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Record W2604976329 · doi:10.1161/circ.133.suppl_1.mp39

Abstract MP39: Plasma Phospholipid Fatty Acid and Coronary Heart Disease Risk

2016· article· en· W2604976329 on OpenAlexaff
Qing Liu, Alice H. Lichtenstein, Lesley F. Tinker, Marian L. Neuhouser, Linda Van Horn, Barbara V. Howard, JoAnn E. Manson, Jacques E. Rossouw, Nirupa R. Matthan, Matthew Allison, Lisa W. Martin, Wenjun Li, Linda Snetselaar, Lu Wang, Charles B. Eaton

Bibliographic record

VenueCirculation · 2016
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsMount Allison University
Fundersnot available
KeywordsMedicinePolyunsaturated fatty acidBody mass indexInternal medicineFatty acidLogistic regressionEndocrinologyPhysiologyCardiologyBiochemistryBiology

Abstract

fetched live from OpenAlex

Introduction: The 2013 AHA/ACC Guideline recommended limiting saturated fatty acids (SFA) and substituting SFA with monounsaturated fatty acids (MUFA) or polyunsaturated fatty acids (PUFA) in diet. Dietary intake data derived from self-reports is prone to measurement error and studies have been criticized for this reason. An objective alternative approach to examining the role of different dietary fatty acids on coronary heart disease (CHD) risk is to measure plasma phospholipid fatty acid (PL-FA) profiles, which reflect both dietary intake and endogenous metabolism, hence are proximal to the pathophysiologic processes of CHD. Our aim was to evaluate the role of plasma PL-FA profiles on CHD risk both directly and by estimating the theoretical effects of plasma “substitution” of various fatty acids for each other on CHD risk, and evaluate the consistence with the AHA/ACC Guideline. Hypothesis: Plasma SFA is associated with increased CHD risk, and “substituting” plasma SFA with MUFA, PUFA n-6 or n-3 is associated with lower CHD risk. Methods: We performed a nested case-control study with 2428 postmenopausal women in Women’s Health Initiative Observational Study (1214 CHD cases-controls pairs matched for baseline age, enrollment date, ethnicity and hysterectomy). Plasma PL-FA profiles were measured using gas chromatography methodology and expressed as molar percentage (mol%). Multivariate conditional logistic regression was used to calculate ORs for CHD risk associated with 1 mol% PL-FA increase, and “substitutions” of various fatty acids for each other, adjusting for matching factors, physical activity, body mass index (BMI), smoking, family history of diabetes, anticoagulant medication, hormone therapy, hypertension, diabetes mellitus, dyslipidemia and alternative healthy eating index. In addition, we tested the interactions of BMI and hormone therapy with PL-FAs. Results: In multivariate conditional logistic regression analysis, plasma PL SFA was associated with increased CHD risk (OR=1.16, 95% CI 1.09 to 1.24) and PUFA n-3 was associated with decreased CHD risk (OR=0.92, 95% CI 0.86 to 0.97). No significant associations were observed for MUFA, PUFA n-6 and trans -fatty acids. “Substituting” 1 mol% SFA with an equivalent proportion of MUFA, PUFA n-6, or n-3 were associated with lower CHD risk (OR=0.81, 95% CI 0.74 to 0.88; OR=0.77, 95% CI 0.70 to 0.84;and OR=0.85, 95%CI 0.80 to 0.92, respectively). “Substituting” PUFA n-6 with n-3 was associated with lower CHD risk (OR=0.92, 95% CI 0.86 to 0.98) while no significant effect was found for “substituting” PUFA n-6 with MUFA. No significant interactions were obtained of BMI and hormone therapy with PL-FAs. Conclusion: This plasma PL-FA “substitution” analysis is consistent with the AHA/ACC Guideline and suggests that “substituting” PL SFA with MUFA, PUFA n-6 or n-3, and “substituting” PUFA n-6 with n-3 is associated with lower CHD risk.

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)0.002
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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.025
GPT teacher head0.292
Teacher spread0.267 · 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".

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Citations1
Published2016
Admission routes1
Has abstractyes

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