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Chen J, Xiao WC, Zhao JJ, Heitkamp M, Chen DF, Shan R, Yang ZR, Liu Z. FTO genotype and body mass index reduction in childhood obesity interventions: A systematic review and meta-analysis. Obes Rev 2024; 25:e13715. [PMID: 38320834 DOI: 10.1111/obr.13715] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 10/27/2023] [Accepted: 01/07/2024] [Indexed: 04/18/2024]
Abstract
Numerous guidelines have called for personalized interventions to address childhood obesity. The role of fat mass and obesity-associated gene (FTO) in the risk of childhood obesity has been summarized. However, it remains unclear whether FTO could influence individual responses to obesity interventions, especially in children. To address this, we systematically reviewed 12,255 records across 10 databases/registers and included 13 lifestyle-based obesity interventions (3980 children with overweight/obesity) reporting changes in body mass index (BMI) Z-score, BMI, waist circumference, waist-to-hip ratio, and body fat percentage after interventions. These obesity-related outcomes were first compared between children carrying different FTO genotypes (rs9939609 or its proxy) and then synthesized by random-effect meta-analysis models. The results from single-group interventions showed no evidence of associations between FTO risk allele and changes in obesity-related outcomes after interventions (e.g., BMI Z-score: -0.01; 95% CI: -0.04, 0.01). The results from controlled trials showed that associations between the FTO risk allele and changes in obesity-related outcomes did not differ by intervention/control group. To conclude, the FTO risk allele might play a minor role in the response to obesity interventions among children. Future studies might pay more attention to the accumulation effect of multiple genes in the intervention process among children.
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Affiliation(s)
- Jing Chen
- Department of Maternal and Child Health, School of Public Health, Peking University, Beijing, China
| | - Wu-Cai Xiao
- Department of Maternal and Child Health, School of Public Health, Peking University, Beijing, China
| | - Jia-Jun Zhao
- Department of Nutrition, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Melanie Heitkamp
- Department of Prevention and Sports Medicine, University Hospital "Klinikum rechts der Isar," Technical University of Munich, Munich, Germany
| | - Da-Fang Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Rui Shan
- Department of Maternal and Child Health, School of Public Health, Peking University, Beijing, China
| | - Zhi-Rui Yang
- Department of Hematology, The Fifth Medical Center, The Chinese PLA General Hospital, Beijing, China
| | - Zheng Liu
- Department of Maternal and Child Health, School of Public Health, Peking University, Beijing, China
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2
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Sokary S, Almaghrbi H, Bawadi H. The Interaction Between Body Mass Index Genetic Risk Score and Dietary Intake on Weight Status: A Systematic Review. Diabetes Metab Syndr Obes 2024; 17:925-941. [PMID: 38435632 PMCID: PMC10908334 DOI: 10.2147/dmso.s452660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 02/15/2024] [Indexed: 03/05/2024] Open
Abstract
Background The escalating global obesity epidemic and the emergence of personalized medicine strategies point to the pressing need to investigate the interplay between genetic risk scores (GRSs), dietary intake, and their combined impact on weight status. This systematic review synthesizes evidence from diverse studies to elucidate how dietary patterns and individual foods interact with genetic predisposition to obesity. Methods Literature searches were conducted in the PubMed, Embase, Science Direct, and Scopus databases until August 2023, following PRISMA guidelines. Out of 575 articles, 15 articles examining the interaction between genetic risk score for body mass index and dietary intake on weight outcomes met the inclusion criteria. All included studies were cross-sectional in design and were assessed for quality using the Newcastle‒Ottawa Scale. Results Unhealthy dietary intake exacerbated the genetic predisposition to obesity, evident in studies assessing Western diet, sulfur microbial diet, and individual macronutrients, including saturated fatty acids, sugar-sweetened beverages and fried foods. Conversely, adhering to healthier dietary intake mitigated the genetic predisposition to obesity, as observed in studies involving Alternative Healthy Eating Index, Alternative Mediterranean Diet, Dietary Approach to Stop Hypertension scores, healthy plant-based diets, and specific foods such as fruits, vegetables, and n-3 polyunsaturated fatty acids. Conclusion This is the first systematic review to explore the interaction between genetics and dietary intake in shaping obesity outcomes. The findings have implications for tailored interventions; however, more controlled clinical trials with robust designs are needed to be able to recommend personalized nutrition based on nutrition for obesity prevention and management.
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Affiliation(s)
- Sara Sokary
- Department of Human Nutrition, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Heba Almaghrbi
- Department of Biomedical Science, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Hiba Bawadi
- Department of Human Nutrition, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
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Szczerbinski L, Florez JC. Precision medicine of obesity as an integral part of type 2 diabetes management - past, present, and future. Lancet Diabetes Endocrinol 2023; 11:861-878. [PMID: 37804854 DOI: 10.1016/s2213-8587(23)00232-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/29/2023] [Accepted: 08/01/2023] [Indexed: 10/09/2023]
Abstract
Obesity is a complex and heterogeneous condition that leads to various metabolic complications, including type 2 diabetes. Unfortunately, for some, treatment options to date for obesity are insufficient, with many people not reaching sustained weight loss or having improvements in metabolic health. In this Review, we discuss advances in the genetics of obesity from the past decade-with emphasis on developments from the past 5 years-with a focus on metabolic consequences, and their potential implications for precision management of the disease. We also provide an overview of the potential role of genetics in guiding weight loss strategies. Finally, we propose a vision for the future of precision obesity management that includes developing an obesity-centred multidisease management algorithm that targets both obesity and its comorbidities. However, further collaborative efforts and research are necessary to fully realise its potential and improve metabolic health outcomes.
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Affiliation(s)
- Lukasz Szczerbinski
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland; Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Jose C Florez
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
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Freuer D, Linseisen J, O’Mara TA, Leitzmann M, Baurecht H, Baumeister SE, Meisinger C. Body Fat Distribution and Risk of Breast, Endometrial, and Ovarian Cancer: A Two-Sample Mendelian Randomization Study. Cancers (Basel) 2021; 13:cancers13205053. [PMID: 34680200 PMCID: PMC8534230 DOI: 10.3390/cancers13205053] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 09/27/2021] [Accepted: 10/06/2021] [Indexed: 12/18/2022] Open
Abstract
Simple Summary The causal impact of body fat distribution on female-specific cancers is largely unknown. For the first time we used a two-sample multivariable Mendelian randomization (MR) approach to elucidate the role and causal relations of body composition assessed by segmental bioelectrical impedance analysis on the risks of breast, endometrial and ovarian cancers and their subtypes. We found that abdominal fat content increases the risk for ovarian cancer and its endometrioid and clear cell subtypes independent of overall fat content. General adiposity has a protective effect on risk of breast cancer and its ER- and ER+ subtypes but increases the risk for endometrial cancer, ovarian cancer, and the endometrioid ovarian cancer subtype. This study extends the literature by addressing specifically the causal role of visceral fat on female-specific cancers. Abstract Background: Mounting evidence shows that adiposity increases female-specific cancer risk, but the role of body fat distribution is less clear. We used a two-sample Mendelian randomization (MR) approach to elucidate causal relations of body fat distribution to the risks of breast, endometrial and ovarian cancers and their subtypes. Methods: Body composition was assessed using segmental bioelectrical impedance analysis, yielding trunk, arm, and leg fat ratios (TFR, AFR, LFR) and BMI including 195,043 and 434,794 European women, respectively. The sample sizes for the outcomes ranged between 58,396 and 228,951. Causal effects were estimated per one standard deviation increment in the respective exposure within the radial regression framework. Robust sensitivity analyses were performed to verify MR assumptions. In a multivariable MR setting, the proportion of risk attributable to overall and abdominal fat content was assessed. Results: TFR, which represents abdominal fat content, was associated with ovarian cancer and its clear cell and endometrioid histotypes independent of overall fat content. BMI was inversely associated with breast cancer and its ER− and ER+ subtypes, but positively with endometrial cancer and ovarian cancer, including its endometrioid histotype. These estimates were confirmed using AFR as proxy for overall body fat. Conclusions: Visceral adiposity seems to be a driver of elevated ovarian cancer risk, particularly of the endometrioid and clear cell ovarian cancer histotypes. General adiposity decreases the risk of breast cancer but increases the risk of endometrial and ovarian cancer.
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Affiliation(s)
- Dennis Freuer
- Chair of Epidemiology, University of Augsburg, University Hospital Augsburg, 86156 Augsburg, Germany; (J.L.); (C.M.)
- Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-Universität München, 81377 Munich, Germany
- Correspondence: ; Tel.: +49-821-598-6474
| | - Jakob Linseisen
- Chair of Epidemiology, University of Augsburg, University Hospital Augsburg, 86156 Augsburg, Germany; (J.L.); (C.M.)
- Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-Universität München, 81377 Munich, Germany
- German Research Center for Environmental Health, Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Tracy A. O’Mara
- Genetics and Computational Biology Department, QIMR Berghofer Medical Research Institute, Brisbane 4006, Australia;
| | - Michael Leitzmann
- Department of Epidemiology and Preventive Medicine, University of Regensburg, 93053 Regensburg, Germany; (M.L.); (H.B.)
| | - Hansjörg Baurecht
- Department of Epidemiology and Preventive Medicine, University of Regensburg, 93053 Regensburg, Germany; (M.L.); (H.B.)
| | | | - Christa Meisinger
- Chair of Epidemiology, University of Augsburg, University Hospital Augsburg, 86156 Augsburg, Germany; (J.L.); (C.M.)
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Hiraike Y, Yang CT, Liu WJ, Yamada T, Lee CL. FTO Obesity Variant-Exercise Interaction on Changes in Body Weight and BMI: The Taiwan Biobank Study. J Clin Endocrinol Metab 2021; 106:e3673-e3681. [PMID: 33929497 DOI: 10.1210/clinem/dgab295] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Indexed: 02/05/2023]
Abstract
CONTEXT Gene-exercise interaction on cross-sectional body mass index (BMI) has been extensively studied and is well established. However, gene-exercise interaction on changes in body weight/BMI remains controversial. OBJECTIVE To examine the interaction between the FTO obesity variant and regular exercise on changes in body weight/BMI. PARTICIPANTS Taiwan Biobank participants aged 30-70 years (N = 20 906) were examined at both baseline and follow-up visit (mean follow-up duration: 3.7 years). MAIN OUTCOME MEASURES The interaction between the FTO obesity variant rs1421085 and regular exercise habit (no exercise, ≤20 metabolic equivalent of tasks (METs)/week exercise, >20 METs/week exercise) on changes in body weight/BMI. RESULTS Individuals with the risk allele of rs1421085 gained more weight and increased BMI than those without the risk allele if they did not exercise. In contrast, individuals with the risk allele gained less weight and BMI if they exercised regularly, indicating an interaction between rs1421085 and regular exercise habit (P = .030 for Δbody weight and P = .034 for ΔBMI). The effect of exercise on maintaining body weight was larger in those with the risk allele of rs1421085. When we focused on individuals without regular exercise at baseline, individuals with the risk allele again tended to lose more weight than those with a nonrisk allele if they had acquired an exercise habit by the follow-up visit. CONCLUSION The beneficial effect of exercise is greater in individuals genetically prone to obesity due to the interaction between the FTO obesity variant rs1421085 and regular exercise on changes in body weight and BMI.
