The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
The metabolic syndrome or the insulin resistance syndrome? Different names, different concepts, and different goals
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Citations
Cites
Rubins, H. B., Robins, S. J., Collins, D., Fye, C. L., Anderson, J. W., Elam, M. B., Faas, F. H., Linares, E., Schaefer, E. J., Schectman, G., et al. (1999) · New England Journal of Medicine · doi:10.1056/nejm199908053410604 · Cited by 3,439
Insulin Resistance in Essential Hypertension
Ferrannini, E., Buzzigoli, G., Bonadonna, R., Giorico, M. A., Oleggini, M., Graziadei, L., Pedrinelli, R., Brandi, L., & Bevilacqua, S. (1987) · New England Journal of Medicine · doi:10.1056/nejm198708063170605 · Cited by 2,344
Lillioja, S., Mott, D. M., Spraul, M., Ferraro, R., Foley, J. E., Ravussin, E., Knowler, W. C., Bennett, P. H., & Bogardus, C. (1993) · New England Journal of Medicine · doi:10.1056/nejm199312303292703 · Cited by 1,508
Relation of triglyceride metabolism and coronary artery disease. Studies in the postprandial state
Patsch, J. R., Miesenböck, G., Hopferwieser, T., Mühlberger, V, Knapp, E., Dunn, J. K., Gotto, A. M., & Patsch, W. (1992) · Arteriosclerosis and Thrombosis: A Journal of Vascular Biology · doi:10.1161/01.atv.12.11.1336 · Cited by 1,122 · cited 1×
“Although not cited as one of the criteria for diagnosing the metabolic syndrome, the atherogenic lipoprotein profile associated with insulin resistance also includes a decrease in LDL-particle diameter (small dense LDL) and the postprandial accumulation of triglyceride-rich remnants lipoproteins [21,22], and these cha…”
BackgroundHypertriglyceridemia as a cardiovascular risk factor
Austin, M. A., Hokanson, J. E., & Edwards, K. L. (1998) · The American Journal of Cardiology · doi:10.1016/s0002-9149(98)00031-9 · Cited by 1,066 · cited 1×
“The ability of a low HDL-C to predict risk of CVD has been known for many years [91], and although issues have been raised concerning the role of an increase in triglyceride concentration as an ‘‘independent’’ CVD risk factor [92], there is evidence in support of that notion [93].”
BackgroundWarram, J. H., Martin, B. C., Krolewski, A. S., Soeldner, J. S., & Kahn, C. R. (1990) · Annals of Internal Medicine · doi:10.7326/0003-4819-113-12-909 · Cited by 1,038
Ferrannini, E., Natali, A., Bell, P., Cavallo-Perin, P., Lalic, N., & Mingrone, G. (1997) · Journal of Clinical Investigation · doi:10.1172/jci119628 · Cited by 898
Hulley, S. B., Rosenman, R. H., Bawol, R. D., & Brand, R. J. (1980) · New England Journal of Medicine · doi:10.1056/nejm198006193022503 · Cited by 702
Insulin resistance as a predictor of age-related diseases
Facchini, F. S., Hua, N., Abbasi, F., & Reaven, G. M. (2001) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.86.8.7763 · Cited by 594
Self-citationGinsberg, H., Kimmerling, G., Olefsky, J. M., & Reaven, G. M. (1975) · Journal of Clinical Investigation · doi:10.1172/jci107951 · Cited by 225
Self-citation
Cited by
Grundy, S. M., Cleeman, J. I., Daniels, S. R., Donato, K. A., Eckel, R. H., Franklin, B. A., Gordon, D. J., Krauss, R. M., Savage, P. J., Smith, S. C., et al. (2005) · Circulation · doi:10.1161/circulationaha.105.169404 · Cited by 11,961
Talbot, K., Wang, H.-Y., Kazi, H., Han, L.-Y., Bakshi, K. P., Stucky, A., Fuino, R. L., Kawaguchi, K. R., Samoyedny, A. J., Wilson, R. S., et al. (2012) · Journal of Clinical Investigation · doi:10.1172/jci59903 · Cited by 1,871
Copps, K. D., & White, M. F. (2012) · Diabetologia · doi:10.1007/s00125-012-2644-8 · Cited by 1,017
“Insulin resistance, defined as reduced responsiveness of tissues to normal insulin concentrations, is a principal feature of type 2 diabetes, which leads to compensatory hyperinsulinaemia [1].”
BackgroundTHE INSULIN RESISTANCE SYNDROME: Definition and Dietary Approaches to Treatment
Reaven, G. M. (2005) · Annual Review of Nutrition · doi:10.1146/annurev.nutr.24.012003.132155 · Cited by 516
Self-citationBiddinger, S. B., Hernandez-Ono, A., Rask-Madsen, C., Haas, J. T., Alemán, J. O., Suzuki, R., Scapa, E. F., Agarwal, C., Carey, M. C., Stephanopoulos, G., et al. (2008) · Cell Metabolism · doi:10.1016/j.cmet.2007.11.013 · Cited by 469
Metadata
- Authors
- Reaven, G.
- Venue
- Endocrinology and Metabolism Clinics of North America
- Publisher
- Elsevier BV
- Published
- 2004-06
- Volume
- 33
- Issue
- 2
- Pages
- 283-303
- ISSN
- 0889-8529 (ISSN-L)
- OpenAlex
- W2009033372
- Cited by
- 507
- References
- 12
- FWCI
- 24.81
- Type
- journal article
- Language
- en
- Open access
- closed
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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The metabolic syndrome or the insulin resistance syndrome? Different names, different concepts, and different goals. https://app.cochat.ai/papers/10.1016/j.ecl.2004.03.002
- The paper
- Reaven, G. (2004). The metabolic syndrome or the insulin resistance syndrome? Different names, different concepts, and different goals. Endocrinology and Metabolism Clinics of North America. https://doi.org/10.1016/j.ecl.2004.03.002
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
