Associations between dietary patterns, FTO genotype and obesity in adults from seven European countries.
Adults
Dietary patterns
FTO genotype
Obesity
Waist circumference
Journal
European journal of nutrition
ISSN: 1436-6215
Titre abrégé: Eur J Nutr
Pays: Germany
ID NLM: 100888704
Informations de publication
Date de publication:
Sep 2022
Sep 2022
Historique:
received:
18
07
2021
accepted:
02
03
2022
pubmed:
22
3
2022
medline:
12
8
2022
entrez:
21
3
2022
Statut:
ppublish
Résumé
High-fat and low-fibre discretionary food intake and FTO genotype are each associated independently with higher risk of obesity. However, few studies have investigated links between obesity and dietary patterns based on discretionary food intake, and the interaction effect of FTO genotype are unknown. Thus, this study aimed to derive dietary patterns based on intake of discretionary foods, saturated fatty acids (SFA) and fibre, and examine cross-sectional associations with BMI and waist circumference (WC), and interaction effects of FTO genotype. Baseline data on 1280 adults from seven European countries were included (the Food4Me study). Dietary intake was estimated from a Food Frequency Questionnaire. Reduced rank regression was used to derive three dietary patterns using response variables of discretionary foods, SFA and fibre density. DNA was extracted from buccal swabs. Anthropometrics were self-measured. Linear regression analyses were used to examine associations between dietary patterns and BMI and WC, with an interaction for FTO genotype. Dietary pattern 1 (positively correlated with discretionary foods and SFA, and inversely correlated with fibre) was associated with higher BMI (β:0.64; 95% CI 0.44, 0.84) and WC (β:1.58; 95% CI 1.08, 2.07). There was limited evidence dietary pattern 2 (positively correlated with discretionary foods and SFA) and dietary pattern 3 (positively correlated with SFA and fibre) were associated with anthropometrics. FTO risk genotype was associated with higher BMI and WC, with no evidence of a dietary interaction. Consuming a dietary pattern low in discretionary foods and high-SFA and low-fibre foods is likely to be important for maintaining a healthy weight, regardless of FTO predisposition to obesity. Clinicaltrials.gov NCT01530139. Registered 9 February 2012 https://clinicaltrials.gov/ct2/show/NCT01530139.
Identifiants
pubmed: 35307761
doi: 10.1007/s00394-022-02858-3
pii: 10.1007/s00394-022-02858-3
pmc: PMC9363276
doi:
Substances chimiques
Dietary Fiber
0
Fatty Acids
0
Alpha-Ketoglutarate-Dependent Dioxygenase FTO
EC 1.14.11.33
FTO protein, human
EC 1.14.11.33
Banques de données
ClinicalTrials.gov
['NCT01530139']
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2953-2965Subventions
Organisme : FP7 Food, Agriculture and Fisheries, Biotechnology
ID : 265494
Organisme : national health and medical research council
ID : APP1173803
Informations de copyright
© 2022. The Author(s).
Références
World Health Organization (2020) Overweight and oesity WHO Fact Sheet. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight . Accessed 9 March 2021
Eurostat (2021) Body mass index (BMI) by sex, age and educational attainment level. https://ec.europa.eu/eurostat/databrowser/view/HLTH_EHIS_BM1E__custom_813099/default/table?lang=en .
