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Patterson R, Ogilvie D, Hoenink JC, Burgoine T, Sharp SJ, Hajna S, Panter J. Combined associations of takeaway food availability and walkability with adiposity: Cross-sectional and longitudinal analyses. Health Place 2025; 91:103405. [PMID: 39826337 DOI: 10.1016/j.healthplace.2024.103405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2024] [Revised: 11/27/2024] [Accepted: 12/16/2024] [Indexed: 01/22/2025]
Abstract
BACKGROUND Diet and physical activity are important determinants of energy balance, body weight and chronic health conditions. Peoples' health and behaviour are shaped by their environment. For example, the availability of unhealthy takeaway food in residential neighbourhoods and the ability to easily walk to a range of local destinations (high "walkability") influence diets and physical activity levels. Most existing evidence on the associations between residential neighbourhood and adiposity is cross-sectional and examines either walkability or takeaway availability, but not both in combination.We examined the cross-sectional and longitudinal associations of residential neighbourhood walkability and takeaway food availability with markers of adiposity separately and combined. METHODS With data from the Fenland Study (Cambridgeshire, UK; n = 12,435), we used linear regression to estimate associations for walkability and takeaway availability separately and in mutually adjusted models, in addition to combining both into a measure of neighbourhood supportiveness for active living and healthy eating. Objective measures of BMI were examined cross-sectionally at baseline (2005-2015) and as change between baseline and follow-up (2014-2020). Additional outcomes (percentage body fat, waist circumference and hip circumference) were also examined both cross-sectionally and longitudinally. RESULTS Complete case analyses indicated that neighbourhoods with greater walkability and lower takeaway availability were associated with lower BMI (n = 10,607) and more favourable trends over time (n = 5508). For example, compared with the lowest exposure group (Q1), Q4 of walkability and takeaway food availability was associated with a difference in BMI of -0.69 kg/m2 (95% CI = -1.09 to -0.29) and 0.99 kg/m2 (95% CI = 0.58 to 1.39) respectively. These associations were more consistent when both neighbourhood measures were included in mutually adjusted models. The combined supportiveness measure was associated with lower BMI. High walkability and low takeaway availability were also associated with lower body fat percentage, waist circumference and hip circumference. CONCLUSIONS These findings are consistent with the residential environment having a role in shaping people's health and behaviour. Living in an area that supports walking and cycling and affords less access to unhealthy food may support people to maintain a healthy lifestyle. It was important to consider walkability and takeaway food availability together because to examine them separately risks unobserved confounding by the other. Future research could incorporate additional environmental measures, especially those likely to be correlated.
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Affiliation(s)
- Richard Patterson
- MRC Epidemiology Unit, University of Cambridge, Box 285 Institute of Metabolic Science, CB2 0QQ, Cambridge, UK.
| | - David Ogilvie
- MRC Epidemiology Unit, University of Cambridge, Box 285 Institute of Metabolic Science, CB2 0QQ, Cambridge, UK
| | - Jody C Hoenink
- MRC Epidemiology Unit, University of Cambridge, Box 285 Institute of Metabolic Science, CB2 0QQ, Cambridge, UK
| | - Thomas Burgoine
- MRC Epidemiology Unit, University of Cambridge, Box 285 Institute of Metabolic Science, CB2 0QQ, Cambridge, UK
| | - Stephen J Sharp
- MRC Epidemiology Unit, University of Cambridge, Box 285 Institute of Metabolic Science, CB2 0QQ, Cambridge, UK
| | - Samantha Hajna
- Faculty of Applied Health Sciences, Brock University, St. Catherines, ON, L2S 3A1, Canada
| | - Jenna Panter
- MRC Epidemiology Unit, University of Cambridge, Box 285 Institute of Metabolic Science, CB2 0QQ, Cambridge, UK
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O'Donnell L, Corron L, Hill EC, Perez J, O'Donnell M, Wyatt B. Skeletal and Adipose Manifestations of Stress in a Contemporary Pediatric Sample. AMERICAN JOURNAL OF BIOLOGICAL ANTHROPOLOGY 2025; 186:e25058. [PMID: 39823172 DOI: 10.1002/ajpa.25058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2024] [Revised: 11/07/2024] [Accepted: 12/29/2024] [Indexed: 01/19/2025]
Abstract
INTRODUCTION Adverse experiences leading to physiological disruptions (stress) in early life produce cascade effects on various biological systems, including the endocrine and metabolic systems, which, in turn, shape the developing skeletal system. To evaluate the effects of stress on adipose and skeletal tissues, we examine the relationship between skeletal indicators of stress (porotic hyperostosis [PH] and cribra orbitalia [CO]), bone mineral density (BMD), vertebral neural canal (VNC) diameters, and adipose tissue distribution in a contemporary pediatric autopsy sample. METHODS Data is from 702 (409 males, 293 females) individuals from a pediatric (0.5-20.9 years) autopsy sample from New Mexico who died between 2011 and 2022. Data includes visceral adipose tissue (VAT) in the abdomen, heart, and liver, CO/PH, VNC size of the fifth lumbar vertebra, and BMD. RESULTS We find that adipose tissue distribution and location are differentially associated with CO/PH, BMD, and VNC size; VNC size is smaller, and liver adiposity is higher in those with CO/PH. Further, increased VAT and small VNC size are associated with PH presence and low BMD. Body mass index categories do not correspond with porous cranial lesion presence. CONCLUSIONS This paper provides evidence for the complex relationship between skeletal markers of early-life stress (CO/PH, reduced VNC size, low BMD) and endocrine system function. VAT distribution and VNC size are partly shaped by stressors during gestation, likely through alterations of the HPA axis. It is possible that alterations of the HPA axis due to gestational stress also shape the expression of porous cranial lesions during exposure to childhood stressors.
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Affiliation(s)
- Lexi O'Donnell
- College of Population Health, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
- Department of Pathology, University of New Mexico School of Medicine, Albuquerque, New Mexico, USA
- Department of Anthropology, University of New Mexico, Albuquerque, New Mexico, USA
| | - Louise Corron
- Department of Anthropology, University of Nevada, Reno, Nevada, USA
| | - Ethan C Hill
- Division of Physical Therapy, Department of Orthopaedics and Rehabilitation, University of New Mexico School of Medicine, Albuquerque, New Mexico, USA
| | - Jordan Perez
- College of Population Health, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
| | - Michael O'Donnell
- Bureau of Business and Economic Research, University of New Mexico, Albuquerque, New Mexico, USA
| | - Bronwyn Wyatt
- School of Anthropology and Archaeology, The Australian National University, Canberra, Australian Capital Territory, Australia
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Slurink IAL, Kupper N, Smeets T, Soedamah-Muthu SS. Dairy consumption and risk of prediabetes and type 2 diabetes in the Fenland study. Clin Nutr 2024; 43:69-79. [PMID: 39353264 DOI: 10.1016/j.clnu.2024.09.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 08/25/2024] [Accepted: 09/11/2024] [Indexed: 10/04/2024]
Abstract
BACKGROUND & AIMS Limited observational evidence suggests that a higher intake of high-fat dairy may be associated with lower prediabetes risk, while opposite associations have been observed for low-fat milk intake. This study aimed to examine associations between baseline and changes in dairy consumption, risk of prediabetes, and glycaemic status. METHODS 7521 participants from the prospective UK Fenland study were included (mean age 48.7 ± 2.0 years, 51.9 % female). Dairy intake was measured using self-reported food frequency questionnaires. Associations with prediabetes risk and glycaemic status were analysed using Poisson regression models adjusted for social demographics, health behaviours, family history of diabetes and food group intake. RESULTS At a mean follow-up of 6.7 ± 2.0 years, 290 participants developed prediabetes (4.3 %). Most dairy products were not significantly associated with prediabetes risk. A higher baseline intake of high-fat dairy (RRservings/day 1.20, 95%CI 1.03-1.39) and high-fat milk (RRservings/day 1.22, 1.01-1.47) were associated with higher prediabetes risk. Conversely, low-fat milk was associated with lower prediabetes risk (RRservings/day 0.86, 0.75-0.98). In the analyses evaluating dietary changes over time, increases in high-fat milk were inversely associated with risk of progressing from normoglycaemia to prediabetes or type 2 diabetes (RRservings/day 0.86, 95%CI 0.75-0.99). CONCLUSIONS This population-based study showed that most dairy products are not associated with prediabetes risk or progression in glycaemic status. Positive associations of high-fat dairy, high-fat milk, and the inverse association of low-fat milk with prediabetes risk found were inconsistent with prior literature and suggestive of the need for future research on environmental, behavioural, and biological factors that explain the available evidence.
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Affiliation(s)
- Isabel A L Slurink
- Center of Research on Psychological Disorders and Somatic Diseases (CoRPS), Department of Medical and Clinical Psychology, Tilburg University, PO Box 90153, 5000 LE, Tilburg, the Netherlands.
| | - Nina Kupper
- Center of Research on Psychological Disorders and Somatic Diseases (CoRPS), Department of Medical and Clinical Psychology, Tilburg University, PO Box 90153, 5000 LE, Tilburg, the Netherlands
| | - Tom Smeets
- Center of Research on Psychological Disorders and Somatic Diseases (CoRPS), Department of Medical and Clinical Psychology, Tilburg University, PO Box 90153, 5000 LE, Tilburg, the Netherlands
| | - Sabita S Soedamah-Muthu
- Center of Research on Psychological Disorders and Somatic Diseases (CoRPS), Department of Medical and Clinical Psychology, Tilburg University, PO Box 90153, 5000 LE, Tilburg, the Netherlands; Institute for Food, Nutrition and Health, University of Reading, Reading RG6 6AR, United Kingdom
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Broadaway KA, Yin X, Williamson A, Parsons VA, Wilson EP, Moxley AH, Vadlamudi S, Varshney A, Jackson AU, Ahuja V, Bornstein SR, Corbin LJ, Delgado GE, Dwivedi OP, Fernandes Silva L, Frayling TM, Grallert H, Gustafsson S, Hakaste L, Hammar U, Herder C, Herrmann S, Højlund K, Hughes DA, Kleber ME, Lindgren CM, Liu CT, Luan J, Malmberg A, Moissl AP, Morris AP, Perakakis N, Peters A, Petrie JR, Roden M, Schwarz PEH, Sharma S, Silveira A, Strawbridge RJ, Tuomi T, Wood AR, Wu P, Zethelius B, Baldassarre D, Eriksson JG, Fall T, Florez JC, Fritsche A, Gigante B, Hamsten A, Kajantie E, Laakso M, Lahti J, Lawlor DA, Lind L, März W, Meigs JB, Sundström J, Timpson NJ, Wagner R, Walker M, Wareham NJ, Watkins H, Barroso I, O'Rahilly S, Grarup N, Parker SC, Boehnke M, Langenberg C, Wheeler E, Mohlke KL. Loci for insulin processing and secretion provide insight into type 2 diabetes risk. Am J Hum Genet 2023; 110:284-299. [PMID: 36693378 PMCID: PMC9943750 DOI: 10.1016/j.ajhg.2023.01.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 01/03/2023] [Indexed: 01/25/2023] Open
Abstract
Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p value < 5 × 10-8), which validated 12 previously reported loci for proinsulin and ten additional loci previously identified for another glycemic trait. Half of the alleles associated with higher proinsulin showed higher rather than lower effects on glucose levels, corresponding to different mechanisms. Proinsulin loci included genes that affect prohormone convertases, beta-cell dysfunction, vesicle trafficking, beta-cell transcriptional regulation, and lysosomes/autophagy processes. We colocalized 11 proinsulin signals with islet expression quantitative trait locus (eQTL) data, suggesting candidate genes, including ARSG, WIPI1, SLC7A14, and SIX3. The NKX6-3/ANK1 proinsulin signal colocalized with a T2D signal and an adipose ANK1 eQTL signal but not the islet NKX6-3 eQTL. Signals were enriched for islet enhancers, and we showed a plausible islet regulatory mechanism for the lead signal in the MADD locus. These results show how detailed genetic studies of an intermediate phenotype can elucidate mechanisms that may predispose one to disease.
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Affiliation(s)
- K Alaine Broadaway
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Xianyong Yin
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Alice Williamson
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK; University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Department of Clinical Biochemistry, University of Cambridge, Cambridge, UK
| | - Victoria A Parsons
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Emma P Wilson
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Anne H Moxley
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | | | - Arushi Varshney
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Anne U Jackson
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Vasudha Ahuja
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Stefan R Bornstein
- Department of Internal Medicine, Metabolic and Vascular Medicine, MedicCal Faculty Carl Gustav Carus, Dresden, Germany; Helmholtz Zentrum München, Paul Langerhans Institute Dresden, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Laura J Corbin
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Om P Dwivedi
- University of Helsinki, Helsinki, Finland; Folkhälsan Research Center, Helsinki, Finland
| | | | | | - Harald Grallert
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Stefan Gustafsson
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Liisa Hakaste
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Ulf Hammar
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Christian Herder
- German Center for Diabetes Research, Neuherberg, Germany; Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sandra Herrmann
- Department of Internal Medicine, Prevention and Care of Diabetes, Medical Faculty Carl Gustav Carus, Dresden, Germany; Helmholtz Zentrum München, Paul Langerhans Institute Dresden, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany
| | | | - David A Hughes
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Marcus E Kleber
- Medical Faculty Mannheim, Heidelberg University, Mannheim, BW, Germany; SYNLAB MVZ Humangenetik Mannheim, Mannheim, BW, Germany
| | - Cecilia M Lindgren
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Nuffield Department of Population Health, University of Oxford, Oxford, UK; Wellcome Trust Centre Human Genetics, University of Oxford, Oxford, UK; Broad Institute, Cambridge, MA, USA
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Anni Malmberg
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Angela P Moissl
- Institute of Nutritional Sciences, Friedrich-Schiller-University, Jena, Germany; Competence Cluster for Nutrition and Cardiovascular Health, Halle-Jena-Leipzig, Germany; Medical Faculty Mannheim, Heidelberg University, Mannheim, BW, Germany
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Nikolaos Perakakis
- Department of Internal Medicine, Metabolic and Vascular Medicine, MedicCal Faculty Carl Gustav Carus, Dresden, Germany; Helmholtz Zentrum München, Paul Langerhans Institute Dresden, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - John R Petrie
- School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Peter E H Schwarz
- Department of Internal Medicine, Prevention and Care of Diabetes, Medical Faculty Carl Gustav Carus, Dresden, Germany; Helmholtz Zentrum München, Paul Langerhans Institute Dresden, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Sapna Sharma
- German Center for Diabetes Research, Neuherberg, Germany; Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Chair of Food Chemistry and Molecular Sensory Science, Technische Universität München, Freising, Germany
| | - Angela Silveira
- Department of Medicine Solna, Division of Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden; Oxford Biomedical Research Centre, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Rona J Strawbridge
- Institute of Health and Wellbeing, Mental Health and Wellbeing, University of Glasgow, Glasgow, UK; Department of Medicine Solna, Division of Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Tiinamaija Tuomi
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland; Folkhälsan Research Center, Helsinki, Finland; Abdominal Center, Endocrinology, Helsinki University Hospital, Helsinki, Finland
| | - Andrew R Wood
- Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Peitao Wu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Björn Zethelius
- Department of Geriatrics, Uppsala University, Uppsala, Sweden
| | - Damiano Baldassarre
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy; Cardiovascular Prevention Area, Centro Cardiologico Monzino I.R.C.C.S., Milan, Italy
| | - Johan G Eriksson
- Department of General Practice and Primary Health Care, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Folkhälsan Research Centre, Helsinki, Finland; Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University Singapore, Singapore, Singapore
| | - Tove Fall
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Jose C Florez
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Andreas Fritsche
- Department of Internal Medicine, Diabetology, Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases, Helmholtz Center Munich, University of Tübingen, Tübingen, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Bruna Gigante
- Department of Medicine Solna, Division of Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Anders Hamsten
- Department of Medicine Solna, Division of Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Eero Kajantie
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland; PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway; Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Markku Laakso
- Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Jari Lahti
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Deborah A Lawlor
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Lars Lind
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Winfried März
- Synlab Academy, SYNLAB Holding Deutschland GmbH, Mannheim, BW, Germany; Medical Faculty Mannheim, Heidelberg University, Mannheim, BW, Germany
| | - James B Meigs
- Department of Medicine, Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Johan Sundström
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Nicholas J Timpson
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Robert Wagner
- Department of Internal Medicine, Diabetology, Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases, Helmholtz Center Munich, University of Tübingen, Tübingen, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Mark Walker
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK; Health Data Research UK, Gibbs Building, London, UK
| | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research, Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Stephen O'Rahilly
- MRC Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Stephen Cj Parker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Michael Boehnke
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK; Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
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Kentistou KA, Luan J, Wittemans LBL, Hambly C, Klaric L, Kutalik Z, Speakman JR, Wareham NJ, Kendall TJ, Langenberg C, Wilson JF, Joshi PK, Morton NM. Large scale phenotype imputation and in vivo functional validation implicate ADAMTS14 as an adiposity gene. Nat Commun 2023; 14:307. [PMID: 36658113 PMCID: PMC9852585 DOI: 10.1038/s41467-022-35563-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 12/09/2022] [Indexed: 01/20/2023] Open
Abstract
Obesity remains an unmet global health burden. Detrimental anatomical distribution of body fat is a major driver of obesity-mediated mortality risk and is demonstrably heritable. However, our understanding of the full genetic contribution to human adiposity is incomplete, as few studies measure adiposity directly. To address this, we impute whole-body imaging adiposity phenotypes in UK Biobank from the 4,366 directly measured participants onto the rest of the cohort, greatly increasing our discovery power. Using these imputed phenotypes in 392,535 participants yielded hundreds of genome-wide significant associations, six of which replicate in independent cohorts. The leading causal gene candidate, ADAMTS14, is further investigated in a mouse knockout model. Concordant with the human association data, the Adamts14-/- mice exhibit reduced adiposity and weight-gain under obesogenic conditions, alongside an improved metabolic rate and health. Thus, we show that phenotypic imputation at scale offers deeper biological insights into the genetics of human adiposity that could lead to therapeutic targets.
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Affiliation(s)
- Katherine A Kentistou
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, EH16 4TJ, UK
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Laura B L Wittemans
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Catherine Hambly
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 2TZ, UK
| | - Lucija Klaric
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Zoltán Kutalik
- Centre for Primary Care and Public Health, University of Lausanne, Lausanne, 1010, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
| | - John R Speakman
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 2TZ, UK
- Centre for Energy Metabolism and Reproduction, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Key Laboratory of Metabolic Health, CAS Centre of Excellence in Animal Evolution and Genetics, Kunming, China
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Timothy J Kendall
- Centre for Inflammation Research, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, CB2 0QQ, UK
- Computational Medicine, Berlin Institute of Health (BIH) Charité University Medicine, Berlin, Germany
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK
| | - Nicholas M Morton
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, EH16 4TJ, UK.
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6
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Mann JP, Pietzner M, Wittemans LB, Rolfe EDL, Kerrison ND, Imamura F, Forouhi NG, Fauman E, Allison ME, Griffin JL, Koulman A, Wareham NJ, Langenberg C. Insights into genetic variants associated with NASH-fibrosis from metabolite profiling. Hum Mol Genet 2020; 29:3451-3463. [PMID: 32720691 PMCID: PMC7116726 DOI: 10.1093/hmg/ddaa162] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 06/15/2020] [Accepted: 07/16/2020] [Indexed: 12/16/2022] Open
Abstract
Several genetic discoveries robustly implicate five single-nucleotide variants in the progression of non-alcoholic fatty liver disease to non-alcoholic steatohepatitis and fibrosis (NASH-fibrosis), including a recently identified variant in MTARC1. To better understand these variants as potential therapeutic targets, we aimed to characterize their impact on metabolism using comprehensive metabolomics data from two population-based studies. A total of 9135 participants from the Fenland study and 9902 participants from the EPIC-Norfolk cohort were included in the study. We identified individuals with risk alleles associated with NASH-fibrosis: rs738409C>G in PNPLA3, rs58542926C>T in TM6SF2, rs641738C>T near MBOAT7, rs72613567TA>T in HSD17B13 and rs2642438A>G in MTARC1. Circulating levels of 1449 metabolites were measured using targeted and untargeted metabolomics. Associations between NASH-fibrosis variants and metabolites were assessed using linear regression. The specificity of variant-metabolite associations were compared to metabolite associations with ultrasound-defined steatosis, gene variants linked to liver fat (in GCKR, PPP1R3B and LYPLAL1) and gene variants linked to cirrhosis (in HFE and SERPINA1). Each NASH-fibrosis variant demonstrated a specific metabolite profile with little overlap (8/97 metabolites) comprising diverse aspects of lipid metabolism. Risk alleles in PNPLA3 and HSD17B13 were both associated with higher 3-methylglutarylcarnitine and three variants were associated with lower lysophosphatidylcholine C14:0. The risk allele in MTARC1 was associated with higher levels of sphingomyelins. There was no overlap with metabolites that associated with HFE or SERPINA1 variants. Our results suggest a link between the NASH-protective variant in MTARC1 to the metabolism of sphingomyelins and identify distinct molecular patterns associated with each of the NASH-fibrosis variants under investigation.
