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Genetic predisposition, Western dietary pattern, and the risk of type 2 diabetes in men
Open accessAbstract
Background A widely held hypothesis is that interactions between genetic predisposition and Western-type lifestyle contribute to the epidemic of type 2 diabetes (T2D). No study has tested this hypothesis.Objective The objective was to assess whether established genetic variants, mainly from genomewide association studies, modify dietary patterns in predicting diabetes risk.Design We determined 10 polymorphisms in a prospective, nested, case-control study of 1196 diabetic and 1337 nondiabetic men. A genetic risk score (GRS) was generated by using an allele counting method. Baseline dietary intakes were collected by using a semiquantitative food-frequency questionnaire. We used factor analysis to derive Western and "Prudent" dietary patterns from 40 food groups.Results A significant interaction (P = 0.02) was observed between the GRS and Western dietary pattern. The multivariable odds ratios (ORs) of T2D across increasing quartiles for the Western dietary pattern were 1.00, 1.23 (95% CI: 0.88, 1.73), 1.49 (1.06,2.09), and 2.06 (1.48, 2.88) among men with a high GRS (>/=12 risk alleles; P for trend = 0.01). The Western dietary pattern was not associated with diabetes risk among those with a lower GRS. In addition, we found that intakes of processed meat, red meat, and heme iron, which characterized the Western dietary pattern, showed significant interactions with GRS in relation to diabetes risk (P for interaction = 0.029, 0.02, and 0.0004, respectively). The diet-diabetes associations were more evident among men with a high GRS (>/=12) than in those with a low GRS.Conclusion Genetic predisposition may synergistically interact with a Western dietary pattern in determining diabetes risk in men.
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“Among those with a lower GRS, the Western dietary pattern was not associated with diabetes risk.”
From Globalization of Diabetes (2011) · cites this paper as [45]
In the citing paper
“In the Health Professionals’ Follow-up Study, we found a significant interaction between a Western dietary pattern derived from principle component analysis of 40 food groups and a genetic risk score (GRS) of type 2 diabetes susceptibility based on 10 established single nucleotide polymorphisms (P = 0.02) (Fig. 4) (45).”
From Globalization of Diabetes (2011) · cites this paper as [45]
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“The multivariable ORs of diabetes across increasing quartiles of the Western dietary pattern were 1.00, 1.23 (95% CI 0.88–1.73), 1.49 (1.06, 2.09), and 2.06 (1.48, 2.88) among men with a higher GRS (≥12 risk alleles, P for trend = 0.01).”
From Globalization of Diabetes (2011) · cites this paper as [45]
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“In addition, intake of processed meat, red meat, and heme iron, which characterize the Western dietary pattern, showed significant interactions with GRS in relation to diabetes risk (P for interaction = 0.029, 0.02, and 0.0004, respectively).”
From Globalization of Diabetes (2011) · cites this paper as [45]
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Cites
Global and societal implications of the diabetes epidemic
Zimmet, P., Alberti, K. G. M. M., & Shaw, J. (2001) · Nature · doi:10.1038/414782a · Cited by 5,883
A genome-wide association study identifies novel risk loci for type 2 diabetes
Sladek, R., Rocheleau, G., Rung, J., Dina, C., Shen, L., Serre, D., Boutin, P., Vincent, D., Belisle, A., Hadjadj, S., et al. (2007) · Nature · doi:10.1038/nature05616 · Cited by 2,993
Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels
Saxena, R., Voight, B. F., Lyssenko, V., Burtt, N. P., de Bakker, P. I. W., Chen, H., Roix, J. J., Kathiresan, S., Hirschhorn, J. N., Daly, M. J., et al. (2007) · Science · doi:10.1126/science.1142358 · Cited by 2,843
A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants
Scott, L. J., Mohlke, K. L., Bonnycastle, L. L., Willer, C. J., Li, Y., Duren, W. L., Erdos, M. R., Stringham, H. M., Chines, P. S., Jackson, A. U., et al. (2007) · Science · doi:10.1126/science.1142382 · Cited by 2,753
Replication of Genome-Wide Association Signals in UK Samples Reveals Risk Loci for Type 2 Diabetes
Zeggini, E., Weedon, M. N., Lindgren, C. M., Frayling, T. M., Elliott, K. S., Lango, H., Timpson, N. J., Perry, J. R. B., Rayner, N. W., Freathy, R. M., et al. (2007) · Science · doi:10.1126/science.1142364 · Cited by 2,194
Prospective study of alcohol consumption and risk of coronary disease in men.