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Affiliation(s)
- Yuta Hiraike
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Department of Cell Biology, Harvard Medical School , Boston, MA 02115, USA
| | - Chao-Tung Yang
- Department of Computer Science, Tunghai University, Taichung, Taiwan
- Research Center for Smart Sustainable Circular Economy, Tunghai University, Taiwan
- Research Center for Nanotechnology, Tunghai University, Taiwan
| | - Wei-Ju Liu
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Tomohide Yamada
- Institute of Population Health, King's College London, London, UK
| | - Chia-Lin Lee
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
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Brunner EJ, Maruyama K, Shipley M, Cable N, Iso H, Hiyoshi A, Stallone D, Kumari M, Tabak A, Singh-Manoux A, Wilson J, Langenberg C, Wareham N, Boniface D, Hingorani A, Kivimäki M, Llewellyn C. Appetite disinhibition rather than hunger explains genetic effects on adult BMI trajectory. Int J Obes (Lond) 2021; 45:758-765. [PMID: 33446837 PMCID: PMC8005371 DOI: 10.1038/s41366-020-00735-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 10/19/2020] [Accepted: 12/16/2020] [Indexed: 11/17/2022]
Abstract
Background/objectives The mediating role of eating behaviors in genetic susceptibility to weight gain during mid-adult life is not fully understood. This longitudinal study aims to help us understand contributions of genetic susceptibility and appetite to weight gain. Subjects/methods We followed the body-mass index (BMI) trajectories of 2464 adults from 45 to 65 years of age by measuring weight and height on four occasions at 5-year intervals. Genetic risk of obesity (gene risk score: GRS) was ascertained, comprising 92 BMI-associated single-nucleotide polymorphisms and split at a median (=high and low risk). At the baseline, the Eating Inventory was used to assess appetite-related traits of ‘disinhibition’, indicative of opportunistic eating or overeating and ‘hunger’ which is susceptibility to/ability to cope with the sensation of hunger. Roles of the GRS and two appetite-related scores for BMI trajectories were examined using a mixed model adjusted for the cohort effect and sex. Results Disinhibition was associated with higher BMI (β = 2.96; 95% CI: 2.66–3.25 kg/m2), and accounted for 34% of the genetically-linked BMI difference at age 45. Hunger was also associated with higher BMI (β = 1.20; 0.82–1.59 kg/m2) during mid-life and slightly steeper weight gain, but did not attenuate the effect of disinhibition. Conclusions Appetite disinhibition is most likely to be a defining characteristic of genetic susceptibility to obesity. High levels of appetite disinhibition, rather than hunger, may underlie genetic vulnerability to obesogenic environments in two-thirds of the population of European ancestry.
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Affiliation(s)
- Eric J Brunner
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Koutatsu Maruyama
- Institute of Epidemiology and Health Care, University College London, London, UK. .,Public Health, Department of Social Medicine, Osaka University Graduate School of Medicine, Suita, Japan. .,Laboratory of Community Health and Nutrition, Department of Bioscience, Graduate School of Agriculture, Ehime University, Matsuyama, Japan.
| | - Martin Shipley
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Noriko Cable
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Hiroyasu Iso
- Public Health, Department of Social Medicine, Osaka University Graduate School of Medicine, Suita, Japan
| | - Ayako Hiyoshi
- Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden
| | | | - Meena Kumari
- Institute of Epidemiology and Health Care, University College London, London, UK.,Institute for Social and Economic Research, University of Essex, Colchester, UK
| | - Adam Tabak
- Institute of Epidemiology and Health Care, University College London, London, UK.,Department of Internal Medicine and Ocology, Faculty of Medicine, Semmelweis University, Budapest, Hungary.,Department of Public Health, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Archana Singh-Manoux
- Institute of Epidemiology and Health Care, University College London, London, UK.,Epidemiology of Ageing and Neurodegenerative diseases, Université de Paris, Paris, France
| | - John Wilson
- North Devon Medical Education Centre, North Devon District Hospital, Barnstaple, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Nick Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - David Boniface
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Aroon Hingorani
- Institute of Cardiovascular Science, University College London, London, UK
| | - Mika Kivimäki
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Clare Llewellyn
- Institute of Epidemiology and Health Care, University College London, London, UK
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Heitkamp M, Siegrist M, Molnos S, Brandmaier S, Wahl S, Langhof H, Grallert H, Halle M. Obesity Genes and Weight Loss During Lifestyle Intervention in Children With Obesity. JAMA Pediatr 2021; 175:e205142. [PMID: 33315090 PMCID: PMC7737153 DOI: 10.1001/jamapediatrics.2020.5142] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
IMPORTANCE Genome-wide association studies have identified genetic loci influencing obesity risk in children. However, the importance of these loci in the associations with weight reduction through lifestyle interventions has not been investigated in large intervention trials. OBJECTIVE To evaluate the associations between various obesity susceptibility loci and changes in body weight in children during an in-hospital, lifestyle intervention program. DESIGN, SETTING, AND PARTICIPANTS Long-term Effects of Lifestyle Intervention in Obesity and Genetic Influence in Children (LOGIC), an interventional prospective cohort study, enrolled 1429 children with overweight or obesity to participate in an in-hospital lifestyle intervention program. Genotyping of 56 validated obesity single-nucleotide variants (SNVs) was performed, and the associations between the SNVs and body weight reduction during the intervention were evaluated using linear mixed-effects models for each SNV. The LOGIC study was conducted from January 6, 2006, to October 19, 2013; data analysis was performed from July 15, 2015, to November 6, 2016. EXPOSURES A 4- to 6-week standardized in-hospital lifestyle intervention program (daily physical activity, calorie-restricted diet, and behavioral therapy). MAIN OUTCOMES AND MEASURES The association between 56 obesity-relevant SNVs and changes in body weight and body mass index. RESULTS Of 1429 individuals enrolled in the LOGIC Study, 1198 individuals (mean [SD] age, 14.0 [2.2] years; 670 [56%] girls) were genotyped. A mean (SD) decrease was noted in body weight of -8.7 (3.6) kg (95% CI, -15.7 to -1.8 kg), and body mass index (calculated as weight in kilograms divided by height in meters squared) decreased by -3.3 (1.1) (95% CI, -5.4 to -1.1) (both P < .05). Five of 56 obesity SNVs were statistically significantly associated with a reduction of body weight or body mass index (all P < 8.93 × 10-4 corresponding to Bonferroni correction for 56 tests). Compared with homozygous participants without the risk allele, homozygous carriers of the rs7164727 (LOC100287559: 0.42 kg; 95% CI, 0.31-0.53 kg, P = 4.00 × 10-4) and rs12940622 (RPTOR: 0.35 kg; 95% CI, 0.18-0.52 kg; P = 1.86 × 10-5) risk alleles had a lower reduction of body weight, whereas carriers of the rs13201877 (IFNGR1: 0.65 kg; 95% CI, 0.51-0.79 kg; P = 2.39 × 10-5), rs10733682 (LMX1B: 0.45 kg; 95% CI, 0.27-0.63 kg; P = 6.37 × 10-4), and rs2836754 (ETS2: 0.56 kg; 95% CI, 0.38-0.74 kg; P = 1.51 × 10-4) risk alleles were associated with a greater reduction of body weight after adjustment for age and sex. CONCLUSIONS AND RELEVANCE Genes appear to play a minor role in weight reduction by lifestyle in children with overweight or obesity. The findings suggest that environmental, social, and behavioral factors are more important to consider in obesity treatment strategies.
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Affiliation(s)
- Melanie Heitkamp
- Department of Prevention and Sports Medicine, Centre for Sports Cardiology, University Hospital “Klinikum rechts der Isar,” Technical University of Munich, Munich, Germany
| | - Monika Siegrist
- Department of Prevention and Sports Medicine, Centre for Sports Cardiology, University Hospital “Klinikum rechts der Isar,” Technical University of Munich, Munich, Germany
| | - Sophie Molnos
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany,German Center for Diabetes Research, Helmholtz Zentrum München, Neuherberg, Germany
| | - Stefan Brandmaier
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany,German Center for Diabetes Research, Helmholtz Zentrum München, Neuherberg, Germany
| | - Simone Wahl
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany,German Center for Diabetes Research, Helmholtz Zentrum München, Neuherberg, Germany,Now with Roche Diagnostics, Bavaria, Germany
| | - Helmut Langhof
- Rehabilitation Clinic “Klinik Schönsicht,” Berchtesgaden, Germany
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany,German Center for Diabetes Research, Helmholtz Zentrum München, Neuherberg, Germany
| | - Martin Halle
- Department of Prevention and Sports Medicine, Centre for Sports Cardiology, University Hospital “Klinikum rechts der Isar,” Technical University of Munich, Munich, Germany,German Center for Cardiovascular Research, partner site Munich Heart Alliance, Munich, Germany
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Lega IC, Lipscombe LL. Review: Diabetes, Obesity, and Cancer-Pathophysiology and Clinical Implications. Endocr Rev 2020; 41:5625127. [PMID: 31722374 DOI: 10.1210/endrev/bnz014] [Citation(s) in RCA: 163] [Impact Index Per Article: 32.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 11/11/2019] [Indexed: 02/07/2023]
Abstract
Obesity and diabetes have both been associated with an increased risk of cancer. In the face of increasing obesity and diabetes rates worldwide, this is a worrying trend for cancer rates. Factors such as hyperinsulinemia, chronic inflammation, antihyperglycemic medications, and shared risk factors have all been identified as potential mechanisms underlying the relationship. The most common obesity- and diabetes-related cancers are endometrial, colorectal, and postmenopausal breast cancers. In this review, we summarize the existing evidence that describes the complex relationship between obesity, diabetes, and cancer, focusing on epidemiological and pathophysiological evidence, and also reviewing the role of antihyperglycemic agents, novel research approaches such as Mendelian Randomization, and the methodological limitations of existing research. In addition, we also describe the bidirectional relationship between diabetes and cancer with a review of the evidence summarizing the risk of diabetes following cancer treatment. We conclude this review by providing clinical implications that are relevant for caring for patients with obesity, diabetes, and cancer and provide recommendations for improving both clinical care and research for patients with these conditions.