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Affiliation(s)
- Jake P Mann
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0SL, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0SL, UK
| | - Laura B Wittemans
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0SL, UK
| | - Emmanuela De Lucia Rolfe
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0SL, UK
| | - Nicola D Kerrison
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0SL, UK
| | - Fumiaki Imamura
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0SL, UK
| | - Nita G Forouhi
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0SL, UK
| | - Eric Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02142, USA
| | - Michael E Allison
- Liver Unit, Department of Medicine, Cambridge Biomedical Research Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - Jules L Griffin
- MRC Human Nutrition Research, University of Cambridge, Cambridge CB1 9NL, UK
- Department of Biochemistry, Cambridge Systems Biology Centre, University of Cambridge, Cambridge CB2 1GA, UK
| | - Albert Koulman
- MRC Human Nutrition Research, University of Cambridge, Cambridge CB1 9NL, UK
- Department of Biochemistry, Cambridge Systems Biology Centre, University of Cambridge, Cambridge CB2 1GA, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0SL, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0SL, UK
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7
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Hirschler V, Edit S, Miorin C, Guntsche Z, Maldonado N, Garcia C, Lapertosa S, Gonzalez CD. Association Between High Birth Weight and Later Central Obesity in 9-Year-Old Schoolchildren. Metab Syndr Relat Disord 2020; 19:213-217. [PMID: 33290153 DOI: 10.1089/met.2020.0127] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Background and Objective: Studies have suggested that birth weight (BW) is associated with body mass index (BMI), but its association with waist circumference (WC) in children should be further explored. To determine the association between central obesity (OB) in 9-year-old Argentinean schoolchildren and high BW. Methods: Schoolchildren (n = 2567, 1157 males) aged 8.7 ± 2.1 years from 10 elementary schools in 5 states in Argentina were examined between April 2017 and September 2019. Mothers submitted children's BW information. Pediatricians assessed anthropometric measures and blood pressure (BP). Central OB was defined for children as WC ≥90th percentile for age and gender. Results: The prevalence of overweight (OW) and OB (OW/OB) was 42.7% (1095) and that of central OB was 34.8% (856) in 9-year-old children. The prevalence of low BW (<2500 grams) and high BW (>4000 grams) was 6.6% (n = 169) and 7.4% (n = 190), respectively. BW (3.25 vs. 3.36 kg), weight (31.38 vs. 42.88 kg), BMI (17.29 vs. 22.25 kg/m2), BMI z-scores (z-BMI; 0.25 vs. 1.63), systolic BP (96 vs. 98 mmHg), and diastolic BP (59 vs. 60 mmHg) were significantly lower in 9-year-old children without central OB than in those with central OB, respectively. Multiple logistic regression analysis using central OB as the dependent variable showed that high BW [odds ratio, 1.98 (95% confidence interval 1.44-2.73)] was associated with central OB, adjusted for age, gender, and systolic and diastolic BP. Conclusion: This study shows that central OB in 9-year-old children was associated with high BW. Future longitudinal studies should be performed to confirm this finding. Clinical Registration number, IATIMET-08102019.
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Affiliation(s)
- Valeria Hirschler
- Department of Nutrition and Diabetes, University of Buenos Aires, Buenos Aires, Argentina
| | - Scaiola Edit
- Department of Nutrition and Diabetes, University of Buenos Aires, Buenos Aires, Argentina
| | - Cecilia Miorin
- Department of Nutrition and Diabetes, University of La Plata, La Plata, Argentina
| | - Zelmira Guntsche
- Department of Nutrition and Diabetes, University of Cuyo, Mendoza, Argentina
| | - Natacha Maldonado
- Department of Nutrition and Diabetes, University of Rosario, Rosario, Argentina
| | - Concepcion Garcia
- Department of Nutrition and Diabetes, University of Buenos Aires, Buenos Aires, Argentina
| | - Silvia Lapertosa
- Department of Nutrition and Diabetes, University of Buenos Aires, Buenos Aires, Argentina
| | - Claudio D Gonzalez
- Department of Nutrition and Diabetes, University of Buenos Aires, Buenos Aires, Argentina
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8
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Norris T, Crozier SR, Cameron N, Godfrey KM, Inskip H, Johnson W. Fetal growth does not modify the relationship of infant weight gain with childhood adiposity and blood pressure in the Southampton women's survey. Ann Hum Biol 2020; 47:150-158. [PMID: 32429761 PMCID: PMC7261399 DOI: 10.1080/03014460.2020.1717616] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Background: Rapid infant weight gain is a risk factor for childhood obesity. This relationship may depend on whether infant weight gain is preceded by in-utero growth restriction. Aim: Examine whether fetal growth modifies the relationship between infant weight gain and childhood adiposity and blood pressure. Subjects and methods: 786 children in the Southampton Women’s Survey. We related infant weight gain (weight at 2 years-birth weight) to body mass index (BMI), %body fat, trunk fat (kg), systolic (SBP) and diastolic blood pressure (DBP) at age 6–7 years. Mean estimated fetal weight (EFW) between 19–34 weeks and change in EFW (19–34 weeks) were added to models as effect modifiers. Results: Infant weight gain was positively associated with all childhood outcomes. We found no evidence that these effects were modified by fetal growth (p > .1 for all interaction terms). For example, a 1 standard deviation (SD) increase in infant weight gain was associated with an increase in BMI z-score of 0.51 (95% CI 0.37;0.64) when EFW-change was set at -2 SD-scores compared with an increase of 0.41 (95% CI 0.27;0.54, p(interaction)=.48) when set at 2 SD-scores. Conclusion: The documented adverse consequences of rapid infant weight gain may occur regardless of whether growth was constrained in-utero.
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Affiliation(s)
- Tom Norris
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
| | - Sarah R Crozier
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Noël Cameron
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
| | - Keith M Godfrey
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Hazel Inskip
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - William Johnson
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
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9
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Wagata M, Ishikuro M, Obara T, Nagai M, Mizuno S, Nakaya N, Nakamura T, Hirata T, Tsuchiya N, Metoki H, Ogishima S, Hozawa A, Kinoshita K, Kure S, Yaegashi N, Yamamoto M, Kuriyama S, Sugawara J. Low birth weight and abnormal pre-pregnancy body mass index were at higher risk for hypertensive disorders of pregnancy. Pregnancy Hypertens 2020; 22:119-125. [PMID: 32791355 DOI: 10.1016/j.preghy.2020.08.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Revised: 07/12/2020] [Accepted: 08/03/2020] [Indexed: 12/12/2022]
Abstract
Low birth weight is known to be associated with hypertension, cardiovascular disease and hypertensive disorders of pregnancy (HDP); however, this association might vary by race/ethnicity. This study aimed to clarify the association between women's own birth weight and their subsequent risk for HDP in a Japanese population, in combination with pre-pregnancy body mass index (BMI). We conducted a cohort study as part of the Tohoku Medical Megabank Birth and Three-Generation Cohort Study in Miyagi, Japan. Our study's population included 4810 women. A multivariate logistic regression analysis was performed to calculate the adjusted odds ratio (aOR) and the 95% confidence interval (CI) of the women's own birth weight for HDP, in the combination categories of birth weight and pre-pregnancy BMI. As a result, the group with a low birth weight of <2500 g had a significant association with HDP (the aOR, 1.50; 95% CI, 1.02-2.21). In the subtype analysis, the odds ratio for only preeclampsia was significantly increased in the low birth weight group (aOR, 3.37; 95% CI, 1.84-6.16). In the group with a low birth weight, the prevalence of HDP was higher in both the underweight and overweight groups. In conclusion, there was a significant association between low birth weight and subsequent HDP in Japanese women. Furthermore, a significant association with HDP was found for women born with a low birth weight who were underweight or overweight as adults. Maintaining a normal weight may be effective for preventing HDP even if a woman was born small.
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Affiliation(s)
- Maiko Wagata
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku University Graduate School of Medicine, Sendai, Japan; Tohoku University Hospital, Tohoku University, Sendai, Japan
| | - Mami Ishikuro
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Taku Obara
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku University Graduate School of Medicine, Sendai, Japan; Tohoku University Hospital, Tohoku University, Sendai, Japan
| | - Masato Nagai
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Satoshi Mizuno
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Naoki Nakaya
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Tomohiro Nakamura
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Takumi Hirata
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Naho Tsuchiya
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Hirohito Metoki
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku Medical Pharmaceutical University, Sendai, Japan
| | - Soichi Ogishima
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Atsushi Hozawa
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kengo Kinoshita
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Shigeo Kure
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku University Graduate School of Medicine, Sendai, Japan; Tohoku University Hospital, Tohoku University, Sendai, Japan
| | - Nobuo Yaegashi
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku University Graduate School of Medicine, Sendai, Japan; Tohoku University Hospital, Tohoku University, Sendai, Japan
| | - Masayuki Yamamoto
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Shinichi Kuriyama
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Junichi Sugawara
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; Tohoku University Graduate School of Medicine, Sendai, Japan.
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10
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Yuan S, Kar S, Carter P, Vithayathil M, Mason AM, Burgess S, Larsson SC. Is Type 2 Diabetes Causally Associated With Cancer Risk? Evidence From a Two-Sample Mendelian Randomization Study. Diabetes 2020; 69:1588-1596. [PMID: 32349989 PMCID: PMC7306131 DOI: 10.2337/db20-0084] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 04/25/2020] [Indexed: 12/15/2022]
Abstract
We conducted a two-sample Mendelian randomization study to investigate the causal associations of type 2 diabetes mellitus (T2DM) with risk of overall cancer and 22 site-specific cancers. Summary-level data for cancer were extracted from the Breast Cancer Association Consortium and UK Biobank. Genetic predisposition to T2DM was associated with higher odds of pancreatic, kidney, uterine, and cervical cancer and lower odds of esophageal cancer and melanoma but not associated with 16 other site-specific cancers or overall cancer. The odds ratios (ORs) were 1.13 (95% CI 1.04, 1.22), 1.08 (1.00, 1.17), 1.08 (1.01, 1.15), 1.07 (1.01, 1.15), 0.89 (0.81, 0.98), and 0.93 (0.89, 0.97) for pancreatic, kidney, uterine, cervical, and esophageal cancer and melanoma, respectively. The association between T2DM and pancreatic cancer was also observed in a meta-analysis of this and a previous Mendelian randomization study (OR 1.08; 95% CI 1.02, 1.14; P = 0.009). There was limited evidence supporting causal associations between fasting glucose and cancer. Genetically predicted fasting insulin levels were positively associated with cancers of the uterus, kidney, pancreas, and lung. The current study found causal detrimental effects of T2DM on several cancers. We suggest reinforcing the cancer screening in T2DM patients to enable the early detection of cancer.
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Affiliation(s)
- Shuai Yuan
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Siddhartha Kar
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, U.K
| | - Paul Carter
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, U.K
| | | | - Amy M Mason
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, U.K
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, U.K
| | - Stephen Burgess
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, U.K
- MRC Biostatistics Unit, University of Cambridge, Cambridge, U.K
| | - Susanna C Larsson
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
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11
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Tong TYN, Koulman A, Griffin JL, Wareham NJ, Forouhi NG, Imamura F. A Combination of Metabolites Predicts Adherence to the Mediterranean Diet Pattern and Its Associations with Insulin Sensitivity and Lipid Homeostasis in the General Population: The Fenland Study, United Kingdom. J Nutr 2020; 150:568-578. [PMID: 31665391 PMCID: PMC7315099 DOI: 10.1093/jn/nxz263] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 07/15/2019] [Accepted: 09/27/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Cardiometabolic benefits of the Mediterranean diet have been recognized, but underlying mechanisms are not fully understood. OBJECTIVES We aimed to investigate how the Mediterranean diet could influence circulating metabolites and how the metabolites could mediate the associations of the diet with cardiometabolic risk factors. METHODS Among 10,806 participants (58.9% women, mean age = 48.4 y) in the Fenland Study (2004-2015) in the United Kingdom, we assessed dietary consumption with FFQs and conducted a targeted metabolomics assay for 175 plasma metabolites (acylcarnitines, amines, sphingolipids, and phospholipids). We examined cross-sectional associations of the Mediterranean diet score (MDS) and its major components with each metabolite, modeling multivariable-adjusted linear regression. We used the regression estimates to summarize metabolites associated with the MDS into a metabolite score as a marker of the diet. Subsequently, we assessed how much metabolite subclasses and the metabolite score would mediate the associations of the MDS with circulating lipids, homeostasis model assessment of insulin resistance (HOMA-IR), and other metabolic factors by comparing regression estimates upon adjustment for the metabolites. RESULTS Sixty-six metabolites were significantly associated with the MDS (P ≤ 0.003, corrected for false discovery rate) (Spearman correlations, r: -0.28 to +0.28). The metabolite score was moderately correlated with the MDS (r = 0.43). Of MDS components, consumption of nuts, cereals, and meats contributed to variations in acylcarnitines; fruits, to amino acids and amines; and fish, to phospholipids. The metabolite score was estimated to explain 37.2% of the inverse association of the MDS with HOMA-IR (P for mediation < 0.05). The associations of the MDS with cardiometabolic factors were estimated to be mediated by acylcarnitines, sphingolipids, and phospholipids. CONCLUSIONS Multiple metabolites relate to the Mediterranean diet in a healthy general British population and highlight the potential to identify a set of biomarkers for an overall diet. The associations may involve pathways of phospholipid metabolism, carnitine metabolism, and development of insulin resistance and dyslipidemia.
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Affiliation(s)
- Tammy Y N Tong
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Albert Koulman
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
- National Institute for Health Research Biomedical Research Centres Core Nutritional Biomarker Laboratory, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
- National Institute for Health Research Biomedical Research Centres Core Metabolomics and Lipidomics Laboratory, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
- MRC Elsie Widdowson Laboratory, Cambridge, United Kingdom
| | - Julian L Griffin
- MRC Elsie Widdowson Laboratory, Cambridge, United Kingdom
- Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Nita G Forouhi
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Fumiaki Imamura
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
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12
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Nakano Y. Adult-Onset Diseases in Low Birth Weight Infants: Association with Adipose Tissue Maldevelopment. J Atheroscler Thromb 2019; 27:397-405. [PMID: 31866623 PMCID: PMC7242223 DOI: 10.5551/jat.rv17039] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Low birth weight (LBW) infants have higher risk of developing insulin resistance and its comorbidities later in life. The concept of “developmental origins of health and disease” suggests that intrauterine and postnatal environments have an important role in increasing these risks. The risk of such adult-onset diseases in LBW infants might be associated with adipose tissue maldevelopment including altered body composition and increased amount of visceral fat, which is the same mechanism as that in children and adults with metabolic syndrome. However, LBW infants often have different characteristics: they are not always overweight or obese over their life course. The inconsistency might be associated with the thrifty phenotype, which is produced in response to impaired growth potential and decreased lean body mass. LBW infants tend to be obese within the limits of impaired growth potential. Through our previous investigations evaluating longitudinal changes in adiponectin levels at an early stage of life, we speculated that probably, the intrauterine life of term infants or the period up to term-equivalent age in preterm infants might be the key age for the development of adipose tissues including fat cells. Because of that, we hypothesized that the smaller number of adipocytes in LBW infants might be associated with overloading of single adipocytes and impaired adipose tissue expandability. The possible mechanisms are discussed from the perspective of adipose tissue maldevelopment in LBW infants.
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Affiliation(s)
- Yuya Nakano
- Department of Pediatrics, Showa University School of Medicine
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13
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Grunnet LG, Lund ASQ, Laigaard KK, Alibegovic AC, Jensen RT, Henriksen NS, Astrup A, Vaag A, Brøns C. Abdominal fat distribution measured by ultrasound and aerobic fitness in young Danish men born with low and normal birth weight. Obes Res Clin Pract 2019; 13:529-532. [PMID: 31757746 DOI: 10.1016/j.orcp.2019.10.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 09/17/2019] [Accepted: 10/29/2019] [Indexed: 12/14/2022]
Abstract
Abdominal subcutaneous and visceral adipose tissue thickness was examined by ultrasound in 17 men with low birth weight (LBW) and 26 with normal BW control individuals to determine if abdominal obesity in LBW individuals is due to increased visceral or subcutaneous fat mass/thickness, or both. Men born with LBW had an increased waist-to-hip ratio (P = 0.04), greater abdominal fat thickness (P = 0.05) and increased visceral (VAT) and subcutaneous adipose tissue (SAT) thickness compared with controls, however the latter not statistically significant (P = 0.08, P = 0.10). A significant difference between birth weight groups in both SAT (P = 0.04) and VAT (P = 0.03) was found after adjustment for weight, whereas no significant difference in either SAT (P = 0.93) or VAT (P = 0.30) was found after adjustment for BMI. Increased waist-to-hip ratio in LBW individuals is due to increased total abdominal fat including both subcutaneous and visceral fat.
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Affiliation(s)
- Louise G Grunnet
- Steno Diabetes Center, Gentofte, Denmark; Department of Endocrinology, Rigshospitalet, Copenhagen, Denmark
| | - Anne-Sofie Q Lund
- Steno Diabetes Center, Gentofte, Denmark; Department of Nutrition, Exercise and Sports (NEXS), University of Copenhagen, Denmark
| | - Klaus K Laigaard
- Steno Diabetes Center, Gentofte, Denmark; Department of Nutrition, Exercise and Sports (NEXS), University of Copenhagen, Denmark
| | | | - Rasmus T Jensen
- Department of Endocrinology, Rigshospitalet, Copenhagen, Denmark
| | | | - Arne Astrup
- Department of Nutrition, Exercise and Sports (NEXS), University of Copenhagen, Denmark
| | - Allan Vaag
- Cardiovascular and Metabolic Disease (CVMD) Translational Medicine Unit, Early Clinical Development, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden
| | - Charlotte Brøns
- Steno Diabetes Center, Gentofte, Denmark; Department of Endocrinology, Rigshospitalet, Copenhagen, Denmark.
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14
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Larqué E, Labayen I, Flodmark CE, Lissau I, Czernin S, Moreno LA, Pietrobelli A, Widhalm K. From conception to infancy - early risk factors for childhood obesity. Nat Rev Endocrinol 2019; 15:456-478. [PMID: 31270440 DOI: 10.1038/s41574-019-0219-1] [Citation(s) in RCA: 111] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/10/2019] [Indexed: 12/25/2022]
Abstract
Maternal lifestyle during pregnancy, as well as early nutrition and the environment infants are raised in, are considered relevant factors for the prevention of childhood obesity. Several models are available for the prediction of childhood overweight and obesity, yet most have not been externally validated. Moreover, the factors considered in the models differ among studies as the outcomes manifest after birth and depend on maturation processes that vary between individuals. The current Review examines and interprets data on the early determinants of childhood obesity to provide relevant strategies for daily clinical work. We evaluate a selection of prenatal and postnatal factors associated with child adiposity. Actions to be considered for preventing childhood obesity include the promotion of healthy maternal nutrition and weight status at reproductive age and during pregnancy, as well as careful monitoring of infant growth to detect early excessive weight gain. Paediatricians and other health-care professionals should provide scientifically validated, individual nutritional advice to families to counteract excessive adiposity in children. Based on systematic reviews, original papers and scientific reports, we provide information to help with setting up public health strategies to prevent overweight and obesity in childhood.
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Affiliation(s)
- Elvira Larqué
- Department of Physiology, University of Murcia, Murcia, Spain
| | - Idoia Labayen
- Institute for Innovation & Sustainable Development in Food Chain (IS-FOOD) and Department of Health Sciences, Public University of Navarra, Pamplona, Spain
| | - Carl-Erik Flodmark
- Childhood Obesity Unit, Department of Pediatrics, Skane University Hospital, Malmo, Sweden
- Department of Clinical Sciences, Faculty of Medicine, University of Lund, Lund, Sweden
| | - Inge Lissau
- Childhood Obesity Unit, Department of Pediatrics, Skane University Hospital, Malmo, Sweden
- Department of Clinical Sciences, Faculty of Medicine, University of Lund, Lund, Sweden
- Clinical Research Centre, University Hospital Copenhagen, Hvidovre, Denmark
| | - Sarah Czernin
- Deptartment of Pediatrics, Division of Nutrition and Metabolism and Austrian Academic institute for Clinical Nutrition, Vienna, Austria
| | - Luis A Moreno
- Growth, Exercise, Nutrition and Development Research Group, Universidad de Zaragoza, Zaragoza, Spain.
- Instituto Agroalimentario de Aragón (IA2) and Instituto de Investigación Sanitaria de Aragón (IIS Aragón), Zaragoza, Spain.
