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Record W2195293762 · doi:10.2337/db14-1706

Gut Peptides Are Novel Regulators of Intestinal Lipoprotein Secretion: Experimental and Pharmacological Manipulation of Lipoprotein Metabolism

2015· review· en· W2195293762 on OpenAlexafffund
Changting Xiao, Satya Dash, Cecilia Morgantini, Khosrow Adeli, Gary F. Lewis

Bibliographic record

VenueDiabetes · 2015
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsPostprandialHyperlipidemiaIncretinLipoproteinGlucagon-like peptide-1EndocrinologyInternal medicineSecretionGlucagon-like peptide-2Diabetes mellitusType 2 diabetesMedicineBiologyCholesterolBiochemistryPeptide

Abstract

fetched live from OpenAlex

Individuals with metabolic syndrome and frank type 2 diabetes are at increased risk of atherosclerotic cardiovascular disease, partially due to the presence of lipid and lipoprotein abnormalities. In these conditions, the liver and intestine overproduce lipoprotein particles, exacerbating the hyperlipidemia of fasting and postprandial states. Incretin-based, antidiabetes therapies (i.e., glucagon-like peptide [GLP]-1 receptor agonists and dipeptidyl peptidase-4 inhibitors) have proven efficacy for the treatment of hyperglycemia. Evidence is accumulating that these agents also improve fasting and postprandial lipemia, the latter more significantly than the former. In contrast, the gut-derived peptide GLP-2, cosecreted from intestinal L cells with GLP-1, has recently been demonstrated to enhance intestinal lipoprotein release. Understanding the roles of these emerging regulators of intestinal lipoprotein secretion may offer new insights into the regulation of intestinal lipoprotein assembly and secretion and provide new opportunities for devising novel strategies to attenuate hyperlipidemia, with the potential for cardiovascular disease reduction.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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)0.0000.000

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 head0.068
GPT teacher head0.334
Teacher spread0.266 · 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.

Study designOther design
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

Citations59
Published2015
Admission routes2
Has abstractyes

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