Rimm, E. B., Giovannucci, E. L., Willett, W. C., Colditz, G. A., Ascherio, A., Rosner, B., & Stampfer, M. J. (1991) · The Lancet · doi:10.1016/0140-6736(91)90542-w · Cited by 1,239
Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire
Hu, F. B., Rimm, E., Smith-Warner, S. A., Feskanich, D., Stampfer, M. J., Ascherio, A., Sampson, L., & Willett, W. C. (1999) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/69.2.243 · Cited by 1,165
A variant in CDKAL1 influences insulin response and risk of type 2 diabetes
Steinthorsdottir, V., Thorleifsson, G., Reynisdottir, I., Benediktsson, R., Jonsdottir, T., Walters, G. B., Styrkarsdottir, U., Gretarsdottir, S., Emilsson, V., Ghosh, S., et al. (2007) · Nature Genetics · doi:10.1038/ng2043 · Cited by 1,072
Prospective study of major dietary patterns and risk of coronary heart disease in men
Hu, F. B., Rimm, E. B., Stampfer, M. J., Ascherio, A., Spiegelman, D. L., & Willett, W. C. (2000) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/72.4.912 · Cited by 1,066
Dietary Patterns and Risk for Type 2 Diabetes Mellitus in U.S. Men
van Dam, R. M., Rimm, E. B., Willett, W. C., Stampfer, M. J., & Hu, F. B. (2002) · Annals of Internal Medicine · doi:10.7326/0003-4819-136-3-200202050-00008 · Cited by 801
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Hardy, D. S., Racette, S. B., Garvin, J. T., Gebrekristos, H. T., & Baye, T. M. (2021) · Research Square · doi:10.21203/rs.3.rs-362376/v1 · Cited by 1
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Swindell, N. J. (2018) · Cronfa (Swansea University) · doi:10.23889/suthesis.43702
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Bykhovets, M. Y., Rykov, S. O., & Natrus, L. V. (2021) · Archive of Ukrainian Ophthalmology · doi:10.22141/2309-8147.6.3.2018.165211
(2010) · Current Opinion in Lipidology · doi:10.1097/mol.0b013e32833592e7
Chacko, T. K. (2022) · International Journal of Science and Healthcare Research · doi:10.52403/ijshr.20220140
Wang, W., Liu, Z., Liu, L., Han, T., Yang, X., & Sun, C. (2021) · Figshare · doi:10.6084/m9.figshare.16921757
Wang, W., Liu, Z., Liu, L., Han, T., Yang, X., & Sun, C. (2021) · Figshare · doi:10.6084/m9.figshare.16921763
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Metadata
- Authors
- Qi, L., Cornelis, M. C., Zhang, C., van Dam, R. M., & Hu, F. B.
- Affiliations
- Brigham and Women's Hospital (US)
- Corresponding author
- Qi, Lu
- Venue
- The American Journal of Clinical Nutrition
- Publisher
- Elsevier BV
- Published
- 2009-05
- Volume
- 89
- Issue
- 5
- Pages
- 1453-1458
- ISSN
- 0002-9165 (ISSN-L)
- PMC
- PMC2676999
- OpenAlex
- W2114342853
- Cited by
- 161
- References
- 26
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 5 · 2023: 4 · 2022: 7 · 2021: 16 · 2020: 10 · 2019: 9 · 2018: 13 · 2017: 8 · 2016: 10 · 2015: 13 · 2014: 18 · 2013: 10 · 2012: 13
- FWCI
- 5.62
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- American Journal of Clinical Nutrition (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- American Heart Association (0730094 N)
- National Institutes of Health (0730094N, DK46200, CA 87969)
- National Heart, Lung, and Blood Institute (R01HL071981, HL-071981)
- National Cancer Institute (CA-87969, P01CA087969)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK058845, P30DK046200, DK58845, DK46200.)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Nutritional Studies and Diet
- Keywords
- type 2 diabetes, genetic susceptibility, Western dietary pattern, polygenic risk score, gene-diet interaction, men, processed meat, red meat, heme iron, prudent diet, dietary intake
- MeSH terms
- Adult, Aged, Diabetes Mellitus, Type 2, Diet, Feeding Behavior, Follow-Up Studies, Genotype, Humans, Life Style, Male, Middle Aged, Prospective Studies, Surveys and Questionnaires, Risk Factors, Odds Ratio, Case-Control Studies, Risk Assessment, Developed Countries, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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