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Angelo Pietrobelli
- Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, Verona, Italy
- Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Kurt Widhalm
- Deptartment of Pediatrics, Division of Nutrition and Metabolism and Austrian Academic institute for Clinical Nutrition, Vienna, Austria
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Association of pulse wave velocity with body fat measures at 30 y of age. Nutrition 2019; 61:38-42. [DOI: 10.1016/j.nut.2018.09.037] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2018] [Revised: 08/26/2018] [Accepted: 09/29/2018] [Indexed: 01/09/2023]
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Dietary cost associated with adherence to the Mediterranean diet, and its variation by socio-economic factors in the UK Fenland Study. Br J Nutr 2019; 119:685-694. [PMID: 29553031 DOI: 10.1017/s0007114517003993] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
High cost of healthy foods could be a barrier to healthy eating. We aimed to examine the association between dietary cost and adherence to the Mediterranean diet in a non-Mediterranean country. We evaluated cross-sectional data from 12 417 adults in the UK Fenland Study. Responses to 130-item FFQ were used to calculate a Mediterranean diet score (MDS). Dietary cost was estimated by matching food consumption data with retail prices of five major supermarkets. Using multivariable-adjusted linear regression, we examined the association of MDS and individual foods with dietary cost in absolute and relative scales. Subsequently, we assessed how much the association was explained by education, income, marital status and occupation, by conducting mediation analysis and testing interaction by these variables. High compared with low MDS (top to bottom third) was associated with marginally higher cost by 5·4 % (95 % CI 4·4, 6·4) or £0·20/d (95 % CI 0·16, 0·25). Participants with high adherence had higher cost associated with the healthier components (e.g. vegetables, fruits and fish), and lower cost associated with the unhealthy components (e.g. red meat, processed meat and sweets) (P for trend<0·001 each). In total, 20·7 % (95 % CI 14·3, 27·0) of the MDS-cost association was explained by the selected socio-economic factors, and the MDS-cost association was of greater magnitude in lower socio-economic groups (P interaction<0·005). Overall, greater adherence to the Mediterranean diet was associated with marginally higher dietary cost, partly modified and explained by socio-economic status, but the potential economic barriers of high adherence might be offset by cost saving from reducing unhealthy food consumption.
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Associations of physical activity and sedentary time with body composition in Brazilian young adults. Sci Rep 2019; 9:5444. [PMID: 30931983 PMCID: PMC6443682 DOI: 10.1038/s41598-019-41935-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 03/21/2019] [Indexed: 11/19/2022] Open
Abstract
The findings of studies on the association between physical activity and adiposity are not consistent, and most are cross-sectional and used only self-reported measures. The aims of this study were to evaluate: 1) independent and combined cross-sectional associations of objectively-measured physical activity and sedentary time with body composition outcomes at 30 years, and 2) prospective associations of changes in self-reported physical activity from 23 to 30 years with the same outcomes in participants from the 1982 Pelotas (Brazil) Birth Cohort. Body mass index, waist circumference, visceral abdominal fat, fat mass index, and android/gynoid fat ratio were the outcomes. 3,206 participants were analysed. In cross-sectional analyses, higher objectively-measured moderate-to-vigorous physical activity was associated with lower body mass index (β = 0.017, 95%CI: −0.026; −0.009), waist circumference (β = −0.043, 95%CI: −0.061; −0.025), visceral abdominal fat (β = −0.006, 95%CI: −0.009; −0.003), and fat mass index (β = −0.015, 95%CI: −0.021; −0.009), independent of sedentary time. Sedentary time was independently associated only with higher fat mass index (β = 0.003, 95%CI: 0.001; 0.005). In longitudinal analyses, using self-reported measure, adiposity was lower among those who were consistently active or who became active. Adiposity was similar among the “became inactive” and “consistently inactive” subjects. Our findings suggest metabolic benefits from engagement in physical activity throughout young adulthood, with stronger associations on concurrent levels.
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Khalatbari-Soltani S, Imamura F, Brage S, De Lucia Rolfe E, Griffin SJ, Wareham NJ, Marques-Vidal P, Forouhi NG. The association between adherence to the Mediterranean diet and hepatic steatosis: cross-sectional analysis of two independent studies, the UK Fenland Study and the Swiss CoLaus Study. BMC Med 2019; 17:19. [PMID: 30674308 PMCID: PMC6345041 DOI: 10.1186/s12916-019-1251-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 01/04/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND AND AIMS The risk of hepatic steatosis may be reduced through changes to dietary intakes, but evidence is sparse, especially for dietary patterns including the Mediterranean diet. We investigated the association between adherence to the Mediterranean diet and prevalence of hepatic steatosis. METHODS Cross-sectional analysis of data from two population-based adult cohorts: the Fenland Study (England, n = 9645, 2005-2015) and CoLaus Study (Switzerland, n = 3957, 2009-2013). Habitual diet was assessed using cohort-specific food frequency questionnaires. Mediterranean diet scores (MDSs) were calculated in three ways based on adherence to the Mediterranean dietary pyramid, dietary cut-points derived from a published review, and cohort-specific tertiles of dietary consumption. Hepatic steatosis was assessed by abdominal ultrasound and fatty liver index (FLI) in Fenland and by FLI and non-alcoholic fatty liver disease (NAFLD) score in CoLaus. FLI includes body mass index (BMI), waist circumference, gamma-glutamyl transferase, and triglyceride; NAFLD includes diabetes, fasting insulin level, fasting aspartate-aminotransferase (AST), and AST/alanine transaminase ratio. Associations were assessed using Poisson regression. RESULTS In Fenland, the prevalence of hepatic steatosis was 23.9% and 27.1% based on ultrasound and FLI, respectively, and in CoLaus, 25.3% and 25.7% based on FLI and NAFLD score, respectively. In Fenland, higher adherence to pyramid-based MDS was associated with lower prevalence of hepatic steatosis assessed by ultrasound (prevalence ratio (95% confidence interval), 0.86 (0.81, 0.90) per one standard deviation of MDS). This association was attenuated [0.95 (0.90, 1.00)] after adjustment for body mass index (BMI). Associations of similar magnitude were found for hepatic steatosis assessed by FLI in Fenland [0.82 (0.78, 0.86)] and in CoLaus [0.85 (0.80, 0.91)], and these were also attenuated after adjustment for BMI. Findings were similar when the other two MDS definitions were used. CONCLUSIONS Greater adherence to the Mediterranean diet was associated with lower prevalence of hepatic steatosis, largely explained by adiposity. These findings suggest that an intervention promoting a Mediterranean diet may reduce the risk of hepatic steatosis.
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Affiliation(s)
- Saman Khalatbari-Soltani
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK. .,Department of Internal Medicine, Internal Medicine, Lausanne University Hospital (CHUV), rue du Bugnon 46, 1011, Lausanne, Switzerland.
| | - Fumiaki Imamura
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK
| | - Soren Brage
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK
| | - Emanuella De Lucia Rolfe
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK
| | - Simon J Griffin
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK
| | - Nicholas J Wareham
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK
| | - Pedro Marques-Vidal
- Department of Internal Medicine, Internal Medicine, Lausanne University Hospital (CHUV), rue du Bugnon 46, 1011, Lausanne, Switzerland
| | - Nita G Forouhi
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK.
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Nutrition, the visceral immune system, and the evolutionary origins of pathogenic obesity. Proc Natl Acad Sci U S A 2018; 116:723-731. [PMID: 30598443 PMCID: PMC6338860 DOI: 10.1073/pnas.1809046116] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
The global obesity epidemic is the subject of an immense, diversely specialized research effort. An evolutionary analysis reveals connections among disparate findings, starting with two well-documented facts: Obesity-associated illnesses (e.g., type-2 diabetes and cardiovascular disease), are especially common in: (i) adults with abdominal obesity, especially enlargement of visceral adipose tissue (VAT), a tissue with important immune functions; and (ii) individuals with poor fetal nutrition whose nutritional input increases later in life. I hypothesize that selection favored the evolution of increased lifelong investment in VAT in individuals likely to suffer lifelong malnutrition because of its importance in fighting intraabdominal infections. Then, when increased nutrition violates the adaptive fetal prediction of lifelong nutritional deficit, preferential VAT investment could contribute to abdominal obesity and chronic inflammatory disease. VAT prioritization may help explain several patterns of nutrition-related disease: the paradoxical increase of chronic disease with increased food availability in recently urbanized and migrant populations; correlations between poor fetal nutrition, improved childhood (catch-up) growth, and adult metabolic syndrome; and survival differences between children with marasmus and kwashiorkor malnutrition. Fats and sugars can aggravate chronic inflammation via effects on intestinal bacteria regulating gut permeability to visceral pathogens. The extremes in a nutrition-sensitive trade-off between visceral (immune-function) vs. subcutaneous (body shape) adiposity may have been favored by selection in highly stratified premedicine societies. Altered adipose allocation in populations with long histories of social stratification and malnutrition may be the result of genetic accommodation of developmental responses to poor maternal/fetal conditions, increasing their vulnerability to inflammatory disease.
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Clifton EAD, Perry JRB, Imamura F, Lotta LA, Brage S, Forouhi NG, Griffin SJ, Wareham NJ, Ong KK, Day FR. Genome-wide association study for risk taking propensity indicates shared pathways with body mass index. Commun Biol 2018; 1:36. [PMID: 30271922 PMCID: PMC6123697 DOI: 10.1038/s42003-018-0042-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 03/14/2018] [Indexed: 01/08/2023] Open
Abstract
Risk-taking propensity is a trait of significant public health relevance but few specific genetic factors are known. Here we perform a genome-wide association study of self-reported risk-taking propensity among 436,236 white European UK Biobank study participants. We identify genome-wide associations at 26 loci (P < 5 × 10-8), 24 of which are novel, implicating genes enriched in the GABA and GABA receptor pathways. Modelling the relationship between risk-taking propensity and body mass index (BMI) using Mendelian randomisation shows a positive association (0.25 approximate SDs of BMI (SE: 0.06); P = 6.7 × 10-5). The impact of individual SNPs is heterogeneous, indicating a complex relationship arising from multiple shared pathways. We identify positive genetic correlations between risk-taking and waist-hip ratio, childhood obesity, ever smoking, attention-deficit hyperactivity disorder, bipolar disorder and schizophrenia, alongside a negative correlation with women's age at first birth. These findings highlight that behavioural pathways involved in risk-taking propensity may play a role in obesity, smoking and psychiatric disorders.
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Affiliation(s)
- Emma A D Clifton
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK.
| | - John R B Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK
| | - Fumiaki Imamura
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK
| | - Luca A Lotta
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK
| | - Soren Brage
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK
| | - Nita G Forouhi
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK
| | - Simon J Griffin
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK
- Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge, CB2 0SR, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK
| | - Ken K Ong
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK
| | - Felix R Day
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0SL, UK.
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SANTOS MAMD, VERÇOSA MDF, GOMES TNQF, MAIA JAR, LEANDRO CG. Birth weight, physical growth and body composition in children: A longitudinal study. REV NUTR 2018. [DOI: 10.1590/1678-98652018000300003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
ABSTRACT Objective To describe children’s physical growth (body mass and height) velocity and body composition (fat percentage and Fat Free Mass); to investigate the magnitude of interindividual differences according to age, gender and birth weight categories, as well as to examine the differences in the average trajectories of children with Low Birth Weight and Normal Weight according to international references. Methods The sample consisted of 534 children (279 boys and 255 girls, 7 to 10 years old) evaluated in the first year of study and followed for 3 years with overlap between the ages of 7 and 9 years. Physical growth and body composition measurements included: height, body mass, fat percentage (%Fat) and Fat Free Mass. Multilevel Modelling was used. Results Birth weight was not associated with physical growth and body composition markers at 7 years old or with the velocity of their changes (p>0.05). There were significant interindividual differences in the trajectories of physical growth (height and body mass; p<0.001) and body composition (%Fat and Fat Free Mass; p<0.001). In plotting on international percentile charts, the trajectories of growth and body composition were within expected values for age and gender, regardless of birth weight. Conclusion There are significant differences in the dynamics of stature growth, body mass and Fat Free Mass, and Low Birth Weight has no influence on this trajectory. In addition, values are within the expected range for age and sex.
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Sociodemographic characteristics and frequency of consuming home-cooked meals and meals from out-of-home sources: cross-sectional analysis of a population-based cohort study. Public Health Nutr 2018; 21:2255-2266. [PMID: 29637874 DOI: 10.1017/s1368980018000812] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE To identify sociodemographic characteristics associated with frequency of consuming home-cooked meals and meals from out-of-home sources. DESIGN Cross-sectional analysis of a population-based cohort study. Frequency of consuming home-cooked meals, ready meals, takeaways and meals out were derived from a participant questionnaire. Sociodemographic characteristics regarding sex, age, ethnicity, working overtime and socio-economic status (SES; measured by household income, educational attainment, occupational status and employment status) were self-reported. Sociodemographic differences in higher v. lower meal consumption frequency were explored using logistic regression, adjusted for other key sociodemographic variables. SETTING Cambridgeshire, UK. SUBJECTS Fenland Study participants (n 11 326), aged 29-64 years at baseline. RESULTS Eating home-cooked meals more frequently was associated with being female, older, of higher SES (measured by greater educational attainment and household income) and not working overtime. Being male was associated with a higher frequency of consumption for all out-of-home meal types. Consuming takeaways more frequently was associated with lower SES (measured by lower educational attainment and household income), whereas eating out more frequently was associated with higher SES (measured by greater educational attainment and household income) and working overtime. CONCLUSIONS Sociodemographic characteristics associated with frequency of eating meals from different out-of-home sources varied according to meal source. Findings may be used to target public health policies and interventions for promoting healthier diets and dietary-related health towards people consuming home-cooked meals less frequently, such as men, those with lower educational attainment and household income, and overtime workers.
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Intrauterine growth restriction combined with a maternal high-fat diet increased adiposity and serum corticosterone levels in adult rat offspring. J Dev Orig Health Dis 2018; 9:315-328. [DOI: 10.1017/s2040174418000016] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
AbstractIntrauterine growth restriction (IUGR) and fetal exposure to a maternal high-fat diet (HFD) independently increase the risk of developing obesity in adulthood. Excess glucocorticoids increase obesity. We hypothesized that surgically induced IUGR combined with an HFD would increase adiposity and glucocorticoids more than in non-IUGR offspring combined with the same HFD, findings that would persist despite weaning to a regular diet. Non-IUGR (N) and IUGR (I) rat offspring from dams fed either regular rat chow (R) or an HFD (H) were weaned to either a regular rat chow or an HFD. For non-IUGR and IUGR rats, this study design resulted in three diet groups: offspring from dams fed a regular diet and weaned to a regular diet (NRR and IRR), offspring rats from dams fed an HFD and weaned to a regular diet (NHR and IHR) and offspring from dams fed an HFD and weaned to an HFD (NHH and IHH). Magnetic resonance imaging or fasting visceral and subcutaneous adipose tissue collection occurred at postnatal day 60. IHH male rats had greater adiposity than NHH males, findings that were only partly normalized by weaning to a regular chow. IHH male rats had a 10-fold increase in serum corticosterone levels. IHH female rats had increased adiposity and serum triglycerides. We conclude that IUGR combined with an HFD throughout life increased adiposity, glucocorticoids and triglycerides in a sex-specific manner. Our data suggest that one mechanism through which the perinatal environment programs increased adiposity in IHH male rats may be via increased systemic glucocorticoids.
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Mytton OT, Ogilvie D, Griffin S, Brage S, Wareham N, Panter J. Associations of active commuting with body fat and visceral adipose tissue: A cross-sectional population based study in the UK. Prev Med 2018; 106:86-93. [PMID: 29030265 PMCID: PMC6108416 DOI: 10.1016/j.ypmed.2017.10.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 09/12/2017] [Accepted: 10/06/2017] [Indexed: 12/27/2022]
Abstract
The promotion of active travel (walking and cycling) is one promising approach to prevent the development of obesity and related cardio-metabolic disease. However the associations between active travel and adiposity remain uncertain. We used the Fenland study (a population based-cohort study; Cambridgeshire, UK, 2005-15) to describe the association of commuting means with DEXA measured body fat and visceral adipose tissue (VAT) among commuters (aged 29-65years; n=7680). We stratified our sample into those living near (within five miles) and far (five miles or further) from work, and categorised commuting means differently for each group reflecting their different travel options. Associations were adjusted for age, education, Mediterranean diet score, smoking, alcohol consumption, test site and either self-reported physical activity or objective physical activity. Among those living near to work, people who reported regularly cycling to work had lower body fat than those who only used the car (adjusting for self-reported physical activity: women, -1.74%, 95% CI: -2.27% to -0.76%; men, -1.30%, -2.26% to -0.33%). Among those who lived far from work, people who reported regular car-use with active travel had lower body fat (women; -1.18%, 95% CI: -2.23% to -0.13%; men, -1.19%, -1.93% to -0.44%). Findings were similar for VAT and when adjusting for objectively measured physical activity instead of self-reported physical activity. In conclusion, active commuting may reduce adiposity and help prevent related cardio-metabolic disease. If people live too far from work to walk or cycle the whole journey, incorporating some active travel within the commute is also beneficial.
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Affiliation(s)
- Oliver T Mytton
- MRC Epidemiology Unit and UKCRC Centre for Diet and Activity Research (CEDAR), University of Cambridge School of Clinical Medicine, Box 285, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK.
| | - David Ogilvie
- MRC Epidemiology Unit and UKCRC Centre for Diet and Activity Research (CEDAR), University of Cambridge School of Clinical Medicine, Box 285, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Simon Griffin
- MRC Epidemiology Unit and UKCRC Centre for Diet and Activity Research (CEDAR), University of Cambridge School of Clinical Medicine, Box 285, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK; The Primary Care Unit, Institute of Public Health, University of Cambridge School of Clinical Medicine, Box 113, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK
| | - Søren Brage
- MRC Epidemiology Unit and UKCRC Centre for Diet and Activity Research (CEDAR), University of Cambridge School of Clinical Medicine, Box 285, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Nick Wareham
- MRC Epidemiology Unit and UKCRC Centre for Diet and Activity Research (CEDAR), University of Cambridge School of Clinical Medicine, Box 285, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Jenna Panter
- MRC Epidemiology Unit and UKCRC Centre for Diet and Activity Research (CEDAR), University of Cambridge School of Clinical Medicine, Box 285, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
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de Oliveira PD, Wehrmeister FC, Horta BL, Pérez-Padilla R, de França GVA, Gigante DP, Barros FC, Ong KK, De Lucia Rolfe E, Menezes AMB. Visceral and subcutaneous abdominal adiposity and pulmonary function in 30-year-old adults: a cross-sectional analysis nested in a birth cohort. BMC Pulm Med 2017; 17:157. [PMID: 29179743 PMCID: PMC5704528 DOI: 10.1186/s12890-017-0510-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Accepted: 11/17/2017] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Several studies have verified body fat distribution in association with pulmonary function (PF), mainly waist circumference, but few have used measures able to distinguish abdominal fat compartments. The present study aims to verify the association of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) with PF measures. METHODS In 1982, all hospital births occurring in Pelotas, Brazil, were identified and those livebirths have been followed. In 2012-13, the cohort participants were evaluated and VAT and SAT measured using ultrasound; forced expiratory volume in the first second (FEV1) or forced vital capacity (FVC) were patronized in z-scores stratified by sex. The associations were verified using crude and adjusted linear regressions. RESULTS The present analyses comprised 3438 individuals (1721 women). VAT was inversely associated with spirometric parameters, in both crude and adjusted models. SAT showed inverse associations in the crude analyzes in males and a positive trend after adjustment, except for SAT and FVC in males. To each centimeter of VAT, mean adjusted FEV1 z-scores decreased 0.072 (95% CI -0.107; -0.036) in men and 0.127 (95% CI -0.164; -0.090) in women, and FVC z-scores decreased -0.075 (95% CI -0.111; -0.039) and 0.121 (95% CI -0.158; -0.083), in men and women, respectively. CONCLUSIONS VAT has a consistent inverse association with FEV1 and FVC in both sexes. On the other hand, SAT showed inconsistent results with PF parameters.
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Affiliation(s)
- Paula Duarte de Oliveira
- Federal University of Pelotas - Postgraduate Program in Epidemiology, Rua Marechal Deodoro, 1160, 3° andar, Pelotas, RS, Zip code: 96020-220, Brazil.
| | - Fernando César Wehrmeister
- Federal University of Pelotas - Postgraduate Program in Epidemiology, Rua Marechal Deodoro, 1160, 3° andar, Pelotas, RS, Zip code: 96020-220, Brazil
| | - Bernardo Lessa Horta
- Federal University of Pelotas - Postgraduate Program in Epidemiology, Rua Marechal Deodoro, 1160, 3° andar, Pelotas, RS, Zip code: 96020-220, Brazil
| | - Rogelio Pérez-Padilla
- National Institute of Respiratory Diseases, Calzada De Tlalpan, 4502, Mexico City, DF, Mexico
| | | | - Denise P Gigante
- Federal University of Pelotas - Postgraduate Program in Epidemiology, Rua Marechal Deodoro, 1160, 3° andar, Pelotas, RS, Zip code: 96020-220, Brazil
| | - Fernando C Barros
- Catholic University of Pelotas - Postgraduate Program in Health and Behavior, Rua Gonçalves Chaves, 373, Pelotas, RS, Zip code: 96015-560, Brazil
| | - Ken K Ong
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, UK
| | - Emanuella De Lucia Rolfe
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, UK
| | - Ana Maria Baptista Menezes
- Federal University of Pelotas - Postgraduate Program in Epidemiology, Rua Marechal Deodoro, 1160, 3° andar, Pelotas, RS, Zip code: 96020-220, Brazil
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de Lauzon-Guillain B, Clifton EA, Day FR, Clément K, Brage S, Forouhi NG, Griffin SJ, Koudou YA, Pelloux V, Wareham NJ, Charles MA, Heude B, Ong KK. Mediation and modification of genetic susceptibility to obesity by eating behaviors. Am J Clin Nutr 2017; 106:996-1004. [PMID: 28814400 PMCID: PMC6186415 DOI: 10.3945/ajcn.117.157396] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 07/13/2017] [Indexed: 01/22/2023] Open
Abstract
Background: Many genetic variants show highly robust associations with body mass index (BMI). However, the mechanisms through which genetic susceptibility to obesity operates are not well understood. Potentially modifiable mechanisms, including eating behaviors, are of particular interest to public health.Objective: Here we explore whether eating behaviors mediate or modify genetic susceptibility to obesity.Design: Genetic risk scores for BMI (BMI-GRSs) were calculated for 3515 and 2154 adults in the Fenland and EDEN (Etude des déterminants pré et postnatals de la santé et du développement de l'enfant) population-based cohort studies, respectively. The eating behaviors-emotional eating, uncontrolled eating, and cognitive restraint-were measured through the use of a validated questionnaire. The mediating effect of each eating behavior on the association between the BMI-GRS and measured BMI was assessed by using the Sobel test. In addition, we tested for interactions between each eating behavior and the BMI-GRS on BMI.Results: The association between the BMI-GRS and BMI was mediated by both emotional eating (EDEN: P-Sobel = 0.01; Fenland: P-Sobel = 0.02) and uncontrolled eating (EDEN: P-Sobel = 0.04; Fenland: P-Sobel = 0.0006) in both sexes combined. Cognitive restraint did not mediate this association (P-Sobel > 0.10), except among EDEN women (P-Sobel = 0.0009). Cognitive restraint modified the relation between the BMI-GRS and BMI among men (EDEN: P-interaction = 0.0001; Fenland: P-interaction = 0.04) and Fenland women (P-interaction = 0.0004). By tertiles of cognitive restraint, the association between the BMI-GRS and BMI was strongest in the lowest tertile of cognitive restraint, and weakest in the highest tertile.Conclusions: Genetic susceptibility to obesity was partially mediated by the "appetitive" eating behavior traits (uncontrolled and emotional eating) and, in 3 of the 4 population groups studied, was modified by cognitive restraint. High levels of cognitive control over eating appear to attenuate the genetic susceptibility to obesity. Future research into interventions designed to support restraint may help to protect genetically susceptible individuals from weight gain.
