Tobacco consumption is consistently associated with reduced body weight, creating an incentive to initiate smoking and a disincentive to cease, although the health risks associated with the habit outweigh the benefits of reduced weight. Among smokers however, increasing con-sumption has been associated with increased body weight. To determine whether this contradiction reflects causal processes, Winsløw et al.1 have applied Mendelian ran-domization (MR) in testing the association of a genetic variant, rs1051730 in CHRNA3, with measures of body weight among 80 342 members of the Copenhagen General Population Study. Among smokers, each minor (T) allele carried was associated with an increase of about one cigarette per day, but with a decrease in several meas-ures of body weight, in contrast to the observational re

Dudbridge, Frank

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Dudbridge, F (2015) Commentary: Tobacco consumption and bodyweight: Mendelian randomization across a range of exposure. Inter-national journal of epidemiology. ISSN 0300-5771 DOI: https://doi.org/10.1093/ije/dyv033Downloaded from: http://researchonline.lshtm.ac.uk/2138085/DOI: 10.1093/ije/dyv033Usage GuidelinesPlease refer to usage guidelines at http://researchonline.lshtm.ac.uk/policies.html or alterna-tively contact researchonline@lshtm.ac.uk.Available under license: http://creativecommons.org/licenses/by/2.5/Commentary: Tobacco consumption and bodyweight: Mendelian randomization across arange of exposureFrank DudbridgeDepartment of Non-communicable Disease Epidemiology, London School of Hygiene and TropicalMedicine, Keppel Street, London WC1E 7HT, UK. E-mail: frank.dudbridge@lshtm.ac.ukTobacco consumption is consistently associated withreduced body weight, creating an incentive to initiatesmoking and a disincentive to cease, although the healthrisks associated with the habit outweigh the benefits ofreduced weight. Among smokers however, increasing con-sumption has been associated with increased body weight.To determine whether this contradiction reflects causalprocesses, Winsløw et al.1 have applied Mendelian ran-domization (MR) in testing the association of a geneticvariant, rs1051730 in CHRNA3, with measures of bodyweight among 80 342 members of the CopenhagenGeneral Population Study. Among smokers, each minor(T) allele carried was associated with an increase of aboutone cigarette per day, but with a decrease in several meas-ures of body weight, in contrast to the observational re-sults. These results, in line with other recent studies,2–6suggest that increased tobacco consumption causesreduced body weight, as does smoking itself.Here I remark on two aspects of this study that mayrecur in other MR studies of this type: the restriction ofgenetic effects to current smokers, and the change, in theobservational data, from decreasing to increasing bodyweight as cigarette consumption increases.The associations of rs1051730 with decreased bodyweight are present in current smokers, but not in former ornever smokers, suggesting that the gene acts on bodyweight only through its effect on smoking. However, bystratifying on smoking status, the results are potentiallyprone to collider bias, whereby an association is inducedbetween the gene and confounders of the exposure-outcome association, creating a non-causal association be-tween the gene and the outcome. The question is whethersmoking status should be considered as derived from thequantity consumed (one who smokes zero cigarettes perday is a non-smoker, others are smokers, Figure 1 arrowa), or as an exogenous variable whose value constrains thepossible consumption (non-smokers must smoke nocigarettes per day, smokers must smoke a positive number,Figure 1 arrow b). Under the former, stratifying on smok-ing status would entail a collider bias, and an associationwould be seen between genotype and smoking status; butsuch an association was not observed by Winsløw et al.,nor was any association between rs1051730 and knownconfounders when stratifying on smoking status. Thus thedata support the view that smoking status is distinct (i.e.has distinct determinants) from tobacco consumption(Figure 1 arrow b). Indeed it seems reasonable that individ-uals would generally decide whether or not to smoke as aprecursor to developing their usual consumption.Nevertheless, collider bias