Dietary patterns interact with chromosome 9p21 rs1333048 polymorphism on the risk of obesity and cardiovascular risk factors in apparently healthy Tehrani adults

  • Mehdi Mollahosseini
  • Mohammad Hossein Rahimi
  • Mir Saeed Yekaninejad
  • Zhila MaghbooliEmail author
  • Khadijeh MirzaeiEmail author
Original Contribution



Gene-dietary patterns may contribute to determining body composition and related biochemical indices. The aim of this study was to evaluate interactions between rs1333048 polymorphism and major dietary patterns on body fat percentage, general and central obesity, and related biochemical measurements.


This cross-sectional study was conducted on 265 healthy Tehrani adults with mean age of 35 years (47.5% men, 52.5% women). Dietary patterns (DPs) were extracted by factor analysis. Bioelectrical impedance analysis was used for body analysis and rs1333048 was genotyped by the restriction fragment length polymorphism (PCR-RFLP) method.


Three DPs were extracted: restricted refined grains DP, legumes DP and healthy DP. AA genotype compared to CC genotype had greater odds for general obesity before (OR 3.14; 95% CI 1.008–9.60, P = 0.045) and after (OR 3.11; 95% CI 1.008–9.60, P = 0.048) adjusting for potential confounders. Individuals with AA genotype were more likely to be centrally obese before (OR 2.09; 95% CI 1.006–4.35, P = 0.048) and after (OR 2.63; 95% CI 1.12–6.17, P = 0.026) controlling for potential confounders. Significant interactions were observed between Legumes DP and rs1333048 SNP on waist circumference (P = 0.047), body fat % (BFP) (P = 0.048), hs-Crp (P = 0.042), BMI (P = 0.073), WHtR (P = 0.063) and odds for general obesity (P = 0.051). Following this DP reduced all these items for individuals with CC genotype, whereas increased them for people who carry CA or AA genotypes.


The findings indicate that there are significant associations between AA genotype of rs1333048 SNP and general and central obesity, and significant interaction between alleles of this SNP and major dietary patterns on the odds of general obesity, BFP, waist circumference, BMI, WHtR and hs-Crp.


Diet Genetics Gene–environment interaction CDKN2B Chromosome 9 Obesity 



Adiponectin gene


Alanine aminotransferase


Apolipoprotein B


Aspartate aminotransferase


Body fat percentage


Bioelectrical impedance analysis


Body mass index


Base pair


Coronary artery disease


Cyclin-dependent kinase inhibitor 2B


Dietary pattern


Cardiovascular disease


Deoxyribonucleic acid


Fasting blood sugar


Fat-free mass


Food frequency questionnaire


Fat mass


Fat mass and obesity associated gene


General linear model


Genome-wide association studies


High-density lipoprotein cholesterol


Healthy dietary pattern


High sensitivity C-reactive protein


International Physical Activity Questionnaire


Low-density lipoprotein


Low-density lipoprotein receptor


Legumes dietary pattern


Melanocortin 4 receptor


Metabolic equivalent hours per week


Myocardial infarction


Myosin heavy chain 7


Polymerase chain reaction


Quantitative polymerase chain reaction


Restriction fragment length polymorphism


Restricted refined grains dietary pattern


Subcutaneous adipose tissue


Single nucleotide polymorphism


Total cholesterol




Visceral adipose tissue


Waist circumference


Waist-to-height ratio


World Health Organization



We are thankful to all participants who took part in the study. This study was supported by a Grant from Tehran University of Medical Sciences (Grant ID: 93-04-161-27722).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.


