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Wen W, Zheng W, Okada Y, Takeuchi F, Tabara Y, Hwang JY, Dorajoo R, Li H, Tsai FJ, Yang X, He J, Wu Y, He M, Zhang Y, Liang J, Guo X, Sheu WHH, Delahanty R, Guo X, Kubo M, Yamamoto K, Ohkubo T, Go MJ, Liu JJ, Gan W, Chen CC, Gao Y, Li S, Lee NR, Wu C, Zhou X, Song H, Yao J, Lee IT, Long J, Tsunoda T, Akiyama K, Takashima N, Cho YS, Ong RT, Lu L, Chen CH, Tan A, Rice TK, Adair LS, Gui L, Allison M, Lee WJ, Cai Q, Isomura M, Umemura S, Kim YJ, Seielstad M, Hixson J, Xiang YB, Isono M, Kim BJ, Sim X, Lu W, Nabika T, Lee J, Lim WY, Gao YT, Takayanagi R, Kang DH, Wong TY, Hsiung CA, Wu IC, Juang JMJ, Shi J, Choi BY, Aung T, Hu F, Kim MK, Lim WY, Wang TD, Shin MH, Lee J, Ji BT, Lee YH, Young TL, Shin DH, Chun BY, Cho MC, Han BG, Hwu CM, Assimes TL, Absher D, Yan X, Kim E, Kuo JZ, Kwon S, Taylor KD, Chen YDI, Rotter JI, Qi L, Zhu D, Wu T, Mohlke KL, Gu D, et alWen W, Zheng W, Okada Y, Takeuchi F, Tabara Y, Hwang JY, Dorajoo R, Li H, Tsai FJ, Yang X, He J, Wu Y, He M, Zhang Y, Liang J, Guo X, Sheu WHH, Delahanty R, Guo X, Kubo M, Yamamoto K, Ohkubo T, Go MJ, Liu JJ, Gan W, Chen CC, Gao Y, Li S, Lee NR, Wu C, Zhou X, Song H, Yao J, Lee IT, Long J, Tsunoda T, Akiyama K, Takashima N, Cho YS, Ong RT, Lu L, Chen CH, Tan A, Rice TK, Adair LS, Gui L, Allison M, Lee WJ, Cai Q, Isomura M, Umemura S, Kim YJ, Seielstad M, Hixson J, Xiang YB, Isono M, Kim BJ, Sim X, Lu W, Nabika T, Lee J, Lim WY, Gao YT, Takayanagi R, Kang DH, Wong TY, Hsiung CA, Wu IC, Juang JMJ, Shi J, Choi BY, Aung T, Hu F, Kim MK, Lim WY, Wang TD, Shin MH, Lee J, Ji BT, Lee YH, Young TL, Shin DH, Chun BY, Cho MC, Han BG, Hwu CM, Assimes TL, Absher D, Yan X, Kim E, Kuo JZ, Kwon S, Taylor KD, Chen YDI, Rotter JI, Qi L, Zhu D, Wu T, Mohlke KL, Gu D, Mo Z, Wu JY, Lin X, Miki T, Tai ES, Lee JY, Kato N, Shu XO, Tanaka T. Meta-analysis of genome-wide association studies in East Asian-ancestry populations identifies four new loci for body mass index. Hum Mol Genet 2014; 23:5492-5504. [PMID: 24861553 PMCID: PMC4168820 DOI: 10.1093/hmg/ddu248] [Show More Authors] [Citation(s) in RCA: 163] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2013] [Revised: 03/22/2014] [Accepted: 05/19/2014] [Indexed: 12/28/2022] Open
Abstract
Recent genetic association studies have identified 55 genetic loci associated with obesity or body mass index (BMI). The vast majority, 51 loci, however, were identified in European-ancestry populations. We conducted a meta-analysis of associations between BMI and ∼2.5 million genotyped or imputed single nucleotide polymorphisms among 86 757 individuals of Asian ancestry, followed by in silico and de novo replication among 7488-47 352 additional Asian-ancestry individuals. We identified four novel BMI-associated loci near the KCNQ1 (rs2237892, P = 9.29 × 10(-13)), ALDH2/MYL2 (rs671, P = 3.40 × 10(-11); rs12229654, P = 4.56 × 10(-9)), ITIH4 (rs2535633, P = 1.77 × 10(-10)) and NT5C2 (rs11191580, P = 3.83 × 10(-8)) genes. The association of BMI with rs2237892, rs671 and rs12229654 was significantly stronger among men than among women. Of the 51 BMI-associated loci initially identified in European-ancestry populations, we confirmed eight loci at the genome-wide significance level (P < 5.0 × 10(-8)) and an additional 14 at P < 1.0 × 10(-3) with the same direction of effect as reported previously. Findings from this analysis expand our knowledge of the genetic basis of obesity.
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Affiliation(s)
- Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Yukinori Okada
- Laboratory for Statistical Analysis, Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Joo-Yeon Hwang
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | - Rajkumar Dorajoo
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore, Department of Genomics of Common Disease, School of Public Health, Imperial College London, Hammersmith Hospital, London, UK
| | - Huaixing Li
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai 200031, China
| | - Fuu-Jen Tsai
- School of Chinese Medicine, Department of Medical Genetics, Department of Health and Nutrition Biotechnology, Asia University, Taichung, Taiwan
| | - Xiaobo Yang
- Department of Occupational Health and Environmental Health, School of Public Health, Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Ying Wu
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Meian He
- Department of Occupational and Environmental Health and the Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yi Zhang
- State Key Laboratory of Medical Genetics, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, Shanghai Institute of Hypertension, Shanghai, China
| | - Jun Liang
- Department of Endocrinology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical College, Affiliated Hospital of Southeast University, Xuzhou, Jiangsu 221009, China
| | - Xiuqing Guo
- Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor-UCLA Medical Center, Institute for Translational Genomics and Populations Sciences, Torrance, CA, USA
| | - Wayne Huey-Herng Sheu
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan, National Defense Medical Center, College of Medicine, Taipei, Taiwan, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Ryan Delahanty
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | | | - Ken Yamamoto
- Department of Molecular Genetics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Takayoshi Ohkubo
- Department of Planning for Drug Development and Clinical Evaluation, Tohoku University Graduate School of Pharmaceutical Sciences, Sendai, Japan, Department of Health Science, Shiga University of Medical Science, Otsu, Japan
| | - Min Jin Go
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | - Jian Jun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Wei Gan
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai 200031, China
| | - Ching-Chu Chen
- School of Chinese Medicine, Division of Endocrinology and Metabolism, Department of Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Yong Gao
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China, College of General Practice, Guangxi Medical University, Nanning, Guangxi, China
| | - Shengxu Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Nanette R Lee
- USC-Office of Population Studies Foundation, Inc., University of San Carlos, Cebu, Philippines
| | - Chen Wu
- State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xueya Zhou
- Bioinformatics Division, Tsinghua National Laboratory of Information Science and Technology, Beijing, China
| | - Huaidong Song
- State Key Laboratory of Medical Genomics, Ruijin Hospital, Molecular Medical Center, Shanghai Institute of Endocrinology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Yao
- Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor-UCLA Medical Center, Institute for Translational Genomics and Populations Sciences, Torrance, CA, USA
| | - I-Te Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan, Department of Medicine, Chung-Shan Medical University, Taichung, Taiwan
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | | | - Koichi Akiyama
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Naoyuki Takashima
- Department of Health Science, Shiga University of Medical Science, Otsu, Japan
| | - Yoon Shin Cho
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea, Department of Biomedical Science, Hallym University, Gangwon-do, Republic of Korea
| | - Rick Th Ong
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore, NUS Graduate School for Integrative Science and Engineering, Centre for Molecular Epidemiology, National University of Singapore, Singapore, Singapore
| | - Ling Lu
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai 200031, China
| | - Chien-Hsiun Chen
- School of Chinese Medicine, Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Aihua Tan
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
| | - Treva K Rice
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Linda S Adair
- Department of Nutrition, University of North Carolina, Chapel Hill, NC, USA
| | - Lixuan Gui
- Department of Occupational and Environmental Health and the Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | | | - Wen-Jane Lee
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan, Department of Social Work, Tunghai University, Taichung, Taiwan
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Minoru Isomura
- Department of Functional Pathology, Shimane University School of Medicine, Izumo, Japan
| | - Satoshi Umemura
- Department of Medical Science and Cardiorenal Medicine, Yokohama City University School of Medicine, Yokohama, Japan
| | - Young Jin Kim
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | - Mark Seielstad
- Institute of Human Genetics, University of California, San Francisco, USA
| | - James Hixson
- Human Genetics Center, University of Texas School of Public Health, Houston, TX, USA
| | - Yong-Bing Xiang
- Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Masato Isono
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Bong-Jo Kim
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | - Xueling Sim
- Centre for Molecular Epidemiology, National University of Singapore, Singapore, Singapore
| | - Wei Lu
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Toru Nabika
- Department of Functional Pathology, Shimane University School of Medicine, Izumo, Japan
| | - Juyoung Lee
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | | | - Yu-Tang Gao
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Ryoichi Takayanagi
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Dae-Hee Kang
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore, Department of Ophthalmology, Yong Loo Lin School of Medicine
| | - Chao Agnes Hsiung
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - I-Chien Wu
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Jyh-Ming Jimmy Juang
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Jiajun Shi
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Bo Youl Choi
- Department of Preventive Medicine, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore, Department of Ophthalmology, Yong Loo Lin School of Medicine
| | - Frank Hu
- Department of Epidemiology, Department of Nutrition, Harvard University School of Public Health, Boston, MA, USA
| | - Mi Kyung Kim
- Department of Preventive Medicine, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | | | - Tzung-Dao Wang
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Min-Ho Shin
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea
| | | | - Bu-Tian Ji
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Young-Hoon Lee
- Department of Preventive Medicine & Institute of Wonkwang Medical Science, Wonkwang University College of Medicine, Iksan, Republic of Korea
| | - Terri L Young
- Department of Ophthalmology, Duke University Medical Center, Durham, NC, USA, Division of Neuroscience, Duke-National University of Singapore Graduate Medical School, Singapore, Singapore
| | - Dong Hoon Shin
- Department of Preventive Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Byung-Yeol Chun
- Department of Preventive Medicine, School of Medicine, and Health Promotion Research Center, Kyungpook National University, Daegu, Republic of Korea
| | - Myeong-Chan Cho
- National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | - Bok-Ghee Han
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | - Chii-Min Hwu
- School of Medicine, National Yang-Ming University, Taipei, Taiwan, Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | | | - Devin Absher
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Xiaofei Yan
- Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor-UCLA Medical Center, Institute for Translational Genomics and Populations Sciences, Torrance, CA, USA
| | - Eric Kim
- Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor-UCLA Medical Center, Institute for Translational Genomics and Populations Sciences, Torrance, CA, USA
| | - Jane Z Kuo
- NShiley Eye Center, Department of Ophthalmology, University of California at San Diego, La Jolla, CA, USA
| | - Soonil Kwon
- Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor-UCLA Medical Center, Institute for Translational Genomics and Populations Sciences, Torrance, CA, USA
| | - Kent D Taylor
- Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor-UCLA Medical Center, Institute for Translational Genomics and Populations Sciences, Torrance, CA, USA
| | - Yii-Der I Chen
- Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor-UCLA Medical Center, Institute for Translational Genomics and Populations Sciences, Torrance, CA, USA
| | - Jerome I Rotter
- Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor-UCLA Medical Center, Institute for Translational Genomics and Populations Sciences, Torrance, CA, USA
| | - Lu Qi
- Department of Nutrition, Harvard University 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, USA
| | - Dingliang Zhu
- State Key Laboratory of Medical Genetics, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, Shanghai Institute of Hypertension, Shanghai, China
| | - Tangchun Wu
- Department of Occupational and Environmental Health and the Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Dongfeng Gu
- Department of Evidence Based Medicine, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, and National Center for Cardiovascular Diseases, Beijing, China
| | - Zengnan Mo
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China, Institute of Urology and Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Jer-Yuarn Wu
- School of Chinese Medicine, Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Xu Lin
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai 200031, China
| | - Tetsuro Miki
- Department of Geriatric Medicine, Ehime University Graduate School of Medicine, Toon, Japan
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, and National University Health System, Singapore, Singapore Duke-National University of Singapore Graduate Medical School, Singapore, Singapore
| | - Jong-Young Lee
- Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Republic of Korea
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA,
| | - Toshihiro Tanaka
- Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan, Laboratory for Cardiovascular Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan, Division of Disease Diversity, Bioresource Research Center, Tokyo Medical and Dental University, Tokyo, Japan
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352
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Abstract
Heritability of obesity and body weight variation is high. Molecular genetic studies have led to the identification of mutations in a few genes, with a major effect on obesity (major genes and monogenic forms). Analyses of these genes have helped to unravel important pathways and have created a more profound understanding of body weight regulation. For most individuals, a polygenic basis is relevant for the genetic predisposition to obesity. Small effect sizes are conveyed by the polygenic variants. Hence, only if a number of these variants is harboured, a sizeable phenotypic effect is detectable. Most, if not all, of the genes relevant to weight regulation are expressed in the hypothalamus. This underscores the major role of this region of the brain in body weight regulation.
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Affiliation(s)
- Anke Hinney
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Universitätsklinikum Essen, Essen, Germany.
| | - Anna-Lena Volckmar
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Universitätsklinikum Essen, Essen, Germany.
| | - Jochen Antel
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Universitätsklinikum Essen, Essen, Germany.
