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Record W4252141244 · doi:10.1161/circ.135.suppl_1.41

Abstract 41: Omega-6 Fatty Acid Biomarkers and Incident Type 2 Diabetes: A Pooled Analysis of 20 Cohort Studies

2017· article· en· W4252141244 on OpenAlexaff
Jason Wu, Matti Marklund, Fumiaki Imamura, Nathan Tintle, Andres V Ardisson Korat, Janette de Goede, Xia Zhou, Wei-Sin Yang, Marcia C. de Oliveira Otto, Janine Kröger, Waqas Qureshi, Jyrki K. Virtanen, Julie K. Bassett, Alexis C. Wood, Maria Lankinen, Rachel A. Murphy, Kalina Rajaobelina, Rozenn N. Lemaître, Renata Micha, Dariush Mozaffarian

Bibliographic record

VenueCirculation · 2017
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineType 2 diabetesInternal medicinePolyunsaturated fatty acidProspective cohort studyArachidonic acidDiabetes mellitusBiomarkerCohortCohort studyLinoleic acidConfoundingEndocrinologyFatty acidBiochemistryBiology

Abstract

fetched live from OpenAlex

Background: Emerging evidence suggests that dietary omega-6 polyunsaturated fatty acids (n-6 PUFA) plays a role in the primary prevention of type 2 diabetes (T2D). Aims: To evaluate the relation between blood and adipose tissue levels of n-6 PUFA and incident T2D, including n-6 linoleic acid (LA), the major dietary PUFA abundant in vegetable oils, and n-6 arachidonic acid (AA), a key precursor of endogenous metabolites that modulate glucose metabolism and inflammation. Methods: A global consortium of 20 prospective cohort studies identified by February 2016. Each study measured LA and AA at study baseline among adults>18y without prevalent T2D, and assessed the association of n-6 PUFA biomarkers and T2D risk prospectively using individual-level data, using a pre-specified analytic plan and harmonized exposures, covariates, and effect modifiers. Findings were centrally pooled using fixed-effects meta-analysis. Results: 39,740 men and women from 10 countries were included (range of cohort means, age, 49-76y, and BMI, 25.0-28.1kg/m 2 ), with 4,347 incident cases of T2D observed during follow-up. In continuous multivariate-adjusted analyses, higher LA was associated with 36% lower risk of T2D (RR,0.64, 95% CI,0.59-0.71, P<0.001, I 2 =49%, Figure 1A ) per interquintile range. Similar inverse associations were observed for LA measured in different fractions, and in sensitivity analysis using random effects model. Levels of AA were not associated with T2D risk overall and in studies grouped by different biomarker fractions except total plasma (per interquintile RR=0.74, 95% CI=0.62-0.88, P<0.001, Figure 1B ). The relations of LA and AA with T2D were not significantly modified by age, BMI, sex, race, aspirin use, n-3 PUFA biomarker, or FADS genetic variants ( P ≥ 0.13 for each). Conclusion: Higher blood and adipose tissue LA levels, biomarkers of the major dietary PUFA, were associated with lower risk of T2D among free-living populations worldwide. There was little evidence that AA levels were appreciably associated with risk of T2D.

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)0.031
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.036
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0100.050
Bibliometrics0.0090.010
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.050
GPT teacher head0.358
Teacher spread0.308 · 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 designMeta-analysis
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

Citations5
Published2017
Admission routes1
Has abstractyes

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