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Comparing the Impact of Saturated Fatty Acids from Different Dairy Sources on LDL Particle Size Phenotype

2017· article· en· W2908757132 on OpenAlexaffabout
Daniela Bernic, Didier Brassard, Maude Tessier‐Grenier, Ethendhar Rajendiran, Yongbo She, Vanu Ramprasath, Iris Gigleux, Émile Lévy, Angelo Tremblay, Peter J.H. Jones, Patrick Couture, Benoı̂t Lamarche

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsCentre hospitalier universitaire de QuébecCentre Hospitalier Universitaire Sainte-JustineUniversity of ManitobaUniversité Laval
Fundersnot available
KeywordsPolyunsaturated fatty acidFood scienceCrossover studyWaistCholesterolParticle sizeChemistryBiologyObesityFatty acidEndocrinologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

Background Low‐density lipoproteins (LDL) constitute a heterogeneous class of lipoproteins among which small and dense LDL may be more atherogenic than large LDL particles, independent of cholesterol concentrations. The aim of this study was to examine how consumption of saturated fat (SFA) from different dairy sources modifies the LDL particle size phenotype compared with other dietary fats (monounsaturated ‐ MUFA and polyunsaturated fat‐ PUFA) and a low‐fat diet/high carbohydrate diet. Methods A randomized crossover controlled trial was conducted in 92 men and women with abdominal obesity and relatively low HDL‐C. Subjects were assigned to sequences of 5 isoenergetic diets of 4 weeks each (): 1) a diet rich in SFA from cheese (CHEESE); a diet rich in SFA from butter (BUTTER); a diet rich in MUFA; a diet rich in PUFA and a low‐fat, high carbohydrate diet (CHO). All foods were provided to the participants during the experimental phases. Features of the LDL particle size phenotype (mean LDL particle size, proportion of small and large) were assessed by one dimension nondenaturing polyacrylamide gel electrophoresis. Results LDL particle size at baseline (mean±SD) in women was larger than in men (252.24±2.85 vs. 251.67±3.49, p=0.04), although differences were no longer significant after adjustment for waist circumference. The CHEESE diet had no significant impact on all phenotypic measures of LDL particle size compared with BUTTER, CHO, MUFA and PUFA. Consumption of the BUTTER diet significantly increased LDL mean size compared with CHO (p=0.021) and MUFA (p=0.005). The increase in mean LDL size with BUTTER was more apparent among individuals with small LDL particles at baseline than among those with large LDL (P treatment × baseline LDL size=0.047). There was no difference in the distribution of small and large LDL particles among diets. Conclusions Results from this full‐feeding study suggest 1‐ that SFA from butter and cheese have similar effects on features of the LDL particle size phenotype, and 2‐ that consumption of SFA from butter may be associated with less atherogenic LDL particles. These data are partly consistent with the fact that consumption of cheese and butter are not associated with an increased risk of cardiovascular disease. Support or Funding Information Source of Research Support : Dairy Research Cluster Initiative ( Dairy Farmers of Canada, Agriculture and Agri‐Food Canada, the Canadian Dairy Network and the Canadian Dairy Commission). Macronutrient (% energy), calcium and sodium content of experimental diets CHEESE BUTTER MUFA PUFA CHO Lipids (%) 32.0 32.0 32.0 32.0 25.0 SFA (%) 12.6 12.4 5.8 5.8 5.8 MUFA (%) 12.5 12.3 19.6 12.6 12.6 PUFA (%) 4.8 4.8 4.8 11.5 4.8 Carbohydrates (%) 51.9 52.0 51.9 51.9 58.9 Proteins (%) 16.0 16.0 16.0 16.0 16.0 Calcium (mg/2500 kcal) 1261 811 812 812 842 Sodium (mg/2500 kcal) 2482 2480 2479 2479 2485 Fibers (g/2500kcal) 31 31 31 31 31

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.279
Teacher spread0.254 · 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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Citations2
Published2017
Admission routes2
Has abstractyes

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