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353
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Bartness TJ, Liu Y, Shrestha YB, Ryu V. Neural innervation of white adipose tissue and the control of lipolysis. Front Neuroendocrinol 2014; 35:473-93. [PMID: 24736043 PMCID: PMC4175185 DOI: 10.1016/j.yfrne.2014.04.001] [Citation(s) in RCA: 242] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2013] [Revised: 03/10/2014] [Accepted: 04/04/2014] [Indexed: 01/22/2023]
Abstract
White adipose tissue (WAT) is innervated by the sympathetic nervous system (SNS) and its activation is necessary for lipolysis. WAT parasympathetic innervation is not supported. Fully-executed SNS-norepinephrine (NE)-mediated WAT lipolysis is dependent on β-adrenoceptor stimulation ultimately hinging on hormone sensitive lipase and perilipin A phosphorylation. WAT sympathetic drive is appropriately measured electrophysiologically and neurochemically (NE turnover) in non-human animals and this drive is fat pad-specific preventing generalizations among WAT depots and non-WAT organs. Leptin-triggered SNS-mediated lipolysis is weakly supported, whereas insulin or adenosine inhibition of SNS/NE-mediated lipolysis is strongly supported. In addition to lipolysis control, increases or decreases in WAT SNS drive/NE inhibit and stimulate white adipocyte proliferation, respectively. WAT sensory nerves are of spinal-origin and sensitive to local leptin and increases in sympathetic drive, the latter implicating lipolysis. Transsynaptic viral tract tracers revealed WAT central sympathetic and sensory circuits including SNS-sensory feedback loops that may control lipolysis.
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Affiliation(s)
- Timothy J Bartness
- Department of Biology, Center for Obesity Reversal, Georgia State University, Atlanta, GA 30302-4010, USA; Center for Behavioral Neuroscience, Georgia State University, Atlanta, GA 30302-4010, USA.
| | - Yang Liu
- Department of Biology, Center for Obesity Reversal, Georgia State University, Atlanta, GA 30302-4010, USA; Center for Behavioral Neuroscience, Georgia State University, Atlanta, GA 30302-4010, USA; Metabolic Diseases Branch, NIDDK, National Institutes of Health, Bethesda, MD 20892, USA
| | - Yogendra B Shrestha
- Metabolic Diseases Branch, NIDDK, National Institutes of Health, Bethesda, MD 20892, USA
| | - Vitaly Ryu
- Department of Biology, Center for Obesity Reversal, Georgia State University, Atlanta, GA 30302-4010, USA; Center for Behavioral Neuroscience, Georgia State University, Atlanta, GA 30302-4010, USA; Metabolic Diseases Branch, NIDDK, National Institutes of Health, Bethesda, MD 20892, USA
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354
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Abstract
The heritability of obesity has long been appreciated and the genetics of obesity has been the focus of intensive study for decades. Early studies elucidating genetic factors involved in rare monogenic and syndromic forms of extreme obesity focused attention on dysfunction of hypothalamic leptin-related pathways in the control of food intake as a major contributor. Subsequent genome-wide association studies of common genetic variants identified novel loci that are involved in more common forms of obesity across populations of diverse ethnicities and ages. The subsequent search for factors contributing to the heritability of obesity not explained by these 2 approaches ("missing heritability") has revealed additional rare variants, copy number variants, and epigenetic changes that contribute. Although clinical applications of these findings have been limited to date, the increasing understanding of the interplay of these genetic factors with environmental conditions, such as the increased availability of high calorie foods and decreased energy expenditure of sedentary lifestyles, promises to accelerate the translation of genetic findings into more successful preventive and therapeutic interventions.
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Affiliation(s)
- Jill Waalen
- The Scripps Research Institute and the Scripps Translational Science Institute, La Jolla, California.
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355
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Rs7206790 and rs11644943 in FTO gene are associated with risk of obesity in Chinese school-age population. PLoS One 2014; 9:e108050. [PMID: 25251416 PMCID: PMC4176023 DOI: 10.1371/journal.pone.0108050] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2014] [Accepted: 06/27/2014] [Indexed: 11/25/2022] Open
Abstract
To evaluate the associations between candidate FTO single nucleotide polymorphisms (SNPs) and obesity, a case-control study was conducted among Chinese school-age children, which included 500 obese cases and 500 matched controls (age, gender and location). We selected 24 candidate FTO tag-SNPs via bio-informatics analysis and performed genotyping using SNPScan technology. Results indicated that rs7206790 and rs11644943 were significantly associated with obesity among school-age children in both additive and recessive models (P<0.05) after adjusting confounders. Comparing rs7206790 CC and CG genotype of carriers, those carrying the GG genotype had an increased risk of obesity (adjusted odds ratio [OR], 3.76; 95% Confidence interval [CI], 1.24–11.43). Carriers of the AA allele of rs11644943 had a lower risk of obesity (adjusted OR, 0.16; 95% CI, 0.04–0.72) compared with those of the T allele (TT and TA). These two SNPs (rs7206790 and rs11644943) were not Linkage Disequilibrium (LD) with previous reported obesity-associated SNPs. Under the recessive model adjusted for age and gender and location, rs7206790 GG allele carriers had significantly increased BMIs (P = 0.012), weight (P = 0.012), waist circumferences (WC) (P = 0.045) and hip circumferences (HC) (P = 0.033). Conversely, rs11644943 AA allele carriers had significantly decreased BMIs (P = 0.006), WC (P = 0.037) and Waist-to-height ratios (WHtR) (P = 0.012). A dose-response relationship was found between the number of risk alleles in rs7206790, rs11644943 and rs9939609 and the risk of obesity. The Genetic Risk Score (GRS) of the reference group was 3; in comparison, those of 2, 4, and ≥5 had ORs for obesity of 0.24 (95%CI, 0.05–1.13), 1.49 (95%CI, 1.10–2.01), and 5.20 (95%CI, 1.75–15.44), respectively. This study confirmed the role of FTO variation on genetic susceptibility to obesity. We reported two new obesity-related FTO SNPs (rs7206790 and rs11644943) among Chinese school-age children.
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356
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Wada N, Hirako S, Takenoya F, Kageyama H, Okabe M, Shioda S. Leptin and its receptors. J Chem Neuroanat 2014; 61-62:191-9. [PMID: 25218975 DOI: 10.1016/j.jchemneu.2014.09.002] [Citation(s) in RCA: 97] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2014] [Revised: 09/02/2014] [Accepted: 09/03/2014] [Indexed: 12/11/2022]
Abstract
Leptin is mainly produced in the white adipose tissue before being secreted into the blood and transported across the blood-brain barrier. Leptin binds to a specific receptor (LepR) that has numerous subtypes (LepRa, LepRb, LepRc, LepRd, LepRe, and LepRf). LepRb, in particular, is expressed in several brain nuclei, including the arcuate nucleus, the paraventricular nucleus, and the dorsomedial, lateral and ventromedial regions of the hypothalamus. LepRb is also co-expressed with several neuropeptides, including proopiomelanocortin, neuropeptide Y, galanin, galanin-like peptide, gonadotropin-releasing hormone, tyrosine hydroxylase and neuropeptide W. Functionally, LepRb induces activation of the JAK2/ERK, /STAT3, /STAT5 and IRS/PI3 kinase signaling cascades, which are important for the regulation of energy homeostasis and appetite in mammals. In this review, we discuss the structure, genetics and distribution of the leptin receptors, and their role in cell signaling mechanisms.
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Affiliation(s)
- Nobuhiro Wada
- Department of Anatomy, Showa University School of Medicine, 1-5-8 Hatanodai Shinagawa-ku, Tokyo 142-8555, Japan
| | - Satoshi Hirako
- Department of Anatomy, Showa University School of Medicine, 1-5-8 Hatanodai Shinagawa-ku, Tokyo 142-8555, Japan
| | - Fumiko Takenoya
- Department of Anatomy, Showa University School of Medicine, 1-5-8 Hatanodai Shinagawa-ku, Tokyo 142-8555, Japan; Department of Physical Education, Hoshi University School of Pharmacy and Pharmaceutical Science, Tokyo 142-8501, Japan
| | - Haruaki Kageyama
- Department of Anatomy, Showa University School of Medicine, 1-5-8 Hatanodai Shinagawa-ku, Tokyo 142-8555, Japan; Department of Nutrition, Faculty of Health Care, Kiryu University, 606-7 Kasakakecho Azami, Midori City 379-2392, Gunma, Japan
| | - Mai Okabe
- Department of Anatomy, Showa University School of Medicine, 1-5-8 Hatanodai Shinagawa-ku, Tokyo 142-8555, Japan; Tokyo Shokuryo Dietitian Academy, Tokyo 154-0001, Japan
| | - Seiji Shioda
- Department of Anatomy, Showa University School of Medicine, 1-5-8 Hatanodai Shinagawa-ku, Tokyo 142-8555, Japan.
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Danielewicz H. What the Genetic Background of Individuals with Asthma and Obesity Can Reveal: Is β2-Adrenergic Receptor Gene Polymorphism Important? PEDIATRIC ALLERGY IMMUNOLOGY AND PULMONOLOGY 2014; 27:104-110. [PMID: 25276484 DOI: 10.1089/ped.2014.0360] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Received: 03/14/2014] [Accepted: 05/16/2014] [Indexed: 12/31/2022]
Abstract
The goal of this review was to evaluate the association of β2-adrenergic receptor (ADRB2) gene polymorphisms with asthma and obesity. Asthma is the most common pediatric inflammatory disorder. The prevalence, severity, and hospitalization index for asthma have increased markedly in the last several decades. Interestingly, asthma is often diagnosed along with obesity. Genetic factors are essential for both conditions, and some of the candidate pleiotropic genes thought to be involved in the development of these diseases are ADRB2, vitamin D receptor (VDR), leptin (LEP), protein kinase C alpha (PRKCA), and tumor necrosis factor alpha (TNFα). The ADRB2 has been studied in multiple populations and more than 80 polymorphisms, mainly single-nucleotide polymorphisms, have been identified. For nonsynonymous Arg16Gly, Gln27Glu, and Thr164Ile, functional effects have been shown. In vivo, these polymorphisms have been evaluated to determine their association with both obesity and asthma, but the results are inconsistent and depend on the population studied or how the disease was defined. Currently, there are only few reports describing the genetic background for the comorbidity of asthma and obesity.
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Affiliation(s)
- Hanna Danielewicz
- 1st Department of Pediatrics, Allergy and Cardiology, Wroclaw Medical University , Wrocław, Poland
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358
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Katsuura-Kamano S, Uemura H, Arisawa K, Yamaguchi M, Hamajima N, Wakai K, Okada R, Suzuki S, Taguchi N, Kita Y, Ohnaka K, Kairupan TS, Matsui D, Oze I, Mikami H, Kubo M, Tanaka H. A polymorphism near MC4R gene (rs17782313) is associated with serum triglyceride levels in the general Japanese population: the J-MICC Study. Endocrine 2014; 47:81-9. [PMID: 24880622 DOI: 10.1007/s12020-014-0306-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2014] [Accepted: 05/15/2014] [Indexed: 01/22/2023]
Abstract
Previously reported associations of a common polymorphism near melanocortin-4 receptor (MC4R) gene (rs17782313) with BMI/obesity were inconsistent, especially in East Asia, and the associations of the polymorphism with serum lipid levels have not been fully elucidated. This study evaluated the association between rs17782313 and obesity-related traits and serum lipid levels in the general Japanese population. A total of 2,035 subjects (aged 35-69 years, 1,024 males and 1,011 females) enrolled in the Japan Multi-Institutional Collaborative Cohort (J-MICC) Study. We examined the associations between near MC4R polymorphism (rs17782313) and obesity-related traits [height, weight, body mass index (BMI), weight change from 20 years old], serum lipid levels (triglycerides, total and HDL-cholesterol), and intake of nutrients (total energy and macronutrients). Polymorphism of rs17782313 (minor C allele) was positively associated with serum triglyceride levels (P for trend = 0.020) adjusted for age and sex. Analysis using a general linear model revealed that the number of minor C alleles was positively associated with serum triglyceride levels after adjustment for age, sex, and potential confounders (P for trend = 0.004). Statistical significance did not change after further adjustment for total energy intake and BMI. There was no significant association between rs17782313 and obesity-related traits including BMI. Interactions between rs17782313 and sex, BMI, or total energy intake for triglyceride levels were not significant. To our knowledge, this study demonstrated for the first time that rs17782313 was associated with serum triglyceride levels in Asian population. Further studies are needed to confirm this result.
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Affiliation(s)
- Sakurako Katsuura-Kamano
- Department of Preventive Medicine, Institute of Health Biosciences, The University of Tokushima Graduate School, 3-18-15, Kuramoto-cho, Tokushima, Japan,
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359
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Genetic strategies to understand physiological pathways regulating body weight. Mamm Genome 2014; 25:377-83. [PMID: 25154910 PMCID: PMC4164839 DOI: 10.1007/s00335-014-9541-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2014] [Accepted: 08/11/2014] [Indexed: 11/29/2022]
Abstract
Body weight is a highly heritable trait across species. In humans, genetic variation plays a major role in determining the inter-individual differences in susceptibility or resistance to environmental factors which influence energy intake and expenditure. In this review, I discuss how genetic studies have contributed to our understanding of the central pathways that govern energy homeostasis. The study of individuals harboring highly penetrant genetic variants that disrupt the leptin–melanocortin pathway has informed our understanding of the physiological pathways involved in mammalian energy homeostasis.
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360
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Scannell Bryan M, Argos M, Pierce B, Tong L, Rakibuz-Zaman M, Ahmed A, Rahman M, Islam T, Yunus M, Parvez F, Roy S, Jasmine F, Baron JA, Kibriya MG, Ahsan H. Genome-wide association studies and heritability estimates of body mass index related phenotypes in Bangladeshi adults. PLoS One 2014; 9:e105062. [PMID: 25133637 PMCID: PMC4136799 DOI: 10.1371/journal.pone.0105062] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2014] [Accepted: 07/18/2014] [Indexed: 11/29/2022] Open
Abstract
Many health outcomes are influenced by a person's body mass index, as well as by the trajectory of body mass index through a lifetime. Although previous research has established that body mass index related traits are influenced by genetics, the relationship between these traits and genetics has not been well characterized in people of South Asian ancestry. To begin to characterize this relationship, we analyzed the association between common genetic variation and five phenotypes related to body mass index in a population-based sample of 5,354 Bangladeshi adults. We discovered a significant association between SNV rs347313 (intron of NOS1AP) and change in body mass index in women over two years. In a linear mixed-model, the G allele was associated with an increase of 0.25 kg/m2 in body mass index over two years (p-value of 2.3·10−8). We also estimated the heritability of these phenotypes from our genotype data. We found significant estimates of heritability for all of the body mass index-related phenotypes. Our study evaluated the genetic determinants of body mass index related phenotypes for the first time in South Asians. The results suggest that these phenotypes are heritable and some of this heritability is driven by variation that differs from those previously reported. We also provide evidence that the genetic etiology of body mass index related traits may differ by ancestry, sex, and environment, and consequently that these factors should be considered when assessing the genetic determinants of the risk of body mass index-related disease.
