Evidence map›Paper›PMID 31243447›Full record

ArticleInternational journal of epidemiology2019

The genetic interplay between body mass index, breast size and breast cancer risk: a Mendelian randomization analysis.

Brandon Nick Sern Ooi, Huiwen Loh, Peh Joo Ho, Roger L Milne, Graham Giles, Chi Gao, Peter Kraft, Esther M John, Anthony Swerdlow, Hermann Brenner and 14 more

Open access · hybridAbstract read
In one paragraph

Article in International journal of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 3 pooled it
3.0field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 3 syntheses or guidelines pooled it, 66 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors at 15 institutions in 9 countries.

Brandon Nick Sern OoiHuman Genetics, Genome Institute of Singapore, Singapore, Singapore.
Huiwen LohHuman Genetics, Genome Institute of Singapore, Singapore, Singapore.
Peh Joo HoHuman Genetics, Genome Institute of Singapore, Singapore, Singapore.
Roger L MilneCancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.
Graham GilesCancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.
Chi GaoProgram in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health Boston, USA.
Peter KraftProgram in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health Boston, USA.
Esther M JohnDepartment of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
Anthony SwerdlowDivision of Genetics and Epidemiology and Division of Breast Cancer Research, Institute of Cancer Research, London UK.
Hermann BrennerDivision of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ) Heidelberg, Germany.
Anna H WuDepartment of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Christopher HaimanDepartment of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
D Gareth EvansGenomic Medicine, Division of Evolution & Genomic Sciences, The University of Manchester Manchester, UK.
Wei ZhengDivision of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Centre, Vanderbilt University Nashville, USA.
Peter A FaschingDepartment of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Germany.
Jose Esteban CastelaoOncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo, Spain.
Ava KwongDepartment of Surgery, The University of Hong Kong Pok Fu Lam, Hong Kong.
Xia ShenDepartment of Medical Epidemiology and Biostatistics, Karolinska Insititute Stockholm, Sweden.
Kamila CzeneOncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo, Spain.
Per HallOncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo, Spain.
Alison DunningCentre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.
Douglas EastonCentre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.
Mikael HartmanDepartment of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Jingmei LiHuman Genetics, Genome Institute of Singapore, Singapore, Singapore.
Genome Institute of Singapore · SGServicio Gallego de Salud · ESCancer Council Victoria · AUHarvard University · USNational University of Singapore · SGUniversity of Cambridge · GBUniversity of Southern California · USGerman Cancer Research Center · DEInstitute of Cancer Research · GBStanford University · USSun Yat-sen University · CNUniversitätsklinikum Erlangen · DEUniversity of Hong Kong · HKUniversity of Manchester · GBVanderbilt University Medical Center · US

Funding

Epidemiologic StudiesU19CA148065 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI AHSAN, HABIBUL, BRUGGE, JOAN SIEFERT · 2010 to 2014
$10.6M
Cancer Research UK 16563Cancer Research UK 16565Cancer Research UK C1287/A10118Cancer Research UK C1287/A10710Cancer Research UK C1287/A16563NCI NIH HHS U19 CA148065
6 · The paper itself

Abstract

backgroundEvidence linking breast size to breast cancer risk has been inconsistent, and its interpretation is often hampered by confounding factors such as body mass index (BMI). Here, we used linkage disequilibrium score regression and two-sample Mendelian randomization (MR) to examine the genetic associations between BMI, breast size and breast cancer risk.

methodsSummary-level genotype data from 23andMe, Inc (breast size, n = 33 790), the Breast Cancer Association Consortium (breast cancer risk, n = 228 951) and the Genetic Investigation of ANthropometric Traits (BMI, n = 183 507) were used for our analyses. In assessing causal relationships, four complementary MR techniques [inverse variance weighted (IVW), weighted median, weighted mode and MR-Egger regression] were used to test the robustness of the results.

resultsThe genetic correlation (rg) estimated between BMI and breast size was high (rg = 0.50, P = 3.89x10-43). All MR methods provided consistent evidence that higher genetically predicted BMI was associated with larger breast size [odds ratio (ORIVW): 2.06 (1.80-2.35), P = 1.38x10-26] and lower overall breast cancer risk [ORIVW: 0.81 (0.74-0.89), P = 9.44x10-6]. No evidence of a relationship between genetically predicted breast size and breast cancer risk was found except when using the weighted median and weighted mode methods, and only with oestrogen receptor (ER)-negative risk. There was no evidence of reverse causality in any of the analyses conducted (P > 0.050).

conclusionOur findings indicate a potential positive causal association between BMI and breast size and a potential negative causal association between BMI and breast cancer risk. We found no clear evidence for a direct relationship between breast size and breast cancer risk.

Indexed as

Body Mass IndexBreastBreast NeoplasmsFemaleHumansLinkage DisequilibriumMendelian Randomization AnalysisOrgan SizeReceptors, EstrogenReceptors, Estrogenbody mass indexbreast cancer riskBreast sizegenetic correlationgenetic epidemiologyLDSC regressionMendelian randomization

Identifiers

PMID31243447
PMCPMC6659372
OpenAlexW2951320675

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.