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      The Quest for the Optimal Assessment of Global Cardiovascular Risk: Are Traditional Risk Factors and Metabolic Syndrome Partners in Crime?

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          Global risk calculators such as the Framingham risk score generally take into account traditional risk factors such as age, sex, blood pressure, smoking status, total cholesterol and high-density lipoprotein cholesterol levels, and the presence of diabetes which are recommended to be used in clinical practice to estimate patients’ cardiovascular disease (CVD) risk. Over the last decades, the prevalence of obesity has dramatically increased all over the world. The deleterious form of obesity, visceral obesity, is the most prevalent form of the so-called metabolic syndrome, a constellation of risk factors associated with perturbations of the lipoprotein-lipid profile and of the plasma glucose-insulin homeostasis also likely to be associated with increased blood pressure and a proinflammatory and prothrombotic state. To this date, metabolic syndrome is still in need of a place in global CVD risk prediction. As the metabolic syndrome is not likely to replace currently used global risk scoring algorithms, both traditional risk factors and emerging metabolic markers associated with the metabolic syndrome should be incorporated in future risk scoring systems to be developed in order to adapt CVD risk prediction tools to the epidemic of obesity which has direct consequences on the daily life of health professionals.

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          Most cited references 68

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          Excess deaths associated with underweight, overweight, and obesity.

          As the prevalence of obesity increases in the United States, concern over the association of body weight with excess mortality has also increased. To estimate deaths associated with underweight (body mass index [BMI] or =30) in the United States in 2000. We estimated relative risks of mortality associated with different levels of BMI (calculated as weight in kilograms divided by the square of height in meters) from the nationally representative National Health and Nutrition Examination Survey (NHANES) I (1971-1975) and NHANES II (1976-1980), with follow-up through 1992, and from NHANES III (1988-1994), with follow-up through 2000. These relative risks were applied to the distribution of BMI and other covariates from NHANES 1999-2002 to estimate attributable fractions and number of excess deaths, adjusted for confounding factors and for effect modification by age. Number of excess deaths in 2000 associated with given BMI levels. Relative to the normal weight category (BMI 18.5 to or =30) was associated with 111,909 excess deaths (95% confidence interval [CI], 53,754-170,064) and underweight with 33,746 excess deaths (95% CI, 15,726-51,766). Overweight was not associated with excess mortality (-86,094 deaths; 95% CI, -161,223 to -10,966). The relative risks of mortality associated with obesity were lower in NHANES II and NHANES III than in NHANES I. Underweight and obesity, particularly higher levels of obesity, were associated with increased mortality relative to the normal weight category. The impact of obesity on mortality may have decreased over time, perhaps because of improvements in public health and medical care. These findings are consistent with the increases in life expectancy in the United States and the declining mortality rates from ischemic heart disease.
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            Metabolic syndrome and risk of incident cardiovascular events and death: a systematic review and meta-analysis of longitudinal studies.

            The purpose of this research was to assess the association between the metabolic syndrome (MetSyn) and cardiovascular events and mortality by meta-analyses of longitudinal studies. Controversy exists regarding the cardiovascular risk associated with MetSyn. We searched electronic reference databases through March 2005, studies that referenced Reaven's seminal article, abstracts presented at meetings in 2003 to 2004, and queried experts. Two reviewers independently assessed eligibility. Longitudinal studies reporting associations between MetSyn and cardiovascular events or mortality were eligible. Two reviewers independently used a standardized form to collect data from published reports. Authors were contacted. Study quality was assessed by the control of selection, detection, and attrition biases. We found 37 eligible studies that included 43 cohorts (inception 1971 to 1997) and 172,573 individuals. Random effects meta-analyses showed MetSyn had a relative risk (RR) of cardiovascular events and death of 1.78 (95% confidence interval [CI] 1.58 to 2.00). The association was stronger in women (RR 2.63 vs. 1.98, p = 0.09), in studies enrolling lower risk (<10%) individuals (RR 1.96 vs. 1.43, p = 0.04), and in studies using factor analysis or the World Health Organization definition (RR 2.68 and 2.06 vs. 1.67 for National Cholesterol Education Program definition and 1.35 for other definitions; p = 0.005). The association remained after adjusting for traditional cardiovascular risk factors (RR 1.54, 95% CI 1.32 to 1.79). The best available evidence suggests that people with MetSyn are at increased risk of cardiovascular events. These results can help clinicians counsel patients to consider lifestyle interventions, and should fuel research of other preventive interventions.
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              Use and misuse of the receiver operating characteristic curve in risk prediction.

              The c statistic, or area under the receiver operating characteristic (ROC) curve, achieved popularity in diagnostic testing, in which the test characteristics of sensitivity and specificity are relevant to discriminating diseased versus nondiseased patients. The c statistic, however, may not be optimal in assessing models that predict future risk or stratify individuals into risk categories. In this setting, calibration is as important to the accurate assessment of risk. For example, a biomarker with an odds ratio of 3 may have little effect on the c statistic, yet an increased level could shift estimated 10-year cardiovascular risk for an individual patient from 8% to 24%, which would lead to different treatment recommendations under current Adult Treatment Panel III guidelines. Accepted risk factors such as lipids, hypertension, and smoking have only marginal impact on the c statistic individually yet lead to more accurate reclassification of large proportions of patients into higher-risk or lower-risk categories. Perfectly calibrated models for complex disease can, in fact, only achieve values for the c statistic well below the theoretical maximum of 1. Use of the c statistic for model selection could thus naively eliminate established risk factors from cardiovascular risk prediction scores. As novel risk factors are discovered, sole reliance on the c statistic to evaluate their utility as risk predictors thus seems ill-advised.

                Author and article information

                S. Karger AG
                March 2009
                30 October 2008
                : 113
                : 1
                : 35-49
                Departments of aAnatomy and Physiology and bMedicine, and cDivision of Kinesiology, Faculty of Medicine, Université Laval, and dQuébec Heart Institute, Hôpital Laval Research Centre, Québec, Qué., Canada
                165919 Cardiology 2009;113:35–49
                © 2008 S. Karger AG, Basel

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                Page count
                Figures: 6, References: 111, Pages: 15


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