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Record W4362523816 · doi:10.1158/1055-9965.epi-22-0724

Exploring the Complex Relationship between Gut Microbiota and Risk of Colorectal Neoplasia Using Bidirectional Mendelian Randomization Analysis

2023· review· en· W4362523816 on OpenAlexaff
Wanxin Li, Xuan Zhou, Shuai Yuan, Lijuan Wang, Lili Yu, Jing Sun, Jie Chen, Qian Xiao, Zhongxiao Wan, Ju‐Sheng Zheng, Cai‐Xia Zhang, Susanna C. Larsson, Susan M. Farrington, Philip Law, Richard S. Houlston, Ian Tomlinson, Kefeng Ding, Malcolm G. Dunlop, Evropi Τheodoratou, Xue Li

Bibliographic record

VenueCancer Epidemiology Biomarkers & Prevention · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsCentre for Global Health Research
FundersNatural Science Foundation of Zhejiang ProvinceMedical Research CouncilCancer Research UKVetenskapsrådetHjärt-LungfondenCancerfondenScience Fund for Distinguished Young Scholars of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsMendelian randomizationColorectal cancerGut floraBiologyMetabolomeMicrobiomeGammaproteobacteriaGeneticsCancerBioinformaticsImmunologyMetabolomicsGeneGenotypeGenetic variants

Abstract

fetched live from OpenAlex

BACKGROUND: Human gut microbiome has complex relationships with the host, contributing to metabolism, immunity, and carcinogenesis. METHODS: Summary-level data for gut microbiota and metabolites were obtained from MiBioGen, FINRISK and human metabolome consortia. Summary-level data for colorectal cancer were derived from a genome-wide association study meta-analysis. In forward Mendelian randomization (MR), we employed genetic instrumental variables (IV) for 24 gut microbiota taxa and six bacterial metabolites to examine their causal relationship with colorectal cancer. We also used a lenient threshold for nine apriori gut microbiota taxa as secondary analyses. In reverse MR, we explored association between genetic liability to colorectal neoplasia and abundance of microbiota studied above using 95, 19, and 7 IVs for colorectal cancer, adenoma, and polyps, respectively. RESULTS: Forward MR did not find evidence indicating causal relationship between any of the gut microbiota taxa or six bacterial metabolites tested and colorectal cancer risk. However, reverse MR supported genetic liability to colorectal adenomas was causally related with increased abundance of two taxa: Gammaproteobacteria (β = 0.027, which represents a 0.027 increase in log-transformed relative abundance values of Gammaproteobacteria for per one-unit increase in log OR of adenoma risk; P = 7.06×10-8), Enterobacteriaceae (β = 0.023, P = 1.29×10-5). CONCLUSIONS: We find genetic liability to colorectal neoplasia may be associated with abundance of certain microbiota taxa. It is more likely that subset of colorectal cancer genetic liability variants changes gut biology by influencing both gut microbiota and colorectal cancer risk. IMPACT: This study highlights the need of future complementary studies to explore causal mechanisms linking both host genetic variation with gut microbiome and colorectal cancer susceptibility.

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)0.040
metaresearch head score (Gemma)0.066
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: Review · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.066
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.006
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.363
GPT teacher head0.444
Teacher spread0.081 · 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
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

Citations10
Published2023
Admission routes1
Has abstractyes

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