Gakidou E, Afshin A, Abajobir AA, Abate KH, Abbafati C, Abbas KM, Abd-Allah F, Abdulle AM, Abera SF, Aboyans V, Abu-Raddad LJ, Abu-Rmeileh NME, Abyu GY, Adedeji IA, Adetokunboh O et al (2017) Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet 390(10100):1345–1422. https://doi.org/10.1016/S0140-6736(17)32366-8
doi: 10.1016/S0140-6736(17)32366-8
Brand-Miller JC, Barclay AW (2017) Declining consumption of added sugars and sugar-sweetened beverages in Australia: a challenge for obesity prevention. Am J Clin Nutr 105(4):854–863. https://doi.org/10.3945/ajcn.116.145318%JTheAmericanJournalofClinicalNutrition
doi: 10.3945/ajcn.116.145318%JTheAmericanJournalofClinicalNutrition
pubmed: 28275129
Sacks FM, Lichtenstein AH, Wu JHY, Appel LJ, Creager MA, Kris-Etherton PM, Miller M, Rimm EB, Rudel LL, Robinson JG, Stone NJ, Horn LVV (2017) Dietary Fats and Cardiovascular Disease: A Presidential Advisory From the American Heart Association. 136 (3):e1-e23. doi:doi: https://doi.org/10.1161/CIR.0000000000000510
Howard Barbara V, Wylie-Rosett J (2002) Sugar and cardiovascular disease. Circulation 106(4):523–527. https://doi.org/10.1161/01.CIR.0000019552.77778.04
doi: 10.1161/01.CIR.0000019552.77778.04
pubmed: 12135957
Anderson JJ, Gray SR, Welsh P, Mackay DF, Celis-Morales CA, Lyall DM, Forbes J, Sattar N, Gill JMR, Pell JP (2020) The associations of sugar-sweetened, artificially sweetened and naturally sweet juices with all-cause mortality in 198,285 UK Biobank participants: a prospective cohort study. BMC Med 18(1):97. https://doi.org/10.1186/s12916-020-01554-5
doi: 10.1186/s12916-020-01554-5
pubmed: 32326961
pmcid: 7181499
Singh GM, Micha R, Khatibzadeh S, Lim S, Ezzati M, Mozaffarian D (2015) Estimated global, regional, and national disease burdens related to sugar-sweetened beverage consumption in 2010. 132 (8):639-666. doi:doi: https://doi.org/10.1161/CIRCULATIONAHA.114.010636
Schulze MB, Martínez-González MA, Fung TT, Lichtenstein AH, Forouhi NG (2018) Food based dietary patterns and chronic disease prevention. BMJ 361. 10.1136/bmj.k2396
Hoffmann K, Schulze MB, Schienkiewitz A, Nöthlings U, Boeing H (2004) Application of a new statistical method to derive dietary patterns in nutritional epidemiology. Am J Epidemiol 159(10):935–944. https://doi.org/10.1093/aje/kwh134
doi: 10.1093/aje/kwh134
pubmed: 15128605
Livingstone KM, McNaughton S (2017) Dietary patterns by reduced rank regression are associated with obesity and hypertension in Australian adults. Br J Nutr. https://doi.org/10.1017/S0007114516004505
doi: 10.1017/S0007114516004505
pubmed: 28120736
Australian Government National Health and Medical Research Council Department of Health and Ageing (2013) Eat for Health. Australian Dietary Guidelines.
Aljadani H, Patterson A, Sibbritt D, Collins C (2013) The association between diet quality and weight change in adults over time: a systematic review of prospective cohort studies. In: Preedy VR, Hunter L-A, Patel VB (eds) Diet quality: an evidence-based approach, Volume 2. Springer New York, New York, NY, pp 3–27. https://doi.org/10.1007/978-1-4614-7315-2_1
Hosseini-Esfahani F, Koochakpoor G, Daneshpour MS, Sedaghati-Khayat B, Mirmiran P, Azizi F (2017) Mediterranean dietary pattern adherence modify the association between FTO genetic variations and obesity phenotypes. Nutrients 9 (10). https://doi.org/10.3390/nu9101064