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Affiliation(s)
- Iliana C Lega
- Department of Medicine, Women's College Hospital, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada.,IC/ES, Toronto, ON, Canada
| | - Lorraine L Lipscombe
- Department of Medicine, Women's College Hospital, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada.,IC/ES, Toronto, ON, Canada.,Institute for Health Policy, Management and Evaluation, University of Toronto; Toronto, ON, Canada
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Ramos-Lopez O, Cuervo M, Goni L, Milagro FI, Riezu-Boj JI, Martinez JA. Modeling of an integrative prototype based on genetic, phenotypic, and environmental information for personalized prescription of energy-restricted diets in overweight/obese subjects. Am J Clin Nutr 2020; 111:459-470. [PMID: 31751449 DOI: 10.1093/ajcn/nqz286] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 10/25/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Interindividual variability in weight loss and metabolic responses depends upon interactions between genetic, phenotypic, and environmental factors. OBJECTIVE We aimed to model an integrative (nutri) prototype based on genetic, phenotypic, and environmental information for the personalized prescription of energy-restricted diets with different macronutrient distribution. METHODS A 4-mo nutritional intervention was conducted in 305 overweight/obese volunteers involving 2 energy-restricted diets (30% restriction) with different macronutrient distribution: a moderately high-protein (MHP) diet (30% proteins, 30% lipids, and 40% carbohydrates) and a low-fat (LF) diet (22% lipids, 18% proteins, and 60% carbohydrates). A total of 201 subjects with good dietary adherence were genotyped for 95 single nucleotide polymorphisms (SNPs) related to energy homeostasis. Genotyping was performed by targeted next-generation sequencing. Two weighted genetic risk scores for the MHP (wGRS1) and LF (wGRS2) diets were computed using statistically relevant SNPs. Multiple linear regression models were performed to estimate percentage BMI decrease depending on the dietary macronutrient composition. RESULTS After energy restriction, both the MHP and LF diets induced similar significant decreases in adiposity, body composition, and blood pressure, and improved the lipid profile. Furthermore, statistically relevant differences in anthropometric and biochemical markers depending on sex and age were found. BMI decrease in the MHP diet was best predicted at ∼28% (optimism-corrected adjusted R2 = 0.279) by wGRS1 and age, whereas wGRS2 and baseline energy intake explained ∼29% (optimism-corrected adjusted R2 = 0.287) of BMI decrease variability in the LF diet. The incorporation of these predictive models into a decision algorithm allowed the personalized prescription of the MHP and LF diets. CONCLUSIONS Different genetic, phenotypic, and exogenous factors predict BMI decreases depending on the administration of a hypocaloric MHP diet or an LF diet. This holistic approach may help to personalize dietary advice for the management of excessive body weight using precision nutrition variables.This trial was registered at clinicaltrials.gov as NCT02737267.
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Affiliation(s)
- Omar Ramos-Lopez
- Department of Nutrition, Food Science, and Physiology, and Center for Nutrition Research, University of Navarra, Pamplona, Spain.,Faculty of Medicine and Psychology, Autonomous University of Baja California, Tijuana, Mexico
| | - Marta Cuervo
- Department of Nutrition, Food Science, and Physiology, and Center for Nutrition Research, University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research, Pamplona, Spain.,Biomedical Research Center Network in Physiopathology of Obesity and Nutrition (CIBERobn), Carlos III Health Institute, Madrid, Spain
| | - Leticia Goni
- Department of Nutrition, Food Science, and Physiology, and Center for Nutrition Research, University of Navarra, Pamplona, Spain
| | - Fermin I Milagro
- Department of Nutrition, Food Science, and Physiology, and Center for Nutrition Research, University of Navarra, Pamplona, Spain.,Biomedical Research Center Network in Physiopathology of Obesity and Nutrition (CIBERobn), Carlos III Health Institute, Madrid, Spain
| | - Jose I Riezu-Boj
- Department of Nutrition, Food Science, and Physiology, and Center for Nutrition Research, University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research, Pamplona, Spain
| | - J Alfredo Martinez
- Department of Nutrition, Food Science, and Physiology, and Center for Nutrition Research, University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research, Pamplona, Spain.,Biomedical Research Center Network in Physiopathology of Obesity and Nutrition (CIBERobn), Carlos III Health Institute, Madrid, Spain
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10
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Dybala MP, Brady MJ, Hara M. Disparity in Adiposity among Adults with Normal Body Mass Index and Waist-to-Height Ratio. iScience 2019; 21:612-623. [PMID: 31731199 PMCID: PMC6889773 DOI: 10.1016/j.isci.2019.10.062] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 06/10/2019] [Accepted: 10/28/2019] [Indexed: 11/28/2022] Open
Abstract
Body mass index (BMI) is commonly used to define obesity. However, concerns about its accuracy in predicting adiposity have been raised. The feasibility of using BMI as well as waist-height ratio (WHtR) in assessing adiposity was examined in relation to a more direct measurement of percent body fat (%BF). We analyzed the relation between dual-energy X-ray absorptiometry (DXA)-measured fat mass and BMI and WHtR using the US 1999-2004 National Health and Nutrition Examination Survey (NHANES) data. A considerable proportion of subjects in the healthy BMI range 20-25 were found to have excess adiposity, including 33.1% of males and 51.9% of females. The use of WHtR also supports the notion of normal-weight central obesity (NWCO), which increases with age. These findings have important implications not only for clinical practice but also for many comparative studies where control subjects are usually selected based on age, sex, and BMI.
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Affiliation(s)
- Michael P Dybala
- Department of Medicine, The University of Chicago, 5841 South Maryland Avenue, MC1027, Chicago, IL 60637, USA
| | - Matthew J Brady
- Department of Medicine, The University of Chicago, 5841 South Maryland Avenue, MC1027, Chicago, IL 60637, USA
| | - Manami Hara
- Department of Medicine, The University of Chicago, 5841 South Maryland Avenue, MC1027, Chicago, IL 60637, USA.
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11
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Ramos-Lopez O, Riezu-Boj JI, Milagro FI, Cuervo M, Goni L, Martinez JA. Models Integrating Genetic and Lifestyle Interactions on Two Adiposity Phenotypes for Personalized Prescription of Energy-Restricted Diets With Different Macronutrient Distribution. Front Genet 2019; 10:686. [PMID: 31417605 PMCID: PMC6683656 DOI: 10.3389/fgene.2019.00686] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 07/01/2019] [Indexed: 12/13/2022] Open
Abstract
Aim: To analyze the influence of genetics and interactions with environmental factors on adiposity outcomes [waist circumference reduction (WCR) and total body fat loss (TFATL)] in response to energy-restricted diets in subjects with excessive body weight. Materials and Methods: Two hypocaloric diets (30% energy restriction) were prescribed to overweight/obese subjects during 16 weeks, which had different targeted macronutrient distribution: a low-fat (LF) diet (22% energy from lipids) and a moderately high-protein (MHP) diet (30% energy from proteins). At the end of the trial, a total of 201 participants (LF diet = 105; MHP diet = 96) who presented good/regular dietary adherence were genotyped for 95 single nucleotide polymorphisms (SNPs) previously associated with weight loss through next-generation sequencing from oral samples. Four unweighted (uGRS) and four weighted (wGRS) genetic risk scores were computed using statistically relevant SNPs for each outcome by diet. Predictions of WCR and TFATL by diet were modeled through recognized multiple linear regression models including genetic (single SNPs, uGRS, and wGRS), phenotypic (age, sex, and WC, or TFAT at baseline), and environment variables (physical activity level and energy intake at baselines) as well as eventual interactions between genes and environmental factors. Results: Overall, 26 different SNPs were associated with differential adiposity outcomes, 9 with WCR and 17 with TFATL, most of which were specific for each dietary intervention. In addition to conventional predictors (age, sex, lifestyle, and adiposity status at baseline), the calculated uGRS/wGRS and interactions with environmental factors were major contributors of adiposity responses. Thus, variances in TFATL-LF diet, TFATL-MHP diet, WCR-LF diet, and WCR-MHP diet were predicted by approximately 38% (optimism-corrected adj. R2 = 0.3792), 32% (optimism-corrected adj. R2 = 0.3208), 22% (optimism-corrected adj. R2 = 0.2208), and 21% (optimism-corrected adj. R2 = 0.2081), respectively. Conclusions: Different genetic variants and interactions with environmental factors modulate the differential individual responses to MHP and LF dietary interventions. These insights and models may help to optimize personalized nutritional strategies for modeling the prevention and management of excessive adiposity through precision nutrition approaches taking into account not only genetic information but also the lifestyle/clinical factors that interplay in addition to age and sex.
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Affiliation(s)
- Omar Ramos-Lopez
- Department of Nutrition, Food Science and Physiology, and Center for Nutrition Research, University of Navarra, Pamplona, Spain.,Medical and Psychology School, Autonomous University of Baja California, Tijuana, Baja California, Mexico
| | - Jose I Riezu-Boj
- Department of Nutrition, Food Science and Physiology, and Center for Nutrition Research, University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
| | - Fermin I Milagro
- Department of Nutrition, Food Science and Physiology, and Center for Nutrition Research, University of Navarra, Pamplona, Spain.,CIBERobn, Fisiopatología de la Obesidad y la Nutrición; Carlos III Health Institute, Madrid, Spain
| | - Marta Cuervo
- Department of Nutrition, Food Science and Physiology, and Center for Nutrition Research, University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdiSNA), Pamplona, Spain.,CIBERobn, Fisiopatología de la Obesidad y la Nutrición; Carlos III Health Institute, Madrid, Spain
| | - Leticia Goni
- Department of Nutrition, Food Science and Physiology, and Center for Nutrition Research, University of Navarra, Pamplona, Spain
| | - J Alfredo Martinez
- Department of Nutrition, Food Science and Physiology, and Center for Nutrition Research, University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdiSNA), Pamplona, Spain.,CIBERobn, Fisiopatología de la Obesidad y la Nutrición; Carlos III Health Institute, Madrid, Spain.,Madrid Institute of Advanced Studies (IMDEA Food), Madrid, Spain
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12
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Liu CT, Merino J, Rybin D, DiCorpo D, Benke KS, Bragg-Gresham JL, Canouil M, Corre T, Grallert H, Isaacs A, Kutalik Z, Lahti J, Marullo L, Marzi C, Rasmussen-Torvik LJ, Rocheleau G, Rueedi R, Scapoli C, Verweij N, Vogelzangs N, Willems SM, Yengo L, Bakker SJL, Beilby J, Hui J, Kajantie E, Müller-Nurasyid M, Rathmann W, Balkau B, Bergmann S, Eriksson JG, Florez JC, Froguel P, Harris T, Hung J, James AL, Kavousi M, Miljkovic I, Musk AW, Palmer LJ, Peters A, Roussel R, van der Harst P, van Duijn CM, Vollenweider P, Barroso I, Prokopenko I, Dupuis J, Meigs JB, Bouatia-Naji N. Genome-wide Association Study of Change in Fasting Glucose over time in 13,807 non-diabetic European Ancestry Individuals. Sci Rep 2019; 9:9439. [PMID: 31263163 PMCID: PMC6602949 DOI: 10.1038/s41598-019-45823-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 05/29/2019] [Indexed: 01/13/2023] Open
Abstract
Type 2 diabetes (T2D) affects the health of millions of people worldwide. The identification of genetic determinants associated with changes in glycemia over time might illuminate biological features that precede the development of T2D. Here we conducted a genome-wide association study of longitudinal fasting glucose changes in up to 13,807 non-diabetic individuals of European descent from nine cohorts. Fasting glucose change over time was defined as the slope of the line defined by multiple fasting glucose measurements obtained over up to 14 years of observation. We tested for associations of genetic variants with inverse-normal transformed fasting glucose change over time adjusting for age at baseline, sex, and principal components of genetic variation. We found no genome-wide significant association (P < 5 × 10-8) with fasting glucose change over time. Seven loci previously associated with T2D, fasting glucose or HbA1c were nominally (P < 0.05) associated with fasting glucose change over time. Limited power influences unambiguous interpretation, but these data suggest that genetic effects on fasting glucose change over time are likely to be small. A public version of the data provides a genomic resource to combine with future studies to evaluate shared genetic links with T2D and other metabolic risk traits.
