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Record W2981207334 · doi:10.1111/bph.14888

Dietary antioxidants remodel DNA methylation patterns in chronic disease

2019· review· en· W2981207334 on OpenAlexaff
Megan Beetch, Sadaf Harandi‐Zadeh, Kate Shen, Katarzyna Lubecka, David D. Kitts, Heather M. O’Hagan, Barbara Stefañska

Bibliographic record

VenueBritish Journal of Pharmacology · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsUniversity of British Columbia
FundersNational Center for Advancing Translational Sciences
KeywordsEpigeneticsDNA methylationEpigenomeBiologyMethylationOxidative stressEpigenomicsAntioxidantChromatinBioinformaticsGeneticsDNAGene expressionGeneBiochemistry

Abstract

fetched live from OpenAlex

Chronic diseases account for over 60% of all deaths worldwide according to the World Health Organization reports. Majority of cases are triggered by environmental exposures that lead to aberrant changes in the epigenome, specifically, the DNA methylation patterns. These changes result in altered expression of gene networks and activity of signalling pathways. Dietary antioxidants, including catechins, flavonoids, anthocyanins, stilbenes and carotenoids, demonstrate benefits in the prevention and/or support of therapy in chronic diseases. This review provides a comprehensive discussion of potential epigenetic mechanisms of antioxidant compounds in reversing altered patterns of DNA methylation in chronic disease. Antioxidants remodel the DNA methylation patterns through multiple mechanisms, including regulation of epigenetic enzymes and chromatin remodelling complexes. These effects can further contribute to antioxidant properties of the compounds. On the other hand, decrease in oxidative stress itself can impact DNA methylation delivering additional link between antioxidant mechanisms and epigenetic effects of the compounds. LINKED ARTICLES: This article is part of a themed section on The Pharmacology of Nutraceuticals. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v177.6/issuetoc.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.051
GPT teacher head0.375
Teacher spread0.324 · 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 designNot applicable
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

Citations96
Published2019
Admission routes1
Has abstractyes

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