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Record W3012157230 · doi:10.1016/j.annonc.2020.01.066

Insulin-like growth factor-1, insulin-like growth factor-binding protein-3, and breast cancer risk: observational and Mendelian randomization analyses with ∼430 000 women

2020· article· en· W3012157230 on OpenAlexfundno aff
Neil Murphy, Anika Knüppel, Nikos Papadimitriou, Richard M. Martin, Konstantinos K. Tsilidis, Karl Smith-Byrne, Georgina K. Fensom, Aurora Perez‐Cornago, Ruth C. Travis, Timothy J. Key, Marc J. Gunter

Bibliographic record

VenueAnnals of Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicGrowth Hormone and Insulin-like Growth Factors
Canadian institutionsnot available
FundersNational Cancer InstituteMedical Research CouncilWorld Health OrganizationEuropean CommissionWellcome TrustNIHR Bristol Biomedical Research CentreNational Institutes of HealthCancer Research UKGovernment of CanadaMinistère de l'Économie, de la Science et de l'Innovation - QuébecCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchGenome Canada
KeywordsMendelian randomizationMedicineBreast cancerHazard ratioInternal medicineProportional hazards modelOncologyRisk factorOdds ratioInsulin-like growth factorConfidence intervalConfoundingCancerGrowth factorGeneticsBiologyGenotype

Abstract

fetched live from OpenAlex

Background Epidemiological evidence supports a positive association between circulating insulin-like growth factor-1 (IGF-1) concentrations and breast cancer risk, but both the magnitude and causality of this relationship are uncertain. We conducted observational analyses with adjustment for regression dilution bias, and Mendelian randomization (MR) analyses allowed for causal inference. Patients and methods We investigated the associations between circulating IGF-1 concentrations and incident breast cancer risk in 206 263 women in the UK Biobank. Multivariable hazard ratios (HRs) and 95% confidence intervals (CI) were estimated using Cox proportional hazards models. HRs were corrected for regression dilution using repeat IGF-1 measures available in a subsample of 6711 women. For the MR analyses, genetic variants associated with circulating IGF-1 and IGF-binding protein-3 (IGFBP-3) levels were identified and their association with breast cancer was examined with two-sample MR methods using genome-wide data from 122 977 cases and 105 974 controls. Results In the UK Biobank, after a median follow-up of 7.1 years, 4360 incident breast cancer cases occurred. In the multivariable-adjusted models corrected for regression dilution, higher IGF-1 concentrations were associated with a greater risk of breast cancer (HR per 5 nmol/l increment of IGF-1 = 1.11, 95% CI = 1.07–1.16). Similar positive associations were found by follow-up time, menopausal status, body mass index, and other risk factors. In the MR analyses, a 5 nmol/l increment in genetically-predicted IGF-1 concentration was associated with a greater breast cancer risk (odds ratio = 1.05, 95% CI = 1.01–1.10; P = 0.02), with a similar effect estimate for estrogen-positive (ER + ) tumours, but no effect found for estrogen-negative (ER − ) tumours. Genetically-predicted IGFBP-3 concentrations were not associated with breast cancer risk (odds ratio per 1-standard deviation increment = 1.00, 95% CI = 0.97–1.04; P = 0.98). Conclusion Our results support a probable causal relationship between circulating IGF-1 concentrations and breast cancer, suggesting that interventions targeting the IGF pathway may be beneficial in preventing breast tumorigenesis.

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)0.024
metaresearch head score (Gemma)0.041
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 score0.024
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.100
GPT teacher head0.344
Teacher spread0.244 · 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".

Quick stats

Citations165
Published2020
Admission routes1
Has abstractyes

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