obesity and genetics research paper

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Genetic epidemiology of obesity

Affiliation.

• 1 Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, New Orleans, LA 70112, USA.
• PMID: 17566051
• DOI: 10.1093/epirev/mxm004

Obesity has become a global epidemic and contributes to the increasing burden of type 2 diabetes, cardiovascular disease, stroke, some types of cancer, and premature death worldwide. Obesity is highly heritable and arises from the interactions of multiple genes, environmental factors, and behavior. In this paper, the authors reviewed recent developments in genetic epidemiologic research, focusing particularly on several promising genomic regions and obesity-related genes. Gene-gene and gene-environment interactions of obesity were also discussed. Published studies were accessed through the MEDLINE database. The authors also searched the Obesity Gene Map Database (http://obesitygene.pbrc.edu/) and conducted a manual search using references cited in relevant papers. Heritabilities for obesity-related phenotypes varied from 6% to 85% among various populations. As of October 2005, 253 quantitative trait loci for obesity-related phenotypes have been localized in 61 genome-wide linkage scans, and genetic variants in 127 biologic candidate genes have been reported to be associated with obesity-related phenotypes from 426 positive findings. Gene-gene interactions were also observed in several genes, and some genes were found to influence the effect of dietary intake and physical activity on obesity-related phenotypes. Integration of genetic epidemiology with functional genomics and proteomics studies will be required to fully understand the role of genetic variants in the etiology and prevention of obesity.

PubMed Disclaimer

Similar articles

• The human obesity gene map: the 2004 update. Pérusse L, Rankinen T, Zuberi A, Chagnon YC, Weisnagel SJ, Argyropoulos G, Walts B, Snyder EE, Bouchard C. Pérusse L, et al. Obes Res. 2005 Mar;13(3):381-490. doi: 10.1038/oby.2005.50. Obes Res. 2005. PMID: 15833932
• The human obesity gene map: the 2003 update. Snyder EE, Walts B, Pérusse L, Chagnon YC, Weisnagel SJ, Rankinen T, Bouchard C. Snyder EE, et al. Obes Res. 2004 Mar;12(3):369-439. doi: 10.1038/oby.2004.47. Obes Res. 2004. PMID: 15044658 Review.
• The human obesity gene map: the 2002 update. Chagnon YC, Rankinen T, Snyder EE, Weisnagel SJ, Pérusse L, Bouchard C. Chagnon YC, et al. Obes Res. 2003 Mar;11(3):313-67. doi: 10.1038/oby.2003.47. Obes Res. 2003. PMID: 12634430 Review.
• The human obesity gene map: the 2005 update. Rankinen T, Zuberi A, Chagnon YC, Weisnagel SJ, Argyropoulos G, Walts B, Pérusse L, Bouchard C. Rankinen T, et al. Obesity (Silver Spring). 2006 Apr;14(4):529-644. doi: 10.1038/oby.2006.71. Obesity (Silver Spring). 2006. PMID: 16741264 Review.
• Health-related disparities: influence of environmental factors. Olden K, White SL. Olden K, et al. Med Clin North Am. 2005 Jul;89(4):721-38. doi: 10.1016/j.mcna.2005.02.001. Med Clin North Am. 2005. PMID: 15925646 Review.
• The Evaluation of the Genetic Variation Types of the Uridine Diphosphate Glucuronosyl Transferase 1A1 Gene by Next-Generation Sequencing and Their Effects on Bilirubin Levels in Obese Children. Aslantas M, Kilicaslan O, Eröz R, Kocabay K. Aslantas M, et al. Genet Test Mol Biomarkers. 2024 Jul;28(7):275-280. doi: 10.1089/gtmb.2023.0365. Epub 2024 Jun 25. Genet Test Mol Biomarkers. 2024. PMID: 38916116
• Determinants of obesity in Latin America. Ferreira SRG, Macotela Y, Velloso LA, Mori MA. Ferreira SRG, et al. Nat Metab. 2024 Mar;6(3):409-432. doi: 10.1038/s42255-024-00977-1. Epub 2024 Mar 4. Nat Metab. 2024. PMID: 38438626 Review.
• Neurogenetics and Epigenetics of Loneliness. Bowirrat A, Elman I, Dennen CA, Gondré-Lewis MC, Cadet JL, Khalsa J, Baron D, Soni D, Gold MS, McLaughlin TJ, Bagchi D, Braverman ER, Ceccanti M, Thanos PK, Modestino EJ, Sunder K, Jafari N, Zeine F, Badgaiyan RD, Barh D, Makale M, Murphy KT, Blum K. Bowirrat A, et al. Psychol Res Behav Manag. 2023 Nov 29;16:4839-4857. doi: 10.2147/PRBM.S423802. eCollection 2023. Psychol Res Behav Manag. 2023. PMID: 38050640 Free PMC article. Review.
• Prevalence of Overweight and Obesity Among Students of Government and Private High Schools in an Indian State With Significant Tribal Population: A Cross-Sectional Analytical Study. Hedayetullah S, Sagar V, Singh SB, Minz P, Kujur A, Kashyap V, Haque MS, Kumar D, Sahu S, Wasnik A. Hedayetullah S, et al. Cureus. 2023 Aug 7;15(8):e43105. doi: 10.7759/cureus.43105. eCollection 2023 Aug. Cureus. 2023. PMID: 37692751 Free PMC article.
• Obesity and the risk of cardiometabolic diseases. Valenzuela PL, Carrera-Bastos P, Castillo-García A, Lieberman DE, Santos-Lozano A, Lucia A. Valenzuela PL, et al. Nat Rev Cardiol. 2023 Jul;20(7):475-494. doi: 10.1038/s41569-023-00847-5. Epub 2023 Mar 16. Nat Rev Cardiol. 2023. PMID: 36927772 Review.

Publication types

• Search in MeSH

Related information

Linkout - more resources, full text sources.

• Ovid Technologies, Inc.
• Silverchair Information Systems
• MedlinePlus Health Information

Research Materials

• NCI CPTC Antibody Characterization Program

• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• PubMed/Medline
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Genetics of obesity in humans: a clinical review.

1. Introduction

2. obesity-related genes and defects, 2.1. leptin, 2.2. proopiomelanocortin (pomc) deficiency, 2.3. melanocortin-4 receptor, 2.4. fto (fat mass and obesity associated gene), 2.5. chromosomal defects and obesity, 3. obesity-related syndromes, 3.1. prader–willi syndrome, 3.2. alstrom syndrome, 3.3. fragile x syndrome (fxs), 3.4. down syndrome, 3.5. bardet–biedl syndrome, 3.6. albright hereditary osteodystrophy, 3.7. wagr syndrome, 3.8. cohen syndrome, 3.9. smith–magenis syndrome, 3.10. kallmann syndrome, 4. management of genetic obesity, author contributions, informed consent statement, data availability statement, conflicts of interest.

