Ramachandran S. Vasan

BACKGROUND: Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. METHODS AND RESULTS: We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions ("general CVD" algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P<0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non-laboratory-based predictors. CONCLUSIONS: A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.

Identification of key factors associated with the risk of developing cardiovascular disease and quantification of this risk using multivariable prediction algorithms are among the major advances made in preventive cardiology and cardiovascular epidemiology in the 20th century. The ongoing discovery of new risk markers by scientists presents opportunities and challenges for statisticians and clinicians to evaluate these biomarkers and to develop new risk formulations that incorporate them. One of the key questions is how best to assess and quantify the improvement in risk prediction offered by these new models. Demonstration of a statistically significant association of a new biomarker with cardiovascular risk is not enough. Some researchers have advanced that the improvement in the area under the receiver-operating-characteristic curve (AUC) should be the main criterion, whereas others argue that better measures of performance of prediction models are needed. In this paper, we address this question by introducing two new measures, one based on integrated sensitivity and specificity and the other on reclassification tables. These new measures offer incremental information over the AUC. We discuss the properties of these new measures and contrast them with the AUC. We also develop simple asymptotic tests of significance. We illustrate the use of these measures with an example from the Framingham Heart Study. We propose that scientists consider these types of measures in addition to the AUC when assessing the performance of newer biomarkers.

BACKGROUND: Extreme obesity is recognized to be a risk factor for heart failure. It is unclear whether overweight and lesser degrees of obesity also pose a risk. METHODS: We investigated the relation between the body-mass index (the weight in kilograms divided by the square of the height in meters) and the incidence of heart failure among 5881 participants in the Framingham Heart Study (mean age, 55 years; 54 percent women). With the use of Cox proportional-hazards models, the body-mass index was evaluated both as a continuous variable and as a categorical variable (normal, 18.5 to 24.9; overweight, 25.0 to 29.9; and obese, 30.0 or more). RESULTS: During follow-up (mean, 14 years), heart failure developed in 496 subjects (258 women and 238 men). After adjustment for established risk factors, there was an increase in the risk of heart failure of 5 percent for men and 7 percent for women for each increment of 1 in body-mass index. As compared with subjects with a normal body-mass index, obese subjects had a doubling of the risk of heart failure. For women, the hazard ratio was 2.12 (95 percent confidence interval, 1.51 to 2.97); for men, the hazard ratio was 1.90 (95 percent confidence interval, 1.30 to 2.79). A graded increase in the risk of heart failure was observed across categories of body-mass index. The hazard ratios per increase in category were 1.46 in women (95 percent confidence interval, 1.23 to 1.72) and 1.37 in men (95 percent confidence interval, 1.13 to 1.67). CONCLUSIONS: In our large, community-based sample, increased body-mass index was associated with an increased risk of heart failure. Given the high prevalence of obesity in the United States, strategies to promote optimal body weight may reduce the population burden of heart failure.

BACKGROUND: Visceral adipose tissue (VAT) compartments may confer increased metabolic risk. The incremental utility of measuring both visceral and subcutaneous abdominal adipose tissue (SAT) in association with metabolic risk factors and underlying heritability has not been well described in a population-based setting. METHODS AND RESULTS: Participants (n=3001) were drawn from the Framingham Heart Study (48% women; mean age, 50 years), were free of clinical cardiovascular disease, and underwent multidetector computed tomography assessment of SAT and VAT volumes between 2002 and 2005. Metabolic risk factors were examined in relation to increments of SAT and VAT after multivariable adjustment. Heritability was calculated using variance-components analysis. Among both women and men, SAT and VAT were significantly associated with blood pressure, fasting plasma glucose, triglycerides, and high-density lipoprotein cholesterol and with increased odds of hypertension, impaired fasting glucose, diabetes mellitus, and metabolic syndrome (P range < 0.01). In women, relations between VAT and risk factors were consistently stronger than in men. However, VAT was more strongly correlated with most metabolic risk factors than was SAT. For example, among women and men, both SAT and VAT were associated with increased odds of metabolic syndrome. In women, the odds ratio (OR) of metabolic syndrome per 1-standard deviation increase in VAT (OR, 4.7) was stronger than that for SAT (OR, 3.0; P for difference between SAT and VAT < 0.0001); similar differences were noted for men (OR for VAT, 4.2; OR for SAT, 2.5). Furthermore, VAT but not SAT contributed significantly to risk factor variation after adjustment for body mass index and waist circumference (P < or = 0.01). Among overweight and obese individuals, the prevalence of hypertension, impaired fasting glucose, and metabolic syndrome increased linearly and significantly across increasing VAT quartiles. Heritability values for SAT and VAT were 57% and 36%, respectively. CONCLUSIONS: Although both SAT and VAT are correlated with metabolic risk factors, VAT remains more strongly associated with an adverse metabolic risk profile even after accounting for standard anthropometric indexes. Our findings are consistent with the hypothesized role of visceral fat as a unique, pathogenic fat depot. Measurement of VAT may provide a more complete understanding of metabolic risk associated with variation in fat distribution.