Yasuyuki Nakamura1,2, Takashi Tamura3, Akira Narita4, Atsushi Shimizu5,6, Yoichi Sutoh5, Naoyuki Takashima7,8, Kenji Matsui7,9, Naoko Miyagawa7,10, Aya Kadota7,11, Katsuyuki Miura7,11, Jun Otonari12,13, Hiroaki Ikezaki14,15, Asahi Hishida3, Mako Nagayoshi3, Rieko Okada3, Yoko Kubo3, Keitaro Tanaka16, Chisato Shimanoe17, Rie Ibusuki18, Daisaku Nishimoto18,19, Isao Oze20, Hidemi Ito21,22, Etsuko Ozaki23, Daisuke Matsui23, Haruo Mikami24, Miho Kusakabe24, Sadao Suzuki25, Miki Watanabe25, Kokichi Arisawa26, Sakurako Katsuura-Kamano26, Kiyonori Kuriki27, Masahiro Nakatochi28, Yukihide Momozawa29, Michiaki Kubo29, Kenji Takeuchi3, Kenji Wakai3. 1. Department of Public Health, Shiga University of Medical Science, Otsu, Japan. nakamura@belle.shiga-med.ac.jp. 2. Yamashina Racto Clinic and Medical Examination Center, Kyoto, Japan. nakamura@belle.shiga-med.ac.jp. 3. Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan. 4. Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan. 5. Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa-gun, Iwate, Japan. 6. Division of Biomedical Information Analysis, Institute for Biomedical Sciences, Iwate Medical University, Shiwa-gun, Iwate, Japan. 7. Department of Public Health, Shiga University of Medical Science, Otsu, Japan. 8. Department of Public Health, Kindai University Faculty of Medicine, Osaka-Sayama, Japan. 9. Division of Bioethics and Healthcare Law, The National Cancer Center, Tokyo, Japan. 10. Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan. 11. NCD Epidemiology Research Center, Shiga University of Medical Science, Otsu, Japan. 12. Department of Psychosomatic Medicine, International University of Health and Welfare Narita Hospital, Narita, Japan. 13. Department of Psychosomatic Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan. 14. Department of Comprehensive General Internal Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan. 15. Department of General Internal Medicine, Kyushu University Hospital, Fukuoka, Japan. 16. Department of Preventive Medicine, Faculty of Medicine, Saga University, Saga, Japan. 17. Department of Pharmacy, Saga University Hospital, Saga, Japan. 18. School of Health Sciences, Faculty of Medicine, Kagoshima University, Kagoshima, Japan. 19. Department of International Island and Community Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan. 20. Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan. 21. Division of Cancer Information and Control, Aichi Cancer Center Research Institute, Nagoya, Japan. 22. Division of Descriptive Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan. 23. Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan. 24. Cancer Prevention Center, Chiba Cancer Center Research Institute, Chiba, Japan. 25. Department of Public Health, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan. 26. Department of Preventive Medicine, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan. 27. Laboratory of Public Health, Division of Nutritional Sciences, School of Food and Nutritional Sciences, University of Shizuoka, Shizuoka, Japan. 28. Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan. 29. Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan.
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.
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.
Authors: Teresa T Fung; Rob M van Dam; Susan E Hankinson; Meir Stampfer; Walter C Willett; Frank B Hu Journal: Ann Intern Med Date: 2010-09-07 Impact factor: 25.391
Authors: Thomas L Halton; Walter C Willett; Simin Liu; JoAnn E Manson; Christine M Albert; Kathryn Rexrode; Frank B Hu Journal: N Engl J Med Date: 2006-11-09 Impact factor: 91.245
Authors: Tian Hu; Katherine T Mills; Lu Yao; Kathryn Demanelis; Mohamed Eloustaz; William S Yancy; Tanika N Kelly; Jiang He; Lydia A Bazzano Journal: Am J Epidemiol Date: 2012-10-01 Impact factor: 4.897
Authors: Sara B Seidelmann; Brian Claggett; Susan Cheng; Mir Henglin; Amil Shah; Lyn M Steffen; Aaron R Folsom; Eric B Rimm; Walter C Willett; Scott D Solomon Journal: Lancet Public Health Date: 2018-08-17