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Affiliation(s)
- Molly Scannell Bryan
- Department of Health Studies, University of Chicago, Chicago, Illinois, United States of America
| | - Maria Argos
- Department of Health Studies, University of Chicago, Chicago, Illinois, United States of America
| | - Brandon Pierce
- Department of Health Studies, University of Chicago, Chicago, Illinois, United States of America
| | - Lin Tong
- Department of Health Studies, University of Chicago, Chicago, Illinois, United States of America
| | | | | | | | | | - Muhammad Yunus
- International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Faruque Parvez
- Department of Environmental Health Sciences, Columbia University, New York, New York, United States of America
| | - Shantanu Roy
- Department of Health Studies, University of Chicago, Chicago, Illinois, United States of America
| | - Farzana Jasmine
- Department of Health Studies, University of Chicago, Chicago, Illinois, United States of America
| | - John A. Baron
- Department of Medicine, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Muhammad G. Kibriya
- Department of Health Studies, University of Chicago, Chicago, Illinois, United States of America
| | - Habibul Ahsan
- Department of Health Studies, University of Chicago, Chicago, Illinois, United States of America
- * E-mail:
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361
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Common genetic variation in and near the melanocortin 4 receptor gene (MC4R) is associated with body mass index in American Indian adults and children. Hum Genet 2014; 133:1431-41. [PMID: 25103139 PMCID: PMC4185108 DOI: 10.1007/s00439-014-1477-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2014] [Accepted: 08/01/2014] [Indexed: 01/30/2023]
Abstract
Six rare functional coding mutations were previously identified in melanocortin 4 receptor (MC4R) in 6,760 American Indians. Individuals heterozygous for one of these mutations become obese while young. We now investigate whether common non-coding variation near MC4R also contributes to obesity. Fifty-six tag single-nucleotide polymorphisms (SNPs) were genotyped in 3,229 full-heritage Pima Indians, and nine of these SNPs which showed evidence for association were genotyped in additional 3,852 mixed-heritage American Indians. Associations of SNPs with maximum body mass index (BMI) in adulthood (n = 5,918), BMI z score in childhood (n = 5,350), percent body fat (n = 864), energy expenditure (n = 358) and ad libitum food intake (n = 178) were assessed. Conditional analyses demonstrated that SNPs, rs74861148 and rs483125, were independently associated with BMI in adulthood (β = 0.68 kg/m2 per risk allele, p = 5 × 10−5; β = 0.58 kg/m2, p = 0.002, respectively) and BMI z score in childhood (β = 0.05, p = 0.02; β = 0.07, p = 0.01, respectively). One haplotype (frequency = 0.35) of the G allele at rs74861148 and the A allele at rs483125 provided the strongest evidence for association with adult BMI (β = 0.89 kg/m2, p = 5.5 × 10−7), and was also associated with childhood BMI z score (β = 0.08, p = 0.001). In addition, a promoter SNP rs11872992 was nominally associated with adult BMI (β = 0.61 kg/m2, p = 0.05) and childhood BMI z score (β = 0.11, p = 0.01), where the risk allele also modestly decreased transcription in vitro by 12 % (p = 0.005). This risk allele was further associated with increased percent body fat (β = 2.2 %, p = 0.002), increased food intake (β = 676 kcal/day, p = 0.007) and decreased energy expenditure (β = −53.4 kcal/day, p = 0.054). Common and rare variation in MC4R contributes to obesity in American Indians.
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Kong X, Zhang X, Zhao Q, He J, Chen L, Zhao Z, Li Q, Ge J, Chen G, Guo X, Lu J, Weng J, Jia W, Ji L, Xiao J, Shan Z, Liu J, Tian H, Ji Q, Zhu D, Zhou Z, Shan G, Yang W. Obesity-related genomic loci are associated with type 2 diabetes in a Han Chinese population. PLoS One 2014; 9:e104486. [PMID: 25093408 PMCID: PMC4122466 DOI: 10.1371/journal.pone.0104486] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Accepted: 07/09/2014] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND AIMS Obesity is a well-known risk factor for type 2 diabetes. Genome-wide association studies have identified a number of genetic loci associated with obesity. The aim of this study is to examine the contribution of obesity-related genomic loci to type 2 diabetes in a Chinese population. METHODS We successfully genotyped 18 obesity-related single nucleotide polymorphisms among 5338 type 2 diabetic patients and 4663 controls. Both individual and joint effects of these single nucleotide polymorphisms on type 2 diabetes and quantitative glycemic traits (assessing β-cell function and insulin resistance) were analyzed using logistic and linear regression models, respectively. RESULTS Two single nucleotide polymorphisms near MC4R and GNPDA2 genes were significantly associated with type 2 diabetes before adjusting for body mass index and waist circumference (OR (95% CI) = 1.14 (1.06, 1.22) for the A allele of rs12970134, P = 4.75×10(-4); OR (95% CI) = 1.10 (1.03, 1.17) for the G allele of rs10938397, P = 4.54×10(-3)). When body mass index and waist circumference were further adjusted, the association of MC4R with type 2 diabetes remained significant (P = 1.81×10(-2)) and that of GNPDA2 was attenuated (P = 1.26×10(-1)), suggesting the effect of the locus including GNPDA2 on type 2 diabetes may be mediated through obesity. Single nucleotide polymorphism rs2260000 within BAT2 was significantly associated with type 2 diabetes after adjusting for body mass index and waist circumference (P = 1.04×10(-2)). In addition, four single nucleotide polymorphisms (near or within SEC16B, BDNF, MAF and PRL genes) showed significant associations with quantitative glycemic traits in controls even after adjusting for body mass index and waist circumference (all P values<0.05). CONCLUSIONS This study indicates that obesity-related genomic loci were associated with type 2 diabetes and glycemic traits in the Han Chinese population.
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Affiliation(s)
- Xiaomu Kong
- Department of Endocrinology, Key Laboratory of Diabetes Prevention and Control of China-Japan Friendship Hospital, Beijing, China
| | - Xuelian Zhang
- Department of Endocrinology, Key Laboratory of Diabetes Prevention and Control of China-Japan Friendship Hospital, Beijing, China
| | - Qi Zhao
- Department of Epidemiology, Tulane School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
| | - Jiang He
- Department of Epidemiology, Tulane School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
| | - Li Chen
- Department of Endocrinology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Zhigang Zhao
- Department of Endocrinology, Henan Province People's Hospital, Zhengzhou, Henan, China
| | - Qiang Li
- Department of Endocrinology, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Jiapu Ge
- Department of Endocrinology, Xinjiang Uygur Autonomous Region's Hospital, Urmqi, Xinjiang, China
| | - Gang Chen
- Department of Endocrinology, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Xiaohui Guo
- Department of Endocrinology, Peking University First Hospital, Beijing, China
| | - Juming Lu
- Department of Endocrinology, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Jianping Weng
- Department of Endocrinology, Sun Yat-sen University Third Hospital, Guangzhou, Guangdong, China
| | - Weiping Jia
- Department of Endocrinology, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Linong Ji
- Department of Endocrinology, Peking University People's Hospital, Beijing, China
| | - Jianzhong Xiao
- Department of Endocrinology, Key Laboratory of Diabetes Prevention and Control of China-Japan Friendship Hospital, Beijing, China
| | - Zhongyan Shan
- Department of Endocrinology, First Affiliated Hospital, Chinese Medical University, Shenyang, Liaoning, China
| | - Jie Liu
- Department of Endocrinology, Shanxi Province People's Hospital, Taiyuan, Shanxi, China
| | - Haoming Tian
- Department of Endocrinology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qiuhe Ji
- Department of Endocrinology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Dalong Zhu
- Department of Endocrinology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Zhiguang Zhou
- Department of Endocrinology, Xiangya Second Hospital, Changsha, Hunan, China
| | - Guangliang Shan
- Department of Epidemiology, Peking Union Medical College, Beijing, China
| | - Wenying Yang
- Department of Endocrinology, Key Laboratory of Diabetes Prevention and Control of China-Japan Friendship Hospital, Beijing, China
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363
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Lute B, Jou W, Lateef DM, Goldgof M, Xiao C, Piñol RA, Kravitz AV, Miller NR, Huang YG, Girardet C, Butler AA, Gavrilova O, Reitman ML. Biphasic effect of melanocortin agonists on metabolic rate and body temperature. Cell Metab 2014; 20:333-45. [PMID: 24981835 PMCID: PMC4126889 DOI: 10.1016/j.cmet.2014.05.021] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2013] [Revised: 04/03/2014] [Accepted: 05/22/2014] [Indexed: 11/30/2022]
Abstract
The melanocortin system regulates metabolic homeostasis and inflammation. Melanocortin agonists have contradictorily been reported to both increase and decrease metabolic rate and body temperature. We find two distinct physiologic responses occurring at similar doses. Intraperitoneal administration of the nonselective melanocortin agonist MTII causes a melanocortin-4 receptor (Mc4r)-mediated hypermetabolism/hyperthermia. This is preceded by a profound, transient hypometabolism/hypothermia that is preserved in mice lacking any one of Mc1r, Mc3r, Mc4r, or Mc5r. Three other melanocortin agonists also caused hypothermia, which is actively achieved via seeking a cool environment, vasodilation, and inhibition of brown adipose tissue thermogenesis. These results suggest that the hypometabolic/hypothermic effect of MTII is not due to a failure of thermoregulation. The hypometabolism/hypothermia was prevented by dopamine antagonists, and MTII selectively activated arcuate nucleus dopaminergic neurons, suggesting that these neurons may contribute to the hypometabolism/hypothermia. We propose that the hypometabolism/hypothermia is a regulated response, potentially beneficial during extreme physiologic stress.
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Affiliation(s)
- Beth Lute
- Mouse Metabolism Core, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD 20892, USA
| | - William Jou
- Mouse Metabolism Core, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD 20892, USA
| | - Dalya M Lateef
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD 20892, USA
| | - Margalit Goldgof
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD 20892, USA
| | - Cuiying Xiao
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD 20892, USA
| | - Ramón A Piñol
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD 20892, USA
| | - Alexxai V Kravitz
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD 20892, USA
| | - Nicole R Miller
- Molecular Neuropharmacology Section, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD 20892, USA
| | - Yuning George Huang
- Kidney Disease Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD 20892, USA
| | - Clemence Girardet
- Department of Metabolism and Aging, The Scripps Research Institute, Jupiter, FL 33458, USA
| | - Andrew A Butler
- Department of Metabolism and Aging, The Scripps Research Institute, Jupiter, FL 33458, USA
| | - Oksana Gavrilova
- Mouse Metabolism Core, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD 20892, USA
| | - Marc L Reitman
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD 20892, USA.
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364
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Grarup N, Sandholt CH, Hansen T, Pedersen O. Genetic susceptibility to type 2 diabetes and obesity: from genome-wide association studies to rare variants and beyond. Diabetologia 2014; 57:1528-41. [PMID: 24859358 DOI: 10.1007/s00125-014-3270-4] [Citation(s) in RCA: 134] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2013] [Accepted: 04/22/2014] [Indexed: 12/29/2022]
Abstract
During the past 7 years, genome-wide association studies have shed light on the contribution of common genomic variants to the genetic architecture of type 2 diabetes, obesity and related intermediate phenotypes. The discoveries have firmly established more than 175 genomic loci associated with these phenotypes. Despite the tight correlation between type 2 diabetes and obesity, these conditions do not appear to share a common genetic background, since they have few genetic risk loci in common. The recent genetic discoveries do however highlight specific details of the interplay between the pathogenesis of type 2 diabetes, insulin resistance and obesity. The focus is currently shifting towards investigations of data from targeted array-based genotyping and exome and genome sequencing to study the individual and combined effect of low-frequency and rare variants in metabolic disease. Here we review recent progress as regards the concepts, methodologies and derived outcomes of studies of the genetics of type 2 diabetes and obesity, and discuss avenues to be investigated in the future within this research field.
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Affiliation(s)
- Niels Grarup
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, DIKU Building, Universitetsparken 1, 2100, Copenhagen Ø, Denmark,
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365
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Yilmaz Z, Kaplan AS, Tiwari AK, Levitan RD, Piran S, Bergen AW, Kaye WH, Hakonarson H, Wang K, Berrettini WH, Brandt HA, Bulik CM, Crawford S, Crow S, Fichter MM, Halmi KA, Johnson CL, Keel PK, Klump KL, Magistretti P, Mitchell JE, Strober M, Thornton LM, Treasure J, Woodside DB, Knight J, Kennedy JL. The role of leptin, melanocortin, and neurotrophin system genes on body weight in anorexia nervosa and bulimia nervosa. J Psychiatr Res 2014; 55:77-86. [PMID: 24831852 PMCID: PMC4191922 DOI: 10.1016/j.jpsychires.2014.04.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2014] [Revised: 03/05/2014] [Accepted: 04/04/2014] [Indexed: 12/26/2022]
Abstract
OBJECTIVE Although low weight is a key factor contributing to the high mortality in anorexia nervosa (AN), it is unclear how AN patients sustain low weight compared with bulimia nervosa (BN) patients with similar psychopathology. Studies of genes involved in appetite and weight regulation in eating disorders have yielded variable findings, in part due to small sample size and clinical heterogeneity. This study: (1) assessed the role of leptin, melanocortin, and neurotrophin genetic variants in conferring risk for AN and BN; and (2) explored the involvement of these genes in body mass index (BMI) variations within AN and BN. METHOD Our sample consisted of 745 individuals with AN without a history of BN, 245 individuals with BN without a history of AN, and 321 controls. We genotyped 20 markers with known or putative function among genes selected from leptin, melanocortin, and neurotrophin systems. RESULTS There were no significant differences in allele frequencies among individuals with AN, BN, and controls. AGRP rs13338499 polymorphism was associated with lowest illness-related BMI in those with AN (p = 0.0013), and NTRK2 rs1042571 was associated with highest BMI in those with BN (p = 0.0018). DISCUSSION To our knowledge, this is the first study to address the issue of clinical heterogeneity in eating disorder genetic research and to explore the role of known or putatively functional markers in genes regulating appetite and weight in individuals with AN and BN. If replicated, our results may serve as an important first step toward gaining a better understanding of weight regulation in eating disorders.
