vrieling A, Bueno-de-Mesquita HB, Boshuizen HC, Michaud DS, Severinsen MT, Overvad K, Olsen A, Tjønneland A, Clavel-Chapelon F, Boutron-Ruault MC, Kaaks R, Rohrmann S, Boeing H, Nöthlings U, Trichopoulou A, Moutsiou E, Dilis V, Palli D, Krogh V, Panico S, Tumino R, Vineis P, van Gils CH, Peeters PHM, Lund E, Gram IT, Rodríguez L, Agudo A, Larrañaga N, Sánchez MJ, Navarro C, Barricarte A, Manjer J, Lindkvist B, Sund M, Ye W, Bingham S, Khaw KT, Roddam A, Key T, Boffetta P, Duell EJ, Jenab M, Gallo V, Riboli E. Cigarette smoking, environmental tobacco smoke exposure and pancreatic cancer risk in the European Prospective Investigation into Cancer and Nutrition. Int J Cancer 2010; 126:2394-403. [PMID: 19790196 DOI: 10.1002/ijc.24907] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Cigarette smoking is an established risk factor for pancreatic cancer. However, prospective data for most European countries are lacking, and epidemiologic studies on exposure to environmental tobacco smoke (ETS) in relation to pancreatic cancer risk are scarce. We examined the association of cigarette smoking and exposure to ETS with pancreatic cancer risk within the European Prospective Investigation into Cancer and Nutrition (EPIC). This analysis was based on 465,910 participants, including 524 first incident pancreatic cancer cases diagnosed after a median follow-up of 8.9 years. Estimates of risk were obtained by Cox proportional hazard models and adjusted for weight, height, and history of diabetes mellitus. An increased risk of pancreatic cancer was found for current cigarette smokers compared with never smokers (HR = 1.71, 95% CI = 1.36-2.15), and risk increased with greater intensity and pack-years. Former cigarette smokers who quit for less than 5 years were at increased risk of pancreatic cancer (HR = 1.78, 95% CI = 1.23-2.56), but risk was comparable to never smokers after quitting for 5 years or more. Pancreatic cancer risk was increased among never smokers daily exposed to ETS (for many hours) during childhood (HR = 2.61, 95% CI = 0.96-7.10) and exposed to ETS at home and/or work (HR = 1.54, 95% CI = 1.00-2.39). These results suggest that both active cigarette smoking, as well as exposure to ETS, is associated with increased risk of pancreatic cancer and that risk is reduced to levels of never smokers within 5 years of quitting.
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Affiliation(s)
- Alina Vrieling
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
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