The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
Red meat consumption and risk of type 2 diabetes: 3 cohorts of US adults and an updated meta-analysis
Open accessAbstract
Background The relation between consumption of different types of red meats and risk of type 2 diabetes (T2D) remains uncertain.Objective We evaluated the association between unprocessed and processed red meat consumption and incident T2D in US adults.Design We followed 37,083 men in the Health Professionals Follow-Up Study (1986-2006), 79,570 women in the Nurses' Health Study I (1980-2008), and 87,504 women in the Nurses' Health Study II (1991-2005). Diet was assessed by validated food-frequency questionnaires, and data were updated every 4 y. Incident T2D was confirmed by a validated supplementary questionnaire.Results During 4,033,322 person-years of follow-up, we documented 13,759 incident T2D cases. After adjustment for age, BMI, and other lifestyle and dietary risk factors, both unprocessed and processed red meat intakes were positively associated with T2D risk in each cohort (all P-trend <0.001). The pooled HRs (95% CIs) for a one serving/d increase in unprocessed, processed, and total red meat consumption were 1.12 (1.08, 1.16), 1.32 (1.25, 1.40), and 1.14 (1.10, 1.18), respectively. The results were confirmed by a meta-analysis (442,101 participants and 28,228 diabetes cases): the RRs (95% CIs) were 1.19 (1.04, 1.37) and 1.51 (1.25, 1.83) for 100 g unprocessed red meat/d and for 50 g processed red meat/d, respectively. We estimated that substitutions of one serving of nuts, low-fat dairy, and whole grains per day for one serving of red meat per day were associated with a 16-35% lower risk of T2D.Conclusion Our results suggest that red meat consumption, particularly processed red meat, is associated with an increased risk of T2D.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Consistent with this, while a minority of individual studies suggest similar cardiovascular risk for unprocessed red meat vs. processed meats,170, 171 many other individual studies and meta-analyses support much stronger effects of processed meats, including low-fat deli meats, on CVD (Figure 6).172-174”
From Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and Obesity: A Comprehensive Review (2016) · cites this paper as [172]
In the citing paper
“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 [172]
In the citing paper
“Consistent with this, while a minority of individual studies suggest similar cardiovascular risk for unprocessed red meat vs. processed meats,170, 171 many other individual studies and meta-analyses support much stronger effects of processed meats, including low-fat deli meats, on CVD (Figure 6).172-174”
From Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and Obesity: A Comprehensive Review (2016) · cites this paper as [173]
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 [173]
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 [2]
In the citing paper
“Furthermore, the red meat-associated diabetes risk was also observed in models in which red meat was substituted by foods with a similar macronutrient content (2), suggesting the relation to be independent of macronutrient composition of the diet.”
From Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes (2015) · cites this paper as [2]
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 [2]
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 [2]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & for the PRISMA Group (2009) · BMJ · doi:10.1136/bmj.b2535 · Cited by 83,823
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.
(2009) · PubMed Central · Cited by 44,859
Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus
The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus* (2002) · Diabetes Care · doi:10.2337/diacare.25.2007.s5 · Cited by 10,325
IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040
Ogurtsova, K., Fernandes, J. D. D. R., Huang, Y., Linnenkamp, U., Guariguata, L., Cho, N. H., Cavan, D. A., Shaw, J. E., & Makaroff, L. (2017) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2017.03.024 · Cited by 7,121
Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus
The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus (1997) · Diabetes Care · doi:10.2337/diacare.20.7.1183 · Cited by 6,220
Sacks, F. M., Svetkey, L. P., Vollmer, W. M., Appel, L. J., Bray, G. A., Harsha, D., Obarzanek, E., Conlin, P. R., Miller, E. R., Simons-Morton, D. G., et al. (2001) · New England Journal of Medicine · doi:10.1056/nejm200101043440101 · Cited by 5,638
Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance.