• Kaur, Y.; de Souza, R.J.; Gibson, W.T.; Meyre, D. A systematic review of genetic syndromes with obesity. Obes. Rev. 2017 , 18 , 603–634. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Purnell, J.Q. Definitions, Classification, and Epidemiology of Obesity. In Endotext ; Feingold, K.R., Anawalt, B., Boyce, A., Chrousos, G., de Herder, W.W., Dhatariya, K., Dungan, K., Hershman, J.M., Hofland, J., Kalra, S., et al., Eds.; MDText.com, Inc.: South Dartmouth, MA, USA, 2000. [ Google Scholar ]
• Wardle, J.; Carnell, S.; Haworth, C.M.; Plomin, R. Evidence for a strong genetic influence on childhood adiposity despite the force of the obesogenic environment. Am. J. Clin. Nutr. 2008 , 87 , 398–404. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Singh, R.K.; Kumar, P.; Mahalingam, K. Molecular genetics of human obesity: A comprehensive review. Comptes Rendus Biol. 2017 , 340 , 87–108. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Duis, J.; Butler, M.G. Syndromic and Nonsyndromic Obesity: Underlying Genetic Causes in Humans. Adv. Biol. 2022 , e2101154. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Butler, M.G. Single Gene and Syndromic Causes of Obesity: Illustrative Examples. Prog. Mol. Biol. Transl. Sci. 2016 , 140 , 1–45. [ Google Scholar ]
• Farooqi, I.S. Genetic and hereditary aspects of childhood obesity. Best Pract. Res. Clin. Endocrinol. Metab. 2005 , 19 , 359–374. [ Google Scholar ] [ CrossRef ]
• Xia, Q.; Grant, S.F. The genetics of human obesity. Ann. N. Y. Acad. Sci. 2013 , 1281 , 178–190. [ Google Scholar ] [ CrossRef ]
• Choquet, H.; Meyre, D. Genetics of Obesity: What have we Learned? Curr. Genom. 2011 , 12 , 169–179. [ Google Scholar ] [ CrossRef ]
• Lyon, H.N.; Hirschhorn, J.N. Genetics of common forms of obesity: A brief overview. Am. J. Clin. Nutr. 2005 , 82 (Suppl. S1), 215S–217S. [ Google Scholar ] [ CrossRef ]
• Dietrich, J.; Lovell, S.; Veatch, O.J.; Butler, M.G. PHIP gene variants with protein modeling, interactions, and clinical phenotypes. Am. J. Med. Genet. Part A 2022 , 188 , 579–589. [ Google Scholar ] [ CrossRef ]
• Friedman, J.M.; Halaas, J.L. Leptin and the regulation of body weight in mammals. Nature 1998 , 395 , 763–770. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Vohra, M.S.; Benchoula, K.; Serpell, C.J.; Hwa, W.E. AgRP/NPY and POMC neurons in the arcuate nucleus and their potential role in treatment of obesity. Eur. J. Pharmacol. 2022 , 915 , 174611. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Franks, P.W.; Brage, S.; Luan, J.; Ekelund, U.; Rahman, M.; Farooqi, I.S.; Halsall, I.; O’Rahilly, S.; Wareham, N.J. Leptin predicts a worsening of the features of the metabolic syndrome independently of obesity. Obes. Res. 2005 , 13 , 1476–1484. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Krude, H.; Gruters, A. Implications of proopiomelanocortin (POMC) mutations in humans: The POMC deficiency syndrome. Trends Endocrinol. Metab. 2000 , 11 , 15–22. [ Google Scholar ] [ CrossRef ]
• Hilado, M.A.; Randhawa, R.S. A novel mutation in the proopiomelanocortin (POMC) gene of a Hispanic child: Metformin treatment shows a beneficial impact on the body mass index. J. Pediatr. Endocrinol. Metab. 2018 , 31 , 815–819. [ Google Scholar ] [ CrossRef ]
• Gregoric, N.; Groselj, U.; Bratina, N.; Debeljak, M.; Zerjav Tansek, M.; Suput Omladic, J.; Kovac, J.; Battelino, T.; Kotnik, P.; Avbelj Stefanija, M. Two Cases With an Early Presented Proopiomelanocortin Deficiency-A Long-Term Follow-Up and Systematic Literature Review. Front. Endocrinol. 2021 , 12 , 689387. [ Google Scholar ] [ CrossRef ]
• Yeo, G.S.; Farooqi, I.S.; Aminian, S.; Halsall, D.J.; Stanhope, R.G.; O’Rahilly, S. A frameshift mutation in MC4R associated with dominantly inherited human obesity. Nat. Genet. 1998 , 20 , 111–112. [ Google Scholar ] [ CrossRef ]
• Tao, Y.X. The melanocortin-4 receptor: Physiology, pharmacology, and pathophysiology. Endocr. Rev. 2010 , 31 , 506–543. [ Google Scholar ] [ CrossRef ]
• Frayling, T.M.; Timpson, N.J.; Weedon, M.N.; Zeggini, E.; Freathy, R.M.; Lindgren, C.M.; Perry, J.R.; Elliott, K.S.; Lango, H.; Rayner, N.W.; et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 2007 , 316 , 889–894. [ Google Scholar ] [ CrossRef ]
• Scuteri, A.; Sanna, S.; Chen, W.M.; Uda, M.; Albai, G.; Strait, J.; Najjar, S.; Nagaraja, R.; Orru, M.; Usala, G.; et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 2007 , 3 , e115. [ Google Scholar ] [ CrossRef ]
• Dina, C.; Meyre, D.; Gallina, S.; Durand, E.; Korner, A.; Jacobson, P.; Carlsson, L.M.; Kiess, W.; Vatin, V.; Lecoeur, C.; et al. Variation in FTO contributes to childhood obesity and severe adult obesity. Nat. Genet. 2007 , 39 , 724–726. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Cho, Y.S.; Go, M.J.; Kim, Y.J.; Heo, J.Y.; Oh, J.H.; Ban, H.J.; Yoon, D.; Lee, M.H.; Kim, D.J.; Park, M.; et al. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat. Genet. 2009 , 41 , 527–534. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wen, W.; Cho, Y.S.; Zheng, W.; Dorajoo, R.; Kato, N.; Qi, L.; Chen, C.H.; Delahanty, R.J.; Okada, Y.; Tabara, Y.; et al. Meta-analysis identifies common variants associated with body mass index in east Asians. Nat. Genet. 2012 , 44 , 307–311. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Okada, Y.; Kubo, M.; Ohmiya, H.; Takahashi, A.; Kumasaka, N.; Hosono, N.; Maeda, S.; Wen, W.; Dorajoo, R.; Go, M.J.; et al. Common variants at CDKAL1 and KLF9 are associated with body mass index in east Asian populations. Nat. Genet. 2012 , 44 , 302–306. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kalantari, N.; Keshavarz Mohammadi, N.; Izadi, P.; Gholamalizadeh, M.; Doaei, S.; Eini-Zinab, H.; Salonurmi, T.; Rafieifar, S.; Janipoor, R.; Azizi Tabesh, G. A complete linkage disequilibrium in a haplotype of three SNPs in Fat Mass and Obesity associated (FTO) gene was strongly associated with anthropometric indices after controlling for calorie intake and physical activity. BMC Med. Genet. 2018 , 19 , 146. [ Google Scholar ] [ CrossRef ]
• Loos, R.J.F.; Yeo, G.S.H. The genetics of obesity: From discovery to biology. Nat. Rev. Genet. 2022 , 23 , 120–133. [ Google Scholar ] [ CrossRef ]
• Castro, G.V.; Latorre, A.F.S.; Korndorfer, F.P.; de Carlos Back, L.K.; Lofgren, S.E. The Impact of Variants in Four Genes: MC4R, FTO, PPARG and PPARGC1A in Overweight and Obesity in a Large Sample of the Brazilian Population. Biochem. Genet. 2021 , 59 , 1666–1679. [ Google Scholar ] [ CrossRef ]
• Dastgheib, S.A.; Bahrami, R.; Setayesh, S.; Salari, S.; Mirjalili, S.R.; Noorishadkam, M.; Sadeghizadeh-Yazdi, J.; Akbarian, E.; Neamatzadeh, H. Evidence from a meta-analysis for association of MC4R rs17782313 and FTO rs9939609 polymorphisms with susceptibility to obesity in children. Diabetes Metab. Syndr. 2021 , 15 , 102234. [ Google Scholar ] [ CrossRef ]
• Resende, C.M.M.; Silva, H.; Campello, C.P.; Ferraz, L.A.A.; de Lima, E.L.S.; Beserra, M.A.; Muniz, M.T.C.; da Silva, L.M.P. Polymorphisms on rs9939609 FTO and rs17782313 MC4R genes in children and adolescent obesity: A systematic review. Nutrition 2021 , 91–92 , 111474. [ Google Scholar ] [ CrossRef ]
• Dumesic, D.A.; Oberfield, S.E.; Stener-Victorin, E.; Marshall, J.C.; Laven, J.S.; Legro, R.S. Scientific Statement on the Diagnostic Criteria, Epidemiology, Pathophysiology, and Molecular Genetics of Polycystic Ovary Syndrome. Endocr. Rev. 2015 , 36 , 487–525. [ Google Scholar ] [ CrossRef ]
• Glueck, C.J.; Goldenberg, N. Characteristics of obesity in polycystic ovary syndrome: Etiology, treatment, and genetics. Metab. Clin. Exp. 2019 , 92 , 108–120. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ewens, K.G.; Jones, M.R.; Ankener, W.; Stewart, D.R.; Urbanek, M.; Dunaif, A.; Legro, R.S.; Chua, A.; Azziz, R.; Spielman, R.S.; et al. FTO and MC4R gene variants are associated with obesity in polycystic ovary syndrome. PLoS ONE 2011 , 6 , e16390. [ Google Scholar ] [ CrossRef ]
• Tu, X.; Yu, C.; Gao, M.; Zhang, Y.; Zhang, Z.; He, Y.; Yao, L.; Du, J.; Sun, Y.; Sun, Z. LEPR gene polymorphism and plasma soluble leptin receptor levels are associated with polycystic ovary syndrome in Han Chinese women. Pers. Med. 2017 , 14 , 299–307. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Dasouki, M.J.; Youngs, E.L.; Hovanes, K. Structural Chromosome Abnormalities Associated with Obesity: Report of Four New subjects and Review of Literature. Curr. Genom. 2011 , 12 , 190–203. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Cheon, C.K. Genetics of Prader-Willi syndrome and Prader-Will-Like syndrome. Ann. Pediatr. Endocrinol. Metab. 2016 , 21 , 126–135. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Bellad, A.; Bandari, A.K.; Pandey, A.; Girimaji, S.C.; Muthusamy, B. A Novel Missense Variant in PHF6 Gene Causing Borjeson-Forssman-Lehman Syndrome. J. Mol. Neurosci. 2020 , 70 , 1403–1409. [ Google Scholar ] [ CrossRef ]
• Hidestrand, P.; Vasconez, H.; Cottrill, C. Carpenter syndrome. J. Craniofac. Surg. 2009 , 20 , 254–256. [ Google Scholar ] [ CrossRef ]
• Gupta, D.; Goyal, S. Cornelia de-Lange syndrome. J. Indian Soc. Pedod. Prev. Dent. 2005 , 23 , 38–41. [ Google Scholar ] [ CrossRef ]
• Raible, S.E.; Mehta, D.; Bettale, C.; Fiordaliso, S.; Kaur, M.; Medne, L.; Rio, M.; Haan, E.; White, S.M.; Cusmano-Ozog, K.; et al. Clinical and molecular spectrum of CHOPS syndrome. Am. J. Med. Genet. Part A 2019 , 179 , 1126–1138. [ Google Scholar ] [ CrossRef ]
• Abidi, F.E.; Cardoso, C.; Lossi, A.M.; Lowry, R.B.; Depetris, D.; Mattei, M.G.; Lubs, H.A.; Stevenson, R.E.; Fontes, M.; Chudley, A.E.; et al. Mutation in the 5’ alternatively spliced region of the XNP/ATR-X gene causes Chudley-Lowry syndrome. Eur. J. Hum. Genet. 2005 , 13 , 176–183. [ Google Scholar ] [ CrossRef ]
• Rogers, R.C.; Abidi, F.E. Coffin-Lowry Syndrome. In GeneReviews((R)) ; Adam, M.P., Mirzaa, G.M., Pagon, R.A., Wallace, S.E., Bean, L.J.H., Gripp, K.W., Amemiya, A., Eds.; University of Washington: Seattle, WA, USA, 1993. [ Google Scholar ]
• Kleefstra, T.; de Leeuw, N. Kleefstra, T.; de Leeuw, N. Kleefstra Syndrome. In GeneReviews((R)) ; Adam, M.P., Mirzaa, G.M., Pagon, R.A., Wallace, S.E., Bean, L.J.H., Gripp, K.W., Amemiya, A., Eds.; University of Washington: Seattle, WA, USA, 1993. [ Google Scholar ]
• Milani, D.; Manzoni, F.M.; Pezzani, L.; Ajmone, P.; Gervasini, C.; Menni, F.; Esposito, S. Rubinstein-Taybi syndrome: Clinical features, genetic basis, diagnosis, and management. Ital. J. Pediatr. 2015 , 41 , 4. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Kagami, M.; Nagasaki, K.; Kosaki, R.; Horikawa, R.; Naiki, Y.; Saitoh, S.; Tajima, T.; Yorifuji, T.; Numakura, C.; Mizuno, S.; et al. Temple syndrome: Comprehensive molecular and clinical findings in 32 Japanese patients. Genet. Med. 2017 , 19 , 1356–1366. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yearwood, E.L.; McCulloch, M.R.; Tucker, M.L.; Riley, J.B. Care of the patient with Prader-Willi syndrome. Medsurg. Nurs. 2011 , 20 , 113–122. [ Google Scholar ] [ PubMed ]
• Cassidy, S.B.; Driscoll, D.J. Prader-Willi syndrome. Eur. J. Hum. Genet. EJHG 2009 , 17 , 3–13. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Bittel, D.C.; Butler, M.G. Prader-Willi syndrome: Clinical genetics, cytogenetics and molecular biology. Expert Rev. Mol. Med. 2005 , 7 , 1–20. [ Google Scholar ] [ CrossRef ]
• Gardner, R.M.; Sutherland, G.R.; Shaffer, L.G. Chromosome Abnormalities and Genetic Counseling , 4th ed.; Oxford University Press: New York, NY, USA, 2012. [ Google Scholar ]
• Bachere, N.; Diene, G.; Delagnes, V.; Molinas, C.; Moulin, P.; Tauber, M. Early diagnosis and multidisciplinary care reduce the hospitalization time and duration of tube feeding and prevent early obesity in PWS infants. Horm. Res. 2008 , 69 , 45–52. [ Google Scholar ] [ CrossRef ]
• Butler, J.V.; Whittington, J.E.; Holland, A.J.; McAllister, C.J.; Goldstone, A.P. The transition between the phenotypes of Prader-Willi syndrome during infancy and early childhood. Dev. Med. Child Neurol. 2010 , 52 , e88–e93. [ Google Scholar ] [ CrossRef ]
• Oldzej, J.; Manazir, J.; Gold, J.A.; Mahmoud, R.; Osann, K.; Flodman, P.; Cassidy, S.B.; Kimonis, V.E. Molecular subtype and growth hormone effects on dysmorphology in Prader-Willi syndrome. Am. J. Med. Genet. Part A 2020 , 182 , 169–175. [ Google Scholar ] [ CrossRef ]
• Mahmoud, R.; Leonenko, A.; Butler, M.G.; Flodman, P.; Gold, J.A.; Miller, J.L.; Roof, E.; Dykens, E.; Driscoll, D.J.; Kimonis, V. Influence of molecular classes and growth hormone treatment on growth and dysmorphology in Prader-Willi syndrome: A multicenter study. Clin. Genet. 2021 , 100 , 29–39. [ Google Scholar ] [ CrossRef ]
• Miller, J.L.; Lynn, C.H.; Driscoll, D.C.; Goldstone, A.P.; Gold, J.A.; Kimonis, V.; Dykens, E.; Butler, M.G.; Shuster, J.J.; Driscoll, D.J. Nutritional phases in Prader-Willi syndrome. Am. J. Med. Genet. Part A 2011 , 155 , 1040–1049. [ Google Scholar ] [ CrossRef ]
• Bereket, A.; Atay, Z. Current status of childhood obesity and its associated morbidities in Turkey. J. Clin. Res. Pediatr. Endocrinol. 2012 , 4 , 1–7. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Brambilla, P.; Crino, A.; Bedogni, G.; Bosio, L.; Cappa, M.; Corrias, A.; Delvecchio, M.; Di Candia, S.; Gargantini, L.; Grechi, E.; et al. Metabolic syndrome in children with Prader-Willi syndrome: The effect of obesity. Nutr. Metab. Cardiovasc. Dis. NMCD 2011 , 21 , 269–276. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Miller, J.L.; Goldstone, A.P.; Couch, J.A.; Shuster, J.; He, G.; Driscoll, D.J.; Liu, Y.; Schmalfuss, I.M. Pituitary abnormalities in Prader-Willi syndrome and early onset morbid obesity. Am. J. Med. Genet. Part A 2008 , 146 , 570–577. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Butler, J.V.; Whittington, J.E.; Holland, A.J.; Boer, H.; Clarke, D.; Webb, T. Prevalence of, and risk factors for, physical ill-health in people with Prader-Willi syndrome: A population-based study. Dev. Med. Child Neurol. 2002 , 44 , 248–255. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Li, G.; Vega, R.; Nelms, K.; Gekakis, N.; Goodnow, C.; McNamara, P.; Wu, H.; Hong, N.A.; Glynne, R. A role for Alstrom syndrome protein, alms1, in kidney ciliogenesis and cellular quiescence. PLoS Genet. 2007 , 3 , e8. [ Google Scholar ] [ CrossRef ]
• Choudhury, A.R.; Munonye, I.; Sanu, K.P.; Islam, N.; Gadaga, C. A review of Alstrom syndrome: A rare monogenic ciliopathy. Intractable Rare Dis. Res. 2021 , 10 , 257–262. [ Google Scholar ] [ CrossRef ]
• Hunter, J.E.; Berry-Kravis, E.; Hipp, H.; Todd, P.K. FMR1 Disorders. In GeneReviews((R)) ; Adam, M.P., Mirzaa, G.M., Pagon, R.A., Wallace, S.E., Bean, L.J.H., Gripp, K.W., Amemiya, A., Eds.; University of Washington: Seattle, WA, USA, 1993. [ Google Scholar ]
• Gantois, I.; Popic, J.; Khoutorsky, A.; Sonenberg, N. Metformin for Treatment of Fragile X Syndrome and Other Neurological Disorders. Annu. Rev. Med. 2019 , 70 , 167–181. [ Google Scholar ] [ CrossRef ]
• Nowicki, S.T.; Tassone, F.; Ono, M.Y.; Ferranti, J.; Croquette, M.F.; Goodlin-Jones, B.; Hagerman, R.J. The Prader-Willi phenotype of fragile X syndrome. J. Dev. Behav. Pediatr. JDBP 2007 , 28 , 133–138. [ Google Scholar ] [ CrossRef ]
• Raspa, M.; Bailey, D.B.; Bishop, E.; Holiday, D.; Olmsted, M. Obesity, food selectivity, and physical activity in individuals with fragile X syndrome. Am. J. Intellect. Dev. Disabil. 2010 , 115 , 482–495. [ Google Scholar ] [ CrossRef ]
• Kidd, S.A.; Lachiewicz, A.; Barbouth, D.; Blitz, R.K.; Delahunty, C.; McBrien, D.; Visootsak, J.; Berry-Kravis, E. Fragile X syndrome: A review of associated medical problems. Pediatrics 2014 , 134 , 995–1005. [ Google Scholar ] [ CrossRef ]
• Choo, T.H.; Xu, Q.; Budimirovic, D.; Lozano, R.; Esler, A.N.; Frye, R.E.; Andrews, H.; Velinov, M. Height and BMI in fragile X syndrome: A longitudinal assessment. Obesity 2022 , 30 , 743–750. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Presson, A.P.; Partyka, G.; Jensen, K.M.; Devine, O.J.; Rasmussen, S.A.; McCabe, L.L.; McCabe, E.R. Current estimate of Down Syndrome population prevalence in the United States. J. Pediatr. 2013 , 163 , 1163–1168. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Asim, A.; Kumar, A.; Muthuswamy, S.; Jain, S.; Agarwal, S. Down syndrome: An insight of the disease. J. Biomed. Sci. 2015 , 22 , 41. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hendrix, C.G.; Prins, M.R.; Dekkers, H. Developmental coordination disorder and overweight and obesity in children: A systematic review. Obes. Rev. 2014 , 15 , 408–423. [ Google Scholar ] [ CrossRef ]
• Liou, T.H.; Pi-Sunyer, F.X.; Laferrere, B. Physical disability and obesity. Nutr. Rev. 2005 , 63 , 321–331. [ Google Scholar ] [ CrossRef ]
• Maiano, C.; Normand, C.L.; Aime, A.; Begarie, J. Lifestyle interventions targeting changes in body weight and composition among youth with an intellectual disability: A systematic review. Res. Dev. Disabil. 2014 , 35 , 1914–1926. [ Google Scholar ] [ CrossRef ]
• Magge, S.N.; O’Neill, K.L.; Shults, J.; Stallings, V.A.; Stettler, N. Leptin levels among prepubertal children with Down syndrome compared with their siblings. J. Pediatr. 2008 , 152 , 321–326. [ Google Scholar ] [ CrossRef ]
• Hill, D.L.; Parks, E.P.; Zemel, B.S.; Shults, J.; Stallings, V.A.; Stettler, N. Resting energy expenditure and adiposity accretion among children with Down syndrome: A 3-year prospective study. Eur. J. Clin. Nutr. 2013 , 67 , 1087–1091. [ Google Scholar ] [ CrossRef ]
• Nordstrom, M.; Retterstol, K.; Hope, S.; Kolset, S.O. Nutritional challenges in children and adolescents with Down syndrome. Lancet Child Adolesc. Health 2020 , 4 , 455–464. [ Google Scholar ] [ CrossRef ]
• Fructuoso, M.; Rachdi, L.; Philippe, E.; Denis, R.G.; Magnan, C.; Le Stunff, H.; Janel, N.; Dierssen, M. Increased levels of inflammatory plasma markers and obesity risk in a mouse model of Down syndrome. Free Radic. Biol. Med. 2018 , 114 , 122–130. [ Google Scholar ] [ CrossRef ]
• Florea, L.; Caba, L.; Gorduza, E.V. Bardet-Biedl Syndrome-Multiple Kaleidoscope Images: Insight into Mechanisms of Genotype-Phenotype Correlations. Genes 2021 , 12 , 1353. [ Google Scholar ] [ CrossRef ]
• Mantovani, G.; Elli, F.M. Inactivating PTH/PTHrP Signaling Disorders. Front. Horm. Res. 2019 , 51 , 147–159. [ Google Scholar ] [ PubMed ]
• Thiele, S.; de Sanctis, L.; Werner, R.; Grotzinger, J.; Aydin, C.; Juppner, H.; Bastepe, M.; Hiort, O. Functional characterization of GNAS mutations found in patients with pseudohypoparathyroidism type Ic defines a new subgroup of pseudohypoparathyroidism affecting selectively Gsalpha-receptor interaction. Hum. Mutat. 2011 , 32 , 653–660. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Butler, M.G. Imprinting disorders in humans: A review. Curr. Opin. Pediatr. 2020 , 32 , 719–729. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ong, K.K.; Amin, R.; Dunger, D.B. Pseudohypoparathyroidism--another monogenic obesity syndrome. Clin. Endocrinol. 2000 , 52 , 389–391. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Delaval, K.; Wagschal, A.; Feil, R. Epigenetic deregulation of imprinting in congenital diseases of aberrant growth. BioEssays 2006 , 28 , 453–459. [ Google Scholar ] [ CrossRef ]
• Fischbach, B.V.; Trout, K.L.; Lewis, J.; Luis, C.A.; Sika, M. WAGR syndrome: A clinical review of 54 cases. Pediatrics 2005 , 116 , 984–988. [ Google Scholar ] [ CrossRef ]
• Breslow, N.E.; Norris, R.; Norkool, P.A.; Kang, T.; Beckwith, J.B.; Perlman, E.J.; Ritchey, M.L.; Green, D.M.; Nichols, K.E.; National Wilms Tumor Study Group. Characteristics and outcomes of children with the Wilms tumor-Aniridia syndrome: A report from the National Wilms Tumor Study Group. J. Clin. Oncol. 2003 , 21 , 4579–4585. [ Google Scholar ] [ CrossRef ]
• Gul, D.; Ogur, G.; Tunca, Y.; Ozcan, O. Third case of WAGR syndrome with severe obesity and constitutional deletion of chromosome (11)(p12p14). Am. J. Med. Genet. 2002 , 107 , 70–71. [ Google Scholar ] [ CrossRef ]
• Marlin, S.; Couet, D.; Lacombe, D.; Cessans, C.; Bonneau, D. Obesity: A new feature of WAGR (del 11p) syndrome. Clin. Dysmorphol. 1994 , 3 , 255–257. [ Google Scholar ] [ CrossRef ]
• Tiberio, G.; Digilio, M.C.; Giannotti, A. Obesity and WAGR syndrome. Clin. Dysmorphol. 2000 , 9 , 63–64. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Han, J.C.; Liu, Q.R.; Jones, M.; Levinn, R.L.; Menzie, C.M.; Jefferson-George, K.S.; Adler-Wailes, D.C.; Sanford, E.L.; Lacbawan, F.L.; Uhl, G.R.; et al. Brain-derived neurotrophic factor and obesity in the WAGR syndrome. N. Engl. J. Med. 2008 , 359 , 918–927. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Rodrigues, J.M.; Fernandes, H.D.; Caruthers, C.; Braddock, S.R.; Knutsen, A.P. Cohen Syndrome: Review of the Literature. Cureus 2018 , 10 , e3330. [ Google Scholar ] [ CrossRef ]
• Wang, H.; Falk, M.J.; Wensel, C.; Traboulsi, E.I. Cohen Syndrome. In GeneReviews((R)) ; Adam, M.P., Mirzaa, G.M., Pagon, R.A., Wallace, S.E., Bean, L.J.H., Gripp, K.W., Amemiya, A., Eds.; University of Washington: Seattle, WA, USA, 1993. [ Google Scholar ]
• Limoge, F.; Faivre, L.; Gautier, T.; Petit, J.M.; Gautier, E.; Masson, D.; Jego, G.; El Chehadeh-Djebbar, S.; Marle, N.; Carmignac, V.; et al. Insulin response dysregulation explains abnormal fat storage and increased risk of diabetes mellitus type 2 in Cohen Syndrome. Hum. Mol. Genet. 2015 , 24 , 6603–6613. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Smith, A.C.M.; Boyd, K.E.; Brennan, C.; Charles, J.; Elsea, S.H.; Finucane, B.M.; Foster, R.; Gropman, A.; Girirajan, S.; Haas-Givler, B. Smith-Magenis Syndrome. In GeneReviews((R)) ; Adam, M.P., Mirzaa, G.M., Pagon, R.A., Wallace, S.E., Bean, L.J.H., Gripp, K.W., Amemiya, A., Eds.; University of Washington: Seattle, WA, USA, 1993. [ Google Scholar ]
• Stamou, M.I.; Georgopoulos, N.A. Kallmann syndrome: Phenotype and genotype of hypogonadotropic hypogonadism. Metabolism 2018 , 86 , 124–134. [ Google Scholar ] [ CrossRef ]
• Markham, A. Setmelanotide: First Approval. Drugs 2021 , 81 , 397–403. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Farooqi, I.S.; Matarese, G.; Lord, G.M.; Keogh, J.M.; Lawrence, E.; Agwu, C.; Sanna, V.; Jebb, S.A.; Perna, F.; Fontana, S.; et al. Beneficial effects of leptin on obesity, T cell hyporesponsiveness, and neuroendocrine/metabolic dysfunction of human congenital leptin deficiency. J. Clin. Investig. 2002 , 110 , 1093–1103. [ Google Scholar ] [ CrossRef ]
• Lindgren, A.C.; Hagenas, L.; Muller, J.; Blichfeldt, S.; Rosenborg, M.; Brismar, T.; Ritzen, E.M. Effects of growth hormone treatment on growth and body composition in Prader-Willi syndrome: A preliminary report. The Swedish National Growth Hormone Advisory Group. Acta Paediatr. 1997 , 423 , 60–62. [ Google Scholar ] [ CrossRef ]
• Lindgren, A.C.; Hagenas, L.; Muller, J.; Blichfeldt, S.; Rosenborg, M.; Brismar, T.; Ritzen, E.M. Growth hormone treatment of children with Prader-Willi syndrome affects linear growth and body composition favourably. Acta Paediatr. 1998 , 87 , 28–31. [ Google Scholar ] [ CrossRef ]
• Eiholzer, U.; Gisin, R.; Weinmann, C.; Kriemler, S.; Steinert, H.; Torresani, T.; Zachmann, M.; Prader, A. Treatment with human growth hormone in patients with Prader-Labhart-Willi syndrome reduces body fat and increases muscle mass and physical performance. Eur. J. Pediatr. 1998 , 157 , 368–377. [ Google Scholar ] [ CrossRef ]
• Whitman, B.Y.; Myers, S.; Carrel, A.; Allen, D. The behavioral impact of growth hormone treatment for children and adolescents with Prader-Willi syndrome: A 2-year, controlled study. Pediatrics 2002 , 109 , E35. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Goldstone, A.P.; Holland, A.J.; Hauffa, B.P.; Hokken-Koelega, A.C.; Tauber, M.; Speakers Contributors at the Second Expert Meeting of the Comprehensive Care of Patients with PWS. Recommendations for the diagnosis and management of Prader-Willi syndrome. J. Clin. Endocrinol. Metab. 2008 , 93 , 4183–4197. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Festen, D.A.; de Lind van Wijngaarden, R.; van Eekelen, M.; Otten, B.J.; Wit, J.M.; Duivenvoorden, H.J.; Hokken-Koelega, A.C. Randomized controlled GH trial: Effects on anthropometry, body composition and body proportions in a large group of children with Prader-Willi syndrome. Clin. Endocrinol. 2008 , 69 , 443–451. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Medscape. FDA Approves First Drug to Treat Children with Prader-Willi Syndrome Medscape Medical News [Online], 2000. Available online: http://www.medscape.com/viewarticle/411964 (accessed on 14 May 2010).
• Butler, M.G.; Smith, B.K.; Lee, J.; Gibson, C.; Schmoll, C.; Moore, W.V.; Donnelly, J.E. Effects of growth hormone treatment in adults with Prader-Willi syndrome. Growth Horm. IGF Res. 2013 , 23 , 81–87. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]

Click here to enlarge figure

Share and Cite

Mahmoud, R.; Kimonis, V.; Butler, M.G. Genetics of Obesity in Humans: A Clinical Review. Int. J. Mol. Sci. 2022 , 23 , 11005. https://doi.org/10.3390/ijms231911005

Mahmoud R, Kimonis V, Butler MG. Genetics of Obesity in Humans: A Clinical Review. International Journal of Molecular Sciences . 2022; 23(19):11005. https://doi.org/10.3390/ijms231911005

Mahmoud, Ranim, Virginia Kimonis, and Merlin G. Butler. 2022. "Genetics of Obesity in Humans: A Clinical Review" International Journal of Molecular Sciences 23, no. 19: 11005. https://doi.org/10.3390/ijms231911005

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

• Search Menu
• Sign in through your institution
• Advance articles
• Author Guidelines
• Submission Site
• Open Access
• About Epidemiologic Reviews
• About the Johns Hopkins Bloomberg School of Public Health
• Editorial Board
• Advertising and Corporate Services
• Journals Career Network
• Self-Archiving Policy
• Dispatch Dates
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

Heritability of obesity, monogenic obesity, genome-wide linkage studies, candidate gene association studies, gene-gene interaction in obesity, gene-environment interaction in obesity, genes and diet interaction, genes and physical activity interaction, future directions, conclusions.