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Affiliation(s)
- Zeynep Yilmaz
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Clinical Research Department, Centre for Addiction and Mental Health, Toronto, Canada
| | - Allan S Kaplan
- Clinical Research Department, Centre for Addiction and Mental Health, Toronto, Canada; Institute of Medical Science, University of Toronto, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Arun K Tiwari
- Neurogenetics Section, Centre for Addiction and Mental Health, Toronto, Canada
| | - Robert D Levitan
- Institute of Medical Science, University of Toronto, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Mood and Anxiety Program, Centre for Addiction and Mental Health, Toronto, Canada
| | - Sara Piran
- Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Andrew W Bergen
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
| | - Walter H Kaye
- Department of Psychiatry, University of California, San Diego, CA, USA
| | - Hakon Hakonarson
- Joseph Stokes Jr. Research Institute, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kai Wang
- Department of Psychiatry, University of Southern California, Los Angeles, CA, USA
| | - Wade H Berrettini
- Department of Psychiatry, Center of Neurobiology and Behavior, University of Pennsylvania, Philadelphia, PA, USA
| | - Harry A Brandt
- Department of Psychiatry, Sheppard Pratt Health System, Towson, MD, USA
| | - Cynthia M Bulik
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steven Crawford
- Department of Psychiatry, Sheppard Pratt Health System, Towson, MD, USA
| | - Scott Crow
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - Manfred M Fichter
- Department of Psychiatry, University of Munich (LMU), Munich, Germany; Roseneck Hospital for Behavioral Medicine, Prien, Germany
| | - Katherine A Halmi
- Department of Psychiatry, Weill Cornell Medical College, New York, NY, USA
| | | | - Pamela K Keel
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Kelly L Klump
- Department of Psychology, Michigan State University, East Lansing, MI, USA
| | - Pierre Magistretti
- Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - James E Mitchell
- Department of Clinical Neuroscience, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, USA; Neuropsychiatric Research Institute, Fargo, ND, USA
| | - Michael Strober
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Laura M Thornton
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Janet Treasure
- Department of Academic Psychiatry, King's College London, Institute of Psychiatry, London, United Kingdom
| | - D Blake Woodside
- Department of Psychiatry, University of Toronto, Toronto, Canada; Eating Disorders Program, Toronto General Hospital, Toronto, Canada
| | - Joanne Knight
- Institute of Medical Science, University of Toronto, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Neurogenetics Section, Centre for Addiction and Mental Health, Toronto, Canada
| | - James L Kennedy
- Institute of Medical Science, University of Toronto, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Neurogenetics Section, Centre for Addiction and Mental Health, Toronto, Canada.
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Solak M, Ozdemir Erdogan M, Yildiz SH, Ucok K, Yuksel S, Arıkan Terzi ES, Bestepe A. Association of obesity with rs1421085 and rs9939609 polymorphisms of FTO gene. Mol Biol Rep 2014; 41:7381-6. [DOI: 10.1007/s11033-014-3627-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2013] [Accepted: 07/21/2014] [Indexed: 01/03/2023]
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367
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Obesity gene NEGR1 associated with white matter integrity in healthy young adults. Neuroimage 2014; 102 Pt 2:548-57. [PMID: 25072390 DOI: 10.1016/j.neuroimage.2014.07.041] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2014] [Revised: 06/23/2014] [Accepted: 07/22/2014] [Indexed: 12/14/2022] Open
Abstract
Obesity is a crucial public health issue in developed countries, with implications for cardiovascular and brain health as we age. A number of commonly-carried genetic variants are associated with obesity. Here we aim to see whether variants in obesity-associated genes--NEGR1, FTO, MTCH2, MC4R, LRRN6C, MAP2K5, FAIM2, SEC16B, ETV5, BDNF-AS, ATXN2L, ATP2A1, KCTD15, and TNN13K--are associated with white matter microstructural properties, assessed by high angular resolution diffusion imaging (HARDI) in young healthy adults between 20 and 30 years of age from the Queensland Twin Imaging study (QTIM). We began with a multi-locus approach testing how a number of common genetic risk factors for obesity at the single nucleotide polymorphism (SNP) level may jointly influence white matter integrity throughout the brain and found a wide spread genetic effect. Risk allele rs2815752 in NEGR1 was most associated with lower white matter integrity across a substantial portion of the brain. Across the area of significance in the bilateral posterior corona radiata, each additional copy of the risk allele was associated with a 2.2% lower average FA. This is the first study to find an association between an obesity risk gene and differences in white matter integrity. As our subjects were young and healthy, our results suggest that NEGR1 has effects on brain structure independent of its effect on obesity.
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368
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Kooijman S, Boon MR, Parlevliet ET, Geerling JJ, van de Pol V, Romijn JA, Havekes LM, Meurs I, Rensen PCN. Inhibition of the central melanocortin system decreases brown adipose tissue activity. J Lipid Res 2014; 55:2022-32. [PMID: 25016380 DOI: 10.1194/jlr.m045989] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The melanocortin system is an important regulator of energy balance, and melanocortin 4 receptor (MC4R) deficiency is the most common monogenic cause of obesity. We investigated whether the relationship between melanocortin system activity and energy expenditure (EE) is mediated by brown adipose tissue (BAT) activity. Therefore, female APOE*3-Leiden.CETP transgenic mice were fed a Western-type diet for 4 weeks and infused intracerebroventricularly with the melanocortin 3/4 receptor (MC3/4R) antagonist SHU9119 or vehicle for 2 weeks. SHU9119 increased food intake (+30%) and body fat (+50%) and decreased EE by reduction in fat oxidation (-42%). In addition, SHU9119 impaired the uptake of VLDL-TG by BAT. In line with this, SHU9119 decreased uncoupling protein-1 levels in BAT (-60%) and induced large intracellular lipid droplets, indicative of severely disturbed BAT activity. Finally, SHU9119-treated mice pair-fed to the vehicle-treated group still exhibited these effects, indicating that MC4R inhibition impairs BAT activity independent of food intake. These effects were not specific to the APOE*3-Leiden.CETP background as SHU9119 also inhibited BAT activity in wild-type mice. We conclude that inhibition of central MC3/4R signaling impairs BAT function, which is accompanied by reduced EE, thereby promoting adiposity. We anticipate that activation of MC4R is a promising strategy to combat obesity by increasing BAT activity.
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Affiliation(s)
- Sander Kooijman
- Department of Endocrinology and Metabolic Diseases, Leiden University Medical Center, Leiden, The Netherlands Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Mariëtte R Boon
- Department of Endocrinology and Metabolic Diseases, Leiden University Medical Center, Leiden, The Netherlands Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Edwin T Parlevliet
- Department of Endocrinology and Metabolic Diseases, Leiden University Medical Center, Leiden, The Netherlands Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands Department of Internal Medicine, Academic Medical Center, University of Amsterdam, The Netherlands
| | - Janine J Geerling
- Department of Endocrinology and Metabolic Diseases, Leiden University Medical Center, Leiden, The Netherlands Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Vera van de Pol
- Department of Endocrinology and Metabolic Diseases, Leiden University Medical Center, Leiden, The Netherlands Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Johannes A Romijn
- Department of Internal Medicine, Academic Medical Center, University of Amsterdam, The Netherlands
| | - Louis M Havekes
- Department of Endocrinology and Metabolic Diseases, Leiden University Medical Center, Leiden, The Netherlands Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands Netherlands Organization for Applied Scientific Research, Gaubius Laboratory, Leiden, The Netherlands
| | - Illiana Meurs
- Department of Endocrinology and Metabolic Diseases, Leiden University Medical Center, Leiden, The Netherlands Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Patrick C N Rensen
- Department of Endocrinology and Metabolic Diseases, Leiden University Medical Center, Leiden, The Netherlands Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
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369
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Caricilli AM, Saad MJA. Gut microbiota composition and its effects on obesity and insulin resistance. Curr Opin Clin Nutr Metab Care 2014; 17:312-8. [PMID: 24848531 DOI: 10.1097/mco.0000000000000067] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
PURPOSE OF REVIEW Rising evidence suggest that variation in the gut microbiome at gene and species levels defines subsets of individuals who have increased risk of obesity-related metabolic disorders, including insulin resistance and type 2 diabetes, which is influenced by diet and genetic profile of the host. Our goal in this review is gathering the newest findings concerning gut microbiota composition and effects on host's metabolism. RECENT FINDINGS Dietary changes have been shown as the most prominent shaper of gut microbiota composition, reflecting major phenotypes, which can also be transmitted to other individuals, in spite of genetic variances. Gut microbiota composition has also been presented as diversity, which may have important implications in metabolite production and consequent interference with inflammatory activation, insulin resistance, and obesity. SUMMARY Specific approaches made it possible to comprehend some of the interactions between certain bacterial strains and their host, and how their metabolites may interfere with host's cell signaling, changing its metabolic profile. Herein, we discuss some of the mechanisms by which alterations in the gut microbiota composition may contribute to the pathophysiology of obesity and its related comorbidities.
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Affiliation(s)
- Andrea M Caricilli
- Department of Internal Medicine, State University of Campinas, Campinas, Sau Paulo, Brazil
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370
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Poveda A, Ibáñez ME, Rebato E. Common variants in BDNF, FAIM2, FTO, MC4R, NEGR1, and SH2B1 show association with obesity-related variables in Spanish Roma population. Am J Hum Biol 2014; 26:660-9. [PMID: 24948161 DOI: 10.1002/ajhb.22576] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2013] [Revised: 04/29/2014] [Accepted: 05/28/2014] [Indexed: 12/30/2022] Open
Abstract
OBJECTIVES The objective of this study is to investigate the association between previously GWAS identified genetic variants predisposing to obesity in Europeans and obesity-related phenotypes in Roma population. METHODS A total of 24 representative single nucleotide polymorphisms (SNPs) were genotyped in 372 individuals belonging to 50 extended families of Roma population. SNPs were tested for association with seven quantitative obesity-related phenotypes in the PLINK program. RESULTS Risk variants in NEGR1, FAIM2, FTO, and SH2B1 genes were associated with increased adiposity accumulation in Roma population with effect sizes between 0.21 and 0.34 Z-scores for each copy of the BMI increasing allele. Additionally, variants in BDNF and MC4R were significantly associated with adiposity distribution but not with overall fatness. No significant association was detected between obesity-related phenotypes and variants in the first intron of the FTO gene (e.g., rs9939609). CONCLUSIONS The results of this study suggest that SNPs in or near six genes (BDNF, FAIM2, FTO, MC4R, NEGR1, and SH2B1) are significantly associated with body fat accumulation and distribution in Roma people. However, the association observed among variants in the first intron of FTO and obesity in European derived populations is not evident in the analyzed Roma sample.
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Affiliation(s)
- Alaitz Poveda
- Department of Genetics, Physical Anthropology and Animal Physiology, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), 48080, Bilbao, Spain
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371
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Lin L, Hales CM, Garber K, Jin P. Fat mass and obesity-associated (FTO) protein interacts with CaMKII and modulates the activity of CREB signaling pathway. Hum Mol Genet 2014; 23:3299-306. [PMID: 24488767 PMCID: PMC4030783 DOI: 10.1093/hmg/ddu043] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2013] [Revised: 01/15/2014] [Accepted: 01/27/2014] [Indexed: 01/09/2023] Open
Abstract
Polymorphisms in the fat mass and obesity-associated (FTO) gene have been associated with obesity in humans. FTO is a nuclear protein and its physiological function remains largely unknown, but alterations in its expression in mice influence energy expenditure, food intake and, ultimately, body weight. To understand the molecular functions of FTO, we performed a yeast two-hybrid screen to identify the protein(s) that could directly interact with human FTO protein. Using multiple assays, we demonstrate that FTO interacts with three isoforms of calcium/calmodulin-dependent protein kinase II: α, β and γ, which are protein kinases that phosphorylate a broad range of substrates. This interaction is functional; overexpression of FTO delays the dephosphorylation of cAMP response element-binding protein (CREB) in human neuroblastoma (SK-N-SH) cells, which in turn leads to a dramatic increase in the expression of the CREB targets neuropeptide receptor 1 (NPY1R) and brain-derived neurotrophic factor (BDNF), which already are known to regulate food intake and energy homeostasis. Thus, our results suggest that FTO could modulate obesity by regulating the activity of the CREB signaling pathway.
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Affiliation(s)
- Li Lin
- Department of Human Genetics and
| | - Chadwick M Hales
- Department of Neurology and Center for Neurodegenerative Diseases, Emory University School of Medicine, Atlanta, GA 30322, USA
| | | | - Peng Jin
- Department of Human Genetics and
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372
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Dearden L, Balthasar N. Sexual dimorphism in offspring glucose-sensitive hypothalamic gene expression and physiological responses to maternal high-fat diet feeding. Endocrinology 2014; 155:2144-54. [PMID: 24684305 PMCID: PMC4183922 DOI: 10.1210/en.2014-1131] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
A wealth of animal and human studies demonstrate that early life environment significantly influences adult metabolic balance, however the etiology for offspring metabolic misprogramming remains incompletely understood. Here, we determine the effect of maternal diet per se on offspring sex-specific outcomes in metabolic health and hypothalamic transcriptome regulation in mice. Furthermore, to define developmental periods of maternal diet misprogramming aspects of offspring metabolic balance, we investigated offspring physiological and transcriptomic consequences of maternal high-fat/high-sugar diet feeding during pregnancy and/or lactation. We demonstrate that female offspring of high-fat/high-sugar diet-fed dams are particularly vulnerable to metabolic perturbation with body weight increases due to postnatal processes, whereas in utero effects of the diet ultimately lead to glucose homeostasis dysregulation. Furthermore, glucose- and maternal-diet sensitive gene expression modulation in the paraventricular hypothalamus is strikingly sexually dimorphic. In summary, we uncover female-specific, maternal diet-mediated in utero misprogramming of offspring glucose homeostasis and a striking sexual dimorphism in glucose- and maternal diet-sensitive paraventricular hypothalamus gene expression adjustment. Notably, female offspring metabolic vulnerability to maternal high-fat/high-sugar diet propagates a vicious cycle of obesity and type 2 diabetes in subsequent generations.
