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Record W2121877750 · doi:10.1161/atvb.35.suppl_1.686

Abstract 686: Intervention with Naringenin Enhances Weight Loss, Potentiates Improvements in Metabolic Dysregulation and Halts Progression of Atherosclerosis Induced by a High-Fat Diet in LDLr-/- Mice.

2015· article· en· W2121877750 on OpenAlexaff
Amy C. Burke, Brian G. Sutherland, Julia M. Assini, Murray W. Huff

Bibliographic record

VenueArteriosclerosis Thrombosis and Vascular Biology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsWestern University
Fundersnot available
KeywordsNaringeninEndocrinologyInternal medicineInsulin resistanceHyperlipidemiaMedicineDyslipidemiaImpaired glucose toleranceCholesterolMetabolic syndromeDiabetes mellitusBiologyFlavonoidBiochemistry

Abstract

fetched live from OpenAlex

Previous studies demonstrate that the addition of naringenin, a grapefruit flavonoid, to a high-fat diet prevents the development of many disorders of the metabolic syndrome and atherosclerosis in Ldlr-/- mice. Furthermore, in intervention studies, the addition of naringenin to a high-fat, high cholesterol (HFHC) diet reversed pre-established obesity, hyperlipidemia, hepatic steatosis, insulin resistance and improved atherosclerotic lesion pathology, but not lesion size. In the present intervention study, we tested the hypothesis that addition of naringenin to a chow diet would further improve pre-established metabolic dysregulation and attenuate lesion development, compared to chow alone. Ldlr-/- mice were fed a HFHC diet for 12 weeks to induce metabolic dysregulation. Subsequently, mice received one of 3 diets for another 12 weeks: 1) continuation of the HFHC diet, 2) an isoflavone-free chow diet or 3) isoflavone-free chow with 3% naringenin. At 12 weeks, the HFHC diet induced significant weight gain and increased adiposity. Intervention with chow alone reduced the weight gained during induction by 22%, whereas the addition of naringenin to chow induced a weight loss of 71%. Specifically, the reduction in adiposity was 2.75-times greater in naringenin-treated mice, compared to chow alone. The HFHC diet increased VLDL cholesterol 20-fold and LDL cholesterol 5-fold, which were reduced by intervention with both chow (>60%) and chow supplemented with naringenin (>80%). The HFHC diet induced insulin resistance and glucose intolerance. Naringenin improved insulin tolerance (plasma glucose AUC -38%) and glucose tolerance (plasma glucose AUC -58%), which was accompanied by normalization of plasma insulin and glucose. HFHC-induction promoted the development of intermediate atherosclerotic lesions. Continuation of the HFHC diet doubled lesion size. Intervention with chow alone attenuated lesion size progression by 65%. The addition of naringenin to chow slowed lesion progression by 90%, resulting in smaller lesions compared to chow intervention alone (P=0.042). We conclude that intervention with naringenin-supplemented chow enhances weight loss, improves metabolic dysregulation and halts the progression of atherosclerosis.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.017
GPT teacher head0.268
Teacher spread0.251 · 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 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

Citations2
Published2015
Admission routes1
Has abstractyes

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