Lack of association between FTO gene variations and metabolic healthy obese (MHO) phenotype: Tehran Cardio-metabolic Genetic Study (TCGS)

  • Bahareh Sedaghati-khayat
  • Maryam Barzin
  • Mahdi Akbarzadeh
  • Kamran Guity
  • Mohammad-Sadegh Fallah
  • Hoda Pourhassan
  • Fereidoun Azizi
  • Maryam S. Daneshpour
Original Article



Obesity is currently an international epidemic and metabolic derangements pose these individuals at greater risk for future morbidity and mortality. Genetics and environmental factors have undeniable effects and among genetic risk factors, FTO/CETP genes are important. The current study examines the interaction between obesity phenotypes and FTO/CETP SNPs and their effects on lipid profile changes.

Materials and methods

We selected 954 adult subjects from TCGS (47.9% male). Participants were stratified according to their BMI and presence of metabolic syndrome according to the Joint Interim Statement (JIS) definition. Nine selected polymorphisms from FTO/CETP genes were genotyped using Tetra ARMS-PCR method. After age and sex adjustment the interaction of 9 markers with lipid profiles among phenotypes were tested by PASW.


In three main groups, HDL_C level had a strong significant association with CETP markers: (rs3764261, β(95% CI) − 0.48(− 0.61 to − 0.35), P = 1.0 × 10−11), (rs1800775, β(95% CI) 0.5(0.36;0.65), P = 1.0 × 10−6) and (rs1864163, β(95% CI) 0.3(0.16;0.43), P = 9.1 × 10−5). This association was also seen in rs7202116 within the total population. In only unhealthy metabolic obese (MUHO) subgroups four new FTO markers (rs1421085, rs1121980, rs1558902 and rs8050136) (P value < 0.01) demonstrated significant association, even after lipid profile adjustment.


In the present study, we investigated the association between obesity phenotypes and some variations in FTO/CETP genes for the first time. Our study showed that four markers in the first intron of the FTO gene should be the risk marker in MUHO participants.

Level of Evidence

Level III, case-control study


Obesity Metabolic syndrome Fat mass and obesity-associated protein Cholesteryl ester transfer protein 



The study was done under supervision of Cellular and Molecular Endocrine Research Center and Obesity research center in the Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Compliance with ethical standards

Conflict of interest

The authors declare that they do not have any conflict of interests.

Ethics approval

The study protocol was approved by the ethics committee of the Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Informed consent

Written consent was obtained from each subject.

Consent for publication.

Not applicable.