San-Cristobal R, Navas-Carretero S, Livingstone KM, Celis-Morales C, Macready AL, Fallaize R, O’Donovan CB, Lambrinou CP, Moschonis G, Marsaux CFM, Manios Y, Jarosz M, Daniel H, Gibney ER, Brennan L et al (2017) Mediterranean diet adherence and genetic background roles within a web-based nutritional intervention: the food4me study. Nutrients 9(10):1107. https://doi.org/10.3390/nu9101107
doi: 10.3390/nu9101107
pmcid: 5691723
Yeo GSH (2014) The role of the FTO (Fat Mass and Obesity Related) locus in regulating body size and composition. Mol Cell Endocrinol 397(1):34–41. https://doi.org/10.1016/j.mce.2014.09.012
doi: 10.1016/j.mce.2014.09.012
pubmed: 25224490
Livingstone KM, Celis-Morales C, Lara J, Ashor AW, Lovegrove JA, Martinez JA, Saris WH, Gibney M, Manios Y, Traczyk I, Drevon CA, Daniel H, Gibney ER, Brennan L, Bouwman J et al (2015) Associations between FTO genotype and total energy and macronutrient intake in adults: a systematic review and meta-analysis. Obes Rev 16(8):666–678. https://doi.org/10.1111/obr.12290
doi: 10.1111/obr.12290
pubmed: 26016642
Naja F, Itani L, Hammoudeh S, Manzoor S, Abbas N, Radwan H, Saber-Ayad M (2021) Dietary patterns and their associations with the FTO and FGF21 gene variants among emirati adults. Frontiers in Nutrition 8 (211). doi: https://doi.org/10.3389/fnut.2021.668901
Lal A, Peeters A, Brown V, Nguyen P, Tran HNQ, Nguyen T, Tonmukayakul U, Sacks G, Calache H, Martin J, Moodie M, Ananthapavan J (2020) The modelled population obesity-related health benefits of reducing consumption of discretionary foods in Australia. Nutrients 12(3):649. https://doi.org/10.3390/nu12030649
doi: 10.3390/nu12030649
pmcid: 7146305
Livingstone KM, Sexton-Dhamu MJ, Pendergast FJ, Worsley A, Brayner B, McNaughton SA (2021) Energy-dense dietary patterns high in free sugars and saturated fat and associations with obesity in young adults. Eur J Nutr. https://doi.org/10.1007/s00394-021-02758-y
doi: 10.1007/s00394-021-02758-y
pubmed: 34870745
pmcid: 8921009
Bell L, Edwards S, Grieger J (2015) The Relationship between dietary patterns and metabolic health in a representative sample of adult Australians. Nutrients 7(8):5295
doi: 10.3390/nu7085295
Food4Me (2016) An integrated analysis of opportunities and challenges for personalised nutrition 2016. http://www.food4me.org/ . 2021
Baecke JA, Burema J, Frijters JE (1982) A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr 36(5):936–942. https://doi.org/10.1093/ajcn/36.5.936
doi: 10.1093/ajcn/36.5.936
pubmed: 7137077
Forster H, Fallaize R, Gallagher C, O’Donovan CB, Woolhead C, Walsh MC, Macready AL, Lovegrove JA, Mathers JC, Gibney MJ, Brennan L, Gibney ER (2014) Online dietary intake estimation: the Food4Me food frequency questionnaire. J Med Internet Res 16(6):e150. https://doi.org/10.2196/jmir.3105
doi: 10.2196/jmir.3105
pubmed: 24911957
pmcid: 4071230
Fallaize R, Forster H, Macready AL, Walsh MC, Mathers JC, Brennan L, Gibney ER, Gibney MJ, Lovegrove JA (2014) Online dietary intake estimation: reproducibility and validity of the Food4Me food frequency questionnaire against a 4-day weighed food record. J Med Internet Res 16(8):e190. https://doi.org/10.2196/jmir.3355
doi: 10.2196/jmir.3355
pubmed: 25113936
pmcid: 4147714
Marshall SJ, Livingstone KM, Celis-Morales C, Forster H, Fallaize R, O'Donovan CB, Woolhead C, Marsaux CF, Macready AL, Navas-Carretero S, San-Cristobal R, Kolossa S, Tsirigoti L, Lambrinou CP, Moschonis G, Godlewska M, Surwiłło A, Drevon CA, Manios Y, Traczyk I, Martínez JA, Saris WH, Daniel H, Gibney ER, Brennan L, Walsh MC, Lovegrove JA, Gibney M, Mathers JC (2016) Food4Me Study. Reproducibility of the Online Food4Me Food-Frequency Questionnaire for Estimating Dietary Intakes across Europe. J Nutr 146(5):1068–1075. https://doi.org/10.3945/jn.115.225078