• < Previous

Genetic Epidemiology of Obesity

• Article contents
• Figures & tables
• Supplementary Data

Wenjie Yang, Tanika Kelly, Jiang He, Genetic Epidemiology of Obesity, Epidemiologic Reviews , Volume 29, Issue 1, 2007, Pages 49–61, https://doi.org/10.1093/epirev/mxm004

• Permissions Icon Permissions

Obesity has become a global epidemic and contributes to the increasing burden of type 2 diabetes, cardiovascular disease, stroke, some types of cancer, and premature death worldwide. Obesity is highly heritable and arises from the interactions of multiple genes, environmental factors, and behavior. In this paper, the authors reviewed recent developments in genetic epidemiologic research, focusing particularly on several promising genomic regions and obesity-related genes. Gene-gene and gene-environment interactions of obesity were also discussed. Published studies were accessed through the MEDLINE database. The authors also searched the Obesity Gene Map Database ( http://obesitygene.pbrc.edu/ ) and conducted a manual search using references cited in relevant papers. Heritabilities for obesity-related phenotypes varied from 6% to 85% among various populations. As of October 2005, 253 quantitative trait loci for obesity-related phenotypes have been localized in 61 genome-wide linkage scans, and genetic variants in 127 biologic candidate genes have been reported to be associated with obesity-related phenotypes from 426 positive findings. Gene-gene interactions were also observed in several genes, and some genes were found to influence the effect of dietary intake and physical activity on obesity-related phenotypes. Integration of genetic epidemiology with functional genomics and proteomics studies will be required to fully understand the role of genetic variants in the etiology and prevention of obesity.

Obesity is characterized as an excess of adipose tissue. The most commonly used measurement to assess weight status is body mass index, defined as weight (kg)/height (m) 2 . The World Health Organization (WHO) recommends the following body mass index cutpoints to classify weight status in adults 20 years of age or older: <18.5 kg/m 2 (underweight), 18.5–24.9 kg/m 2 (normal weight), 25.0–29.9 kg/m 2 (overweight), 30.0–39.9 kg/m 2 (obese), and ≥40 kg/m 2 (extremely obese) ( 1 ). Although it is by far the most commonly used index for classifying general obesity in an adult, body mass index cannot distinguish obese from muscular individuals, such as athletes, who have more lean muscle than body fat. Body fat mass and percentage body fat, which are measured by dual energy x-ray absorptiometry, can provide a more accurate estimate of obesity status. Percentage total body fat is calculated as fat mass/(fat mass + lean mass + bone mineral content) ( 2 ). The WHO-recommended cutoff point for obesity corresponds to a percentage body fat of 25 percent and 35 percent in men and women, respectively ( 3 ). Waist circumference and waist/hip ratio (WHR) are other indicators commonly used to determine abdominal obesity status. The American Heart Association and the National Heart, Lung, and Blood Institute recommend waist circumference cutpoints for determining abdominal obesity status as ≥102 cm in men and ≥88 cm in women of non-Asian origin and ≥90 cm in Asian men and ≥80 cm in Asian women ( 4 ). According to guidelines from the WHO, abdominal obesity status can be identified as a WHR of >0.90 in men and >0.85 in women ( 5 ).

Obesity is becoming an increasingly important clinical and public health challenge throughout the world. Recently, the International Obesity Taskforce estimated a total of 1.1 billion overweight, including 320 million obese, adults worldwide ( 6 ). Economically developed regions have a higher prevalence of overweight and obesity compared with developing regions of the world ( 7 ). For example, results of the 1999–2002 National Health and Nutrition Examination Survey indicated that an estimated 65 percent of US adults aged 20 years or older (over 131 million people) were either overweight or obese and that 30 percent of adults (over 60 million people) were obese ( 8 ). Despite these estimates, the developing world actually faces a larger absolute burden of overweight and obesity because of a larger population size ( 9 , 10 ). Obesity is a major risk factor for type 2 diabetes, cardiovascular disease, stroke, some types of cancer, and premature death ( 11–18 ).

Human obesity arises from the interactions of multiple genes, environmental factors, and behavior, and this complex etiology makes management and prevention of obesity especially challenging. While a genetic basis for obesity exists, defining the genetic contribution has proven to be a formidable task. Genetic epidemiologic methods for the gene discovery of complex traits, such as obesity, can be divided into two broad classes: hypothesis-free (genome-wide linkage and genome-wide association) and hypothesis-driven (candidate gene and biologic pathway) approaches.

The hypothesis-free approach does not involve any specific biologic hypothesis about the trait of interest. Genome-wide linkage analysis, typically using a 10-centimorgan (cM) (or denser 2-cM) marker density usually containing 400 (or 2,000) microsatellite markers evenly covering the entire human genome, identifies broad intervals of several megabases that might contain hundreds of susceptibility genes for diseases of interest. This method has been remarkably successful in identifying disease genes for monogenic disorders ( 19 ). When applied to the common complex disease, however, linkage analysis has less power, and success has been limited. In addition, relatively high costs and the family-based data requirement make linkage analysis more restricted in practice. Falling genotype costs and the recent advancements in the International HapMap Project have made genome-wide association studies of complex disease popular ( 20 ). For example, the first genome-wide association study of obesity conducted by Herbert et al. ( 21 ) identified a common genetic variant near the insulin-induced gene 2 ( INSIG2 ) associated with obesity in Framingham Heart Study participants. Genome-wide association studies have been expected to be more powerful than linkage studies because of their resolution and ability to narrow down the genomic target region more precisely and to detect even small gene effects. In addition, the number of genotyped variants could be dramatically reduced, taking advantage of linkage disequilibrium between variants ( 22 ). The practical necessity of having a fixed set of genome-wide association markers has obvious advantages. For example, a linkage disequilibrium-based set of tag single nucleotide polymorphisms (SNPs) can maximize the amount of variation captured per SNP by 300,000 SNPs of Illumina HumanHap300 BeadChip or by 500,000 SNPs of Illumina HumanHap500 BeadChip (Illumina, Inc., San Diego, California). A set of SNPs that ignores linkage disequilibrium patterns can be selected to distribute approximately randomly across the genome available from Affymetrix 111,000 and 500,000 array sets (Affymetrix, Inc., Santa Clara, California). A combination of these two methods is also a good choice, consisting of a set of “random” SNPs augmented by a carefully chosen fill-in set. Therefore, a research group should make decisions about which genome-wide association genotyping platform to use in order to balance efficiency, redundancy, and completeness regarding the different marker panels and populations being studied ( 23 ).

The hypothesis-driven approach (candidate gene or biologic pathway analysis) needs an a priori hypothesis that the genetic polymorphisms in a candidate gene or a biologic pathway being studied are causal variants or in strong linkage disequilibrium with a causal variant for a particular phenotype of interest. This approach is now considered to be an efficient strategy for identifying genetic variants with small or modest effects that underlie susceptibility to common disease, including obesity. The selection of candidate genes (or biologic pathways) should consider both the relevance of the candidate gene (or biologic pathway) to the pathogenesis of the disease of interest and the functional effects of a particular polymorphism ( 24 ). Candidate gene analysis is an indirect test of association to examine the relation between a dense map of SNPs and disease, while candidate SNP analysis is a direct test of association between putatively functional variants and disease risk ( 25 ). The advantage of indirect association is that it does not require prior determination of which SNP might be functionally important; however, the disadvantage is that larger numbers of SNPs need to be genotyped ( 25 ). A combination of functionally important SNPs with a collection of tag SNPs covering the entire candidate gene has been used in many candidate gene association studies. Genetic variants in multiple candidate genes within the same biologic pathway can be examined, and their interaction can be tested in pathway analysis. It also makes sense to do fine mapping in significant linkage peaks by association analysis with the knowledge of candidate genes that reside in these regions and are involved in biologic pathways for developing the disease of interest. One of the main weaknesses of candidate gene analysis is that it depends on an a prior hypothesis about disease mechanisms, so that the discovery of new genetic variants or novel genes is precluded by previously unknown pathways ( 26 ).

This paper reviews recent developments in genetic epidemiology research of obesity in human populations and includes current information from heritability studies, genome-wide linkage studies, and candidate gene association studies, focusing particularly on several important genomic regions and obesity-related genes. Gene-gene and gene-environment interactions of obesity are also discussed.

Twin, adoption, and family studies have established that obesity is highly heritable, and an individual's risk of obesity is increased when one has relatives who are obese ( 27–29 ). Heritability estimates ranged from 16 percent to 85 percent for body mass index ( 30–34 ), from 37 percent to 81 percent for waist circumference ( 35–37 ), from 6 percent to 30 percent for WHR ( 38–40 ), and from 35 percent to 63 percent for percentage body fat ( 40–43 ). The Framingham Heart Study reported a moderate heritability estimate for body mass index (40–50 percent) ( 32 ). In contrast, the National Heart, Lung, and Blood Institute family heart study and twin studies observed higher estimates of heritability for body mass index (40–80 percent), and they also reported a heritability of 70–80 percent for weight gain ( 27 , 44–46 ). Davey et al. ( 46 ) reported that the heritability estimate exceeded 90 percent for abdominal fat accumulation in an Indian population, while a family study in an Old Order Amish community showed a heritability of 37 percent for waist circumference and 13 percent for WHR ( 35 ). A twin study and HERITAGE (HEalth, RIsk factors, exercise Training, And GEnetics ) Family Study reported similar heritabilities of 63 percent and 62 percent, respectively, for percentage body fat ( 41 , 42 ), while the maximal heritability estimate in a Taiwanese population was 35 percent ( 43 ).

In recent years, molecular approaches have advanced the understanding of some forms of monogenic obesity in humans. These forms of obesity are rare and very severe, generally starting in childhood ( 47 ). For example, mutations in human genes coding for leptin ( LEP ), leptin receptor ( LEPR ), proopiomelanocortin ( POMC ), and melanocortin-4 receptor ( MC4R ) have been associated with juvenile-onset morbid obesity ( 48–51 ). To date, 176 human obesity cases due to single-gene mutations in 11 different genes have been reported, 50 loci related to Mendelian syndromes relevant to human obesity have been mapped to a genomic region, and causal genes or strong candidates have been identified for most of these syndromes ( 52 ).

Obesity is a complex, heterogeneous group of disorders, which develops predominantly from a polygenic multifactorial trait, with interplay of genetic and environmental factors. As of October 2005, 253 quantitative trait loci for obesity-related phenotypes have been localized from 61 genome-wide linkage studies in human populations. A total of 52 genomic regions harbor quantitative trait loci supported by two or more studies ( 52 ).

The genome-wide linkage studies have linked body mass index to almost every chromosomal region except Y. Table 1 lists studies that showed evidence for the presence of linkage with body mass index (logarithm of the odds (LOD) score: ≥3) ( 36 , 53–73 ). The strongest linkage evidence was observed in a multipoint analysis with a LOD score of 9.2 in Utah pedigrees ( 60 ).

Evidence for the presence of linkage with body mass index

LOD score: In genetics, a statistical estimate of whether two loci (the sites of genes) are likely to lie near each other on a chromosome and are therefore likely to be inherited together as a package. “LOD” stands for logarithm of the odds (to the base 10). (A LOD score of three means that the odds are a thousand to one in favor of genetic linkage.)

Few studies have found evidence of linkage with waist circumference or WHR ( 74–76 ). A LOD score of 3.71 was observed for waist circumference, which was located at 1q21-q25, in the Hong Kong Family Diabetes Study, and evidence of linkage with waist circumference was shown in the 6q23-25 region in the Framingham Heart Study ( 74 , 77 ). Suggestive linkage was found in European Americans and African Americans, both with LOD scores of 2.7 at the Xp21.3 and Xp11.3 regions ( 75 ).

Some studies have found evidence of linkage with percentage body fat ( 56 , 67 , 72 , 78–80 ). LOD scores of 4.27 and 4.21 were observed with the same genetic marker, D21S1446, in chromosome 21q22.3 by Li et al. ( 67 ) and Dong et al. ( 80 ). The HyperGEN Sudy reported a LOD score of 3.0 for men in chromosome 15q25.3 with marker D15S655 and 3.8 for women in chromosome 12q24 with markers D12S395–D12S2078 in non-Hispanic Whites and African-American populations ( 79 ), while in the same year, a LOD score of 3.8 was observed in chromosome 12q24 with marker D12S2070 in European-American families ( 80 ).

Most reports of chromosomal regions linked to obesity and body composition are not robust; only a few regions have been replicated in some studies. As for body mass index, the most promising genomic regions (in chromosomes 2, 3, 6, 11, 13, and 20) were replicated in multiple studies. For example, two studies reported linkage in the 2q14.3 region with marker D2S347 ( 54 , 55 ), and two studies obtained evidence of linkage in the 2p22.3 region with marker D2S1788 ( 53 , 81 ). Three studies reported evidence of linkage at chromosome 3q26.33 with marker D3S2427 ( 57–59 ). In chromosome 6, two studies found linkage in the 6q22.31 region with marker D6S462 ( 71 , 82 ). In chromosome 11, three studies showed evidence of linkage or suggestive linkage in the 11q24.3 region with marker D11S912 ( 60 , 66 , 81 ), and three studies observed suggestive linkage (LOD scores of 2.3, 2.7, and 2.8, respectively) in chromosome 11q24.1 with marker D11S4464 ( 60 , 83 , 84 ). Li et al. ( 67 ) and Dong et al. ( 80 ) reported two regions with suggestive linkage located at 13q21.32 and 13q32.2 with the same markers, D13S800 and D13S779, respectively, and North et al. ( 70 ) found evidence of linkage at 13q13.2 with marker D13S1493, which was also reported by Li et al. ( 67 ) with suggestive linkage. In chromosome 20, at the 20q12 region with marker D20S438, one study observed linkage, and another study found suggestive linkage ( 60 , 73 ). As for waist circumference, two studies observed evidence of linkage in the 12q24.21 region with marker D12S2070 ( 67 , 80 ). As for percentage body fat, aside from chromosomes 12q24 and 21q22.3, suggestive linkage was reported at the chromosome 20q13.31-qter region with marker D20S149, which has already been observed by Lee et al. ( 72 ) and Dong et al. ( 85 ).

The general lack of replication of genome scan results across data sets has been an ongoing concern for genetic epidemiologic studies. The inconsistency between studies may be attributed partially to varying sample sizes from study to study. Relatively small study sample sizes tend to limit the power of genome scans to detect linkage. In addition, the multiple statistical tests performed in each scan increase the risk of type I error. The problem of false positives could be solved by applying more stringent statistical significance criteria ( 86 ). The study population is also an important issue when considering inconsistencies of results. Population heterogeneity decreases the power to detect the true linkage signals within studies and makes it difficult to compare them across studies ( 87 ). The genome-scan meta-analysis method would be useful for combining evidence from multiple studies and could confirm evidence for regions highlighted in more than one scan or identify new regions where weak but consistent evidence for linkage has been seen across studies ( 87 ).

Obesity is a complex trait, which does not show a typical Mendelian transmission pattern and may depend on several susceptibility genes with low or moderate effects. There is firm evidence that genes influencing energy homeostasis and thermogenesis, adipogenesis, leptin-insulin signaling transduction, and hormonal signaling peptides play a role in the development of obesity ( 88 ). The number of studies reporting associations between DNA sequence variation in specific genes and obesity phenotypes has increased considerably, with 426 findings of positive associations in 127 candidate genes. A promising observation is that 22 genes are each supported by at least five positive studies ( 52 ). A selective list of candidate genes according to biologic pathway is presented in table 2 ( 52 ).

Candidate genes associated with obesity and body composition according to biologic pathways *

Please refer to Rankinen et al. ( 52 ) for a more comprehensive summarization of obesity-candidate gene associations.

SAH, SA hypertension-associated homolog (rat) ; SH3, src homology-3; ATPase, adenosine triphosphatase; ATP, adenosine triphosphate; TNF, tumor necrosis factor.

It is necessary to clarify the biologic mechanism underlying the putative pathogenic association despite the level of statistical evidence in favor of an allele-obesity association. For example, SNP Pro12Ala of the peroxisome proliferative activated receptor, gamma gene ( PPARG ), is responsible for the association with elevated body mass index in some studies ( 89–91 ). The lower trans -activation capacity of the Ala variant of PPARG suggests a potential molecular mechanism underlying the association of this allele with lower body mass index and higher insulin sensitivity. The Ala isoform may lead to less efficient stimulation of PPARG target genes and predispose individuals with this variant to lower levels of adipose tissue mass accumulation, which in turn may be responsible for improved insulin sensitivity ( 92 ).

Recently, the best evidence for a causal role in the etiology of obesity, other than the rare autosomal recessive forms of obesity, stems from findings pertaining to diverse mutations in the melanocortin 4 receptor gene ( MC4R ), of which more than 40 mutations have been detected so far ( 93 ). The MC4R V103I polymorphism has been found to be negatively associated with obesity in a meta-analysis encompassing over 7,500 individuals, which revealed that, among obese cases, the carrier frequency is about 2.0 percent, whereas in nonobese controls the rate is 3.5 percent ( 94 ). These results indicate that large-scale association studies are most likely required to pick up such small effects, particularly among alleles with frequencies below 5 percent.

