Skip to main content

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.

  • Article
  • Published:

Genetics and Epigenetics

Single-nucleotide polymorphisms in a cohort of significantly obese women without cardiometabolic diseases

Abstract

Background/Objectives

Obesity is an important risk factor for the development of diseases such as diabetes mellitus, hypertension, and dyslipidemia; however, a small number of individuals with long-standing obesity do not present with these cardiometabolic diseases. Such individuals are referred to as metabolically healthy obese (MHO) and potentially represent a subgroup of the general population with a protective genetic predisposition to obesity-related diseases. We hypothesized that individuals who were metabolically healthy, but significantly obese (BMI ≥ 35 kg/m2) would represent a highly homogenous subgroup, with which to investigate potential genetic associations to obesity. We further hypothesized that such a cohort may lend itself well to investigate potential genotypes that are protective with respect to the development of cardiometabolic disease.

Subjects/Methods

In the present study, we implemented this novel selection strategy by screening 892 individuals diagnosed as Class 2 or Class 3 obese and identified 38 who presented no manifestations of cardiometabolic disease. We then assessed these subjects for single-nucleotide polymorphisms (SNPs) that associated with this phenotype.

Results

Our analysis identified 89 SNPs that reach statistical significance (p < 1 × 10−5), some of which are associated with genes of biological pathways that influences dietary behavior; others are associated with genes previously linked to obesity and cardiometabolic disease as well as neuroimmune disease. This study, to the best of our knowledge, represents the first genetic screening of a cardiometabolically healthy, but significantly obese population.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Ogden CL, Carroll, MD, Fryar, CD, Flegal, KM Prevalence of Obesity Among Adults and Youth: United States, 2011–2014. Centers for Disease Control and Prevention, November 2015;288:1-2

  2. Hruby A, Manson JE, Qi L, Malik VS, Rimm EB, Sun Q, et al. Determinants and consequences of obesity. Am J Public Health. 2016;106:1656–62.

    Article  Google Scholar 

  3. Stunkard AJ, Sorensen TI. Obesity and socioeconomic status--a complex relation. N Engl J Med. 1993;329:1036–7.

    Article  CAS  Google Scholar 

  4. Reddon H, Gueant JL, Meyre D. The importance of gene-environment interactions in human obesity. Clin Sci. 2016;130:1571–97.

    Article  CAS  Google Scholar 

  5. Goodarzi MO. Genetics of obesity: what genetic association studies have taught us about the biology of obesity and its complications. Lancet Diabetes Endocrinol. 2018;6:223–36

  6. Sims EA. Are there persons who are obese, but metabolically healthy? Metabolism. 2001;50:1499–504.

    Article  CAS  Google Scholar 

  7. Karelis AD, St-Pierre DH, Conus F, Rabasa-Lhoret R, Poehlman ET. Metabolic and body composition factors in subgroups of obesity: what do we know? J Clin Endocrinol Metab. 2004;89:2569–75.

    Article  CAS  Google Scholar 

  8. Garcia-Moll X. Obesity and prognosis: time to forget about metabolically healthy obesity. Eur Heart J. 2018;39:407–9

  9. Munoz-Garach A, Cornejo-Pareja I, Tinahones FJ. Does metabolically healthy obesity exist? Nutrients. 2016;8:1–10

  10. Espinosa De Ycaza AE, Donegan D, Jensen MD. Long-term metabolic risk for the metabolically healthy overweight/obese phenotype. Int J Obesity. 2018;42:302–9

  11. Iglesias Molli AE, Penas Steinhardt A, Lopez AP, Gonzalez CD, Vilarino J, Frechtel GD, et al. Metabolically healthy obese individuals present similar chronic inflammation level but less insulin-resistance than obese individuals with metabolic syndrome. PLoS ONE. 2017;12:e0190528.

    Article  Google Scholar 

  12. Bluher M. The distinction of metabolically ‘healthy’ from ‘unhealthy’ obese individuals. Curr Opin Lipidol. 2010;21:38–43.

    Article  Google Scholar 

  13. National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Third Report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report. Circulation. 2002;106:3143–421.

    Article  Google Scholar 

  14. Smith AK, Fang H, Whistler T, Unger ER, Rajeevan MS. Convergent genomic studies identify association of GRIK2 and NPAS2 with chronic fatigue syndrome. Neuropsychobiology. 2011;64:183–94.

    Article  CAS  Google Scholar 

  15. Gauderman WJMJ. QUANTO 1.2.4: a computer program for power and sample size calculations for genetic–epidemiology studies. Los Angeles, CA: University of Southern California; 2009.

    Google Scholar 

  16. den Hoed M, Luan J, Langenberg C, Cooper C, Sayer AA, Jameson K, et al. Evaluation of common genetic variants identified by GWAS for early onset and morbid obesity in population-based samples. Int J Obes. 2013;37:191–6.

