OBJECTIVE: To examine the clinical and epidemiological utility of the concepts of metabolic syndrome and insulin resistance syndrome in two prospective cohort studies of white men. METHODS: Men aged 45-63 years were screened for evidence of ischaemic heart disease (IHD) between 1979 and 1982 and followed up at regular intervals thereafter. Non-fatal coronary events were validated from hospital records and fatal coronary events from death certificates. RESULTS: Analysis of serum insulin concentrations in non-diabetic individuals measured at entry to the study showed no independent contribution to the prediction of subsequent IHD at 10 year follow up. Blood glucose concentrations, however, showed a small independent contribution in the combined cohort in the upper fifth of the distribution. Three different models of metabolic syndrome among non-diabetic individuals were defined based on tertiles, medians, and clusters. The predictive value of each model was assessed using logistic regression before and after adjustment for conventional and metabolic risk factors. After adjustment the odds were non-significant and close to unity. CONCLUSIONS: This study did not detect any complex relation among the five variables defining metabolic syndrome; the excess risk seems to be no greater than can be explained by individual effects of the defining variables in a multiple logistic model.
OBJECTIVE: To examine the clinical and epidemiological utility of the concepts of metabolic syndrome and insulin resistance syndrome in two prospective cohort studies of white men. METHODS:Men aged 45-63 years were screened for evidence of ischaemic heart disease (IHD) between 1979 and 1982 and followed up at regular intervals thereafter. Non-fatal coronary events were validated from hospital records and fatal coronary events from death certificates. RESULTS: Analysis of serum insulin concentrations in non-diabetic individuals measured at entry to the study showed no independent contribution to the prediction of subsequent IHD at 10 year follow up. Blood glucose concentrations, however, showed a small independent contribution in the combined cohort in the upper fifth of the distribution. Three different models of metabolic syndrome among non-diabetic individuals were defined based on tertiles, medians, and clusters. The predictive value of each model was assessed using logistic regression before and after adjustment for conventional and metabolic risk factors. After adjustment the odds were non-significant and close to unity. CONCLUSIONS: This study did not detect any complex relation among the five variables defining metabolic syndrome; the excess risk seems to be no greater than can be explained by individual effects of the defining variables in a multiple logistic model.
Authors: Roberto Elosua; Carla Lluís-Ganella; Isaac Subirana; Aki Havulinna; Kristi Läll; Gavin Lucas; Sergi Sayols-Baixeras; Arto Pietilä; Maris Alver; Antonio Cabrera de León; Mariano Sentí; David Siscovick; Olle Mellander; Krista Fischer; Veikko Salomaa; Jaume Marrugat Journal: Circ Cardiovasc Genet Date: 2016-04-21
Authors: Lisa de las Fuentes; Giovanni de Simone; Donna K Arnett; Víctor G Dávila-Román Journal: Endocr Metab Immune Disord Drug Targets Date: 2010-06 Impact factor: 2.895
Authors: Latifa M Baynouna; Anthony D Revel; Nico J D Nagelkerke; Tariq M Jaber; Aziza O Omar; Nader M Ahmed; Mohammad K Nazirudeen; Mamdouh F Al Sayed; Fuad A Nour; Sameh Abdouni Journal: Cardiovasc Diabetol Date: 2009-04-16 Impact factor: 9.951