  1. 1.
    Karelis AD, St-Pierre DH, Conus F, Rabasa-Lhoret R, Poehlman ET (2004) Metabolic and body composition factors in subgroups of obesity: what do we know? J Clin Endocrinol Metab 89(6):2569–2575. CrossRefPubMedGoogle Scholar
  2. 2.
    Meigs JB, Wilson PW, Fox CS, Vasan RS, Nathan DM, Sullivan LM, D’Agostino RB (2006) Body mass index, metabolic syndrome, and risk of type 2 diabetes or cardiovascular disease. J Clin Endocrinol Metab 91(8):2906–2912. CrossRefPubMedGoogle Scholar
  3. 3.
    Daneshpour MS, Rebai A, Houshmand M, Alfadhli S, Zeinali S, Hedayati M, Zarkesh M, Azizi F (2011) 8q24. 3 and 11q25 chromosomal loci association with low HDL-C in metabolic syndrome. Eur J Clin Investig 41(10):1105–1112. CrossRefGoogle Scholar
  4. 4.
    Kramer CK, Zinman B, Retnakaran R (2013) Are metabolically healthy overweight and obesity benign conditions?: a systematic review and meta-analysis. Ann Intern Med 159(11):758–769. CrossRefPubMedGoogle Scholar
  5. 5.
    Cheung CY, Tso AW, Cheung BM, Xu A, Ong KL, Law LS, Wat NM, Janus ED, Sham PC, Lam KS (2011) Genetic variants associated with persistent central obesity and the metabolic syndrome in a 12-year longitudinal study. Eur J Endocrinol 164(3):381–388. CrossRefPubMedGoogle Scholar
  6. 6.
    de Luis DA, Aller R, Conde R, Izaola O, de la Fuente B, Sagrado MG (2013) Relation of the rs9939609 gene variant in FTO with metabolic syndrome in obese female patients. J Diabetes Complic 27(4):346–350. CrossRefGoogle Scholar
  7. 7.
    Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, Perry JR, Elliott KS, Lango H, Rayner NW, Shields B, Harries LW, Barrett JC, Ellard S, Groves CJ, Knight B, Patch AM, Ness AR, Ebrahim S, Lawlor DA, Ring SM, Ben-Shlomo Y, Jarvelin MR, Sovio U, Bennett AJ, Melzer D, Ferrucci L, Loos RJ, Barroso I, Wareham NJ, Karpe F, Owen KR, Cardon LR, Walker M, Hitman GA, Palmer CN, Doney AS, Morris AD, Smith GD, Hattersley AT, McCarthy MI (2007) A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316(5826):889–894. CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Daneshpour MS, Sedaghatikhayat B, Hedayati M, Azizi F (2015) From genome to gene: a review of genes and genetic variations to be associated with metabolic syndrome. Iran J Diabetes Lipid Disord 14(4):225–234Google Scholar
  9. 9.
    Muller TD, Hinney A, Scherag A, Nguyen TT, Schreiner F, Schafer H, Hebebrand J, Roth CL, Reinehr T (2008) ‘Fat mass and obesity associated’ gene (FTO): no significant association of variant rs9939609 with weight loss in a lifestyle intervention and lipid metabolism markers in German obese children and adolescents. BMC Med Genet 9:85. CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, Beckmann JS, Bragg-Gresham JL, Chang HY, Demirkan A, Den Hertog HM, Do R, Donnelly LA, Ehret GB, Esko T, Feitosa MF, Ferreira T, Fischer K, Fontanillas P, Fraser RM, Freitag DF, Gurdasani D, Heikkila K, Hypponen E, Isaacs A, Jackson AU, Johansson A, Johnson T, Kaakinen M, Kettunen J, Kleber ME, Li X, Luan J, Lyytikainen LP, Magnusson PK, Mangino M, Mihailov E, Montasser ME, Muller-Nurasyid M, Nolte IM, O’Connell JR, Palmer CD, Perola M, Petersen AK, Sanna S, Saxena R, Service SK, Shah S, Shungin D, Sidore C, Song C, Strawbridge RJ, Surakka I, Tanaka T, Teslovich TM, Thorleifsson G, Van den Herik EG, Voight BF, Volcik KA, Waite LL, Wong A, Wu Y, Zhang W, Absher D, Asiki G, Barroso I, Been LF, Bolton JL, Bonnycastle LL, Brambilla P, Burnett MS, Cesana G, Dimitriou M, Doney AS, Doring A, Elliott P, Epstein