Gene-carbohydrate and gene-fiber interactions and type 2 diabetes in diverse populations from the National Health and Nutrition Examination Surveys (NHANES) as part of the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study
Open accessAbstract
BACKGROUND: Both environmental and genetic factors impact type 2 diabetes (T2D). To identify such modifiers, we genotyped 15 T2D-associated variants from genome-wide association studies (GWAS) in 6,414 non-Hispanic whites, 3,073 non-Hispanic blacks, and 3,633 Mexican American participants from the National Health and Nutrition Examination Surveys (NHANES) and evaluated interactions between these variants and carbohydrate intake and fiber intake. RESULTS: We calculated a genetic risk score (GRS) with the 15 SNPs. The odds ratio for T2D with each GRS point was 1.10 (95% CI: 1.05-1.14) for non-Hispanic whites, 1.07 (95% CI: 1.02-1.13) for non-Hispanic blacks, and 1.11 (95% CI: 1.06-1.17) for Mexican Americans. We identified two gene-carbohydrate interactions (P < 0.05) in non-Hispanic whites (with CDKAL1 rs471253 and FTO rs8050136), two in non-Hispanic blacks (with IGFBP2 rs4402960 and THADA rs7578597), and two in Mexican Americans (with NOTCH2 rs1092398 and TSPAN8-LGRS rs7961581). We found three gene-fiber interactions in non-Hispanic whites (with ADAMT59 rs4607103, CDKN2A/2B rs1801282, and FTO rs8050136), two in non-Hispanic blacks (with ADAMT59 rs4607103 and THADA rs7578597), and two in Mexican Americans (with THADA rs7578597 and TSPAN8-LGRS rs796158) at the P < 0.05 level. Interactions between the GRS and nutrients failed to reach significance in all the racial/ethnic groups. CONCLUSION: Our results suggest that dietary carbohydrates and fiber may modify T2D-associated variants, highlighting the importance of dietary nutrients in predicting T2D risk.
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Candidate gene approaches have identified several potential gene-diet interactions for traits including blood cholesterol levels.
“While candidate genes approaches have identified several potential gene-diet interactions for traits including blood cholesterol levels, a major challenge has been lack of replication.74-77”
From Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and Obesity: A Comprehensive Review (2016) · cites this paper as [77]
A major challenge for candidate gene approaches has been lack of replication.
“While candidate genes approaches have identified several potential gene-diet interactions for traits including blood cholesterol levels, a major challenge has been lack of replication.74-77”
From Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and Obesity: A Comprehensive Review (2016) · cites this paper as [77]
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Multiple significance tests: the Bonferroni method
Bland, M., & Altman, D. G. (1995) · BMJ · doi:10.1136/bmj.310.6973.170 · Cited by 3,898 · see citing works
New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk
DIAGRAM Consortium, Dupuis, J., GIANT Consortium, Global BPgen Consortium, Anders Hamsten on behalf of Procardis Consortium, the MAGIC investigators, Langenberg, C., Prokopenko, I., Saxena, R., Soranzo, N., et al. (2010) · Nature Genetics · doi:10.1038/ng.520 · Cited by 2,166 · see citing works
the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Morris, A. P., Voight, B. F., Teslovich, T. M., Ferreira, T., Segrè, A. V., Steinthorsdottir, V., Strawbridge, R. J., Khan, H., Grallert, H., et al. (2012) · Nature Genetics · doi:10.1038/ng.2383 · Cited by 1,912 · see citing works
Carbohydrates, dietary fiber, and incident type 2 diabetes in older women
Meyer, K. A., Kushi, L. H., Jacobs, D. R., Slavin, J., Sellers, T. A., & Folsom, A. R. (2000) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/71.4.921 · Cited by 1,271 · see citing works
