Smoking and body weight: Evidence using genetic instruments
Highlights
► Smoking may have heterogeneous effects on low versus high body weight quantiles. ► Genetic instruments may be useful for identifying behavioral effects. ► Instruments should be validated through literature review and statistical tests. ► Replication with large samples and strong instruments is needed. ► The study highlights the advantages and challenges of genetic instruments.
Introduction
Obesity has become one of the most prevalent population health risks in several developed countries over the past 20 years. Obesity significantly increases cardiovascular disease, diabetes, other chronic health conditions, mortality, and health care costs (Finkelstein et al., 2009). Also, obesity and overweight may reduce employment (Han et al., 2009) and wage rates (Baum and Ford, 2004), in part due to increasing insurance premiums (Bhattacharya and Bundorf, 2009).
Body weight and obesity are complex traits, likely with several determinants and contributors. Lifestyle including high caloric and fast food consumption and physical inactivity, food advertisements, restaurant availability, and changes in economic wellbeing and security may play a role (Chou et al., 2004, Smith et al., 2009). Furthermore, body weight and obesity may have a strong genetic etiologic component. Twin studies estimate an 80% heritability of the body mass index (BMI) (Hjelmborg et al., 2008), 73% heritability of obesity (Watson et al., 2006) and up to 45–60% heritability of eating behaviors (Keskitalo et al., 2008, Tholin et al., 2005). Several genes may contribute to obesity, some of which have been identified and found to be significantly correlated to obesity across multiple studies (Martinez et al., 2007).1
Risk behaviors may also contribute to obesity. There has been a wide interest in understanding the effects of smoking on body weight and several economic and epidemiological studies have evaluated these effects. However, the direction and magnitude of effects vary across studies. There is a general perception that smoking may decrease body weight by decreasing appetite and caloric intake, enhancing metabolism, and reducing fat accumulation. This may occur through the effects of nicotine on brain regulation of appetite and energy expenditure. Studies in mice have shown appetite decrease, weight and fat loss, and changes in the expression of uncoupling proteins in the fat tissue and of brain neuropeptide Y levels, which are involved in metabolism-related processes (Chen et al., 2005, Chen et al., 2008). However, smoking may also decrease exercise by constraining respiratory functioning, which may counteract the previously mentioned effects on appetite and metabolism resulting in an overall no effect or increase in body weight. Therefore, the biologic pathways suggest a rather ambiguous net effect of smoking on body weight.
This paper reports an application of genetic instrumental variables to identify the effects of smoking on BMI. Furthermore, we evaluate whether these effects vary at different locations of the BMI distribution. The paper discusses the advantages and challenges of employing genetic instrumental variables particularly when studying behavioral effects. The paper proceeds as follows: Section 2 summarizes the existing knowledge and the contributions of this paper; Section 3 describes the data and methods; Section 4 reports the results; Section 5 includes the discussion and conclusions.
Section snippets
Background and contributions
Researchers have questioned the extent to which decreases in smoking rates over the past two decades may have contributed to rising population obesity rates. Fig. 1 shows the smoking and obesity rates among adults between 1971 and 2006 in the United States (US). These rates were changing in opposite directions, with smoking rates decreasing as obesity rates were climbing. About 34% of adults were obese in 2007–2008 in the US, and another 34% were overweight (Flegal et al., 2010). In 1971–1974,
Analytical approach
Body weight (W) is studied as a function of smoking (S) and socioeconomic and demographic factors that may relate to preferences for body weight, information about health risks and economic wellbeing (E) as follows:where i represents an individual and γ represents the smoking effect. As discussed above, smoking is endogenous to body weight, suggesting that direct estimation of Eq. (1) will result in biased estimates of γ. A common approach is to utilize instruments for smoking
Instrument effects and validity
Table 2 reports the coefficients of the cigarette function. The instruments have significant effects on cigarette number, with a joint F-statistic of 3.4 (p = 0.0025). Relative to the CC genotype, the TT genotype of rs1435252 decreases average daily cigarettes by 1.3 cigarettes, while the CT genotype increases smoking by 0.74 cigarettes per day. The AG genotype of rs1930139 increases daily cigarettes by about one cigarette (relative to AA).
Some of these effects may seem unreasonably large as they
Discussion and conclusions
The study finds that smoking may increase BMI for women at low/moderate BMI levels, but may decrease BMI for those at high BMI levels. These heterogeneities are completely masked by evaluating the smoking effects at BMI mean. This effect heterogeneity implies that those with more risk factors for high BMI levels, who are at the extreme right margin of the BMI distribution, may experience weight loss due to smoking, while those with fewer risk factors for high BMI may experience weight gain.
Acknowledgements
Data analysis was supported in part by NIH/NIDCR grants R03 DE018394 and R01 DE20895. This research was supported in part by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences. The authors thank research seminar participants and Robert Wallace at the University of Iowa and Shin-Yi Chou at Lehigh University for helpful comments.
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