Abstract
Background/objectives
Low-carbohydrate diets (LCD) are useful for weight reduction, and 50–55% carbohydrate consumption is associated with minimal risk. Genetic differences were related to nutritional consumption, food preferences, and dietary patterns, but whether particular genetic differences in individuals influence LCD adherence is unknown.
Subjects/methods
We conducted a GWAS on adherence to LCD utilizing 14,076 participants from the Japan Multi-Institutional Collaborative Cohort study. We used a previously validated semiquantitative food frequency questionnaire to estimate food consumption. Association of the imputed variants with the LCD score by Halton et al. we used linear regression analysis adjusting for sex, age, total dietary energy consumption, and components 1 to 10 by principal component analysis. We repeated the analysis with adjustment for alcohol consumption (g/day) in addition to the above-described variables.
Results
Men and women combined analysis without adjustment for alcohol consumption; we found 395 variants on chromosome 12 associated with the LCD score having P values <5 × 10−8. A conditional analysis with the addition of the dosage data of rs671 on chromosome 12 as a covariate, P values for all 395 SNPs on chromosome 12 turned out to be insignificant. In the analysis with additional adjustment for alcohol consumption, we did not identify any SNPs associated with the LCD score.
Conclusion
We found rs671 was inversely associated with adherence to LCD, but that was strongly confounded by alcohol consumption.
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References
World Health Organization. Obesity and overweight. 2021. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight.
Santos FL, Esteves SS, da Costa Pereira A, Yancy WS Jr, Nunes JP. Systematic review and meta-analysis of clinical trials of the effects of low carbohydrate diets on cardiovascular risk factors. Obes Rev. 2012;13:1048–66.
Krieger JW, Sitren HS, Daniels MJ, Langkamp-Henken B. Effects of variation in protein and carbohydrate intake on body mass and composition during energy restriction: a meta-regression 1. Am J Clin Nutr. 2006;83:260–74.
Hu T, Mills KT, Yao L, Demanelis K, Eloustaz M, William S, et al. Effects of low-carbohydrate diets versus low-fat diets on metabolic risk factors: a meta-analysis of randomized controlled clinical trials. Am J Epidemiol. 2012;176 Suppl. 7:S44–54.
Clifton PM, Condo D, Keogh JB. Long-term weight maintenance after advice to consume low carbohydrate, higher protein diets – a systematic review and meta-analysis. Nutr Metab Cardiovasc Dis. 2014;24:224–35.
Halton TL, Willett WC, Liu S, Manson JE, Albert CM, Rexrode K, et al. Low-carbohydrate-diet score and the risk of coronary heart disease in women. N Engl J Med. 2006;355:1991–2002.
Fung TT, van Dam RM, Hankinson SE, Stampfer M, Willett WC, Hu FB. Low-carbohydrate diets and all-cause and cause-specific mortality: two cohort studies. Ann Intern Med. 2010;153:289–98.
Sjögren P, Becker W, Warensjö E, Olsson E, Byberg L, Gustafsson IG, et al. Mediterranean, and carbohydrate-restricted diets and mortality among elderly men: a cohort study in Sweden. Am J Clin Nutr. 2010;92:967–74.
Lagiou P, Sandin S, Lof M, Trichopoulos D, Adami HO, Weiderpass E. Low carbohydrate-high protein diet and incidence of cardiovascular diseases in Swedish women: prospective cohort study. BMJ. 2012;344:e4026.
Trichopoulou A, Psaltopoulou T, Orfanos P, Hsieh CC, Trichopoulos D. Low-carbohydrate-high-protein diet and long-term survival in a general population cohort. Eur J Clin Nutr. 2007;61:575–81.
Seidelmann SB, Claggett B, Cheng S, Henglin M, Shah A, Steffen LM, et al. Dietary carbohydrate intake and mortality: a prospective cohort study and meta-analysis. Lancet Public Health. 2018;3:e419–28.
Hasselbalch A, Heiman B, Kiviak K, Sørensen T. Studies of twins indicate that genetics influence dietary intake. J Nutr. 2008;138:2406–12.
Guénard F, Bouchard-Mercier A, Rudkowska I, Lemieux S, Couture P, Vohl MC. Genome-wide association study of dietary pattern scores. Nutrients. 2017;9:1–17.
Chu AY, Workalemahu T, Paynter NP, Rose LM, Giuliani F, Tanaka T, et al. Novel locus including FGF21 is associated with dietary macronutrient intake. Hum Mol Genet. 2013;22:1895–902.