National Diabetes Data Group (1979) · Diabetes · doi:10.2337/diab.28.12.1039 · Cited by 4,909
Reproducibility and validity of a semiquantitative food frequency questionnaire
WILLETT, W. C., SAMPSON, L., STAMPFER, M. J., ROSNER, B., BAIN, C., WITSCHI, J., HENNEKENS, C. H., & SPEIZER, F. E. (1985) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a114086 · Cited by 4,274
Self-citationEconomic Costs of Diabetes in the U.S. in 2002
American Diabetes Association (2003) · Diabetes Care · doi:10.2337/diacare.26.3.917 · Cited by 4,147
Diet, lifestyle, and the risk of type 2 diabetes mellitus in women.
Hu, F. B., Manson, J. E., Stampfer, M. J., Colditz, G., Liu, S., Solomon, C. G., & Willett, W. C. (2001) · New England Journal of Medicine · doi:10.1056/nejmoa010492 · Cited by 2,901
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Items 601–700 of 713
Plant-Based Diets and Diabetes
Babich, J. S., & Gulati, M. (2024) · Springer eBooks · doi:10.1007/978-3-031-63083-5_24 · Cited by 1
Sustainable Dietary Patterns and Alternative Food Sources
Aksoy, A. S. (2024) · Bilecik Şeyh Edebali Üniversitesi Sağlık Bilimleri Fakültesi Dergisi · doi:10.61535/bseusbfd.1537888 · Cited by 1
Ibrahim, F. M., Osikabor, B., Akinosho, H. O., Adesope, A. A., Ibrahim, A. G., Olatunji, B. T., Akanni, O., & Ogunwale, O. G. (2014) · Food and public health · Cited by 1
Red meat consumption and type 2 diabetes mellitus risk
Derbyshire, E. J., & Ruxton, C. H. S. (2015) · Nutrition & Food Science · doi:10.1108/nfs-12-2014-0103 · Cited by 2
Krebszellen mögen Zucker, aber noch mehr lieben sie Fett und tierisches Eiweiß
Jacob, L., & Weis, N. (2012) · Deutsche Zeitschrift für Onkologie · doi:10.1055/s-0032-1314699 · Cited by 1
Role of Diet on Risk of Diabetes
Ristić-Medić, D., & Radjen, S. (2017) · Elsevier eBooks · doi:10.1016/b978-0-08-100596-5.21340-1 · Cited by 1
GYVENSENOS VEIKSNIŲ SĄSAJOS SU GLIUKOZĖS KIEKIU KAPILIARINIAME KRAUJYJE
Kirvaitienė, J., Volskienė, R., Ambrožienė, L., & Vaičiulevičienė, A. (2017) · Sveikatos mokslai · doi:10.5200/sm-hs.2017.094 · Cited by 1
Sa’ad-Aldin, K., & Altamimi, M. (2018) · International Journal of Diabetes in Developing Countries · doi:10.1007/s13410-018-0689-z · Cited by 2
Diet and Nutrition: Metabolic Diseases
Rao, G. H. R. (2018) · Global Journal of Nutrition & Food Science · doi:10.33552/gjnfs.2018.01.000504 · Cited by 1
Association Between Toothbrushing and Cardiovascular Disease Risk Factors: A Systematic Review
Zoraya, S. I., & Azhar, A. A. B. (2020) · Sriwijaya International Conference of Public Health (SICPH) · doi:10.2991/ahsr.k.200612.004 · Cited by 1
Should we reduce consumption of red meat?
