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Record W4414367349 · doi:10.1093/jncics/pkaf086

Pre-diagnostic circulating bile acid concentrations and liver cancer risk: a nested case-control analysis of 12 cohorts

2025· article· en· W4414367349 on OpenAlexfundno aff
Cody Z. Watling, Jessica L. Petrick, Barry I. Graubard, Matthew J Barnett, Julie E. Buring, Yu Chen, A. Heather Eliassen, J. Michael Gaziano, Jonathan N. Hofmann, Wen‐Yi Huang, Jae H. Kang, Jill Koshiol, Erikka Loftfield, I‐Min Lee, Steven C. Moore, Lorelei A. Mucci, Marian L Neuhouser, Christina C. Newton, Julie R. Palmer, Mark P. Purdue, Lynn Rosenberg, Howard D. Sesso, Martha J. Shrubsole, Lesley F. Tinker, Matthew Triplette, Caroline Y. Um, Kala Visvanathan, Eleanor L. Watts, Jean Wactawski‐Wende, Walter C. Willett, Peter T. Campbell, Dinesh Kumar Barupal, Katherine A. McGlynn

Bibliographic record

VenueJNCI Cancer Spectrum · 2025
Typearticle
Languageen
FieldMedicine
TopicDrug Transport and Resistance Mechanisms
Canadian institutionsnot available
FundersStanford Center for Carbon Storage, Stanford UniversityDivision of Cancer Epidemiology and Genetics, National Cancer InstituteNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNiilo Helanderin SäätiöU.S. Public Health ServiceWisconsin Historical SocietyCenters for Disease Control and PreventionNational Cancer InstituteU.S. Department of Health and Human ServicesNational Institutes of HealthNational Institute of Environmental Health SciencesCanadian Institutes of Health ResearchPresbyterian Historical Society
KeywordsBile acidLiver cancerCancerCohort studyNested case-control study

Abstract

fetched live from OpenAlex

BACKGROUND: Bile acids are produced in the liver and are important for lipid digestion. Higher-circulating bile acid levels, however, have been linked to metabolic disorders, inflammation, and gut microbiota dysbiosis, which have been implicated in liver carcinogenesis. To date, few epidemiological studies have explored the association between circulating bile acids and liver cancer risk. METHODS: We conducted a nested case-control study among 12 prospective cohort studies located in the United States. Fifteen prediagnostic circulating bile acids were measured from blood samples among 872 individuals who developed liver cancer and 872 matched control participants. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using multivariable-adjusted conditional logistic regression analysis of circulating bile acid levels and liver cancer risk. RESULTS: Primary conjugated bile acid concentrations were positively associated with higher risk of liver cancer (OR per doubling in concentrations [log2] and 95% CI of glycocholic acid: 1.32, 1.24 to 1.40; glycochenodeoxycholic acid: 1.33, 1.24 to 1.43; taurocholic acid: 1.28, 1.22 to 1.35; and taurchenodeoxycholic acid: 1.32, 1.24 to 1.39). Secondary conjugated bile acids were also positively associated with liver cancer risk (doubling of concentrations OR ranged from 1.11 to 1.22). Unconjugated bile acid concentrations were generally not associated with liver cancer risk, except lithocholic acid (OR per doubling: 1.27, 1.16 to 1.39). When analyses were separated into the 2 main subtypes of liver cancer, hepatocellular carcinoma (HCC; 438 cases/438 controls) and intrahepatic cholangiocarcinoma (ICC; 111 cases/111 controls), significant heterogeneity was observed for primary conjugated bile acid concentrations (all P < .001) that showed positive significant associations with HCC but not ICC. CONCLUSIONS: These results suggest that bile acids may be important markers of HCC risk and contribute to hepatocarcinogenesis; however, further research using serial measurements is needed.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
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)0.0010.000

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 head0.007
GPT teacher head0.266
Teacher spread0.259 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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