Merino J, Dashti HS, Li SX, Sarnowski C, Justice AE, Graff M, et al. Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium. Mol Psychiatry. 2019;24:1920–32.
Meddens SFW, de Vlaming R, Bowers P, Burik CAP, Linnér RK, Lee C, et al. Genomic analysis of diet composition finds novel loci and associations with health and lifestyle. Mol Psychiatry. 2021;26:2056–69.
Nakagawa-Senda H, Hachiya T, Shimizu A, Hosono S, Oze I, Watanabe M, et al. A genome-wide association study in the Japanese population identifies the 12q24 locus for habitual coffee consumption: the J-MICC Study. Sci Rep. 2018;8:1493.
Mozaffarian D, Dashti HS, Wojczynski MK, Chu AY, Nettleton JA, Männistö S, et al. Genome-wide association meta-analysis of fish and EPA+DHA consumption in 17 US and European cohorts. PLoS ONE 2017;12:e0186456.
Igarashi M, Nogawa S, Kawafune K, Hachiya T, Takahashi S, Jia H, et al. Identification of the 12q24 locus associated with fish intake frequency by genome-wide meta-analysis in Japanese populations. Genes Nutr. 2019;14:21.
Suzuki T, Nakamura Y, Matsuo K, Imaeda N, Goto C, Narita A, et al. A genome-wide association study on fish consumption in a Japanese population—the Japan Multi-Institutional Collaborative Cohort study. Eur J Clin Nut. 2021;75:480–8.
Suzuki T, Nakamura Y, Doi Y, Narita A, Shimizu A, Imaeda N, et al. A genome-wide association study on confection consumption in a Japanese population: the Japan Multi-Institutional Collaborative Cohort Study. Br J Nutr. 2021;126:1843–51.
Nakamura Y, Ueshima H, Okamura T, Kadowaki T, Hayakawa T, Kita Y, et al. A Japanese diet and 19-year mortality: national integrated project for prospective observation of non-communicable diseases and its trends in the aged, 1980. Br J Nutr. 2009;101:1696–705.
Nakamura Y, Okuda N, Okamura T, Kadota A, Miyagawa N, Hayakawa T, et al. Low-carbohydrate diets and cardiovascular and total mortality in Japanese: a 29-year follow-up of NIPPON DATA80. Br J Nutr. 2014;112:916–24.
Hamajima N. The Japan Multi-Institutional Collaborative Cohort Study (J-MICC Study) to detect gene-environment interactions for cancer. Asian Pac J Cancer Prev. 2007;8:317–23.
Delongchamp R, Faramawi MF, Feingold E, Chung D, Abouelenein S. The association between SNPs and a quantitative trait: power calculation. Eur J Environ Public Health. 2018;2:10.
Tokudome S, Goto C, Imaeda N, Tokudome Y, Ikeda M, Maki D, et al. Development of a data-based short food frequency questionnaire for assessing nutrient intake by middle-aged Japanese. Asian Pac J Cancer Prev. 2004;5:40–3.
Imaeda N, Goto C, Sasakabe T, Mikami H, Oze I, Hosono A, et al. Reproducibility and validity of food group intake in a short food frequency questionnaire for the middle-aged Japanese population. Environ Health Preventive Med. 2021;26:28.
Wakai K. A review of food frequency questionnaires developed and validated in Japan. J Epidemiol. 2009;19:1–11.
Report of the Subdivision on Resources The Council for Science and Technology Ministry of Education, Culture, Sports, Science, and Technology, JAPAN. 2005. Standard Tables of Food Composition in Japan, Fifth Revised and Enlarged. Official Gazette Cooperation of Japan, Tokyo.
Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38:904–9.
Patterson N, Price AL, Reich D. Population structure and eigen analysis. PLoS Genet. 2006;2:e190.
Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, 1000 Genomes Project Consortium, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491:56–65.
Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, 1000 Genomes Project Consortium, et al. A global reference for human genetic variation. Nature. 2015;526:68–74.
Yamaguchi-Kabata Y, Nakazono K, Takahashi A, Saito S, Hosono N, Kubo M, et al. Japanese population structure, based on SNP genotypes from 7003 individuals compared to other ethnic groups: effects on population-based association studies. Am J Hum Genet. 2008;83:445–56.
Delaneau O, Marchini J, Zagury J. A linear complexity phasing method for thousands of genomes. Nat Methods. 2011;9:179–81.
Das S, Forer L, Schönherr S, Sidore C, Locke AE, Kwong A, et al. Next-generation genotype imputation service and methods. Nat Genet. 2016;48:1284–7.