doi: 10.3945/jn.115.225078
pubmed: 27052541
Food Standards Agency (2002) McCance and Widdowson's The Composition of Foods. Sixth summary edition edn. Royal Society of Chemistry, Cambridge
Food Standards Scotland (2018) Briefing paper on discretionary foods. https://www.foodstandards.gov.scot/publications-and-research/publications/briefing-on-discretionary-foods . Accessed 20 Jan 2021
Livingstone KM, Celis-Morales C, Navas-Carretero S, San-Cristobal R, Forster H, Woolhead C, O’Donovan CB, Moschonis G, Manios Y, Traczyk I, Gundersen TE, Drevon CA, Marsaux CFM, Fallaize R, Macready AL et al (2021) Personalised nutrition advice reduces intake of discretionary foods and beverages: findings from the Food4Me randomised controlled trial. Int J Behav Nutr Phys Act 18(1):70. https://doi.org/10.1186/s12966-021-01136-5
doi: 10.1186/s12966-021-01136-5
pubmed: 34092234
pmcid: 8183081
Hoffmann K (2004) Application of a new statistical method to derive dietary patterns in nutritional epidemiology. Am J Epidemiol 159(10):935–944. https://doi.org/10.1093/aje/kwh134
doi: 10.1093/aje/kwh134
pubmed: 15128605
Public Health England (2020) National Diet and Nutrition Survey: results from Years 9 to 11 (combined) of the rolling programme for 2016 to 2017 and 2018 to 2019.
Food Standards Scotland (2018) Briefing paper on discretionary foods.
World Health Organization (2003) Diet, nutrition and the prevention of chronic diseases. Joint FAO/WHO expert consultation. WHO technical report series. World Health Organization, Geneva
Celis-Morales C, Livingstone KM, Woolhead C, Forster H, O’Donovan CB, Macready AL, Fallaize R, Marsaux CFM, Tsirigoti L, Efstathopoulou E, Moschonis G, Navas-Carretero S, San-Cristobal R, Kolossa S, Klein UL et al (2015) How reliable is internet-based self-reported identity, socio-demographic and obesity measures in European adults? Genes Nutr 10(5):28. https://doi.org/10.1007/s12263-015-0476-0
doi: 10.1007/s12263-015-0476-0
pubmed: 26143178
pmcid: 4491331
World Health Organization (2015) BMI classification. http://apps.who.int/bmi/index.jsp?intro-Page=intro_3.html .
World Health Organization (2008) Waist Circumference and Waist-Hip Ratio: Report of a WHO Expert Consultation. Geneva
European Commission (2015) European skills, competences, qualifications and occupations. https://ec.europa.eu/esco/web/guest/hierarchybrowser/-/browser/Occupation (accessed June 2020).
World Health Organization (2010) Global Recommendations on Physical Activity for Health. https://apps.who.int/iris/bitstream/handle/10665/44399/9789241599979_eng.pdf;jsessionid=C4CC87EEAC23E919A8D59FB1BE5879BB?sequence=1 . 14 July 2020
Henry CJK (2005) Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutr 8(7a):1133–1152. https://doi.org/10.1079/PHN2005801
doi: 10.1079/PHN2005801
pubmed: 16277825
Hébert JR, Peterson KE, Hurley TG, Stoddard AM, Cohen N, Field AE, Sorensen G (2001) The effect of social desirability trait on self-reported dietary measures among multi-ethnic female health center employees. Ann Epidemiol 11(6):417–427. https://doi.org/10.1016/s1047-2797(01)00212-5
doi: 10.1016/s1047-2797(01)00212-5
pubmed: 11454501
