Metabolic Syndrome vs Framingham Risk Score for Prediction of Coronary Heart Disease, Stroke, and Type 2 Diabetes Mellitus

Department of Primary Care and Population Sciences, Royal Free and University College Medical School, Rowland Hill Street, London NW3 2PF, England.
Archives of Internal Medicine (Impact Factor: 17.33). 12/2005; 165(22):2644-50. DOI: 10.1001/archinte.165.22.2644
Source: PubMed


We sought to compare metabolic syndrome (MetS) with the Framingham Risk Score (FRS) as predictors of coronary heart disease (CHD), stroke, and type 2 diabetes mellitus (DM2) in middle-aged men.
A prospective study of 5128 men aged 40 to 59 years with no history of cardiovascular disease (CVD) (CHD or stroke) or DM2 drawn from general practices in 24 British towns and observed for 20 years. Metabolic syndrome was defined as the presence of 3 or more metabolic abnormalities based on modified National Cholesterol Education Program criteria.
Men with MetS at baseline (26%) showed significantly higher relative risk (RR) than men without MetS of developing CHD (RR, 1.64; 95% confidence interval [CI], 1.41-1.90), stroke (RR, 1.61 95% CI, 1.26-2.06), and DM2 (RR, 3.57; 95% CI, 2.83-4.50). The probability of developing CVD or DM2 over 20 years increased from 11.9% in those with no abnormalities to 31.2% in those with 3 abnormalities to 40.8% in those with 4 or 5 abnormalities. The FRS was a better predictor of CHD and stroke than MetS but was less predictive of DM2. Areas under the receiver-operating characteristic curves for FRS vs the number of metabolic abnormalities were 0.68 vs 0.59 for CHD, 0.60 vs 0.70 for DM2, and 0.66 vs 0.55 for stroke (P< .001 for all).
Presence of MetS is a significant predictor of CVD and DM2 but is a stronger predictor of DM2 than of CHD. Although MetS does not predict CHD as well as the FRS, it serves well as a simple clinical tool for identifying high-risk subjects predisposed to CVD or DM2.

