Authors

Robert Carreras-Torres, International Agency for Research on Cancer (IARC), France
Mattias Johansson, International Agency for Research on Cancer (IARC), France
Philip C. Haycock, University of Bristol, UK
Kaitlin H. Wade, University of Bristol, UK
Caroline L. Relton, University of Bristol, UK
Richard M. Martin, University of Bristol, UK
George Davey Smith, University of Bristol, UK
Demetrius Albanes, National Institutes of Health
Melinda C. Aldrich, Vanderbilt University
Angeline Andrew, Norris Cotton Cancer Center
Susanne M. Arnold, University of KentuckyFollow
Heike Bickeböller, University Medical Center Göettingen, Germany
Stig E. Bojesen, University of Copenhagen, Denmark
Hans Brunnström, Lund University, Sweden
Jonas Manjer, Lund University, Sweden
Irene Brüske, Helmholtz Zentrum München, Germany
Neil E. Caporaso, National Institutes of Health
Chu Chen, Fred Hutchinson Cancer Research Center
David C. Christiani, Harvard University
Warren Jay Christian, University of KentuckyFollow
Jennifer A. Doherty, Dartmouth College
Eric J. Duell, Catalan Institute of Oncology (ICO-IDIBELL), Spain
John K. Field, University of Liverpool, UK
Michael P. A. Davies, University of Liverpool, UK
Michael W. Marcus, University of Liverpool, UK
Gary E. Goodman, Fred Hutchinson Cancer Research Center
Kjell Grankvist, Umeå University, Sweden
Aage Haugen, National Institute of Occupational Health, Norway
Yun-Chul Hong, Seoul National University, South Korea
Lambertus A. Kiemeney, Radboud University, The Netherlands

Abstract

Background

Assessing the relationship between lung cancer and metabolic conditions is challenging because of the confounding effect of tobacco. Mendelian randomization (MR), or the use of genetic instrumental variables to assess causality, may help to identify the metabolic drivers of lung cancer.

Methods and findings

We identified genetic instruments for potential metabolic risk factors and evaluated these in relation to risk using 29,266 lung cancer cases (including 11,273 adenocarcinomas, 7,426 squamous cell and 2,664 small cell cases) and 56,450 controls. The MR risk analysis suggested a causal effect of body mass index (BMI) on lung cancer risk for two of the three major histological subtypes, with evidence of a risk increase for squamous cell carcinoma (odds ratio (OR) [95% confidence interval (CI)] = 1.20 [1.01–1.43] and for small cell lung cancer (OR [95%CI] = 1.52 [1.15–2.00]) for each standard deviation (SD) increase in BMI [4.6 kg/m2]), but not for adenocarcinoma (OR [95%CI] = 0.93 [0.79–1.08]) (Pheterogeneity = 4.3x10-3). Additional analysis using a genetic instrument for BMI showed that each SD increase in BMI increased cigarette consumption by 1.27 cigarettes per day (P = 2.1x10-3), providing novel evidence that a genetic susceptibility to obesity influences smoking patterns. There was also evidence that low-density lipoprotein cholesterol was inversely associated with lung cancer overall risk (OR [95%CI] = 0.90 [0.84–0.97] per SD of 38 mg/dl), while fasting insulin was positively associated (OR [95%CI] = 1.63 [1.25–2.13] per SD of 44.4 pmol/l). Sensitivity analyses including a weighted-median approach and MR-Egger test did not detect other pleiotropic effects biasing the main results.

Conclusions

Our results are consistent with a causal role of fasting insulin and low-density lipoprotein cholesterol in lung cancer etiology, as well as for BMI in squamous cell and small cell carcinoma. The latter relation may be mediated by a previously unrecognized effect of obesity on smoking behavior.

Document Type

Article

Publication Date

6-8-2017

Notes/Citation Information

Published in PLOS ONE, v. 12, no. 6, e0177875, p. 1-16.

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Due to the large number of authors, only the first 30 and the authors affiliated with the University of Kentucky are listed in the author section above. For the complete list of authors, please download this article.

Digital Object Identifier (DOI)

https://doi.org/10.1371/journal.pone.0177875

Funding Information

RCT, MJ, PCH, KHW, CR, RMM, GDS, and PB are investigators or researchers on a Cancer Research UK (C18281/A19169) Programme Grant (the Integrative Cancer Epidemiology Programme). RMM is supported by the National Institute for Health Research (NIHR) Bristol Nutritional Biomedical Research Unit based at University Hospitals Bristol NHS Foundation Trust and the University of Bristol. CLR and GDS are supported by funding from the MRC Integrative Epidemiology Unit at the University of Bristol (MC_UU_12013/1, MC_UU_12013/2). PCH is supported by a Cancer Research UK Population Research Postdoctoral Fellowship (C52724/A20138). SMA was supported by the Department of Defense under award number: 10153006 (W81XWH-11-1-0781) and by the UK Center for Clinical and Translational Science, (UL1TR000117). JMY is partially supported by the U.S. National Institutes of Health Grants (R01 CA144034 and UM1 CA182876). CARET investigators would like to thank the study participants for their involvement and acknowledge the National Cancer Institute and National Institute of Health for their grant support: 5-UM1-CA-167462, (PI Gary E. Goodman), U01-CA63673 (PIs G. Omenn, G. Goodman), RO1-CA111703 (PI Chu Chen), and 5R01-CA151989-01A1 (PI Jennifer Doherty).

