Dietary Fat and Total Energy Intake Modifies the Association of Genetic Profile Risk Score on Obesity: Evidence from 48 170 UK Biobank Participants

Overview

Journal Int J Obes (Lond)

Specialty Endocrinology

Date 2017 Jul 25

PMID 28736445

Citations 22

Authors

C A Celis-Morales

D M Lyall

S R Gray

L Steell

J Anderson

S Iliodromiti

P Welsh

Y Guo

F Petermann

D F Mackay

M E S Bailey

J P Pell

J M R Gill

N Sattar

Affiliations

Soon will be listed here.

Abstract

Background: Obesity is a multifactorial condition influenced by both genetics and lifestyle. The aim of this study was to investigate whether the association between a validated genetic profile risk score for obesity (GPRS-obesity) and body mass index (BMI) or waist circumference (WC) was modified by macronutrient intake in a large general population study.

Methods: This study included cross-sectional data from 48 170 white European adults, aged 37-73 years, participating in the UK Biobank. Interactions between GPRS-obesity and macronutrient intake (including total energy, protein, fat, carbohydrate and dietary fibre intake) and its effects on BMI and WC were investigated.

Results: The 93-single-nucleotide polymorphism (SNP) GPRS was associated with a higher BMI (β: 0.57 kg m per s.d. increase in GPRS (95% confidence interval: 0.53-0.60); P=1.9 × 10) independent of major confounding factors. There was a significant interaction between GPRS and total fat intake (P=0.007). Among high-fat-intake individuals, BMI was higher by 0.60 (0.52, 0.67) kg m per s.d. increase in GPRS-obesity; the change in BMI with GPRS was lower among low-fat-intake individuals (β: 0.50 (0.44, 0.57) kg m). Significant interactions with similar patterns were observed for saturated fat intake (high β: 0.66 (0.59, 0.73) versus low β: 0.49 (0.42, 0.55) kg m, P=2 × 10) and for total energy intake (high β: 0.58 (0.51, 0.64) versus low β: 0.49 (0.42, 0.56) kg m, P=0.019), but not for protein intake, carbohydrate intake and fibre intake (P >0.05). The findings were broadly similar using WC as the outcome.

Conclusions: These data suggest that the benefits of reducing the intake of fats and total energy intake may be more important in individuals with high genetic risk for obesity.

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References

Celis-Morales C, Lara J, Mathers J . Personalising nutritional guidance for more effective behaviour change. Proc Nutr Soc. 2014; 74(2):130-8. DOI: 10.1017/S0029665114001633. View

Parnell L, Blokker B, Dashti H, Nesbeth P, Cooper B, Ma Y . CardioGxE, a catalog of gene-environment interactions for cardiometabolic traits. BioData Min. 2014; 7:21. PMC: 4217104. DOI: 10.1186/1756-0381-7-21. View

Livingstone K, Celis-Morales C, Lara J, Ashor A, Lovegrove J, Martinez J . Associations between FTO genotype and total energy and macronutrient intake in adults: a systematic review and meta-analysis. Obes Rev. 2015; 16(8):666-78. DOI: 10.1111/obr.12290. View

Celis-Morales C, Lyall D, Guo Y, Steell L, Llanas D, Ward J . Sleep characteristics modify the association of genetic predisposition with obesity and anthropometric measurements in 119,679 UK Biobank participants. Am J Clin Nutr. 2017; 105(4):980-990. DOI: 10.3945/ajcn.116.147231. View

Qi Q, Chu A, Kang J, Huang J, Rose L, Jensen M . Fried food consumption, genetic risk, and body mass index: gene-diet interaction analysis in three US cohort studies. BMJ. 2014; 348:g1610. PMC: 3959253. DOI: 10.1136/bmj.g1610. View

Hooper L, Abdelhamid A, Moore H, Douthwaite W, Skeaff C, Summerbell C . Effect of reducing total fat intake on body weight: systematic review and meta-analysis of randomised controlled trials and cohort studies. BMJ. 2012; 345:e7666. PMC: 3516671. DOI: 10.1136/bmj.e7666. View

Rukh G, Sonestedt E, Melander O, Hedblad B, Wirfalt E, Ericson U . Genetic susceptibility to obesity and diet intakes: association and interaction analyses in the Malmö Diet and Cancer Study. Genes Nutr. 2013; 8(6):535-47. PMC: 3824829. DOI: 10.1007/s12263-013-0352-8. View

Collins R . What makes UK Biobank special?. Lancet. 2012; 379(9822):1173-4. DOI: 10.1016/S0140-6736(12)60404-8. View

Perusse L, Bouchard C . Gene-diet interactions in obesity. Am J Clin Nutr. 2000; 72(5 Suppl):1285S-1290S. DOI: 10.1093/ajcn/72.5.1285s. View

10.

Tyrrell J, Wood A, Ames R, Yaghootkar H, Beaumont R, Jones S . Gene-obesogenic environment interactions in the UK Biobank study. Int J Epidemiol. 2017; 46(2):559-575. PMC: 5837271. DOI: 10.1093/ije/dyw337. View

11.

. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2001; 894:i-xii, 1-253. View

12.

Celis-Morales C, Lyall D, Anderson J, Iliodromiti S, Fan Y, Ntuk U . The association between physical activity and risk of mortality is modulated by grip strength and cardiorespiratory fitness: evidence from 498 135 UK-Biobank participants. Eur Heart J. 2017; 38(2):116-122. PMC: 5837781. DOI: 10.1093/eurheartj/ehw249. View

13.

Qi Q, Li Y, Chomistek A, Kang J, Curhan G, Pasquale L . Television watching, leisure time physical activity, and the genetic predisposition in relation to body mass index in women and men. Circulation. 2012; 126(15):1821-7. PMC: 3667660. DOI: 10.1161/CIRCULATIONAHA.112.098061. View

14.

Maes H, Neale M, Eaves L . Genetic and environmental factors in relative body weight and human adiposity. Behav Genet. 1997; 27(4):325-51. DOI: 10.1023/a:1025635913927. View

15.

Perusse L, Bouchard C . Genotype-environment interaction in human obesity. Nutr Rev. 1999; 57(5 Pt 2):S31-7; discussion S37-8. DOI: 10.1111/j.1753-4887.1999.tb01785.x. View

16.

Hill J . Understanding and addressing the epidemic of obesity: an energy balance perspective. Endocr Rev. 2006; 27(7):750-61. DOI: 10.1210/er.2006-0032. View

17.

Anderson J, Celis-Morales C, Mackay D, Iliodromiti S, Lyall D, Sattar N . Adiposity among 132 479 UK Biobank participants; contribution of sugar intake vs other macronutrients. Int J Epidemiol. 2016; 46(2):492-501. PMC: 5837311. DOI: 10.1093/ije/dyw173. View

18.

Bouchard C . Gene-environment interactions in the etiology of obesity: defining the fundamentals. Obesity (Silver Spring). 2008; 16 Suppl 3:S5-S10. DOI: 10.1038/oby.2008.528. View

19.

Palmer L . UK Biobank: bank on it. Lancet. 2007; 369(9578):1980-1982. DOI: 10.1016/S0140-6736(07)60924-6. View

20.

Eichler E, Flint J, Gibson G, Kong A, Leal S, Moore J . Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet. 2010; 11(6):446-50. PMC: 2942068. DOI: 10.1038/nrg2809. View