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    • "HDL was considered reduced at b 50 mg/dL in women or b 40 mg/dL in men (Grundy et al., 2005). To account for non-fasted blood samples, triglycerides were considered elevated at ≥200 mg/dL (Wannamethee et al., 2005). "
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    ABSTRACT: Objective: To assess levels of physical activity the use of objective physical activity measures like accelerometers is promising. We investigated characteristics associated with non-participation in accelerometry within an apparently healthy sample. Methods: Among German participants of a cardiovascular examination program (CEP; 2012-2013), 470 participants aged 40-75. years were invited to wear an accelerometer for 7. days. We used multivariate logistic regression to estimate the association between non-participation and the following characteristics of participants: sex, age, education, smoking, setting of recruitment for the CEP (general medical practices, job agencies, statutory health insurance), self-reported general health, and objective health criteria such as cardiorespiratory fitness and absolute number of cardiometabolic risk factors (elevated waist circumference, blood pressure, triglycerides, blood glucose, and reduced high-density lipoprotein). Subsequently, we stratified this analysis by sex. Results: Among all invited individuals, N = 235 (60.0% women) gave consent to participate in accelerometry. Women were more likely to decline participation (odds ratio, 1.7; 95% confidence interval, 1.1-2.7) compared to men. Stratified analyses revealed the absolute number of risk factors as predictor of non-participation for men (1.4; 1.01-2.0), while there was no predictor found in women. Conclusion: We found a self-selection bias in participation in accelerometry. Women declined study participation more likely than men. The number of cardiometabolic risk factors decreased compliance only in men. Future studies should consider strategies to reduce this bias.
    05/2015; 2(08/09). DOI:10.1016/j.pmedr.2015.05.003
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    • "This pattern of stronger associations when predicting type 2 diabetes to weaker associations with CVD is not unexpected. Studies have found clinical indicators of risk, including metabolic syndrome status and a count of metabolic syndrome risk components, to have stronger associations with incident type 2 diabetes than incident CVD [23,24]. "
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    ABSTRACT: Indicators of cardiometabolic risk typically include non-clinical factors (e.g., smoking). While the incorporation of non-clinical factors can improve absolute risk prediction, it is impossible to study the contribution of non-clinical factors when they are both predictors and part of the outcome measure. Metabolic syndrome, incorporating only clinical measures, seems a solution yet provides no information on risk severity. The aims of this study were: 1) to construct two continuous clinical indices of cardiometabolic risk (cCICRs), and assess their accuracy in predicting 10-year incident cardiovascular disease and/or type 2 diabetes; and 2) to compare the predictive accuracies of these cCICRs with existing risk indicators that incorporate non-clinical factors (Framingham Risk Scores). Data from a population-based biomedical cohort (n = 4056) were used to construct two cCICRs from waist circumference, mean arteriole pressure, fasting glucose, triglycerides and high density lipoprotein: 1) the mean of standardised risk factors (cCICR-Z); and 2) the weighted mean of the two first principal components from principal component analysis (cCICR-PCA). The predictive accuracies of the two cCICRs and the Framingham Risk Scores were assessed and compared using ROC curves. Both cCICRs demonstrated moderate accuracy (AUCs 0.72 - 0.76) in predicting incident cardiovascular disease and/or type 2 diabetes, among men and women. There were no significant differences between the predictive accuracies of the cCICRs and the Framingham Risk Scores. cCICRs may be useful in research investigating associations between non-clinical factors and health by providing suitable alternatives to current risk indicators which include non-clinical factors.
    BMC Cardiovascular Disorders 02/2014; 14(1):27. DOI:10.1186/1471-2261-14-27 · 1.88 Impact Factor
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    • "The metabolic syndrome (MetS), characterized by abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C) level, high blood pressure (BP), and increased fasting glucose level, predisposes individuals to a high risk for cardiovascular disease (CVD) and diabetes [1-4]. The MetS is a common disorder in China. "
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    ABSTRACT: In 2009, a unified definition of metabolic syndrome (MetS) was proposed, of which, the glycemic component is defined on the basis of fasting plasma glucose (FPG) level. Recently, the American Diabetes Association (ADA) recommended the use of glycated hemoglobin (HbA1c) as an alternative to FPG to define prediabetes. Hence, we aim to compare the performance of HbA1c and FPG in the definition of glycemic component of the MetS among Chinese adults. We conducted a cross-sectional analysis of 7641 Chinese participants aged >=18 years using data from the China Health and Nutrition Survey 2009. MetS was defined according to the consensus criteria in 2009. We compared the use of HbA1c versus FPG in the definition of the glycemic component of MetS. Increased HbA1c value was defined following the criterion of HbA1c cut-off point of >=5.7% recommended by the ADA. Overall, 1136 (14.9%) had MetS according to FPG >= 5.6 mmol/l, and 1640 (21.5%) had MetS according to HbA1c >= 5.7%. Compared with individuals with FPG-based diagnosis of MetS, individuals with HbA1c-based diagnosis of MetS were older, had higher levels of LDL-C, magnesium, and transferrin, and lower levels of uric acid. Of those found to have MetS according to either FPG or HbA1c (n = 2008), overlap between HbA1c- and FPG-based diagnosis of MetS was limited (n = 768, 38.2%). The overlap index regarding MetS diagnosed by FPG or HbA1c persisted low in each evaluated subgroup (<= 50.0%). We note limited overlap and poor agreement between FPG- and HbA1c-based diagnosis of MetS. Screening MetS through introduction of HbA1c in addition to FPG could contribute to identification of more people with MetS.
    BMC Public Health 11/2013; 13(1):1045. DOI:10.1186/1471-2458-13-1045 · 2.26 Impact Factor
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