OBJECTIVE: The metabolic syndrome has been promoted as a method for identifying high-risk individuals for type 2 diabetes and cardiovascular disease (CVD). We therefore sought to compare this syndrome, as defined by the National Cholesterol Education Program, to the Diabetes Predicting Model and the Framingham Risk Score as predictors of type 2 diabetes and CVD, respectively. RESEARCH DESIGN AND METHODS: A population-based sample of 1,709 initially nondiabetic San Antonio Heart Study (SAHS) participants were followed for 7.5 years, 195 of whom developed type 2 diabetes. Over the same time interval, 156 of 2,570 SAHS participants experienced a cardiovascular event. A population-based sample of 1,353 initially nondiabetic Mexico City Diabetes Study (MCDS) participants were followed for 6.5 years, 125 of whom developed type 2 diabetes. Baseline measurements included medical history, age, sex, ethnicity, smoking status, BMI, blood pressure, fasting and 2-h plasma glucose levels, and fasting serum total and HDL cholesterol and triglycerides. RESULTS: The sensitivities for predicting diabetes with the metabolic syndrome were 66.2 and 62.4% in the SAHS and the MCDS, respectively, and the false-positive rates were 27.8 and 38.7%, respectively. The sensitivity and false-positive rates for predicting CVD with the metabolic syndrome in the SAHS were 67.3 and 34.2%, respectively. At corresponding false-positive rates, the two predicting models had significantly higher sensitivities and, at corresponding sensitivities, significantly lower false-positive rates than the metabolic syndrome for both end points. Combining the metabolic syndrome with either predicting model did not improve the prediction of either end point. CONCLUSIONS: The metabolic syndrome is inferior to established predicting models for either type 2 diabetes or CVD.
OBJECTIVE: The metabolic syndrome has been promoted as a method for identifying high-risk individuals for type 2 diabetes and cardiovascular disease (CVD). We therefore sought to compare this syndrome, as defined by the National Cholesterol Education Program, to the Diabetes Predicting Model and the Framingham Risk Score as predictors of type 2 diabetes and CVD, respectively. RESEARCH DESIGN AND METHODS: A population-based sample of 1,709 initially nondiabetic San Antonio Heart Study (SAHS) participants were followed for 7.5 years, 195 of whom developed type 2 diabetes. Over the same time interval, 156 of 2,570 SAHSparticipants experienced a cardiovascular event. A population-based sample of 1,353 initially nondiabetic Mexico City Diabetes Study (MCDS) participants were followed for 6.5 years, 125 of whom developed type 2 diabetes. Baseline measurements included medical history, age, sex, ethnicity, smoking status, BMI, blood pressure, fasting and 2-h plasma glucose levels, and fasting serum total and HDL cholesterol and triglycerides. RESULTS: The sensitivities for predicting diabetes with the metabolic syndrome were 66.2 and 62.4% in the SAHS and the MCDS, respectively, and the false-positive rates were 27.8 and 38.7%, respectively. The sensitivity and false-positive rates for predicting CVD with the metabolic syndrome in the SAHS were 67.3 and 34.2%, respectively. At corresponding false-positive rates, the two predicting models had significantly higher sensitivities and, at corresponding sensitivities, significantly lower false-positive rates than the metabolic syndrome for both end points. Combining the metabolic syndrome with either predicting model did not improve the prediction of either end point. CONCLUSIONS: The metabolic syndrome is inferior to established predicting models for either type 2 diabetes or CVD.
Authors: Véronique L Roger; Alan S Go; Donald M Lloyd-Jones; Emelia J Benjamin; Jarett D Berry; William B Borden; Dawn M Bravata; Shifan Dai; Earl S Ford; Caroline S Fox; Heather J Fullerton; Cathleen Gillespie; Susan M Hailpern; John A Heit; Virginia J Howard; Brett M Kissela; Steven J Kittner; Daniel T Lackland; Judith H Lichtman; Lynda D Lisabeth; Diane M Makuc; Gregory M Marcus; Ariane Marelli; David B Matchar; Claudia S Moy; Dariush Mozaffarian; Michael E Mussolino; Graham Nichol; Nina P Paynter; Elsayed Z Soliman; Paul D Sorlie; Nona Sotoodehnia; Tanya N Turan; Salim S Virani; Nathan D Wong; Daniel Woo; Melanie B Turner Journal: Circulation Date: 2011-12-15 Impact factor: 29.690
Authors: Véronique L Roger; Alan S Go; Donald M Lloyd-Jones; Robert J Adams; Jarett D Berry; Todd M Brown; Mercedes R Carnethon; Shifan Dai; Giovanni de Simone; Earl S Ford; Caroline S Fox; Heather J Fullerton; Cathleen Gillespie; Kurt J Greenlund; Susan M Hailpern; John A Heit; P Michael Ho; Virginia J Howard; Brett M Kissela; Steven J Kittner; Daniel T Lackland; Judith H Lichtman; Lynda D Lisabeth; Diane M Makuc; Gregory M Marcus; Ariane Marelli; David B Matchar; Mary M McDermott; James B Meigs; Claudia S Moy; Dariush Mozaffarian; Michael E Mussolino; Graham Nichol; Nina P Paynter; Wayne D Rosamond; Paul D Sorlie; Randall S Stafford; Tanya N Turan; Melanie B Turner; Nathan D Wong; Judith Wylie-Rosett Journal: Circulation Date: 2010-12-15 Impact factor: 29.690
Authors: Revathi Balakrishnan; Jeffrey S Berger; Lisa Tully; Anish Vani; Binita Shah; Joseph Burdowski; Edward Fisher; Arthur Schwartzbard; Steven Sedlis; Howard Weintraub; James A Underberg; Ann Danoff; James A Slater; Eugenia Gianos Journal: Diabetes Metab Res Rev Date: 2015-05-12 Impact factor: 4.876
Authors: Miranda J Spratlen; Maria Grau-Perez; Lyle G Best; Joseph Yracheta; Mariana Lazo; Dhananjay Vaidya; Poojitha Balakrishnan; Mary V Gamble; Kevin A Francesconi; Walter Goessler; Shelley A Cole; Jason G Umans; Barbara V Howard; Ana Navas-Acien Journal: Am J Epidemiol Date: 2018-08-01 Impact factor: 4.897