Fadnes, L. T., Arnesen, E., & Aune, D. (2020) · Tidsskrift for Den norske legeforening · doi:10.4045/tidsskr.19.0786 · Cited by 1
Chicken consumption and insulin resistance in non-diabetic older adults
Yeum, K., Kim, Y., & Joo, N. (2020) · Journal of Nutrition and Health · doi:10.4163/jnh.2020.53.4.356 · Cited by 1
Healthy diets as a guide to responsible food systems
Kassam, S., Jenkins, D. J. A., Bristor, D., & Kassam, Z. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-816410-5.00015-3 · Cited by 2
Health Risks of Heating Food II
Mathias, D. (2022) · Springer eBooks · doi:10.1007/978-3-662-65961-8_39 · Cited by 1
de Gavelle, E. (2019) · theses.fr (ABES) · doi:10.70675/d6d39b06zcce4z4d31zaa98zc85aea185844 · Cited by 1
Dietary change after gestational diabetes mellitus for prevention of type two diabetes mellitus
Cinnadaio, N. (2013) · Research Online (University of Wollongong)
Red Meat and Type 2 Diabetes Mellitus—Reply
Pan, A., & Hu, F. B. (2014) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2013.12839
Self-citationOkonkwo, P. O. (2013) · IOSR Journal of Pharmacy (IOSRPHR) · doi:10.9790/3013-0310045-50
Acceptability, Short Term Impacts, and Relationships of Variables of a Processed Food Module
Corbett, J. (2016) · Digital Commons - URI (University of Rhode Island) · doi:10.23860/thesis-corbett-jacqueline-2016
Comparative analysis of meat and vegetable based meals in wistar rats
Essiet, G. A. (2016) · Advances in Biomedicine and Pharmacy · doi:10.19046/abp.v03i02.05
Righter, A., & Wasserman, A. (2015) · Journal of Agricultural Science and Technology B · doi:10.17265/2161-6264/2015.08.008
Dietary determinants, inflammation, and type 2 diabetes: insights from observational studies
Woudenbergh, G. J. G.-V. (2013) · doi:10.18174/280209
Duncan, A. (2016) · Research Portal (King's College London)
Differences in fecal metabolite profiles from geographically distinct populations of adolescents
Moncivaiz, J. D. (2015) · OhioLink ETD Center (Ohio Library and Information Network)
Obesity and the Risk for Type 2 Diabetes
Bray, G. A. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-802928-2.00030-8
Muñoz, V. A. (2017) · Communities in ADDI (Universidad del Pais Vasco)
The effect of dairy on insulin sensitivity
Peters, J. L. (2017) · Adelaide Research & Scholarship (AR&S) (University of Adelaide) · doi:10.4225/55/592e59a09b82d
Chapman, L. E., Alexander, R., Keyserling, T. C., & Ammerman, A. S. (2018) · Springer eBooks · doi:10.1007/978-3-319-71812-5_6
OBSOLETE: Nutrition - Macronutrients
Nakamura, T. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.99599-0
Auch Huhn auf dem Grill steigert Diabetesrisiko
Müssig, K. (2018) · Info Diabetologie · doi:10.1007/s15034-018-1301-9
Swindell, N. J. (2018) · Cronfa (Swansea University) · doi:10.23889/suthesis.43702
Évolution de la consommation de viande rouge et risque de diabète de type 2
Hansel, B. (2014) · Médecine des Maladies Métaboliques · doi:10.1016/s1957-2557(14)70768-0
Alharbi, O. (2018) · TSpace (University of Toronto)
Bridge, A. D. (2019) · Brock University Digital Repository (Brock University)
Zhang, X. (2018) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/99f4-gf26
Dairy Products and Cardiometabolic Health Outcomes
Korat, A. V. A., & Andrés, V. L. G. (2018) · Digital Access to Scholarship at Harvard (DASH) (Harvard University)
Consumo de carnes rojas y procesadas: ¿qué nos deja la reciente polémica?
Jenny Ruedlinger, S., & Catterina Ferreccio, R. (2019) · Revista del Instituto de Salud Pública de Chile · doi:10.34052/rispch.v3i2.88
GIycemic Index and Women’s Health
Philippou, E. (2016) · CRC Press eBooks · doi:10.1201/9781315371764-11
Charcuteries et maladies cardio-métaboliques
Lecerf, J. (2019) · Cahiers de Nutrition et de Diététique · doi:10.1016/s0007-9960(20)30010-9
¿Tienen los Hábitos de Vida y de Alimentación Impacto en la Calidad Seminal?
