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Association between dietary meat consumption and incident type 2 diabetes: the EPIC-InterAct study
Open accessAbstract
Aims/hypothesis A diet rich in meat has been reported to contribute to the risk of type 2 diabetes. The present study aims to investigate the association between meat consumption and incident type 2 diabetes in the EPIC-InterAct study, a large prospective case-cohort study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study.Methods During 11.7 years of follow-up, 12,403 incident cases of type 2 diabetes were identified among 340,234 adults from eight European countries. A centre-stratified random subsample of 16,835 individuals was selected in order to perform a case-cohort design. Prentice-weighted Cox regression analyses were used to estimate HR and 95% CI for incident diabetes according to meat consumption.Results Overall, multivariate analyses showed significant positive associations with incident type 2 diabetes for increasing consumption of total meat (50 g increments: HR 1.08; 95% CI 1.05, 1.12), red meat (HR 1.08; 95% CI 1.03, 1.13) and processed meat (HR 1.12; 95% CI 1.05, 1.19), and a borderline positive association with meat iron intake. Effect modifications by sex and class of BMI were observed. In men, the results of the overall analyses were confirmed. In women, the association with total and red meat persisted, although attenuated, while an association with poultry consumption also emerged (HR 1.20; 95% CI 1.07, 1.34). These associations were not evident among obese participants.Conclusions/interpretation This prospective study confirms a positive association between high consumption of total and red meat and incident type 2 diabetes in a large cohort of European adults.
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“Interestingly, both red and processed meats, regardless of fat content, are linked to higher incidence of diabetes, although with approximately double the risk, gram-for-gram, for processed meats than for unprocessed red meats.172, 178”
From Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and Obesity: A Comprehensive Review (2016) · cites this paper as [178]
In the citing paper
“Large studies evaluating poultry and incident diabetes have shown mixed results including higher risk, no risk, and lower risk.178, 190-195”
From Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and Obesity: A Comprehensive Review (2016) · cites this paper as [178]
In the citing paper
“While unprocessed red meats and butter may be relatively neutral for CHD, no studies demonstrate appreciable benefits, all meats appear to increase diabetes, and processed meats are strongly linked to CHD (see Meats, above).20, 29, 173, 178”
From Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and Obesity: A Comprehensive Review (2016) · cites this paper as [178]
In the citing paper
“Potential confounders to be included as covariates were a priori selected based on the literature on the association between red meat intake and diabetes risk (2, 3) and prior investigations in EPIC-Potsdam regarding risk factors of type 2 diabetes (5), dietary patterns (27), and determinants of metabolic profiles (28,29).”
From Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes (2015) · cites this paper as [3]
In the citing paper
“Red meat consumption has consistently been associated with a higher risk of type 2 diabetes mellitus in several large cohort studies and summarized by several meta-analyses (1-3).”
From Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes (2015) · cites this paper as [3]
In the citing paper
“Still, an elevated diabetes risk was consistently observed for both unprocessed and processed red meat (1-3).”
From Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes (2015) · cites this paper as [3]
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Cites
Guh, D. P., Zhang, W., Bansback, N., Amarsi, Z., Birmingham, C. L., & Anis, A. H. (2009) · BMC Public Health · doi:10.1186/1471-2458-9-88 · Cited by 3,855
Systematic review and meta-analysis
Gillies, A. (2019) · CRC Press eBooks · doi:10.1201/9781315377315-19 · Cited by 2,876
Riboli, E., Hunt, K., Slimani, N., Ferrari, P., Norat, T., Fahey, M., Charrondière, U., Hémon, B., Casagrande, C., Vignat, J., et al. (2002) · Public Health Nutrition · doi:10.1079/phn2002394 · Cited by 1,866
Micha, R., Wallace, S. K., & Mozaffarian, D. (2010) · Circulation · doi:10.1161/circulationaha.109.924977 · Cited by 1,244
Dietary fat intake and risk of type 2 diabetes in women
Salmerón, J., Hu, F. B., Manson, J. E., Stampfer, M. J., Colditz, G. A., Rimm, E. B., & Willett, W. C. (2001) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/73.6.1019 · Cited by 818
Pan, A., Sun, Q., Bernstein, A. M., Schulze, M. B., Manson, J. E., Willett, W. C., & Hu, F. B. (2011) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.111.018978 · Cited by 709
Analysis of Case-Cohort Designs
Barlow, W. E., Ichikawa, L., Rosner, D., & Izumi, S. (1999) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(99)00102-x · Cited by 682
Body Iron Stores in Relation to Risk of Type 2 Diabetes in Apparently Healthy Women
Jiang, R. (2004) · JAMA · doi:10.1001/jama.291.6.711 · Cited by 598
The Prevention or Delay of Type 2 Diabetes
American Diabetes Association, & National Institute of Diabetes, D. A. K. D. (2003) · Diabetes Care · doi:10.2337/diacare.26.2007.s62 · Cited by 520
Feskens, E. J. M., Virtanen, S. M., Räsänen, L., Tuomilehto, J. O., Stengård, J., Pekkanen, J., Nissinen, A. M., & Kromhout, D. (1995) · Diabetes Care · doi:10.2337/diacare.18.8.1104 · Cited by 504
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Zarkoob, H., Lewinsky, S., Almgren, P., Melander, O., & Fakhrai-Rad, H. (2017) · PLoS ONE · doi:10.1371/journal.pone.0180180 · Cited by 4
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Richardson, L. C. (2015) · ScholarWorks (Walden University)
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Tay, J., & Tay, J. (2016) · Adelaide Research & Scholarship (AR&S) (University of Adelaide) · doi:10.25909/5b63b9ac0685f
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Moran, M. (2021) · International Journal of Complementary & Alternative Medicine · doi:10.15406/ijcam.2021.14.00526
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Chrysant, S. G., & Chrysant, G. S. (2020) · doi:10.22541/au.159188513.38865745
Zhang, F., Chai, Y., Ren, J., Xu, X., Jing, C., Zhang, H., Jiang, Y., & Xie, H. (2024) · Frontiers in Nutrition · doi:10.3389/fnut.2024.1438953 · Cited by 1
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Gadgil, M. D., STROEBEL, B. M., King, S. M., Bergeron, N., Chiu, S., & Krauss, R. M. (2026) · Journal of Nutrition · doi:10.1016/j.tjnut.2026.101458
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Metadata
- Authors
- The InterAct Consortium
- Venue
- Diabetologia
- Published
- 2012-09-16
- Volume
- 56
- Issue
- 1
- Pages
- 47-59
- ISSN
- 0012-186X (ISSN-L)
- 1432-0428
- OpenAlex
- W2394915398
- Cited by
- 154
- References
- 51
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 11 · 2023: 9 · 2022: 14 · 2021: 16 · 2020: 12 · 2019: 13 · 2018: 13 · 2017: 13 · 2016: 9 · 2015: 14 · 2014: 11 · 2013: 12
- FWCI
- 11.44
- Type
- journal article
- Language
- en
- Open access
- green
- research.wur.nl
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Diabetologia (publishedVersion)
- PubMed (publishedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion)
- Institutional Research Information System University of Turin (University of Turin) (submittedVersion)
- VBN Forskningsportal (Aalborg Universitet) (submittedVersion)
- Rivm (National Institute for Public Health and the Environment) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Cancer Research UK
- National Institute for Health and Care Research (NF-SI-0512-10114)
- Medical Research Council (MC_UU_12015/1)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Nutritional Studies and Diet
- Physical Sciences › Environmental Science › Ecology › Agriculture Sustainability and Environmental Impact
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diet, Metabolism, and Disease
- Keywords
- type 2 diabetes, meat consumption, red meat, processed meat, poultry, iron intake, sex differences, obesity, diabetes risk factors
- MeSH terms
- Adult, Aged, Aged, 80 and over, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Diet, Europe, Female, Follow-Up Studies, Humans, Male, Meat, Meat Products, Middle Aged, Prospective Studies, Risk, Sex Characteristics, Cohort Studies, Incidence, Case-Control Studies, Iron, Dietary, Young Adult
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