Abstract
Despite considerable research underscoring the importance of carbohydrate intake in relation to the risk of type 2 diabetes (T2D), a comprehensive assessment of this relationship is currently lacking. We aimed to examine the associations of various types and food sources of dietary carbohydrate intake with the risk of T2D, to evaluate potential effect modification by other factors, including genetic susceptibility, and to explore the potential mediators for such associations. The present study included 161,872 participants of the UK Biobank who were free of prevalent cancer, cardiovascular disease, or diabetes, and had at least one validated 24-h dietary recall assessment. Multivariable-adjusted age-stratified Cox proportional hazard regression models were applied to estimate hazard ratios (HRs) and 95% confidence intervals (CI) for the associations of various types and food sources of dietary carbohydrate intake with risk of T2D. During a median follow-up of 13.6 years, 4,176 incident cases of T2D were identified. In the multivariable-adjusted models, a greater intake of fiber, carbohydrates from whole grains, and carbohydrates from non-starchy vegetables was significantly associated with a lower risk of T2D (highest vs. lowest quantile, HR [95% CI]=0.70 [0.62–0.79], 0.74 [0.67–0.82], and 0.83 [0.75–0.92], respectively, all P for trend <0.005). In contrast, a higher intake of starch and carbohydrate from starchy vegetables was associated with an increased risk of T2D (highest vs. lowest quantile, HR [95% CI]=1.31 [1.16–1.48] and 1.19 [1.09–1.31], respectively, both P for trend <0.005). Replacing one serving of refined grains or starchy vegetables with an equal amount of whole grains or non-starchy vegetables was associated with 4% to 10% lower risk of T2D (all P values <0.001). The observed associations were generally similar across population subgroups, including individuals with different genetic susceptibility to T2D. Mediation analyses of the inverse association between T2D risk and isocaloric substitution of carbohydrates from refined grains with carbohydrate from whole grains demonstrated that 39.6%, 43.4%, 44.0%, 27.8%, and 34.9% were mediated through body mass index, waist-to-hip ratio, glycosylated hemoglobin, high-density lipoprotein cholesterol, and C-reactive protein, respectively. In addition, the inverse association between the isocaloric substitution of carbohydrates from starchy vegetables with carbohydrates from non-starchy vegetables and T2D was partially mediated through high-density lipoprotein cholesterol (15.9%). These findings underscore the importance of dietary modifications of carbohydrates, particularly considering types and food sources of carbohydrate intake, in the primary prevention of T2D.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data sharing
Data from UK Biobank are available to all researchers upon submitting an application. This research was performed using the UK Biobank Resource under Application 98583.
References
Agency, F.S. (2002). McCance and Widdowson’s the Composition of Foods, Sixth Ed. Royal Society of Chemistry.
Ahmad, E., Lim, S., Lamptey, R., Webb, D.R., and Davies, M.J. (2022). Type 2 diabetes. Lancet 400, 1803–1820.
AlEssa, H.B., Bhupathiraju, S.N., Malik, V.S., Wedick, N.M., Campos, H., Rosner, B., Willett, W.C., and Hu, F.B. (2015). Carbohydrate quality and quantity and risk of type 2 diabetes in US women. Am J Clin Nutr 102, 1543–1553.
Aston, L.M., Jackson, D., Monsheimer, S., Whybrow, S., Handjieva-Darlenska, T., Kreutzer, M., Kohl, A., Papadaki, A., Martinez, J.A., Kunova, V., et al. (2010). Developing a methodology for assigning glycaemic index values to foods consumed across Europe. Obesity Rev 11, 92–100.
Barrett, E.M., Amoutzopoulos, B., Batterham, M.J., Ray, S., and Beck, E.J. (2020). Whole grain intake compared with cereal fibre intake in association to CVD risk factors: a cross-sectional analysis of the National Diet and Nutrition Survey (UK). Public Health Nutr 23, 1392–1403.
