MétaCan
Menu
Back to cohort
Record W2808581515 · doi:10.2217/epi-2017-0164

The Effect of Inflammation-Related Lifestyle Exposures and Interactions with Gene Variants on Long Interspersed Nuclear Element-1 DNA Methylation

2018· article· en· W2808581515 on OpenAlexafffund
Priyanka Gogna, Dylan E. O’Sullivan, Will D. King

Bibliographic record

VenueEpigenomics · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsQueen's University
FundersNational Institute of Mental HealthCanadian Institutes of Health Research
KeywordsBiologyDNA methylationGeneticsGeneInflammationMethylationDNAEpigeneticsComputational biologyBioinformaticsGene expressionImmunology

Abstract

fetched live from OpenAlex

AIM: To examine the relationship between inflammation-related lifestyle factors and long interspersed nuclear element-1 (LINE-1) DNA methylation, and test for interaction by gene variants involved in one-carbon metabolism. PATIENTS & METHODS: The study population consisted of 280 individuals undergoing colonoscopy screening. Multivariable linear regression was employed to examine associations of physical activity, BMI and NSAID use with LINE-1 DNA methylation and interactions with MTR and MTHFR gene variants. RESULTS: The highest quartile of physical activity compared with the lowest was associated with higher LINE-1 DNA methylation (p = 0.005). Long-term NSAID use and a normal BMI were associated with increased LINE-1 DNA methylation among individuals with the variant MTR allele (p = 0.02; p = 0.03). CONCLUSION: This study provides evidence that inflammation-related exposures may influence LINE-1 DNA methylation.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.237
Teacher spread0.233 · 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

Citations9
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueEpigenomicsSame topicEpigenetics and DNA MethylationFrench-language works237,207