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Affiliation(s)
- Laura Dearden
- School of Physiology and Pharmacology, University of Bristol, Bristol BS8 1TD, United Kingdom
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373
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Salloum NC, McCarthy MJ, Leckband SG, Kelsoe JR. Towards the clinical implementation of pharmacogenetics in bipolar disorder. BMC Med 2014; 12:90. [PMID: 24885933 PMCID: PMC4039055 DOI: 10.1186/1741-7015-12-90] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2013] [Accepted: 04/29/2014] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Bipolar disorder (BD) is a psychiatric illness defined by pathological alterations between the mood states of mania and depression, causing disability, imposing healthcare costs and elevating the risk of suicide. Although effective treatments for BD exist, variability in outcomes leads to a large number of treatment failures, typically followed by a trial and error process of medication switches that can take years. Pharmacogenetic testing (PGT), by tailoring drug choice to an individual, may personalize and expedite treatment so as to identify more rapidly medications well suited to individual BD patients. DISCUSSION A number of associations have been made in BD between medication response phenotypes and specific genetic markers. However, to date clinical adoption of PGT has been limited, often citing questions that must be answered before it can be widely utilized. These include: What are the requirements of supporting evidence? How large is a clinically relevant effect? What degree of specificity and sensitivity are required? Does a given marker influence decision making and have clinical utility? In many cases, the answers to these questions remain unknown, and ultimately, the question of whether PGT is valid and useful must be determined empirically. Towards this aim, we have reviewed the literature and selected drug-genotype associations with the strongest evidence for utility in BD. SUMMARY Based upon these findings, we propose a preliminary panel for use in PGT, and a method by which the results of a PGT panel can be integrated for clinical interpretation. Finally, we argue that based on the sufficiency of accumulated evidence, PGT implementation studies are now warranted. We propose and discuss the design for a randomized clinical trial to test the use of PGT in the treatment of BD.
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Affiliation(s)
| | | | | | - John R Kelsoe
- Department of Psychiatry (0603), University of California San Diego, La Jolla, CA 92093, USA.
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374
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Tapsoba JDD, Kooperberg C, Reiner A, Wang CY, Dai JY. Robust estimation for secondary trait association in case-control genetic studies. Am J Epidemiol 2014; 179:1264-72. [PMID: 24723002 DOI: 10.1093/aje/kwu039] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Secondary trait genetic association provides insight into the genetic architecture of disease etiology but requires caution in estimation. Ignoring case-control sampling may introduce bias into secondary trait association. In this paper, we compare the efficiency and robustness of various inverse probability weighted (IPW) estimators and maximum likelihood (ML) estimators. ML methods have been proposed but require correct modeling of both the secondary and the primary trait associations for valid inference. We show that ML methods using a misspecified primary trait model can severely inflate the type I error. IPW estimators are typically less efficient than ML estimators but are robust against model misspecification. When the secondary trait is available for the entire cohort, the IPW estimator with selection probabilities estimated nonparametrically and the augmented IPW estimator improve efficiency over the simple IPW estimator. We conclude that in large genetic association studies with complex sampling schemes, IPW-based estimators offer flexibility and robustness, and therefore are a viable option for analysis.
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375
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Evans DS, Calton MA, Kim MJ, Kwok PY, Miljkovic I, Harris T, Koster A, Liu Y, Tranah GJ, Ahituv N, Hsueh WC, Vaisse C. Genetic association study of adiposity and melanocortin-4 receptor (MC4R) common variants: replication and functional characterization of non-coding regions. PLoS One 2014; 9:e96805. [PMID: 24820477 PMCID: PMC4018404 DOI: 10.1371/journal.pone.0096805] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2013] [Accepted: 04/11/2014] [Indexed: 11/29/2022] Open
Abstract
Common genetic variants 3' of MC4R within two large linkage disequilibrium (LD) blocks spanning 288 kb have been associated with common and rare forms of obesity. This large association region has not been refined and the relevant DNA segments within the association region have not been identified. In this study, we investigated whether common variants in the MC4R gene region were associated with adiposity-related traits in a biracial population-based study. Single nucleotide polymorphisms (SNPs) in the MC4R region were genotyped with a custom array and a genome-wide array and associations between SNPs and five adiposity-related traits were determined using race-stratified linear regression. Previously reported associations between lower BMI and the minor alleles of rs2229616/Val103Ile and rs52820871/Ile251Leu were replicated in white female participants. Among white participants, rs11152221 in a proximal 3' LD block (closer to MC4R) was significantly associated with multiple adiposity traits, but SNPs in a distal 3' LD block (farther from MC4R) were not. In a case-control study of severe obesity, rs11152221 was significantly associated. The association results directed our follow-up studies to the proximal LD block downstream of MC4R. By considering nucleotide conservation, the significance of association, and proximity to the MC4R gene, we identified a candidate MC4R regulatory region. This candidate region was sequenced in 20 individuals from a study of severe obesity in an attempt to identify additional variants, and the candidate region was tested for enhancer activity using in vivo enhancer assays in zebrafish and mice. Novel variants were not identified by sequencing and the candidate region did not drive reporter gene expression in zebrafish or mice. The identification of a putative insulator in this region could help to explain the challenges faced in this study and others to link SNPs associated with adiposity to altered MC4R expression.
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Affiliation(s)
- Daniel S. Evans
- California Pacific Medical Center Research Institute, San Francisco, California, United States of America
| | - Melissa A. Calton
- Diabetes Center and Department of Medicine, University of California, San Francisco, California, United States of America
| | - Mee J. Kim
- Department of Bioengineering and Therapeutic Sciences and Institute for Human Genetics, University of California, San Francisco, California, United States of America
| | - Pui-Yan Kwok
- Cardiovascular Research Institute, Institute for Human Genetics, and Department of Dermatology, University of California, San Francisco, California, United States of America
| | - Iva Miljkovic
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Tamara Harris
- National Institute on Aging, Bethesda, Maryland, United States of America
| | - Annemarie Koster
- National Institute on Aging, Bethesda, Maryland, United States of America
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University, Winston-Salem, North Carolina, United States of America
| | - Gregory J. Tranah
- California Pacific Medical Center Research Institute, San Francisco, California, United States of America
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences and Institute for Human Genetics, University of California, San Francisco, California, United States of America
| | - Wen-Chi Hsueh
- Diabetes Center and Department of Medicine, University of California, San Francisco, California, United States of America
| | - Christian Vaisse
- Diabetes Center and Department of Medicine, University of California, San Francisco, California, United States of America
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376
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Larsen SC, Angquist L, Ahluwalia TS, Skaaby T, Roswall N, Tjønneland A, Halkjær J, Overvad K, Pedersen O, Hansen T, Linneberg A, Husemoen LLN, Toft U, Heitmann BL, Sørensen TIA. Dietary ascorbic acid and subsequent change in body weight and waist circumference: associations may depend on genetic predisposition to obesity--a prospective study of three independent cohorts. Nutr J 2014; 13:43. [PMID: 24886192 PMCID: PMC4024624 DOI: 10.1186/1475-2891-13-43] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2014] [Accepted: 04/29/2014] [Indexed: 01/23/2023] Open
Abstract
Background Cross-sectional data suggests that a low level of plasma ascorbic acid positively associates with both Body Mass Index (BMI) and Waist Circumference (WC). This leads to questions about a possible relationship between dietary intake of ascorbic acid and subsequent changes in anthropometry, and whether such associations may depend on genetic predisposition to obesity. Hence, we examined whether dietary ascorbic acid, possibly in interaction with the genetic predisposition to a high BMI, WC or waist-hip ratio adjusted for BMI (WHR), associates with subsequent annual changes in weight (∆BW) and waist circumference (∆WC). Methods A total of 7,569 participants’ from MONICA, the Diet Cancer and Health study and the INTER99 study were included in the study. We combined 50 obesity associated single nucleotide polymorphisms (SNPs) in four genetic scores: a score of all SNPs and a score for each of the traits (BMI, WC and WHR) with which the SNPs associate. Linear regression was used to examine the association between ascorbic acid intake and ΔBW or ΔWC. SNP-score × ascorbic acid interactions were examined by adding product terms to the models. Results We found no significant associations between dietary ascorbic acid and ∆BW or ∆WC. Regarding SNP-score × ascorbic acid interactions, each additional risk allele of the 14 WHR associated SNPs associated with a ∆WC of 0.039 cm/year (P = 0.02, 95% CI: 0.005 to 0.073) per 100 mg/day higher ascorbic acid intake. However, the association to ∆WC only remained borderline significant after adjustment for ∆BW. Conclusion In general, our study does not support an association between dietary ascorbic acid and ∆BW or ∆WC, but a diet with a high content of ascorbic acid may be weakly associated to higher WC gain among people who are genetically predisposed to a high WHR. However, given the quite limited association any public health relevance is questionable.
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Affiliation(s)
- Sofus C Larsen
- Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospitals, the Capital Region, Nordre Fasanvej 57, Hovedvejen, entrance 5, ground floor, 2000, Frederiksberg Copenhagen, Denmark.
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377
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Fang Y, Xiang Ding C, Li Jun T, Jie S, Ding You L, Fang Z, Bao Yong S, Hong Wen D. Genome wide association study: searching for genes underlying body mass index in the Chinese. BIOMEDICAL AND ENVIRONMENTAL SCIENCES : BES 2014; 27:360-370. [PMID: 24827717 PMCID: PMC4537185 DOI: 10.3967/bes2014.061] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 04/29/2013] [Accepted: 10/24/2013] [Indexed: 06/03/2023]
Abstract
OBJECTIVE Obesity is becoming a worldwide health problem. The genome wide association (GWA) study particularly for body mass index (BMI) has not been successfully conducted in the Chinese. In order to identify novel genes for BMI variation in the Chinese, an initial GWA study and a follow up replication study were performed. METHODS Affymetrix 500K SNPs were genotyped for initial GWA of 597 Northern Chinese. After quality control, 281,533 SNPs were included in the association analysis. Three SNPs were genotyped in a Southern Chinese replication sample containing 2 955 Chinese Han subjects. Association analyses were performed by Plink software. RESULTS Eight SNPs were significantly associated with BMI variation after false discovery rate (FDR) correction (P=5.45×10⁻⁷-7.26×10⁻⁶, FDR q=0.033-0.048). Two adjacent SNPs (rs4432245 & rs711906) in the eukaryotic translation initiation factor 2 alpha kinase 4 (EIF2AK4) gene were significantly associated with BMI (P=6.38×10⁻⁶ & 4.39×10⁻⁶, FDR q=0.048). In the follow-up replication study, we confirmed the associations between BMI and rs4432245, rs711906 in the EIF2AKE gene (P=0.03 & 0.01, respectively). CONCLUSION Our study suggests novel mechanisms for BMI, where EIF2AK4 has exerted a profound effect on the synthesis and storage of triglycerides and may impact on overall energy homeostasis associated with obesity. The minor allele frequencies for the two SNPs in the EIF2AK4 gene have marked ethnic differences between Caucasians and the Chinese. The association of the EIF2AK4 gene with BMI is suggested to be 'ethnic specific' in the Chinese.
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Affiliation(s)
- Yang Fang
- Department of Epidemiology and Biostatistics, School of Public Health, Central South University, Changsha 410078, Hunan, China
| | - Chen Xiang Ding
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China
| | - Tan Li Jun
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China
| | - Shen Jie
- Department of Endocrinology, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, Guangdong, China
| | - Li Ding You
- Department of Pediatrics, University of Missouri Kansas City School of Medicine, Division of Gastroenterology, Children’s Mercy Hospital, Kansas City, Missouri 64108, USA
| | - Zhang Fang
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China
| | - Sha Bao Yong
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China
| | - Deng Hong Wen
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China
- Systematic Biomedicine Research Center, University of Shanghai for Science and Technology, Shanghai 200093, China
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA 70112, USA
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379
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Scheid JL, Carr KA, Lin H, Fletcher KD, Sucheston L, Singh PK, Salis R, Erbe RW, Faith MS, Allison DB, Epstein LH. FTO polymorphisms moderate the association of food reinforcement with energy intake. Physiol Behav 2014; 132:51-6. [PMID: 24768648 DOI: 10.1016/j.physbeh.2014.04.029] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2012] [Revised: 02/19/2014] [Accepted: 04/14/2014] [Indexed: 11/15/2022]
Abstract
Food reinforcement (RRVfood) is related to increased energy intake, cross-sectionally related to obesity, and prospectively related to weight gain. The fat mass and obesity-associated (FTO) gene is related to elevated body mass index and increased energy intake. The primary purpose of the current study was to determine whether any of 68 FTO single nucleotide polymorphisms (SNPs) or a FTO risk score moderate the association between food reinforcement and energy or macronutrient intake. Energy and macronutrient intake was measured using a laboratory ad libitum snack food consumption task in 237 adults of varying BMI. Controlling for BMI, the relative reinforcing value of reading (RRVreading) and proportion of African ancestry, RRVfood predicted 14.2% of the variance in energy intake, as well as predicted carbohydrate, fat, protein and sugar intake. In individual analyses, six FTO SNPs (rs12921970, rs9936768, rs12446047, rs7199716, rs8049933 and rs11076022, spanning approximately 251kbp) moderated the relationship between RRVfood and energy intake to predict an additional 4.9-7.4% of variance in energy intake. We created an FTO risk score based on 5 FTO SNPs (rs9939609, rs8050136, rs3751812, rs1421085, and rs1121980) that are related to BMI in multiple studies. The FTO risk score did not increase variance accounted for beyond individual FTO SNPs. rs12921970 and rs12446047 served as moderators of the relationship between RRVfood and carbohydrate, fat, protein, and sugar intake. This study shows for the first time that the relationship between RRVfood and energy intake is moderated by FTO SNPs. Research is needed to understand how these processes interact to predict energy and macronutrient intake.