The β 3 -adrenergic receptor gene ( ADRB3 ) is predominantly expressed in adipose tissue and regulates lipid metabolism and thermogenesis ( 95 ). Therefore, an impairment of ADRB3 function may lead to obesity through its effect on energy expenditure of fat tissue. A meta-analysis including 31 studies with more than 9,000 individuals demonstrated a significant association of the Trp64Arg polymorphism of the ADRB3 gene with body mass index ( 96 ). For the first time, an association among diverse population groups exhibited a relatively similar strength, and the ADRB3 locus has been shown to be a genetic factor associated with body weight in a universal manner. More recently, another meta-analysis conducted in Japanese populations supported the hypothesis that the ADRB3 gene Trp64Arg polymorphism is associated with body mass index ( 97 ).

Uncoupling proteins, designated as “UCPs,” are a family of proteins whose function is to uncouple oxidative phosphorylation of adenosine diphosphate to adenosine triphosphate, leading to the generation of heat ( 98 ). Three different proteins have been discovered. Uncoupling protein 1 (UCP-1) is expressed in brown adipose tissue ( 99 ), uncoupling protein 2 (UCP-2) is expressed in most tissues including white adipose tissue, and uncoupling protein 3 (UCP-3) is expressed in skeletal muscle ( 100 , 101 ). The 3′ insertion/deletion (I/D) polymorphism in the UCP-2 gene had a reported association with obesity and body mass index in different populations ( 102–105 ). This variant might have an effect on UCP-2 messenger RNA stability. This, in turn, could affect protein expression and determine body weight by influencing energy expenditure and thermogenesis, which was supported by a study that found reduced UCP-2 messenger RNA levels in the visceral fat of obese but not lean subjects ( 106 ). Although several studies had conflicting findings about the −G866A polymorphism of the UCP-2 gene with obesity, results from one group indicated that the this polymorphism may increase the risk of central obesity in Chinese and Indian men ( 107 ), and results from a Spanish population indicated that the presence of the A allele increased the likelihood of developing obesity in the future ( 108 ).

Other genes, such as the LEPR gene and the glucocorticoid receptor gene ( GRL ), have been reported to be associated with an increased body mass index, an increased weight gain, or obesity in some populations. However, findings from two recent meta-analyses indicated that there was no compelling evidence of an association between these two genes and obesity ( 109 , 110 ).

Although genetic association studies offer a potentially powerful approach to detect genetic variants that influence susceptibility to common disease, the failure to replicate findings across these studies is a serious concern of this approach. Several possibilities have been proposed to explain the inconsistent findings from association studies ( 111 ). First, the association may be due to false positive results. Recently, a meta-analysis suggested that the false positives were probably responsible for many failures to replicate associations between common variants and complex traits; similarly, the estimate of the genetic effect in the first positive report was always biased upward ( 111 ). Second, a true association may fail to be replicated in an underpowered replication attempt (false negative), especially for complex diseases with modest genetic effects ( 112 , 113 ). Third, population stratification results in inconsistency in replication, reflecting different ancestral history that includes responses to natural selection, migration patterns, and founder events ( 111 , 112 ). Thus, a true association in one population is not true in another population because of heterogeneity in genetic or environmental background. Unmeasured factors, selection bias, and differential misclassification of exposure may also be responsible for some nonreplication in association studies ( 114 ).

In light of the seemingly high proportion of false positive reports in the literature, more stringent criteria for interpreting association studies are needed ( 111 ). A single, nominally significant association should be viewed as tentative until it has been independently replicated in other studies. In addition, large association studies should be encouraged, with collaborative efforts probably required to achieve the sample size of many thousands of case-control pairs that is necessary for definitive studies of common variants with modest genetic effects. Finally, using large samples to test previously reported associations, perhaps focusing initially on those associations that have already been replicated at least once, would probably identify a significant number of variants that affect the risk of common disease, such as obesity. The International HapMap Project has identified appropriate sets of tag SNPs that span the genome, greatly facilitating an efficient, linkage disequilibrium-based approach ( 115 ).

The risk of obesity is determined by not only specific genotypes but also significant gene-gene interactions. There is a growing awareness that the failure to replicate single-locus association studies for obesity may be due to underlying genetic interactions between genes. Unfortunately, difficulty in detecting gene-gene interactions is a common problem for current epidemiologic studies. Certain association study designs have been shown to be more effective in identifying gene-gene interactions compared with others. For example, case-only and unmatched case-control studies have been shown to be more powerful than matched case-control studies and family-based designs for detecting interaction; however, both of these study designs are particularly sensitive to population stratification ( 116–118 ). Additionally, study sample sizes are often calculated with the purpose of capturing the main effect of the candidate gene and are therefore underpowered to detect any gene-gene interactions ( 119 ). Moreover, if an association study fails to detect the marginal effect of a single locus, subsequent identification of interactions including that locus may be unlikely ( 120 , 121 ). A recent paper by Marchini et al. ( 120 ) illustrates the utility of two- and three-locus models for identifying multiloci interactions in genome-wide association studies.

Recently, some examples of interactions of known genes on obesity have been reported. Peroxisome proliferator-activated receptor genes ( PPAR s) are ligand-activated nuclear receptors implicated in adipocyte differentiation and lipid and glucose metabolism ( 122 ), whereas the ADRB3 gene is expressed in adipocytes and mediates the rate of lipolysis in response to catecholamines ( 123 ). A gene-gene interaction was reported between Pro12Ala of the PPARG2 gene and Trp64Arg of the ADRB3 gene, where subjects with both gene variants had significantly higher body mass index, insulin, and leptin levels than those with only the PPARG2 gene variant in Mexican Americans ( 124 ); a synergistic effect between these two polymorphisms was also found for obesity risk in a Spanish population ( 125 ).

In the Quebec Family Study, gene-gene interactions were observed among the markers in the α2-, β2-, and β3-adrenergic receptor genes ( ADR s) contributing to the phenotypic variability in abdominal obesity ( 126 ). An interaction was also found in women between the β1- and β3-adrenergic receptors. Women with Gly/Gly genotypes at the β1-adrenergic receptor gene ( ADRB1 ) and carrying at least one β3-Arg allele showed notable increases in body mass index ( 127 ). Additionally, the simultaneous existence of the ADRB1/ADRB3 gene with the UCP-1 gene and/or the lipoprotein lipase gene ( LPL ) might play a role in the development of obesity or weight gain and have synergistic effects when combined with each other ( 128–131 ). Genetic interactions between LEP –G2548A and LEPR Q223R may promote immune dysfunction associated with obesity ( 132 ).

Some potential chromosome regions have also been detected by allowing for interaction between obesity-susceptibility loci, such as chromosome regions 2p25-p24 and 13q13-21, 20q and chromosome 10 centromere, and the TBC1 domain family member 1 gene ( TBC1D1 ) and the 4q34-q35 region ( 80 , 85 , 133 ).

The rapidly increasing prevalence of obesity, in spite of an unchanged gene pool, makes it interesting to search for responsible environmental factors that increase the susceptibility for obesity at the individual level. Migration studies help support the impact of environmental factors on obesity development. For example, Japanese people who have migrated to Hawaii and California are more overweight than their relatives who remained in Japan ( 134 ). Perhaps the genetic background of most people is not prepared for the current abundance of food and sedentary lifestyle. However, even in the obesity-promoting environment, not every individual becomes obese. Therefore, the importance of a gene-environment interaction is demonstrated when an individual with a high-risk genetic profile enters a high-risk environment, and the effects on risk are so great that obesity develops ( 135 ).

Eating behavioral traits aggregate in families. The familial environment seems to be the major determinant of correlations in weight status between parents and their offspring, although a genetic contribution cannot be excluded ( 136 ). At the population level, people with high risk of obesity could benefit from early diet intervention. However, it is well documented that there are considerable interindividual differences in the response of plasma lipid concentrations to alterations in the amount of fat and cholesterol in the diet ( 137 ). Therefore, in tailoring prevention and treatment programs for eating behavior, both an individual's genetic makeup and family environment should be considered.

Some potential susceptibility genes, which relate to energy homeostasis, appetite, satiety, lipoprotein metabolism, and a number of peripheral signaling peptides, may be involved in variable responses to diets ( 138 ). Genes regulating energy homeostasis and thermogenesis include neuropeptide Y ( NPY ), agouti-related protein ( AGRP ), melanocortin pathway factors ( MC4R ), uncoupling proteins ( UCPs ), and fatty acid binding protein ( FABP ) ( 80 , 138 ). Diet intake control may be affected by genes encoding taste receptors and a number of peripheral signaling peptides, such as insulin ( INS ), LEP , ghrelin ( GHRL ), and cholecystokinin ( CCK ) ( 138 ). Levels of uncoupling proteins ( UCP-2 and UCP-3 ) were reported to increase during starvation without changing heat production ( 85 ). LEPR genes showed an association with energy balance in an overfeeding experiment ( 139 ). Clusters of α-amylase genes ( AMY1A, AMY2A , and AMY2B ), involved in the digestion of starch, and the insulin-like growth factor 1 gene ( IGF1 ) may be linked to carbohydrate and protein intakes ( 140 , 141 ). The MC4R gene and the neuromedin beta gene ( NMB ) were identified as having an association with the control of eating behavior ( 142 , 143 ).

Some genes have been identified and linked to variable responses to diet in the lipoprotein metabolism pathway, including apolipoprotein E ( APOE) , apolipoprotein B ( APOB) , apolipoprotein AIV ( APOA4) , apolipoprotein CIII (APOC3) , low-density lipoprotein receptor ( LDLR), FABP, LPL , microsomal transfer protein ( MTP ), cholesteryl ester transfer protein ( CETP ), and hepatic lipase ( HPL ) ( 144 ). For example, the subjects with APOA4 T allele showed a better reduction in low-density lipoprotein cholesterol under dietary intervention, and subjects with the FABP 54Thr allele exhibited a much better lowering of triglyceride with dietary intervention ( 144 ).

Physical activity is a determinant of energy and substrate metabolism. However, recent cultural changes have engineered physical activity out of the daily lives of humans. More than 60 percent of American adults are not regularly active, and 25 percent are sedentary ( 145 ). Physical activity deficiency is predicted to disrupt the optimized expression of the “thrifty” genes and genotype for the physical activity-rest cycle. Some of these “thrifty” genes could have been initially selected to conserve glycogen stores by oxidizing greater quantities of fatty acids to maximize survival during famine and exercise. Therefore, the present sedentary lifestyle has led to discordance in gene-environmental interactions ( 146 ).

A review showed heritability coefficients between 0.29 and 0.62 for daily physical activity, suggesting significant genetic effects ( 147 ). Some genes have been reported to influence human physical performance and physical activity, such as the angiotensin I converting enzyme gene ( ACE ), the guanine nucleotide binding protein, beta polypeptide 3 gene ( GNB3 ), the β 2 -adrenergic receptor gene ( ADRB2 ), MC4R , the cocaine- and amphetamine-regulated transcript gene ( CART ), UCP-2 , and UCP-3 ( 148–153 ). For example, duration of exercise improved significantly for those with the II and ID genotype of the ACE gene but not for those with DD genotype ( 148 ). Furthermore, the CART gene may modify the effect of the MC4R genotype ( 151 ). The hypoxia-inducible factor 1 gene ( HIF1 ) and the titin gene ( TTN ) were associated with maximal oxygen consumption after aerobic exercise training ( 154–156 ). Linkage studies also discovered some genes related to maximal oxygen uptake in the sedentary state and in response to training; these included the following: the skeletal muscle-specific creatine kinase gene ( CKMM ), the β-sarcoglycan gene ( SGCB ), the syntrophin β-1 gene ( SNTB1 ), the γ-sarcoglycan gene ( SGCG ), the dystrophin-associated glycoprotein 1 gene ( DAG1 ), the lamin A/C gene ( LMNA ), the liver glycogen phosphorylase gene ( PYGL ), the guanosine triphosphate cyclohydrolase I gene ( GCH1 ), and the sulfonylurea receptor gene ( SUR ) ( 157 ).

With the completion of the Human Genome Project and recent advancements in the International HapMap Project, our capability in understanding the genetic mechanisms underlying human obesity is rapidly increasing. High-throughput genotyping and decreased genotyping costs have made whole-genome association studies a feasible option, and future studies will likely utilize this method for identification of novel genes involved in obesity pathogenesis ( 158 ). However, complete elucidation of this complex trait will require the integration of many disciplines, combining advances in genetic epidemiology with the fields of functional genomics and proteomics. The advent of the DNA microarray has made gene-expression profiling a powerful tool for simultaneous investigation of the expression of a large number of genes that may provide more clues regarding the molecular basis of obesity with its continued use ( 159–161 ). Several studies have already examined gene expression in adipose tissue in obese and nonobese subjects, identifying some novel genes of interest along with genes mapped to regions with suggestive linkage to obesity ( 162–164 ). Rodent models may also play an important role in not only identifying novel genes for further exploration in human populations but also testing the functional effects of candidate genes already identified in studies from human populations ( 26 ). Additionally, because a gene can be posttranscriptionally or posttranslationally modified into many different protein products, other methods of studying the molecular mechanisms of obesity must be used. The recent emergence of proteomics, defined as the analysis of proteins and their interactions in an organism, holds great promise as an adjunct technique for unraveling the pathogenesis and pathophysiology of this complex trait ( 165 , 166 ).

The global emergence of obesity is one of the greatest challenges in public health research today. Unhealthy diet and physical inactivity have been identified as primary determinants of the increase in the incidence of obesity. It is likely and reasonable to assume that acute changes in behavior and the environment have contributed to the rapid increase in obesity and that genetic factors may be important in determining an individual's susceptibility to obesity. Its complex etiology makes the prevention and treatment of obesity especially challenging. While exciting advancements in molecular technology are rapidly expanding the field of genetic epidemiology and the capabilities of the genetic epidemiologist, it should be noted that limitations of genetic epidemiologic studies of obesity still exist. For example, body mass index is the most widely used phenotype for defining obesity status; however, recent epidemiologic studies have indicated that it is not the best predictor for the risk of cardiovascular disease compared with other obesity measures ( 167 ). Moreover, common confounders such as diet and physical activity are very difficult to measure accurately, which can result in residual confounding in examining the association between candidate gene and obesity-related phenotypes ( 168 , 169 ). Additionally, the effect size of individual genetic variants on a polygenic disorder such as obesity is typically moderate to small; therefore, very large sample sizes may be necessary to detect these effects, particularly when adjusting for other confounders or when examining gene-gene or gene-environment interactions ( 170 ). With the increasing utilization of large-scale case-control studies of unrelated individuals, special attention to population stratification is also warranted ( 114 , 171 ). While methods of genomic control have been established to correct for population substructure, recent evidence has shown that these methods may not always be adequate, and, therefore, issues of population stratification should be considered in the study design phase ( 172 ). Finally, with the advances in genotyping technology and the emergence of genome-wide association studies, statistical methods for correcting for multiple comparisons have become challenging.

Generally speaking, researchers must always understand the limitations and potential pitfalls of their study. However, with careful planning and attention to study design, methods, conduct, and analytical issues, genetic epidemiologic studies of obesity can yield important and valid results. For the novice researcher initiating a linkage study, information from Teare and Barrett ( 173 ) and Botstein and Risch ( 174 ) would be helpful, and important guidelines for interpreting and reporting linkage results have been illustrated by Lander and Kruglyak ( 86 ). Moreover, recently published reviews have demonstrated design and statistical issues involved in population-based or family-based association studies that should also be kept in mind ( 116 , 171 , 175 ). In addition, gene-gene and gene-environmental interaction methodological reviews can also be useful references ( 118 , 119 , 176 , 177 ).

Abbreviations

logarithm of the odds; SNP, single nucleotide polymorphism

World Health Organization

waist/hip ratio

Conflict of interest: none declared.

Google Scholar

• genetic epidemiology
• candidate disease gene

Email alerts

Citing articles via.

• Recommend to your Library

Affiliations

• Online ISSN 1478-6729
• Copyright © 2024 Johns Hopkins Bloomberg School of Public Health
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Preprints with The Lancet is part of SSRN´s First Look, a place where journals identify content of interest prior to publication. Authors have opted in at submission to The Lancet family of journals to post their preprints on Preprints with The Lancet. The usual SSRN checks and a Lancet-specific check for appropriateness and transparency have been applied. Preprints available here are not Lancet publications or necessarily under review with a Lancet journal. These preprints are early stage research papers that have not been peer-reviewed. The findings should not be used for clinical or public health decision making and should not be presented to a lay audience without highlighting that they are preliminary and have not been peer-reviewed. For more information on this collaboration, see the comments published in The Lancet about the trial period, and our decision to make this a permanent offering, or visit The Lancet´s FAQ page, and for any feedback please contact [email protected] .

Association of Multiple Trait Polygenic Risk Score with Obesity and Cardiometabolic Diseases in Korean Population

31 Pages Posted: 9 Sep 2024

Seoul National University

Je Hyun Seo

Veterans Health Service Medical Center

Sungkyoung Choi

Hanyang University

Eun Kyung Choe

Jang won son.

Catholic University of Korea

Seoul National University - Department of Public Health Science

In this study, we conducted a comprehensive analysis of genetic factors associated with obesity in a cohort of 93,673 Korean subjects. Participants were categorized based on body mass index and waist circumference, using both Korean-specific and global criteria. We employed advanced computational techniques to develop various genetic risk models for obesity and related diseases, including conventional single-trait and multiple-trait approaches combined with the PRSsum technique. Our analysis revealed significant genetic effects shared across different ancestry groups, with higher heritability observed for body mass index compared to waist circumference, and in moderate obesity compared to severe obesity. Additionally, single-trait analyses indicated that individuals with low genetic risk had a reduced risk of obesity, while those with high genetic risk were more susceptible to obesity and related conditions such as hypertension and type 2 diabetes. Furthermore, multiple-trait polygenic risk scores indicated greater risks for obesity and related diseases beyond single-trait approaches. In conclusion, our study identifies specific genetic loci associated with obesity in the Korean population and highlights shared genetic correlations across East Asian and non-Hispanic White populations. Our advanced risk models provide a comprehensive understanding of obesity risks, underscoring the importance of tailored, population-specific genetic research in addressing obesity. Funding: This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI22C0154), the National Research Foundation (NRF) grants (NRF-2021R1A5A1033157) and the Ministry of Science and ICT (MSIT), Republic of Korea (RS-2024-00346850). Statistical analyses were supported by the National Supercomputing Center of Korea with supercomputing resources including technical support (KSC- 2022-CRE-0319 and KSC-2023-CRE-0117). This study was supported by Research Grant (Grant No. KSSO-D- 2021002) from Korean Society for the Study of Obesity. Declaration of Interest: The authors have declared no competing interests. Ethical Approval: This research was designed in accordance with the principles of Declaration of Helsinki, and conducted after approval of the institutional review board of Seoul National University (IRB No. E2303/004-012). The institutional review boards of the Veterans Health Service Medical Center approved this study protocol and informed consent waiver (IRB No. 2020-02-015 and IRB No. 2021-05-005).