Das S. DosageConvertor. https://genome.sph.umich.edu/wiki/DosageConvertor. 2017.
Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82.
Zhu X, Li S, Cooper RS, Elston RC. A unified association analysis approach for family and unrelated samples correcting for stratifications. Am J Hum Genet. 2008;82:352–65.
Gogarten SM, Bhangale T, Conomos MP, Laurie CA, McHugh CP, Painter I, et al. GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies. Bioinformatics. 2012;28:3329–31.
Turner SD. qqman: an R package for visualizing GWAS results using Q-Q and Manhattan plots. biorXiv. 2014. https://doi.org/10.1101/005165.
Tyner C, Barber GP, Casper J, Clawson H, Diekhans M, Eisenhart C, et al. The UCSC Genome Browser database: 2017 update. Nucleic Acids Res. 2017;45:D626–34.
Aken BL, Achuthan P, Akanni W, Amode MR, Bernsdorff F, Bhai J, et al. Ensembl 2017. Nucleic Acids. Res. 2017;45:D635–42.
Li H, Borinskaya S, Yoshimura K, Kal’ina N, Marusin A, Stepanov VA, et al. Refined geographic distribution of the oriental ALDH2∗504Lys (nee 487Lys) variant. Ann Hum Gen. 2009;73:335–45.
da Cunha Veloso MC, da Silva VM, Santos GV, de Andrade JB. Determination of aldehydes in fish by high-performance liquid chromatography. J Chromatogr Sci. 2001;39:173–6.
Matoba N, Akiyama M, Ishigaki K, Kanai M, Takahashi A, Momozawa Y, et al. GWAS of 165,084 Japanese individuals identified nine loci associated with dietary habits. Nat Hum Behav 2020;4:308–16.
Tanaka T, Muramatus K, Kim HR, Watanabe T, Takeyasu M, Kanai Y, et al. Comparison of volatile compounds from Chungkuk-Jang and Itohiki-Natto. Biosci Biotechnol Biochem. 1998;62:1440–4.
Nakamura Y, Ueshima H, Okuda N, Miura K, Kita Y, Miyagawa N, et al. Relationship of three different types of low-carbohydrate diet to cardiometabolic risk factors in a Japanese population: the INTERMAP/INTERLIPID Study. Eur J Nutr. 2016;55:1515–24.
Tavani A, Negri E, La, Vecchia C. Determinants of body mass index: a study from northern Italy. Int J Obes Relat Metab Disord. 1994;18:497–502.
Lichtman SW, Pisarska K, Berman ER, Pestone M, Dowling H, Offenbacher E, et al. Discrepancy between self-reported and actual caloric intake and exercise in obese subjects. N Engl J Med. 1992;327:1893–8.
Acknowledgements
We would like to express our special thanks to all the faculty at RIKEN, the Laboratory for Genotyping Development, the Center for Integrative Medical Sciences, and the faculty of the BioBank Japan project. We would like to thank Drs. Nobuyuki Hamajima and Hideo Tanaka, the former principal investigators of the J-MICC, for their continuous support for our study. We also would like to thank Dr. Yoshiyuki Kita for his constant effort in promoting the J-MICC study.
Funding
This study was supported by JSPS KAKENHI Grants (No. 16H06277) from the Japanese Ministry of Education, Culture, Sports, Science, and Technology. And by a Grants-in-Aid for Scientific Research for Priority Areas of Cancer (No. 17015018) and Innovative Areas (No. 221S0001). It was also supported by the Ministry of Education, Culture, Sports, Science, and Technology from April 2003 to March 2015. and by funding for the BioBank Japan Project from the Japan Agency for Medical Research and Development from April 2015.
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YN, TT, and KW: designed the research; NT, KeM, NM, AK, KaM, JO, HIk, AH, MNag, RO, YK, KTan, CS, RI, DN, IO, HIt, EO, DM, HM, MKus, SS, MW, KA, SKK, KK, and KTak: conducted the research; YN, AN, AS, YS, MNak, YM, and MKub: analyzed data and performed statistical analysis; YN, TT, YS, and KW: wrote the paper and had primary responsibility for final content; and all authors: read and approved the final paper.
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Nakamura, Y., Tamura, T., Narita, A. et al. A genome-wide association study on adherence to low-carbohydrate diets in Japanese. Eur J Clin Nutr 76, 1103–1110 (2022). https://doi.org/10.1038/s41430-022-01090-w
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DOI: https://doi.org/10.1038/s41430-022-01090-w