SE, Eyjolfsson GI, Gigante B, Goodarzi MO, Grallert H, Gravito ML, Groves CJ, Hallmans G, Hartikainen AL, Hayward C, Hernandez D, Hicks AA, Holm H, Hung YJ, Illig T, Jones MR, Kaleebu P, Kastelein JJ, Khaw KT, Kim E, Klopp N, Komulainen P, Kumari M, Langenberg C, Lehtimaki T, Lin SY, Lindstrom J, Loos RJ, Mach F, McArdle WL, Meisinger C, Mitchell BD, Muller G, Nagaraja R, Narisu N, Nieminen TV, Nsubuga RN, Olafsson I, Ong KK, Palotie A, Papamarkou T, Pomilla C, Pouta A, Rader DJ, Reilly MP, Ridker PM, Rivadeneira F, Rudan I, Ruokonen A, Samani N, Scharnagl H, Seeley J, Silander K, Stancakova A, Stirrups K, Swift AJ,Tiret L, Uitterlinden AG, van Pelt LJ, Vedantam S, Wainwright N, Wijmenga C, Wild SH, Willemsen G, Wilsgaard T, Wilson JF, Young EH, Zhao JH, Adair LS, Arveiler D,Assimes TL, Bandinelli S, Bennett F, Bochud M, Boehm BO, Boomsma DI, Borecki IB, Bornstein SR, Bovet P, Burnier M, Campbell H, Chakravarti A, Chambers JC, Chen YD, Collins FS,Cooper RS, Danesh J, Dedoussis G, de Faire U, Feranil AB, Ferrieres J, Ferrucci L,Freimer NB, Gieger C, Groop LC, Gudnason V, Gyllensten U, Hamsten A, Harris TB, Hingorani A, Hirschhorn JN, Hofman A, Hovingh GK, Hsiung CA, Humphries SE, Hunt SC, Hveem K,Iribarren C, Jarvelin MR, Jula A, Kahonen M, Kaprio J, Kesaniemi A, Kivimaki M, Kooner JS, Koudstaal PJ, Krauss RM, Kuh D, Kuusisto J, Kyvik KO, Laakso M, Lakka TA, Lind L, Lindgren CM, Martin NG, Marz W, McCarthy MI, McKenzie CA, Meneton P, Metspalu A,Moilanen L, Morris AD, Munroe PB, Njolstad I, Pedersen NL, Power C, Pramstaller PP,Price JF, Psaty BM, Quertermous T, Rauramaa R, Saleheen D, Salomaa V, Sanghera DK,Saramies J, Schwarz PE, Sheu WH, Shuldiner AR, Siegbahn A, Spector TD, Stefansson K, Strachan DP, Tayo BO, Tremoli E, Tuomilehto J, Uusitupa M, van Duijn CM, Vollenweider P, Wallentin L, Wareham NJ, Whitfield JB, Wolffenbuttel BH, Ordovas JM, Boerwinkle E, Palmer CN, Thorsteinsdottir U, Chasman DI, Rotter JI, Franks PW, Ripatti S, Cupples LA, Sandhu MS, Rich SS, Boehnke M, Deloukas P, Kathiresan S, Mohlke KL, Ingelsson E, Abecasis GR (2013) Discovery and refinement of loci associated with lipid levels. Nat Genet 45(11):1274–1283. CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Gerken T, Girard CA, Tung YC, Webby CJ, Saudek V, Hewitson KS, Yeo GS, McDonough MA, Cunliffe S, McNeill LA, Galvanovskis J, Rorsman P, Robins P, Prieur X, Coll AP, Ma M, Jovanovic Z, Farooqi IS, Sedgwick B, Barroso I, Lindahl T, Ponting CP, Ashcroft FM, O’Rahilly S, Schofield CJ (2007) The obesity-associated FTO gene encodes a 2-oxoglutarate-dependent nucleic acid demethylase. Science 318(5855):1469–1472. CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Kuivenhoven JA, Jukema JW, Zwinderman AH, de Knijff P, McPherson R, Bruschke AV, Lie KI, Kastelein JJ (1998) The role of a common variant of the cholesteryl ester transfer protein gene in the progression of coronary atherosclerosis. The Regression Growth Evaluation Statin Study Group. N Engl J Med 338(2):86–93. CrossRefPubMedGoogle Scholar
  13. 13.
    Okamoto H, Yonemori F, Wakitani K, Minowa T, Maeda K, Shinkai H (2000) A cholesteryl ester transfer protein inhibitor attenuates atherosclerosis in rabbits. Nature 406(6792):203–207. CrossRefPubMedGoogle Scholar
  14. 14.