Keywords: Obesity, Cardiometabolic diseases, GWAS, PRS, Multiple trait PRS

Suggested Citation: Suggested Citation

Jinyeon Jo (Contact Author)

Seoul national university ( email ).

Kwanak-gu Seoul, 151-742 Korea, Republic of (South Korea)

Veterans Health Service Medical Center ( email )

Seoul Korea, Republic of (South Korea)

Hanyang University ( email )

Catholic university of korea ( email ), seoul national university - department of public health science ( email ), click here to go to thelancet.com, paper statistics, related ejournals, preprints with the lancet.

Subscribe to this free journal for more curated articles on this topic

Medical Genetics & Genomics eJournal

Subscribe to this fee journal for more curated articles on this topic

Warning: The NCBI web site requires JavaScript to function. more...

An official website of the United States government

The .gov means it's official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• Browse Titles

NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Genetics and obesity.

Ekta Tirthani ; Mina S. Said ; Anis Rehman .

Affiliations

Last Update: July 31, 2023 .

• Continuing Education Activity

Obesity is closely linked to genetics and environmental factors. The newest studies in the field of epigenetics further our understanding of the effect of the environment on genetics. This article describes the genetic causes of obesity, including syndromic, monogenic, and polygenic causes, and cites specific examples of epigenetic modifications associated with obesity. This activity reviews the evaluation and treatment of genetically predisposed obesity and highlights the role of the interprofessional team in evaluating and treating patients with this condition.

• Describe the need for genome and epigenome-wide association studies to understand better how obesity can be inherited.
• Identify the important genes associated with syndromic and non-syndromic obesity.
• Describe the crucial times in human life when epigenetic modifications can occur.
• Explain the importance of a detailed history, physical examination, and genetic tests that can be used to diagnose genetically and epigenetically inherited causes of obesity.
• Introduction

The obesity epidemic around the world affects not only adults but also children. About 50% of the time, obesity in childhood is carried into adulthood in a phenomenon known as "tracking." Per the latest data from the World Health Organization, the number of overweight and obese children under five years of age is estimated to be close to 39 million. In the United States, 1 in 3 adult Americans is obese, and the Centers for Disease Control has estimated that the prevalence of obesity among children is 19.3% per data from the year 2017-2018. By 2030 some epidemiologists suggest that 20% of the world's population will be obese, i.e., having a body mass index (BMI) of more than 30 kg/m² in adults, or a BMI ≥95th percentile for age and sex in children aged 2 to 18 years. Obesity as a disease itself is multifactorial and occurs due to complex interactions occurring between genetics and the environment.

The Human Genome project was carried out between the years 1990 to 2003 to map out the human genome. Genome-Wide Association Studies (GWAS) have been ongoing since 2007 to help associate specific genetic variations with certain diseases. Around 250 genes are now associated with obesity. The FTO gene on chromosome 16 is the most important and carries the highest risk of the obesity phenotype. [1] [2] [3] [4]  

The Genetic Investigation of Anthropomorphic Traits Consortium is the organization involved in furthering research in GWAS. [5]  However, genetic mutations alone cannot explain the heritability of obesity perfectly. The concept of epigenetics was introduced to help understand the heritability of obesity better. Waddington first introduced the definition of epigenetics was first introduced in the 1940s by Waddington and subsequently elaborated by Holiday in 1990. However, the modern definition of epigenetics comes from Riggs et al. in 1996. Epigenetics is defined as "the study of mitotically heritable changes in gene expression that occur without changes in the DNA sequence." Epigenetic marks on the genome alter the way each gene is read to produce a distinct phenotype. This provides a better explanation of how the environment plays a significant role in affecting how genes are expressed. [6] [7]  

Epigenome-wide Association Studies (EWAS) began in 2013 to map the epigenome and understand the varied expressions of genes in different tissues. GWAS and EWAS have heralded a new era in the study of genetics and obesity. [6] [8]

Genetic and epigenetic variations contribute to obesity by influencing the function of metabolic pathways in the body and regulating neural pathways and appetite centers. Subsequently, these variations influence insulin resistance, dyslipidemia, inflammation, hypertension, and ectopic fat deposition-especially in the liver, which are the markers of obesity. [2]  Genetic mutations can be inherited in an autosomal dominant or autosomal recessive manner and are influenced by genetic mechanisms of deletion, genetic imprinting, and translocation. However, epigenetic modifications are more complex and occur at any given time and can be passed on from generation to generation to cause obesity. Geneticists have identified some crucial periods when epigenetic changes occur, especially during the growth of the fetus. Factors influencing these epigenetic changes include:

1. Maternal nutrition-both maternal over and undernutrition give rise to epigenetic changes that can affect the fetus and have intergenerational and transgenerational effects. Maternal undernutrition and intrauterine growth retardation are known to be risk factors for permanent changes in fetal insulin metabolism. Although this is a survival adaptation mechanism in fetal life, when these children are born and exposed to a nutrient-rich environment, it predisposes them to develop obesity and type 2 diabetes. This concept is widely known as the thrifty phenotype hypothesis, which was put forth by Hales and Barker 1992, published in the Journal Diabetologia. The new terminology for this concept is the "Developmental Origins of Health and Disease hypothesis." [9]  

Human studies that elaborated this concept include-the Dutch Hunger Winter study of victims of the Dutch famine of 1944-1945, which looked at the changes in the IGF2 gene, the Chinese famine study, the Kiang West longitudinal population study in the Gambia, which looked at differences in populations born in the wet and dry season with special focus on the POMC gene. Maternal overnutrition, on the other hand, including low protein and high-fiber diets, have been studied to cause fetal obesity. The effect of maternal diet on fetal health is widely known as the theory of fetal programming. [7] [10] [11]  The rising prevalence of obesity and type 2 diabetes in developing countries like India and sub-Saharan Africa confounded epidemiologists for the longest time and is now known to have its origins explained by the theory of fetal programming. [9]

2.  Maternal exposure to toxins like organochlorides, polycyclic aromatic hydrocarbons, arsenic [which can cause gestational diabetes mellitus and thus fetal metabolic syndrome], and cigarette smoking can cause epigenetic modifications. An example of this is- changes in the GF11 gene seen in mothers who smoke >15 cigarettes a day. Researchers are now terming these factors as "obesogens" or "endocrine-disrupting chemicals." [10] [11]

3. Maternal stress has been associated with diet-induced obesity in rat models. The Quebec Ice Storm Study in humans implied the association between type 2 diabetes and children born to pregnant mothers experiencing grief after the storm. [10]

4. Maternal diabetes, younger maternal age, low pre-pregnancy weight have been studied in association with fetal metabolic derangements and later childhood obesity. [12] [13] [14]

5. Nutritional disturbances in the postnatal environments and early childhood nutrition in twin studies have been linked to childhood obesity and metabolic abnormalities in early adulthood. [7]

6. Altered gut microbial flora with antibiotic use in the first year of life and even in adulthood is linked to obesity and non-alcoholic fatty liver disease (NAFLD). [10]  Microbial metabolites can cause epigenetic modifications, change gene expression profiles, and cause genome reprogramming. [15]

7. Paternal nutrition-overnutrition, prediabetes, and low protein diets are linked to epigenetic modifications associated with fetal obesity. There is a new interest in this field of "Paternal Origins of Health and Disease." [7] [10]

8. A high intake of sugary beverages, fried foods, high saturated fats, sleep disturbances, and a sedentary lifestyle in adulthood has been linked to epigenetic modifications, e.g., DNA methylation of PGC1 alpha encoded by PPARGC1A. [10] [5]

In the laboratory, the mammals used to study epigenetics include sheep, pigs, mice, rats, macaques, and drosophila. The tissues used in human epigenetic studies include peripheral blood-leukocytes and CD4+ T cells, cord blood, liver, pancreas, skeletal muscle, subcutaneous adipose tissue from the abdomen and buttock. [7]

• Issues of Concern

Genetic Obesity Can Be Classified as Monogenic and Polygenic Obesity [included under nonsyndromic obesity] or Syndromic Obesity

1. Syndromic obesity : This can be further classified as obesity caused by chromosomal rearrangements like Prader-Willi syndrome, WAGR syndrome, SIM1 syndrome, and pleiotropic syndromes, including Bardet-Biedl syndrome, Fragile X syndrome, Cohen syndrome, etc.

Prader-Willi syndrome (PWS) is caused by the deletion of paternal 15q.11-13-the Prader-Willi Critical Region (PWCR) in most cases or by maternal uniparental disomy in 20 to 30% of cases. The PWCR on the maternal chromosome is normally genetically imprinted; hence the loss of the paternal PWCR causes Prader-Willi syndrome. PWS characterized by mental retardation, dysmorphic facies, hypotonia, short stature, and hormonal deficiencies in addition to obesity. It is associated with severe hyperphagia and food compulsivity in childhood. The genes in the PWCR that are lost include NPAP1, MAGEL2, SNURF-SNRPN, MKRN3, and NDN, which leads to lower expression of proconvertase-1 in the hypothalamus, contributing to obesity.

Bardet-Biedl syndrome (BBS) is an autosomal recessive disease seen more in families with a history of consanguinity. It is characterized by problems in the BBSome, which is a unit of motility for cilia. Sixteen genes have been implicated in various forms of BBS. Children affected by this disorder present with learning disabilities, dyslexia, progressive rod-cone dystrophy, hypogonadism, type 2 diabetes, labile behavior, renal abnormalities, and polydactyly.

Other causes of syndromic obesity include 5p13 microdeletion syndrome, 16p11.2 deletion, Albright hereditary osteodystrophy associated with GNAS mutation, Alstrom syndrome-ALMS1 mutation, CHOPS syndrome-AFF4 mutation, Carpenter syndrome-RAB23 mutation, Cohen syndrome-VPS13B/COH1 mutation, Rubinstein Tayabi syndrome-CREBBP mutation, OBHD syndrome-NTRK2 mutation, Kleefstra syndrome- EHMT1 mutation, etc. [11] [3] [16]

2. Monogenic obesity:  Monogenic obesity can be further classified into autosomal dominant or autosomal recessively inherited forms of genetic obesity. Monogenic obesity generally involves mutations in the leptin signaling pathway leading to suppression of anorexigenic and activation of orexigenic pathways. To understand the many forms of monogenic obesity, it is crucial to understand the intricate functioning of the leptin signaling pathway. Normally, leptin acts on the leptin receptor [LEPR], which increases the levels of proopiomelanocortin [POMC] and cocaine and amphetamine-regulated transcript [CART]. POMC, in turn, increases the levels of proprotein convertase 1/3, which increases the formation of alpha melanocyte stimulating hormone [alpha-MSH]. Alpha-MSH then acts on the melanocortin 4 receptor [MC4R] in the hypothalamus to initiate the feeling of satiety. Also, leptin normally suppresses the neuropeptide Y (NPY)-agouti-related peptide (AgRP)-Y1R orexigenic pathway. [16]

• Autosomal recessive inheritance: Mutations in the leptin gene located on chromosome 7, leptin receptor located on chromosome 1, PCSK 1 located on chromosome 5, and POMC located on chromosome 2 are examples of mutated genes that have an autosomal recessive inheritance. Homozygous mutations in the leptin gene are generally seen in consanguineous families and can be treated with metreleptin. Mutations in leptin receptors are frameshift, missense, or nonsense mutations that cannot be treated with metreleptin. POMC generates both alpha MSH and Adrenocorticotropic hormone (ACTH). Patients with POMC mutations develop central adrenal insufficiency and skin hyperpigmentation. Mutations of POMC can be inactivating or nonsense mutations, and patients can be treated with setmelanotide and hydrocortisone. [16]
• Autosomal dominant inheritance: Mutations in genes SH2B1 located on chromosome 16, MRAP2 located on chromosome 6, and LPR2 located on chromosome 2 are examples of mutated genes with an autosomal dominant inheritance. Mutations in genes-BDNF located on chromosome 11, SIM1 located on chromosome 6, NTRK2 located on chromosome 9 give rise to abnormal proteins involved in hypothalamic neuronal differentiation leading to the development of severe obesity and cognitive impairment. Mutations in MC4R have codominant inheritance and constitute the commonest cause of monogenic obesity, with a prevalence of 0.5% to 6% in different populations. Setmelanotide cannot be used in these cases of impaired or loss of function of MC4R because its action depends on the normal downstream signaling of MC4R. [16] [11] [17]
• Other gene mutations that can cause obesity include NPY gene mutations, ghrelin receptor mutations, MC3R gene mutations, and FTO mutations (the most significant gene mutations contributing to obesity in adults and children). [3]

3. Polygenic obesity:  Sixty percent of inherited obesity is polygenic. Polygenic obesity is associated with mutations in CYP27A1, TFAP2B, PARK2, IFNGR1, as well as UCP2 & UCP3-which code for uncoupling proteins in skeletal and brown adipose tissue, ADRB1-3 which code for the beta-adrenergic receptors affecting energy utilization and lipolysis, and SLC6A14-which regulates tryptophan accessibility for serotonin synthesis which affects appetite control and energy balance. [3] [16] [2]

Epigenetic Modifications Linked to Obesity

• DNA methylation/demethylation-the most common mechanism of epigenetic modifications seen throughout the genome. Methylation is governed by the action of DNA methyltransferase 1 (DNMT1), and demethylation is carried out by ten-eleven-translocation (TET) enzymes. Variations in the methylation of CpGs in the genome constitute the "Differentially Methylated Regions" (DMRs).
• Histamine modification by acetylation and methylation. Histone modification regulates five essential adipogenesis genes, including Pref-1, c/EBP beta, C/EBP alpha, PPAR gamma, and aP2.
• Histone variants: Histone macroH2A1.2 inhibits adipogenesis and increases leanness while promoting metabolic health. 
• ATP-dependent chromatin remodeling complexes' involvement leads to further acetylation, phosphorylation, or methylation of genes.
• The addition of micro-RNAs, long non-coding RNAs, piRNAs, or siRNAs leads to pre and post-transcriptional variations in RNA. [10] [7] [11]

Cross-sectional and longitudinal studies have identified differential methylation sites in CPT1A, ABCG1, and SREBF1 genes in the blood, associated with BMI variation. Differential methylation of LY86 in blood leukocytes is seen between obese and lean people. Variation in the waist to hip ratios varies with ADRB3 methylation in blood. Other significant epigenetic changes causing variations in BMI have been seen in PGC1A, HIF3A, FTO, TCF7L2, FASN, CCRL2, ELOVL2 genes. [7]  

In prenatal famine, differential methylation in CDH23, SMAD7, INSR, CPT1A, KLF13, RFTN1 genes has been studied from adult whole blood samples. In intrauterine growth retardation, pancreatic islet failure and insulin resistance are linked to decreased acetylation of histones 3 and 4. Maternal high-fat diets have been linked to adipose tissue hyperplasia by reduced methylation of promoter Scd1. Maternal obesity has been associated with hypermethylation of POMC in the fetal brain and hypomethylation of dopamine reuptake transporter promoting fat and sugar cravings in children. [10]  The gut flora in adult life changes based on diet and can induce epigenetic modifications like histone deacetylation and lower levels of methylation of FFAR3 and TLR. Thus epigenetic variations need to be studied in specific tissues due to the differential expression of genes in body tissues. [15]

• Clinical Significance

When diagnosing genetic causes of obesity, good history taking, and physical exam skills are extremely important. A detailed history includes personal history, family history, medication history, psychosocial history, diet and activity/exercise history, and history of weight gain. Endocrine causes of obesity like hypothyroidism, growth hormone deficiency, hypothalamic obesity, and Cushing's disease must be ruled out early with history, physical examination, and lab work. Syndromic obesity can sometimes be distinctly diagnosed based on the presence of physical features, like in Prader-Willi syndrome or Albright's hereditary osteodystrophy. After basic lab work is done, including a complete blood count, comprehensive metabolic panel, growth hormone, thyroid-stimulating hormone, and dexamethasone suppression test, physicians can check leptin, insulin, and proinsulin levels. If all the above blood work is negative genetic testing can be carried out.

These genetic tests are expensive and done in limited centers across the United States. They include linkage analysis to look for familial aggregation of Mendelian traits, Sanger sequencing, chromosomal microarrays, next-generation sequencing with whole genome and whole exome sequencing, as well as rare variant association tests. [16]

The Food and Drug Administration (FDA) has approved two drugs that target patients with genetic causes of obesity-metreleptin and setmelanotide. The other drugs like semaglutide, liraglutide, phentermine-topiramate, and naltrexone-bupropion are approved for weight loss in the general population and may be used to treat patients with genetic obesity.

