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Record W2514982885 · doi:10.1371/journal.pmed.1002105

Genetically Predicted Body Mass Index and Breast Cancer Risk: Mendelian Randomization Analyses of Data from 145,000 Women of European Descent

2016· article· en· W2514982885 on OpenAlexaff
Yan Guo, Shaneda Warren Andersen, Xiao‐Ou Shu, Kyriaki Michailidou, Manjeet K. Bolla, Qin Wang, Montserrat García‐Closas, Roger L. Milne, Marjanka K. Schmidt, Jenny Chang‐Claude, Alison M. Dunning, Stig E. Bojesen, Habibul Ahsan, Kristiina Aittomäki, Irene L. Andrulis, Hoda Anton‐Culver, Volker Arndt, Matthias W. Beckmann, Alicia Beeghly‐Fadiel, Javier Benı́tez, Natalia Bogdanova, Bernardo Bonanni, Anne‐Lise Børresen‐Dale, Judith S. Brand, Hiltrud Brauch, Hermann Brenner, Thomas Brüning, Barbara Burwinkel, Graham Casey, Georgia Chenevix‐Trench, Fergus J. Couch, Angela Cox, Simon S. Cross, Kamila Czene, Peter Devilee, Thilo Dörk, Martine Dumont, Peter A. Fasching, Jonine D. Figueroa, Dieter Flesch‐Janys, Olivia Fletcher, Henrik Flyger, Florentia Fostira, Marilie D. Gammon, Graham G. Giles, Pascal Guénel, Christopher A. Haiman, Ute Hamann, Maartje J. Hooning, John L. Hopper, Anna Jakubowska, Farzana Jasmine, Mark A. Jenkins, Esther M. John, Nichola Johnson, Michael E. Jones, Maria Kabisch, Muhammad G. Kibriya, Julia A. Knight, Linetta B. Koppert, Veli‐Matti Kosma, Vessela N. Kristensen, Loı̈c Le Marchand, Eunjung Lee, Jingmei Li, Annika Lindblom, Robert Luben, Jan Lubiński, Kathi Malone, Sara Margolin, Frederik Marmé, Catriona McLean, Hanne Meijers‐Heijboer, Alfons Meindl, Susan L. Neuhausen, Heli Nevanlinna, Patrick Neven, Janet E. Olson, José Ignacio Arias Pérez, Barbara Perkins, Paolo Peterlongo, Kelly‐Anne Phillips, Katri Pylkäs, Anja Rudolph, Regina M. Santella, Elinor J. Sawyer, Rita K. Schmutzler, Caroline Seynaeve, Mitul Shah, Martha J. Shrubsole, Melissa C. Southey, Anthony J. Swerdlow, Amanda E. Toland, Ian Tomlinson, Diana Torres, Thérèse Truong, Giske Ursin, Rob B. van der Luijt, Senno Verhoef, Alice S. Whittemore, Robert Winqvist, Hui Zhao, Shilin Zhao, Per Hall, Jacques Simard, Peter Kraft, Paul D.P. Pharoah, David J. Hunter, Douglas F. Easton, Wei Zheng

Bibliographic record

VenuePLoS Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsPublic Health OntarioUniversité LavalCentre hospitalier universitaire de QuébecMount Sinai HospitalLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
FundersNational Cancer InstituteCancer Research UKFrancis Crick InstituteVanderbilt UniversityWorld Health Organization
KeywordsMendelian randomizationBody mass indexBreast cancerMedicineGeneticsIndex (typography)DemographyCancerOncologyBiologyBioinformaticsInternal medicineGenotypeGeneGenetic variants

Abstract

fetched live from OpenAlex

BACKGROUND: Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or environmental factors. METHODS: We applied Mendelian randomization to evaluate the association between BMI and risk of breast cancer occurrence using data from two large breast cancer consortia. We created a weighted BMI genetic score comprising 84 BMI-associated genetic variants to predicted BMI. We evaluated genetically predicted BMI in association with breast cancer risk using individual-level data from the Breast Cancer Association Consortium (BCAC) (cases = 46,325, controls = 42,482). We further evaluated the association between genetically predicted BMI and breast cancer risk using summary statistics from 16,003 cases and 41,335 controls from the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Project. Because most studies measured BMI after cancer diagnosis, we could not conduct a parallel analysis to adequately evaluate the association of measured BMI with breast cancer risk prospectively. RESULTS: In the BCAC data, genetically predicted BMI was found to be inversely associated with breast cancer risk (odds ratio [OR] = 0.65 per 5 kg/m2 increase, 95% confidence interval [CI]: 0.56-0.75, p = 3.32 × 10-10). The associations were similar for both premenopausal (OR = 0.44, 95% CI:0.31-0.62, p = 9.91 × 10-8) and postmenopausal breast cancer (OR = 0.57, 95% CI: 0.46-0.71, p = 1.88 × 10-8). This association was replicated in the data from the DRIVE consortium (OR = 0.72, 95% CI: 0.60-0.84, p = 1.64 × 10-7). Single marker analyses identified 17 of the 84 BMI-associated single nucleotide polymorphisms (SNPs) in association with breast cancer risk at p < 0.05; for 16 of them, the allele associated with elevated BMI was associated with reduced breast cancer risk. CONCLUSIONS: BMI predicted by genome-wide association studies (GWAS)-identified variants is inversely associated with the risk of both pre- and postmenopausal breast cancer. The reduced risk of postmenopausal breast cancer associated with genetically predicted BMI observed in this study differs from the positive association reported from studies using measured adult BMI. Understanding the reasons for this discrepancy may reveal insights into the complex relationship of genetic determinants of body weight in the etiology of breast cancer.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.322
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations291
Published2016
Admission routes1
Has abstractyes

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