    Daneshpour MS, Fallah M-S, Sedaghati-Khayat B, Guity K, Khalili D, Hedayati M, Ebrahimi A, Hajsheikholeslami F, Mirmiran P, Ramezani Tehrani F, Momenan A-A, Ghanbarian A, Amouzegar A, Amiri P, Azizi F (2017) Rationale and design of a Genetic Study on Cardiometabolic Risk Factors: protocol for the Tehran Cardiometabolic Genetic Study (TCGS). JMIR Res Protoc 6(2):e28. CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Azizi F, Ghanbarian A, Momenan AA, Hadaegh F, Mirmiran P, Hedayati M, Mehrabi Y, Zahedi-Asl S (2009) Prevention of non-communicable disease in a population in nutrition transition: Tehran Lipid and Glucose Study phase II. Trials 10:5. CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Azizi F, Rahmani M, Emami H, Madjid M (2000) Tehran Lipid and Glucose Study: rationale and design. CVD Prev 3:242–247. Google Scholar
  17. 17.
    Azizi F, Rahmani M, Emami H, Mirmiran P, Hajipour R, Madjid M, Ghanbili J, Ghanbarian A, Mehrabi Y, Saadat N, Salehi P, Mortazavi N, Heydarian P, Sarbazi N, Allahverdian S, Saadati N, Ainy E, Moeini S (2002) Cardiovascular risk factors in an Iranian urban population: Tehran lipid and glucose study (phase 1). Soz Praventivmed 47(6):408–426. CrossRefPubMedGoogle Scholar
  18. 18.
    Daneshpour M, Hedayati M, Eshraghi P, Azizi F (2010) Association of Apo E gene polymorphism with HDL level in a Thehranian population. Eur J Lipid Sci Technol. Google Scholar
  19. 19.
    Virani SS (2011) Non-HDL cholesterol as a metric of good quality of care: opportunities and challenges. Texas Heart Inst J 38(2):160–162Google Scholar
  20. 20.
    Chen Y, Zhang X, Pan B, Jin X, Yao H, Chen B, Zou Y, Ge J, Chen H (2010) A modified formula for calculating low-density lipoprotein cholesterol values. Lipids Health Dis 9:52. CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Miller SA, Dykes DD, Polesky HF (1988) A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res 16(3):1215CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart JC, James WP, Loria CM, Smith SC Jr (2009) Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 120(16):1640–1645. CrossRefPubMedGoogle Scholar
  23. 23.
    Azizi F, Khalili D, Aghajani H, Esteghamati A, Hosseinpanah F, Delavari A, Larijani B, Mirmiran P, Mehrabi Y, Kelishadi R, Hadaegh F (2010) Appropriate waist circumference cut-off points among Iranian adults: the first report of the Iranian National Committee of Obesity. Arch Iran Med 13(3):243–244PubMedGoogle Scholar
  24. 24.
    Liu K, Muse SV (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics 21(9):2128–2129. CrossRefPubMedGoogle Scholar
  25. 25.
    Purcell S (2007) PLINK. A toolset for whole-genome association and population-based linkage analysis. Am J Hum Genet. PubMedPubMedCentralGoogle Scholar
  26. 26.
    Wu J, Xu J, Zhang Z, Ren J, Li Y, Wang J, Cao Y, Rong F, Zhao R, Huang X, Du J (2014) Association of FTO polymorphisms with obesity and metabolic parameters in Han Chinese adolescents. PLoS One 9(6):e98984. CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, Ban HJ, Yoon D, Lee MH, Kim DJ, Park M, Cha SH, Kim JW, Han BG, Min H, Ahn Y, Park MS, Han HR, Jang HY, Cho EY, Lee JE, Cho NH, Shin C, Park T, Park JW, Lee JK, Cardon L, Clarke G, McCarthy MI, Lee JY, Oh B, Kim HL (2009) A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 41(5):527–534. CrossRefPubMedGoogle Scholar
  28. 28.
    Okada Y, Kubo M, Ohmiya H, Takahashi A, Kumasaka N, Hosono N, Maeda S, Wen W, Dorajoo R, Go MJ, Zheng W, Kato N, Wu JY, Lu Q, Tsunoda T, Yamamoto K, Nakamura Y, Kamatani N, Tanaka T (2012) Common variants at CDKAL1 and KLF9 are associated with body mass index in east Asian populations. Nat Genet 44(3):302–306. CrossRefPubMedGoogle Scholar
  29. 29.