    Article  Google Scholar 

  17. Schlauch KA, Khaiboullina SF, De Meirleir KL, Rawat S, Petereit J, Rizvanov AA, et al. Genome-wide association analysis identifies genetic variations in subjects with myalgic encephalomyelitis/chronic fatigue syndrome. Transl Psychiatry. 2016;6:e730.

    Article  CAS  Google Scholar 

  18. Carvalho B, Bengtsson H, Speed TP, Irizarry RA. Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data. Biostatistics. 2007;8:485–99.

    Article  Google Scholar 

  19. Gonzalez JR, Armengol L, Sole X, Guino E, Mercader JM, Estivill X, et al. SNPassoc: an R package to perform whole genome association studies. Bioinformatics. 2007;23:644–5.

    PubMed  Google Scholar 

  20. Jorgenson E, Witte JS. Genome-wide association studies of cancer. Future Oncol. 2007;3:419–27.

    Article  CAS  Google Scholar 

  21. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc. 1995;Series B:289–300.

    Google Scholar 

  22. Devlin B, Roeder K. Genomic control for association studies. Biometrics. 1999;55:997–1004.

    Article  CAS  Google Scholar 

  23. Lehne B, Lewis CM, Schlitt T. From SNPs to genes: disease association at the gene level. PLoS ONE. 2011;6:e20133.

    Article  CAS  Google Scholar 

  24. Radkowski P, Wator G, Skupien J, Bogdali A, Wolkow P. Analysis of gene expression to predict dynamics of future hypertension incidence in type 2 diabetic patients. BMC Proc. 2016;10(Suppl 7):113–7.

    PubMed  PubMed Central  Google Scholar 

  25. Al-Shammari MS, Al-Ali R, Al-Balawi N, Al-Enazi MS, Al-Muraikhi AA, Busaleh FN, et al. Type 2 diabetes associated variants of KCNQ1 strongly confer the risk of cardiovascular disease among the Saudi Arabian population. Genet Mol Biol. 2017;40:586–90.

    Article  Google Scholar 

  26. van Vliet-Ostaptchouk JV, van Haeften TW, Landman GW, Reiling E, Kleefstra N, Bilo HJ, et al. Common variants in the type 2 diabetes KCNQ1 gene are associated with impairments in insulin secretion during hyperglycaemic glucose clamp. PLoS ONE. 2012;7:e32148.

    Article  Google Scholar 

  27. Anveden A, Sjoholm K, Jacobson P, Palsdottir V, Walley AJ, Froguel P, et al. ITIH-5 expression in human adipose tissue is increased in obesity. Obesity. 2012;20:708–14.

    Article  CAS  Google Scholar 

  28. Yeo GS, Connie Hung CC, Rochford J, Keogh J, Gray J, Sivaramakrishnan S, et al. A de novo mutation affecting human TrkB associated with severe obesity and developmental delay. Nat Neurosci. 2004;7:1187–9.

    Article  CAS  Google Scholar 

  29. Dong C, Wong ML, Licinio J. Sequence variations of ABCB1, SLC6A2, SLC6A3, SLC6A4, CREB1, CRHR1 and NTRK2: association with major depression and antidepressant response in Mexican-Americans. Mol Psychiatry. 2009;14:1105–18.

    Article  CAS  Google Scholar 

  30. International Multiple Sclerosis Genetics C, Beecham AH, Patsopoulos NA, Xifara DK, Davis MF, Kemppinen A, et al. Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat Genet. 2013;45:1353–60.

    Article  Google Scholar 

  31. Sturm R, Hattori A. Morbid obesity rates continue to rise rapidly in the United States. Int J Obes. 2013;37:889–91.

    Article  CAS  Google Scholar 

  32. Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999-2010. JAMA. 2012;307:491–7.

    Article  Google Scholar 

  33. Behrens M, Bartelt J, Reichling C, Winnig M, Kuhn C, Meyerhof W. Members of RTP and REEP gene families influence functional bitter taste receptor expression. J Biol Chem. 2006;281:20650–9.

    Article  CAS  Google Scholar 

  34. Tepper BJ, Banni S, Melis M, Crnjar R, Tomassini Barbarossa I. Genetic sensitivity to the bitter taste of 6-n-propylthiouracil (PROP) and its association with physiological mechanisms controlling body mass index (BMI). Nutrients. 2014;6:3363–81.

    Article  CAS  Google Scholar 

  35. Dotson CD, Zhang L, Xu H, Shin YK, Vigues S, Ott SH, et al. Bitter taste receptors influence glucose homeostasis. PLoS ONE. 2008;3:e3974.

