High tobacco consumption lowers body weight: a Mendelian randomization study of the Copenhagen General Population Study

Winsløw, Ulrik; Rode, Line; Nordestgaard, Børge G.

doi:10.1093/ije/dyu276

Cited by 69 publications

(54 citation statements)

References 40 publications

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“…In a similar manner, [22] demonstrates that the smoking heaviness-increasing allele of rs16969968 is associated with lower body mass index in current and former smokers but not in never-smokers , suggesting that heavier smoking causally lowers body weight. The finding has been replicated and extended to other measures of adiposity (e.g., waist circumference) in subsequent MR analyses 23, 24, 25. Similar analyses have shown that smoking leads to higher resting heart rate and may adversely affect kidney function, as it leads to higher estimated glomerular filtration rate 25, 26, as well as all-cause mortality [27].…”

Section: Causal Analyses and Mendelian Randomisation (Mr)mentioning

confidence: 61%

The CHRNA5–A3–B4 Gene Cluster and Smoking: From Discovery to Therapeutics

Lassi

Taylor

Timpson

et al. 2016

Trends in Neurosciences

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Genome-wide association studies (GWASs) have identified associations between the CHRNA5–CHRNA3–CHRNB4 gene cluster and smoking heaviness and nicotine dependence. Studies in rodents have described the anatomical localisation and function of the nicotinic acetylcholine receptors (nAChRs) formed by the subunits encoded by this gene cluster. Further investigations that complemented these studies highlighted the variability of individuals’ smoking behaviours and their ability to adjust nicotine intake. GWASs of smoking-related health outcomes have also identified this signal in the CHRNA5–CHRNA3–CHRNB4 gene cluster. This insight underpins approaches to strengthen causal inference in observational data. Combining genetic and mechanistic studies of nicotine dependence and smoking heaviness may reveal novel targets for medication development. Validated targets can inform genetic therapeutic interventions for smoking cessation and tobacco-related diseases.

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Section: Causal Analyses and Mendelian Randomisation (Mr)mentioning

confidence: 61%

The CHRNA5–A3–B4 Gene Cluster and Smoking: From Discovery to Therapeutics

Lassi

Taylor

Timpson

et al. 2016

Trends in Neurosciences

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“…As BMI-related transitions to poor health and death may differ by sex, we repeated analyses including interactions between sex and BMI in the multinomial logistic models. Finally, since tobacco smoking is associated with increased risk of mortality and morbidity 33, 34 and it is also typically associated with lower weight, 35 we conducted sensitivity analyses among never smokers using a bootstrap method with 50 replicates.…”

Section: Methodsmentioning

confidence: 99%

Body mass index as a predictor of healthy and disease-free life expectancy between ages 50 and 75: a multicohort study

et al. 2017

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Background:While many studies have shown associations between obesity and increased risk of morbidity and mortality, little comparable information is available on how body mass index (BMI) impacts health expectancy. We examined associations of BMI with healthy and chronic disease-free life expectancy in four European cohort studies.Methods:Data were drawn from repeated waves of cohort studies in England, Finland, France and Sweden. BMI was categorized into four groups from normal weight (18.5–24.9 kg m−2) to obesity class II (⩾35 kg m−2). Health expectancy was estimated with two health indicators: sub-optimal self-rated health and having a chronic disease (cardiovascular disease, cancer, respiratory disease and diabetes). Multistate life table models were used to estimate sex-specific healthy life expectancy and chronic disease-free life expectancy from ages 50 to 75 years for each BMI category.Results:The proportion of life spent in good perceived health between ages 50 and 75 progressively decreased with increasing BMI from 81% in normal weight men and women to 53% in men and women with class II obesity which corresponds to an average 7-year difference in absolute terms. The proportion of life between ages 50 and 75 years without chronic diseases decreased from 62 and 65% in normal weight men and women and to 29 and 36% in men and women with class II obesity, respectively. This corresponds to an average 9 more years without chronic diseases in normal weight men and 7 more years in normal weight women between ages 50 and 75 years compared to class II obese men and women. No consistent differences were observed between cohorts.Conclusions:Excess BMI is associated with substantially shorter healthy and chronic disease-free life expectancy, suggesting that tackling obesity would increase years lived in good health in populations.