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Affiliation(s)
- Blandine de Lauzon-Guillain
- Early Origin of Child Health and Development (ORCHAD) Team 6, Center of Research in Epidemiology and UMR 1153 Statistics Sorbonne Paris Cité (CRESS), National Institute of Health and Medical Research (INSERM), Paris, France
- Paris Descartes University, Paris, France
| | | | - Felix R Day
- MRC Epidemiology Unit, Institute of Metabolic Science, and
| | - Karine Clément
- Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
- NutriOmics Team 6, UMRS 1166, National Institute of Health and Medical Research (INSERM), Paris, France; and
- Pierre and Marie Curie University, Sorbonne Universities, Paris, France
| | - Soren Brage
- MRC Epidemiology Unit, Institute of Metabolic Science, and
| | - Nita G Forouhi
- MRC Epidemiology Unit, Institute of Metabolic Science, and
| | - Simon J Griffin
- MRC Epidemiology Unit, Institute of Metabolic Science, and
- Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Yves Akoli Koudou
- Early Origin of Child Health and Development (ORCHAD) Team 6, Center of Research in Epidemiology and UMR 1153 Statistics Sorbonne Paris Cité (CRESS), National Institute of Health and Medical Research (INSERM), Paris, France
| | - Véronique Pelloux
- Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
- NutriOmics Team 6, UMRS 1166, National Institute of Health and Medical Research (INSERM), Paris, France; and
- Pierre and Marie Curie University, Sorbonne Universities, Paris, France
| | | | - Marie-Aline Charles
- Early Origin of Child Health and Development (ORCHAD) Team 6, Center of Research in Epidemiology and UMR 1153 Statistics Sorbonne Paris Cité (CRESS), National Institute of Health and Medical Research (INSERM), Paris, France
- Paris Descartes University, Paris, France
| | - Barbara Heude
- Early Origin of Child Health and Development (ORCHAD) Team 6, Center of Research in Epidemiology and UMR 1153 Statistics Sorbonne Paris Cité (CRESS), National Institute of Health and Medical Research (INSERM), Paris, France
- Paris Descartes University, Paris, France
| | - Ken K Ong
- MRC Epidemiology Unit, Institute of Metabolic Science, and
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Mills S, Brown H, Wrieden W, White M, Adams J. Frequency of eating home cooked meals and potential benefits for diet and health: cross-sectional analysis of a population-based cohort study. Int J Behav Nutr Phys Act 2017; 14:109. [PMID: 28818089 PMCID: PMC5561571 DOI: 10.1186/s12966-017-0567-y] [Citation(s) in RCA: 154] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Accepted: 08/09/2017] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Reported associations between preparing and eating home cooked food, and both diet and health, are inconsistent. Most previous research has focused on preparing, rather than eating, home cooked food; used small, non-population based samples; and studied markers of nutrient intake, rather than overall diet quality or health. We aimed to assess whether frequency of consuming home cooked meals was cross-sectionally associated with diet quality and cardio-metabolic health. METHODS We used baseline data from a United Kingdom population-based cohort study of adults aged 29 to 64 years (n = 11,396). Participants self-reported frequency of consuming home cooked main meals. Diet quality was assessed using the Mediterranean Diet Score, Dietary Approaches to Stop Hypertension (DASH) score, fruit and vegetable intake calculated from a 130-item food frequency questionnaire, and plasma vitamin C. Markers of cardio-metabolic health were researcher-measured body mass index (BMI), percentage body fat, haemoglobin A1c (HbA1c), cholesterol and hypertension. Differences across the three exposure categories were assessed using linear regression (diet variables) and logistic regression (health variables). RESULTS Eating home cooked meals more frequently was associated with greater adherence to DASH and Mediterranean diets, greater fruit and vegetable intakes and higher plasma vitamin C, in adjusted models. Those eating home cooked meals more than five times, compared with less than three times per week, consumed 62.3 g more fruit (99% CI 43.2 to 81.5) and 97.8 g more vegetables (99% CI 84.4 to 111.2) daily. More frequent consumption of home cooked meals was associated with greater likelihood of having normal range BMI and normal percentage body fat. Associations with HbA1c, cholesterol and hypertension were not significant in adjusted models. Those consuming home cooked meals more than five times, compared with less than three times per week, were 28% less likely to have overweight BMI (99% CI 8 to 43%), and 24% less likely to have excess percentage body fat (99% CI 5 to 40%). CONCLUSIONS In a large population-based cohort study, eating home cooked meals more frequently was associated with better dietary quality and lower adiposity. Further prospective research is required to identify whether consumption of home cooked meals has causal effects on diet and health.
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Affiliation(s)
- Susanna Mills
- Institute of Health & Society, Newcastle University, Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX UK
| | - Heather Brown
- Institute of Health & Society, Newcastle University, Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX UK
| | - Wendy Wrieden
- Human Nutrition Research Centre, Institute of Health & Society, Newcastle University, M1.151 William Leech Building, Medical School, Framlington Place, Newcastle upon Tyne, NE2 4HH UK
| | - Martin White
- Institute of Health & Society, Newcastle University, Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX UK
- Centre for Diet and Activity Research (CEDAR), MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Box 285 Biomedical Campus, Cambridge, CB2 0QQ UK
| | - Jean Adams
- Centre for Diet and Activity Research (CEDAR), MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Box 285 Biomedical Campus, Cambridge, CB2 0QQ UK
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Larsson SC, Scott RA, Traylor M, Langenberg CC, Hindy G, Melander O, Orho-Melander M, Seshadri S, Wareham NJ, Markus HS. Type 2 diabetes, glucose, insulin, BMI, and ischemic stroke subtypes: Mendelian randomization study. Neurology 2017; 89:454-460. [PMID: 28667182 PMCID: PMC5539736 DOI: 10.1212/wnl.0000000000004173] [Citation(s) in RCA: 73] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Accepted: 05/04/2017] [Indexed: 01/05/2023] Open
Abstract
OBJECTIVE To implement a mendelian randomization (MR) approach to determine whether type 2 diabetes mellitus (T2D), fasting glucose, fasting insulin, and body mass index (BMI) are causally associated with specific ischemic stroke subtypes. METHODS MR estimates of the association between each possible risk factor and ischemic stroke subtypes were calculated with inverse-variance weighted (conventional) and weighted median approaches, and MR-Egger regression was used to explore pleiotropy. The number of single nucleotide polymorphisms (SNPs) used as instrumental variables was 49 for T2D, 36 for fasting glucose, 18 for fasting insulin, and 77 for BMI. Genome-wide association study data of SNP-stroke associations were derived from METASTROKE and the Stroke Genetics Network (n = 18,476 ischemic stroke cases and 37,296 controls). RESULTS Conventional MR analysis showed associations between genetically predicted T2D and large artery stroke (odds ratio [OR] 1.28, 95% confidence interval [CI] 1.16-1.40, p = 3.3 × 10-7) and small vessel stroke (OR 1.21, 95% CI 1.10-1.33, p = 8.9 × 10-5) but not cardioembolic stroke (OR 1.06, 95% CI 0.97-1.15, p = 0.17). The association of T2D with large artery stroke but not small vessel stroke was consistent in a sensitivity analysis using the weighted median method, and there was no evidence of pleiotropy. Genetically predicted fasting glucose and fasting insulin levels and BMI were not statistically significantly associated with any ischemic stroke subtype. CONCLUSIONS This study provides support that T2D may be causally associated with large artery stroke.
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Affiliation(s)
- Susanna C Larsson
- From the Stroke Research Group (S.L.C., M.T., H.S.M.), Department of Clinical Neurosciences, and MRC Epidemiology Unit (R.A.S., C.C.L., N.J.W.), University of Cambridge, UK; Unit of Nutritional Epidemiology (S.C.L.), Institute of Environmental Medicine, Karolinska Institutet, Stockholm; Department of Clinical Sciences (G.H., O.M., M.O.-M.), Lund University, Malmö, Sweden; and Boston University School of Medicine (S.S.), MA.
| | - Robert A Scott
- From the Stroke Research Group (S.L.C., M.T., H.S.M.), Department of Clinical Neurosciences, and MRC Epidemiology Unit (R.A.S., C.C.L., N.J.W.), University of Cambridge, UK; Unit of Nutritional Epidemiology (S.C.L.), Institute of Environmental Medicine, Karolinska Institutet, Stockholm; Department of Clinical Sciences (G.H., O.M., M.O.-M.), Lund University, Malmö, Sweden; and Boston University School of Medicine (S.S.), MA
| | - Matthew Traylor
- From the Stroke Research Group (S.L.C., M.T., H.S.M.), Department of Clinical Neurosciences, and MRC Epidemiology Unit (R.A.S., C.C.L., N.J.W.), University of Cambridge, UK; Unit of Nutritional Epidemiology (S.C.L.), Institute of Environmental Medicine, Karolinska Institutet, Stockholm; Department of Clinical Sciences (G.H., O.M., M.O.-M.), Lund University, Malmö, Sweden; and Boston University School of Medicine (S.S.), MA
| | - Claudia C Langenberg
- From the Stroke Research Group (S.L.C., M.T., H.S.M.), Department of Clinical Neurosciences, and MRC Epidemiology Unit (R.A.S., C.C.L., N.J.W.), University of Cambridge, UK; Unit of Nutritional Epidemiology (S.C.L.), Institute of Environmental Medicine, Karolinska Institutet, Stockholm; Department of Clinical Sciences (G.H., O.M., M.O.-M.), Lund University, Malmö, Sweden; and Boston University School of Medicine (S.S.), MA
| | - George Hindy
- From the Stroke Research Group (S.L.C., M.T., H.S.M.), Department of Clinical Neurosciences, and MRC Epidemiology Unit (R.A.S., C.C.L., N.J.W.), University of Cambridge, UK; Unit of Nutritional Epidemiology (S.C.L.), Institute of Environmental Medicine, Karolinska Institutet, Stockholm; Department of Clinical Sciences (G.H., O.M., M.O.-M.), Lund University, Malmö, Sweden; and Boston University School of Medicine (S.S.), MA
| | - Olle Melander
- From the Stroke Research Group (S.L.C., M.T., H.S.M.), Department of Clinical Neurosciences, and MRC Epidemiology Unit (R.A.S., C.C.L., N.J.W.), University of Cambridge, UK; Unit of Nutritional Epidemiology (S.C.L.), Institute of Environmental Medicine, Karolinska Institutet, Stockholm; Department of Clinical Sciences (G.H., O.M., M.O.-M.), Lund University, Malmö, Sweden; and Boston University School of Medicine (S.S.), MA
| | - Marju Orho-Melander
- From the Stroke Research Group (S.L.C., M.T., H.S.M.), Department of Clinical Neurosciences, and MRC Epidemiology Unit (R.A.S., C.C.L., N.J.W.), University of Cambridge, UK; Unit of Nutritional Epidemiology (S.C.L.), Institute of Environmental Medicine, Karolinska Institutet, Stockholm; Department of Clinical Sciences (G.H., O.M., M.O.-M.), Lund University, Malmö, Sweden; and Boston University School of Medicine (S.S.), MA
| | - Sudha Seshadri
- From the Stroke Research Group (S.L.C., M.T., H.S.M.), Department of Clinical Neurosciences, and MRC Epidemiology Unit (R.A.S., C.C.L., N.J.W.), University of Cambridge, UK; Unit of Nutritional Epidemiology (S.C.L.), Institute of Environmental Medicine, Karolinska Institutet, Stockholm; Department of Clinical Sciences (G.H., O.M., M.O.-M.), Lund University, Malmö, Sweden; and Boston University School of Medicine (S.S.), MA
| | - Nicholas J Wareham
- From the Stroke Research Group (S.L.C., M.T., H.S.M.), Department of Clinical Neurosciences, and MRC Epidemiology Unit (R.A.S., C.C.L., N.J.W.), University of Cambridge, UK; Unit of Nutritional Epidemiology (S.C.L.), Institute of Environmental Medicine, Karolinska Institutet, Stockholm; Department of Clinical Sciences (G.H., O.M., M.O.-M.), Lund University, Malmö, Sweden; and Boston University School of Medicine (S.S.), MA
| | - Hugh S Markus
- From the Stroke Research Group (S.L.C., M.T., H.S.M.), Department of Clinical Neurosciences, and MRC Epidemiology Unit (R.A.S., C.C.L., N.J.W.), University of Cambridge, UK; Unit of Nutritional Epidemiology (S.C.L.), Institute of Environmental Medicine, Karolinska Institutet, Stockholm; Department of Clinical Sciences (G.H., O.M., M.O.-M.), Lund University, Malmö, Sweden; and Boston University School of Medicine (S.S.), MA
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Accessibility and Affordability of Supermarkets: Associations With the DASH Diet. Am J Prev Med 2017; 53:55-62. [PMID: 28336352 PMCID: PMC5478361 DOI: 10.1016/j.amepre.2017.01.044] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Revised: 12/09/2016] [Accepted: 01/31/2017] [Indexed: 11/05/2022]
Abstract
INTRODUCTION It is unknown whether there is an interplay of affordability (economic accessibility) and proximity (geographic accessibility) of supermarkets in relation to having a Dietary Approaches to Stop Hypertension (DASH)-accordant diet. METHODS Data (collected: 2005-2015, analyzed: 2016) were from the cross-sectional, population-based Fenland Study cohort: 9,274 adults aged 29-64 years, living in Cambridgeshire, United Kingdom. Dietary quality was evaluated using an index of DASH dietary accordance, based on recorded consumption of foods and beverages in a validated 130-item, semi-quantitative food frequency questionnaire. DASH accordance was defined as a DASH score in the top quintile. Dietary costs (£/day) were estimated by attributing a food price variable to the foods consumed according to the questionnaire. Individuals were classified as having low-, medium-, or high-cost diets. Supermarket affordability was determined based on the cost of a 101-item market basket. Distances between home address to the nearest supermarket (geographic accessibility) and nearest economically-appropriate supermarket (economic accessibility) were divided into tertiles. RESULTS Higher-cost diets were more likely to be DASH-accordant. After adjustment for key demographics and exposure to other food outlets, individuals with lowest economic accessibility to supermarkets had lower odds of being DASH-accordant (OR=0.59, 95% CI=0.52, 0.68) than individuals with greatest economic accessibility. This association was stronger than with geographic accessibility alone (OR=0.85, 95% CI=0.74, 0.98). CONCLUSIONS Results suggest that geographic and economic access to food should be taken into account when considering approaches to promote adherence to healthy diets for the prevention of cardiovascular diseases and other chronic disease.
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30
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de França GVA, De Lucia Rolfe E, Horta BL, Gigante DP, Yudkin JS, Ong KK, Victora CG. Genomic ancestry and education level independently influence abdominal fat distributions in a Brazilian admixed population. PLoS One 2017; 12:e0179085. [PMID: 28582437 PMCID: PMC5459508 DOI: 10.1371/journal.pone.0179085] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 05/23/2017] [Indexed: 01/07/2023] Open
Abstract
We aimed to identify the independent associations of genomic ancestry and education level with abdominal fat distributions in the 1982 Pelotas birth cohort study, Brazil. In 2,890 participants (1,409 men and 1,481 women), genomic ancestry was assessed using genotype data on 370,539 genome-wide variants to quantify ancestral proportions in each individual. Years of completed education was used to indicate socio-economic position. Visceral fat depth and subcutaneous abdominal fat thickness were measured by ultrasound at age 29–31y; these measures were adjusted for BMI to indicate abdominal fat distributions. Linear regression models were performed, separately by sex. Admixture was observed between European (median proportion 85.3), African (6.6), and Native American (6.3) ancestries, with a strong inverse correlation between the African and European ancestry scores (ρ = -0.93; p<0.001). Independent of education level, African ancestry was inversely associated with both visceral and subcutaneous abdominal fat distributions in men (both P = 0.001), and inversely associated with subcutaneous abdominal fat distribution in women (p = 0.009). Independent of genomic ancestry, higher education level was associated with lower visceral fat, but higher subcutaneous fat, in both men and women (all p<0.001). Our findings, from an admixed population, indicate that both genomic ancestry and education level were independently associated with abdominal fat distribution in adults. African ancestry appeared to lower abdominal fat distributions, particularly in men.
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Affiliation(s)
- Giovanny Vinícius Araújo de França
- Post-graduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil, Rua Marechal Deodoro, 1160–3° Piso, Bairro Centro—Pelotas, RS
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Hills R, Cambridge, United Kingdom
- * E-mail:
| | - Emanuella De Lucia Rolfe
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Hills R, Cambridge, United Kingdom
| | - Bernardo Lessa Horta
- Post-graduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil, Rua Marechal Deodoro, 1160–3° Piso, Bairro Centro—Pelotas, RS
| | - Denise Petrucci Gigante
- Post-graduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil, Rua Marechal Deodoro, 1160–3° Piso, Bairro Centro—Pelotas, RS
| | | | - Ken K. Ong
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Hills R, Cambridge, United Kingdom
| | - Cesar Gomes Victora
- Post-graduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil, Rua Marechal Deodoro, 1160–3° Piso, Bairro Centro—Pelotas, RS
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31
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Tachmazidou I, Süveges D, Min JL, Ritchie GRS, Steinberg J, Walter K, Iotchkova V, Schwartzentruber J, Huang J, Memari Y, McCarthy S, Crawford AA, Bombieri C, Cocca M, Farmaki AE, Gaunt TR, Jousilahti P, Kooijman MN, Lehne B, Malerba G, Männistö S, Matchan A, Medina-Gomez C, Metrustry SJ, Nag A, Ntalla I, Paternoster L, Rayner NW, Sala C, Scott WR, Shihab HA, Southam L, St Pourcain B, Traglia M, Trajanoska K, Zaza G, Zhang W, Artigas MS, Bansal N, Benn M, Chen Z, Danecek P, Lin WY, Locke A, Luan J, Manning AK, Mulas A, Sidore C, Tybjaerg-Hansen A, Varbo A, Zoledziewska M, Finan C, Hatzikotoulas K, Hendricks AE, Kemp JP, Moayyeri A, Panoutsopoulou K, Szpak M, Wilson SG, Boehnke M, Cucca F, Di Angelantonio E, Langenberg C, Lindgren C, McCarthy MI, Morris AP, Nordestgaard BG, Scott RA, Tobin MD, Wareham NJ, Burton P, Chambers JC, Smith GD, Dedoussis G, Felix JF, Franco OH, Gambaro G, Gasparini P, Hammond CJ, Hofman A, Jaddoe VWV, Kleber M, Kooner JS, Perola M, Relton C, Ring SM, Rivadeneira F, Salomaa V, Spector TD, Stegle O, Toniolo D, Uitterlinden AG, Barroso I, Greenwood CMT, Perry JRB, Walker BR, Butterworth AS, Xue Y, Durbin R, Small KS, Soranzo N, Timpson NJ, Zeggini E. Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits. Am J Hum Genet 2017; 100:865-884. [PMID: 28552196 PMCID: PMC5473732 DOI: 10.1016/j.ajhg.2017.04.014] [Citation(s) in RCA: 118] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Accepted: 04/21/2017] [Indexed: 01/05/2023] Open
Abstract
Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.
