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Record W4412006675 · doi:10.1016/s1470-2045(25)00222-0

Oral contraceptive use and risk of liver cancer: a population-based study, systematic review, and meta-analysis

2025· review· en· W4412006675 on OpenAlexfundaboutno aff
Cody Z. Watling, Siân Sweetland, Aika Wojt, Gisela Butera, Barry I. Graubard, Sarah Floud, Charles E. Matthews, Gillian Reeves, Katherine A. McGlynn

Bibliographic record

VenueThe Lancet Oncology · 2025
Typereview
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNational Institutes of HealthCancer Research UKNHS Health Scotland
KeywordsMeta-analysisMedicineCancerGynecologyObstetricsInternal medicine

Abstract

fetched live from OpenAlex

Background Oral contraceptive use has been suggested to increase the risk of liver cancer. Although the International Agency for Research on Cancer concluded in 1999 that there was sufficient evidence of an association, this was based on case–control studies with few liver cancer cases. We aimed to provide more robust epidemiological evidence on this association by analysing data from two large prospective UK cohorts and additionally conducting a systematic review and meta-analysis of previous observational studies. Methods In our population-based study, the relationship between oral contraceptive use and liver cancer risk was examined using data from the Million Women Study (MWS) and the UK Biobank. We included women from both cohorts who did not have a prevalent cancer at baseline (except non-melanoma skin cancer) and had provided data on oral contraceptive use; incident liver cancer diagnoses were determined using linkage to National Health Service cancer registries. We compared risk in women who had ever used oral contraceptives with women who had never used oral contraceptives. Multivariable Cox proportional hazards regression was used to calculate hazard ratios (HRs) and 95% CIs. For the systematic review and meta-analysis, we searched PubMed, Embase, CINAHL Plus, Web of Science, and Scopus from database inception to June 28, 2024, for existing observational studies. Study-specific log odds ratios (ORs) or log HRs were pooled and we determined the relative risk (RR) between oral contraceptive use and liver cancer across all studies using a fixed-effects model (PROSPERO number CRD42024552518). Findings A total of 2765 (0·21%) of 1 305 024 participants developed liver cancer in the MWS cohort (median follow-up 21·4 years; IQR 18·4–22·4) and 191 (0·08%) of 253 408 participants developed liver cancer in the UK Biobank (median follow-up 12·6 years; IQR 11·8–13·4). No association was observed between ever versus never use of oral contraceptives and liver cancer risk in either the MWS (HR 1·05, 95% CI 0·97–1·13; p=0·27) or the UK Biobank (1·08, 0·76–1·55; p=0·66). The meta-analysis of 23 observational studies, which included 5422 individuals with liver cancer, found no evidence of an association between ever versus never oral contraceptive use and liver cancer (RR 1·04, 0·98–1·11; I 2 45·9%, p=0·0080). In the meta-analysis of duration of oral contraceptive use, there was a slightly increased risk of liver cancer per 5 years of use of oral contraceptives (RR 1·06, 1·02–1·10; I 2 63·9%, p<0·0001), with corresponding subtype-specific RRs of 1·07 (1·00–1·14) for hepatocellular carcinoma and 1·06 (1·01–1·11) for intrahepatic cholangiocarcinoma (p heterogeneity =0·82). Interpretation The totality of observational studies suggests there is no association between ever versus never use of oral contraceptive and liver cancer risk. When looking at associations by duration of oral contraceptive use, there was little or no association with all liver cancer or its two main subtypes. There might be a small increased risk of liver cancer with longer duration of use, but residual confounding cannot be ruled out. Funding Canadian Institutes of Health Research, National Institutes of Health Intramural Program, and Cancer Research UK.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.841
Threshold uncertainty score0.833

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0120.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.180
GPT teacher head0.454
Teacher spread0.274 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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

Citations1
Published2025
Admission routes2
Has abstractyes

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