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Affiliation(s)
- Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, MA, 02118, USA.
| | - Jordi Merino
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Denis Rybin
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, MA, 02118, USA
| | - Daniel DiCorpo
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, MA, 02118, USA
| | - Kelly S Benke
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Jennifer L Bragg-Gresham
- Kidney Epidemiology and Cost Center, Department of Internal Medicine - Nephrology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Mickaël Canouil
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, 59000, Lille, France
| | - Tanguy Corre
- Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Aaron Isaacs
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- CARIM School for Cardiovascular Diseases, Maastricht Centre for Systems Biology (MaCSBio) and Department of Biochemistry, Maastricht University, Maastricht, The Netherlands
| | - Zoltan Kutalik
- Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Jari Lahti
- Helsinki Collegium for Advanced Studies, University of Helsinki, Helsinki, Finland
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Letizia Marullo
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| | - Carola Marzi
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Ghislain Rocheleau
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, 59000, Lille, France
- Preventive and Genomic Cardiology, McGill University Health Centre and Research Institute, Montreal, Quebec, Canada
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Chiara Scapoli
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| | - Niek Verweij
- University Medical Center Groningen, Department of Cardiology, University of Groningen, Groningen, The Netherlands
| | - Nicole Vogelzangs
- Maastricht University, Department of Epidemiology, Cardiovascular Research Institute Maastricht (CARIM) & Maastricht Centre for Systems Biology (MaCSBio), Maastricht, The Netherlands
| | - Sara M Willems
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Loïc Yengo
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, 59000, Lille, France
| | - Stephan J L Bakker
- University of Groningen, University Medical Center Groningen, Department of Internal Medicine, Groningen, The Netherlands
| | - John Beilby
- PathWest Laboratory Medicine of Western Australia, Nedlands WA, 6009, Australia
- School of Pathology and Laboratory Medicine, The University of Western Australia, Nedlands WA, 6009, Australia
- Busselton Population Medical Research Foundation, Sir Charles Gairdner Hospital, Nedlands WA, Australia
| | - Jennie Hui
- PathWest Laboratory Medicine of Western Australia, Nedlands WA, 6009, Australia
- School of Pathology and Laboratory Medicine, The University of Western Australia, Nedlands WA, 6009, Australia
- Busselton Population Medical Research Foundation, Sir Charles Gairdner Hospital, Nedlands WA, Australia
- School of Population and Global Health, The University of Western Australia, Nedlands WA, 6009, Australia
| | - Eero Kajantie
- National Institute for Health and Welfare, Helsinki, Finland
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, Munich, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Wolfgang Rathmann
- Institute for Biometrics and Epidemiology, German Diabetes Centre, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
| | - Beverley Balkau
- CESP Centre for Research in Epidemiology and Population Health, Villejuif, France
- Univ. Paris-Saclay, Univ. Paris Sud, UVSQ, UMRS 1018, F-94807, Villejuif, France
- INSERM U1018, CESP, Renal and Cardiovascular Epidemiology, UVSQ-UPS, Villejuif, France
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Johan G Eriksson
- National Institute for Health and Welfare, Helsinki, Finland
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
- Folkhälsan Research Centre, Helsinki, Finland
| | - Jose C Florez
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Philippe Froguel
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, 59000, Lille, France
- Department of Genomics of Common Disease, Imperial College London, London, United Kingdom
| | - Tamara Harris
- National Institute on Aging, Laboratory of Epidemiology and Population Sciences in Intramural Research Program, Baltimore, MD, USA
| | - Joseph Hung
- Busselton Population Medical Research Foundation, Sir Charles Gairdner Hospital, Nedlands WA, Australia
- School of Medicine and Pharmacology, The University of Western Australia, Nedlands WA, 6009, Australia
| | - Alan L James
- Busselton Population Medical Research Foundation, Sir Charles Gairdner Hospital, Nedlands WA, Australia
- School of Medicine and Pharmacology, The University of Western Australia, Nedlands WA, 6009, Australia
- Department of Pulmonary Physiology, Sir Charles Gairdner Hospital, Nedlands WA, 6009, Australia
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Iva Miljkovic
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Arthur W Musk
- Busselton Population Medical Research Foundation, Sir Charles Gairdner Hospital, Nedlands WA, Australia
- School of Population and Global Health, The University of Western Australia, Nedlands WA, 6009, Australia
- School of Medicine and Pharmacology, The University of Western Australia, Nedlands WA, 6009, Australia
| | - Lyle J Palmer
- School of Public Health, University of Adelaide, Adelaide, Australia
| | - Annette Peters
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
| | - Ronan Roussel
- INSERM U1138 (équipe 2: Pathophysiology and Therapeutics of Vascular and Renal Diseases Related to Diabetes, Centre de Recherches des Cordeliers), Paris, France
- Univ. Paris 7 Denis Diderot, Sorbonne Paris Cité, France
- AP-HP, DHU FIRE, Department of Endocrinology, Diabetology, Nutrition, and Metabolic Diseases, Bichat Claude Bernard Hospital, Paris, France
| | - Pim van der Harst
- University Medical Center Groningen, Department of Cardiology, University of Groningen, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
- Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands
| | - Cornelia M van Duijn
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Inês Barroso
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Inga Prokopenko
- Department of Medicine, Imperial College London, London, United Kingdom
- Wellcome Centre for Human genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, MA, 02118, USA
- The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA
| | - James B Meigs
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Nabila Bouatia-Naji
- INSERM, UMR970 Paris Cardiovascular Research Center (PARCC), Paris, F-75015, France.
- Paris-Descartes University, Sorbonne Paris Cité, Paris, 75006, France.
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13
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Arner P, Andersson DP, Bäckdahl J, Dahlman I, Rydén M. Weight Gain and Impaired Glucose Metabolism in Women Are Predicted by Inefficient Subcutaneous Fat Cell Lipolysis. Cell Metab 2018; 28:45-54.e3. [PMID: 29861390 DOI: 10.1016/j.cmet.2018.05.004] [Citation(s) in RCA: 82] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 04/13/2018] [Accepted: 05/03/2018] [Indexed: 12/14/2022]
Abstract
Adipocyte mobilization of fatty acids (lipolysis) is instrumental for energy expenditure. Lipolysis displays both spontaneous (basal) and hormone-stimulated activity. It is unknown if lipolysis is important for future body weight gain and associated disturbed glucose metabolism, and this was presently investigated in subcutaneous adipocytes from two female cohorts before and after ≥10-year follow-up. High basal and low stimulated lipolysis at baseline predicted future weight gain (odds ratios ≥4.6) as well as development of insulin resistance and impaired fasting glucose/type 2 diabetes (odds ratios ≥3.2). At baseline, weight gainers displayed lower adipose expression of several established lipolysis-regulating genes. Thus, inefficient lipolysis (high basal/low stimulated) involving altered gene expression is linked to future weight gain and impaired glucose metabolism and may constitute a treatment target. Finally, low stimulated lipolysis could be accurately estimated in vivo by simple clinical/biochemical measures and may be used to identify risk individuals for intensified preventive measures.
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Affiliation(s)
- Peter Arner
- Department of Medicine (H7), Karolinska Institutet, Karolinska University Hospital, Huddinge, Stockholm 141 86, Sweden.
| | - Daniel P Andersson
- Department of Medicine (H7), Karolinska Institutet, Karolinska University Hospital, Huddinge, Stockholm 141 86, Sweden
| | - Jesper Bäckdahl
- Department of Medicine (H7), Karolinska Institutet, Karolinska University Hospital, Huddinge, Stockholm 141 86, Sweden
| | - Ingrid Dahlman
- Department of Medicine (H7), Karolinska Institutet, Karolinska University Hospital, Huddinge, Stockholm 141 86, Sweden
| | - Mikael Rydén
- Department of Medicine (H7), Karolinska Institutet, Karolinska University Hospital, Huddinge, Stockholm 141 86, Sweden.
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14
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Hang D, Nan H, Kværner AS, De Vivo I, Chan AT, Hu Z, Shen H, Giovannucci E, Song M. Longitudinal associations of lifetime adiposity with leukocyte telomere length and mitochondrial DNA copy number. Eur J Epidemiol 2018; 33:485-495. [PMID: 29619669 PMCID: PMC8063494 DOI: 10.1007/s10654-018-0382-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 03/20/2018] [Indexed: 12/12/2022]
Abstract
Adiposity may cause adverse health outcomes by increasing oxidative stress and systemic inflammation, which can be reflected by altered telomere length (TL) and mitochondrial DNA copy number (mtCN) in peripheral blood leukocytes. However, little is known about the influence of lifetime adiposity on TL and mtCN in later life. This study was performed to investigate the associations of lifetime adiposity with leukocyte TL and mtCN in 9613 participants from the Nurses' Health Study. A group-based trajectory modelling approach was used to create trajectories of body shape from age 5 through 60 years, and a genetic risk score (GRS) was created based on 97 known adiposity susceptibility variants. Associations of body shape trajectories and GRS with dichotomized TL and mtCN were assessed by logistic regression models. After adjustment for lifestyle and dietary factors, compared with the lean-stable group, the lean-marked increase group had higher odds of having below-median TL (OR = 1.18, 95% CI 1.04, 1.35; P = 0.01), and the medium-marked increase group had higher odds of having below-median mtCN (OR = 1.28, 95% CI 1.00, 1.64; P = 0.047). There was a suggestive trend toward lower mtCN across the GRS quartiles (P for trend = 0.07). In conclusion, telomere attrition may be accelerated by marked weight gain in middle life, whereas mtCN is likely to be reduced persistently by adiposity over the life course. The findings indicate the importance of lifetime weight management to preserve functional telomeres and mitochondria.