Gómez-Gutiérrez, A. M., Ramírez, B. D. G., & Maya, W. D. C. (2020) · Revista Urología Colombiana / Colombian Urology Journal · doi:10.1055/s-0039-3402485
Bernard, K. P., Aliocha, N. N., Roger, K. Z., Baliko, E. L., Eleuthère, K. V., Jean-René, M. K., & Benjamin, L.-M. (2020) · World Journal of Cardiovascular Diseases · doi:10.4236/wjcd.2020.109060
Schmidt, K. A. (2020) · ResearchWorks at the University of Washington (University of Washington)
Francis, E. I., Kwaskebe, J., Okwuoma, A., & James, O. S. (2020) · World Journal of Innovative Research · doi:10.31871/wjir.9.6.41
Improved food intake in OECD countries as a key global climate mitigation driver
Cunha, B. S. L. D., Garaffa, R., & Gurgel, A. (2021)
Nudging meat eaters towards plant-based meat alternatives : an online supermarket experiment
Yu, N. (2021) · MOspace Institutional Repository (University of Missouri) · doi:10.32469/10355/88116
“Red” and “white” meats—terms that lead to confusion
KEETON, J. T., & Dikeman, M. E. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-85125-1.00003-x
Diabetes: Gute Ernährung ist ein therapeutischer Baustein
Pfeiffer, A. F. H. (2015) · Deutsches Ärzteblatt · doi:10.3238/persdia.2015.04.24.05
Belkacemi, L. (2013) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.717993409.793473760
Gesundheitsgefahren durch Erhitzen der Nahrung II
Mathias, D. (2022) · Springer eBooks · doi:10.1007/978-3-662-64209-2_39
Peltzer, K., & Pengpid, S. (2019) · Complementary Medicine Research · doi:10.1159/000499399
Laudańska–Krzemińska, I. (2015) · doi:10.1515/9783110472172
Roy, S., & Kar, D. (2022) · Research Square · doi:10.21203/rs.3.rs-1707156/v1
Argel, N. S. (2022) · El Servicio de Difusión de la Creación Intelectual (National University of La Plata) · doi:10.35537/10915/138278
Patel, P., & Micić, D. (2022) · Springer eBooks · doi:10.1007/978-3-030-94953-2_6
Varraso, R., Li, Z., Bédard, A., & Camargo, C. A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-85419-1.00007-4 · Cited by 2
Meat ( Longissimus lumborum M uscle) Quality in Males of the Family Cervidae
Daszkiewicz, T. (2022) · Acta Universitatis Cibiniensis. Series E: Food Technology · doi:10.2478/aucft-2022-0013
Metabolites in fish and humans as a response to different food ingredients : a metabolomics approach
Brunel, M. (2023) · Acta universitatis agriculturae Sueciae · doi:10.54612/a.524khafcck
Nian, T., Xue, S., Zhou, J. W., Fen Fen, E., Yan, L. Y., Hu, Y., Xin, J. T., Chen, Y. K., Cai, S. Z., Lu, C., et al. (2023) · Preprints.org · doi:10.20944/preprints202306.2082.v1
Monyeki, K. D., Kengne, A. P., Nkeh-Chungag, B. N., & Kemper, H. C. G. (2023) · Frontiers research topics · doi:10.3389/978-2-8325-3068-9
Diniz, H. J. C., da Silva, R. F., Camisasca, L. R., Silva, L. F., Alves, C. D. R., Rezende, L. F. D., de Magalhães, T. A., & Silva, C. S. D. O. E. (2024) · Revista Contemporânea · doi:10.56083/rcv4n2-007
Toprak, K., & Ayaz, A. (2024) · Turkish Journal of Family Medicine & Primary Care · doi:10.21763/tjfmpc.1392271
Butler, F. M., Utt, J., Mathew, R. O., Casiano, C. A., Montgomery, S., Wiafe, S., Lampe, J. W., & Fraser, G. E. (2023) · Figshare · doi:10.6084/m9.figshare.24465108
Zhang, H. (2024) · Research in Health Science · doi:10.22158/rhs.v9n4p73
Fonmboh, D. J., Thierry, N. N., Ntungwen, F., & Ejoh, A. R. (2024) · International Journal of Food Science and Biotechnology · doi:10.11648/j.ijfsb.20240904.16
Rosenkranz, S. K., Dennis-McLeod, B. T., Janss, B., Shahamati, D., Yarrow, L., & Rosenkranz, R. R. (2025) · Journal of American College Health · doi:10.1080/07448481.2025.2479701