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Affiliation(s)
- Ioanna Tachmazidou
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Dániel Süveges
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Josine L Min
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Graham R S Ritchie
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK; Usher Institute of Population Health Sciences & Informatics, University of Edinburgh, Edinburgh EH16 4UX, UK; MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH16 4UX, UK
| | - Julia Steinberg
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Klaudia Walter
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Valentina Iotchkova
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK
| | | | - Jie Huang
- Boston VA Research Institute, Boston, MA 02130, USA
| | - Yasin Memari
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Shane McCarthy
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Andrew A Crawford
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK; BHF Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Cristina Bombieri
- Department of Neurological, Biomedical and Movement Sciences, University of Verona, Verona 37134, Italy
| | - Massimiliano Cocca
- Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste 34100, Italy
| | - Aliki-Eleni Farmaki
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens 17671, Greece
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Pekka Jousilahti
- Department of Health, National Institute for Health and Welfare, Helsinki 00271, Finland
| | - Marjolein N Kooijman
- The Generation R Study Group, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Pediatrics, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | - Benjamin Lehne
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Giovanni Malerba
- Department of Neurological, Biomedical and Movement Sciences, University of Verona, Verona 37134, Italy
| | - Satu Männistö
- Department of Health, National Institute for Health and Welfare, Helsinki 00271, Finland
| | - Angela Matchan
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Carolina Medina-Gomez
- Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Internal Medicine, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | - Sarah J Metrustry
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Abhishek Nag
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Ioanna Ntalla
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Lavinia Paternoster
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Nigel W Rayner
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford OX3 7LJ, UK
| | - Cinzia Sala
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan 20132, Italy
| | - William R Scott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London W2 1PG, UK; Department of Cardiology, Ealing Hospital NHS Trust, Middlesex UB1 3EU, UK
| | - Hashem A Shihab
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Lorraine Southam
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Beate St Pourcain
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK; Max Planck Institute for Psycholinguistics, Nijmegen 6500, the Netherlands
| | - Michela Traglia
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan 20132, Italy
| | - Katerina Trajanoska
- Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Internal Medicine, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | - Gialuigi Zaza
- Renal Unit, Department of Medicine, Verona University Hospital, Verona 37126, Italy
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London W2 1PG, UK; Department of Cardiology, Ealing Hospital NHS Trust, Middlesex UB1 3EU, UK
| | - María S Artigas
- Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Narinder Bansal
- Cardiovascular Epidemiology Unit, Department of Public Health & Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Marianne Benn
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark; Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen 2100, Denmark
| | - Zhongsheng Chen
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Petr Danecek
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark; Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen 2100, Denmark
| | - Wei-Yu Lin
- Cardiovascular Epidemiology Unit, Department of Public Health & Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Adam Locke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; McDonnell Genome Institute, Washington University School of Medicine, Saint Louis, MO 63108, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Alisa K Manning
- Center for Human Genetics Research, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA; Department of Medicine, Harvard University Medical School, Boston, MA 02115, USA
| | - Antonella Mulas
- Istituto di Ricerca Genetica e Biomedica (IRGB-CNR), Cagliari 09100, Italy; Università degli Studi di Sassari, Sassari 07100, Italy
| | - Carlo Sidore
- Istituto di Ricerca Genetica e Biomedica (IRGB-CNR), Cagliari 09100, Italy
| | - Anne Tybjaerg-Hansen
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark; Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen 2100, Denmark
| | - Anette Varbo
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark; Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen 2100, Denmark
| | | | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London WC1E 6BT, UK
| | | | - Audrey E Hendricks
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK; Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA
| | - John P Kemp
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK; University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, QLD 4072, Australia
| | - Alireza Moayyeri
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK; Institute of Health Informatics, University College London, London NW1 2DA, UK
| | | | - Michal Szpak
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Scott G Wilson
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK; School of Medicine and Pharmacology, The University of Western Australia, Crawley, WA 6009, Australia; Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands, WA 6009, Australia
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica (IRGB-CNR), Cagliari 09100, Italy; Università degli Studi di Sassari, Sassari 07100, Italy
| | - Emanuele Di Angelantonio
- Cardiovascular Epidemiology Unit, Department of Public Health & Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge, Cambridge CB1 8RN, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Cecilia Lindgren
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; Li Ka Shing Centre for Health Information and Discovery, The Big Data Institute, University of Oxford, Oxford OX3 7BN, UK
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford OX3 7LJ, UK; Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford OX3 7LJ, UK
| | - Andrew P Morris
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; Department of Biostatistics, University of Liverpool, Liverpool L69 3GL, UK; Estonian Genome Center, University of Tartu, Tartu, Tartumaa 51010, Estonia
| | - Børge G Nordestgaard
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark; Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen 2100, Denmark
| | - Robert A Scott
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Martin D Tobin
- Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK; National Institute for Health Research (NIHR) Leicester Respiratory Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | | | | | - Paul Burton
- D2K Research Group, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - John C Chambers
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London W2 1PG, UK; Department of Cardiology, Ealing Hospital NHS Trust, Middlesex UB1 3EU, UK; Imperial College Healthcare NHS Trust, London W2 1NY, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens 17671, Greece
| | - Janine F Felix
- The Generation R Study Group, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Pediatrics, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | - Oscar H Franco
- Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | - Giovanni Gambaro
- Division of Nephrology and Dialysis, Columbus-Gemelli University Hospital, Catholic University, Rome 00168, Italy
| | - Paolo Gasparini
- Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste 34100, Italy; Medical Genetics, Institute for Maternal and Child Health IRCCS "Burlo Garofolo", Trieste 34100, Italy
| | - Christopher J Hammond
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Pediatrics, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | - Marcus Kleber
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim 68167, Germany
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital NHS Trust, Middlesex UB1 3EU, UK; Imperial College Healthcare NHS Trust, London W2 1NY, UK; National Heart and Lung Institute, Imperial College London, Hammersmith Hospital Campus, London W12 0NN, UK
| | - Markus Perola
- Department of Health, National Institute for Health and Welfare, Helsinki 00271, Finland; Estonian Genome Center, University of Tartu, Tartu, Tartumaa 51010, Estonia; Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki 00290, Finland
| | - Caroline Relton
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Susan M Ring
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Fernando Rivadeneira
- Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Internal Medicine, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | - Veikko Salomaa
- Department of Health, National Institute for Health and Welfare, Helsinki 00271, Finland
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Oliver Stegle
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK
| | - Daniela Toniolo
- Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan 20132, Italy
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands; Department of Internal Medicine, Erasmus Medical Center, University Medical Center, Rotterdam 3000 CA, the Netherlands
| | | | | | | | - Inês Barroso
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK; University of Cambridge Metabolic Research Laboratories, and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC H3T 1E2, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC H3A 1A2, Canada; Department of Oncology, McGill University, Montréal, QC H2W 1S6, Canada
| | - John R B Perry
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK; MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Brian R Walker
- BHF Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Adam S Butterworth
- Cardiovascular Epidemiology Unit, Department of Public Health & Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge, Cambridge CB1 8RN, UK
| | - Yali Xue
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Richard Durbin
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Nicole Soranzo
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK; The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge, Cambridge CB1 8RN, UK; Department of Haematology, University of Cambridge, Cambridge CB2 0AH, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Eleftheria Zeggini
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK.
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Abstract
Endocrine-disrupting chemicals (EDCs) might increase the risk of childhood diseases by disrupting hormone-mediated processes that are critical for growth and development during gestation, infancy and childhood. The fetus, infant and child might have enhanced sensitivity to environmental stressors such as EDCs due to their rapid development and increased exposure to some EDCs as a consequence of development-specific behaviour, anatomy and physiology. In this Review, I discuss epidemiological studies examining the relationship between early-life exposure to bisphenol A (BPA), phthalates, triclosan and perfluoroalkyl substances (PFAS) with childhood neurobehavioural disorders and obesity. The available epidemiological evidence suggest that prenatal exposure to several of these ubiquitous EDCs is associated with adverse neurobehaviour (BPA and phthalates) and excess adiposity or increased risk of obesity and/or overweight (PFAS). Quantifying the effects of EDC mixtures, improving EDC exposure assessment, reducing bias from confounding, identifying periods of heightened vulnerability and elucidating the presence and nature of sexually dimorphic EDC effects would enable stronger inferences to be made from epidemiological studies than currently possible. Ultimately, improved estimates of the causal effects of EDC exposures on child health could help identify susceptible subpopulations and lead to public health interventions to reduce these exposures.
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Affiliation(s)
- Joseph M. Braun
- Department of Epidemiology, Brown University, Providence, RI 02912
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Kuhle S, Maguire B, Ata N, MacInnis N, Dodds L. Birth Weight for Gestational Age, Anthropometric Measures, and Cardiovascular Disease Markers in Children. J Pediatr 2017; 182:99-106. [PMID: 28012695 DOI: 10.1016/j.jpeds.2016.11.067] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Revised: 10/10/2016] [Accepted: 11/23/2016] [Indexed: 01/27/2023]
Abstract
OBJECTIVE To examine the association of birth weight for gestational age with anthropometric measures and cardiometabolic markers in a population-based sample of Canadian children. STUDY DESIGN The study used data from 2016 children aged 6-12 years from the first 2 cycles of the Canadian Health Measures Survey, a population-based survey of Canadian residents. The main exposure was birth weight for gestational age (small [SGA], large [LGA], and appropriate for gestational age [AGA]). The outcomes were anthropometric measures, blood pressure, and laboratory cardiovascular disease markers. The association between the exposure and the outcomes was examined using multiple regression. Analyses were weighted to account for the complex sampling design and for nonresponse. RESULTS SGA infants had lower and LGA infants had higher z scores for anthropometric measures compared with the AGA group but most differences were not statistically significant. There were no differences between the SGA or LGA infants and the AGA group in blood pressure or individual cardiometabolic markers but SGA infants were significantly less likely to have elevated levels of 3 or more components of the metabolic syndrome compared with their AGA peers. CONCLUSIONS Former SGA and LGA infants have lower (SGA) and higher (LGA) body mass index and waist circumference, respectively, than their AGA peers. The known long-term increased cardiovascular disease risk among SGA or LGA infants was not reflected in the blood pressure and laboratory measurements at age 6-12 years.
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Affiliation(s)
- Stefan Kuhle
- Perinatal Epidemiology Research Unit, Departments of Obstetrics and Gynaecology and Pediatrics, Dalhousie University, Halifax, Nova Scotia, Canada.
| | - Bryan Maguire
- Perinatal Epidemiology Research Unit, Departments of Obstetrics and Gynaecology and Pediatrics, Dalhousie University, Halifax, Nova Scotia, Canada; Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Nicole Ata
- School of Health and Human Performance, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Natasha MacInnis
- Perinatal Epidemiology Research Unit, Departments of Obstetrics and Gynaecology and Pediatrics, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Linda Dodds
- Perinatal Epidemiology Research Unit, Departments of Obstetrics and Gynaecology and Pediatrics, Dalhousie University, Halifax, Nova Scotia, Canada
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Clifton EAD, Day FR, De Lucia Rolfe E, Forouhi NG, Brage S, Griffin SJ, Wareham NJ, Ong KK. Associations between body mass index-related genetic variants and adult body composition: The Fenland cohort study. Int J Obes (Lond) 2017; 41:613-619. [PMID: 28096530 PMCID: PMC5382973 DOI: 10.1038/ijo.2017.11] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Revised: 11/16/2016] [Accepted: 11/29/2016] [Indexed: 12/24/2022]
Abstract
BACKGROUND/OBJECTIVE Body mass index (BMI) is a surrogate measure of adiposity but does not distinguish fat from lean or bone mass. The genetic determinants of BMI are thought to predominantly influence adiposity but this has not been confirmed. Here we characterise the association between BMI-related genetic variants and body composition in adults. SUBJECTS/METHODS Among 9667 adults aged 29-64 years from the Fenland study, a genetic risk score for BMI (BMI-GRS) was calculated for each individual as the weighted sum of BMI-increasing alleles across 96 reported BMI-related variants. Associations between the BMI-GRS and body composition, estimated by dual-energy X-ray absorptiometry (DXA) scans, were examined using age-adjusted linear regression models, separately by sex. RESULTS The BMI-GRS was positively associated with all fat, lean and bone variables. Across body regions, associations of the greatest magnitude were observed for adiposity variables, for example, for each s.d. increase in BMI-GRS predicted BMI, we observed a 0.90 s.d. (95% confidence interval (CI): 0.71, 1.09) increase in total fat mass for men (P=3.75 × 10-21) and a 0.96 s.d. (95% CI: 0.77, 1.16) increase for women (P=6.12 × 10-22). Associations of intermediate magnitude were observed with lean variables, for example, total lean mass: men: 0.68 s.d. (95% CI: 0.49, 0.86; P=1.91 × 10-12); women: 0.85 s.d. (95% CI: 0.65, 1.04; P=2.66 × 10-17) and of a lower magnitude with bone variables, for example, total bone mass: men: 0.39 s.d. (95% CI: 0.20, 0.58; P=5.69 × 10-5); women: 0.45 s.d. (95% CI: 0.26, 0.65; P=3.96 × 106). Nominally significant associations with BMI were observed for 28 single-nucleotide polymorphisms. All 28 were positively associated with fat mass and 13 showed adipose-specific effects. CONCLUSIONS In adults, genetic susceptibility to elevated BMI influences adiposity more than lean or bone mass. This mirrors the association between BMI and body composition. The BMI-GRS can be used to model the effects of measured BMI and adiposity on health and other outcomes.
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Affiliation(s)
- E A D Clifton
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - F R Day
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - E De Lucia Rolfe
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - N G Forouhi
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - S Brage
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - S J Griffin
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.,Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge, UK
| | - N J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - K K Ong
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
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Mohammad A, De Lucia Rolfe E, Sleigh A, Kivisild T, Behbehani K, Wareham NJ, Brage S, Mohammad T. Validity of visceral adiposity estimates from DXA against MRI in Kuwaiti men and women. Nutr Diabetes 2017; 7:e238. [PMID: 28067890 PMCID: PMC5301039 DOI: 10.1038/nutd.2016.38] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2016] [Revised: 05/12/2016] [Accepted: 07/20/2016] [Indexed: 01/17/2023] Open
Abstract
OBJECTIVES The prevalence of obesity and diabetes in the Middle East is among the highest in the world. Valid measures of abdominal adiposity are essential to understanding the metabolic consequences of obesity. Dual-energy X-ray absorptiometry (DXA) is increasingly being utilised to assess body composition in population studies, and has recently been used to estimate visceral adipose tissue (VAT). The aim of this study was to determine the accuracy of DXA-derived VAT in a Middle Eastern population using magnetic resonance imaging (MRI) as the criterion measure. METHOD VAT was estimated from abdominal DXA measures in 237 adult men (n=130) and women (n=107), aged 18-65 years, participating in the Kuwait Wellbeing Study. These estimates were compared with MRI measures of the corresponding anatomical region. The agreement between methods was assessed using Bland-Altman as well as correlation analysis. RESULTS Median MRI VAT was 1148.5 cm3 (95% confidence interval: 594.2-1734.6) in men and 711.3 cm3 (95% confidence interval: 395.5-1042.8) in women. DXA estimates of VAT showed high correlations with corresponding MRI measures (r=0.94 (P<0.0001) in men; r=0.93 (P<0.0001) in women). DXA overestimated VAT with a mean bias (95% limits of agreement) of 79.7 cm3 (-767 to 963) in men and 46.8 cm3 (-482 to 866) in women. The imprecision of DXA increased with increasing VAT level in both men and women. CONCLUSION DXA estimates of VAT are valid for use in Middle Eastern populations, although accuracy decreases with increasing level of visceral adiposity.
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Affiliation(s)
- A Mohammad
- Department of Public Health Research, Dasman Diabetes Institute, Kuwait City, Kuwait
| | - E De Lucia Rolfe
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - A Sleigh
- Wolfson Brain Imaging Centre, University of Cambridge School of Clinical Medicine, and NIHR/Wellcome Trust Clinical Research Facility, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, UK
| | - T Kivisild
- Department of Biological Anthropology, University of Cambridge, Cambridge, UK
| | - K Behbehani
- Department of Public Health Research, Dasman Diabetes Institute, Kuwait City, Kuwait
| | - N J Wareham
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - S Brage
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - T Mohammad
- Department of Biological Anthropology, University of Cambridge, Cambridge, UK
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36
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Stansfield BK, Fain ME, Bhatia J, Gutin B, Nguyen JT, Pollock NK. Nonlinear Relationship between Birth Weight and Visceral Fat in Adolescents. J Pediatr 2016; 174:185-92. [PMID: 27174144 PMCID: PMC5711485 DOI: 10.1016/j.jpeds.2016.04.012] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Revised: 03/01/2016] [Accepted: 04/06/2016] [Indexed: 12/19/2022]
Abstract
OBJECTIVE To determine the association of birth weight with abdominal fat distribution and markers known to increase risk for cardiovascular disease and type 2 diabetes in adolescents. STUDY DESIGN In 575 adolescents aged 14-18 years (52% female, 46% black), birth weight was obtained by parental recall. Fasting blood samples were measured for glucose, insulin, lipids, adiponectin, leptin, and C-reactive protein. Subcutaneous abdominal adipose tissue and visceral adipose tissue were assessed by magnetic resonance imaging. RESULTS When we compared markers of cardiometabolic risk across tertiles of birth weight, adjusting for age, sex, race, Tanner stage, physical activity, socioeconomic status, and body mass index, there were significant U-shaped trends for homeostasis model assessment of insulin resistance, leptin, and visceral adipose tissue (all Pquadratic < .05). A significant linear downward trend across tertiles of birth weight was observed for triglycerides (Plinear = .03). There were no differences in fasting glucose, blood pressure, total cholesterol, low-density lipoprotein-cholesterol, high-density lipoprotein-cholesterol, adiponectin, C-reactive protein, or subcutaneous abdominal adipose tissue across tertiles of birth weight. CONCLUSIONS Our data suggest that both low and high birth weights are associated with greater visceral adiposity and biomarkers implicated in insulin resistance and inflammation in adolescents.
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Affiliation(s)
- Brian K. Stansfield
- Division of Neonatology, Department of Pediatrics, Medical College of Georgia, Augusta University, Augusta, GA,Vascular Biology Center, Augusta University, Augusta, GA
| | - Mary Ellen Fain
- Georgia Prevention Institute, Augusta University, Augusta, GA
| | - Jatinder Bhatia
- Division of Neonatology, Department of Pediatrics, Medical College of Georgia, Augusta University, Augusta, GA
| | - Bernard Gutin
- Department of Nutrition, University of North Carolina, Chapel Hill, NC
| | | | - Norman K. Pollock
- Georgia Prevention Institute, Augusta University, Augusta, GA,Department of Pediatrics, Medical College of Georgia, Augusta University, Augusta, GA
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Scott RA, Freitag DF, Li L, Chu AY, Surendran P, Young R, Grarup N, Stancáková A, Chen Y, Varga TV, Yaghootkar H, Luan J, Zhao JH, Willems SM, Wessel J, Wang S, Maruthur N, Michailidou K, Pirie A, van der Lee SJ, Gillson C, Al Olama AA, Amouyel P, Arriola L, Arveiler D, Aviles-Olmos I, Balkau B, Barricarte A, Barroso I, Garcia SB, Bis JC, Blankenberg S, Boehnke M, Boeing H, Boerwinkle E, Borecki IB, Bork-Jensen J, Bowden S, Caldas C, Caslake M, Cupples LA, Cruchaga C, Czajkowski J, den Hoed M, Dunn JA, Earl HM, Ehret GB, Ferrannini E, Ferrieres J, Foltynie T, Ford I, Forouhi NG, Gianfagna F, Gonzalez C, Grioni S, Hiller L, Jansson JH, Jørgensen ME, Jukema JW, Kaaks R, Kee F, Kerrison ND, Key TJ, Kontto J, Kote-Jarai Z, Kraja AT, Kuulasmaa K, Kuusisto J, Linneberg A, Liu C, Marenne G, Mohlke KL, Morris AP, Muir K, Müller-Nurasyid M, Munroe PB, Navarro C, Nielsen SF, Nilsson PM, Nordestgaard BG, Packard CJ, Palli D, Panico S, Peloso GM, Perola M, Peters A, Poole CJ, Quirós JR, Rolandsson O, Sacerdote C, Salomaa V, Sánchez MJ, Sattar N, Sharp SJ, Sims R, Slimani N, Smith JA, Thompson DJ, Trompet S, Tumino R, van der A DL, van der Schouw YT, Virtamo J, Walker M, Walter K, Abraham JE, Amundadottir LT, Aponte JL, Butterworth AS, Dupuis J, Easton DF, Eeles RA, Erdmann J, Franks PW, Frayling TM, Hansen T, Howson JMM, Jørgensen T, Kooner J, Laakso M, Langenberg C, McCarthy MI, Pankow JS, Pedersen O, Riboli E, Rotter JI, Saleheen D, Samani NJ, Schunkert H, Vollenweider P, O'Rahilly S, Deloukas P, Danesh J, Goodarzi MO, Kathiresan S, Meigs JB, Ehm MG, Wareham NJ, Waterworth DM. A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease. Sci Transl Med 2016; 8:341ra76. [PMID: 27252175 PMCID: PMC5219001 DOI: 10.1126/scitranslmed.aad3744] [Citation(s) in RCA: 92] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2015] [Accepted: 05/10/2016] [Indexed: 02/06/2023]
Abstract
Regulatory authorities have indicated that new drugs to treat type 2 diabetes (T2D) should not be associated with an unacceptable increase in cardiovascular risk. Human genetics may be able to guide development of antidiabetic therapies by predicting cardiovascular and other health endpoints. We therefore investigated the association of variants in six genes that encode drug targets for obesity or T2D with a range of metabolic traits in up to 11,806 individuals by targeted exome sequencing and follow-up in 39,979 individuals by targeted genotyping, with additional in silico follow-up in consortia. We used these data to first compare associations of variants in genes encoding drug targets with the effects of pharmacological manipulation of those targets in clinical trials. We then tested the association of those variants with disease outcomes, including coronary heart disease, to predict cardiovascular safety of these agents. A low-frequency missense variant (Ala316Thr; rs10305492) in the gene encoding glucagon-like peptide-1 receptor (GLP1R), the target of GLP1R agonists, was associated with lower fasting glucose and T2D risk, consistent with GLP1R agonist therapies. The minor allele was also associated with protection against heart disease, thus providing evidence that GLP1R agonists are not likely to be associated with an unacceptable increase in cardiovascular risk. Our results provide an encouraging signal that these agents may be associated with benefit, a question currently being addressed in randomized controlled trials. Genetic variants associated with metabolic traits and multiple disease outcomes can be used to validate therapeutic targets at an early stage in the drug development process.