can only be completely ruledout if the whole sample is analysed, and here the genetic as-sociations remained significant owing to the sufficientlyhigh proportion of smokers among them.The lack of genetic association among never smokerscould be interpreted as evidence against alternative path-ways to that through smoking, in line with MR assump-tions. However, Taylor et al.6 reported positive associationbetween rs1051730 and body mass index (BMI)(P¼ 6.4 105) in a sample of 66 809 never smokers. Thistrend is apparent in the 32 937 never smokers in Winsløwet al. (P¼ 0.07, their Figure 4) and would have reached sig-nificance (P¼ 0.01) if the same trend were observed in66 809 subjects. Thus the exclusion restriction seems to beviolated, but as the effect is in the opposite direction toVC The Author 2015. Published by Oxford University Press on behalf of the International Epidemiological Association e1This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permitsunrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.International Journal of Epidemiology, 2016, e1–e3doi: 10.1093/ije/dyv033Advance Access Publication Date: 30 March 2015CommentaryDownloaded from https://academic.oup.com/ije/article-abstract/45/6/e1/3038099by London School of Hygiene & Tropical Medicine useron 11 May 2018that observed in smokers, it need not conflict with thequalitative conclusion that increased tobacco consumptionreduces body weight.Observational data show a decrease in body weight atlow tobacco consumption, with increases in body weightat higher levels of consumption (Figure 2 of Winsløwet al.). This raises the question of whether the J-shaped pat-tern reflects causal effects: the marginal inverse associationof rs1051730 may be driven by effects at certain consump-tion levels. Perhaps there is a particularly strong inversegenetic association at low consumption levels, which out-weighs positive genetic associations at higher levels. Theconclusion that increased smoking reduces weight mightonly be applicable at certain low levels of smoking.Similar questions have recently been addressed in rela-tion to alcohol consumption. rs1229984 in ADH1B isassociated with reduced alcohol intake and also withreduced incidence of coronary heart disease (CHD) amongdrinkers, but is not associated with CHD among non-drinkers.7 This suggests beneficial effects of reduced in-take, but observational data show a J-shape with increasedrisk in abstainers compared with moderate drinkers.Furthermore, for alcohol it is more plausible that absten-tion is a special case of low intake, perhaps due to unpleas-ant side effects associated with ADH1B variation.rs1229984 is indeed associated with drinking status,7 sostratification on drinking status is less defensible than it isfor smoking status.To address these questions, methods have recently beenproposed to estimate localised causal effects that can indi-cate whether the direction of causal effect is constant overthe range of exposure. A foundational result is that themarginal treatment effect is a weighted sum of local aver-age treatment effects (LATEs), defined for subgroups ofsubjects with the same potential outcomes.8 Silverwoodet al.9 considered the effect of alcohol intake on markers ofcardiovascular disease. Using rs1229984 in ADH1B, theystratified subjects according to their potential alcohol in-take if homozygous for the common allele. With someassumptions their approach explicitly calculates LATEs,and was used to infer J-shaped causal associations of alco-hol intake on several cardiovascular markers including sys-tolic blood pressure and BMI. Burgess et al.10 proposed amore general approach, also based on considering poten-tial levels of exposure for a set genetic value. Because the‘instrument-free exposure’ is by construction not associ-ated with the genotype, stratification on it does not inducea collider bias. With arbitrary stratification, their approachestimates localized causal effects, distinct from formalLATEs but also able to identify non-linear patterns in thecausal dose-response curve.These approaches show promise for developing insightinto the pattern of causal effects when observational datashow variation in effect size or direction across the rangeof exposure. One limitation is that the localized effectsapply to levels of the instrument-free exposure rather thanthe exposure