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Affiliation(s)
- Jennifer L Scheid
- Department of Pediatrics, University at Buffalo, School of Medicine and Biomedical Sciences, United States
| | - Katelyn A Carr
- Department of Pediatrics, University at Buffalo, School of Medicine and Biomedical Sciences, United States
| | - Henry Lin
- Department of Pediatrics, University at Buffalo, School of Medicine and Biomedical Sciences, United States
| | - Kelly D Fletcher
- Department of Pediatrics, University at Buffalo, School of Medicine and Biomedical Sciences, United States
| | - Lara Sucheston
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, United States
| | - Prashant K Singh
- Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, United States
| | - Robbert Salis
- Department of Pediatrics, Niagara Falls Memorial Hospital, United States
| | - Richard W Erbe
- Department of Pediatrics, University at Buffalo, School of Medicine and Biomedical Sciences, United States
| | - Myles S Faith
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, United States
| | - David B Allison
- Office of Energetics and Nutrition Obesity Research Center, University of Alabama at Birmingham School of Public Health, United States
| | - Leonard H Epstein
- Department of Pediatrics, University at Buffalo, School of Medicine and Biomedical Sciences, United States.
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380
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Winkler TW, Day FR, Croteau-Chonka DC, Wood AR, Locke AE, Mägi R, Ferreira T, Fall T, Graff M, Justice AE, Luan J, Gustafsson S, Randall JC, Vedantam S, Workalemahu T, Kilpeläinen TO, Scherag A, Esko T, Kutalik Z, Heid IM, Loos RJF. Quality control and conduct of genome-wide association meta-analyses. Nat Protoc 2014; 9:1192-212. [PMID: 24762786 DOI: 10.1038/nprot.2014.071] [Citation(s) in RCA: 353] [Impact Index Per Article: 32.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Rigorous organization and quality control (QC) are necessary to facilitate successful genome-wide association meta-analyses (GWAMAs) of statistics aggregated across multiple genome-wide association studies. This protocol provides guidelines for (i) organizational aspects of GWAMAs, and for (ii) QC at the study file level, the meta-level across studies and the meta-analysis output level. Real-world examples highlight issues experienced and solutions developed by the GIANT Consortium that has conducted meta-analyses including data from 125 studies comprising more than 330,000 individuals. We provide a general protocol for conducting GWAMAs and carrying out QC to minimize errors and to guarantee maximum use of the data. We also include details for the use of a powerful and flexible software package called EasyQC. Precise timings will be greatly influenced by consortium size. For consortia of comparable size to the GIANT Consortium, this protocol takes a minimum of about 10 months to complete.
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Affiliation(s)
- Thomas W Winkler
- Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Felix R Day
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK
| | - Damien C Croteau-Chonka
- 1] Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA. [2] Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Andrew R Wood
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Adam E Locke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Teresa Ferreira
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Tove Fall
- 1] Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden. [2] Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Anne E Justice
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Jian'an Luan
- Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK
| | - Stefan Gustafsson
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | | | - Sailaja Vedantam
- 1] Divisions of Endocrinology and Genetics and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, Massachusetts, USA. [2] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA. [3] Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Tuomas O Kilpeläinen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - André Scherag
- 1] Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany. [2] Clinical Epidemiology, Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany
| | - Tonu Esko
- 1] Estonian Genome Center, University of Tartu, Tartu, Estonia. [2] Divisions of Endocrinology and Genetics and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, Massachusetts, USA. [3] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA. [4] Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA
| | - Zoltán Kutalik
- 1] Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland. [2] Institute of Social and Preventive Medicine (IUMSP), Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland. [3] Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Iris M Heid
- 1] Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany. [2]
| | - Ruth J F Loos
- 1] The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA. [2] The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA. [3] The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, USA. [4]
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381
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382
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Abstract
Major depression is the commonest psychiatric disorder and in the U.S. has the greatest impact of all biomedical diseases on disability. Here we review evidence of the genetic contribution to disease susceptibility and the current state of molecular approaches. Genome-wide association and linkage results provide constraints on the allele frequencies and effect sizes of susceptibility loci, which we use to interpret the voluminous candidate gene literature. We consider evidence for the genetic heterogeneity of the disorder and the likelihood that subtypes exist that represent more genetically homogenous conditions than have hitherto been analyzed.
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Affiliation(s)
- Jonathan Flint
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN.
| | - Kenneth S Kendler
- Virginia Commonwealth University, Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA 23298-0126, USA
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383
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Larsen SC, Ängquist L, Ahluwalia TS, Skaaby T, Roswall N, Tjønneland A, Halkjær J, Overvad K, Pedersen O, Hansen T, Linneberg A, Husemoen LLN, Toft U, Heitmann BL, Sørensen TI. Interaction between genetic predisposition to obesity and dietary calcium in relation to subsequent change in body weight and waist circumference. Am J Clin Nutr 2014; 99:957-65. [PMID: 24500147 DOI: 10.3945/ajcn.113.076596] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Studies indicate an effect of dietary calcium on change in body weight (BW) and waist circumference (WC), but the results are inconsistent. Furthermore, a relation could depend on genetic predisposition to obesity. OBJECTIVE The objective was to examine whether genetic predisposition to higher body mass index (BMI), WC, or waist-hip ratio (WHR) interacts with dietary calcium in relation to subsequent annual change in BW (ΔBW) and WC (ΔWC). DESIGN The study was based on 7569 individuals from the MONItoring trends and determinants of CArdiovascular disease Study, a sample from the Danish Diet, Cancer and Health Study and the INTER99 study, with information on diet; 54 single-nucleotide polymorphisms (SNPs) associated with BMI, WC, or WHR adjusted for BMI; and potential confounders. The SNPs were combined in 4 scores as indicators of genetic predisposition; all SNPs in a general score and a score for each of 3 phenotypes: BMI, WC, and WHR. Linear regression was used to examine the association between calcium intake and ΔBW or ΔWC adjusted for concurrent ΔBW. SNP score × calcium interactions were examined by adding product terms to the models. RESULTS We found a significant ΔBW of -0.076 kg (P = 0.021; 95% CI: -0.140, -0.012) per 1000 mg Ca. No significant association was observed between dietary calcium and ΔWC. In the analyses with ΔBW as outcome, we found no significant interactions between the developed predisposition scores and calcium. However, we found a significant interaction between a score of 6 WC-associated SNPs and calcium in relation to ΔWC. Each risk allele was associated with a ΔWC of -0.043 cm (P = 0.038; 95% CI: -0.083, -0.002) per 1000 mg Ca. CONCLUSIONS Our study suggests that dietary calcium relates weakly to BW loss. We found no evidence of a general association between calcium and ΔWC, but calcium may reduce WC among people genetically predisposed to a high WC. However, further replication of this finding is needed.
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Affiliation(s)
- Sofus C Larsen
- Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospitals, the Capital Region, Copenhagen, Denmark (SCL, LÄ, BLH, and TIAS); the Novo Nordisk Foundation Center for Basic Metabolic Research, Section on Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark (TSA, OP, TH, and TIAS); the Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark (TS, AL, LLNH, UT, and BLH); the Danish Cancer Society Research Center, Copenhagen, Denmark (NR, AT, and JH); the Section of Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark (KO); the Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark (KO); the National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark (BLH); and the Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, The University of Sydney, Sydney, Australia (BLH)
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384
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Albuquerque D, Nóbrega C, Rodríguez-López R, Manco L. Association study of common polymorphisms in MSRA, TFAP2B, MC4R, NRXN3, PPARGC1A, TMEM18, SEC16B, HOXB5 and OLFM4 genes with obesity-related traits among Portuguese children. J Hum Genet 2014; 59:307-13. [PMID: 24670271 DOI: 10.1038/jhg.2014.23] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2013] [Revised: 02/06/2014] [Accepted: 03/03/2014] [Indexed: 02/01/2023]
Abstract
At least 52 genetic loci were associated with obesity-related traits. However, little is known about the genetic basis of obesity among children. This study aims to test whether 10 polymorphisms in obesity-related genes methionine sulfoxide reductase A (MSRA), transcription factor AP-2 beta (TFAP2B), melanocortin 4 receptor (MC4R), neurexin 3 (NRXN3), peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PPARGC1A), transmembrane protein 18 (TMEM18), homolog of S. cerevisiae Sec16 (SEC16B), homeobox B5 (HOXB5) and olfactomedin 4 (OLFM4) are associated with the risk of obesity in Portuguese children. A total of 730 children aging from 6 to 12 years old, recruited randomly from public schools in Portugal, were analysed. Anthropometric measurements were obtained and children were classified into three phenotypic groups, normal weight (n=256), overweight (n=320) and obese (n=154), according to the International Obesity Task Force cutoffs. Polymorphisms were genotyped by allelic discrimination TaqMan assays. The MC4R rs12970134 polymorphism was nominally associated with body mass index (BMI) (P=0.035), BMI Z-score (P=0.043) and waist circumference (P=0.020), and borderline associated with weight (P=0.053). Near nominal associations were also found for the PPARGC1A rs8192678 polymorphism with weight (P=0.061), and for the MSRA rs545854 polymorphism with BMI (P=0.055) and BMI Z-score (P=0.056). Furthermore, logistic regression showed that MC4R rs12970134 and TFAP2B rs987237 were nominally, respectively, associated (P=0.029) and borderline associated (P=0.056) with the obese phenotype. This study highlighted the possible association of MC4R, PPARGC1A, MSRA and TFAP2B polymorphisms with several obesity-related traits in a sample of Portuguese children. The two significant associated TFAP2B rs987237 and MC4R rs12970134 polymorphisms showed an opposite direction of effect to that in the original reports.
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Affiliation(s)
- David Albuquerque
- Department of Life Sciences, Research Centre for Anthropology and Health (CIAS), University of Coimbra, Coimbra, Portugal
| | - Clévio Nóbrega
- Center for Neurosciences and Cell Biology, University of Coimbra, Coimbra, Portugal
| | | | - Licínio Manco
- Department of Life Sciences, Research Centre for Anthropology and Health (CIAS), University of Coimbra, Coimbra, Portugal
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385
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Zhu J, Loos RJF, Lu L, Zong G, Gan W, Ye X, Sun L, Li H, Lin X. Associations of genetic risk score with obesity and related traits and the modifying effect of physical activity in a Chinese Han population. PLoS One 2014; 9:e91442. [PMID: 24626232 PMCID: PMC3953410 DOI: 10.1371/journal.pone.0091442] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2013] [Accepted: 02/11/2014] [Indexed: 11/30/2022] Open
Abstract
Background/Objectives Recent large-scale genome-wide association studies have identified multiple loci robustly associated with BMI, predominantly in European ancestry (EA) populations. However, associations of these loci with obesity and related traits have not been well described in Chinese Hans. This study aimed to investigate whether BMI-associated loci are, individually and collectively, associated with adiposity-related traits and obesity in Chinese Hans and whether these associations are modified by physical activity (PA). Subjects/Methods We genotyped 28 BMI-associated single nucleotide polymorphisms (SNPs) in a population-based cohort including 2,894 unrelated Han Chinese. Genetic risk score (GRS), EA and East Asian ancestry (EAA) GRSs were calculated by adding BMI-increasing alleles based on all, EA and EAA identified SNPs, respectively. Interactions of GRS and PA were examined by including the interaction-term in the regression model. Results Individually, 26 of 28 SNPs showed directionally consistent effects on BMI, and associations of four loci (TMEM18, PCSK1, BDNF and MAP2K5) reached nominal significance (P<0.05). The GRS was associated with increased BMI, trunk fat and body fat percentages; and increased risk of obesity and overweight (all P<0.05). Effect sizes (0.11 vs. 0.17 kg/m2) and explained variance (0.90% vs. 1.45%) of GRS for BMI tended to be lower in Chinese Hans than in Europeans. The EA GRS and EAA GRS were associated with 0.11 and 0.13 kg/m2 higher BMI, respectively. In addition, we found that PA attenuated the effect of the GRS on BMI (Pinteraction = 0.022). Conclusions Our observations suggest that the combined effect of obesity-susceptibility loci on BMI tended to be lower in Han Chinese than in EA. The overall, EA and EAA GRSs exert similar effects on adiposity traits. Genetic predisposition to increased BMI is attenuated by PA in this population of Han Chinese.
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Affiliation(s)
- Jingwen Zhu
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate University of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, The Mindich Child Health and Development Institute, The Genetics of Obesity and Related Metabolic Traits Program, Department of Preventive Medicine, The Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Ling Lu
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate University of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Geng Zong
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate University of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Wei Gan
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate University of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Xingwang Ye
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate University of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Liang Sun
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate University of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Huaixing Li
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate University of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
- * E-mail: (HL); (XL)
| | - Xu Lin
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate University of the Chinese Academy of Sciences, Shanghai, People’s Republic of China
- * E-mail: (HL); (XL)
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386
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Albuquerque D, Estévez MN, Víbora PB, Giralt PS, Balsera AM, Cortés PG, López MJ, Luego LM, Gervasini G, Hernández SB, Arroyo-Díez J, Vacas MA, Nóbrega C, Manco L, Rodríguez-López R. Novel Variants in theMC4RandLEPRGenes among Severely Obese Children from the Iberian Population. Ann Hum Genet 2014; 78:195-207. [DOI: 10.1111/ahg.12058] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2013] [Accepted: 01/21/2014] [Indexed: 12/12/2022]
Affiliation(s)
- David Albuquerque
- Research Centre for Anthropology and Health (CIAS); Department of Life Sciences; University of Coimbra; Portugal
| | | | - Pilar Beato Víbora
- Department of Dietician; Endocrinologist Service; Infanta Cristina Hospital; Badajoz Spain
| | - Plácida Sánchez Giralt
- Department of Dietician; Endocrinologist Service; Infanta Cristina Hospital; Badajoz Spain
| | | | - Pedro Gil Cortés
- Department of Dietician; Endocrinologist Service; Infanta Cristina Hospital; Badajoz Spain
| | - Mercedes Jiménez López
- Department of Medical & Surgical Therapeutics; Medical School; University of Extremadura; Badajoz Spain
| | - Luis Miguel Luego
- Department of Dietician; Endocrinologist Service; Infanta Cristina Hospital; Badajoz Spain
| | - Guillermo Gervasini
- Department of Medical & Surgical Therapeutics; Medical School; University of Extremadura; Badajoz Spain
| | | | | | | | - Clévio Nóbrega
- Center for Neurosciences & Cell Biology; University of Coimbra; Portugal
| | - Licínio Manco
- Research Centre for Anthropology and Health (CIAS); Department of Life Sciences; University of Coimbra; Portugal
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387
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Abstract
Metabolic syndrome is not a disease per se, but is a term that highlights traits that may have an increased risk of disease, approximately 2-fold for cardiovascular disease and 5-fold or more for type 2 diabetes mellitus. Obesity and insulin resistance are believed to be at the core of most cases of metabolic syndrome, although further research is required to truly understand the pathophysiology behind the syndrome and the gene-environment interactions that increase susceptibility. The mainstay of treatment remains lifestyle changes with exercise and diet to induce weight loss and pharmacologic intervention to treat atherogenic dyslipidemia, hypertension, and hyperglycemia.