    Article  Google Scholar 

  36. Clark AA, Dotson CD, Elson AE, Voigt A, Boehm U, Meyerhof W, et al. TAS2R bitter taste receptors regulate thyroid function. FASEB J. 2015;29:164–72.

    Article  CAS  Google Scholar 

  37. Vong QP, Leung WH, Houston J, Li Y, Rooney B, Holladay M, et al. TOX2 regulates human natural killer cell development by controlling T-BET expression. Blood. 2014;124:3905–13.

    Article  CAS  Google Scholar 

  38. Stolarczyk E, Vong CT, Perucha E, Jackson I, Cawthorne MA, Wargent ET, et al. Improved insulin sensitivity despite increased visceral adiposity in mice deficient for the immune cell transcription factor T-bet. Cell Metab. 2013;17:520–33.

    Article  CAS  Google Scholar 

  39. Kasuga M. KCNQ1, a susceptibility gene for type 2 diabetes. J Diabetes Investig. 2011;2:413–4.

    Article  CAS  Google Scholar 

  40. Tavira B, Coto E, Diaz-Corte C, Ortega F, Arias M, Torres A, et al. KCNQ1 gene variants and risk of new-onset diabetes in tacrolimus-treated renal-transplanted patients. Clin Transplant. 2011;25:E284–91.

    Article  CAS  Google Scholar 

  41. Al-Haggar M, Madej-Pilarczyk A, Kozlowski L, Bujnicki JM, Yahia S, Abdel-Hadi D, et al. A novel homozygous p.Arg527Leu LMNA mutation in two unrelated Egyptian families causes overlapping mandibuloacral dysplasia and progeria syndrome. Eur J Hum Genet. 2012;20:1134–40.

    Article  CAS  Google Scholar 

  42. Cordovado SK, Hendrix M, Greene CN, Mochal S, Earley MC, Farrell PM, et al. CFTR mutation analysis and haplotype associations in CF patients. Mol Genet Metab. 2012;105:249–54.

    Article  CAS  Google Scholar 

  43. Farh KK, Marson A, Zhu J, Kleinewietfeld M, Housley WJ, Beik S, et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature. 2015;518:337–43.

    Article  CAS  Google Scholar 

  44. San Francisco IF, Rojas PA, Torres-Estay V, Smalley S, Cerda-Infante J, Montecinos VP, et al. Association of RNASEL and 8q24 variants with the presence and aggressiveness of hereditary and sporadic prostate cancer in a Hispanic population. J Cell Mol Med. 2014;18:125–33.

    Article  CAS  Google Scholar 

  45. Judge AD, Zhang X, Fujii H, Surh CD, Sprent J. Interleukin 15 controls both proliferation and survival of a subset of memory-phenotype CD8(+) T cells. J Exp Med. 2002;196:935–46.

    Article  CAS  Google Scholar 

  46. Farooqi S, O’Rahilly S. Genetics of obesity in humans. Endocr Rev. 2006;27:710–18.

    Article  CAS  Google Scholar 

  47. Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316:889–94.

    Article  CAS  Google Scholar 

  48. Yako YY, Guewo-Fokeng M, Balti EV, Bouatia-Naji N, Matsha TE, Sobngwi E, et al. Genetic risk of type 2 diabetes in populations of the African continent: a systematic review and meta-analyses. Diabetes Res Clin Pract. 2016;114:136–50.

    Article  CAS  Google Scholar 

  49. Manriquez V, Aviles J, Salazar L, Saavedra N, Seron P, Lanas F et al. Polymorphisms in genes involved in the leptin-melanocortin pathway are associated with obesity-related cardiometabolic alterations in a Southern Chilean Population. Mol Diagnosis Therapy. 2017.

  50. Suzuki R, Matsumoto M, Fujikawa A, Kato A, Kuboyama K, Yonehara K, et al. SPIG1 negatively regulates BDNF maturation. J Neurosci. 2014;34:3429–42.

    Article  CAS  Google Scholar 

  51. Abbott GW, Tai KK, Neverisky DL, Hansler A, Hu Z, Roepke TK, et al. KCNQ1, KCNE2, and Na+-coupled solute transporters form reciprocally regulating complexes that affect neuronal excitability. Sci Signal. 2014;7:ra22.

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful to the Coriell Genotyping and Microarray Center at Coriell Institute for Medical Research in Camden, NJ, and to its former Director, Dr. Norman P. Gerry, for their assistance in processing our specimens. This work was funded in part by the National Institute of General Medical Sciences (GM103440).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to András Palotás or Vincent C. Lombardi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schlauch, K.A., Kulick, D., Subramanian, K. et al. Single-nucleotide polymorphisms in a cohort of significantly obese women without cardiometabolic diseases. Int J Obes 43, 253–262 (2019). https://doi.org/10.1038/s41366-018-0181-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41366-018-0181-3

This article is cited by

Search

Quick links