• Metreleptin is a leptin analog used to treat patients with congenital generalized lipodystrophy in leptin-deficient patients with mutations in the leptin gene. However, metreleptin cannot be used in patients with leptin receptor mutations or mutations downstream in the leptin signally pathway. The use of this drug is monitored by the Risk Evaluation and Mitigation Strategies (REMS) group of the FDA. The dose is generally 0.06 mg/kg/dose once daily for patients with weight <40kg and 2.5 to 5 mg a day for weight >40 kg or adult patients. [18]
• Setmelanotide is an MC4R agonist used in obese patients with genetic mutations in POMC, PCSK1, or LEPR genes and Bardet Biedl syndrome. The advantage of this drug is that it acts directly on the MC4R receptor bypassing multiple targets, which could be mutated in the leptin pathway. It is generally administered as a 2 mg daily subcutaneous dose. [19]

Other drugs studied in Prader-Willi syndrome include beloranib-a MetAP2 inhibitor and nasal oxytocin, but these are not FDA approved. [11]  Bariatric surgery with Roux en Y gastric bypass, sleeve gastrectomy, and laparoscopic gastric banding has been shown to benefit patients with genetic causes of obesity. The only exception is in patients with complete loss of MC4R function where bariatric surgery was not found to be effective. [16]

It is now well studied that distinct interventions can modify the epigenome of the body to benefit patients with obesity. Examples of this are:

• Bariatric surgery can cause changes in adipocyte-derived exosomal micro-RNA and cause epigenetic changes in differential methylated regions in HOXB1, PRKCZ, SLC38A10, SECTM1 genes. [11] [7]
• Regular exercise can cause widespread changes in DNA methylation in the RUNX1, NDUFC2, THADA, MEF2A, PRKAA2 genes. For patients who maintain their weight loss, the DNA methylation profiles resemble lean individuals, as seen in RYR1, TUBA3C, and BDNF genes. [10] [6] [7]
• Fasting can cause changes in DNA methylation of genes-LEP (leptin) and ADIPOQ (adiponectin). [10]
• The use of probiotics, prebiotics, and fecal transplant can restore gut flora and cause positive epigenetic modifications in patients with obesity. [15]
• Other Issues

In genome-wide association studies done so far, most subjects have European ancestry. However, 47% or the vast majority of patients grappling with the burden of obesity in the United States are of African-American and Hispanic/Latino descent. The African Ancestry Anthropometry Genetics Consortium (AAAGC) and the Hispanic and Latino Consortium (HISLA) were created to study specific alleles related to obesity in these populations. The Population Architecture using Genomics and Epidemiology (PAGE) study performed large-scale genotyping involving 54,000 participants of African-American, Hispanic/Latino, East Asian, Native Hawaiian, and Native American descent to study specific genetic variants associated with obesity. [20]

The future of genetic and epigenetics is promising, especially in the area of obesity and metabolic disease. Epigenetic studies form the basis of precision medicine with distinct targets for gene modulation. The reversible nature of epigenetic marks gives geneticists and clinicians a chance to suggest changes in lifestyle with dietary modification and exercise and avoidance of tobacco, alcohol, and potential obesogens before patients and their offspring suffer the consequences. The use of histone deacetylators is now being suggested beyond the boundaries of Hematology/Oncology for its use in lifestyle medicine, and research in this field is ongoing. Methylation Quantitative Trait Locus (meQTL) studies are now being used to further epigenetic studies. New Nutri-pharmacogenomic studies are expanding our understanding of how nutrition affects genetics. [10] [11] [6]

• Enhancing Healthcare Team Outcomes

With the help of GWAS and EWAS, we now understand how genetics and epigenetics play an important role in obesity. Diagnosing, managing, and supporting patients with genetically predisposed obesity requires a dedicated team of health care professionals experienced in their various fields with good teamwork. Since the origins of obesity are directly associated with maternal health, a team of obstetricians, pediatricians, nutritionists, geneticists, psychologists can help mitigate risk factors associated with maternal and childhood obesity.

Pediatric endocrinologists play a significant role in diagnosing early childhood obesity. In contrast, adult endocrinologists can help treat and control diabetes and other cardiometabolic parameters that cause epigenome changes passed on from generation to generation. Early lifestyle interventions, bariatric surgery, and medications form the basis of the treatment of genetically predisposed obesity.

• Review Questions
• Access free multiple choice questions on this topic.
• Comment on this article.

Disclosure: Ekta Tirthani declares no relevant financial relationships with ineligible companies.

Disclosure: Mina Said declares no relevant financial relationships with ineligible companies.

Disclosure: Anis Rehman declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Tirthani E, Said MS, Rehman A. Genetics and Obesity. [Updated 2023 Jul 31]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

In this Page

Bulk download.

• Bulk download StatPearls data from FTP

Related information

• PMC PubMed Central citations
• PubMed Links to PubMed

Similar articles in PubMed

• The effectiveness of web-based programs on the reduction of childhood obesity in school-aged children: A systematic review. [JBI Libr Syst Rev. 2012] The effectiveness of web-based programs on the reduction of childhood obesity in school-aged children: A systematic review. Antwi F, Fazylova N, Garcon MC, Lopez L, Rubiano R, Slyer JT. JBI Libr Syst Rev. 2012; 10(42 Suppl):1-14.
• Identification of autosomal cis expression quantitative trait methylation (cis eQTMs) in children's blood. [Elife. 2022] Identification of autosomal cis expression quantitative trait methylation (cis eQTMs) in children's blood. Ruiz-Arenas C, Hernandez-Ferrer C, Vives-Usano M, Marí S, Quintela I, Mason D, Cadiou S, Casas M, Andrusaityte S, Gutzkow KB, et al. Elife. 2022 Mar 18; 11. Epub 2022 Mar 18.
• Review Screening and Interventions for Childhood Overweight [ 2005] Review Screening and Interventions for Childhood Overweight Whitlock EP, Williams SB, Gold R, Smith P, Shipman S. 2005 Jul
• Review Recent developments on the role of epigenetics in obesity and metabolic disease. [Clin Epigenetics. 2015] Review Recent developments on the role of epigenetics in obesity and metabolic disease. van Dijk SJ, Tellam RL, Morrison JL, Muhlhausler BS, Molloy PL. Clin Epigenetics. 2015; 7:66. Epub 2015 Jul 11.
• Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas. [Cochrane Database Syst Rev. 2022] Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas. Crider K, Williams J, Qi YP, Gutman J, Yeung L, Mai C, Finkelstain J, Mehta S, Pons-Duran C, Menéndez C, et al. Cochrane Database Syst Rev. 2022 Feb 1; 2(2022). Epub 2022 Feb 1.

Recent Activity

• Genetics and Obesity - StatPearls Genetics and Obesity - StatPearls

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

Connect with NLM

National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894

Web Policies FOIA HHS Vulnerability Disclosure

Help Accessibility Careers

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 10 September 2024

Prevalence and determinants of obesity and its association with upper gastrointestinal diseases in people aged 40–69 years in Yangzhong, southeast China

• Xiang Feng 1 ,
• Jinhua Zhu 1 , 2 ,
• Zhaolai Hua 1 ,
• Shenghua Yao 3 ,
• Hongjun Yin 3 ,
• Qiuping Shi 1 &
• Jinyi Zhou 4  

Scientific Reports volume  14 , Article number:  21153 ( 2024 ) Cite this article

Metrics details

• Cancer prevention
• Endocrinology
• Gastroenterology
• Risk factors

Several international epidemiological studies have established a link between obesity and upper gastrointestinal cancer (UGC), but Chinese evidence is limited. This study aimed to determine the prevalence of obesity, especially central obesity, while investigating its association with upper gastrointestinal diseases in the high-risk population of Yangzhong, a typical high-risk area for UGC in southeastern China. We conducted a cross-sectional study from November 2017 to June 2021 involving 6736 residents aged 40–69. Multivariate logistic regression was used to assess independent factors influencing overweight/obesity and central obesity. We also analyzed the relationship between obesity and upper gastrointestinal diseases using multinomial logistic regression. The prevalence of overweight, obesity, waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR)-central obesity were 40.6%, 12.0%, 49.9%, 79.4%, and 63.7%, respectively. Gender, age, smoking, tea consumption, sufficient vegetable, pickled food, spicy food, eating speed, physical activity, family history of cancer, and family history of common chronic disease were associated with overweight /obesity and central obesity. Besides, education and missing teeth were only associated with central obesity. General and central obesity were positively associated with UGC, while general obesity was negatively associated with UGC precancerous diseases. There were no significant associations between obesity and UGC precancerous lesions. Subgroup analyses showed that general and central obesity was positively associated with gastric cancer but not significantly associated with esophageal cancer. Obesity is negatively and positively associated with gastric and esophageal precancerous diseases, respectively. In conclusion, general and central obesity were at high levels in the target population in this study. Most included factors influenced overweight/obesity and central obesity simultaneously. Policymakers should urgently develop individualized measures to reduce local obesity levels according to obesity characteristics. Besides, obesity increases the risk of UGC but decreases the risk of UGC precancerous diseases, especially in the stomach. The effect of obesity on the precancerous diseases of the gastric and esophagus appears to be the opposite. No significant association between obesity and upper gastrointestinal precancerous lesions was found in the study. This finding still needs to be validated in cohort studies.

Similar content being viewed by others

The prevalence and determinant of overweight and obesity among residents aged 40–69 years in high-risk regions for upper gastrointestinal cancer in southeast China

Multifaceted association of overweight and metabolically unhealthy with the risk of Barrett's esophagus in the UK Biobank cohort

Obesity and “obesity-related” cancers: are there body mass index cut-points?

Introduction.

Obesity and overweight have become public health issues globally and are related to many chronic diseases, such as hypertension, hyperlipidemia, and diabetes 1 , 2 . It is shown that from 1975 to 2016, the age-standardized worldwide prevalence of obesity (body mass index, BMI ≥ 30 kg/m 2 ) increased from 3 to 12% in men and 7% to 16% in women. This change has increased obese adults by over 6 times, from 100 to 671 million 3 . From 1995 to 2016, an enormous absolute increase in obesity occurred among men (from 9 to 30%) in high-income countries in the West and women (from 12 to 35%) in Central Asia, the Middle East, and North Africa 3 . As in the world, overweight and obesity have become serious concerns in China. In 2018, among Chinese adults aged 18–69, an estimated 85 million people were obese (48 million for men and 37 million for women), 3 times the number of obesity in 2004 4 . The age-standardized prevalence of obesity increased from 3.1 to 8.1%, and BMI increased faster in men than in women 4 . Most strikingly, during this period, rural men's BMI levels have always been lower than urban ones. However, the BMI level of urban and rural women changed in 2010 (the increased levels of BMI among rural women began to exceed that of urban ones). By 2018, rural women's BMI levels have surpassed urban women's 4 . Furthermore, a British study of over 112 million adults reported that the average BMI of women in rural areas increased by 2.09 kg/m 2 , and that of men increased by 2.10 kg/m 2 from 1985 to 2017 worldwide. In contrast, women's average BMI in urban areas increased by 1.35 kg/m 2 , and men's increased by 1.59 kg/m 2 . This led to a higher BMI for rural women than their urban counterparts in regions such as East Asia, South Asia, Southeast Asia, and Oceania in 2017, especially in low- and middle-income countries 5 . Overall, significant increases in the BMI of rural residents contributed to a 60% increase in average BMI for women and a 57% increase in average BMI for men globally 5 . These findings challenge the current mainstream view of urban life and urbanization as the main driving force of the global obesity epidemic, reminding us that the obesity problem of rural residents is also worthy of attention.

Obesity has been confirmed to be associated with the risk of at least 13 cancers, including esophageal cancers (EC) and gastric cancers (GC) 6 . A systematic review and meta-analysis of 141 studies found that increased BMI was associated with a higher risk of esophageal adenocarcinoma (EAC) both in men (RR = 1.52, 95% CI 1.33–1.74) and women (RR = 1.51, 95% CI 1.31–1.74) 7 . Another study found that EAC risk was greater with BMI (RR per 5 kg/m 2  = 1.47, 95% CI 1.34–1.61) 8 . Besides, some studies reported an indisputable positive relationship between central obesity and EAC. For instance, the European Prospective Investigation into Cancer and Nutrition showed that waist circumference (WC) was strongly associated with EAC risk 9 . Singh et al. 10 found that central adiposity increased the EAC risk (aOR = 2.51, 95% CI 1.54–4.06). In contrast, some previous studies have revealed that higher BMI and central obesity were associated with a lower risk of esophageal squamous cell carcinoma (ESCC), the most common form of EC in China 8 , 11 , 12 , 13 .

Overall, physical measurement indicators are positively correlated with GC risk. However, there is evidence that the association may differ when GC is divided into cardia gastric (CGC) and non-cardia gastric cancer (NCGC). Specifically, the risk of CGC increases with increasing BMI (RR per 5 kg/m 2  = 1.23, 95% CI 1.07–1.40) 14 and WC (RR = 1.87, 95% CI 1.19–2.54) 15 . The evidence for NCGC is conflicting, with studies suggesting that BMI and central obesity were not associated with it 9 , 16 and a few reporting that central obesity was associated with its increased risk 15 .

Although the association between obesity and upper gastrointestinal cancer (UGC, EC/GC) is almost proven internationally, evidence from the Chinese-specific population and studies on the relationship between obesity and upper gastrointestinal diseases, especially for precancerous lesions/diseases, are limited. As we all know, the precancerous stage of UGC has important practical implications for the cost and prognosis of clinical interventions. Meanwhile, we have explored the current status of overweight and obesity in people at high risk of UGC in the previous literature 17 . However, we failed to include central obesity and multiple types of confounding variables due to study design limitations. Therefore, our study first focused on the prevalence of obesity, especially central obesity, among residents aged 40–69 (defined as the high-risk groups for UGC in China) from a high-risk area for UGC and then explored the relationship between upper gastrointestinal diseases and obesity. We used the secondary data from The National Cohort of Esophageal Cancer-Prospective Cohort Study of Esophageal Cancer and Precancerous Lesions based on High-Risk Population (NCEC-HRP) and rural Upper Gastrointestinal Cancer Early Diagnosis and Treatment Project (UGCEDTP) implemented in Yangzhong to conduct this study.

Study design and population

Yangzhong is located in Jiangsu Province of southeast China, which has high morbidity and mortality of EC and GC 18 , 19 , and it has been listed as a project site in the UGCEDTP since 2006 18 , 19 . The project mainly uses endoscopy and pathological diagnosis to find early UGC and its precancerous lesions among high-risk groups as much as possible and promote the tertiary prevention of UGC 18 , 19 . The project site has also been included in the NCEC-HRP in recent years 20 . Both programs target local people aged 40–69 years in regions with a high incidence of UGC. Additional details of the UGCEDTP and NCEC-HRP can be found elsewhere 18 , 19 , 20 . Therefore, Yangzhong was chosen for this continuity study because of its good experience in cancer screening and research projects, stable screening staff, and high incidence rate of UGC.

The sampling method for this study was multistage stratified cluster sampling 19 , 21 . The sample was taken in six health areas of Yangzhong (Baqiao, Youfang, Sanmao, Xinba, Xinglong, and Xilaiqiao). In the first stage, regions were stratified by income and geographic location. Three regions (townships/subdistricts) were randomly selected. In the second stage, five administration villages or communities were randomly selected from each site. In the third stage, each resident group was selected from the chosen sites. In the fourth stage, all residents (individuals with local household registration) aged 40–69 years were invited unless they were unwilling to participate or had a history of UGC/mental disorder or contraindication for endoscopy 18 , 19 , 20 . All participants were given informed consent. The study adhered to the Declaration of Helsinki. The academic and ethics committee of Yangzhong People's Hospital approved the study (approval number: 202152). Participants with complete data on general information (demographic and socio-economic information), health-related characteristics, physical examination, and screening results from November 2017 to June 2021 were included in our study. Finally, 6736 high-risk (40–69 years) residents were included in our analysis, with 163 participants excluded as missing information on socio-demographic characteristics (age, marital status and education, etc.), height, weight, WC, and screening outcomes (Fig.  1 ).

Flow diagram of the study participants with number excluded and reason for exclusion.

Study procedure and data collection

Well-trained epidemiological investigators, physicians, and pathologists from the People's Hospital of Yangzhong conducted the cross-sectional study, including the questionnaire survey, anthropometrical measurements, endoscopy, and pathological diagnosis guided by the study protocol for NCEC-HRP and Technical Programme for the UGCEDTP 20 , 22 .

Firstly, a face-to-face questionnaire survey for eligible respondents is based on a structured and validated questionnaire (authorized by the Cancer Hospital of the Chinese Academy of Medical Sciences) derived from the NCEC-HRP, with the whole process taking about 20–30 min. Survey respondents were asked about socio-demographic characteristics (gender, age, marital status, residence, education, and average annual household income). Health-related characteristics included smoking (frequency, type, amount of cigarettes in a typical day, duration), drinking (frequency, type, amount of alcohol consumed in a typical day, and duration), tea consumption (frequency, type, amount of tea consumed in a typical day, duration and preference for tea temperature), and vegetable intake (frequency and consumption in a typical day), fruit intake (frequency and consumption in a typical day), scalding (frequency in the past year), pickled (frequency in the past year), leftovers (frequency in the past year), fried (frequency in the past year), irregular (frequency in the past year), and spicy diet (frequency in the past year), eating too fast (Yes/No), indoor air pollution (frequency of cooking and type of cooking fuel), missing teeth (Yes/No), diet taste (salty, medium, light), family history of cancer (Yes/No) and common chronic disease (Yes/No), were also collected. In addition, occupational and leisure-time physical activity (type and duration of physical activity per week) were recorded.

Secondly, physical measurements, including height, weight, hip circumference (HC), and WC, were done following the standard methods. The measurement methods of height and weight can be found in the literature mentioned above 17 , 18 . We measured the widest part of the pelvis as the HC, with accuracy to the nearest 0.1 cm 23 . WC was measured at the midpoint between the lower margin of the last rib and the top of the hip bone (umbilicus level) at the expiration's end, with accuracy to the nearest 0.1 cm 23 , 24 . The measurement was conducted by two experienced staff.

Thirdly, the endoscopic centre's clinical staff conducted an endoscopy and biopsy of suspicious lesions/high-incidence sites biopsy. Biopsy tissue at the gastric antrum was placed in a rapid urease assay reagent to determine whether Helicobacter pylori infection was present 19 . Pathologists then performed pathological diagnoses of the biopsied tissue. The examination procedure followed the technical program and the expert consensus recommendation on UGC 20 , 22 , 25 . Participants would be included in the follow-up process if diagnosed with precancerous lesions/diseases. If early-stage or advanced cancer is detected, targeted interventions would be made to interrupt the cancer process. Corresponding follow-up and treatment methods can also be found in the technical program 20 , 22 .

Definition of variables

The formulae for BMI can be found in the previous literature 17 , 18 . Waist-to-hip ratio (WHR) = WC (cm)/HC (cm), and waist-to-height ratio (WHtR) = WC (cm)/height (cm) 23 , 26 . Underweight/Normal weight, overweight, and obesity were defined as BMI < 24.0 kg/m 2 , 24.0 ≤ BMI < 28.0 kg/m 2 , and BMI ≥ 28.0 kg/m 2 , respectively, according to the Chinese classifications 27 . The WC cut-off points of the International Diabetes Federation for central obesity (WC ≥ 90.0 cm for men, WC ≥ 80.0 cm for women) were also used 28 . The WHR cut-off point for central obesity was 0.90 for men and 0.85 for women, respectively 23 . The cut-off point of WHtR for central obesity was 0.5 for both sexes 29 .