Livingstone KM, Celis-Morales C, Macready AL, Fallaize R, Forster H, Woolhead C, O’Donovan CB, Marsaux CF, Navas-Carretero S, San-Cristobal R, Kolossa S, Tsirigoti L, Lambrinou CP, Moschonis G, Surwiłło A et al (2017) Characteristics of European adults who dropped out from the Food4Me Internet-based personalised nutrition intervention. Public Health Nutr 20(1):53–63. https://doi.org/10.1017/s1368980016002020
doi: 10.1017/s1368980016002020
pubmed: 27492149
Johnson L, Mander AP, Jones LR, Emmett PM, Jebb SA (2008) Energy-dense, low-fiber, high-fat dietary pattern is associated with increased fatness in childhood. Am J Clin Nutr 87(4):846–854. https://doi.org/10.1093/ajcn/87.4.846
doi: 10.1093/ajcn/87.4.846
pubmed: 18400706
Sprake EF, Russell JM, Cecil JE, Cooper RJ, Grabowski P, Pourshahidi LK, Barker ME (2018) Dietary patterns of university students in the UK: a cross-sectional study. Nutr J 17(1):90. https://doi.org/10.1186/s12937-018-0398-y
doi: 10.1186/s12937-018-0398-y
pubmed: 30290816
pmcid: 6172790
Pryer JA, Nichols R, Elliott P, Thakrar B, Brunner E, Marmot M (2001) Dietary patterns among a national random sample of British adults. J Epidemiol Community Health 55(1):29–37. https://doi.org/10.1136/jech.55.1.29
doi: 10.1136/jech.55.1.29
pubmed: 11112948
pmcid: 1731768
Appannah G, Pot GK, Huang RC, Oddy WH, Beilin LJ, Mori TA, Jebb SA, Ambrosini GL (2015) Identification of a dietary pattern associated with greater cardiometabolic risk in adolescence. Nutr Metab Cardiovasc Dis 25(7):643–650. https://doi.org/10.1016/j.numecd.2015.04.007
doi: 10.1016/j.numecd.2015.04.007
pubmed: 26026208
pmcid: 4510146
Mendoza JA, Drewnowski A, Christakis DA (2007) Dietary Energy Density Is Associated With Obesity and the Metabolic Syndrome in U.S. Adults. Diabetes Care 30 (4):974–979. doi: https://doi.org/10.2337/dc06-2188
Livingstone KM, Celis-Morales C, Navas-Carretero S, San-Cristobal R, Forster H, O’Donovan CB, Woolhead C, Marsaux CFM, Macready AL, Fallaize R, Kolossa S, Tsirigoti L, Lambrinou CP, Moschonis G, Godlewska M et al (2016) Fat mass- and obesity-associated genotype, dietary intakes and anthropometric measures in European adults: the Food4Me study. Br J Nutr 115(3):440–448. https://doi.org/10.1017/S0007114515004675
doi: 10.1017/S0007114515004675
pubmed: 26620191
Hosseini-Esfahani F, Koochakpoor G, Daneshpour MS, Mirmiran P, Sedaghati-khayat B, Azizi F (2017) The interaction of fat mass and obesity associated gene polymorphisms and dietary fiber intake in relation to obesity phenotypes. Sci Rep 7(1):18057. https://doi.org/10.1038/s41598-017-18386-8
doi: 10.1038/s41598-017-18386-8
pubmed: 29273742
pmcid: 5741758
Razquin C, Martinez JA, Martinez-Gonzalez MA, Bes-Rastrollo M, Fernández-Crehuet J, Marti A (2010) A 3-year intervention with a Mediterranean diet modified the association between the rs9939609 gene variant in FTO and body weight changes. Int J Obes (Lond) 34(2):266–272. https://doi.org/10.1038/ijo.2009.233
doi: 10.1038/ijo.2009.233
Saber-Ayad M, Manzoor S, Radwan H, Hammoudeh S, Wardeh R, Ashraf A, Jabbar H, Hamoudi R (2019) The FTO genetic variants are associated with dietary intake and body mass index amongst Emirati population. PLoS ONE 14(10):e0223808. https://doi.org/10.1371/journal.pone.0223808
doi: 10.1371/journal.pone.0223808
pubmed: 31622411
pmcid: 6797190
Castillo JJ, Orlando RA, Garver WS (2017) Gene-nutrient interactions and susceptibility to human obesity. Genes Nutr 12:29–29. https://doi.org/10.1186/s12263-017-0581-3
doi: 10.1186/s12263-017-0581-3
pubmed: 29093760
pmcid: 5663124