  1. 1.
    Franco LP, Morais CC, Cominetti C (2016) Normal-weight obesity syndrome: diagnosis, prevalence, and clinical implications. Nutr Rev 74(9):558–570. CrossRefPubMedGoogle Scholar
  2. 2.
    Mahan LK, Raymond JL (2016) Krause’s food and the nutrition care process, 14 edn. Elsevier, Oxford, p 388Google Scholar
  3. 3.
    Jensen MD (2008) Role of body fat distribution and the metabolic complications of obesity. J Clin Endocrinol Metab 93(11 Suppl 1):S57–S63. CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y (2000) Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr 72(3):694–701CrossRefGoogle Scholar
  5. 5.
    Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH (1999) The disease burden associated with overweight and obesity. JAMA 282(16):1523–1529CrossRefGoogle Scholar
  6. 6.
    WHO obesity and overweight. Accessed 18 July 2016
  7. 7.
    Pang Q, Zhang JY, Qu K, Song SD, Liu SS, Liu C (2015) Central obesity induces a greater risk of hepatocellular carcinoma than general obesity. Hepatology 62(3):979–980. CrossRefPubMedGoogle Scholar
  8. 8.
    Andreasen CH, Andersen G (2009) Gene–environment interactions and obesity—further aspects of genomewide association studies. Nutrition (Burbank, Los Angeles County, Calif) 25(10):998–1003CrossRefGoogle Scholar
  9. 9.
    Garaulet M, Ordovas JM, Madrid JA (2010) The chronobiology, etiology and pathophysiology of obesity. Int J Obes 34(12):1667–1683CrossRefGoogle Scholar
  10. 10.
    Krajmalnik-Brown R, Ilhan Z-E, Kang D-W, DiBaise JK (2012) Effects of gut microbes on nutrient absorption and energy regulation. Nutr Clin Pract 27(2):201–214CrossRefGoogle Scholar
  11. 11.
    Hinney A, Nguyen TT, Scherag A, Friedel S, Brönner G, Müller TD, Grallert H, Illig T, Wichmann H-E, Rief W (2007) Genome wide association (GWA) study for early onset extreme obesity supports the role of fat mass and obesity associated gene (FTO) variants. PLoS One 2(12):e1361CrossRefGoogle Scholar
  12. 12.
    Hu E, Liang P, Spiegelman BM (1996) AdipoQ is a novel adipose-specific gene dysregulated in obesity. J Biol Chem 271(18):10697–10703CrossRefGoogle Scholar
  13. 13.
    Lönnqvist F, Arner P, Nordfors L, Schalling M (1995) Overexpression of the obese (ob) gene in adipose tissue of human obese subjects. Nat Med 1(9):950–953CrossRefGoogle Scholar
  14. 14.
    Mollahosseini M, Shab-Bidar S, Rahimi MH, Djafarian K (2017) Effect of whey protein supplementation on long and short term appetite: a meta-analysis of randomized controlled trials. Clin Nutr ESPEN 20:34CrossRefGoogle Scholar
  15. 15.
    Samani NJ, Erdmann J, Hall AS, Hengstenberg C, Mangino M, Mayer B, Dixon RJ, Meitinger T, Braund P, Wichmann H-E (2007) Genomewide association analysis of coronary artery disease. N Engl J Med 357(5):443–453CrossRefGoogle Scholar
  16. 16.
    Schunkert H, Götz A, Braund P, McGinnis R, Tregouet D-A, Mangino M, Linsel-Nitschke P, Cambien F, Hengstenberg C, Stark K (2008) Repeated replication and a prospective meta-analysis of the association between chromosome 9p21. 3 and coronary artery disease. Circulation 117(13):1675–1684CrossRefGoogle Scholar
  17. 17.
    Holdt LM, Teupser D (2012) Recent studies of the human chromosome 9p21 locus, which is associated with atherosclerosis in human populations. Arterioscler Thromb Vasc Biol 32(2):196–206CrossRefGoogle Scholar
  18. 18.
    Burton PR, Clayton DG, Cardon LR, Craddock N, Deloukas P, Duncanson A, Kwiatkowski DP, McCarthy MI, Ouwehand WH, Samani NJ (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3000 shared controls. Nature 447(7145):661–678CrossRefGoogle Scholar
  19. 19.
    McPherson R, Visel A, Zhu Y, May D, McPherson R, Pertsemlidis A, Kavaslar N, Helgadottir A, Thorleifsson G, Magnusson K (2010) Chromosome 9p21 and coronary artery disease. N Engl J Med 362(18):1736CrossRefGoogle Scholar
  20. 20.
    Kathiresan S, Voight BF, Purcell S, Musunuru K, Ardissino D, Mannucci PM, Anand S, Engert JC, Samani NJ, Schunkert H (2009) Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nat Genet 41(3):334–341CrossRefGoogle Scholar
  21. 21.