Smoking included current smokers (who have smoked cigarettes in the past 28 days and the consumption of cigarettes was up to 100 in their lifetime), ever smokers (who have not consumed cigarettes in the past 28 days, and the consumption of cigarettes was up to 100 in their lifetime) and never smokers (who have consumed the cigarettes less than 100 in their lifetime) 19 , 21 . Drinking included current drinkers (who have drunk alcohol at least once per week in the past year) and never-drinkers 19 , 21 . Tea drinking included current tea drinkers (who have drunk tea at least once per week in the past year) and never-tea drinkers. Adequate vegetable and fruit consumption was defined as consuming at least 300 g and 200 g, respectively, per day in the past year, according to the recommendations of the Chinese Dietary Guidelines 30 . The scalding diet was categorized into "Yes" (including self-reported consumption of scalding tea/food at least once per week in the past year ) and "No" 31 , 32 . Pickled, leftovers, fried, irregular and spicy diets were defined as the self-reported frequency of at least once a week in the past year 18 , 19 . We defined self-reported having the experience of cooking and using coal or wood as the main cooking fuel as indoor air pollution 33 . The occupational and leisure-time physical activity were merged and grouped into low, moderate, or high, whose definitions, specific consolidation, and grouping methods can be found in the previous studies 1 , 34 . Precancerous diseases in this study included reflux oesophagitis, gastric polyps, gastric ulcers, and intestinal metaplasia/atrophic gastritis diagnosed by endoscopy or pathology. Precancerous lesions included low-grade intraepithelial neoplasia and high-grade intraepithelial neoplasia diagnosed by pathology. Cancers are defined as EC and GC diagnosed by pathology.

Statistical analysis

All data were analyzed using SPSS version 27.0. As continuous data did not pass the Kolmogorov–Smirnov test ( P  < 0.001), they were expressed as the median and interquartile range (IQR). Frequencies and percentages were used to express categorical variables. Mann–Whitney U and χ 2 tests were applied to evaluate the differentials in the socio-demographic and health-related characteristics among different genders and obesity. Besides, differences in screening results for the population with different obesity status were assessed. Multivariate logistic regression was adopted to explore the independent factors influencing overweight/obesity and central obesity with adjusted odds ratios (AOR) and corresponding 95% confidence intervals (CI) estimated. Besides, multinomial logistic regression was applied to explore the relationship between upper gastrointestinal diseases [others (reference), precancerous diseases, precancerous lesions and cancers)] and different types of obesity, with AOR and 95%CI estimated. There was no collinearity between all variables (VIF < 2); therefore, all variables were considered in the multivariate model. Only factors with P  < 0.05 in the final multivariate regression analysis were considered statistically significant.

Basic information of the study population

Table 1 shows the gender-stratified characteristics of the study sample. A total of 6736 participants (2947 men and 3789 women) [mean age, 56.0 (IQR, 25th–75th percentiles: 51.0–63.0) years] were analyzed in this study. Most participants were female (56.3%), married (93.6%), rural residents (78.0%), and nearly half had an education level of junior middle school (47.2%). More participants had an average annual household income of ≥ 110,000 Chinese Yuan (CNY) (34.9%). Differences in all presented characteristics were statistically significant for males and females (all P  < 0.05), except for age, family history of cancer, and common chronic disease ( P > 0.05). Besides, the median BMI was 24.2 (IQR: 22.2–26.1) kg/m 2 [24.4 (IQR: 22.3–26.5) kg/m 2 for men and 24.0 (IQR: 22.0–26.0) kg/m 2 for women, P  < 0.001], and the median WC was 84.0 (IQR: 78.0–89.0) cm [86.0 (IQR: 81.0–92.0) cm for men and 82.0 (IQR: 77.0–87.0) cm for women, P  < 0.001)] among the participants. The median WHR was 0.9 (IQR: 0.9–1.0) [0.9 (IQR: 0.9–1.0) for men and women, P  < 0.001], and the median value of WHtR was 0.5 (IQR: 0.5–0.5) [0.5 (IQR: 0.5–0.5) for men and 0.5 (IQR:0.5–0.6) for women, P  < 0.001].

Prevalence of general and central obesity

Table 2 presents the prevalence of general and central obesity across different socio-demographic characteristics. The prevalence of overweight, obesity, WC-central obesity, WHR-central obesity, and WHtR-central obesity was 40.6%, 12.0%, 49.9%, 79.4%, and 63.7%, respectively. The prevalence of overweight and obesity was higher among males than females, while central obesity was higher among females than males. The prevalence of central obesity increases progressively with age, with obesity decreasing. Besides, married participants had higher rates of overweight and obesity but lower rates of central obesity. Urban residents had higher rates of overweight, obesity, and WC-central obesity but lower rates of WHR/WHtR-central obesity. There is an upward trend in overweight and obesity rates and a downward trend in central obesity as educational attainment and average annual household income increase. There were statistically significant differences in the distribution of general obesity and central obesity among participants with different socio-demographic characteristics, except for the distribution of WC-central obesity among people with different average annual household income, WHR-central obesity among people with different marital status and WHtR-central obesity among people with different residence had no significant difference ( P  > 0.05).

Prevalence of upper gastrointestinal diseases

Table 3 shows the distribution characteristics of upper gastrointestinal diseases under different obesity indicators. The prevalence of precancerous diseases, lesions, cancers, and others in the upper gastrointestinal tract was 42.0%, 8.5%, 0.7%, and 48.8%, respectively. The prevalence of precancerous diseases and lesions was lower in the overweight/obese population than in the under/normal-weight population, but the prevalence of cancers was higher ( P  < 0.05). The prevalence of precancerous diseases, lesions, and cancers was higher in the WHR centrally obese population than in their counterparts ( P  < 0.05). In addition, the distribution of upper gastrointestinal diseases in the WC centrally obese and WHtR centrally obese populations, although different, was not statistically significant ( P > 0.05). Distributional features of gastric and esophageal diseases can be seen in the supplementary Tables S1 and S2 .

Associated factors for overweight/obesity and central obesity

Multivariate regression analyses were conducted to evaluate the influencing factors of overweight/obesity or central obesity (Table 4 ). After adjusting for all confounding variables, the result of the multivariate regression analysis showed that females (OR = 0.752, 95% CI 0.640–0.884), current smokers (OR = 0.761, 95% CI 0.642–0.902), and participants with moderate physical activity (OR = 0.854, 95% CI 0.767–0.950) had lower odds of overweight/obesity. There was strong evidence that participants aged 50–59 years (OR = 1.155, 95% CI 1.018–1.309), drinking tea (OR = 1.241, 95% CI 1.052–1.465), intake of sufficient vegetable (OR = 1.166, 95% CI 1.050–1.295), pickled food (OR = 1.247, 95% CI 1.127–1.380), spicy food (OR = 1.191, 95% CI 1.021–1.389), eating too fast (OR = 1.590, 95% CI 1.416–1.785) and having family history of cancer (OR = 1.110, 95% CI 1.003–1.230) or common chronic disease (OR = 1.321, 95% CI 1.194–1.462) were more likely to be overweight/obesity.

The result of the multivariate regression analysis showed that younger age, higher education level, and participants with moderate physical activity were protective factors for central obesity while drinking tea, intake of pickled food, and eating too fast were risk factors of central obesity ( P  < 0.05). Besides, females (OR = 3.083, 95% CI 2.611–3.640), ever-smokers (OR = 1.310, 95% CI 1.013–1.694), participants consuming spicy food (OR = 1.187, 95% CI 1.016–1.387) and having a family history of cancer (OR = 1.131, 95% CI 1.018–1.256) or common chronic disease (OR = 1.171, 95% CI 1.055–1.299) had higher odds of WC-central obesity. Current smoking decreased the risk of WHR/WHtR-central obesity. Intake of sufficient vegetable (OR = 1.240, 95% CI 1.090–1.409) and missing teeth (OR = 1.168, 95% CI 1.023–1.334) were risk factors for WHR-central obesity. Participants with a family history of cancer (OR = 1.134, 95% CI 1.020–1.260) or common chronic disease (OR = 1.166, 95% CI 1.050–1.294) were more likely to be WHtR-central obesity.

Association of upper gastrointestinal diseases with obesity indicators

Table 5 presents the multivariate logistic regression analysis of determinants related to obesity of upper gastrointestinal diseases. After fully adjusting, the result reported that being overweight or obese (OR = 0.889, 95% CI 0.801–0.987) was less likely to have upper gastrointestinal precancerous diseases but more likely to have UGC (OR = 2.103, 95% CI 1.078–4.101), which was similar with WHtR-central obesity (OR = 2.233, 95% CI 1.056–4.718). WC- and WHR-central obesity were not significantly associated with upper gastrointestinal diseases ( P > 0.05). Subgroup analyses showed that those who were overweight or obese (OR = 0.855, 95% CI 0.772–0.946) and those who were centrally obese in WC (OR = 0.892, 95% CI 0.804–0.990) were less likely to have gastric precancerous diseases but more likely to have GC. Participants with WHtR-central obesity were more likely to have GC (OR = 2.997, 95% CI 1.124–7.995). Overweight and obesity (OR = 1.638, 95% CI 1.105–2.429), WC-central obesity (OR = 1.956, 95% CI 1.329–2.879), and WHtR-central obesity (OR = 2.281, 95% CI 1.459–3.566) were more likely to have esophageal precancerous diseases.

We reran the multinomial logistic regression model as a sensitivity analysis after combining cancers with precancerous lesions, and the association between obesity and precancerous diseases did not change substantially (Supplementary Table S3 ).

The present study focused on describing the prevalence of obesity, especially central obesity, among high-risk residents in a typical high-risk area of UGC, southeastern China. Furthermore, we explored the determinants of obesity and the possible relationship between obesity and upper gastrointestinal disease. The study showed that nearly 50% of the participants were overweight/obese or WC-centrally obese, and most participants were WHR/WHtR -centrally obese. After controlling for the possible confounders, different factors were associated with overweight/obesity and central obesity. Overweight/obesity was significantly associated with precancerous diseases and cancers of the upper gastrointestinal tract. WHtR-central obesity was associated with UGC. Subgroup analyses showed that being overweight or obese and WC-central obesity were significantly associated with gastric precancerous diseases and GC. WHtR-central obesity was associated with GC. Being overweight/obese and WC/WHtR-central obesity was only associated with esophageal precancerous diseases. This study did not find a significant correlation between general obesity/central obesity and any precancerous lesions.

With socio-economic development and continuous lifestyle changes, the obesity level of Chinese adults is increasing 4 , 35 . China Chronic Disease Risk Factor Surveillance (CCDRFS) data demonstrated that the prevalence of general obesity (BMI ≥ 28 kg/m 2 ) among Chinese adults (≥ 18 years) from 2013 to 2014 was 14% (males, 14.0% vs. females, 14.1%), and the prevalence of WC-central obesity (90 cm for men; 85 cm for women) was 31.5% (males, 30.7% vs. females, 32.4%) 36 . Moreover, since 2004, the prevalence of general and central obesity has increased by about 90% and more than 50%, respectively 36 . It was estimated by Li et al. that the prevalence of overweight, obesity, and WC-central obesity between 2007 and 2017 among Chinese adults increased from 20.3 to 20.8%, 31.9 to 37.2% and 25.9 to 35.4%, respectively 37 . The prevalence of overweight and obesity in this study was 40.6% and 12.0%, respectively, which were higher than the rates of 25.8% and 7.9%, respectively, found by Hu et al. 38 and 26.97% and 7.13%, respectively, reported in Hunan Province, China recently 39 . In addition, the prevalence of overweight in our study was higher than that reported in previous studies, while obesity was similar to or lower than that in the same studies 40 , 41 . Nevertheless, the prevalence of overweight and obesity is more in line with our previous findings in the same context 17 . The prevalence of WC-central obesity in this study was 49.9%, much higher than that reported in different settings 36 , 37 , 42 . Similar findings were found in the prevalence of central obesity determined by WHR and WHtR 43 , 44 , 45 . Numerous studies regarding the prevalence of obesity and central obesity have been conducted in China. Due to the different survey subjects, definitions, and data sources, the findings varied. In brief, we found that more than half of the population in our study were overweight or obese, nearly half were WC-central obesity, and most were WHR/WHtR-central obesity. This high prevalence of different types of obesity indicates that obesity is a common occurrence in the high-risk population of UGC and deserves the attention of policymakers. Meanwhile, we also found that the prevalence of overweight in the survey population was dominant, giving a window of weight improvement. Therefore, it is urgent to adopt intervention strategies to slow or reverse the obesity trend of those people.

We found some independent factors that influence obesity. More specifically, gender, age, current smoking, consumption of adequate vegetable, and pickled food were independent factors related to being overweight or obese, consistent with our previous finding 17 . In addition, we found tea drinking, spicy food, eating too fast, moderate physical activity, and family history of cancer and common chronic disease were influencing factors for being overweight/obese and centrally obese. Green tea consumption is usually considered to improve obesity levels. Numerous studies have shown that tea and its bioactive polyphenolic constituent, caffeine, can prevent and treat some metabolic disorders, including obesity 46 , 47 , 48 . Tea may also prevent obesity by decreasing appetite, reducing food consumption, reducing digestive system absorption, and altering fat metabolism 49 . This inconsistent result may be partly attributable to the fact that the type of tea consumption was not categorized in this study but also to the fact that residents may increase the frequency of tea consumption after perceiving obesity. Consistent with previous studies 50 , 51 , we found that spicy food increased the risk of general and central obesity. Many previous studies have confirmed that eating too fast is one of the risk factors for obesity 52 , 53 , 54 . On the one hand, eating too fast may cause us to eat more food due to a lack of satiety, thus increasing the potential for overnutrition 53 . It may also cause elevated blood sugar and insulin resistance, culminating in overeating 54 . On the other hand, a decrease in chewing leads to a reduction in the activation of histamine neurons in the brain, whose function includes suppressing appetite and accelerating lipolysis of visceral fat cells 54 . Physical activity is known to be a protective factor for obesity 55 , 56 , 57 . Similarly, among participants with moderate physical activity, we found that the risk of overweight/obesity and central obesity was about 0.8–0.9 times that of participants with low physical activity. Compared with participants without a family history of cancer and common chronic disease, we found that individuals with a family history were more likely to be obese, consistent with other findings 58 , 59 . This result may be achieved by increasing the risk of high-risk chronic diseases and poor lifestyles 60 , 61 , 62 .

We also found some heterogeneity in the independent influences on central and general obesity. Firstly, the risk of overweight/obesity was higher in males than in females, while the females tended to be central obese, consistent with previous studies 24 , 38 , 41 . However, other studies have found that females tend to be overweight/obese 2 , 29 . One possible explanation for the difference might be rapid changes in female hormones, the onset of menopause, and decreased metabolic exertion and physical activity levels 24 , 38 , 41 , 63 . Secondly, we observed that the risk of being overweight/obese decreased with ageing. However, a positive association between central obesity and ageing was observed, consistent with previous studies 1 , 24 . The changes in subcutaneous fat, lean mass, visceral fat accumulation 1 , 24 , 38 , 64 , 65 , level of physical activity, and physical health may partly be the reason for the difference 63 . We also observed that a high education level was a protective factor for central obesity, which may benefit from relatively adequate health awareness and literacy. However, in some other studies, their relationship was positive, especially among men 24 , 66 . We found that ever-smokers were more likely to be centrally obese, as in the study by Wang et al. 24 . Hence, residents who quit smoking should combine appropriate education and supportive treatment to reduce obesity to avoid weight gain becoming a barrier to quitting 67 . Our study found a positive association between missing teeth and central obesity. Still, we did not observe a meaningful association in general obesity, which aligns with the study by Singh et al. 68 . Previous studies have shown that missing teeth may affect chewing function and fruit and fibre intake, thus adversely affecting weight management 69 .

This study found that being overweight/obese and WHtR-central obese were more likely to get UGC. Subgroup analysis showed that in addition to the same association between the above obesity types and GC, WC-central obesity was also positively associated with GC, consistent with previous studies' results 70 , 71 . Several previous studies have found a positive association between overweight and obesity and GC, particularly among men and non-Asian populations 72 , 73 , 74 . Relevant mechanisms include excessive fat accumulation, especially abdominal obesity leading to insulin resistance, high leptin, low adiponectin, altered ghrelin, and abnormally increased blood levels of insulin-like growth factor 74 , 75 . In addition, gastroesophageal reflux diseases, strongly associated with GC, appear more prevalent in obese individuals 74 , 75 , 76 . However, we did not find any significant association between any obesity type and EC, which is inconsistent with the results of previous related studies 8 , 9 , 10 , 11 , 77 , 78 . This may be related to the small number of EC cases in this study (n = 13). Besides, it is well known that there is heterogeneity in the association of ESCC and EAC with obesity, and combining them in this study due to the small number of cases may have obscured some of the associations. We found no significant association between obesity indicators and any precancerous lesions, including gastric and esophagus sites. Earlier studies in a local high-risk area found an inverse association between general/central obesity and ESCC and its precancerous lesions 33 , 78 . One explanation for this finding may be the differences in study design, origin and characteristics of the participants, and the type and number of confounding factors included. We found that being overweight or obese was a protective factor for upper gastrointestinal precancerous diseases. Subgroup analysis showed that both overweight/obesity and WC-central obesity reduced the risk of gastric precancerous diseases. A recent study by Bae et al. found that overweight is a protective factor for GC in adult Asians, which is consistent with the results of this study 79 . The reason for this phenomenon may be related to the fact that overweight or obese patients have a greater experience of consultation and care 80 , which undoubtedly increases their likelihood of detecting their precancerous diseases. It may also be associated with altered digestive or eating habits in people with precancerous conditions, which can lead to weight loss 81 . We found that overweight and obesity, as well as WC/WHtR-central obesity, increase the risk of precancerous esophageal diseases (reflux esophagitis). This finding was confirmed by the studies of Chang et al. and Friedenberg et al. 82 , 83 .

Although this study is the first to investigate a high-risk population in a high-risk area for UGC to describe the prevalence of different types of obesity and explore the possible association between obesity and upper gastrointestinal diseases through standardized screening data, there are still some limitations. Firstly, the target population was 40–69 years, not representing the whole population. Secondly, participants were recruited in Yangzhong, and the findings may not be generalizable to other areas. Thirdly, the cross-sectional study can describe the status of interested variables at a certain time but cannot determine the causal association between exposure and outcome. Fourth, the small number of cancer cases in this study did not allow for subgroup analyses of different pathological types of cancer. Therefore, further evidence is needed. We will further construct prospective cohort studies based on the NCEC-HRP and UGCEDTP to demonstrate their association among high-risk Chinese populations.