Roychowdhury, P., Ghosh, P. S., Chatterjee, M., & Lahiri, S. (2025) · Current Functional Foods · doi:10.2174/0126668629375807250608065611
Eshel, G. (2025) · The MIT Press eBooks · doi:10.7551/mitpress/13832.001.0001
Himeno, N., Matsuda, T., Morita, Y., Kodama, T., Baba, R., Egusa, G., Nagano, G., Hattori, N., & Ohno, H. (2025) · Journal of Diabetes Investigation · doi:10.1111/jdi.70148
Saidi, H., Bounihi, A., Benbaibeche, H., Benhedouga, A., Saiah, W., & Koceir, E.-A. (2025) · Nutrition Bulletin · doi:10.1111/nbu.70036
Júnior, P., & Corrêa, D. A. (2012) · Aleph UCLA Undergraduate Research Journal for the Humanities and Social Sciences
Willits-Smith, A. M., Taillie, L. S., Jaacks, L. M., Frank, S. M., & Grummon, A. H. (2024) · Figshare · doi:10.6084/m9.figshare.25608662
Additional file 1 of Food intake and cardiometabolic risk factors in rural Uganda
Holmager, T. L. F., Meyrowitsch, D. W., Bahendeka, S., & Nielsen, J. (2021) · Figshare · doi:10.6084/m9.figshare.14121576
Allaire, B. T., Tjaden, A. H., Venditti, E. M., Apolzan, J. W., Dabelea, D., Delahanty, L. M., Edelstein, S. L., Hoskin, M. A., Temple, K. A., Wylie-Rosett, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13467993
Cao, L., Wen, Y., Fan, K., Wang, Q., Zhang, Y., Li, Z., Wang, N., & Zhang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26701036
Zhou, C., Liu, C., Zhang, Z., Liu, M., Zhang, Y., Li, H., He, P., Li, Q., & Qin, X. (2022) · Figshare · doi:10.6084/m9.figshare.18318467
Cao, L., Wen, Y., Fan, K., Wang, Q., Zhang, Y., Li, Z., Wang, N., & Zhang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26701045
Papier, K., Fensom, G. K., Knüppel, A., Appleby, P. N., Tong, T. Y. N., Schmidt, J. A., Travis, R. C., Key, T. J., & Perez‐Cornago, A. (2021) · Figshare · doi:10.6084/m9.figshare.14140042
Additional file 2 of Food intake and cardiometabolic risk factors in rural Uganda
Holmager, T. L. F., Meyrowitsch, D. W., Bahendeka, S., & Nielsen, J. (2021) · Figshare · doi:10.6084/m9.figshare.14121579
Frank, S. M., Jaacks, L. M., Meyer, K., Rose, D., Adair, L. S., Avery, C. L., & Taillie, L. S. (2024) · Figshare · doi:10.6084/m9.figshare.25530270
Cao, L., Wen, Y., Fan, K., Wang, Q., Zhang, Y., Li, Z., Wang, N., & Zhang, X. (2024) · Figshare · doi:10.6084/m9.figshare.25530279
Cao, L., Wen, Y., Fan, K., Wang, Q., Zhang, Y., Li, Z., Wang, N., & Zhang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26701030
Cao, L., Wen, Y., Fan, K., Wang, Q., Zhang, Y., Li, Z., Wang, N., & Zhang, X. (2024) · Figshare · doi:10.6084/m9.figshare.26701024
Cao, L., Wen, Y., Fan, K., Wang, Q., Zhang, Y., Li, Z., Wang, N., & Zhang, X. (2024) · Figshare · doi:10.6084/m9.figshare.25530421
Macronutrient quality and colorectal cancer outcomes: evidence from the PLCO screening trial
Feng, D., Xiang, L., Wen, K., Xiao, Y., Gu, H.-T., Peng, L., Luo, Y., Wang, Y., Liu, D., Zhou, Z., et al. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1656275
Animal-Derived Foods in Our Diets: Nutrition, Health and Social Implications
(2026) · Frontiers research topics · doi:10.3389/978-2-8325-7400-3
Wang, L., Guo, J., Guan, Y., Zhang, C., Wang, R., Li, K., Zhu, R., & He, J. (2026) · Nutrients · doi:10.3390/nu18040669
Alimentos tipo hambúrguer à base de plantas
Luz, G. M. (2022) · doi:10.47749/t/unicamp.2022.1523550
A metabolomic exploration of processed meat intake : dietary biomarkers and metabolic effects
Wedekind, R. (2020) · theses.fr (ABES) · doi:10.70675/9feb21c7z42f5z4b13z84edz05b650b15f0f
Mota, J. D. O. (2020) · theses.fr (ABES) · doi:10.70675/83d60308z5af5z4077zb799ze79efe7dfdc7
Red Meat in Human Evolution, Health, and Disease: From A Blessing to A Curse?
