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Record W2989791957 · doi:10.3168/jds.2019-17308

Symposium review: The dairy matrix—Bioaccessibility and bioavailability of nutrients and physiological effects

2019· review· en· W2989791957 on OpenAlexafffund
Sylvie L. Turgeon, G. J. Brisson

Bibliographic record

VenueJournal of Dairy Science · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBioavailabilityFood scienceCaseinDigestion (alchemy)Whey proteinPostprandialNutrientChemistryWhey protein isolateBiotechnologyBiologyChromatographyInsulin

Abstract

fetched live from OpenAlex

Several studies have linked food structure and texture to different kinetics of nutrients delivery. Changes in some nutrients' release rate, such as proteins and lipids, could induce different physiological effects (e.g., satiety effect, reduction of postprandial lipemia). Recently, experts are proposing to consider the food as a whole instead of looking at specific nutrients, as the combination of food components and the way they are structured could change their physiological effects. This review highlights recent knowledge linking the different levels of structure of dairy products to their digestion, absorption, and physiological effects. Two examples, yogurt and cheese, will be presented to showcase the contributions of dairy food structure to nutrient release rates. One study aimed to validate whether changes in the casein:whey protein ratio or addition of fiber could influence the digestion kinetics of protein and, subsequently, satiety. A static in vitro digestion model has been used on experimental yogurts differing by their casein:whey protein ratio or dietary fiber content. A human trial with healthy men (n = 20) consuming 5 isocaloric and isoproteinemic yogurt snacks before monitoring lunch intake revealed that the yogurt formulation with increased whey protein content significantly reduced subsequent energy intake compared with its control. This result was linked to slower in vitro disintegration rate and soluble protein release for yogurts with increased whey protein, whereas no difference was observed for yogurts with fiber. A second study allowed discrimination between the effects of cheese attributes on lipid release and absorption. Nine commercial cheeses were digested in vitro, and 2 were selected for the in vivo study, in which plasma concentrations of triglycerides (TAG) were followed before and after meal consumption. The in vivo study revealed that cream cheese, but not cheddar, induced a greater increase in TAG concentrations at 2 h than did butter; this difference was linked to their in vitro disintegration. These studies demonstrate that the dairy food matrix per se modulates foods' nutritional properties. Other studies recently published on this topic will also be included, to put in perspective the important role of the dairy food matrix on release of nutrients and their physiological effects, and how this can be compared with other foods.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.006

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 head0.030
GPT teacher head0.341
Teacher spread0.311 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations39
Published2019
Admission routes2
Has abstractyes

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