MétaCan
Menu
← Back to cohort
Record W4312193573 · doi:10.1101/2022.12.09.22283290

Lower Omega-3 Status Associated with Higher Erythrocyte Distribution Width and Neutrophil-Lymphocyte Ratio in UK Biobank Cohort

2022· preprint· en· W4312193573 on OpenAlexaff
Michael I. McBurney, Nathan Tintle, William S. Harris

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsUniversity of Guelph
FundersMedical Research Council
KeywordsRed blood cell distribution widthMedicineCohortInternal medicineBiomarkerNeutrophil to lymphocyte ratioBiobankLymphocyteSystemic inflammationCohort studyInflammationImmunologyGastroenterologyBiologyBioinformatics

Abstract

fetched live from OpenAlex

Abstract High red blood distribution width (RDW) is associated with decreased red blood cell deformability, and high neutrophil-lymphocyte ratio (NLR) is a biomarker of systemic inflammation and innate-adaptive immune system imbalance. Both RDW and NLR are predictors of chronic disease risk and mortality. Omega-3 index (O3I) values have previously been shown to be inversely associated with RDW and NLR levels. Our objective was to determine if total plasma long chain omega-3 fatty acids (Omega3%) measured in the UK Biobank cohort were associated with RDW and NLR values. RDW- and NLR-relationships with Omega3% were characterized in 109,191 adults (58.4% female). RDW- and NLR-Omega3% relationships were inversely associated with Omega3% (both p<0.0001). These cross-sectional associations confirm previous findings that increasing RDW and NLR values are associated with low O3I. The hypothesis that RDW and/or NLR values can be reduced in individuals with less-than optimal long chain omega 3 values need to be tested in randomized controlled intervention trials using EPA and/or DHA.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.021
GPT teacher head0.286
Teacher spread0.265 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuemedRxiv→Same topicFatty Acid Research and Health→French-language works237,207→