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 · see citing works
TCF7L2 Polymorphisms and Progression to Diabetes in the Diabetes Prevention Program
Florez, J. C., Jablonski, K. A., Bayley, N., Pollin, T. I., de Bakker, P. I. W., Shuldiner, A. R., Knowler, W. C., Nathan, D. M., & Altshuler, D. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa062418 · Cited by 834 · see citing works
Implications of Total Energy Intake for Epidemiologic Analyses
Willett, W. C. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199754038.003.0011 · Cited by 645 · see citing works
Genome-wide association studies in diverse populations
Rosenberg, N. A., Huang, L., Jewett, E. M., Szpiech, Z. A., Jankovic, I., & Boehnke, M. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2760 · Cited by 566 · see citing works
Implications of Total Energy Intake for Epidemiologic Analyses
Willett, W. C. (1998) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195122978.003.11 · Cited by 310 · see citing works
Florez, J. C. (2008) · Diabetologia · doi:10.1007/s00125-008-1025-9 · Cited by 290 · see citing works
Cornelis, M. C., Qi, L., Zhang, C., Kraft, P. L., Manson, J. E., Cai, T., Hunter, D. J., & Hu, F. B. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-150-8-200904210-00008 · Cited by 228 · see citing works
Hivert, M.-F., Jablonski, K. A., Perreault, L., Saxena, R., McAteer, J. B., Franks, P. W., Hamman, R. F., Kahn, S. E., Haffner, S., the DIAGRAM Consortium, et al. (2011) · Diabetes · doi:10.2337/db10-1119 · Cited by 191 · see citing works
Matise, T. C., Ambite, J. L., Buyske, S. G., Carlson, C. S., Cole, S. A., Crawford, D. C., Haiman, C. A., HEISS, G. M., Kooperberg, C. L., Marchand, L. L., et al. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwr160 · Cited by 166 · see citing works
Self-citationGenetic predisposition, Western dietary pattern, and the risk of type 2 diabetes in men
Qi, L., Cornelis, M. C., Zhang, C., van Dam, R. M., & Hu, F. B. (2009) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.2008.27249 · Cited by 161 · see citing works
Nettleton, J. A., McKeown, N. M., Kanoni, S., Lemaître, R. N., Hivert, M., Ngwa, J. S., van Rooij, F. J. A., Sonestedt, E., Wojczynski, M. K., Ye, Z., et al. (2010) · Diabetes Care · doi:10.2337/dc10-1150 · Cited by 139 · see citing works
Wannamethee, S. G., Whincup, P. H., Thomas, M. C., & Sattar, N. A. (2009) · Diabetes Care · doi:10.2337/dc09-0477 · Cited by 135 · see citing works
Fisher, E., Boeing, H., Fritsche, A., Doering, F., Joost, H., & Schulze, M. B. (2008) · British Journal Of Nutrition · doi:10.1017/s0007114508020369 · Cited by 100 · see citing works
Role of TCF7L2 risk variant and dietary fibre intake on incident type 2 diabetes
Hindy, G., Sonestedt, E., Ericson, U., Jing, X., Zhou, Y., Hansson, O., Renström, E., Wirfält, E., & Orho‐Melander, M. (2012) · Diabetologia · doi:10.1007/s00125-012-2634-x · Cited by 91 · see citing works
Sonestedt, E., Lyssenko, V., Ericson, U., Gullberg, B., Wirfält, E., Groop, L., & Orho‐Melander, M. (2012) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2011-2444 · Cited by 55 · see citing works
Genetic Risk Assessment of Type 2 Diabetes–Associated Polymorphisms in African Americans
Bailey, J. N. C., Ng, M. C. Y., Palmer, N. D., An, S. S., Hester, J. M., Freedman, B. I., Langefeld, C. D., & Bowden, D. W. (2012) · Diabetes Care · doi:10.2337/dc11-0957 · Cited by 54 · see citing works
Consistent Directions of Effect for Established Type 2 Diabetes Risk Variants Across Populations
Haiman, C. A., Fesinmeyer, M. D., Spencer, K. L., Bůžková, P., Voruganti, V. S., Wan, P. C., Haessler, J., Franceschini, N., Monroe, K. R., Howard, B. V., et al. (2012) · Diabetes · doi:10.2337/db11-1296 · Cited by 52 · see citing works
Self-citationPendergrass, S. A., DUDEK, S. M., Crawford, D. C., & Ritchie, M. D. (2010) · BioData Mining · doi:10.1186/1756-0381-3-10 · Cited by 42 · see citing works