itself, and this distorts their interpretation.11Thus we can only make statements about subjects withgiven potential levels of exposure, rather than their actualexposures which are arguably more relevant. Another limi-tation is an assumption that the effect of genotype on ex-posure is constant for all subjects. This will not hold whenthere is a gene-environment interaction, as in the presentcase in which the gene only affects tobacco consumption inthose who are smokers (Figure 1 arrow c); stratification onexposure levels may be required, with attendant possibil-ities of collider bias.An increasing number of MR studies will considerdosage of exposures that are present among somesubjects only. Such studies must carefully consider the rela-tionship between presence and dosage of exposure,together with their genetic associations. When observa-tional effects vary over the range of exposure, it will beinformative to apply emerging methods for localized ef-fects to infer non-linear causal effects. Such approacheswill be particularly valuable when there are conflicts be-tween causal and observational results, which meritexploring in greater detail.+ve-ve(c)(b)(a) Genotype Tobacco consumpon Body weight ConfoundersSmoker Y/NFigure 1. Directed acyclic graph showing causal relationships between genotype, smoking status and consumption, and body weight outcomes.Arrow (a) applies if smoking status is defined by the quantity consumed. Arrow (b) applies if smoking status has distinct determinants and definesthe possible range of consumption. Arrow (c) is absent for non-smokers under (b). Y/N, yes or no; þve, positive; -ve, negative.e2 International Journal of Epidemiology, 2016, Vol. 45, No. 6Downloaded from https://academic.oup.com/ije/article-abstract/45/6/e1/3038099by London School of Hygiene & Tropical Medicine useron 11 May 2018FundingThis work was funded by the MRC (K006215).Conflict of interest: None declared.References1. Winsløw UC, Rode L, Nordestgaard BG. High tobacco con-sumption lowers body weight: a Mendelian randomization studyof the Copenhagen General Population Study. Int J Epidemiol2015; doi:10.1093/ije/dyu276.2. Freathy RM, Kazeem GR, Morris RW et al. Genetic variation atCHRNA5-CHRNA3-CHRNB4 interacts with smoking status toinfluence body mass index. Int J Epidemiol 2011;40:1617–28.3. Asvold BO, Bjorngaard JH, Carslake D et al. Causal associationsof tobacco smoking with cardiovascular risk factors: aMendelian randomization analysis of the HUNT Study inNorway. Int J Epidemiol 2014;43:1458–70.4. Kaakinen M, Ducci F, Sillanpaa MJ, Laara E, Jarvelin MR.Associations between variation in CHRNA5-CHRNA3-CHRNB4, body mass index and blood pressure in the NorthernFinland Birth Cohort 1966. PLoSOne 2012;7:e46557.5. Varga TV, Hallmans G, Hu FB, Renstrom F, Franks PW. Smokingstatus, snus use, and variation at the CHRNA5-CHRNA3-CHRNB4 locus in relation to obesity: the GLACIER study. Am JEpidemiol 2013;178:31–37.6. Taylor AE, Morris RW, Fluharty ME et al. Stratification bysmoking status reveals an association of CHRNA5-A3-B4 geno-type with body mass index in never smokers. PLoS Genet 2014;10:e1004799.7. Holmes MV, Dale CE, Zuccolo L et al. Association between al-cohol and cardiovascular disease: Mendelian randomizationanalysis based on individual participant data. BMJ 2014;349:g4164.8. Angrist JD, Imbens GW. Two-stage least squares estimation ofaverage causal effects in models with variable treatment inten-sity. J Am Stat Assoc 1995;90:431–42.9. Silverwood RJ, Holmes MV, Dale CE et al. Testing for non-linear causal effects using a binary genotype in a Mendelianrandomization study: application to alcohol and cardiovasculartraits. Int J Epidemiol 2014;43:1781–90.10. Burgess S, Davies NM, Thompson SG. Instrumental variableanalysis with a nonlinear exposure-outcome relationship.Epidemiology 2014;25:877–85.11. Small DS. Commentary: Interpretation and sensitivity analysisfor the localized average causal effect curve. Epidemiology 2014;25:886–88.International Journal of Epidemiology, 2016, Vol. 45, No. 6 e3Downloaded from https://academic.oup.com/ije/article-abstract/45/6/e1/3038099by London School of Hygiene & Tropical Medicine useron 11 May 2018

Commentary: Tobacco consumption and body weight: Mendelian randomization across a range of exposure.

Frank Dudbridge

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