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“…The MR GxE approach has been used to investigate the effect of smoking heaviness on traits such as body mass index (BMI) and depression/anxiety (15–20). A causal effect of a genetic variant known to affect smoking heaviness should, if the effect is solely via tobacco intake, be seen in participants who are previous or current smokers, but not in participants who have never smoked.…”

Section: Introductionmentioning

confidence: 99%

MR-pheWAS with stratification and interaction: Searching for the causal effects of smoking heaviness identified an effect on facial aging

Millard¹,

Munafò²,

Tilling³

et al. 2018

Preprint

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Mendelian randomization (MR) is an established approach for estimating the causal effect of an environmental exposure on a downstream outcome. The gene x environment (GxE) study design can be used within an MR framework to determine whether MR estimates may be biased if the genetic instrument affects the outcome through pathways other than via the exposure of interest (known as horizontal pleiotropy). MR phenome-wide association studies (MR-pheWAS) search for the effects of an exposure, and a recently published tool (PHESANT) means that it is now possible to do this comprehensively, across thousands of traits in UK Biobank. In this study, we introduce the GxE MR-pheWAS approach, and search for the causal effects of smoking heaviness – stratifying on smoking status (ever versus never) – as an exemplar. If a genetic variant is associated with smoking heaviness (but not smoking initiation), and this variant affects an outcome (at least partially) via tobacco intake, we would expect the effect of the variant on the outcome to differ in ever versus never smokers. If this effect is entirely mediated by tobacco intake, we would expect to see an effect in ever smokers but not never smokers. We used PHESANT to search for the causal effects of smoking heaviness, instrumented by genetic variant rs16969968, among never and ever smokers respectively, in UK Biobank. We ranked results by: 1) strength of effect of rs16969968 among ever smokers, and 2) strength of interaction between ever and never smokers. We replicated previously established causal effects of smoking heaviness, including a detrimental effect on lung function and pulse rate. Novel results included a detrimental effect of heavier smoking on facial aging. We have demonstrated how GxE MR-pheWAS can be used to identify causal effects of an exposure, while simultaneously assessing the extent that results may be biased by horizontal pleiotropy. Author summary Mendelian randomization uses genetic variants associated with an exposure to investigate causality. For instance, a genetic variant that relates to how heavily a person smokes has been used to test whether smoking causally affects health outcomes. Mendelian randomization is biased if the genetic variant also affects the outcome via other pathways. We exploit additional information – that the effect of heavy smoking only occurs in people who actually smoke – to overcome this problem. By testing associations in ever and never smokers separately we can assess whether the genetic variant affects an outcome via smoking or another pathway. If the effect is entirely via smoking heaviness, we would expect to see an effect in ever but not never smokers, and this would suggest that smoking causally influences the outcome. Previous Mendelian randomization studies of smoking heaviness focused on specific outcomes – here we searched for the causal effects of smoking heaviness across over 18,000 traits. We identified previously established effects (e.g. a detrimental effect on lung function) and novel results includi...

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High tobacco consumption lowers body weight: a Mendelian randomization study of the Copenhagen General Population Study

Cited by 69 publications

References 40 publications

The CHRNA5–A3–B4 Gene Cluster and Smoking: From Discovery to Therapeutics

The CHRNA5–A3–B4 Gene Cluster and Smoking: From Discovery to Therapeutics

Body mass index as a predictor of healthy and disease-free life expectancy between ages 50 and 75: a multicohort study

MR-pheWAS with stratification and interaction: Searching for the causal effects of smoking heaviness identified an effect on facial aging

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