Self-citationEricson, U., Rukh, G., Stojkovic, I., Sonestedt, E., Gullberg, B., Wirfält, E., Wallström, P., & Orho‐Melander, M. (2012) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.112.046474 · Cited by 35 · see citing works
Third National Health and Nutrition Examination Survey
National Cancer Institute (2020) · Definitions · doi:10.32388/b9orlz · Cited by 30 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Willet W Stampfer M Willet W Implications of total energy intake for epidemiological analysis Nutritional Epidemiology, Volume 30 1998 2 New York: Oxford University Press 273 301
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Mozaffarian, D. (2016) · Circulation · doi:10.1161/circulationaha.115.018585 · Cited by 2,216 · see citing works
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Mambiya, M., Shang, M., Wang, Y., Li, Q., Liu, S., Yang, L., Zhang, Q., Zhang, K., Liu, M., Nie, F., et al. (2019) · Frontiers in Public Health · doi:10.3389/fpubh.2019.00349 · Cited by 106 · see citing works
Li, S. X., Imamura, F., Ye, Z., Schulze, M. B., Zheng, J., Ardanáz, E., Arriola, L., Boeing, H., Dow, C., Fagherazzi, G., et al. (2017) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.116.150094 · Cited by 59 · see citing works
Dietary and genetic risk scores and incidence of type 2 diabetes
Ericson, U., Hindy, G., Drake, I., Schulz, C., Brunkwall, L., Hellstrand, S., Almgren, P., & Orho‐Melander, M. (2018) · Genes & Nutrition · doi:10.1186/s12263-018-0599-1 · Cited by 45 · see citing works
Alharbi, K. K., Khan, I. A., Syed, R., Alharbi, F. K., Mohammed, A. K., Vinodson, B., & Al‐Daghri, N. M. (2015) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-015-0091-7 · Cited by 39 · see citing works
Huang, T., Ley, S. H., Zheng, Y., Wang, T., Bray, G. A., Sacks, F. M., & Qi, L. (2016) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.115.121186 · Cited by 37 · see citing works
Reversing the tide — diagnosis and prevention of T2DM in populations of African descent
Utumatwishima, J. N., Chung, S. T., Bentley, A. R., Udahogora, M., & Sumner, A. E. (2017) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2017.127 · Cited by 35 · see citing works
Kabisch, S., Weickert, M. O., & Pfeiffer, A. F. H. (2022) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2022.2141190 · Cited by 31 · see citing works
Go, M. J., Lee, Y., Park, S., Kwak, S. H., Kim, B.-J., & Lee, J. (2016) · Journal of Human Genetics · doi:10.1038/jhg.2016.93 · Cited by 30 · see citing works
Hosseini‐Esfahani, F., Koochakpoor, G., DANESHPOUR, M. S., Mirmiran, P., Sedaghati-khayat, B., & Azizi, F. (2017) · Scientific Reports · doi:10.1038/s41598-017-18386-8 · Cited by 28 · see citing works
Li, S. X., Imamura, F., Schulze, M. B., Zheng, J., Ye, Z., Agudo, A., Ardanáz, E., Aune, D., Boeing, H., Dorronsoro, M., et al. (2018) · Diabetologia · doi:10.1007/s00125-018-4586-2 · Cited by 26 · see citing works
Saha, A., Samadder, A., & Nandi, S. (2022) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026623666221201150933 · Cited by 23 · see citing works
Dhillon, J., Jacobs, A., Flores, S. O., & Rios, L. K. D. (2022) · Advances in Nutrition · doi:10.1093/advances/nmac002 · Cited by 20 · see citing works
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Kodama, S., Fujihara, K., Ishiguro, H., Horikawa, C., Ohara, N., Yachi, Y., Tanaka, S., Shimano, H., Kato, K., Hanyu, O., et al. (2018) · Journal of Epidemiology · doi:10.2188/jea.je20160151 · Cited by 18 · see citing works
Watanabe, D., Kuranuki, S., Sunto, A., Matsumoto, N., & Nakamura, T. (2018) · Nutrients · doi:10.3390/nu10121834 · Cited by 17 · see citing works
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Malinowski, J. R., Goodloe, R., Brown-Gentry, K. D., & Crawford, D. C. (2015) · Frontiers in Genetics · doi:10.3389/fgene.2015.00317 · Cited by 11 · see citing works