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Affiliation(s)
- Robert A Scott
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK.
| | - Daniel F Freitag
- Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK. The Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK
| | - Li Li
- Statistical Genetics, Projects, Clinical Platforms, and Sciences (PCPS), GlaxoSmithKline, Research Triangle Park, NC 27709, USA
| | - Audrey Y Chu
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Praveen Surendran
- Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK
| | - Robin Young
- Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK
| | - Niels Grarup
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Alena Stancáková
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, FI-70211 Kuopio, Finland
| | - Yuning Chen
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Tibor V Varga
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, SE-205 Malmö, Sweden
| | - Hanieh Yaghootkar
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK
| | - Jian'an Luan
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Jing Hua Zhao
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Sara M Willems
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK. Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, 3000 CE Rotterdam, Netherlands
| | - Jennifer Wessel
- Department of Epidemiology, Fairbanks School of Public Health, Indianapolis, IN 46202, USA. Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Shuai Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Nisa Maruthur
- Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD 21205, USA. Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge CB1 8RN, UK
| | - Ailith Pirie
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge CB1 8RN, UK
| | - Sven J van der Lee
- Department of Epidemiology, Erasmus University Medical Center, 3000 CA Rotterdam, Netherlands
| | - Christopher Gillson
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Ali Amin Al Olama
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge CB1 8RN, UK
| | - Philippe Amouyel
- University of Lille, INSERM, Centre Hospitalier Régional Universitaire de Lille, Institut Pasteur de Lille, UMR 1167, RID-AGE, F-59000 Lille, France
| | - Larraitz Arriola
- Public Health Division of Gipuzkoa, San Sebastian 20013, Spain. Instituto BIO-Donostia, Basque Government, San Sebastian 20014, Spain. CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
| | - Dominique Arveiler
- Department of Epidemiology and Public Health (EA3430), University of Strasbourg, 67085 Strasbourg, France
| | - Iciar Aviles-Olmos
- Sobell Department of Motor Neuroscience, UCL Institute of Neurology, London WC1N 3BG, UK
| | - Beverley Balkau
- INSERM, Centre de Recherche en Epidémiologie et Santé des Populations (CESP), 94807 Villejuif, France. Univeristy of Paris-Sud, F-94805 Villejuif, France
| | - Aurelio Barricarte
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain. Navarre Public Health Institute (ISPN), Pamplona 31003, Spain
| | - Inês Barroso
- The Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK. University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge CB2 0QQ, UK
| | - Sara Benlloch Garcia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge CB1 8RN, UK
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA
| | - Stefan Blankenberg
- Department of General and Interventional Cardiology, University Heart Center Hamburg, 20246 Hamburg, Germany
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109-2029, USA
| | - Heiner Boeing
- German Institute of Human Nutrition, Potsdam-Rehbruecke, 14558 Nuthetal, Germany
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77025, USA. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ingrid B Borecki
- Department of Genetics, Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Jette Bork-Jensen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Sarah Bowden
- Cancer Research UK Clinical Trials Unit, Institute for Cancer Studies, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Carlos Caldas
- Cancer Research UK Cambridge Institute and Department of Oncology, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, UK
| | | | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA. Framingham Heart Study, National Heart, Lung, and Blood Institute (NHLBI), Framingham, MA 01702-5827, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jacek Czajkowski
- Division of Statistical Genomics, Department of Genetics and Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Marcel den Hoed
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, SE-752 37 Uppsala, Sweden
| | - Janet A Dunn
- Warwick Clinical Trials Unit, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - Helena M Earl
- University of Cambridge and National Institute of Health Research Cambridge Biomedical Research Centre, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, UK
| | - Georg B Ehret
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Ele Ferrannini
- Consiglio Nazionale delle Ricerche (CNR), Institute of Clinical Physiology, 56124 Pisa, Italy
| | - Jean Ferrieres
- Department of Epidemiology, UMR 1027, INSERM, Centre Hospitalier Universitaire (CHU) de Toulouse, 31000 Toulouse, France
| | - Thomas Foltynie
- Sobell Department of Motor Neuroscience, UCL Institute of Neurology, London WC1N 3BG, UK
| | - Ian Ford
- University of Glasgow, Glasgow G12 8QQ, UK
| | - Nita G Forouhi
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Francesco Gianfagna
- Department of Clinical and Experimental Medicine, Research Centre in Epidemiology and Preventive Medicine, University of Insubria, 21100 Varese, Italy. Department of Epidemiology and Prevention, Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS), Istituto Neurologico Mediterraneo Neuromed, 86077 Pozzilli, Italy
| | | | - Sara Grioni
- Epidemiology and Prevention Unit, 20133 Milan, Italy
| | - Louise Hiller
- Warwick Clinical Trials Unit, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - Jan-Håkan Jansson
- Research Unit, 931 41 Skellefteå, Sweden. Department of Public Health & Clinical Medicine, Umeå University, 901 85 Umeå, Sweden
| | - Marit E Jørgensen
- Steno Diabetes Center, 2820 Gentofte, Denmark. National Institute of Public Health, Southern Denmark University, DK-1353 Odense, Denmark
| | - J Wouter Jukema
- Leiden University Medical Center, 2333 ZA Leiden, Netherlands
| | - Rudolf Kaaks
- German Cancer Research Centre (DKFZ), 69120 Heidelberg, Germany
| | - Frank Kee
- UK Clinical Research Collaboration (UKCRC) Centre of Excellence for Public Health, Queen's University Belfast, Northern Ireland, Belfast BT12 6BJ, UK
| | - Nicola D Kerrison
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | | | - Jukka Kontto
- National Institute for Health and Welfare, FI-00271 Helsinki, Finland
| | | | - Aldi T Kraja
- Division of Statistical Genomics, Department of Genetics and Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Kari Kuulasmaa
- National Institute for Health and Welfare, FI-00271 Helsinki, Finland
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, FI-70211 Kuopio, Finland. Kuopio University Hospital, FL 70029 Kuopio, Finland
| | - Allan Linneberg
- Research Centre for Prevention and Health, Capital Region, DK-2600 Copenhagen, Denmark. Department of Clinical Experimental Research, Rigshospitalet, 2100 Glostrup, Denmark. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Chunyu Liu
- Framingham Heart Study, Population Sciences Branch, NHLBI/National Institutes of Health (NIH), Bethesda, MD 20892, USA
| | - Gaëlle Marenne
- The Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599-7264, USA
| | - Andrew P Morris
- Department of Biostatistics, University of Liverpool, Liverpool L69 3GL, UK. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Kenneth Muir
- Centre for Epidemiology, Institute of Population Health, University of Manchester, Oxford Road, Manchester M13 9PT, UK. University of Warwick, Coventry CV4 7AL, UK
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764 Neuherberg, Germany. Department of Medicine I, Ludwig Maximilians University Munich, 80336 Munich, Germany. DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, 80802 Munich, Germany
| | - Patricia B Munroe
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Carmen Navarro
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain. Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia 30008, Spain
| | - Sune F Nielsen
- Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, University of Copenhagen, 2730 Copenhagen, Denmark
| | | | - Børge G Nordestgaard
- Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, University of Copenhagen, 2730 Copenhagen, Denmark
| | | | - Domenico Palli
- Cancer Research and Prevention Institute (ISPO), 50141 Florence, Italy
| | - Salvatore Panico
- Dipartimento di Medicina Clinica e Chirurgia, Federico II University, 80131 Naples, Italy
| | - Gina M Peloso
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA. Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
| | - Markus Perola
- National Institute for Health and Welfare, FI-00271 Helsinki, Finland. Institute of Molecular Medicine Finland (FIMM), University of Helsinki, FI-00014 Helsinki, Finland
| | - Annette Peters
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, 80802 Munich, Germany. Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764 Neuherberg, Germany
| | - Christopher J Poole
- University of Warwick, Coventry CV4 7AL, UK. Department of Medical Oncology, Arden Cancer Centre, University Hospital Coventry and Warwickshire, West Midlands CV2 2DX, UK
| | - J Ramón Quirós
- Public Health Directorate, 33006 Oviedo, Asturias, Spain
| | | | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, Citta' della Salute e della Scienza Hospital, University of Turin, 10126 Torino, Italy. Center for Cancer Prevention (CPO), 10126 Torino, Italy. Human Genetics Foundation, 10126 Torino, Italy
| | - Veikko Salomaa
- National Institute for Health and Welfare, FI-00271 Helsinki, Finland
| | - María-José Sánchez
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain. Escuela Andaluza de Salud Pública, Instituto de Investigación Biosanitaria ibs.GRANADA. Hospitales Universitarios de Granada/Universidad de Granada, Granada 18012, Spain
| | | | - Stephen J Sharp
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Rebecca Sims
- Institute of Psychological Medicine and Clinical Neuroscience, MRC Centre, Cardiff University, Cardiff CF24 4HQ, UK
| | - Nadia Slimani
- International Agency for Research on Cancer, 69372 Lyon, France
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109-2029, USA
| | - Deborah J Thompson
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge CB1 8RN, UK
| | - Stella Trompet
- Leiden University Medical Center, 2333 ZA Leiden, Netherlands
| | - Rosario Tumino
- Cancer Registry and Histopathology Unit, "Civic-M.P. Arezzo" Hospital, ASP Ragusa, 97100 Ragusa, Italy
| | - Daphne L van der A
- National Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven, Netherlands
| | | | - Jarmo Virtamo
- National Institute for Health and Welfare, FI-00271 Helsinki, Finland
| | - Mark Walker
- Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Klaudia Walter
- The Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK
| | - Jean E Abraham
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge CB1 8RN, UK
| | - Laufey T Amundadottir
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - Jennifer L Aponte
- Genetics, PCPS, GlaxoSmithKline, Research Triangle Park, NC 27709, USA
| | - Adam S Butterworth
- Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge CB1 8RN, UK. Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge CB1 8RN, UK
| | - Rosalind A Eeles
- The Institute of Cancer Research, London SM2 5NG, UK. Royal Marsden NHS Foundation Trust, Fulham and Sutton, London and Surrey SW3 6JJ, UK
| | - Jeanette Erdmann
- Institut für Integrative und Experimentelle Genomik, Universität zu Lübeck, 23562 Lübeck, Germany
| | - Paul W Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, SE-205 Malmö, Sweden. Department of Public Health & Clinical Medicine, Umeå University, 901 85 Umeå, Sweden. Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Joanna M M Howson
- Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK
| | - Torben Jørgensen
- Research Centre for Prevention and Health, DK-2600 Capital Region, Denmark. Department of Public Health, Institute of Health Science, University of Copenhagen, 1014 Copenhagen, Denmark. Faculty of Medicine, Aalborg University, 9220 Aalborg, Denmark
| | - Jaspal Kooner
- National Heart and Lung Institute, Imperial College London, London SW3 6LY, UK. Imperial College Healthcare NHS Trust, London W2 1NY, UK. Ealing Hospital NHS Trust, Middlesex UB1 3HW, UK
| | - Markku Laakso
- Department of Medicine, University of Kuopio, FI-70211 Kuopio, Finland
| | - Claudia Langenberg
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK. Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), University of Oxford, Oxford, UK
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN 55455-0381, USA
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Elio Riboli
- School of Public Health, Imperial College London, London W2 1PG, UK
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-University of California, Los Angeles Medical Center, Torrance, CA 90502, USA
| | - Danish Saleheen
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester LE3 9QP, UK. National Institute for Health Research, Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, UK
| | - Heribert Schunkert
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, 80802 Munich, Germany. Deutsches Herzzentrum München, Technische Universität München, 80636 Munich, Germany
| | - Peter Vollenweider
- Department of Internal Medicine, BH10-462, Internal Medicine, Lausanne University Hospital (CHUV), CH-1011 Lausanne, Switzerland
| | - Stephen O'Rahilly
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge CB2 0QQ, UK. MRC Metabolic Diseases Unit, Cambridge CB2 0QQ, UK. National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge CB2 0QQ, UK
| | - Panos Deloukas
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - John Danesh
- Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK. The Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Sekar Kathiresan
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA. Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA. Cardiology Division, Center for Human Genetic Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - James B Meigs
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA. Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Margaret G Ehm
- Genetics, PCPS, GlaxoSmithKline, Research Triangle Park, NC 27709, USA
| | - Nicholas J Wareham
- Medical Research Council (MRC) Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK.
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Jornayvaz FR, Vollenweider P, Bochud M, Mooser V, Waeber G, Marques-Vidal P. Low birth weight leads to obesity, diabetes and increased leptin levels in adults: the CoLaus study. Cardiovasc Diabetol 2016; 15:73. [PMID: 27141948 PMCID: PMC4855501 DOI: 10.1186/s12933-016-0389-2] [Citation(s) in RCA: 178] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Accepted: 04/20/2016] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Low birth weight is associated with increased rates of obesity, insulin resistance and type 2 diabetes, but the precise mechanisms for this association remain unclear. We aimed to assess the relationships between birth weight and markers of glucose homeostasis or obesity in adults. METHODS Cross-sectional population-based study on 1458 women and 1088 men aged 35-75 years living in Lausanne, Switzerland. Birth weight was self-reported and categorized into ≤ 2.5, 2.6-3.5, 3.6-4.0 and >4.0 kg. Body composition was assessed by bioimpedance. Leptin and adiponectin levels were measured by ELISA. RESULTS Women with low birth weight (≤ 2.5 kg) had higher levels of fasting plasma glucose, insulin, HOMA, diabetes and metabolic syndrome; a non significant similar trend was seen in men. In both genders, height increased with birth weight, whereas a U-shaped association was found between birth weight and body mass index, waist circumference and body fat percentage. After adjusting for age, smoking status, physical activity and fat mass, an inverse association was found between leptin and birth weight categories: adjusted mean ± standard error 17.3 ± 0.7, 16.2 ± 0.3, 15.6 ± 0.5 and 14.0 ± 0.8 ng/dL for birth weight categories ≤ 2.5, 2.6-3.5, 3.6-4.0 and >4.0 kg, respectively, in women (p < 0.05) and 9.8 ± 0.8, 9.1 ± 03, 7.8 ± 0.4 and 7.7 ± 0.5 ng/dL in men (p < 0.05). An inverse association was also found between reported birth weight and leptin to fat mass ratio: mean ± standard error 0.77 ± 0.04, 0.73 ± 0.02, 0.69 ± 0.03 and 0.62 ± 0.04 in women (p < 0.05); 0.46 ± 0.05, 0.45 ± 0.02, 0.39 ± 0.02 and 0.38 ± 0.03 in men (p < 0.05). No differences in adiponectin levels were found between birth weight groups. CONCLUSIONS Middle-aged adults born with a low weight present a higher prevalence of diabetes and obesity and also higher leptin levels and leptin to fat mass ratio than adults born with a normal weight. The higher leptin levels and leptin to fat mass ratio among adults born with a low weight might be related to nutritional factors during childhood or to the development of leptin resistance and/or higher leptin production by body fat unit. Subjects born with a low weight should be counselled regarding the risks of developing diabetes and/or cardiovascular disease.
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Affiliation(s)
- François R. Jornayvaz
- />Service of Endocrinology, Diabetes, and Metabolism, Lausanne University Hospital, Lausanne, Switzerland
| | - Peter Vollenweider
- />Department of Medicine, Department of Internal Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | - Murielle Bochud
- />Institute of Social and Preventive Medicine (IUMSP), Lausanne, Switzerland
| | - Vincent Mooser
- />Department of Medical Biology, Lausanne University Hospital, Lausanne, Switzerland
| | - Gérard Waeber
- />Department of Medicine, Department of Internal Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | - Pedro Marques-Vidal
- />Department of Medicine, Department of Internal Medicine, Lausanne University Hospital, Lausanne, Switzerland
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Foley L, Panter J, Heinen E, Prins R, Ogilvie D. Changes in active commuting and changes in physical activity in adults: a cohort study. Int J Behav Nutr Phys Act 2015; 12:161. [PMID: 26682539 PMCID: PMC4683976 DOI: 10.1186/s12966-015-0323-0] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Accepted: 12/12/2015] [Indexed: 11/10/2022] Open
Abstract
Background Active travel is associated with greater physical activity, but there is a dearth of research examining this relationship over time. We examined the longitudinal associations between change in time spent in active commuting and changes in recreational and total physical activity. Methods Adult commuters working in Cambridge, United Kingdom completed questionnaires in 2009 and 2012, and a sub-set completed objective physical activity monitoring in 2010 and 2012. Commuting was assessed using a validated seven-day travel to work record. Moderate-to-vigorous physical activity was assessed using the Recent Physical Activity Questionnaire and combined heart rate and movement sensing. We used multivariable multinomial logistic regression models to examine associations between change in time spent in active commuting and tertiles of changes in time spent in recreational and total physical activity. Results Four hundred sixty-nine participants (67 % female, mean age 44 years) provided valid travel and self-reported physical activity data. Seventy-one participants (54 % female, mean age 45 years) provided valid travel and objectively measured physical activity data. A decrease in active commuting was associated with a greater likelihood of a decrease in self-reported total physical activity (relative risk ratio [RRR] 2.1, 95 % CI 1.1, 4.1). Correspondingly, an increase in active commuting was associated with a borderline significantly greater likelihood of an increase in self-reported total physical activity (RRR 1.8, 95 % CI 1.0, 3.4). No associations were seen between change in time spent in active commuting and change in time spent in either self-reported recreational physical activity or objectively measured physical activity. Conclusions Changes in active commuting were associated with commensurate changes in total self-reported physical activity and we found no compensatory changes in self-reported recreational physical activity. Promoting active commuting has potential as a public health strategy to increase physical activity. Future longitudinal research would be useful to verify these findings. Electronic supplementary material The online version of this article (doi:10.1186/s12966-015-0323-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Louise Foley
- MRC Epidemiology Unit and UKCRC Centre for Diet and Activity Research (CEDAR), University of Cambridge School of Clinical Medicine, Box 285, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
| | - Jenna Panter
- MRC Epidemiology Unit and UKCRC Centre for Diet and Activity Research (CEDAR), University of Cambridge School of Clinical Medicine, Box 285, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
| | - Eva Heinen
- MRC Epidemiology Unit and UKCRC Centre for Diet and Activity Research (CEDAR), University of Cambridge School of Clinical Medicine, Box 285, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
| | - Richard Prins
- MRC Epidemiology Unit and UKCRC Centre for Diet and Activity Research (CEDAR), University of Cambridge School of Clinical Medicine, Box 285, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
| | - David Ogilvie
- MRC Epidemiology Unit and UKCRC Centre for Diet and Activity Research (CEDAR), University of Cambridge School of Clinical Medicine, Box 285, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
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Yuan ZP, Yang M, Liang L, Fu JF, Xiong F, Liu GL, Gong CX, Luo FH, Chen SK, Zhang DD, Zhang S, Zhu YM. Possible role of birth weight on general and central obesity in Chinese children and adolescents: a cross-sectional study. Ann Epidemiol 2015. [DOI: 10.1016/j.annepidem.2015.05.011] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Associations of birth weight, linear growth and relative weight gain throughout life with abdominal fat depots in adulthood: the 1982 Pelotas (Brazil) birth cohort study. Int J Obes (Lond) 2015; 40:14-21. [PMID: 26395747 PMCID: PMC4722236 DOI: 10.1038/ijo.2015.192] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Revised: 08/10/2015] [Accepted: 09/07/2015] [Indexed: 02/03/2023]
Abstract
Background: Several studies have reported on associations of size at birth and early growth with general and central obesity; however, few have examined the potential effects of birth weight and postnatal growth on separate abdominal fat compartments. We investigated the effects of size at birth, linear growth and relative weight gain from birth to adulthood on visceral (VFT) and subcutaneous abdominal (SAFT) fat thicknesses at age 30 years. Methods: A total of 2663 participants from the 1982 Pelotas (Brazil) birth cohort study had complete information on ultrasound measures of abdominal fat at age 30 years, and anthropometric measurements for at least five visits (0/2/4/23/30 years). We estimated weight and height Z-score changes, conditional relative weight gain and conditional height at several ages. Results: In both men and women, VFT and SAFT showed positive associations with conditional relative weight gain during all age periods beyond 2 years and birth, respectively (all P⩽0.01). Women born with intrauterine growth restriction (IUGR) had greater VFT than other women (difference=0.15 s.d., 95% CI: 0.01–0.29), and they showed a stronger positive influence of infant weight gain 0–2 years on VFT (IUGR: β=0.17 s.d., 95% CI: 0.05–0.29; non-IUGR: β=0.01 s.d., 95% CI: −0.04 to 0.06; Pinteraction=0.02). Stunting at 2 years was associated with lower SAFT but not VFT, and it modified the influence of weight gain 2–4 years on SAFT in both sexes (both Pinteraction<0.05). Conclusions: Our findings reinforce the advantages of being born with an appropriate birth weight, and the hazards of rapid postnatal gains in weight relative to linear growth, particularly after the critical window of the first 1000 days.