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Affiliation(s)
- Dong Hang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Room 371, Bldg. 2, 665 Huntington Avenue, Boston, MA, 02115, USA
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Hongmei Nan
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, USA
- Indiana University Melvin and Bren Simon Cancer Center, Indianapolis, IN, USA
| | - Ane Sørlie Kværner
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Room 371, Bldg. 2, 665 Huntington Avenue, Boston, MA, 02115, USA
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Immaculata De Vivo
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew Tan Chan
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Room 906, 55 Fruit Street, Boston, MA, 02114, USA
| | - Zhibin Hu
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Edward Giovannucci
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Room 371, Bldg. 2, 665 Huntington Avenue, Boston, MA, 02115, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Mingyang Song
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Room 371, Bldg. 2, 665 Huntington Avenue, Boston, MA, 02115, USA.
- Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Room 906, 55 Fruit Street, Boston, MA, 02114, USA.
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15
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Goodarzi MO. Genetics of obesity: what genetic association studies have taught us about the biology of obesity and its complications. Lancet Diabetes Endocrinol 2018; 6:223-236. [PMID: 28919064 DOI: 10.1016/s2213-8587(17)30200-0] [Citation(s) in RCA: 259] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 05/24/2017] [Accepted: 05/24/2017] [Indexed: 01/01/2023]
Abstract
Genome-wide association studies (GWAS) for BMI, waist-to-hip ratio, and other adiposity traits have identified more than 300 single-nucleotide polymorphisms (SNPs). Although there is reason to hope that these discoveries will eventually lead to new preventive and therapeutic agents for obesity, this will take time because such developments require detailed mechanistic understanding of how an SNP influences phenotype (and this information is largely unavailable). Fortunately, absence of functional information has not prevented GWAS findings from providing insights into the biology of obesity. Genes near loci regulating total body mass are enriched for expression in the CNS, whereas genes for fat distribution are enriched in adipose tissue itself. Gene by environment and lifestyle interaction analyses have revealed that our increasingly obesogenic environment might be amplifying genetic risk for obesity, yet those at highest risk could mitigate this risk by increasing physical activity and possibly by avoiding specific dietary components. GWAS findings have also been used in mendelian randomisation analyses probing the causal association between obesity and its many putative complications. In supporting a causal association of obesity with diabetes, coronary heart disease, specific cancers, and other conditions, these analyses have clinical relevance in identifying which outcomes could be preventable through weight loss interventions.
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Affiliation(s)
- Mark O Goodarzi
- Division of Endocrinology, Diabetes, and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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16
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Impact of Genetic Variants on the Individual Potential for Body Fat Loss. Nutrients 2018; 10:nu10030266. [PMID: 29495392 PMCID: PMC5872684 DOI: 10.3390/nu10030266] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 02/09/2018] [Accepted: 02/23/2018] [Indexed: 12/21/2022] Open
Abstract
The past decade has witnessed the discovery of obesity-related genetic variants and their functions through genome-wide association studies. Combinations of risk alleles can influence obesity phenotypes with different degrees of effectiveness across various individuals by interacting with environmental factors. We examined the interaction between genetic variation and changes in dietary habits or exercise that influences body fat loss from a large Korean cohort (n = 8840). Out of 673 obesity-related SNPs, a total of 100 SNPs (37 for carbohydrate intake; 19 for fat intake; 44 for total calories intake; 25 for exercise onset) identified to have gene-environment interaction effect in generalized linear model were used to calculate genetic risk scores (GRS). Based on the GRS distribution, we divided the population into four levels, namely, “very insensitive”, “insensitive”, “sensitive”, and “very sensitive” for each of the four categories, “carbohydrate intake”, “fat intake”, “total calories intake”, and “exercise”. Overall, the mean body fat loss became larger when the sensitivity level was increased. In conclusion, genetic variants influence the effectiveness of dietary regimes for body fat loss. Based on our findings, we suggest a platform for personalized body fat management by providing the most suitable and effective nutrition or activity plan specific to an individual.
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17
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Song M, Zheng Y, Qi L, Hu FB, Chan AT, Giovannucci EL. Longitudinal Analysis of Genetic Susceptibility and BMI Throughout Adult Life. Diabetes 2018; 67:248-255. [PMID: 29212779 PMCID: PMC5780056 DOI: 10.2337/db17-1156] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 11/21/2017] [Indexed: 01/01/2023]
Abstract
Little is known about the genetic influence on BMI trajectory throughout adulthood. We created a genetic risk score (GRS) comprising 97 adult BMI-associated variants among 9,971 women and 6,405 men of European ancestry. Serial measures of BMI were assessed from 18 (women) or 21 (men) years to 85 years of age. We also examined BMI change in early (from 18 or 21 to 45 years of age), middle (from 45 to 65 years of age), and late adulthood (from 65 to 80 years of age). GRS was positively associated with BMI across all ages, with stronger associations in women than in men. The associations increased from early to middle adulthood, peaked at 45 years of age in men and at 60 years of age in women (0.91 and 1.35 kg/m2 per 10-allele increment, respectively) and subsequently declined in late adulthood. For women, each 10-allele increment in the GRS was associated with an average BMI gain of 0.54 kg/m2 in early adulthood, whereas no statistically significant association was found for BMI change in middle or late adulthood or for BMI change in any life period in men. Our findings indicate that genetic predisposition exerts a persistent effect on adiposity throughout adult life and increases early adulthood weight gain in women.
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Affiliation(s)
- Mingyang Song
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, and Harvard Medical School, Boston, MA
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Yan Zheng
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
| | - Lu Qi
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, and Harvard Medical School, Boston, MA
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA
| | - Edward L Giovannucci
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
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18
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Skaaby T, Taylor AE, Thuesen BH, Jacobsen RK, Friedrich N, Møllehave LT, Hansen S, Larsen SC, Völker U, Nauck M, Völzke H, Hansen T, Pedersen O, Jørgensen T, Paternoster L, Munafò M, Grarup N, Linneberg A. Estimating the causal effect of body mass index on hay fever, asthma and lung function using Mendelian randomization. Allergy 2018; 73:153-164. [PMID: 28675761 DOI: 10.1111/all.13242] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/02/2017] [Indexed: 12/26/2022]
Abstract
BACKGROUND Observational studies have shown that body mass index (BMI) is positively associated with asthma. However, observational data are prone to confounding and reverse causation. In Mendelian randomization, genetic variants are used as unconfounded markers of exposures to examine causal effects. We examined the causal effect of BMI on asthma, hay fever, allergic sensitization, serum total immunoglobulin E (IgE), forced expiratory volume in one-second (FEV1) and forced vital capacity (FVC). METHODS We included 490 497 participants in the observational and 162 124 participants in the genetic analyses. A genetic risk score (GRS) was created using 26 BMI-associated single nucleotide polymorphisms (SNPs). Results were pooled in meta-analyses and expressed as odds ratios (ORs) or β-estimates with 95% confidence interval (CI). RESULTS The GRS was significantly associated with asthma (OR=1.009; 95% CI: 1.004, 1.013), but not with hay fever (OR= 0.998; 95% CI: 0.994, 1.002) or allergic sensitization (OR=0.999; 95% CI: 0.986, 1.012) per BMI-increasing allele. The GRS was significantly associated with decrease in FEV1: β=-0.0012 (95% CI: -0.0019, -0.0006) and FVC: β=-0.0022 (95% CI: -0.0031, -0.0014) per BMI-increasing allele. Effect sizes estimated by instrumental variable analyses were OR=1.07 (95% CI: 1.03, 1.10) for asthma, a 9 ml decrease in FEV1 (95% CI: 2.0-15 mL decrease) and a 16 ml decrease in FVC (95% CI: 7.0-24 mL decrease) per 1 kg/m2 higher BMI. CONCLUSIONS The results support the conclusion that increasing BMI is causally related to higher prevalence of asthma and decreased lung function, but not with hay fever or biomarkers of allergy.
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Affiliation(s)
- T. Skaaby
- Research Centre for Prevention and Health Centre for Health Capital Region of Denmark Copenhagen Denmark
| | - A. E. Taylor
- MRC Integrative Epidemiology Unit (IEU) The University of Bristol Bristol UK
- UK Centre for Tobacco and Alcohol Studies School of Experimental Psychology University of Bristol Bristol UK
| | - B. H. Thuesen
- Research Centre for Prevention and Health Centre for Health Capital Region of Denmark Copenhagen Denmark
| | - R. K. Jacobsen
- Research Centre for Prevention and Health Centre for Health Capital Region of Denmark Copenhagen Denmark
| | - N. Friedrich
- Research Centre for Prevention and Health Centre for Health Capital Region of Denmark Copenhagen Denmark
- Institute of Clinical Chemistry and Laboratory Medicine University Medicine Greifswald Greifswald Germany
| | - L. T. Møllehave
- Research Centre for Prevention and Health Centre for Health Capital Region of Denmark Copenhagen Denmark
| | - S. Hansen
- Research Centre for Prevention and Health Centre for Health Capital Region of Denmark Copenhagen Denmark
| | - S. C. Larsen
- Research unit for Dietary Studies The Parker Institute Frederiksberg and Bispebjerg Hospitals The Capital Region Frederiksberg Denmark
| | - U. Völker
- Interfaculty Institute for Genetics and Functional Genomics University Medicine and Ernst‐Moritz‐Arndt University Greifswald Greifswald Germany
| | - M. Nauck
- Institute of Clinical Chemistry and Laboratory Medicine University Medicine Greifswald Greifswald Germany
| | - H. Völzke
- Institute for Community Medicine University Medicine Greifswald Greifswald Germany
| | - T. Hansen
- Section on Metabolic Genetics Faculty of Health and Medical Sciences The Novo Nordisk Foundation Center for Basic Metabolic Research University of Copenhagen Copenhagen Denmark
| | - O. Pedersen
- Section on Metabolic Genetics Faculty of Health and Medical Sciences The Novo Nordisk Foundation Center for Basic Metabolic Research University of Copenhagen Copenhagen Denmark
| | - T. Jørgensen
- Research Centre for Prevention and Health Centre for Health Capital Region of Denmark Copenhagen Denmark
- Department of Public Health Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark
- Faculty of Medicine Aalborg University Aalborg Denmark
| | - L. Paternoster
- MRC Integrative Epidemiology Unit (IEU) The University of Bristol Bristol UK
| | - M. Munafò
- MRC Integrative Epidemiology Unit (IEU) The University of Bristol Bristol UK
- UK Centre for Tobacco and Alcohol Studies School of Experimental Psychology University of Bristol Bristol UK
| | - N. Grarup
- Section on Metabolic Genetics Faculty of Health and Medical Sciences The Novo Nordisk Foundation Center for Basic Metabolic Research University of Copenhagen Copenhagen Denmark
| | - A. Linneberg
- Research Centre for Prevention and Health Centre for Health Capital Region of Denmark Copenhagen Denmark
- Department of Clinical Experimental Research Rigshospitalet Glostrup Denmark
- Department of Clinical Medicine Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark
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19
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Konttinen H, Llewellyn C, Silventoinen K, Joensuu A, Männistö S, Salomaa V, Jousilahti P, Kaprio J, Perola M, Haukkala A. Genetic predisposition to obesity, restrained eating and changes in body weight: a population-based prospective study. Int J Obes (Lond) 2017; 42:858-865. [PMID: 29158543 DOI: 10.1038/ijo.2017.278] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 10/09/2017] [Accepted: 10/30/2017] [Indexed: 01/06/2023]
Abstract
OBJECTIVES There is no consensus on whether cognitive control over food intake (that is, restrained eating) is helpful, merely ineffective or actually harmful in weight management. We examined the interplay between genetic risk of obesity, restrained eating and changes in body weight and size. METHODS Participants were Finnish aged 25-74 years who attended the DIetary, Lifestyle and Genetic determinants of Obesity and Metabolic syndrome study at baseline in 2007 and follow-up in 2014. At baseline (n=5024), height, weight and waist circumference (WC) were measured in a health examination and participants self-reported their weight at age 20 years. At follow-up (n=3735), height, weight and WC were based on measured or self-reported information. We calculated 7-year change in body mass index (BMI) and WC and annual weight change from age 20 years to baseline. Three-Factor Eating Questionnaire-R18 was used to assess restrained eating. Genetic risk of obesity was assessed by calculating a polygenic risk score of 97 known BMI-related loci. RESULTS Cross-lagged autoregressive models indicated that baseline restrained eating was unrelated to 7-year change in BMI (β=0.00; 95% confidence interval (CI)=-0.01, 0.02). Instead, higher baseline BMI predicted greater 7-year increases in restrained eating (β=0.08; 95% CI=0.05, 0.11). Similar results were obtained with WC. Polygenic risk score correlated positively with restrained eating and obesity indicators in both study phases, but it did not predict 7-year change in BMI or WC. However, individuals with higher genetic risk of obesity tended to gain more weight from age 20 years to baseline, and this association was more pronounced in unrestrained eaters than in restrained eaters (P=0.038 for interaction). CONCLUSIONS Our results suggest that restrained eating is a marker for previous weight gain rather than a factor that leads to future weight gain in middle-aged adults. Genetic influences on weight gain from early to middle adulthood may vary according to restrained eating, but this finding needs to be replicated in future studies.