Benjamin, D.J., Berger, J.O., Johannesson, M., Nosek, B.A., Wagenmakers, E.J., Berk, R., Bollen, K.A., Brembs, B., Brown, L., Camerer, C., et al. (2018). Redefine statistical significance. Nat Hum Behav 2, 6–10.
Bradbury, K.E., Young, H.J., Guo, W., and Key, T.J. (2018). Dietary assessment in UK Biobank: an evaluation of the performance of the touchscreen dietary questionnaire. J Nutr Sci 7, e6.
Chiavaroli, L., Lee, D., Ahmed, A., Cheung, A., Khan, T.A., Blanco, S., Mejia, S., Mirrahimi, A., Jenkins, D.J.A., Livesey, G., et al. (2021). Effect of low glycaemic index or load dietary patterns on glycaemic control and cardiometabolic risk factors in diabetes: systematic review and meta-analysis of randomised controlled trials. BMJ 374, n1651.
Eastwood, S.V., Mathur, R., Atkinson, M., Brophy, S., Sudlow, C., Flaig, R., de Lusignan, S., Allen, N., Chaturvedi, N., and Herder, C. (2016). Algorithms for the capture and adjudication of prevalent and incident diabetes in UK Biobank. PLoS One 11, e0162388.
Forouhi, N.G. (2023). Embracing complexity: making sense of diet, nutrition, obesity and type 2 diabetes. Diabetologia 66, 786–799.
Greenwood, D.C., Hardie, L.J., Frost, G.S., Alwan, N.A., Bradbury, K.E., Carter, M., Elliott, P., Evans, C.E.L., Ford, H.E., Hancock, N., et al. (2019). Validation of the Oxford WebQ online 24-hour dietary questionnaire using biomarkers. Am J Epidemiol 188, 1858–1867.
Greenwood, D.C., Threapleton, D.E., Evans, C.E.L., Cleghorn, C.L., Nykjaer, C., Woodhead, C., and Burley, V.J. (2013). Glycemic index, glycemic load, carbohydrates, and type 2 diabetes. Diabetes Care 36, 4166–4171.
Hajihashemi, P., Azadbakht, L., Hashemipour, M., Kelishadi, R., Saneei, P., and Esmaillzadeh, A. (2021). Whole grain intake favorably affects blood glucose and serum triacylglycerols in overweight and obese children: a randomized controlled crossover clinical trial. Nutrition 87–88, 111200.
Hu, Y., Ding, M., Sampson, L., Willett, W.C., Manson, J.A.E., Wang, M., Rosner, B., Hu, F.B., and Sun, Q. (2020). Intake of whole grain foods and risk of type 2 diabetes: results from three prospective cohort studies. BMJ 370, m2206.
Ibsen, D.B., Laursen, A.S.D., Würtz, A.M.L., Dahm, C.C., Rimm, E.B., Parner, E.T., Overvad, K., and Jakobsen, M.U. (2021). Food substitution models for nutritional epidemiology. Am J Clin Nutr 113, 294–303.
Kim, S.Y., Woo, H.W., Lee, Y.H., Shin, D.H., Shin, M.H., Choi, B.Y., and Kim, M.K. (2020). Association of dietary glycaemic index, glycaemic load, and total carbohydrates with incidence of type-2 diabetes in adults aged 3=40 years: the Multi-Rural Communities Cohort (MRCohort). Diabetes Res Clin Pract 160, 108007.
Li, Z., Nie, Q., and Nie, S.P. (2024). Comprehensive insights: unraveling the mechanisms of gut commensals in glucose metabolism regulation. Sci China Life Sci 67, 414–417.
Liu, B., Young, H., Crowe, F.L., Benson, V.S., Spencer, E.A., Key, T.J., Appleby, P.N., and Beral, V. (2011). Development and evaluation of the Oxford WebQ, a low-cost, web-based method for assessment of previous 24 h dietary intakes in large-scale prospective studies. Public Health Nutr 14, 1998–2005.
Luo, J., Liang, S., and Jin, F. (2024). Gut microbiota and healthy longevity. Sci China Life Sci, doi: https://doi.org/10.1007/s11427-023-2595-5.