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Abstract
Evidence suggests that diets meeting recommendations for fruit and vegetable (F&V) intake are more costly. Dietary costs may be a greater constraint on the diet quality of people of lower socioeconomic position (SEP). The aim of this study was to examine whether dietary costs are more strongly associated with F&V intake in lower-SEP groups than in higher-SEP groups. Data on individual participants’ education and income were available from a population-based, cross-sectional study of 10 020 British adults. F&V intake and dietary costs (GBP/d) were derived from a semi-quantitative FFQ. Dietary cost estimates were based on UK food prices. General linear models were used to assess associations between SEP, quartiles of dietary costs and F&V intake. Effect modification of SEP gradients by dietary costs was examined with interaction terms. Analysis demonstrated that individuals with lowest quartile dietary costs, low income and low education consumed less F&V than individuals with higher dietary costs, high income and high education. Significant interaction between SEP and dietary costs indicated that the association between dietary costs and F&V intake was stronger for less-educated and lower-income groups. That is, socioeconomic differences in F&V intake were magnified among individuals who consumed lowest-cost diets. Such amplification of socioeconomic inequalities in diet among those consuming low-cost diets indicates the need to address food costs in strategies to promote healthy diets. In addition, the absence of socioeconomic inequalities for individuals with high dietary costs suggests that high dietary costs can compensate for lack of other material, or psychosocial resources.
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Abstract
Unhealthy dietary behaviours may contribute to obesity along with energy imbalance. Both positive and null associations of snacking and BMI have been reported, but the association between snacking and total adiposity or pattern of fat deposition remains unevaluated. The objective of this study was to investigate the associations between snacking frequency and detailed adiposity measurements. A total of 10 092 adults residing in Cambridgeshire, England, self-completed eating pattern snacking frequency, FFQ and physical activity questionnaires. Measurements included anthropometry, body composition using dual-energy X-ray absorptiometry scan and ultrasound and assessment of physical activity energy expenditure using heart rate and movement sensing. Linear regression analyses were conducted adjusted for age, socio-demographics, dietary quality, energy intake, PAEE and screen time by sex and BMI status. Among normal-weight individuals (BMI<25 kg/m2), each additional snack was inversely associated with obesity measures: lower total body fat in men and women (−0·41 (95 % CI −0·74, −0·07) %, −0·41 (−0·67, −0·15) %, respectively) and waist circumference (−0·52 (−0·90, −0·14) cm) in men. In contrast, among the overweight/obese (BMI≥25 kg/m2), there were positive associations: higher waist circumference (0·80 (0·34, 0·28) cm) and subcutaneous fat (0·06 (0·01, 0·110) cm) in women and waist circumference (0·37 (0·00, 0·73) cm) in men. Comparing intakes of snack-type foods showed that participants with BMI≥25 kg/m2 had higher intakes of crisps, sweets, chocolates and ice-creams and lower intakes of yoghurt and nuts compared with normal-weight participants. Adjusting for these foods in a model that included a BMI–snacking interaction term attenuated all the associations to null. Snacking frequency may be associated with higher or lower adiposity, with the direction of association being differential by BMI status and dependent on snack food choice. Improving snack choices could contribute to anti-obesity public health interventions.
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Nead KT, Sharp SJ, Thompson DJ, Painter JN, Savage DB, Semple RK, Barker A, Perry JRB, Attia J, Dunning AM, Easton DF, Holliday E, Lotta LA, O'Mara T, McEvoy M, Pharoah PDP, Scott RJ, Spurdle AB, Langenberg C, Wareham NJ, Scott RA. Evidence of a Causal Association Between Insulinemia and Endometrial Cancer: A Mendelian Randomization Analysis. J Natl Cancer Inst 2015; 107:djv178. [PMID: 26134033 PMCID: PMC4572886 DOI: 10.1093/jnci/djv178] [Citation(s) in RCA: 118] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Revised: 04/08/2015] [Accepted: 05/28/2015] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Insulinemia and type 2 diabetes (T2D) have been associated with endometrial cancer risk in numerous observational studies. However, the causality of these associations is uncertain. Here we use a Mendelian randomization (MR) approach to assess whether insulinemia and T2D are causally associated with endometrial cancer. METHODS We used single nucleotide polymorphisms (SNPs) associated with T2D (49 variants), fasting glucose (36 variants), fasting insulin (18 variants), early insulin secretion (17 variants), and body mass index (BMI) (32 variants) as instrumental variables in MR analyses. We calculated MR estimates for each risk factor with endometrial cancer using an inverse-variance weighted method with SNP-endometrial cancer associations from 1287 case patients and 8273 control participants. RESULTS Genetically predicted higher fasting insulin levels were associated with greater risk of endometrial cancer (odds ratio [OR] per standard deviation = 2.34, 95% confidence internal [CI] = 1.06 to 5.14, P = .03). Consistently, genetically predicted higher 30-minute postchallenge insulin levels were also associated with endometrial cancer risk (OR = 1.40, 95% CI = 1.12 to 1.76, P = .003). We observed no associations between genetic risk of type 2 diabetes (OR = 0.91, 95% CI = 0.79 to 1.04, P = .16) or higher fasting glucose (OR = 1.00, 95% CI = 0.67 to 1.50, P = .99) and endometrial cancer. In contrast, endometrial cancer risk was higher in individuals with genetically predicted higher BMI (OR = 3.86, 95% CI = 2.24 to 6.64, P = 1.2x10(-6)). CONCLUSION This study provides evidence to support a causal association of higher insulin levels, independently of BMI, with endometrial cancer risk.
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Affiliation(s)
- Kevin T Nead
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - Stephen J Sharp
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - Deborah J Thompson
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - Jodie N Painter
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - David B Savage
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - Robert K Semple
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - Adam Barker
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - John R B Perry
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - John Attia
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - Alison M Dunning
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - Douglas F Easton
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - Elizabeth Holliday
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - Luca A Lotta
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - Tracy O'Mara
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - Mark McEvoy
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - Paul D P Pharoah
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - Rodney J Scott
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - Amanda B Spurdle
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS)
| | - Robert A Scott
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK (KTN, SJS, AB, JRBP, LAL, CL, NJW, RAS); Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA (KTN); Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (DJT, DFE, PDPP); Queensland Institute of Medical Research, Brisbane, Australia (JNP, ANECS, TO, ABS); University of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Cambridge, UK (DBS, RKS); Hunter Medical Research Institute, John Hunter Hospital, Newcastle, Australia (JA, EH, RJS); Centre for Clinical Epidemiology and Biostatistics, School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia (JA, MM); Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK (AMD, DFE, PDPP); Centre for Information Based Medicine, School of Medicine and Public Health, University of Newcastle, Australia (EH, RJS).
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Association between weight at birth and body composition in childhood: A Brazilian cohort study. Early Hum Dev 2015; 91:445-9. [PMID: 26025334 DOI: 10.1016/j.earlhumdev.2015.05.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2014] [Revised: 04/07/2015] [Accepted: 05/08/2015] [Indexed: 11/23/2022]
Abstract
BACKGROUND AND AIM Previous studies have shown that the association between birth weight and obesity later in life apparently follows a U-shaped curve. However, due to the continuous increase of mean birth weight in several countries worldwide, it is expected that higher birth weight will play a more important role as a risk factor for further obesity than low birth weight. This study investigated the association between birth weight and body composition of children in order to establish their relationship in an earlier period of life. STUDY DESIGN AND SUBJECTS Prospective cohort study carried out from 1997 to 2006 in Jundiai city, Brazil, involving 486 children at birth and from 5 to 8 years of age. The following anthropometric measurements were determined: birth weight, weight, height, waist circumference and triceps skinfold thickness. Fat mass percentage, fat mass and fat-free mass were measured by electrical bioimpedance analysis by the 310 Body Composition Analyzer, Biodynamics(®). Five multiple linear regression models were developed considering waist circumference, triceps skinfold thickness, fat mass percentage, fat mass and fat-free mass as markers of body composition, and outcomes. RESULTS Significant positive associations were observed between birth weight and waist circumference (p<0.001), triceps skinfold thickness (p=0.006), fat mass (p=0.007) and fat-free mass (p<0.001). Approximately 10% of the children presented excess body fat assessed by bioimpedance, and 27.6% of them had central adiposity (waist circumference ≥95th percentile). CONCLUSIONS Intrauterine growth, assessed by weight at birth, was positively associated with body composition of children aged 5-8 years, indicating that those with the highest birth weight are more at risk for obesity, and probably to chronic diseases in adulthood.
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Østergaard SD, Mukherjee S, Sharp SJ, Proitsi P, Lotta LA, Day F, Perry JRB, Boehme KL, Walter S, Kauwe JS, Gibbons LE, Larson EB, Powell JF, Langenberg C, Crane PK, Wareham NJ, Scott RA. Associations between Potentially Modifiable Risk Factors and Alzheimer Disease: A Mendelian Randomization Study. PLoS Med 2015; 12:e1001841; discussion e1001841. [PMID: 26079503 PMCID: PMC4469461 DOI: 10.1371/journal.pmed.1001841] [Citation(s) in RCA: 145] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2014] [Accepted: 05/08/2015] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Potentially modifiable risk factors including obesity, diabetes, hypertension, and smoking are associated with Alzheimer disease (AD) and represent promising targets for intervention. However, the causality of these associations is unclear. We sought to assess the causal nature of these associations using Mendelian randomization (MR). METHODS AND FINDINGS We used SNPs associated with each risk factor as instrumental variables in MR analyses. We considered type 2 diabetes (T2D, NSNPs = 49), fasting glucose (NSNPs = 36), insulin resistance (NSNPs = 10), body mass index (BMI, NSNPs = 32), total cholesterol (NSNPs = 73), HDL-cholesterol (NSNPs = 71), LDL-cholesterol (NSNPs = 57), triglycerides (NSNPs = 39), systolic blood pressure (SBP, NSNPs = 24), smoking initiation (NSNPs = 1), smoking quantity (NSNPs = 3), university completion (NSNPs = 2), and years of education (NSNPs = 1). We calculated MR estimates of associations between each exposure and AD risk using an inverse-variance weighted approach, with summary statistics of SNP-AD associations from the International Genomics of Alzheimer's Project, comprising a total of 17,008 individuals with AD and 37,154 cognitively normal elderly controls. We found that genetically predicted higher SBP was associated with lower AD risk (odds ratio [OR] per standard deviation [15.4 mm Hg] of SBP [95% CI]: 0.75 [0.62-0.91]; p = 3.4 × 10(-3)). Genetically predicted higher SBP was also associated with a higher probability of taking antihypertensive medication (p = 6.7 × 10(-8)). Genetically predicted smoking quantity was associated with lower AD risk (OR per ten cigarettes per day [95% CI]: 0.67 [0.51-0.89]; p = 6.5 × 10(-3)), although we were unable to stratify by smoking history; genetically predicted smoking initiation was not associated with AD risk (OR = 0.70 [0.37, 1.33]; p = 0.28). We saw no evidence of causal associations between glycemic traits, T2D, BMI, or educational attainment and risk of AD (all p > 0.1). Potential limitations of this study include the small proportion of intermediate trait variance explained by genetic variants and other implicit limitations of MR analyses. CONCLUSIONS Inherited lifetime exposure to higher SBP is associated with lower AD risk. These findings suggest that higher blood pressure--or some environmental exposure associated with higher blood pressure, such as use of antihypertensive medications--may reduce AD risk.
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Affiliation(s)
- Søren D. Østergaard
- Research Department P, Aarhus University Hospital, Risskov, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Shubhabrata Mukherjee
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Stephen J. Sharp
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Petroula Proitsi
- Department of Basic and Clinical Neuroscience, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
| | - Luca A. Lotta
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Felix Day
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - John R. B. Perry
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Kevin L. Boehme
- Department of Biology, Brigham Young University, Provo, Utah, United States of America
| | - Stefan Walter
- Department of Epidemiology and Biostatistics, School of Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - John S. Kauwe
- Department of Biology, Brigham Young University, Provo, Utah, United States of America
| | - Laura E. Gibbons
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | | | | | | | - Eric B. Larson
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
- Group Health Research Institute, Seattle, Washington, United States of America
| | - John F. Powell
- Department of Basic and Clinical Neuroscience, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Paul K. Crane
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Nicholas J. Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Robert A. Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
- * E-mail:
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Glueck CJ, Wang P, Woo JG, Morrison JA, Khoury PR, Daniels SR. Adolescent and young adult female determinants of visceral adipose tissue at ages 26-28 years. J Pediatr 2015; 166:936-46.e1-3. [PMID: 25641236 DOI: 10.1016/j.jpeds.2014.12.030] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2014] [Revised: 10/17/2014] [Accepted: 12/12/2014] [Indexed: 02/06/2023]
Abstract
OBJECTIVE To assess adolescent and young adult determinants of visceral adipose tissue (VAT) at ages 26-28 years. STUDY DESIGN Prospective study (ages 9-28 years) of cardiometabolic measures, menarche age, menses irregularities, metabolic syndrome, impaired fasting glucose-type 2 diabetes mellitus, and VAT in 400 girls (248 black, 152 white). RESULTS Adolescent (age 14-19) independent variables for greater VAT at ages 26-28 included larger mean waist circumference (partial R(2) = 30.8%), earlier age at menarche (0.9%), and white race (1.8%). Young adult (ages 20-28 years) independent variables for greater VAT included larger mean waist circumference (partial R(2) = 61.7%), greater triglyceride levels (3.3%), lower high-density lipoprotein cholesterol (1.0%), and greater insulin resistance (homeostasis model assessment-estimated insulin resistance; 0.4%). Independent variables for greater VAT when both adolescent and young adult variables were used included waist (tertile rank change from adolescence to young adulthood, partial R(2) = 58.3%), greater young adult triglyceride levels (4.4%), white race (1.8%), greater young adult homeostasis model assessment-estimated insulin resistance (age 20-28, 2.4%), and earlier menarche age (0.7%). Menses irregularities were not independently associated with young adult VAT. CONCLUSIONS Adolescent girls with early menarche and larger waist circumference should be targets for primary prevention of accretion of VAT. In young adulthood, VAT is associated with dysregulated cardiometabolic profiles, which is greater for those with waist circumference increases from adolescence to adulthood. Waist circumference during young adulthood, and to a lesser degree during adolescence, is an inexpensive surrogate for VAT at ages 26-28 years.
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Affiliation(s)
- Charles J Glueck
- Cholesterol Center, Jewish Hospital of Cincinnati, Cincinnati, OH.
| | - Ping Wang
- Cholesterol Center, Jewish Hospital of Cincinnati, Cincinnati, OH
| | - Jessica G Woo
- The Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - John A Morrison
- The Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Philip R Khoury
- The Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
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48
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Storage capacity of subcutaneous fat in Japanese adults. Eur J Clin Nutr 2015; 69:933-8. [PMID: 25649236 DOI: 10.1038/ejcn.2014.292] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Revised: 11/14/2014] [Accepted: 12/13/2014] [Indexed: 11/08/2022]
Abstract
BACKGROUND On the basis of our previous study, which examined the nonlinear relationship between visceral fat area (VFA) and percent regional fat mass in the trunk, we hypothesise the presence of some storage capacity of subcutaneous fat. This study aimed to examine the storage capacity of subcutaneous fat on the basis of subcutaneous fat area (SFA) and VFA in 791 Japanese adult males and 563 females. METHODS Regression analyses by using SFA as a dependent variable and VFA as an independent variable were performed for each group classified by visceral fat obesity (VO): VO (VFA ⩾ 100 cm(2)) and the no-VO (NVO) groups. To statistically identify an optimal critical point for subcutaneous fat accumulation, we changed the cutoff point for the VO group from 50-150 cm(2) in 10-cm(2) increments and confirmed the significance of the correlation between SFA and VFA for each obesity group, the statistical difference in correlations between NVO and VO groups, and the goodness of fit for the two regression lines using the standard error of estimation values. These analyses were conducted for each sex and age (<65 and ⩾ 65 years) group. RESULTS The critical point for subcutaneous fat accumulation appears at the following cutoff points of VFA: 130 cm(2) in <65-year-old males, 110 cm(2) in ⩾ 65-year-old males and 100 cm(2) in both female groups. CONCLUSIONS These results suggest the presence of some storage capacity of subcutaneous fat. As a further application, these findings may serve to improve the risk assessment of obesity-related diseases.
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49
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Wessel J, Chu AY, Willems SM, Wang S, Yaghootkar H, Brody JA, Dauriz M, Hivert MF, Raghavan S, Lipovich L, Hidalgo B, Fox K, Huffman JE, An P, Lu Y, Rasmussen-Torvik LJ, Grarup N, Ehm MG, Li L, Baldridge AS, Stančáková A, Abrol R, Besse C, Boland A, Bork-Jensen J, Fornage M, Freitag DF, Garcia ME, Guo X, Hara K, Isaacs A, Jakobsdottir J, Lange LA, Layton JC, Li M, Hua Zhao J, Meidtner K, Morrison AC, Nalls MA, Peters MJ, Sabater-Lleal M, Schurmann C, Silveira A, Smith AV, Southam L, Stoiber MH, Strawbridge RJ, Taylor KD, Varga TV, Allin KH, Amin N, Aponte JL, Aung T, Barbieri C, Bihlmeyer NA, Boehnke M, Bombieri C, Bowden DW, Burns SM, Chen Y, Chen YD, Cheng CY, Correa A, Czajkowski J, Dehghan A, Ehret GB, Eiriksdottir G, Escher SA, Farmaki AE, Frånberg M, Gambaro G, Giulianini F, Goddard WA, Goel A, Gottesman O, Grove ML, Gustafsson S, Hai Y, Hallmans G, Heo J, Hoffmann P, Ikram MK, Jensen RA, Jørgensen ME, Jørgensen T, Karaleftheri M, Khor CC, Kirkpatrick A, Kraja AT, Kuusisto J, Lange EM, Lee IT, Lee WJ, Leong A, Liao J, Liu C, Liu Y, Lindgren CM, Linneberg A, Malerba G, Mamakou V, Marouli E, Maruthur NM, Matchan A, McKean-Cowdin R, McLeod O, Metcalf GA, Mohlke KL, Muzny DM, Ntalla I, Palmer ND, Pasko D, Peter A, Rayner NW, Renström F, Rice K, Sala CF, Sennblad B, Serafetinidis I, Smith JA, Soranzo N, Speliotes EK, Stahl EA, Stirrups K, Tentolouris N, Thanopoulou A, Torres M, Traglia M, Tsafantakis E, Javad S, Yanek LR, Zengini E, Becker DM, Bis JC, Brown JB, Adrienne Cupples L, Hansen T, Ingelsson E, Karter AJ, Lorenzo C, Mathias RA, Norris JM, Peloso GM, Sheu WHH, Toniolo D, Vaidya D, Varma R, Wagenknecht LE, Boeing H, Bottinger EP, Dedoussis G, Deloukas P, Ferrannini E, Franco OH, Franks PW, Gibbs RA, Gudnason V, Hamsten A, Harris TB, Hattersley AT, Hayward C, Hofman A, Jansson JH, Langenberg C, Launer LJ, Levy D, Oostra BA, O'Donnell CJ, O'Rahilly S, Padmanabhan S, Pankow JS, Polasek O, Province MA, Rich SS, Ridker PM, Rudan I, Schulze MB, Smith BH, Uitterlinden AG, Walker M, Watkins H, Wong TY, Zeggini E, Laakso M, Borecki IB, Chasman DI, Pedersen O, Psaty BM, Shyong Tai E, van Duijn CM, Wareham NJ, Waterworth DM, Boerwinkle E, Linda Kao WH, Florez JC, Loos RJ, Wilson JG, Frayling TM, Siscovick DS, Dupuis J, Rotter JI, Meigs JB, Scott RA, Goodarzi MO. Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility. Nat Commun 2015; 6:5897. [PMID: 25631608 PMCID: PMC4311266 DOI: 10.1038/ncomms6897] [Citation(s) in RCA: 153] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2014] [Accepted: 11/12/2014] [Indexed: 12/30/2022] Open
Abstract
Fasting glucose and insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (β=-0.09±0.01 mmol l(-1), P=3.4 × 10(-12)), T2D risk (OR[95%CI]=0.86[0.76-0.96], P=0.010), early insulin secretion (β=-0.07±0.035 pmolinsulin mmolglucose(-1), P=0.048), but higher 2-h glucose (β=0.16±0.05 mmol l(-1), P=4.3 × 10(-4)). We identify a gene-based association with FG at G6PC2 (pSKAT=6.8 × 10(-6)) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ABO at the putative promoter of an antisense lncRNA, associating with higher FG (β=0.02±0.004 mmol l(-1), P=1.3 × 10(-8)). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.