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Affiliation(s)
- H Konttinen
- Department of Social Research, University of Helsinki, Helsinki, Finland.,Department of Food and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - C Llewellyn
- Department of Behavioural Science and Health, University College London, London, UK
| | - K Silventoinen
- Department of Social Research, University of Helsinki, Helsinki, Finland
| | - A Joensuu
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.,Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
| | - S Männistö
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
| | - V Salomaa
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
| | - P Jousilahti
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
| | - J Kaprio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.,Department of Public Health, University of Helsinki, Helsinki, Finland
| | - M Perola
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.,Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland.,Diabetes and Obesity Research Program, University of Helsinki, Helsinki, Finland.,Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - A Haukkala
- Department of Social Research, University of Helsinki, Helsinki, Finland
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20
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Rohde JF, Ängquist L, Larsen SC, Tolstrup JS, Husemoen LLN, Linneberg A, Toft U, Overvad K, Halkjær J, Tjønneland A, Hansen T, Pedersen O, Sørensen TIA, Heitmann BL. Alcohol consumption and its interaction with adiposity-associated genetic variants in relation to subsequent changes in waist circumference and body weight. Nutr J 2017; 16:51. [PMID: 28841830 PMCID: PMC5574083 DOI: 10.1186/s12937-017-0274-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Accepted: 08/21/2017] [Indexed: 02/06/2023] Open
Abstract
Background Studies have suggested a link between alcohol intake and adiposity. However, results from longitudinal studies have been inconsistent, and a possible interaction with genetic predisposition to adiposity measures has often not been taken into account. Objective To examine the association between alcohol intake recorded at baseline and subsequent annual changes in body weight (∆BW), waist circumference (ΔWC) and WC adjusted for BMI (ΔWCBMI), and to test for interaction with genetic predisposition scores based on single nucleotide polymorphisms (SNPs) associated with various forms of adiposity. Method This study included a total of 7028 adult men and women from MONICA, the Diet, Cancer and Health cohort (DCH), and the Inter99 studies. We combined 50 adiposity-associated SNPs into four scores indicating genetic predisposition to BMI, WC, WHRBMI and all three traits combined. Linear regression was used to examine the association of alcohol intake (drinks of 12 g (g) alcohol/day) with ΔBW, ΔWC, and ΔWCBMI, and to examine possible interactions with SNP-scores. Results from the analyses of the individual cohorts were combined in meta-analyses. Results Each additional drink/day was associated with a ΔBW/year of −18.0 g (95% confidence interval (CI): −33.4, −2.6, P = 0.02) and a ΔWC of −0.3 mm/year (−0.5, −0.0, P = 0.03). In analyses of women only, alcohol intake was associated with a higher ΔWCBMI of 0.5 mm/year (0.2, 0.9, P = 0.002) per drink/day. Overall, we found no statistically significant interactions between the four SNP-scores and alcohol intake in relation to changes in adiposity measures. However in analyses of women separately, we found interaction between the complete score of all 50 SNPs and alcohol intake in relation to ΔBW (P for interaction = 0.03). No significant interaction was observed among the men. Conclusion Alcohol intake was associated with a decrease in BW and WC among men and women, and an increase in WCBMI among women only. We found no strong indication that these associations depend on a genetic predisposition to adiposity. Trial registration Registry: ClinicalTrials.gov Trial number: CT00289237, Registered: 19 September 2005 retrospectively registered. Electronic supplementary material The online version of this article (10.1186/s12937-017-0274-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jeanett F Rohde
- Research Unit for Dietary Studies at the Parker Institute, Bispebjerg and Frederiksberg Hospital, the Capital Region, Copenhagen, Nordre Fasanvej 57, entrance 5, ground floor, 2000, Frederiksberg, Denmark. .,Department of Clinical Epidemiology (Formerly 'Institute of Preventive Medicine'), Bispebjerg and Frederiksberg Hospital, the Capital Region, Nordre Fasanvej 57, Hovedvejen, entrance 5, first floor, 2000, Frederiksberg, Denmark.
| | - Lars Ängquist
- Department of Clinical Epidemiology (Formerly 'Institute of Preventive Medicine'), Bispebjerg and Frederiksberg Hospital, the Capital Region, Nordre Fasanvej 57, Hovedvejen, entrance 5, first floor, 2000, Frederiksberg, Denmark
| | - Sofus C Larsen
- Research Unit for Dietary Studies at the Parker Institute, Bispebjerg and Frederiksberg Hospital, the Capital Region, Copenhagen, Nordre Fasanvej 57, entrance 5, ground floor, 2000, Frederiksberg, Denmark.,Department of Clinical Epidemiology (Formerly 'Institute of Preventive Medicine'), Bispebjerg and Frederiksberg Hospital, the Capital Region, Nordre Fasanvej 57, Hovedvejen, entrance 5, first floor, 2000, Frederiksberg, Denmark
| | - Janne S Tolstrup
- National Institute of Public Health, University of Southern Denmark, Øster Farimagsgade 5 A, 1353, Copenhagen K, Denmark
| | - Lise Lotte N Husemoen
- Research Centre for Prevention and Health, Capital Region of Denmark, Nordre Ringvej 57, building 84-85, 2600, Glostrup, Denmark
| | - Allan Linneberg
- Research Centre for Prevention and Health, Capital Region of Denmark, Nordre Ringvej 57, building 84-85, 2600, Glostrup, Denmark.,Department of Clinical Experimental Research, Rigshospitalet, Nordre Ringvej 57, 2600, Glostrup, Denmark.,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, København N, Denmark
| | - Ulla Toft
- Research Centre for Prevention and Health, Capital Region of Denmark, Nordre Ringvej 57, building 84-85, 2600, Glostrup, Denmark
| | - Kim Overvad
- Section for Epidemiology, Department of Public Health, Aarhus University, Nordre Ringgade 1, 8000, Aarhus C, Denmark.,Department of Cardiology, Aalborg University Hospital, Fredrik Bajers Vej 7-D3, 9220, Aalborg, Denmark
| | - Jytte Halkjær
- Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen Ø, Denmark
| | - Anne Tjønneland
- Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen Ø, Denmark
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research (Section of Metabolic Genetics), and Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Nørre Alle 20, 2200, Copenhagen N, Denmark
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research (Section of Metabolic Genetics), and Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Nørre Alle 20, 2200, Copenhagen N, Denmark
| | - Thorkild I A Sørensen
- Department of Clinical Epidemiology (Formerly 'Institute of Preventive Medicine'), Bispebjerg and Frederiksberg Hospital, the Capital Region, Nordre Fasanvej 57, Hovedvejen, entrance 5, first floor, 2000, Frederiksberg, Denmark.,The Novo Nordisk Foundation Center for Basic Metabolic Research (Section of Metabolic Genetics), and Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Nørre Alle 20, 2200, Copenhagen N, Denmark.,MRC Integrative Epidemiology Unit, Bristol University, Senate House, Tyndall Avenue, Bristol, BS8 1TH, UK
| | - Berit L Heitmann
- Research Unit for Dietary Studies at the Parker Institute, Bispebjerg and Frederiksberg Hospital, the Capital Region, Copenhagen, Nordre Fasanvej 57, entrance 5, ground floor, 2000, Frederiksberg, Denmark.,Department of Clinical Epidemiology (Formerly 'Institute of Preventive Medicine'), Bispebjerg and Frederiksberg Hospital, the Capital Region, Nordre Fasanvej 57, Hovedvejen, entrance 5, first floor, 2000, Frederiksberg, Denmark.,National Institute of Public Health, University of Southern Denmark, Øster Farimagsgade 5 A, 1353, Copenhagen K, Denmark.,The Boden Institute of Obesity, Nutrition, Exercise & Eating Disorders, The University of Sydney, Sydney, NSW, 2006, Australia.,Section for General Practice, Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, entrance Q, 1014, Copenhagen K, Denmark
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21
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Kyrgiou M, Kalliala I, Markozannes G, Gunter MJ, Paraskevaidis E, Gabra H, Martin-Hirsch P, Tsilidis KK. Adiposity and cancer at major anatomical sites: umbrella review of the literature. BMJ 2017; 356:j477. [PMID: 28246088 PMCID: PMC5421437 DOI: 10.1136/bmj.j477] [Citation(s) in RCA: 505] [Impact Index Per Article: 63.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/24/2017] [Indexed: 12/11/2022]
Abstract
Objective To evaluate the strength and validity of the evidence for the association between adiposity and risk of developing or dying from cancer.Design Umbrella review of systematic reviews and meta-analyses.Data sources PubMed, Embase, Cochrane Database of Systematic Reviews, and manual screening of retrieved references.Eligibility criteria Systematic reviews or meta-analyses of observational studies that evaluated the association between indices of adiposity and risk of developing or dying from cancer.Data synthesis Primary analysis focused on cohort studies exploring associations for continuous measures of adiposity. The evidence was graded into strong, highly suggestive, suggestive, or weak after applying criteria that included the statistical significance of the random effects summary estimate and of the largest study in a meta-analysis, the number of cancer cases, heterogeneity between studies, 95% prediction intervals, small study effects, excess significance bias, and sensitivity analysis with credibility ceilings.Results 204 meta-analyses investigated associations between seven indices of adiposity and developing or dying from 36 primary cancers and their subtypes. Of the 95 meta-analyses that included cohort studies and used a continuous scale to measure adiposity, only 12 (13%) associations for nine cancers were supported by strong evidence. An increase in body mass index was associated with a higher risk of developing oesophageal adenocarcinoma; colon and rectal cancer in men; biliary tract system and pancreatic cancer; endometrial cancer in premenopausal women; kidney cancer; and multiple myeloma. Weight gain and waist to hip circumference ratio were associated with higher risks of postmenopausal breast cancer in women who have never used hormone replacement therapy and endometrial cancer, respectively. The increase in the risk of developing cancer for every 5 kg/m2 increase in body mass index ranged from 9% (relative risk 1.09, 95% confidence interval 1.06 to 1.13) for rectal cancer among men to 56% (1.56, 1.34 to 1.81) for biliary tract system cancer. The risk of postmenopausal breast cancer among women who have never used HRT increased by 11% for each 5 kg of weight gain in adulthood (1.11, 1.09 to 1.13), and the risk of endometrial cancer increased by 21% for each 0.1 increase in waist to hip ratio (1.21, 1.13 to 1.29). Five additional associations were supported by strong evidence when categorical measures of adiposity were included: weight gain with colorectal cancer; body mass index with gallbladder, gastric cardia, and ovarian cancer; and multiple myeloma mortality.Conclusions Although the association of adiposity with cancer risk has been extensively studied, associations for only 11 cancers (oesophageal adenocarcinoma, multiple myeloma, and cancers of the gastric cardia, colon, rectum, biliary tract system, pancreas, breast, endometrium, ovary, and kidney) were supported by strong evidence. Other associations could be genuine, but substantial uncertainty remains. Obesity is becoming one of the biggest problems in public health; evidence on the strength of the associated risks may allow finer selection of those at higher risk of cancer, who could be targeted for personalised prevention strategies.