Makki, K., Deehan, E.C., Walter, J., and Bäckhed, F. (2018). The impact of dietary fiber on gut microbiota in host health and disease. Cell Host Microbe 23, 705–715.
Marshall, S., Petocz, P., Duve, E., Abbott, K., Cassettari, T., Blumfield, M., and Fayet-Moore, F. (2020). The effect of replacing refined grains with whole grains on cardiovascular risk factors: a systematic review and meta-analysis of randomized controlled trials with GRADE clinical recommendation. J Acad Nutr Dietetics 120, 1859–1883.e31.
McKeown, N.M., Meigs, J.B., Liu, S., Wilson, P.W., and Jacques, P.F. (2002). Whole-grain intake is favorably associated with metabolic risk factors for type 2 diabetes and cardiovascular disease in the Framingham Offspring Study. Am J Clin Nutr 76, 390–398.
Muraki, I., Rimm, E.B., Willett, W.C., Manson, J.A.E., Hu, F.B., and Sun, Q. (2016). Potato consumption and risk of type 2 diabetes: results from three prospective cohort studies. Diabetes Care 39, 376–384.
Musa-Veloso, K., Poon, T., Harkness, L.S., O’Shea, M., and Chu, Y.F. (2018). The effects of whole-grain compared with refined wheat, rice, and rye on the postprandial blood glucose response: a systematic review and meta-analysis of randomized controlled trials. Am J Clin Nutr 108, 759–774.
Perez-Cornago, A., Pollard, Z., Young, H., van Uden, M., Andrews, C., Piernas, C., Key, T.J., Mulligan, A., and Lentjes, M. (2021). Description of the updated nutrition calculation of the Oxford WebQ questionnaire and comparison with the previous version among 207,144 participants in UK Biobank. Eur J Nutr 60, 4019–4030.
Pokharel, P., Kyrø, C., Olsen, A., Tjønneland, A., Murray, K., Blekkenhorst, L.C., Bondonno, C.P., Hodgson, J.M., and Bondonno, N.P. (2023). Vegetable, but not potato, intake is associated with a lower risk of type 2 diabetes in the danish diet, cancer and health cohort. Diabetes Care 46, 286–296.
Reynolds, A., Mann, J., Cummings, J., Winter, N., Mete, E., and Te Morenga, L. (2019). Carbohydrate quality and human health: a series of systematic reviews and meta-analyses. Lancet 393, 434–445.
Reynolds, A.N., Akerman, A.P., Mann, J., and Ma, R.C.W. (2020). Dietary fibre and whole grains in diabetes management: systematic review and meta-analyses. PLoS Med 17, e1003053.
Roager, H.M., Vogt, J.K., Kristensen, M., Hansen, L.B.S., Ibrügger, S., Mærkedahl, R.B., Bahl, M.I., Lind, M.V., Nielsen, R.L., Frøkiær, H., et al. (2019). Whole grain-rich diet reduces body weight and systemic low-grade inflammation without inducing major changes of the gut microbiome: a randomised cross-over trial. Gut 68, 83–93.
Sanders, L.M., Zhu, Y., Wilcox, M.L., Koecher, K., and Maki, K.C. (2023). Whole grain intake, compared to refined grain, improves postprandial glycemia and insulinemia: a systematic review and meta-analysis of randomized controlled trials. Crit Rev Food Sci Nutr 63, 5339–5357.
Satija, A., Yu, E., Willett, W.C., and Hu, F.B. (2015). Understanding nutritional epidemiology and its role in policy. Adv Nutr 6, 5–18.
Schlesinger, S., Neuenschwander, M., Schwedhelm, C., Hoffmann, G., Bechthold, A., Boeing, H., and Schwingshackl, L. (2019). Food groups and risk of overweight, obesity, and weight gain: a systematic review and dose-response meta-analysis of prospective studies. Adv Nutr 10, 205–218.