    Svensson P-A, Wahlstrand B, Olsson M, Froguel P, Falchi M, Bergman RN, McTernan PG, Hedner T, Carlsson LM, Jacobson P (2014) CDKN2B expression and subcutaneous adipose tissue expandability: possible influence of the 9p21 atherosclerosis locus. Biochem Biophys Res Commun 446(4):1126–1131CrossRefGoogle Scholar
  22. 22.
    Hindy G, Ericson U, Hamrefors V, Drake I, Wirfält E, Melander O, Orho-Melander M (2014) The chromosome 9p21 variant interacts with vegetable and wine intake to influence the risk of cardiovascular disease: a population based cohort study. BMC Med Genet 15(1):1CrossRefGoogle Scholar
  23. 23.
    Do R, Xie C, Zhang X, Männistö S, Harald K, Islam S, Bailey SD, Rangarajan S, McQueen MJ, Diaz R (2011) The effect of chromosome 9p21 variants on cardiovascular disease may be modified by dietary intake: evidence from a case/control and a prospective study. PLoS Med 8(10):e1001106CrossRefGoogle Scholar
  24. 24.
    Mirmiran P, Esfahani FH, Mehrabi Y, Hedayati M, Azizi F (2010) Reliability and relative validity of an FFQ for nutrients in the Tehran Lipid and Glucose Study. Public Health Nutr 13(5):654–662CrossRefGoogle Scholar
  25. 25.
    Garnett S, Baur L, Cowell C (2008) Waist-to-height ratio: a simple option for determining excess central adiposity in young people. Int J Obes 32(6):1028–1030CrossRefGoogle Scholar
  26. 26.
    Esparragon FR, Companioni O, Bello MG, Rios NB, Perez JC (2012) Replication of relevant SNPs associated with cardiovascular disease susceptibility obtained from GWAs in a case–control study in a Canarian population. Dis Mark 32(4):231–239. CrossRefGoogle Scholar
  27. 27.
    Moghaddam MHB, Aghdam FB, Jafarabadi MA, Allahverdipour H, Nikookheslat SD, Safarpour S (2012) The Iranian Version of International Physical Activity Questionnaire (IPAQ) in Iran: content and construct validity, factor structure, internal consistency and stability. World Appl Sci 18(8):1073–1080Google Scholar
  28. 28.
    Byun JS, Han YS, Lee SS (2010) The effects of yellow soybean, black soybean, and sword bean on lipid levels and oxidative stress in ovariectomized rats. Int J Vitamin Nutr Res 80(2):97–106. CrossRefGoogle Scholar
  29. 29.
    Abbasi F, Kohli P, Reaven GM, Knowles JW (2016) Hypertriglyceridemia: a simple approach to identify insulin resistance and enhanced cardio-metabolic risk in patients with prediabetes. Diabetes Res Clin Pract 120:156–161. CrossRefPubMedGoogle Scholar
  30. 30.
    Lofgren IE, Herron KL, West KL, Zern TL, Patalay M, Koo SI, Fernandez ML (2005) Carbohydrate intake is correlated with biomarkers for coronary heart disease in a population of overweight premenopausal women. J Nutr Biochem 16(4):245–250. CrossRefPubMedGoogle Scholar
  31. 31.
    Martinez R, Lopez-Jurado M, Wanden-Berghe C, Sanz-Valero J, Porres JM, Kapravelou G (2016) Beneficial effects of legumes on parameters of the metabolic syndrome: a systematic review of trials in animal models. Br J Nutr 116(3):402–424. CrossRefPubMedGoogle Scholar
  32. 32.
    Srichamroen A, Thomson AB, Field CJ, Basu TK (2009) In vitro intestinal glucose uptake is inhibited by galactomannan from Canadian fenugreek seed (Trigonella foenum graecum L.) in genetically lean and obese rats. Nutr Res 29(1):49–54CrossRefGoogle Scholar
  33. 33.
    Esmaillzadeh A, Kimiagar M, Mehrabi Y, Azadbakht L, Hu FB, Willett WC (2007) Dietary patterns and markers of systemic inflammation among Iranian women. J Nutr 137(4):992–998CrossRefGoogle Scholar
  34. 34.
    Nanri H, Nakamura K, Hara M, Higaki Y, Imaizumi T, Taguchi N, Sakamoto T, Horita M, Shinchi K, Tanaka K (2011) Association between dietary pattern and serum C-reactive protein in Japanese men and women. J epidemiol Jpn Epidemiol Assoc 21(2):122–131CrossRefGoogle Scholar
  35. 35.
    Lee Y, Kang D, Lee SA (2014) Effect of dietary patterns on serum C-reactive protein level. Nutr Metab Cardiovasc Dis 24(9):1004–1011. CrossRefPubMedGoogle Scholar
  36. 36.
    Corley J, Kyle JA, Starr JM, McNeill G, Deary IJ (2015) Dietary factors and biomarkers of systemic inflammation in older people: the Lothian Birth Cohort 1936. Br J Nutr 114(7):1088–1098. CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Neale EP, Batterham MJ, Tapsell LC (2016) Consumption of a healthy dietary pattern results in significant reductions in C-reactive protein levels in adults: a meta-analysis. Nutr Res (New York NY) 36(5):391–401. CrossRefGoogle Scholar
