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Comparing the Impact of Saturated Fatty acids from Different Dairy Sources on the proteome of High Density Lipoproteins

2017· article· en· W2916164516 on OpenAlexaffabout
Daniela Bernic, Didier Brassard, Arnaud Droit, Florence Roux‐Dalvai, 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 Sainte-JustineUniversity of ManitobaCentre hospitalier universitaire de QuébecUniversité Laval
Fundersnot available
KeywordsPolyunsaturated fatty acidFood scienceChemistryCrossover studyCholesterolInternal medicineEndocrinologyBiochemistryFatty acidBiologyMedicine

Abstract

fetched live from OpenAlex

Background High‐density lipoproteins (HDL) play different protective roles in the etiology of atherosclerosis. The anti‐inflammatory and antioxidant properties of HDL are of particular interest in that regard. Because dietary saturated fatty acids (SFAs) are known to increase HDL‐C concentrations, we investigated how consumption of SFAs from different dairy sources modifies HDL functions as reflected by changes in HDL proteomic profile compared to other dietary fats (monounsaturated ‐ MUFAs and polyunsaturated ‐ PUFAs) and a low‐fat diet/high carbohydrate diet. Methods A randomized crossover controlled trial was conducted in 42 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 SFAs 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. HDLs' proteome was assessed by mass‐spectrometry (MS) after isolation of the HDL fraction by sequential ultracentrifugation after each treatment. Results Using conservative analytical procedures, a total of 66 unique proteins were quantified in the HDL fraction. There was no difference between any diets in the levels of HDL proteins known to be involved in oxidative processes and acute phase response. CHEESE increased levels of HDL proteins related to immune response, namely angiotensinogen (vs. BUTTER, P<0.05), and Ig gamma‐1 chain C region, platelet basic protein and vitronectin (vs. CHO diet, P<0.05). BUTTER increased levels of vitronectin (vs. CHO, P<0.05) but had no effect on other immune response proteins. Apolipoproteins A‐IV and C‐IV, which play key roles in cholesterol homeostasis, were significantly increased after CHEESE (vs. CHO, P<0.05) but not after BUTTER. Conclusions This full‐feeding study suggest that SFAs from butter and cheese do not significantly influence the antioxidant and anti‐inflammatory properties of HDL compared with other dietary fats and CHO. However, immune response and cholesterol homeostasis proteins were modified by cheese but not by butter in comparison with CHO, consistent with a modest food matrix effect modifying the impact of SFAs on cardiometabolic health. Functional studies are necessary to confirm these findings. Support or Funding Information National Dairy Council, 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 MU FA (%) 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/2500 kcal) 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)0.001
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.261
Teacher spread0.237 · 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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Citations0
Published2017
Admission routes2
Has abstractyes

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