The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
The metabolic syndrome and incidence of cardiovascular disease in non‐diabetic subjects—a population‐based study comparing three different definitions
Open accessAbstract
Aims Between 1998 and 2005, a number of definitions of the metabolic syndrome (MetS) have been proposed. The aim of this population‐based cohort study was to compare prevalence rates and the prediction of cardiovascular disease (CVD) using different definitions of MetS. Methods A total of 5047 non‐diabetic subjects (66% women), from the city of Malmö, Sweden, were followed. The incidence of fatal and non‐fatal CVD (cardiac events, n = 176, and stroke, n = 171) was monitored over 11 years of follow‐up. MetS was defined in three different ways [by International Diabetes Federation (IDF), National Cholesterol Education Program—Adult Treatment Panel III (NCEP‐ATPIII), or European Group for the study of Insulin Resistance (EGIR) criteria] based on data on waist circumference, blood pressure, serum triglycerides, High‐density lipoprotein cholesterol and fasting blood glucose. The IDF definition identified 21.9% of the subjects having the MetS. Corresponding figures for the NCEP‐ATPIII and EGIR definitions were 20.7 and 18.8%, respectively. Results After taking age, gender, low‐density lipoprotein cholesterol and lifestyle factors into account, the hazard ratio (HR) for CVD event according to the IDF, NCEP‐ATPIII and EGIR definitions were HR 1.11 (95% CI: 0.86–1.44), 1.59 (1.25–2.03) and 1.35 (1.05–1.74), respectively. The results were largely similar for cardiac and stroke events. Conclusions The prevalence of Mets according to the IDF definition was higher in comparison with NCEP‐ATPIII and EGIR definitions, but the IDF definition was not superior to these definitions for prediction of CVD events. This was true for both genders and questions the usefulness of the current IDF criteria of MetS in a North‐European, Caucasian population. In addition, single risk factors such as smoking had an equal prediction as the metabolic syndrome.
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Matthews, D. R., Hosker, J. P., Rudenski, A. S., Naylor, B. A., Treacher, D. F., & Turner, R. C. (1985) · Diabetologia · doi:10.1007/bf00280883 · Cited by 31,502
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
Prediction of coronary heart disease using risk factor categories
Wilson, P. W. F., D’Agostino, R. B., Levy, D., Belanger, A. M., Silbershatz, H., & Kannel, W. B. (1998) · Circulation · doi:10.1161/01.cir.97.18.1837 · Cited by 9,581
The metabolic syndrome—a new worldwide definition
Alberti, K. G. M., Zimmet, P., & Shaw, J. (2005) · The Lancet · doi:10.1016/s0140-6736(05)67402-8 · Cited by 9,352
The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men
Lakka, H.-M. (2002) · JAMA · doi:10.1001/jama.288.21.2709 · Cited by 5,033
Cardiovascular Morbidity and Mortality Associated With the Metabolic Syndrome
Isomaa, B., Almgren, P., Tuomi, T., Forsén, B., Lahti, K., Nissén, M., Taskinen, M.-R., & Groop, L. (2001) · Diabetes Care · doi:10.2337/diacare.24.4.683 · Cited by 4,760
Comment on the provisional report from the WHO consultation
(1999) · Diabetic Medicine · doi:10.1046/j.1464-5491.1999.00059.x · Cited by 1,820
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) · Cardiology in Review · doi:10.1097/01.crd.0000380842.14048.7e · Cited by 1,600
Stern, M. P., Williams, K., González-Villalpando, C., Hunt, K. J., & Haffner, S. M. (2004) · Diabetes Care · doi:10.2337/diacare.27.11.2676 · Cited by 735
Cheal, K. L., Abbasi, F., Lamendola, C., McLaughlin, T., Reaven, G. M., & Ford, E. S. (2004) · Diabetes · doi:10.2337/diabetes.53.5.1195 · Cited by 312
Cited by
(2013) · European Heart Journal · doi:10.1093/eurheartj/eht151 · Cited by 13,655
Mancia, G., Fagard, R., Narkiewicz, K., Redón, J., Zanchetti, A., Böhm, M., Christiaens, T., Cifkova, R., De Backer, G., Dominiczak, A., et al. (2013) · Journal of Hypertension · doi:10.1097/01.hjh.0000431740.32696.cc · Cited by 5,655
Self-citationThe Metabolic Syndrome and Cardiovascular Risk
Mottillo, S., Filion, K. B., Genest, J., Joseph, L., Pilote, L., Poirier, P., Rinfret, S., Schiffrin, E. L., & Eisenberg, M. J. (2010) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2010.05.034 · Cited by 3,009
Cornier, M.-A., Dabelea, D., Hernandez, T. L., Lindstrom, R. C., Steig, A. J., Stob, N. R., Van Pelt, R. E., Wang, H., & Eckel, R. H. (2008) · Endocrine Reviews · doi:10.1210/er.2008-0024 · Cited by 2,162
Huang, Y., Cai, X., Mai, W., Li, M., & Hu, Y. (2016) · BMJ · doi:10.1136/bmj.i5953 · Cited by 1,012
Metadata
- Authors
- Nilsson, P. M., Engström, G., & Hedblad, B.
- Venue
- Diabetic Medicine
- Publisher
- Wiley
- Published
- 2007-03-22
- Volume
- 24
- Issue
- 5
- Pages
- 464-472
- ISSN
- 0742-3071 (ISSN-L)
- 1464-5491
- OpenAlex
- W2089602295
- Cited by
- 207
- References
- 11
- FWCI
- 17.68
- Type
- journal article
- Language
- en
- Open access
- bronze
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