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Affiliation(s)
- Susan L Samson
- Department of Medicine, Baylor College of Medicine, One Baylor Plaza, ABBR R615, Houston, TX 77030, USA
| | - Alan J Garber
- Department of Medicine, Baylor College of Medicine, One Baylor Plaza, BCM 620, Houston, TX 77030, USA; Department of Molecular and Cellular Biology, Baylor College of Medicine, One Baylor Plaza, BCM 620, Houston, TX 77030, USA; Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, BCM 620, Houston, TX 77030, USA.
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388
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Vasan SK, Karpe F, Gu HF, Brismar K, Fall CH, Ingelsson E, Fall T. FTO genetic variants and risk of obesity and type 2 diabetes: a meta-analysis of 28,394 Indians. Obesity (Silver Spring) 2014; 22:964-70. [PMID: 23963770 DOI: 10.1002/oby.20606] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2012] [Accepted: 08/13/2013] [Indexed: 01/05/2023]
Abstract
OBJECTIVE To investigate the magnitude of association of FTO variants with obesity, type 2 diabetes (T2DM), and related traits among Asian Indians. METHODS Random-effect meta-analysis was performed on pooled data from eight studies (n = 28,394) for obesity and related traits and six studies (n = 24,987) for assessment of T2DM risk in Indians where FTO variants were reported. RESULTS The minor A-allele of the FTO variant rs9939609 was associated with increased risk of obesity (OR 1.15, 95% CI 1.08-1.21, p = 2.14 × 10(-) (5) ), BMI (β = 0.30 kg/m2, 95% CI 0.21-0.38, p = 4.78 × 10(-) (11) ) and other regional adiposity measurements [waist (β = 0.74 cm, 95% CI 0.49-0.99), HC (β = 0.52, 95% CI 0.26-0.78), and waist-hip ratio (WHR) (β = 0.002, 95% CI 0.001-0.004)] in Indians (p ≤ 0.001). An increased risk for T2DM (OR 1.11; 95% CI 1.04-1.19, p = 0.002) was observed, which attenuated when adjusted for age, gender, and BMI (OR 1.09; 95%CI 1.02-1.16, p = 0.01). CONCLUSIONS Our study provides evidence of association between common FTO variant and obesity risk among Indians with comparable effect sizes as in Caucasians. The attenuation of FTO-T2DM risk on BMI adjustment reinforces that BMI does not fully account for the adiposity effects among Asian Indians who are more centrally obese.
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Affiliation(s)
- Senthil K Vasan
- Department of Molecular Medicine and Surgery Rolf Luft Research Center for Diabetes and Endocrinology, Karolinska Institutet, Stockholm, Sweden; Department of Endocrinology Diabetes and Metabolism, Christian Medical College, Vellore, Tamil Nadu, India
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389
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Chong PN, Teh CPW, Poh BK, Noor MI. Etiology of Obesity Over the Life Span: Ecological and Genetic Highlights from Asian Countries. Curr Obes Rep 2014; 3:16-37. [PMID: 26626465 DOI: 10.1007/s13679-013-0088-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Obesity is a worldwide pandemic, and the prevalence rate has doubled since the 1980s. Asian countries are also experiencing the global epidemic of obesity with its related health consequences. The prevalence of overweight and obesity are increasing at an alarming rate across all age groups in Asia. These increases are mainly attributed to rapid economic growth, which leads to socio-economic, nutrition and lifestyle transitions, resulting in a positive energy balance. In addition, fat mass and obesity-associated gene variants, copy number variants in chromosomes and epigenetic modifications have shown positive associations with the risk of obesity among Asians. In this review highlights of prevalence and related ecological and genetic factors that could influence the rapid rise in obesity among Asian populations are discussed.
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Affiliation(s)
- Pei Nee Chong
- Nutritional Sciences Programme, School of Healthcare Sciences, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Abdul Aziz, 50300, Kuala Lumpur, Malaysia
| | - Christinal Pey Wen Teh
- UKM Medical Molecular Biology Institute, Universiti Kebangsaan Malaysia, Jalan Ya'acob Latiff, Bandar Tun Razak, 56000, Cheras, Kuala Lumpur, Malaysia
| | - Bee Koon Poh
- Nutritional Sciences Programme, School of Healthcare Sciences, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Abdul Aziz, 50300, Kuala Lumpur, Malaysia.
| | - Mohd Ismail Noor
- Department of Nutrition and Dietetics, Faculty of Health Sciences, MARA University of Technology, 42300, Puncak Alam, Selangor, Malaysia
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390
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Srivastava A, Mittal B, Prakash J, Narain VS, Natu S, Srivastava N. Evaluation of MC4R [rs17782313, rs17700633], AGRP [rs3412352] and POMC [rs1042571] Polymorphisms with Obesity in Northern India. Oman Med J 2014; 29:114-118. [PMID: 24715938 PMCID: PMC3976726 DOI: 10.5001/omj.2014.28] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2013] [Accepted: 02/11/2014] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE Genetic variants of the melanocortin-4 receptor gene (MC4R), agouti related protein (AGRP) and proopiomelanocortin (POMC) are reported to be associated with obesity. Therefore, the aim of this study is to examine MC4R rs17782313, MC4R rs17700633, AGRP rs3412352 and POMCrs1042571 for any association with obesity in North Indian subjects. METHODS The variants were investigated for association in 300 individuals with BMI ≥30 kg/m(2) and 300 healthy non-obese individuals BMI <30 kg/m(2.) The genotyping were analyzed by Taqman probes. The statistical analysis was performed by the SPSS software, ver.19 and p≤0.05 was considered statistically significant. RESULTS The genotypes of MC4R rs17782313 and POMC rs1042571 were significantly associated with obesity (C), (p=0.02; OR=1.7 and p=0.01; OR=1.6, respectively); however, MC4Rrs17700633 (p=0.001; OR=0.55) was associated with low risk. In addition, AGRPrs3412352 (p=0.93; OR=0.96) showed no association with obesity (BMI ≥30 kg/m(2)) in North Indian subjects. CONCLUSION This study provides the report about the significant association of MC4R (rs17782313) and POMC (rs1042571) with morbid obesity (BMI ≥30 kg/m(2)), but MC4R (rs17700633) and AGRP (rs34123523) did not show any association with obesity in the studied North Indian population.
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Affiliation(s)
- Apurva Srivastava
- Department of Physiology, King George's Medical University, (Erstwhile Chhatrapati Shahuji Maharaj Medical University), Chowk, Lucknow, Uttar Pradesh, India 226003
| | - Balraj Mittal
- Department of Medical Genetics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Rae Bareli Road, Lucknow, Uttar Pradesh, India 226014
| | - Jai Prakash
- Department of Pediatrics, King George's Medical University, (Erstwhile Chhatrapati Shahuji Maharaj Medical University), Chowk, Lucknow, Uttar Pradesh, India 226003
| | - Varun Shanker Narain
- Department of Cardiology, King George's Medical University, (Erstwhile Chhatrapati Shahuji Maharaj Medical University), Chowk, Lucknow, Uttar Pradesh, India 226003
| | - S.M. Natu
- Department of Pathology, King George's Medical University, (Erstwhile Chhatrapati Shahuji Maharaj Medical University), Chowk, Lucknow, Uttar Pradesh, India 226003
| | - Neena Srivastava
- Department of Physiology, King George's Medical University, (Erstwhile Chhatrapati Shahuji Maharaj Medical University), Chowk, Lucknow, Uttar Pradesh, India 226003
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391
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Sandholt CH, Allin KH, Toft U, Borglykke A, Ribel-Madsen R, Sparso T, Justesen JM, Harder MN, Jørgensen T, Hansen T, Pedersen O. The effect of GWAS identified BMI loci on changes in body weight among middle-aged Danes during a five-year period. Obesity (Silver Spring) 2014; 22:901-8. [PMID: 23804573 DOI: 10.1002/oby.20540] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2012] [Revised: 04/30/2013] [Accepted: 05/27/2013] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Genome-wide association studies have identified genetic variants associating with BMI, however, it is un-clarified whether the same variants also influence body weight fluctuations. METHODS Among 3,982 adult individuals that attended both a baseline and a five-year follow-up examination in the Danish Inter99 intervention study, a genetic risk score (GRS) was constructed based on 30 BMI variants to address whether it is associated with body weight changes. Moreover, it was examined whether the effect of lifestyle changes was modulated by the GRS. RESULTS The GRS associated strongly with baseline body weight, with a per risk allele increase of 0.45 (0.33-0.58) kg (P = 2.7 × 10(-12) ), corresponding to a body weight difference of 3.41 (2.21-4.60) kg comparing the highest (≥ 30 risk alleles) and lowest (≤ 26 risk alleles) risk allele tertile. No association was observed with changes in body weight during the five years. Changes in lifestyle, including physical activity, diet and smoking habits associated strongly with body weight changes, however, no interactions with the GRS was observed. CONCLUSION The GRS associated with body weight cross-sectionally, but not with changes over a five-year period. Body weight changes were influenced by lifestyle changes, however, independently of the GRS.
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Affiliation(s)
- C 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
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392
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Valcárcel B, Ebbels TMD, Kangas AJ, Soininen P, Elliot P, Ala-Korpela M, Järvelin MR, de Iorio M. Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity. J R Soc Interface 2014; 11:20130908. [PMID: 24573330 PMCID: PMC3973353 DOI: 10.1098/rsif.2013.0908] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Current studies of phenotype diversity by genome-wide association studies (GWAS) are mainly focused on identifying genetic variants that influence level changes of individual traits without considering additional alterations at the system-level. However, in addition to level alterations of single phenotypes, differences in association between phenotype levels are observed across different physiological states. Such differences in molecular correlations between states can potentially reveal information about the system state beyond that reported by changes in mean levels alone. In this study, we describe a novel methodological approach, which we refer to as genome metabolome integrated network analysis (GEMINi) consisting of a combination of correlation network analysis and genome-wide correlation study. The proposed methodology exploits differences in molecular associations to uncover genetic variants involved in phenotype variation. We test the performance of the GEMINi approach in a simulation study and illustrate its use in the context of obesity and detailed quantitative metabolomics data on systemic metabolism. Application of GEMINi revealed a set of metabolic associations which differ between normal and obese individuals. While no significant associations were found between genetic variants and body mass index using a standard GWAS approach, further investigation of the identified differences in metabolic association revealed a number of loci, several of which have been previously implicated with obesity-related processes. This study highlights the advantage of using molecular associations as an alternative phenotype when studying the genetic basis of complex traits and diseases.
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Affiliation(s)
- Beatriz Valcárcel
- Department of Epidemiology and Biostatistics, School of Public Health, MRC-HPA Centre for Environment and Health, Faculty of Medicine, Imperial College London, , London, UK
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393
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Speakman JR. Evolutionary perspectives on the obesity epidemic: adaptive, maladaptive, and neutral viewpoints. Annu Rev Nutr 2014; 33:289-317. [PMID: 23862645 DOI: 10.1146/annurev-nutr-071811-150711] [Citation(s) in RCA: 108] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The prevalence of obesity in modern societies has two major contributory factors-an environmental change that has happened in historical times and a genetic predisposition that has its origins in our evolutionary history. Understanding both aspects is complex. From an evolutionary perspective, three different types of explanation have been proposed. The first is that obesity was once adaptive and enabled us to survive (or sustain fecundity) through periods of famine. People carrying so-called thrifty genes that enabled the efficient storage of energy as fat between famines would be at a selective advantage. In the modern world, however, people who have inherited these genes deposit fat in preparation for a famine that never comes, and the result is widespread obesity. The key problem with this, and any other adaptive scenario, is to understand why, if obesity was historically so advantageous, many people did not inherit these thrifty genes and in modern society are able to remain slim, despite the environmental change favoring fat storage. The second type of explanation is that obesity is not adaptive and may never even have existed in our evolutionary past, but it is favored today as a maladaptive by-product of positive selection on some other trait. An example of this type of explanation is the suggestion that obesity results from variation in brown adipose tissue thermogenesis. Finally, a third class of explanation is that most mutations in the genes that predispose us to obesity are neutral and have been drifting over evolutionary time--so-called drifty genes, leading some individuals to be obesity prone and others obesity resistant. In this article, I review the current evidence for and against these three different scenarios and conclude that the thrifty gene hypothesis is untenable but the other two ideas may provide a cogent explanation of the modern obesity phenomenon.
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Affiliation(s)
- John R Speakman
- Key State Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Chaoyang, Beijing 100101, People's Republic of China.
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394
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Garg G, Kumar J, McGuigan FE, Ridderstråle M, Gerdhem P, Luthman H, Åkesson K. Variation in the MC4R gene is associated with bone phenotypes in elderly Swedish women. PLoS One 2014; 9:e88565. [PMID: 24516669 PMCID: PMC3916440 DOI: 10.1371/journal.pone.0088565] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2013] [Accepted: 12/30/2013] [Indexed: 01/01/2023] Open
Abstract
Osteoporosis is characterized by reduced bone mineral density (BMD) and increased fracture risk. Fat mass is a determinant of bone strength and both phenotypes have a strong genetic component. In this study, we examined the association between obesity associated polymorphisms (SNPs) with body composition, BMD, Ultrasound (QUS), fracture and biomarkers (Homocysteine (Hcy), folate, Vitamin D and Vitamin B12) for obesity and osteoporosis. Five common variants: rs17782313 and rs1770633 (melanocortin 4 receptor (MC4R); rs7566605 (insulin induced gene 2 (INSIG2); rs9939609 and rs1121980 (fat mass and obesity associated (FTO) were genotyped in 2 cohorts of Swedish women: PEAK-25 (age 25, n = 1061) and OPRA (age 75, n = 1044). Body mass index (BMI), total body fat and lean mass were strongly positively correlated with QUS and BMD in both cohorts (r2 = 0.2–0.6). MC4R rs17782313 was associated with QUS in the OPRA cohort and individuals with the minor C-allele had higher values compared to T-allele homozygotes (TT vs. CT vs. CC: BUA: 100 vs. 103 vs. 103; p = 0.002); (SOS: 1521 vs. 1526 vs. 1524; p = 0.008); (Stiffness index: 69 vs. 73 vs. 74; p = 0.0006) after adjustment for confounders. They also had low folate (18 vs. 17 vs. 16; p = 0.03) and vitamin D (93 vs. 91 vs. 90; p = 0.03) and high Hcy levels (13.7 vs 14.4 vs. 14.5; p = 0.06). Fracture incidence was lower among women with the C-allele, (52% vs. 58%; p = 0.067). Variation in MC4R was not associated with BMD or body composition in either OPRA or PEAK-25. SNPs close to FTO and INSIG2 were not associated with any bone phenotypes in either cohort and FTO SNPs were only associated with body composition in PEAK-25 (p≤0.001). Our results suggest that genetic variation close to MC4R is associated with quantitative ultrasound and risk of fracture.
