ReviewThe obesity paradox: Understanding the effect of obesity on mortality among individuals with cardiovascular disease
Introduction
Over the past three decades, the prevalence of obesity has increased substantially in North America (Flegal et al., 2010). A recent study by Flegal and colleagues highlighted that over one third of American adults (36%) are obese and more than two thirds (69%) are overweight (Flegal et al., 2012). In the general population, obesity is associated with an increased risk of death (Adams et al., 2006, Calle et al., 2003, Flegal et al., 2013). An analysis of data from nineteen pooled studies reported all-cause mortality hazard ratios of 1.44 (95% CI 1.38, 1.50) for grade I obesity (BMI 30 to 34.9 kg/m2), 1.88 (95% CI 1.77, 2.00) for grade II obesity (BMI 35 to 39.9 kg/m2), and 2.51 (95% CI 2.30, 2.73) for grade III obesity (BMI ≥ 40 kg/m2) relative to normal weight individuals (Berrington de Gonzalez et al., 2010).
Despite the known association between obesity and mortality in the general population, there have been conflicting reports about the relationship between obesity and mortality among individuals with cardiovascular disease (CVD). Numerous authors have reported that obesity confers a survival advantage in patients with CVD, a phenomenon known as the “obesity paradox” (McAuley and Blair, 2011, Romero-Corral et al., 2006). Among individuals with CVD, studies have reported that obese patients have improved short- and long-term survival, measured by all-cause mortality, relative to normal weight counterparts. Evidence of the obesity paradox has been found among patients with many types of cardiovascular disease, including coronary heart disease, myocardial infarction, hypertension, atrial fibrillation, and heart failure (Angerås et al., 2013, Badheka et al., 2010, Bucholz et al., 2012, Curtis et al., 2005, Lavie et al., 2009a, Lavie et al., 2009b, Nigam et al., 2006, Oreopoulos et al., 2008a, Oreopoulos et al., 2008b, Uretsky et al., 2007). As well, the obesity paradox has been documented among cardiac surgery patients, such as those who have undergone percutaneous coronary intervention, heart valve surgery, and coronary artery bypass surgery (Gruberg et al., 2002, Oreopoulos et al., 2008a, Oreopoulos et al., 2008b, Sarno et al., 2011, Vaduganathan et al., 2012, van der Boon et al., 2013). The obesity paradox has also been reported in patients with other types of chronic disease, including diabetes, cancer, renal disease, and chronic obstructive pulmonary disease (McAuley and Blair, 2011). Several hypotheses have been suggested to explain this phenomenon (Chrysant and Chrysant, 2013, Dixon and Lambert, 2013).
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Physiological explanations for the obesity paradox
Physiological explanations emphasize the biological advantages of excess fat stores during periods of illness. Body fat may act to decrease oxidative stress and inflammation, reduce levels of B-type natriuretic peptide, and improve secretion of amino acids and adipokines, potentially improving survival among obese individuals (Dixon and Lambert, 2013). Certain hormones and cytokines, such as leptin and tumor necrosis factor alpha, have been suggested as possible moderators of the relationship
Methodological explanations for the obesity paradox
There are also a number of hypothesized methodological explanations for the obesity paradox. Firstly, using BMI to define obesity has been identified as a possible design flaw. Authors suggest that BMI does not correspond to the same degree of adiposity in individuals of different height, nor does it account for body composition or the location of adipose tissue (i.e., visceral vs. subcutaneous fat), or differentiate between fat mass and muscle mass (Kopelman, 2000, Lavie et al., 2013,
An alternative explanation
An additional methodologic explanation is that it is due in whole or in part to a form of selection bias known as collider stratification bias (Banack and Kaufman, 2013, Lajous et al., 2014). Selection bias occurs when exposure and disease both affect inclusion into the analysis. In other words, it occurs as the result of conditioning on a common effect of exposure and outcome (Hernán et al., 2004). Conditioning can occur at the study design or analysis stage and may occur through restriction,
Analysis
The following section will provide two examples of how collider stratification bias can produce an apparently protective effect of obesity on mortality among individuals with CVD. The first is a fictitious example intended to provide a demonstration of how the paradox occurs using simple, easy-to-follow, hand calculations. The second example uses data from the Third US National Health and Nutrition Examination Survey (NHANES III) to provide a real-life example of the obesity paradox using
Bias analysis
Bias analysis techniques can be used as sensitivity analyses to understand the magnitude of bias induced by studying a highly selected population drawn from the total cohort. They can be used to quantify the amount of selection bias affecting an estimate of the obesity–mortality relationship among individuals who already have CVD compared with an estimate of the obesity–mortality relationship in the total population. To conduct a bias analysis, one must select values for the bias parameters and
Conclusion
The objective of the present paper was to review the obesity paradox and explore whether it can be explained as an example of collider stratification bias. Both the fictitious example and the NHANES III data suggest that after conditioning on CVD status in the analysis, obesity appears protective among individuals with CVD. Stratifying on CVD status creates an imbalance in the distribution of unmeasured common causes (U) between obese and non-obese individuals.
The bias analysis presented in
Conflict of interest statement
The authors declare that there are no conflicts of interests.
Acknowledgments
We acknowledge helpful comments and critical input received from Dr. M. Maria Glymour.
We thank Genevieve Gariepy for providing feedback on this manuscript.
Hailey Banack was supported by a doctoral research award from the Fonds de la Recherche en Sante du Quebec, a Society for Epidemiologic Research Travel Award, and a CIHR Institute of Circulatory and Respiratory Health Skills Development Award. Jay Kaufman was supported by the Canada Research Chair program.
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