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Affiliation(s)
- Maria Kyrgiou
- Department of Surgery and Cancer, Institute of Reproductive and Developmental Biology, Faculty of Medicine, Imperial College London, London W12 0NN, UK
- West London Gynaecological Cancer Centre, Queen Charlotte's and Chelsea Hospital, Hammersmith Hospital, Imperial Healthcare NHS Trust, London, UK
| | - Ilkka Kalliala
- Department of Surgery and Cancer, Institute of Reproductive and Developmental Biology, Faculty of Medicine, Imperial College London, London W12 0NN, UK
| | - Georgios Markozannes
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Marc J Gunter
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France
| | | | - Hani Gabra
- Department of Surgery and Cancer, Institute of Reproductive and Developmental Biology, Faculty of Medicine, Imperial College London, London W12 0NN, UK
- West London Gynaecological Cancer Centre, Queen Charlotte's and Chelsea Hospital, Hammersmith Hospital, Imperial Healthcare NHS Trust, London, UK
| | - Pierre Martin-Hirsch
- Department of Gynaecologic Oncology, Lancashire Teaching Hospitals, Preston, UK
- Department of Biophysics, University of Lancaster, Lancaster, UK
| | - Konstantinos K Tsilidis
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London UK
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22
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Justesen JM, Andersson EA, Allin KH, Sandholt CH, Jørgensen T, Linneberg A, Jørgensen ME, Hansen T, Pedersen O, Grarup N. Increasing insulin resistance accentuates the effect of triglyceride-associated loci on serum triglycerides during 5 years. J Lipid Res 2016; 57:2193-2199. [PMID: 27777317 PMCID: PMC5321221 DOI: 10.1194/jlr.p068379] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Revised: 10/18/2016] [Indexed: 11/20/2022] Open
Abstract
Blood concentrations of triglycerides are influenced by genetic factors as well as a number of environmental factors, including adiposity and glucose homeostasis. The aim was to investigate the association between a serum triglyceride weighted genetic risk score (wGRS) and changes in fasting serum triglyceride level over 5 years and to test whether the effect of the wGRS was modified by 5 year changes of adiposity, insulin resistance, and lifestyle factors. A total of 3,474 nondiabetic individuals from the Danish Inter99 cohort participated in both the baseline and 5 year follow-up physical examinations and had information on the wGRS comprising 39 genetic variants. In a linear regression model adjusted for age, sex, and baseline serum triglyceride, the wGRS was associated with increased serum triglyceride levels over 5 years [per allele effect = 1.3% (1.0-1.6%); P = 1.0 × 10-17]. This triglyceride-increasing effect of the wGRS interacted with changes in insulin resistance (Pinteraction = 1.5 × 10-6). This interaction indicated that the effect of the wGRS was stronger in individuals who became more insulin resistant over 5 years. In conclusion, our findings suggest that increased genetic risk load is associated with a larger increase in fasting serum triglyceride levels in nondiabetic individuals during 5 years of follow-up. This effect of the wGRS is accentuated by increasing insulin resistance.
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Affiliation(s)
- Johanne M Justesen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ehm A Andersson
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Kristine H Allin
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Camilla H Sandholt
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Jørgensen
- Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Faculty of Medicine, University of Aalborg, Aalborg, Denmark
| | - Allan Linneberg
- Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark; Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Marit E Jørgensen
- Steno Diabetes Center, Gentofte, Denmark; Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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23
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Gao C, Patel CJ, Michailidou K, Peters U, Gong J, Schildkraut J, Schumacher FR, Zheng W, Boffetta P, Stucker I, Willett W, Gruber S, Easton DF, Hunter DJ, Sellers TA, Haiman C, Henderson BE, Hung RJ, Amos C, Pierce BL, Lindström S, Kraft P. Mendelian randomization study of adiposity-related traits and risk of breast, ovarian, prostate, lung and colorectal cancer. Int J Epidemiol 2016; 45:896-908. [PMID: 27427428 PMCID: PMC6372135 DOI: 10.1093/ije/dyw129] [Citation(s) in RCA: 113] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/16/2016] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Adiposity traits have been associated with risk of many cancers in observational studies, but whether these associations are causal is unclear. Mendelian randomization (MR) uses genetic predictors of risk factors as instrumental variables to eliminate reverse causation and reduce confounding bias. We performed MR analyses to assess the possible causal relationship of birthweight, childhood and adult body mass index (BMI), and waist-hip ratio (WHR) on the risks of breast, ovarian, prostate, colorectal and lung cancers. METHODS We tested the association between genetic risk scores and each trait using summary statistics from published genome-wide association studies (GWAS) and from 51 537 cancer cases and 61 600 controls in the Genetic Associations and Mechanisms in Oncology (GAME-ON) Consortium. RESULTS We found an inverse association between the genetic score for childhood BMI and risk of breast cancer [odds ratio (OR) = 0.71 per standard deviation (s.d.) increase in childhood BMI; 95% confidence interval (CI): 0.60, 0.80; P = 6.5 × 10(-5)). We also found the genetic score for adult BMI to be inversely associated with breast cancer risk (OR = 0.66 per s.d. increase in BMI; 95% CI: 0.57, 0.77; P = 2.5 × 10(-7)), and positively associated with ovarian cancer (OR = 1.35; 95% CI: 1.05, 1.72; P = 0.017), lung cancer (OR = 1.27; 95% CI: 1.09, 1.49; P = 2.9 × 10(-3)) and colorectal cancer (OR = 1.39; 95% CI: 1.06, 1.82, P = 0.016). The inverse association between genetically predicted adult BMI and breast cancer risk remained even after adjusting for directional pleiotropy via MR-Egger regression. CONCLUSIONS Findings from this study provide additional understandings of the complex relationship between adiposity and cancer risks. Our results for breast and lung cancer are particularly interesting, given previous reports of effect heterogeneity by menopausal status and smoking status.
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Affiliation(s)
- Chi Gao
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, Department of Electron Microscopy/Molecular Pathology, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus and
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Jian Gong
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Joellen Schildkraut
- Cancer Prevention, Detection & Control Research Program, Duke Cancer Institute, Durham, NC, USA, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Fredrick R Schumacher
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Wei Zheng
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
| | - Paolo Boffetta
- Tisch Cancer institute and Institute for Transitional Epidemiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Isabelle Stucker
- Centre for Research in Epidemiology and Population Health, INSERM, Villejuif, France
| | - Walter Willett
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA, Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Stephen Gruber
- USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, Department of Oncology, University of Cambridge, Cambridge, UK
| | - David J Hunter
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Thomas A Sellers
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center, Tampa, FL, USA
| | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Brian E Henderson
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada
| | - Christopher Amos
- Center for Genomic Medicine, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
| | - Brandon L Pierce
- Department of Public Health Studies, University of Chicago, Chicago, IL, USA
| | - Sara Lindström
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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24
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Larsen SC, Ängquist L, Moldovan M, Huikari V, Sebert S, Cavadino A, Singh Ahluwalia T, Skaaby T, Linneberg A, Husemoen LLN, Toft U, Pedersen O, Hansen T, Herzig KH, Jarvelin MR, Power C, Hyppönen E, Heitmann BL, Sørensen TIA. Serum 25-Hydroxyvitamin D Status and Longitudinal Changes in Weight and Waist Circumference: Influence of Genetic Predisposition to Adiposity. PLoS One 2016; 11:e0153611. [PMID: 27077659 PMCID: PMC4831693 DOI: 10.1371/journal.pone.0153611] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2015] [Accepted: 03/31/2016] [Indexed: 01/10/2023] Open
Abstract
Studies of the relationship between serum 25-hydroxyvitamin D (25(OH)D) and changes in measures of adiposity have shown inconsistent results, and interaction with genetic predisposition to obesity has rarely been examined. We examined whether 25(OH)D was associated with subsequent annual changes in body weight (ΔBW) or waist circumference (ΔWC), and whether the associations were modified by genetic predisposition to a high BMI, WC or waist-hip ratio adjusted for BMI (WHRBMI). The study was based on 10,898 individuals from the Danish Inter99, the 1958 British Birth Cohort and the Northern Finland Birth Cohort 1966. We combined 42 adiposity-associated Single Nucleotide Polymorphisms (SNPs) into four scores indicating genetic predisposition to BMI, WC and WHRBMI, or all three traits combined. Linear regression was used to examine the association between serum 25(OH)D and ΔBW or ΔWC, SNP-score × 25(OH)D interactions were examined, and results from the individual cohorts were meta-analyzed. In the meta-analyses, we found no evidence of an association between 25(OH)D and ΔBW (-9.4 gram/y per 10 nmol/L higher 25(OH)D [95% CI: -23.0, +4.3; P = 0.18]) or ΔWC (-0.06 mm/y per 10 nmol/L higher 25(OH)D [95% CI: -0.17, +0.06; P = 0.33]). Furthermore, we found no statistically significant interactions between the four SNP-scores and 25(OH)D in relation to ΔBW or ΔWC. Thus, in view of the narrow CIs, our results suggest that an association between 25(OH)D and changes in measures of adiposity is absent or marginal. Similarly, the study provided evidence that there is either no or very limited dependence on genetic predisposition to adiposity.