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Affiliation(s)
- Jennifer Wessel
- Department of Epidemiology, Fairbanks School of Public Health, Indianapolis, Indiana 46202, USA
- Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
| | - Audrey Y Chu
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02215, USA
- National Heart, Lung, and Blood Institute (NHLBI) Framingham Heart Study, Framingham, Massachusetts 01702, USA
| | - Sara M Willems
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam 3000 CE, The Netherlands
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0SL, UK
| | - Shuai Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA
| | - Hanieh Yaghootkar
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington 98101, USA
- Department of Medicine, University of Washington, Seattle, Washington 98195, USA
| | - Marco Dauriz
- Massachusetts General Hospital, General Medicine Division, Boston, Massachusetts 02114, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Verona Medical School and Hospital Trust of Verona, Verona 37126, Italy
| | - Marie-France Hivert
- Harvard Pilgrim Health Care Institute, Department of Population Medicine, Harvard Medical School, Boston, Massachusetts 02215, USA
- Division of Endocrinology and Metabolism, Department of Medicine, Université de Sherbrooke, Sherbrooke, Québec, Canada J1K 2R1
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| | - Sridharan Raghavan
- Massachusetts General Hospital, General Medicine Division, Boston, Massachusetts 02114, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Leonard Lipovich
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan 48202, USA
| | - Bertha Hidalgo
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama 35233, USA
| | - Keolu Fox
- Department of Medicine, University of Washington, Seattle, Washington 98195, USA
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Jennifer E Huffman
- National Heart, Lung, and Blood Institute (NHLBI) Framingham Heart Study, Framingham, Massachusetts 01702, USA
- MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh, Scotland EH4 2XU, UK
| | - Ping An
- Division of Statistical Genomics and Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63108, USA
| | - Yingchang Lu
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA
| | - Niels Grarup
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen DK-2200, Denmark
| | - Margaret G Ehm
- Quantitative Sciences, PCPS, GlaxoSmithKline, North Carolina 27709, USA
| | - Li Li
- Quantitative Sciences, PCPS, GlaxoSmithKline, North Carolina 27709, USA
| | - Abigail S Baldridge
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA
| | - Alena Stančáková
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio FI-70211, Finland
| | - Ravinder Abrol
- Department of Medicine and Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California 90048, USA
- Materials and Process Simulation Center, California Institute of Technology, Pasadena, California 91125, USA
| | - Céline Besse
- CEA, Institut de Génomique, Centre National de Génotypage, 2 Rue Gaston Crémieux, EVRY Cedex 91057, France
| | - Anne Boland
- CEA, Institut de Génomique, Centre National de Génotypage, 2 Rue Gaston Crémieux, EVRY Cedex 91057, France
| | - Jette Bork-Jensen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen DK-2200, Denmark
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center, Houston, Texas 77030, USA
| | - Daniel F Freitag
- The Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK
- Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK
| | - Melissa E Garcia
- Intramural Research Program, National Institute on Aging, Bethesda, Maryland 21224, USA
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California 90502, USA
| | - Kazuo Hara
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Aaron Isaacs
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam 3000 CE, The Netherlands
| | | | - Leslie A Lange
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
| | - Jill C Layton
- Indiana University, Fairbanks School of Public Health, Indianapolis, Indiana 46202, USA
| | - Man Li
- Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland 21205, USA
| | - Jing Hua Zhao
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0SL, UK
| | - Karina Meidtner
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal DE-14558, Germany
| | - Alanna C Morrison
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas 77225, USA
| | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland 20892, USA
| | - Marjolein J Peters
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam 3000 CE, The Netherlands
- The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden/Rotterdam 2300 RC, The Netherlands
| | - Maria Sabater-Lleal
- Atherosclerosis Research Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm SE-171 77, Sweden
| | - Claudia Schurmann
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Angela Silveira
- Atherosclerosis Research Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm SE-171 77, Sweden
| | - Albert V Smith
- Icelandic Heart Association, Holtasmari 1, Kopavogur IS-201, Iceland
- University of Iceland, Reykjavik IS-101, Iceland
| | - Lorraine Southam
- The Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK
- Wellcome Trust Centre for Human Genetics, Oxford OX3 7BN, UK
| | - Marcus H Stoiber
- Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Rona J Strawbridge
- Atherosclerosis Research Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm SE-171 77, Sweden
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California 90502, USA
| | - Tibor V Varga
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skåne University Hospital, Malmö SE-205 02, Sweden
| | - Kristine H Allin
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen DK-2200, Denmark
| | - Najaf Amin
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam 3000 CE, The Netherlands
| | - Jennifer L Aponte
- Quantitative Sciences, PCPS, GlaxoSmithKline, North Carolina 27709, USA
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
- Department of Ophthalmology, National University of Singapore and National University Health System, Singapore 119228, Singapore
| | - Caterina Barbieri
- Division of Genetics and Cell Biology, San Raffaele Research Institute, Milano 20132, Italy
| | - Nathan A Bihlmeyer
- Predoctoral Training Program in Human Genetics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Maryland 21205, USA
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Cristina Bombieri
- Section of Biology and Genetics, Department of Life and Reproduction Sciences, University of Verona, Verona 37100, Italy
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA
| | - Sean M Burns
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| | - Yuning Chen
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA
| | - Yii-DerI Chen
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California 90502, USA
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
- Department of Ophthalmology, National University of Singapore and National University Health System, Singapore 119228, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 119228, Singapore
- Office of Clinical Sciences, Duke-NUS Graduate Medical School, National University of Singapore, Singapore 169857, Singapore
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi 39216, USA
| | - Jacek Czajkowski
- Division of Statistical Genomics and Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63108, USA
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam 3000 CE, The Netherlands
| | - Georg B Ehret
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland 21205, USA
- Division of Cardiology, Geneva University Hospital Geneva 1211, Switzerland
| | | | - Stefan A Escher
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skåne University Hospital, Malmö SE-205 02, Sweden
| | - Aliki-Eleni Farmaki
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens 17671, Greece
| | - Mattias Frånberg
- Atherosclerosis Research Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm SE-171 77, Sweden
- Department of Numerical Analysis and Computer Science, SciLifeLab, Stockholm University, Stockholm SE-106 91, Sweden
| | - Giovanni Gambaro
- Division of Nephrology, Department of Internal Medicine and Medical Specialties, Columbus-Gemelli University Hospital, Catholic University, Rome 00168, Italy
| | - Franco Giulianini
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02215, USA
| | - William A Goddard
- Materials and Process Simulation Center, California Institute of Technology, Pasadena, California 91125, USA
| | - Anuj Goel
- Department of Cardiovascular Medicine, The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Omri Gottesman
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Megan L Grove
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas 77225, USA
| | - Stefan Gustafsson
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala SE-751 85, Sweden
| | - Yang Hai
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California 90502, USA
| | - Göran Hallmans
- Department of Biobank Research, Umeå University, Umeå SE-901 87, Sweden
| | - Jiyoung Heo
- Department of Biomedical Technology, Sangmyung University, Chungnam 330-720, Korea
| | - Per Hoffmann
- Institute of Human Genetics, Department of Genomics, Life & Brain Center, University of Bonn, Bonn DE-53127, Germany
- Human Genomics Research Group, Division of Medical Genetics, University Hospital Basel Department of Biomedicine 4031, Basel, Switzerland
- Institute of Neuroscience and Medicine (INM-1) Genomic Imaging Research Center Juelich, Juelich DE-52425, Germany
| | - Mohammad K Ikram
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
- Office of Clinical Sciences, Duke-NUS Graduate Medical School, National University of Singapore, Singapore 169857, Singapore
- Memory Aging & Cognition Centre (MACC), National University Health System, Singapore 117599, Singapore
| | - Richard A Jensen
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington 98101, USA
- Department of Medicine, University of Washington, Seattle, Washington 98195, USA
| | | | - Torben Jørgensen
- Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup DK-2600, Denmark
- Faculty of Medicine, University of Aalborg, Aalborg DK-9220, Denmark
| | | | - Chiea C Khor
- Department of Ophthalmology, National University of Singapore and National University Health System, Singapore 119228, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 119228, Singapore
- Division of Human Genetics, Genome Institute of Singapore, Singapore 138672, Singapore
| | - Andrea Kirkpatrick
- Materials and Process Simulation Center, California Institute of Technology, Pasadena, California 91125, USA
| | - Aldi T Kraja
- Division of Statistical Genomics and Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63108, USA
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio FI-70211, Finland
| | - Ethan M Lange
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
| | - I T Lee
- Division of Endocrine and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 407, Taiwan
- School of Medicine, National Yang-Ming University, Taipei 112, Taiwan
| | - Wen-Jane Lee
- Department of Medical Research, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Aaron Leong
- Massachusetts General Hospital, General Medicine Division, Boston, Massachusetts 02114, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Jiemin Liao
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
- Department of Ophthalmology, National University of Singapore and National University Health System, Singapore 119228, Singapore
| | - Chunyu Liu
- National Heart, Lung, and Blood Institute (NHLBI) Framingham Heart Study, Framingham, Massachusetts 01702, USA
| | - Yongmei Liu
- Department of Epidemiology & Prevention, Division of Public Health Sciences, Wake Forest University, Winston-Salem, North Carolina 27106, USA
| | - Cecilia M Lindgren
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Allan Linneberg
- Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup DK-2600, Denmark
- Department of Clinical Experimental Research, Copenhagen University Hospital Glostrup, Glostrup DK-2600, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen DK-2200, Denmark
| | - Giovanni Malerba
- Section of Biology and Genetics, Department of Life and Reproduction Sciences, University of Verona, Verona 37100, Italy
| | - Vasiliki Mamakou
- National and Kapodistrian University of Athens, Faculty of Medicine, Athens 115 27, Greece
- Dromokaiteio Psychiatric Hospital, Athens 124 61, Greece
| | - Eirini Marouli
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens 17671, Greece
| | - Nisa M Maruthur
- Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland 21205, USA
| | - Angela Matchan
- The Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK
| | - Roberta McKean-Cowdin
- Department of Preventive Medicine, Keck School of Medicine of the University of Southern California, Los Angeles 90033, USA
| | - Olga McLeod
- Atherosclerosis Research Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm SE-171 77, Sweden
| | - Ginger A Metcalf
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
| | - Donna M Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Ioanna Ntalla
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens 17671, Greece
- University of Leicester, Leicester LE1 7RH, UK
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, USA
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina 27106, USA
| | - Dorota Pasko
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK
| | - Andreas Peter
- Department of Internal Medicine, Division of Endocrinology, Metabolism, Pathobiochemistry and Clinical Chemistry and Institute of Diabetes Research and Metabolic Diseases, University of Tübingen, Tübingen DE-72076, Germany
- German Center for Diabetes Research (DZD), Neuherberg DE-85764, Germany
| | - Nigel W Rayner
- The Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK
- Wellcome Trust Centre for Human Genetics, Oxford OX3 7BN, UK
- The Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LE, UK
| | - Frida Renström
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skåne University Hospital, Malmö SE-205 02, Sweden
| | - Ken Rice
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington 98101, USA
- Department of Biostatistics, University of Washington, Seattle, Washington 98195, USA
| | - Cinzia F Sala
- Division of Genetics and Cell Biology, San Raffaele Research Institute, Milano 20132, Italy
| | - Bengt Sennblad
- Atherosclerosis Research Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm SE-171 77, Sweden
- Science for Life Laboratory, Karolinska Institutet, Stockholm SE-171 77, Sweden
| | | | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Nicole Soranzo
- The Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK
- Department of Hematology, Long Road, Cambridge CB2 0XY, UK
| | - Elizabeth K Speliotes
- Department of Internal Medicine, Division of Gastroenterology and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Eli A Stahl
- Division of Psychiatric Genomics, The Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Kathleen Stirrups
- The Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 4NS, UK
| | - Nikos Tentolouris
- First Department of Propaedeutic and Internal Medicine, Athens University Medical School, Laiko General Hospital, Athens 11527, Greece
| | - Anastasia Thanopoulou
- Diabetes Centre, 2nd Department of Internal Medicine, National University of Athens, Hippokration General Hospital, Athens 11527, Greece
| | - Mina Torres
- Department of Preventive Medicine, Keck School of Medicine of the University of Southern California, Los Angeles 90033, USA
| | - Michela Traglia
- Division of Genetics and Cell Biology, San Raffaele Research Institute, Milano 20132, Italy
| | | | - Sundas Javad
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0SL, UK
| | - Lisa R Yanek
- The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Eleni Zengini
- Dromokaiteio Psychiatric Hospital, Athens 124 61, Greece
- University of Sheffield, Sheffield S10 2TN, UK
| | - Diane M Becker
- The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington 98101, USA
- Department of Medicine, University of Washington, Seattle, Washington 98195, USA
| | - James B Brown
- Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
- Department of Statistics, University of California at Berkeley, Berkeley, California 94720, USA
| | - L Adrienne Cupples
- National Heart, Lung, and Blood Institute (NHLBI) Framingham Heart Study, Framingham, Massachusetts 01702, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen DK-2200, Denmark
- Faculty of Health Science, University of Copenhagen, Copenhagen 1165, Denmark
| | - Erik Ingelsson
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala SE-751 85, Sweden
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Andrew J Karter
- Division of Research, Kaiser Permanente, Northern California Region, Oakland, California 94612, USA
| | - Carlos Lorenzo
- Department of Medicine, University of Texas Health Science Center, San Antonio, Texas 77030, USA
| | - Rasika A Mathias
- The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Jill M Norris
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, Colorado 80204, USA
| | - Gina M Peloso
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| | - Wayne H.-H. Sheu
- Division of Endocrine and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 407, Taiwan
- School of Medicine, National Yang-Ming University, Taipei 112, Taiwan
- College of Medicine, National Defense Medical Center, Taipei 114, Taiwan
| | - Daniela Toniolo
- Division of Genetics and Cell Biology, San Raffaele Research Institute, Milano 20132, Italy
| | - Dhananjay Vaidya
- The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Rohit Varma
- Department of Preventive Medicine, Keck School of Medicine of the University of Southern California, Los Angeles 90033, USA
| | - Lynne E Wagenknecht
- Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina 27106, USA
| | - Heiner Boeing
- Department of Epidemiology, German Institute of Human Nutrition Potsdam Rehbrücke, Nuthetal DE-14558, Germany
| | - Erwin P Bottinger
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens 17671, Greece
| | - Panos Deloukas
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 4NS, UK
- Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
- Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah 22254, Saudi Arabia
| | | | - Oscar H Franco
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam 3000 CE, The Netherlands
| | - Paul W Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skåne University Hospital, Malmö SE-205 02, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts 02115, USA
- Department of Public Health & Clinical Medicine, Umeå University, Umeå SE-901 87, Sweden
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Holtasmari 1, Kopavogur IS-201, Iceland
- University of Iceland, Reykjavik IS-101, Iceland
| | - Anders Hamsten
- Atherosclerosis Research Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm SE-171 77, Sweden
| | - Tamara B Harris
- Intramural Research Program, National Institute on Aging, Bethesda, Maryland 21224, USA
| | - Andrew T Hattersley
- Genetics of Diabetes, University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh, Scotland EH4 2XU, UK
| | - Albert Hofman
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam 3000 CE, The Netherlands
| | - Jan-Håkan Jansson
- Department of Public Health & Clinical Medicine, Umeå University, Umeå SE-901 87, Sweden
- Research Unit, Skellefteå SE-931 87, Sweden
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0SL, UK
| | - Lenore J Launer
- Intramural Research Program, National Institute on Aging, Bethesda, Maryland 21224, USA
| | - Daniel Levy
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
- Framingham Heart Study, Framingham, Massachusetts 01702, USA
| | - Ben A Oostra
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam 3000 CE, The Netherlands
| | - Christopher J O'Donnell
- National Heart, Lung, and Blood Institute (NHLBI) Framingham Heart Study, Framingham, Massachusetts 01702, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
- Cardiology Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Stephen O'Rahilly
- University of Cambridge Metabolic Research Laboratories, MRC Metabolic Diseases Unit and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 1TN, UK
| | - Sandosh Padmanabhan
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow G12 8TA, UK
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Ozren Polasek
- Department of Public Health, Faculty of Medicine, University of Split, Split 21000, Croatia
| | - Michael A Province
- Division of Statistical Genomics and Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63108, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia 22908, USA
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02215, USA
- Division of Cardiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Igor Rudan
- Centre for Population Health Sciences, Medical School, University of Edinburgh, Edinburgh, Scotland EH8 9YL, UK
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal DE-14558, Germany
- German Center for Diabetes Research (DZD), Neuherberg DE-85764, Germany
| | - Blair H Smith
- Medical Research Institute, University of Dundee, Dundee DD1 9SY, UK
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam 3000 CE, The Netherlands
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam 3000 CE, The Netherlands
| | - Mark Walker
- Institute of Cellular Medicine, Newcastle University, Newcastle-upon-Tyne NE1 7RU, UK
| | - Hugh Watkins
- Department of Cardiovascular Medicine, The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Tien Y Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
- Department of Ophthalmology, National University of Singapore and National University Health System, Singapore 119228, Singapore
- Office of Clinical Sciences, Duke-NUS Graduate Medical School, National University of Singapore, Singapore 169857, Singapore
| | | | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio FI-70211, Finland
| | - Ingrid B Borecki
- Division of Statistical Genomics and Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63108, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02215, USA
- Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen DK-2200, Denmark
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington 98101, USA
- Department of Medicine, University of Washington, Seattle, Washington 98195, USA
- Department of Epidemiology, University of Washington, Seattle, Washington 98195, USA
- Department of Health Services, University of Washington, Seattle, Washington 98195, USA
- Group Health Research Institute, Group Health Cooperative, Seattle, Washington 98195, USA
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 119228, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Cornelia M van Duijn
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam 3000 CE, The Netherlands
- Center for Medical Systems Biology, Leiden 2300, The Netherlands
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0SL, UK
| | - Dawn M Waterworth
- Genetics, PCPS, GlaxoSmithKline, Philadelphia, Pennsylvania 19104, USA
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas 77225, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - W H Linda Kao
- Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland 21205, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland 21205, USA
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland 21205, USA
| | - Jose C Florez
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
- The Mindich Child Health and Development Institute, The Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi 38677, USA
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK
| | - David S Siscovick
- New York Academy of Medicine, New York, New York 10029, USA
- Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle, Washington 98195, USA
| | - Josée Dupuis
- National Heart, Lung, and Blood Institute (NHLBI) Framingham Heart Study, Framingham, Massachusetts 01702, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California 90502, USA
| | - James B Meigs
- Massachusetts General Hospital, General Medicine Division, Boston, Massachusetts 02114, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Robert A Scott
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0SL, UK
| | - Mark O Goodarzi
- Department of Medicine and Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California 90048, USA
- Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, California 90048, USA
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Relation between birth weight, growth, and subclinical atherosclerosis in adulthood. BIOMED RESEARCH INTERNATIONAL 2015; 2015:926912. [PMID: 25648854 PMCID: PMC4310315 DOI: 10.1155/2015/926912] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2014] [Accepted: 09/24/2014] [Indexed: 01/21/2023]
Abstract
Background and Objectives. Adverse conditions in the prenatal environment and in the first years of life are independently associated with increased risk for cardiovascular disease. This paper aims to study the relation between birthweight, growth in the first year of life, and subclinical atherosclerosis in adults. Methods. 88 adults aged between 20 and 31 were submitted to sociodemographic qualities, anthropometric data, blood pressure measurements, metabolic profile, and evaluation of subclinical atherosclerosis. Results. Birthweight <2,500 grams (g) was negatively correlated with (a) increased waist-to-hip ratio (WHR), according to regression coefficient (RC) equal to −0.323, 95% CI [−0.571, −0.075] P < 0.05; (b) diastolic blood pressure (RC = −4.744, 95% CI [−9.017, −0.470] P < 0.05); (c) low HDL-cholesterol (RC = −0.272, 95% CI [−0.516, −0.029] P < 0.05); (d) frequency of intima-media thickness (IMT) of left carotid >75th percentile (RC = −0.242, 95% CI [−0.476, −0.008] P < 0.05). Birthweight >3,500 g was associated with (a) BMI >25.0 kg/m2, (RC = 0.317, 95% CI [0.782, 0.557] P < 0.05); (b) increased waist circumference (RC = 0.284, 95% CI [0.054, 0.513] P < 0.05); (c) elevated WHR (RC = 0.280, 95% CI [0.054, 0.505] P < 0.05); (d) minimum subcutaneous adipose tissue (SAT) (RC = 4.354, 95% CI [0.821, 7.888] P < 0.05); (e) maximum SAT (RC = 7.095, 95% CI [0.608, 13.583] P < 0.05); (f) right lobe of the liver side (RC = 6.896, 95% CI [1.946, 11.847] P < 0.001); (g) frequency's right lobe of the liver >75th percentile (RC = 0.361, 95% CI [0.169, 0.552] P < 0.001). Weight gain in the first year of life was inversely correlated with (a) mean IMT of left carotid (RC = −0.046, 95% CI [−0.086, −0.006] P < 0.05; (b) frequency IMT of left carotid >75th percentile (RC = −0.253, 95% CI [−0.487, −0.018] P < 0.05); (c) mean IMT (RC = −0.038, 95% CI [0.073, −0.002] P < 0.05); (d) the frequency of the mean IMT >75th percentile (RC = −0.241, 95% CI [−0.442, −0.041] P < 0.05). Conclusions. Adults birthweight <2,500 g and >3,500 g and with insufficient weight gain in the first year of life have showed different metabolic phenotypes, but all of them were related to subclinical atherosclerosis.
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