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Record W1989323244 · doi:10.1194/jlr.m200318-jlr200

Effects of dietary fat type and energy restriction on adipose tissue fatty acid composition and leptin production in rats

2003· article· en· W1989323244 on OpenAlexaff
Geoffrey R. Hynes, Jode T. Heshka, Kris Chadee, Peter J.H. Jones

Bibliographic record

VenueJournal of Lipid Research · 2003
Typearticle
Languageen
FieldNeuroscience
TopicRegulation of Appetite and Obesity
Canadian institutionsMcGill University
Fundersnot available
KeywordsAdipose tissueLeptinComposition (language)ChemistryEndocrinologyInternal medicineDietary fatFood scienceObesityBiochemistryBiologyMedicine

Abstract

fetched live from OpenAlex

To investigate whether dietary fatty acid (FA) composition and energy restriction (ER) interactively influence obese (ob) gene expression, rats consumed diets containing beef tallow, safflower, or fish oil ad libitum (AL) or at 60% AL intake. Circulating leptin concentrations were higher (P < 0.0001) after AL feeding, but were not influenced by dietary fat. ER decreased (P < 0.0001) weight gain and visceral adipose weight, which were positively correlated (r = 0.40 P < 0.001, r = 0.58 P < 0.0001) with circulating leptin levels. Visceral adipose ob mRNA levels were greater in animals fed unsaturated fats, particularly safflower oil, which had the highest ob mRNA levels. Circulating leptin levels did not parallel ob mRNA levels, except for the greater abundance detected in AL adipose in comparison to ER animals. In addition, visceral FA profiles reflected dietary fat source and were influenced by an interaction of dietary fat and energy. These data demonstrate that dietary fat, particularly from a plant or marine source, and ER interactively influence ob mRNA levels; however, alterations in ob mRNA do not confer changes in circulating leptin, with the exception of ER, which is a key determinant. Thus, dietary intake is an important regulator of leptin production; however, the significance of these modest changes in diet-induced obese animals requires further study.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.335
Teacher spread0.285 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations33
Published2003
Admission routes1
Has abstractyes

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