Sluijs, I., van der Schouw, Y.T., van der A, D.L., Spijkerman, A.M., Hu, F.B., Grobbee, D.E., and Beulens, J.W. (2010). Carbohydrate quantity and quality and risk of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition-Netherlands (EPIC-NL) study. Am J Clin Nutr 92, 905–911.
Stefan, N., and Schulze, M.B. (2023). Metabolic health and cardiometabolic risk clusters: implications for prediction, prevention, and treatment. Lancet Diabetes Endocrinol 11, 426–440.
Sun, H., Saeedi, P., Karuranga, S., Pinkepank, M., Ogurtsova, K., Duncan, B.B., Stein, C., Basit, A., Chan, J.C.N., Mbanya, J.C., et al. (2022). IDF Diabetes Atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract 183, 109119.
Tarasuk, V.S., and Brooker, A.S. (1997). Interpreting epidemiologic studies of diet-disease relationships. J Nutr 127, 1847–1852.
Tomova, G.D., Arnold, K.F., Gilthorpe, M.S., and Tennant, P.W. (2022). Adjustment for energy intake in nutritional research: a causal inference perspective. Am J Clin Nutr 115, 189–198.
Tsilas, C.S., de Souza, R.J., Mejia, S.B., Mirrahimi, A., Cozma, A.I., Jayalath, V.H., Ha, V., Tawfik, R., Di Buono, M., Jenkins, A.L., et al. (2017). Relation of total sugars, fructose and sucrose with incident type 2 diabetes: a systematic review and meta-analysis of prospective cohort studies. CMAJ 189, E711–E720.
UK Biobank. (2019). Polygenic Risk Scores in UK Biobank. https://biobank.ctsu.ox.ac.uk/ukb/label.cgi?id=300.
Waddell, I.S., and Orfila, C. (2023). Dietary fiber in the prevention of obesity and obesity-related chronic diseases: from epidemiological evidence to potential molecular mechanisms. Crit Rev Food Sci Nutr 63, 8752–8767.
Wan, Y., Tobias, D.K., Dennis, K.K., Guasch-Ferré, M., Sun, Q., Rimm, E.B., Hu, F.B., Ludwig, D.S., Devinsky, O., and Willett, W.C. (2023). Association between changes in carbohydrate intake and long term weight changes: prospective cohort study. BMJ 382, e073939.
Wang, X., Bao, W., Liu, J., OuYang, Y.Y., Wang, D., Rong, S., Xiao, X., Shan, Z.L., Zhang, Y., Yao, P., et al. (2013). Inflammatory markers and risk of type 2 diabetes. Diabetes Care 36, 166–175.
Yao, T., Wang, H., Lin, K., Wang, R., Guo, S., Chen, P., Wu, H., Liu, T., and Wang, R. (2024). Exercise-induced microbial changes in preventing type 2 diabetes. Sci China Life Sci 67, 892–899.
Zhao, L., Zhang, F., Ding, X., Wu, G., Lam, Y.Y., Wang, X., Fu, H., Xue, X., Lu, C., Ma, J., et al. (2018). Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes. Science 359, 1151–1156.
Acknowledgement
This work was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0508200, 2023ZD0508201), the National Natural Science Foundation of China (82100949, 82470931), the Outstanding Young Investigator Award of Hunan Province (2022JJ10094), and Central South University Research Programme of Advanced Interdisciplinary Studies (2023QYJC008) to J.H. and the Intramural Research Program of the U.S. National Cancer Institute (NCI), National Institutes of Health (NIH) to D.A. We thank participants and staff of the UK Biobank for their contributions to this research.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
The authors declare that they have no conflict of interest.
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Gan, L., Yang, Y., Zhao, B. et al. Dietary carbohydrate intake and risk of type 2 diabetes: a 16-year prospective cohort study. Sci. China Life Sci. 68, 1149–1157 (2025). https://doi.org/10.1007/s11427-024-2804-0
Received:
Accepted:
Published:
Version of record:
Issue date:
DOI: https://doi.org/10.1007/s11427-024-2804-0