    Wen W, Cho YS, Zheng W, Dorajoo R, Kato N, Qi L, Chen CH, Delahanty RJ, Okada Y, Tabara Y, Gu D, Zhu D, Haiman CA, Mo Z, Gao YT, Saw SM, Go MJ, Takeuchi F, Chang LC, Kokubo Y, Liang J, Hao M, Le Marchand L, Zhang Y, Hu Y, Wong TY, Long J, Han BG, Kubo M, Yamamoto K, Su MH, Miki T, Henderson BE, Song H, Tan A, He J, Ng DP, Cai Q, Tsunoda T, Tsai FJ, Iwai N, Chen GK, Shi J, Xu J, Sim X, Xiang YB, Maeda S, Ong RT, Li C, Nakamura Y, Aung T, Kamatani N, Liu JJ, Lu W, Yokota M, Seielstad M, Fann CS, Wu JY, Lee JY, Hu FB, Tanaka T, Tai ES, Shu XO (2012) Meta-analysis identifies common variants associated with body mass index in east Asians. Nat Genet 44(3):307–311. CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Day FR, Loos RJ (2011) Developments in obesity genetics in the era of genome-wide association studies. J Nutrigenet Nutrigenom 4(4):222–238. CrossRefGoogle Scholar
  31. 31.
    Lu Y, Loos RJ (2013) Obesity genomics: assessing the transferability of susceptibility loci across diverse populations. Genome Med. PubMedPubMedCentralGoogle Scholar
  32. 32.
    Stratigopoulos G, Padilla SL, LeDuc CA, Watson E, Hattersley AT, McCarthy MI, Zeltser LM, Chung WK, Leibel RL (2008) Regulation of Fto/Ftm gene expression in mice and humans. Am J Physiol Regul Integr Comp Physiol 294(4):6. CrossRefGoogle Scholar
  33. 33.
    Delous M, Baala L, Salomon R, Laclef C, Vierkotten J, Tory K, Golzio C, Lacoste T, Besse L, Ozilou C, Moutkine I, Hellman NE, Anselme I, Silbermann F, Vesque C, Gerhardt C, Rattenberry E, Wolf MT, Gubler MC, Martinovic J, Encha-Razavi F, Boddaert N, Gonzales M, Macher MA, Nivet H, Champion G, Bertheleme JP, Niaudet P, McDonald F, Hildebrandt F, Johnson CA, Vekemans M, Antignac C, Ruther U, Schneider-Maunoury S, Attie-Bitach T, Saunier S (2007) The ciliary gene RPGRIP1L is mutated in cerebello-oculo-renal syndrome (Joubert syndrome type B) and Meckel syndrome. Nat Genet 39(7):875–881. CrossRefPubMedGoogle Scholar
  34. 34.
    Peters U, North KE, Sethupathy P, Buyske S, Haessler J, Jiao S, Fesinmeyer MD, Jackson RD, Kuller LH, Rajkovic A, Lim U, Cheng I, Schumacher F, Wilkens L, Li R, Monda K, Ehret G, Nguyen KD, Cooper R, Lewis CE, Leppert M, Irvin MR, Gu CC, Houston D, Buzkova P, Ritchie M, Matise TC, Le Marchand L, Hindorff LA, Crawford DC, Haiman CA, Kooperberg C (2013) A systematic mapping approach of 16q12.2/FTO and BMI in more than 20,000 African Americans narrows in on the underlying functional variation: results from the Population Architecture using Genomics and Epidemiology (PAGE) study. PLoS Genet 9(1):17. CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Bahareh Sedaghati-khayat
    • 1
  • Maryam Barzin
    • 2
  • Mahdi Akbarzadeh
    • 1
  • Kamran Guity
    • 1
  • Mohammad-Sadegh Fallah
    • 3
  • Hoda Pourhassan
    • 4
  • Fereidoun Azizi
    • 5
  • Maryam S. Daneshpour
    • 1
  1. 1.Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine SciencesShahid Beheshti University of Medical SciencesTehranIslamic Republic of Iran
  2. 2.Obesity Research Center, Research Institute for Endocrine SciencesShahid Beheshti University of Medical SciencesTehranIslamic Republic of Iran
  3. 3.Kawsar Human Genetics Research CenterTehranIslamic Republic of Iran
  4. 4.Department of Internal MedicineUniversity of California RiversideRiversideUSA
  5. 5.Endocrine Research Center, Research Institute for Endocrine SciencesShahid Beheshti University of Medical SciencesTehranIslamic Republic of Iran

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