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Affiliation(s)
- Sofus C. Larsen
- Research unit for Dietary Studies, the Parker Institute, Frederiksberg and Bispebjerg Hospitals, The Capital Region, Frederiksberg, Denmark
- Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospitals, the Capital Region, Copenhagen, Denmark
- * E-mail:
| | - Lars Ängquist
- Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospitals, the Capital Region, Copenhagen, Denmark
| | - Max Moldovan
- Centre for Population Health Research, School of Health Sciences and Sansom Institute, University of South Australia, Adelaide, Australia
| | - Ville Huikari
- Center for Life-Course Health Research, Faculty of Medicine, P.O.Box 5000, FI-90014 University of Oulu, Oulu, Finland
| | - Sylvain Sebert
- Center for Life-Course Health Research, Faculty of Medicine, P.O.Box 5000, FI-90014 University of Oulu, Oulu, Finland
| | - Alana Cavadino
- Centre for Environmental and Preventive Medicine, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- Population, Policy and Practice, UCL Institute of Child Health, University College London, London, United Kingdom
| | - Tarunveer Singh Ahluwalia
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Steno Diabetes Center, Gentofte, Denmark
| | - Tea Skaaby
- Research Centre for Prevention and Health, Capital Region of Denmark, Copenhagen, Denmark
| | - Allan Linneberg
- Research Centre for Prevention and Health, Capital Region of Denmark, Copenhagen, Denmark
- Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lise Lotte N. Husemoen
- Research Centre for Prevention and Health, Capital Region of Denmark, Copenhagen, Denmark
| | - Ulla Toft
- Research Centre for Prevention and Health, Capital Region of Denmark, Copenhagen, Denmark
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Karl-Heinz Herzig
- Institute of Biomedicine, University of Oulu, Oulu, Finland
- Medical Research Center Oulu and Oulu University Hospital, Oulu, Finland
- Biocenter Oulu, P.O.Box 5000, Aapistie 5A, FI-90014 University of Oulu, Oulu, Finland
- Department of Gastroenterology and Metabolism, Poznan University of Medical Sciences, Poznan, Poland
| | - Marjo-Riitta Jarvelin
- Center for Life-Course Health Research, Faculty of Medicine, P.O.Box 5000, FI-90014 University of Oulu, Oulu, Finland
- Biocenter Oulu, P.O.Box 5000, Aapistie 5A, FI-90014 University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Unit of Primary Care, Oulu University Hospital, Kajaanintie 50, P.O.Box 20, FI-90220 Oulu, 90029 OYS, Finland
| | - Chris Power
- Population, Policy and Practice, UCL Institute of Child Health, University College London, London, United Kingdom
| | - Elina Hyppönen
- Centre for Population Health Research, School of Health Sciences and Sansom Institute, University of South Australia, Adelaide, Australia
- Population, Policy and Practice, UCL Institute of Child Health, University College London, London, United Kingdom
- South Australian Health and Medical Research Institute, Adelaide, Australia
| | - Berit L. Heitmann
- Research unit for Dietary Studies, the Parker Institute, Frederiksberg and Bispebjerg Hospitals, The Capital Region, Frederiksberg, Denmark
- Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospitals, the Capital Region, Copenhagen, Denmark
- The National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark
- The Boden Institute of Obesity, Nutrition, Exercise & Eating Disorders, University of Sydney, Sydney, Australia
| | - Thorkild I. A. Sørensen
- Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospitals, the Capital Region, Copenhagen, Denmark
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
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25
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Loos RJF, Hagberg JM, Pérusse L, Roth SM, Sarzynski MA, Wolfarth B, Rankinen T, Bouchard C. Advances in exercise, fitness, and performance genomics in 2014. Med Sci Sports Exerc 2016; 47:1105-12. [PMID: 25706296 DOI: 10.1249/mss.0000000000000645] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
This is the annual review of the exercise genomics literature in which we report on the highest quality papers published in 2014. We identified a number of noteworthy papers across a number of fields. In 70-89 yr olds, only 19% of angiotensin-converting enzyme (ACE) II homozygotes exhibited significant improvement in gait speed in response to a yearlong physical activity program compared to 30% of ACE D-allele carriers. New studies continue to support the notion that the genetic susceptibility to obesity, as evidenced by a genomic risk score (GRS; based on multiple single nucleotide polymorphisms), is attenuated by 40%-50% in individuals who are physically active, compared to those who are sedentary. One study reported that the polygenic risk for hypertriglyceridemia was reduced by 30%-40% in individuals with high cardiorespiratory fitness. One report showed that there was a significant interaction of a type 2 diabetes GRS with physical activity, with active individuals having the lowest risk of developing diabetes. The protective effect of physical activity was most pronounced in the low GRS tertile (hazard ratio, 0.82). The interaction observed with the diabetes GRS seemed to be dependent on a genetic susceptibility to insulin resistance and not insulin secretion. A significant interaction between PPARα sequence variants and physical activity levels on cardiometabolic risk was observed, with higher activity levels associated with lower risk only in carriers of specific genotypes and haplotypes. The review concludes with a discussion of the importance of replication studies when very large population or intervention discovery studies are not feasible or are cost prohibitive.
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Affiliation(s)
- Ruth J F Loos
- 1The Genetics of Obesity and Related Metabolic Traits Program, The Charles Bronfman Institute for Personalized Medicine, The Mindich Child Health and Development Institute, The Icahn School of Medicine at Mount Sinai, New York, NY; 2Department of Kinesiology, School of Public Health, University of Maryland, College Park, MD; 3Department of Kinesiology, Faculty of Medicine, Laval University, Ste-Foy, Québec, CANADA; 4Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA; 5Department of Sport Medicine, Humboldt University and Charité University School of Medicine, Berlin, GERMANY
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26
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Rukh G, Ahmad S, Ericson U, Hindy G, Stocks T, Renström F, Almgren P, Nilsson PM, Melander O, Franks PW, Orho-Melander M. Inverse relationship between a genetic risk score of 31 BMI loci and weight change before and after reaching middle age. Int J Obes (Lond) 2015; 40:252-9. [PMID: 26374450 PMCID: PMC4753358 DOI: 10.1038/ijo.2015.180] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2015] [Revised: 07/08/2015] [Accepted: 08/13/2015] [Indexed: 01/23/2023]
Abstract
Background/Objective: Genome-wide-association studies have identified numerous body mass index (BMI)-associated variants, but it is unclear how these relate to weight gain in adults at different ages. Methods: We examined the association of a genetic risk score (GRS), consisting of 31 BMI-associated variants, with an annual weight change (AWC) and a substantial weight gain (SWG) of 10% by comparing self-reported weight at 20 years (y) with baseline weight (mean: 58 y; s.d.: 8 y) in 21407 participants from the Malmö Diet and Cancer Study (MDCS), and comparing baseline weight to weight at follow-up (mean: 73 y; s.d.: 6 y) among 2673 participants. Association between GRS and AWG and SWG was replicated in 4327 GLACIER (Gene x Lifestyle interactions And Complex traits Involved in Elevated disease Risk) participants (mean: 45 y; s.d.: 7 y) with 10 y follow-up. Cohort-specific results were pooled by fixed-effect meta-analyses. Results: In MDCS, the GRS was associated with increased AWC (β: 0.003; s.e: 0.01; P: 7 × 10−8) and increased odds for SWG (odds ratio (OR) 1.01 (95% confidence interval (CI): 1.00, 1.02); P: 0.013) per risk-allele from age 20y, but unexpectedly with decreased AWC (β: −0.006; s.e: 0.002; P: 0.009) and decreased odds for SWG OR 0.96 (95% CI: 0.93, 0.98); P: 0.001) between baseline and follow-up. Effect estimates from age 20 y to baseline differed significantly from those from baseline to follow-up (P: 0.0002 for AWC and P: 0.0001 for SWG). Similar to MDCS, the GRS was associated with decreased odds for SWG OR 0.98 (95% CI: 0.96, 1.00); P: 0.029) from baseline to follow-up in GLACIER. In meta-analyses (n=7000), the GRS was associated with decreased AWC (β: −0.005; s.e.m. 0.002; P: 0.002) and decreased odds for SWG OR 0.97 (95% CI: 0.96, 0.99); P: 0.001) per risk-allele. Conclusions: Our results provide convincing evidence for a paradoxical inversed relationship between a high number of BMI-associated risk-alleles and less weight gain during and after middle-age, in contrast to the expected increased weight gain seen in younger age.
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Affiliation(s)
- G Rukh
- Department of Clinical Sciences in Malmö, Lund University Diabetes Center (LUDC), Lund University, Malmö, Sweden
| | - S Ahmad
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Center, Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Sweden
| | - U Ericson
- Department of Clinical Sciences in Malmö, Lund University Diabetes Center (LUDC), Lund University, Malmö, Sweden
| | - G Hindy
- Department of Clinical Sciences in Malmö, Lund University Diabetes Center (LUDC), Lund University, Malmö, Sweden
| | - T Stocks
- Department of Clinical Sciences in Malmö, Lund University Diabetes Center (LUDC), Lund University, Malmö, Sweden.,Department of Surgical and Perioperative Sciences, Urology and Andrology, Umeå University, Umeå, Sweden
| | - F Renström
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Center, Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Sweden
| | - P Almgren
- Department of Clinical Sciences in Malmö, Lund University Diabetes Center (LUDC), Lund University, Malmö, Sweden
| | - P M Nilsson
- Department of Internal Medicine, Skåne University Hospital, Lund University, Malmö, Sweden
| | - O Melander
- Department of Clinical Sciences in Malmö, Lund University Diabetes Center (LUDC), Lund University, Malmö, Sweden
| | - P W Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Center, Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Sweden.,Department of Nutrition, Harvard School of Public Health, Boston, MA, USA.,Department of Public Health and Clinical Medicine, Section for Medicine, Umeå University Hospital, Umeå, Sweden
| | - M Orho-Melander
- Department of Clinical Sciences in Malmö, Lund University Diabetes Center (LUDC), Lund University, Malmö, Sweden
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