In conclusion, we found a high prevalence of general and central obesity in a high-risk setting of Yangzhong, southeast China. Overweight/obesity and central obesity were significantly associated with gender, age, smoking, tea consumption, sufficient vegetable, pickled food, spicy food, eating speed, physical activity, and family history of cancer and common chronic disease. Education and missing teeth were also significantly correlated with central obesity. These findings can inform the development of weight improvement measures by disease control and prevention departments. Obesity significantly increases the risk of UGC but decreases the risk of precancerous diseases, which mainly apply to the stomach. Obesity is not significantly associated with EC but increases the risk of esophageal precancerous diseases. However, no significant association between precancerous lesions and obesity was observed in this study. More studies are needed to verify the association between obesity indicators and upper gastrointestinal diseases in the Chinese population.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Guo, X. et al. An update on overweight and obesity in rural Northeast China: from lifestyle risk factors to cardiometabolic comorbidities. BMC Public Health 14 , 1046 (2014).

Article   PubMed   Google Scholar  

Omar, S. M., Taha, Z., Hassan, A. A., Al-Wutayd, O. & Adam, I. Prevalence and factors associated with overweight and central obesity among adults in the Eastern Sudan. PLoS ONE 15 , e0232624 (2020).

Article   CAS   PubMed   Google Scholar  

Sung, H. et al. Global patterns in excess body weight and the associated cancer burden. CA Cancer J. Clin. 69 , 88–112 (2019).

Wang, L. et al. Body-mass index and obesity in urban and rural China: Findings from consecutive nationally representative surveys during 2004–18. Lancet 398 , 53–63 (2021).

NCD Risk Factor Collaboration (NCD-RisC). Rising rural body-mass index is the main driver of the global obesity epidemic in adults. Nature 569 , 260–264 (2019).

Avgerinos, K. I., Spyrou, N., Mantzoros, C. S. & Dalamaga, M. Obesity and cancer risk: Emerging biological mechanisms and perspectives. Metabolism 92 , 121–135 (2019).

Renehan, A. G., Tyson, M., Egger, M., Heller, R. F. & Zwahlen, M. Body-mass index and incidence of cancer: A systematic review and meta-analysis of prospective observational studies. Lancet 371 , 569–578 (2008).

Vingeliene, S. et al. An update of the WCRF/AICR systematic literature review and meta-analysis on dietary and anthropometric factors and esophageal cancer risk. Ann. Oncol. 28 , 2409–2419 (2017).

Steffen, A. et al. General and abdominal obesity and risk of esophageal and gastric adenocarcinoma in the European prospective investigation into cancer and nutrition. Int. J. Cancer 137 , 646–657 (2015).

Singh, S. et al. Central adiposity is associated with increased risk of esophageal inflammation, metaplasia, and adenocarcinoma: A systematic review and meta-analysis. Clin. Gastroenterol. Hepatol. 11 , 1399-1412.e7 (2013).

Nimptsch, K., Steffen, A. & Pischon, T. Obesity and oesophageal cancer. Recent Res. Cancer Res. 208 , 67–80 (2016).

Article   CAS   Google Scholar  

Yang, X. et al. Adult height, body mass index change, and body shape change in relation to esophageal squamous cell carcinoma risk: A population-based case-control study in China. Cancer Med. 8 , 5769–5778 (2019).

Sanikini, H. et al. Anthropometry, body fat composition and reproductive factors and risk of oesophageal and gastric cancer by subtype and subsite in the UK Biobank cohort. PLoS One 15 , e0240413 (2020).

World Cancer Research Fund/American Institute for Cancer Research. Continuous Update Project Expert Report 2018. Diet, nutrition, physical activity and stomach cancer. Available at https://www.wcrf.org/wp-content/uploads/2021/02/stomach-cancer-report.pdf (2018).

Murphy, N., Jenab, M. & Gunter, M. J. Adiposity and gastrointestinal cancers: Epidemiology, mechanisms and future directions. Nat. Rev. Gastroenterol. Hepatol. 15 , 659–670 (2018).

Du, X., Hidayat, K. & Shi, B. M. Abdominal obesity and gastroesophageal cancer risk: systematic review and meta-analysis of prospective studies. Biosci. Rep. 37 (2017).

Feng, X. et al. The prevalence and determinant of overweight and obesity among residents aged 40–69 years in high-risk regions for upper gastrointestinal cancer in southeast China. Sci. Rep. 13 , 8172 (2023).

Article   ADS   CAS   PubMed   Google Scholar  

Feng, X. et al. Prevalence and coprevalence of modifiable risk factors for upper digestive tract cancer among residents aged 40–69 years in Yangzhong city, China: A cross-sectional study. BMJ Open 11 , e042006 (2021).

Zhu, J. H. et al. Analysis of Helicobacter pylori infection and its association with gastric diseases among a population aged 40–69 years in areas with high incidence of gastric cancer. Chin. J. Cancer Prev. Treat 30 , 765–772 (2023) ( Chinese ).

Google Scholar  

Chen, R. et al. The national cohort of esophageal cancer-prospective cohort study of esophageal cancer and precancerous lesions based on high-risk population in China (NCEC-HRP): Study protocol. BMJ Open 9 , e027360 (2019).

Article   ADS   PubMed   Google Scholar  

Huang, F. et al. Analysis of the association between types of obesity and cardiovascular risk factors in residents aged 40–69 years. Mod. Prev. Med. 20 , 2305–2310 (2023) ( Chinese ).

Wang, G. Q. & Wei, W. Q. Upper Gastrointestinal Cancer Screening and Early Detection and Treatment Technology Programme (2020 Pilot Version) (People's Publishing House, 2020).

Zhang, J., Xu, L., Li, J., Sun, L. & Qin, W. Association between obesity-related anthropometric indices and multimorbidity among older adults in Shandong, China: A cross-sectional study. BMJ Open 10 , e036664 (2020).

Wang, H. et al. Epidemiology of general obesity, abdominal obesity and related risk factors in urban adults from 33 communities of Northeast China: The CHPSNE study. BMC Public Health 12 , 967 (2012).

Ma, D. et al. Chinese expert consensus on screening and endoscopic management of early Esophageal Cancer (Beijing, 2014). Gastroenterology 20 , 220–240 (2015) ( Chinese ).

Kromeyer-Hauschild, K., Neuhauser, H., Schaffrath Rosario, A. & Schienkiewitz, A. Abdominal obesity in German adolescents defined by waist-to-height ratio and its association to elevated blood pressure: the KiGGS study. Obes. Facts 6 , 165–175 (2013).

Wang, H. & Zhai, F. Programme and policy options for preventing obesity in China. Obes. Rev. 14 (Suppl 2), 134–140 (2013).

Alberti, K. G., Zimmet, P. & Shaw, J. The metabolic syndrome—a new worldwide definition. Lancet 366 , 1059–1062 (2005).

Alshamiri, M. Q. et al. Waist-to-height ratio (WHtR) in predicting coronary artery disease compared to body mass index and waist circumference in a single center from Saudi Arabia. Cardiol. Res. Pract. 2020 , 4250793 (2020).

Chinese nutrition society. Dietary guidelines for Chinese residents . (People's Publishing House, 2016).

Sheikh, M. et al. Individual and combined effects of environmental risk factors for esophageal cancer based on results from the golestan cohort study. Gastroenterology 156 (1416–1427), 32 (2019).

Islami, F. et al. A prospective study of tea drinking temperature and risk of esophageal squamous cell carcinoma. Int. J. Cancer 146 , 18–25 (2020).

Liu, M. et al. A model to identify individuals at high risk for esophageal squamous cell carcinoma and precancerous lesions in regions of high prevalence in China. Clin. Gastroenterol. Hepatol. 15 , 1538-1546.e7 (2017).

Hu, G., Tuomilehto, J., Silventoinen, K., Barengo, N. & Jousilahti, P. Joint effects of physical activity, body mass index, waist circumference and waist-to-hip ratio with the risk of cardiovascular disease among middle-aged Finnish men and women. Eur. Heart J. 25 , 2212–2219 (2004).

Afshin, A. et al. Health effects of overweight and obesity in 195 countries over 25 years. N. Engl. J. Med. 377 , 13–27 (2017).

Zhang, X. et al. Geographic variation in prevalence of adult obesity in China: Results from the 2013–2014 national chronic disease and risk factor surveillance. Ann. Intern. Med. 172 , 291–293 (2020).

Li, Y. et al. Changes in the prevalence of obesity and hypertension and demographic risk factor profiles in China over 10 years: Two national cross-sectional surveys. Lancet Reg. Health West Pac. 15 , 100227 (2021).

PubMed   Google Scholar  

Hu, L. et al. Prevalence of overweight, obesity, abdominal obesity and obesity-related risk factors in southern China. PLoS One 12 , e0183934 (2017).

Hua, J. et al. Prevalence of overweight and obesity among people aged 18 years and over between 2013 and 2018 in Hunan, China. Int. J. Environ. Res. Public Health 17 , 4048 (2020).

Li, X. Y. et al. Prevalence and characteristic of overweight and obesity among adults in China, 2010. Zhonghua Yu Fang Yi Xue Za Zhi 46 , 683–686 (2012) ( Chinese ).

Wang, R. et al. Prevalence of overweight and obesity and some associated factors among adult residents of northeast China: A cross-sectional study. BMJ Open 6 , e010828 (2016).

Du, P. et al. Prevalence of abdominal obesity among Chinese adults in 2011. J. Epidemiol. 27 , 282–286 (2017).

Muhammad, T., Boro, B., Kumar, M. & Srivastava, S. Gender differences in the association of obesity-related measures with multi-morbidity among older adults in India: Evidence from LASI, Wave-1. BMC Geriatr. 22 , 171 (2022).

Srivastava, S., Joseph K J, V., Dristhi, D. & Muhammad, T. Interaction of physical activity on the association of obesity-related measures with multimorbidity among older adults: A population-based cross-sectional study in India. BMJ Open 11 , e050245 (2021).

Cao, L. et al. Effects of body mass index, waist circumference, waist-to-height ratio and their changes on risks of dyslipidemia among Chinese adults: The guizhou population health cohort study. Int. J. Environ. Res. Public Health 19 , 341 (2021).

Xu, R., Yang, K., Li, S., Dai, M. & Chen, G. Effect of green tea consumption on blood lipids: A systematic review and meta-analysis of randomized controlled trials. Nutr. J. 19 , 48 (2020).

Gamboa-Gómez, C. I. et al. Plants with potential use on obesity and its complications. EXCLI J 14 , 809–831 (2015).

Hayat, K., Iqbal, H., Malik, U., Bilal, U. & Mushtaq, S. Tea and its consumption: Benefits and risks. Crit. Rev. Food Sci. Nutr. 55 , 939–954 (2015).

Sirotkin, A. V. & Kolesárová, A. The anti-obesity and health-promoting effects of tea and coffee. Physiol. Res. 70 , 161–168 (2021).

Wang, M., Huang, W. & Xu, Y. Effects of spicy food consumption on overweight/obesity, hypertension and blood lipids in China: A meta-analysis of cross-sectional studies. Nutr. J. 22 , 29 (2023).

Yang, K. et al. Association of the frequency of spicy food intake and the risk of abdominal obesity in rural Chinese adults: A cross-sectional study. BMJ Open 9 , e028736 (2019).

Zhu, B., Haruyama, Y., Muto, T. & Yamazaki, T. Association between eating speed and metabolic syndrome in a three-year population-based cohort study. J. Epidemiol. 25 , 332–336 (2015).

Nagahama, S. et al. Self-reported eating rate and metabolic syndrome in Japanese people: Cross-sectional study. BMJ Open 4 , e005241 (2014).

Wu, N. et al. Analysis of the correlation between eating speed and obesity. Chin. J. Endocrine Metab. 38 , 186–189 (2022) ( Chinese ).

ADS   Google Scholar  

Jakicic, J. M. & Davis, K. K. Obesity and physical activity. Psychiatr. Clin. N. Am. 34 , 829–840 (2011).

Article   Google Scholar  

Oppert, J. M. et al. Exercise training in the management of overweight and obesity in adults: Synthesis of the evidence and recommendations from the European association for the study of obesity physical activity working group. Obes. Rev. 22 (Suppl 4), e13273 (2021).

Pojednic, R., D’Arpino, E., Halliday, I. & Bantham, A. The benefits of physical activity for people with obesity, independent of weight loss: A systematic review. Int. J. Environ. Res. Public Health 19 , 4981 (2022).

Van der Sande, M. A. et al. Family history: An opportunity for early interventions and improved control of hypertension, obesity and diabetes. Bull. World Health Organ. 79 , 321–328 (2001).

Cederberg, H., Stančáková, A., Kuusisto, J., Laakso, M. & Smith, U. Family history of type 2 diabetes increases the risk of both obesity and its complications: Is type 2 diabetes a disease of inappropriate lipid storage. J. Intern. Med. 277 , 540–551 (2015).

Valdez, R., Greenlund, K. J., Khoury, M. J. & Yoon, P. W. Is family history a useful tool for detecting children at risk for diabetes and cardiovascular diseases? A public health perspective. Pediatrics 120 (Suppl 2), S78-86 (2007).

Bostean, G., Crespi, C. M. & McCarthy, W. J. Associations among family history of cancer, cancer screening and lifestyle behaviors: A population-based study. Cancer Causes Control 24 , 1491–1503 (2013).

Sá, A. & Peleteiro, B. The effect of chronic disease family history on the adoption of healthier lifestyles. Int. J. Health Plann. Manage 33 , e906–e917 (2018).

Zang, J. & Ng, S. W. Age, period and cohort effects on adult physical activity levels from 1991 to 2011 in China. Int. J. Behav. Nutr. Phys. Act. 13 , 40 (2016).

Borkan, G. A. & Norris, A. H. Fat redistribution and the changing body dimensions of the adult male. Hum. Biol. 49 , 495–513 (1977).

CAS   PubMed   Google Scholar  

Cartwright, M. J., Tchkonia, T. & Kirkland, J. L. Aging in adipocytes: Potential impact of inherent, depot-specific mechanisms. Exp. Gerontol. 42 , 463–471 (2007).

Ariaratnam, S. et al. Prevalence of obesity and its associated risk factors among the elderly in Malaysia: Findings from The National Health and Morbidity Survey (NHMS) 2015. PLoS One 15 , e0238566 (2020).

Sucharda, P. Smoking and obesity. Vnitr Lek 56 , 1053–1057 (2010).

Singh, A. et al. Gender differences in the association between tooth loss and obesity among older adults in Brazil. Rev. Saude Publica 49 , 44 (2015).

Chari, M. & Sabbah, W. The relationships among consumption of fruits, tooth loss and obesity. Community Dent. Health 35 , 148–152 (2018).

Ilic, M. & Ilic, I. Epidemiology of stomach cancer. World J. Gastroenterol. 28 , 1187–1203 (2022).

Thrift, A. P. & El-Serag, H. B. Burden of gastric cancer. Clin. Gastroenterol. Hepatol. 18 , 534–542 (2020).

Lin, X. J. et al. Body mass index and risk of gastric cancer: A meta-analysis. Jpn J. Clin. Oncol. 44 , 783–791 (2014).

Ji, B. T. et al. Body mass index and the risk of cancers of the gastric cardia and distal stomach in Shanghai China. Cancer Epidemiol. Biomark. Prev. 6 , 481–485 (1997).

CAS   Google Scholar  

Azizi, N. et al. Gastric cancer risk in association with underweight, overweight, and obesity: A systematic review and meta-analysis. Cancers (Basel) 15 , 2778 (2023).

Li, Q., Zhang, J., Zhou, Y. & Qiao, L. Obesity and gastric cancer. Front. Biosci. (Landmark Ed) 17 , 2383–2390 (2012).

Kellerman, R. & Kintanar, T. Gastroesophageal reflux disease. Prim. Care 44 , 561–573 (2017).

Uhlenhopp, D. J., Then, E. O., Sunkara, T. & Gaduputi, V. Epidemiology of esophageal cancer: Update in global trends, etiology and risk factors. Clin. J. Gastroenterol. 13 , 1010–1021 (2020).

Lu, P., Gu, J., Zhang, N., Sun, Y. & Wang, J. Risk factors for precancerous lesions of esophageal squamous cell carcinoma in high-risk areas of rural China: A population-based screening study. Medicine (Baltimore) 99 , e21426 (2020).

Bae, J. M. Body mass index and risk of gastric cancer in Asian adults: A meta-epidemiological meta-analysis of population-based cohort studies. Cancer Res. Treat 52 , 369–373 (2020).

Bottone, F. G. et al. Obese older adults report high satisfaction and positive experiences with care. BMC Health Serv. Res. 14 , 220 (2014).

Wei, L. P. et al. Body mass index and the risk of gastric cancer in males: A prospective cohort study. Zhonghua Liu Xing Bing Xue Za Zhi 40 , 1522–1526 (2019).

Chang, P. & Friedenberg, F. Obesity and GERD. Gastroenterol. Clin. N. Am. 43 , 161–173 (2014).

Friedenberg, F. K., Xanthopoulos, M., Foster, G. D. & Richter, J. E. The association between gastroesophageal reflux disease and obesity. Am. J. Gastroenterol. 103 , 2111–2122 (2008).

Download references

Acknowledgements

We would like to thank the residents and staff involved in the present study.

This study was supported by the China Early Gastrointestinal Cancer Physicians Growing Together Program (Grant NO. GTCZ-2021-JS-32-0001), Zhenjiang City key research and development plan (Grant NO. SH2022051) and 2023 Jiangsu Province Preventive Medicine general Project (Ym2023031).

Author information

Authors and affiliations.

Institute of Tumour Prevention and Control, Yangzhong People’s Hospital, Yangzhong, 212200, China

Xiang Feng, Jinhua Zhu, Zhaolai Hua & Qiuping Shi

Department of Gastroenterology, Zhongda Hospital, Southeast University, Nanjing, 210000, China

Department of Gastroenterology, Yangzhong People’s Hospital, Yangzhong, 212200, China

Shenghua Yao & Hongjun Yin

Department of Non-Communicable Disease Prevention and Control, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, 210009, China

You can also search for this author in PubMed   Google Scholar

Contributions

X.F. drafted the manuscript. X.F., J.H.Z., Z.L.H., and J.Y.Z. contributed to the conception and design of the study. X.F., Q.P.S., S.H.Y. and H.J.Y. conducted data collection and fundamental statistical analysis. X.F., Z.L.H., J.H.Z., J.Y.Z. S.H.Y., Q.P.S. and H.J.Y. discussed the result and agreed on the final manuscript.

Corresponding authors

Correspondence to Xiang Feng or Jinhua Zhu .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary tables., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Feng, X., Zhu, J., Hua, Z. et al. Prevalence and determinants of obesity and its association with upper gastrointestinal diseases in people aged 40–69 years in Yangzhong, southeast China. Sci Rep 14 , 21153 (2024). https://doi.org/10.1038/s41598-024-72313-2

Download citation

Received : 11 August 2023

Accepted : 05 September 2024

Published : 10 September 2024

DOI : https://doi.org/10.1038/s41598-024-72313-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.