journal.pone.0177875.s001.pdf (750 kB)
S1 Fig. Power calculations for Mendelian randomization analyses on lung cancer using genetic instruments accounting for different proportion of phenotypic variance (15.0, 10.0, 5.0, 2.5, and 1.0%).

journal.pone.0177875.s002.pdf (469 kB)
S2 Fig. Forest plot of lung cancer risk for each SD increase of waist-to-hip ratio observed in a likelihood-based MR approach.

journal.pone.0177875.s003.pdf (783 kB)
S3 Fig. Funnel plots for the distribution of risk estimates of waist-to-hip ratio-instrumental SNPs along with MR causal effect lung cancer subtypes.

journal.pone.0177875.s004.pdf (465 kB)
S4 Fig. Forest plot of lung cancer risk for each SD increase in HDL observed in a likelihood-based MR approach using the instrument set of common SNPs.

journal.pone.0177875.s005.pdf (460 kB)
S5 Fig. Forest plot of lung cancer risk for each SD increase in HDL observed in a likelihood-based MR approach using the instrument set of rare SNPs.

journal.pone.0177875.s006.pdf (798 kB)
S6 Fig. Funnel plots for the distribution of risk estimates of HDL instrumental common SNPs along with MR causal effect lung cancer subtypes.

journal.pone.0177875.s007.pdf (761 kB)
S7 Fig. Funnel plots for the distribution of risk estimates of HDL instrumental rare SNPs along with MR causal effect lung cancer subtypes.

journal.pone.0177875.s008.pdf (471 kB)
S8 Fig. Forest plot of lung cancer risk for each SD increase in triglycerides observed in a likelihood-based MR approach using the main instrument set of common SNPs.

journal.pone.0177875.s009.pdf (472 kB)
S9 Fig. Forest plot of lung cancer risk for each SD increase in triglycerides observed in a likelihood-based MR approach using the instrument set of rare SNPs.

journal.pone.0177875.s010.pdf (791 kB)
S10 Fig. Funnel plots for the distribution of risk estimates of triglycerides instrumental common SNPs along with MR causal effect lung cancer subtypes.

journal.pone.0177875.s011.pdf (766 kB)
S11 Fig. Funnel plots for the distribution of risk estimates of triglycerides instrumental rare SNPs along with MR causal effect lung cancer subtypes.

journal.pone.0177875.s012.pdf (809 kB)
S12 Fig. Funnel plots for the distribution of risk estimates of LDL instrumental common SNPs along with MR causal effect lung cancer subtypes.

journal.pone.0177875.s013.pdf (463 kB)
S13 Fig. Forest plot of lung cancer risk for each SD increase in LDL observed in a likelihood-based MR approach using the instrument set of rare SNPs.

journal.pone.0177875.s014.pdf (763 kB)
S14 Fig. Funnel plots for the distribution of risk estimates of LDL instrumental rare SNPs along with MR causal effect lung cancer subtypes.

journal.pone.0177875.s015.pdf (468 kB)
S15 Fig. Forest plot of lung cancer risk for each SD increase in total cholesterol observed in a likelihood-based MR approach.

journal.pone.0177875.s016.pdf (833 kB)
S16 Fig. Funnel plots for the distribution of risk estimates of total cholesterol instrumental SNPs along with MR causal effect lung cancer subtypes.

journal.pone.0177875.s017.pdf (807 kB)
S17 Fig. Funnel plots for the distribution of risk estimates of fasting insulin instrumental SNPs along with MR causal effect lung cancer subtypes.

journal.pone.0177875.s018.pdf (466 kB)
S18 Fig. Forest plot of lung cancer risk for each SD increase in fasting glucose observed in a likelihood-based MR approach.

journal.pone.0177875.s019.pdf (770 kB)
S19 Fig. Funnel plots for the distribution of risk estimates of fasting glucose instrumental SNPs along with MR causal effect lung cancer subtypes.

journal.pone.0177875.s020.pdf (465 kB)
S20 Fig. Forest plot of lung cancer risk for each SD increase in glucose 2h post-challenge observed in a likelihood-based MR approach.

journal.pone.0177875.s021.pdf (774 kB)
S21 Fig. Funnel plots for the distribution of risk estimates of glucose 2h post-challenge instrumental SNPs along with MR causal effect lung cancer subtypes.

journal.pone.0177875.s022.pdf (846 kB)
S1 Table. Association parameters of instrumental SNPs for the corresponding metabolic factor and for different lung cancer groups.

journal.pone.0177875.s023.pdf (249 kB)
S2 Table. Risk increase on lung cancer phenotypes for each standard deviation increase in the phenotype provided by weighted median MR approach.

journal.pone.0177875.s024.pdf (268 kB)
S3 Table. Overall pleiotropic effect assessment of causal estimates of potential risk factors on lung cancer phenotypes provided by MR-Egger test.

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