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Affiliation(s)
- Gaurav Garg
- Clinical and Molecular Osteoporosis Research Unit, Department of Clinical Sciences, Lund University and Department of Orthopaedics, Skåne University Hospital, Malmö, Sweden
| | - Jitender Kumar
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Fiona E. McGuigan
- Clinical and Molecular Osteoporosis Research Unit, Department of Clinical Sciences, Lund University and Department of Orthopaedics, Skåne University Hospital, Malmö, Sweden
| | - Martin Ridderstråle
- Clinical Obesity Research, Department of Endocrinology, Skåne University Hospital, Malmö, Sweden
| | - Paul Gerdhem
- Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Department of Orthopaedics, Karolinska University Hospital, Stockholm, Sweden
| | - Holger Luthman
- Medical Genetics Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Kristina Åkesson
- Clinical and Molecular Osteoporosis Research Unit, Department of Clinical Sciences, Lund University and Department of Orthopaedics, Skåne University Hospital, Malmö, Sweden
- * E-mail:
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395
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Simonson MA, McQueen MB, Keller MC. Whole-genome pathway analysis on 132,497 individuals identifies novel gene-sets associated with body mass index. PLoS One 2014; 9:e78546. [PMID: 24497910 PMCID: PMC3908858 DOI: 10.1371/journal.pone.0078546] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2013] [Accepted: 09/14/2013] [Indexed: 01/28/2023] Open
Abstract
Whole genome pathway analysis is a powerful tool for the exploration of the combined effects of gene-sets within biological pathways. This study applied Interval Based Enrichment Analysis (INRICH) to perform whole-genome pathway analysis of body-mass index (BMI). We used a discovery set composed of summary statistics from a meta-analysis of 123,865 subjects performed by the GIANT Consortium, and an independent sample of 8,632 subjects to assess replication of significant pathways. We examined SNPs within nominally significant pathways using linear mixed models to estimate their contribution to overall BMI heritability. Six pathways replicated as having significant enrichment for association after correcting for multiple testing, including the previously unknown relationships between BMI and the Reactome regulation of ornithine decarboxylase pathway, the KEGG lysosome pathway, and the Reactome stabilization of P53 pathway. Two non-overlapping sets of genes emerged from the six significant pathways. The clustering of shared genes based on previously identified protein-protein interactions listed in PubMed and OMIM supported the relatively independent biological effects of these two gene-sets. We estimate that the SNPs located in examined pathways explain ∼20% of the heritability for BMI that is tagged by common SNPs (3.35% of the 16.93% total).
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Affiliation(s)
- Matthew A. Simonson
- Department of Psychology and Neuroscience, Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado, United States of America
- Mayo Clinic, Department of Health Sciences, Division of Biomedical Statistics and Informatics, Rochester, Minnesota, United States of America
- * E-mail:
| | - Matthew B. McQueen
- Department of Integrative Physiology, Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado, United States of America
| | - Matthew C. Keller
- Department of Psychology and Neuroscience, Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado, United States of America
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396
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Pei YF, Zhang L, Liu Y, Li J, Shen H, Liu YZ, Tian Q, He H, Wu S, Ran S, Han Y, Hai R, Lin Y, Zhu J, Zhu XZ, Papasian CJ, Deng HW. Meta-analysis of genome-wide association data identifies novel susceptibility loci for obesity. Hum Mol Genet 2014; 23:820-830. [PMID: 24064335 PMCID: PMC3888264 DOI: 10.1093/hmg/ddt464] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2013] [Revised: 09/02/2013] [Accepted: 09/17/2013] [Indexed: 11/12/2022] Open
Abstract
Obesity is a major public health problem with strong genetic determination. Multiple genetic variants have been implicated for obesity by conducting genome-wide association (GWA) studies, primarily focused on body mass index (BMI). Fat body mass (FBM) is phenotypically more homogeneous than BMI and is more appropriate for obesity research; however, relatively few studies have been conducted on FBM. Aiming to identify variants associated with obesity, we carried out meta-analyses of seven GWA studies for BMI-related traits including FBM, and followed these analyses by de novo replication. The discovery cohorts consisted of 21 969 individuals from diverse ethnic populations and a total of over 4 million genotyped or imputed SNPs. The de novo replication cohorts consisted of 6663 subjects from two independent samples. To complement individual SNP-based association analyses, we also carried out gene-based GWA analyses in which all variations within a gene were considered jointly. Individual SNP-based association analyses identified a novel locus 1q21 [rs2230061, CTSS (Cathepsin S)] that was associated with FBM after the adjustment of lean body mass (LBM) (P = 3.57 × 10(-8)) at the genome-wide significance level. Gene-based association analyses identified a novel gene NLK (nemo-like kinase) in 17q11 that was significantly associated with FBM adjusted by LBM. In addition, we confirmed three previously reported obesity susceptibility loci: 16q12 [rs62033400, P = 1.97 × 10(-14), FTO (fat mass and obesity associated)], 18q22 [rs6567160, P = 8.09 × 10(-19), MC4R (melanocortin 4 receptor)] and 2p25 [rs939583, P = 1.07 × 10(-7), TMEM18 (transmembrane protein 18)]. We also found that rs6567160 may exert pleiotropic effects to both FBM and LBM. Our results provide additional insights into the molecular genetic basis of obesity and may provide future targets for effective prevention and therapeutic intervention.
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Affiliation(s)
- Yu-Fang Pei
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai 200093, P R China
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA 70112, USA
| | - Lei Zhang
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai 200093, P R China
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA 70112, USA
| | - Yongjun Liu
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA 70112, USA
| | - Jian Li
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA 70112, USA
| | - Hui Shen
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA 70112, USA
| | - Yao-Zhong Liu
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA 70112, USA
| | - Qing Tian
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA 70112, USA
| | - Hao He
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA 70112, USA
| | - Shuyan Wu
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai 200093, P R China
| | - Shu Ran
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai 200093, P R China
| | - Yingying Han
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai 200093, P R China
| | - Rong Hai
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai 200093, P R China
| | - Yong Lin
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai 200093, P R China
| | - Jingying Zhu
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai 200093, P R China
| | - Xue-Zhen Zhu
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai 200093, P R China
| | - Christopher J. Papasian
- Department of Basic Medical Science, University of Missouri-Kansas City, Kansas City, MO 64108, USA
| | - Hong-Wen Deng
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai 200093, P R China
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA 70112, USA
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397
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Abstract
OBJECTIVES An increasing number of people are at risk for developing nonalcoholic fatty liver disease (NAFLD). Because obesity is a risk factor for NAFLD, the common variants of obesity-susceptible genes may be associated with NAFLD. Our aim was to identify whether the obesity-susceptible gene variants (rs9939609, rs9930506, and rs4783819 in fat mass and obesity-associated gene (FTO); rs12970134 and rs17782313 in melanocortin-4 receptor gene (MC4R); and rs7566605, rs13428113, and rs9308762 in insulin-induced gene 2 [INSIG2]) were associated with NAFLD. METHODS The case-control study recruited 1027 Chinese children ages 7 to 18 years, including 162 children with NAFLD and 865 children without NAFLD. Anthropometric measurements, alanine transaminase (ALT) detection, liver ultrasound examination, and genotyping of 8 gene variants were performed. RESULTS The A-allele of FTO rs9939609 was associated with increased NAFLD risk (P = 0.029, odds ratio 1.43), but was not significant after being adjusted for body mass index (BMI) (P = 0.268). We also found an association between the 2 variants (rs12970134 in MC4R and rs9308762 in INSIG2) and ALT level. For rs12970134, each additional A-allele increased ALT level by 1.87 IU/L (P = 0.032). For rs9308762, the homozygotes of the C-allele had a higher ALT level than the T-allele carriers (β = 3.19, P = 0.007). After adjustment for BMI, the former association did not exist, whereas the latter reminded significant (P = 0.003). CONCLUSIONS The FTO rs9939609 A-allele increased risk of NAFLD and MC4R rs12970134 was associated with ALT level through an effect on BMI. The association between INSIG2 rs9308762 and ALT level was independent of BMI. The results provided evidence for identifying genetic factors of NAFLD and may be useful for risk assessment and personalized medicine of NAFLD.
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398
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Abstract
Obesity is a highly heritable trait. While acute and chronic changes in body weight or obesity-related comorbidities are heavily influenced by environmental factors, there are still strong genomic modifiers that help account for inter-subject variability in baseline traits and in response to interventions. This review is intended to provide an up-to-date overview of our current understanding of genetic influences on obesity, with emphasis on genetic modifiers of baseline traits and responses to intervention. We begin by reviewing how genetic variants can influence obesity. We then examine genetic modifiers of weight loss via different intervention strategies, focusing on known and potential modifiers of surgical weight loss outcomes. We will pay particular attention to the effects of patient age on outcomes, addressing the risks and benefits of adopting early intervention strategies. Finally, we will discuss how the field of bariatric surgery can leverage knowledge of genetic modifiers to adopt a personalized medicine approach for optimal outcomes across this widespread and diverse patient population.
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Affiliation(s)
- Samantha Sevilla
- Research Center for Genetic Medicine, Children's National Medical Center, 111 Michigan Ave NW, Washington, DC 20010
| | - Monica J Hubal
- Research Center for Genetic Medicine, Children's National Medical Center, 111 Michigan Ave NW, Washington, DC 20010.
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399
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Louwers YV, Rayner NW, Herrera BM, Stolk L, Groves CJ, Barber TM, Uitterlinden AG, Franks S, Laven JSE, McCarthy MI. BMI-associated alleles do not constitute risk alleles for polycystic ovary syndrome independently of BMI: a case-control study. PLoS One 2014; 9:e87335. [PMID: 24498077 PMCID: PMC3909077 DOI: 10.1371/journal.pone.0087335] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2013] [Accepted: 12/20/2013] [Indexed: 12/15/2022] Open
Abstract
Introduction Polycystic Ovary Syndrome (PCOS) has a strong genetic background and the majority of patients with PCOS have elevated BMI levels. The aim of this study was to determine to which extent BMI-increasing alleles contribute to risk of PCOS when contemporaneous BMI is taken into consideration. Methods Patients with PCOS and controls were recruited from the United Kingdom (563 cases and 791 controls) and The Netherlands (510 cases and 2720 controls). Cases and controls were of similar BMI. SNPs mapping to 12 BMI-associated loci which have been extensively replicated across different ethnicities, i.e., BDNF, FAIM2, ETV5, FTO, GNPDA2, KCTD15, MC4R, MTCH2, NEGR1, SEC16B, SH2B1, and TMEM18, were studied in association with PCOS within each cohort using the additive genetic model followed by a combined analysis. A genetic allelic count risk score model was used to determine the risk of PCOS for individuals carrying increasing numbers of BMI-increasing alleles. Results None of the genetic variants, including FTO and MC4R, was associated with PCOS independently of BMI in the meta-analysis. Moreover, no differences were observed between cases and controls in the number of BMI-risk alleles present and no overall trend across the risk score groups was observed. Conclusion In this combined analysis of over 4,000 BMI-matched individuals from the United Kingdom and the Netherlands, we observed no association of BMI risk alleles with PCOS independent of BMI.
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Affiliation(s)
- Yvonne V. Louwers
- Department of Obstetrics and Gynecology, Subdivision of Reproductive Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Nigel W. Rayner
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Blanca M. Herrera
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Lisette Stolk
- Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Christopher J. Groves
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Thomas M. Barber
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Andre G. Uitterlinden
- Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Stephen Franks
- Institute of Reproductive and Developmental Biology, Imperial College London, Hammersmith Hospital, London, United Kingdom
| | - Joop S. E. Laven
- Department of Obstetrics and Gynecology, Subdivision of Reproductive Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- * E-mail: (MIM); (JSEL)
| | - Mark I. McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
- * E-mail: (MIM); (JSEL)
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400
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Fall T, Ingelsson E. Genome-wide association studies of obesity and metabolic syndrome. Mol Cell Endocrinol 2014; 382:740-757. [PMID: 22963884 DOI: 10.1016/j.mce.2012.08.018] [Citation(s) in RCA: 203] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2011] [Revised: 05/04/2012] [Accepted: 08/27/2012] [Indexed: 11/29/2022]
Abstract
Until just a few years ago, the genetic determinants of obesity and metabolic syndrome were largely unknown, with the exception of a few forms of monogenic extreme obesity. Since genome-wide association studies (GWAS) became available, large advances have been made. The first single nucleotide polymorphism robustly associated with increased body mass index (BMI) was in 2007 mapped to a gene with for the time unknown function. This gene, now known as fat mass and obesity associated (FTO) has been repeatedly replicated in several ethnicities and is affecting obesity by regulating appetite. Since the first report from a GWAS of obesity, an increasing number of markers have been shown to be associated with BMI, other measures of obesity or fat distribution and metabolic syndrome. This systematic review of obesity GWAS will summarize genome-wide significant findings for obesity and metabolic syndrome and briefly give a few suggestions of what is to be expected in the next few years.
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Affiliation(s)
- Tove Fall
- Dept. of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Erik Ingelsson
- Dept. of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden.
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