Jaco, J., Banda, K., Varki, A. P., & Gagneux, P. (2026) · The Quarterly Review of Biology · doi:10.1086/741185 · Cited by 1
Fu, P., Zheng, H., Wu, K., Lan, Y., Wu, D., Wu, W., Zheng, H., Cai, Z. F., Wang, Y., Lin, Q., et al. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2026.1802388
Genetics and immunity: Mapping pathways in type II diabetes (T2D)
Khan, A., & Moharana, R. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33590-7.00011-3
Environmental Menu Labels and Food Selection Among US Adults
Reimold, A. E., Wolfson, J. A., Lemmon, B., Musicus, A. A., Kim, B. F., Carr, N., Santo, R. E., Cho, C., Leung, C. W., & Falbe, J. (2026) · JAMA Network Open · doi:10.1001/jamanetworkopen.2026.19650
Kumar, A., Thorat, D. S., Yadav, S., Yadav, P., Pramanik, J., Ushir, Y. V., Singh, S. K., & Prajapati, B. G. (2026) · Food biomacromolecules. · doi:10.1002/fob2.70078
Metadata
- Authors
- Pan, A., Sun, Q., Bernstein, A. M., Schulze, M. B., Manson, J. E., Willett, W. C., & Hu, F. B.
- Affiliations
- Harvard University (US)
- Venue
- The American Journal of Clinical Nutrition
- Publisher
- Elsevier BV
- Published
- 2011-10
- Volume
- 94
- Issue
- 4
- Pages
- 1088-1096
- ISSN
- 0002-9165 (ISSN-L)
- PMC
- PMC3173026
- OpenAlex
- W1995856488
- Cited by
- 709
- References
- 75
- Citations by year
- 2026: 24 · 2025: 27 · 2024: 53 · 2023: 35 · 2022: 60 · 2021: 50 · 2020: 51 · 2019: 59 · 2018: 56 · 2017: 68 · 2016: 51 · 2015: 46 · 2014: 42 · 2013: 39 · 2012: 43
- FWCI
- 39.40
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- American Journal of Clinical Nutrition (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (CA 87969, CA55075)
- National Heart, Lung, and Blood Institute (K99HL098459, CA55075, CA87969)
- National Cancer Institute (CA-87969, CA-55075, P01CA087969, CA50385, R01CA050385)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK058845, DK58845)
- 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
- red meat consumption, type 2 diabetes, processed meat, diabetes risk, dietary risk factors, nuts, low-fat dairy, whole grains, Health Professionals Follow-up Study, Nurses' Health Study
- MeSH terms
- Adult, Aged, Diabetes Mellitus, Type 2, Diet, Female, Follow-Up Studies, Food Handling, Humans, Male, Meat, Middle Aged, Prospective Studies, Surveys and Questionnaires, Risk, United States, Reproducibility of Results, Cohort Studies, Incidence
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review