  38. 38.
    Brighenti F, Valtuena S, Pellegrini N, Ardigo D, Del Rio D, Salvatore S, Piatti P, Serafini M, Zavaroni I (2005) Total antioxidant capacity of the diet is inversely and independently related to plasma concentration of high-sensitivity C-reactive protein in adult Italian subjects. Br J Nutr 93(05):619–625CrossRefGoogle Scholar
  39. 39.
    Esmaillzadeh A, Azadbakht L (2008) Major dietary patterns in relation to general obesity and central adiposity among Iranian women. J Nutr 138(2):358–363CrossRefGoogle Scholar
  40. 40.
    Yu C, Shi Z, Lv J, Du H, Qi L, Guo Y, Bian Z, Chang L, Tang X, Jiang Q, Mu H, Pan D, Chen J, Chen Z, Li L (2015) Major dietary patterns in relation to general and central obesity among Chinese adults. Nutrients 7(7):5834–5849. CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Gaesser GA (2007) Carbohydrate quantity and quality in relation to body mass index. J Am Diet Assoc 107(10):1768–1780CrossRefGoogle Scholar
  42. 42.
    Feliciano Pereira P, das Gracas de Almeida C, Alfenas Rde C (2014) Glycemic index role on visceral obesity, subclinical inflammation and associated chronic diseases. Nutr Hosp 30(2):237–243. CrossRefPubMedGoogle Scholar
  43. 43.
    Helgadottir A, Thorleifsson G, Manolescu A, Gretarsdottir S, Blondal T, Jonasdottir A, Jonasdottir A, Sigurdsson A, Baker A, Palsson A (2007) A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science 316(5830):1491–1493CrossRefGoogle Scholar
  44. 44.
    Paynter NP, Chasman DI, Buring JE, Shiffman D, Cook NR, Ridker PM (2009) Cardiovascular disease risk prediction with and without knowledge of genetic variation at chromosome 9p21. 3. Ann Intern Med 150(2):65–72CrossRefGoogle Scholar
  45. 45.
    Karvanen J, Silander K, Kee F, Tiret L, Salomaa V, Kuulasmaa K, Wiklund PG, Virtamo J, Saarela O, Perret C (2009) The impact of newly identified loci on coronary heart disease, stroke and total mortality in the MORGAM prospective cohorts. Genet Epidemiol 33(3):237–246CrossRefGoogle Scholar
  46. 46.
    Bayoglu B, Cakmak HA, Yuksel H, Can G, Karadag B, Ulutin T, Vural VA, Cengiz M (2013) Chromosome 9p21 rs10757278 polymorphism is associated with the risk of metabolic syndrome. Mol Cell Biochem 379(1–2):77–85. CrossRefPubMedGoogle Scholar
  47. 47.
    Kathiresan S, Srivastava D (2012) Genetics of human cardiovascular disease. Cell 148(6):1242–1257CrossRefGoogle Scholar
  48. 48.
    Choquet H, Meyre D (2011) Genetics of obesity: what have we learned? Curr Genom 12(3):169–179CrossRefGoogle Scholar
  49. 49.
    Paracchini V, Pedotti P, Taioli E (2005) Genetics of leptin and obesity: a HuGE review. Am J Epidemiol 162(2):101–114CrossRefGoogle Scholar
  50. 50.
    Menaa F, Menaa A, Menaa B, Tréton J (2013) Trans-fatty acids, dangerous bonds for health? A background review paper of their use, consumption, health implications and regulation in France. Eur J Nutr 52(4):1289–1302CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Mehdi Mollahosseini
    • 1
    • 2
    • 3
    • 4
  • Mohammad Hossein Rahimi
    • 1
    • 2
  • Mir Saeed Yekaninejad
    • 5
  • Zhila Maghbooli
    • 6
    Email author
  • Khadijeh Mirzaei
    • 1
    Email author
  1. 1.Department of Community Nutrition, School of Nutritional Sciences and DieteticsTehran University of Medical Sciences (TUMS)TehranIran
  2. 2.Endocrinology and Metabolism Clinical Sciences InstituteTehran University of Medical SciencesTehranIran
  3. 3.Department of Nutrition, School of Public HealthShahid Sadoughi University of Medical SciencesYazdIran
  4. 4.Nutrition and Food Security Research CenterShahid Sadoughi University of Medical SciencesYazdIran
  5. 5.Department of Epidemiology and Biostatistics, School of Public HealthTehran University of Medical SciencesTehranIran
  6. 6.MS Research CenterNeurosciences Institute of Tehran University of Medical SciencesTehranIran

Personalised recommendations