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Eriksen, R., Gibson, R., Aresu, M., Heard, A., Chan, Q. M., Εvangelou, Ε., Gao, H., Elliott, P., & Frost, G. S. (2019) · Endocrinology Diabetes & Metabolism · doi:10.1002/edm2.74 · Cited by 7 · see citing works
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Khosravi, A., Kouhsar, M. P., Goliaei, B., Jayaram, B., & Masoudi‐Nejad, A. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.04.037 · Cited by 7 · see citing works
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Cornelis, M. C. (2014) · Current Nutrition Reports · doi:10.1007/s13668-014-0095-1 · Cited by 6 · see citing works
Heianza, Y., & Qi, L. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-802928-2.00029-1 · Cited by 5 · see citing works
Scales for assessing the genetic risk of developing type 2 diabetes mellitus
Kurilova, O. V., Kiseleva, A. V., Meshkov, A. N., Сотникова, Е. А., Ershova, A. I., Иванова, А. А., Limonova, A. S., & Drapkina, O. M. (2021) · Russian Journal of Preventive Medicine · doi:10.17116/profmed202124121115 · Cited by 5 · see citing works
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Park, S., Park, J., Lee, Y. S., Shin, N., & Yang, M. (2025) · Food Supplements and Biomaterials for Health · doi:10.52361/fsbh.2025.5.e13 · Cited by 3 · see citing works
Park, J. M., Choi, J., Song, Y., Kwon, Y., Lee, J., & Hong, K. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1538813 · Cited by 2 · see citing works
Kiseleva, A. V., Soplenkova, A. G., Kutsenko, V. A., Сотникова, Е. А., Vyatkin, Y. V., Zharikova, A. A., Ershova, A. I., Zaicenoka, M., Ramensky, V. E., Скирко, О. П., et al. (2023) · CARDIOVASCULAR THERAPY AND PREVENTION · doi:10.15829/1728-8800-2023-3746 · Cited by 1 · see citing works
Rice: Prevention and Management of Type 2 Diabetes and Coronary Heart Disease
Bergman, C. J. (2020) · Springer eBooks · doi:10.1007/978-3-030-37510-2_9 · Cited by 1 · see citing works
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Banjar, K., Holzmann, C., & BULLERDIEK, J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.19.460947 · Cited by 1 · see citing works
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Crawford, D. C., & Nickerson, D. A. (2015)
Self-citationOhkuma, T., Iwase, M., Higashi, T., Sakamoto, W., & Ago, T. (2026) · Journal of Nutrition · doi:10.1016/j.tjnut.2026.101481
- Authors
- Villegas, R., Goodloe, R. J., McClellan, B. E., Boston, J., & Crawford, D. C.
- Affiliations
- Vanderbilt University Medical Center (US)
- Corresponding author
- Villegas, Raquel
- Venue
- BMC Genetics
- Published
- 2014-06-14
- Volume
- 15
- Issue
- 1
- Pages
- 69-69
- ISSN
- 1471-2156 (ISSN-L)
- PMC
- PMC4094781
- OpenAlex
- W1985071603
- Cited by
- 34
- References
- 24
- Citations by year
- 2026: 1 · 2025: 2 · 2023: 2 · 2022: 3 · 2021: 3 · 2020: 2 · 2019: 3 · 2018: 4 · 2017: 5 · 2016: 4 · 2015: 4 · 2014: 1
- FWCI
- 3.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Genetics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Vanderbilt University
- National Human Genome Research Institute (U01HG004798)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- gene-diet interaction, dietary carbohydrates, dietary fiber, polygenic risk score, CDKAL1, FTO, IGFBP-2, THADA, Notch2, CDKN2A/2B, racial and ethnic differences, non-Hispanic whites, non-Hispanic blacks, Mexican Americans, EAGLE study, NHANES, genetic susceptibility to diabetes
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Diabetes Mellitus, Type 2, Dietary Carbohydrates, Dietary Fiber, Female, Genotype, Humans, Male, Middle Aged, Nutrition Surveys, Risk Factors, Mexican Americans, Polymorphism, Single Nucleotide, Black People, White People, Genome-Wide Association Study, Young Adult, Gene-Environment Interaction
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