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Pooled Cohort Equations to Estimate ASCVD Risk by BMI
Quick Takes
- Obesity is associated with an increase in ASCVD events and mortality but does not add to the predictive value of the ACC/AHA pooled risk equation to predict 10-year risk.
- Because of poor accuracy and much less reproducibility, waist and waist-to-hip ratios reflective of the cardiometabolic disorders are not useful.
- Cost, complexity, and radiation preclude use of computed tomography, magnetic resonance imaging, and dual X-ray absorptometry for measuring and tracking visceral fat. There are many novel inexpensive devices for measuring visceral fat that may prove very valuable for assessing and monitoring risk and treatment effects.
Study Questions:
Does including clinical body mass index (BMI) categories improve the performance of the American College of Cardiology/American Heart Association (ACC/AHA) pooled cohort equation (PCE) used to assess atherosclerotic cardiovascular disease (ASCVD) risk?
Methods:
This cohort study used pooled individual-level data from eight community-based, prospective, longitudinal cohort studies with 10-year ASCVD event follow-up from 1996-2016. Included were all adults ages 40-79 years without baseline ASCVD or statin use, resulting in a sample size of 37,311 participants.
Results:
Among 37,311 participants (mean age, 58.6 [11.8] years; 21,897 [59%] women), there was 380,604 person-years of follow-up. Mean baseline BMI was 29.0 (6.2) kg/m2, and 360 individuals (1.0%) were in the underweight category, 9,937 (26.6%) were in the normal weight category, 13,601 individuals (36.4%) were in the overweight category, 7,783 (20.9%) were in the mild obesity category, and 5,630 (15.1%) were in the moderate to severe obesity category. Median (interquartile range [IQR]) 10-year estimated ASCVD risk was 7.1% (2.5%-15.4%), and 3,709 individuals (9.9%) developed ASCVD over a median (IQR) 10.8 (8.5-12.6) years. The PCE overestimated ASCVD risk in the overall cohort (estimated/observed risk ratio, 1.22) and across all BMI categories except the underweight category. The PCE C-statistic overall was 0.760, with lower discrimination in the moderate or severe obesity group (C-statistic, 0.742) compared with the normal-range BMI group (C-statistic, 0.785). Waist circumference (hazard ratio, 1.07 per 1-standard deviation [SD] increase) and high-sensitivity C-reactive protein (hazard ratio, 1.07 per 1-SD increase), but not BMI, were associated with increased ASCVD risk when added to the PCE. However, these factors did not improve model performance (C-statistic, 0.760) with or without added metrics.
Conclusions:
The findings suggest that the PCE had acceptable model discrimination and were well calibrated at clinical decision thresholds but overestimated risk of ASCVD for individuals in overweight and obese categories, particularly individuals with high estimated risk. Incorporation of the usual clinical measures of obesity did not improve risk estimation of the PCE. Future research is needed to determine whether incorporation of alternative high-risk obesity markers (e.g., weight trajectory or measures of visceral or ectopic fat) into the PCE may improve risk prediction.
Perspective:
The population sample is a good reflection of the United States, with a relatively high mean BMI. That BMI from normal to moderate and severe obesity was not associated with increasing performance accuracy of the PCE is disappointing because of the ease and accuracy of measure. But it is not surprising when one considers the known increase in ASCVD, diabetes, and CV mortality associated with visceral obesity (intestinal and epicardial) compared to generalized obesity, as reflected by the BMI.
Clinical Topics: Diabetes and Cardiometabolic Disease, Noninvasive Imaging, Prevention
Keywords: Atherosclerosis, Body Mass Index, C-Reactive Protein, Diagnostic Imaging, Intra-Abdominal Fat, Metabolic Syndrome, Obesity, Obesity, Abdominal, Obesity, Morbid, Primary Prevention, Risk Assessment, Thinness, Waist Circumference
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