Abstract 4123373: Estimating obesity-attributable cardiovascular disease burden in the United States: a Bayesian network analysis

Background: Obesity is a known contributor to the development of cardiovascular disease (CVD) and increases the risk of subsequent mortality. To fully understand the burden of obesity, accurate information regarding its effects on CVD is necessary. Current estimates are limited by a focus on direct effects, use of non-representative data sources, and a reliance on inaccurate diagnosis codes. Aim: To determine the size of CVD burden attributable to obesity in the US and identify the mechanisms through which obesity affects CVD. Methods: This study used data collected from US adults (aged ≥35 years) participating in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2020 and US Census projections. NHANES data were longitudinally adapted, and participants were excluded if they had missing data for weight history, risk factors, or a given outcome of interest. Bayesian networks (BNs) mapping out the relationship between risk factors and each outcome were constructed to estimate current CVD risk and expected CVD risk in the absence of obesity. Obesity-attributable CVD burden was then calculated for each outcome, and the direct and indirect pathways through which changes in CVD risk occur were characterized. A subgroup analysis was conducted in non-smoking participants aged ≤65 years for whom obesity is likely the primary CVD risk factor. Results: The final sample included data from approximately 29,000 participants. Of these, 1286 (4.3%) had a history of myocardial infarction (MI), 1283 (4.4%) had a history of stroke, 1092 (3.7%) had a history of heart failure (HF), and 1992 (6.7%) had a history of CVD (composite of MI, stroke, angina, coronary heart disease, and HF). Results from the fitted BN indicate that 21% of MI, 16% of stroke, 38% of HF, and 19% of CVD cases were attributable to obesity, with ≥80% of this burden due to the effect of obesity on intermediate risk factors (hypertension, type 2 diabetes, and prior cardiovascular events). In the subgroup analysis, the obesity-attributable CVD burden was approximately twice as high for non-smokers aged ≤65 years, with 39% of MI, 32% of stroke, 65% of HF, and 38% of CVD cases attributable to obesity. Conclusion: Through a sophisticated network modeling approach, we generated estimates of obesity-attributable CVD burden in the US. Results highlight the importance of obesity as a CVD risk factor, particularly in younger non-smokers, and provide insight into the nature of this relationship.