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Record W1977480762 · doi:10.1194/jlr.m600452-jlr200

Single nucleotide polymorphisms in ABCG5 and ABCG8 are associated with changes in cholesterol metabolism during weight loss

2007· article· en· W1977480762 on OpenAlexaff
Sylvia Santosa, Isabelle Demonty, Alice H. Lichtenstein, José M. Ordovás, Peter J.H. Jones

Bibliographic record

VenueJournal of Lipid Research · 2007
Typearticle
Languageen
FieldMedicine
TopicCholesterol and Lipid Metabolism
Canadian institutionsUniversity of ManitobaMcGill University
Fundersnot available
KeywordsSingle-nucleotide polymorphismCholesterolInternal medicineEndocrinologyBiologyWeight lossGenotypeLipid metabolismGeneticsMedicineGeneObesity

Abstract

fetched live from OpenAlex

The purpose of this study was to examine whether changes in cholesterol metabolism after weight loss were affected by single nucleotide polymorphisms (SNPs) in ABCG5 and ABCG8 genes. Thirty-five hypercholesterolemic women lost 11.7 +/- 2.5 kg (P < 0.001). Cholesterol kinetics were assessed using stable isotope techniques. TaqMan PCR was used to detect SNPs in ABCG5/G8. Homozygous Q604E variants in ABCG5 had larger (P < 0.05) reductions in cholesterol absorption and greater increases (P < 0.05) in synthesis in contrast to heterozygous and homozygous wild-type carriers. Heterozygous C54Y carriers had smaller declines (P = 0.047) in synthesis compared with homozygous variant individuals. The presence of at least one Y54 variant was associated with higher (P = 0.042) post-weight-loss synthesis compared with carriers of the C54 genotype. The direction of the results is consistent with cross-sectional studies on the effects of Q604E and C54Y polymorphisms on plasma cholesterol. SNPs in ABCG5/G8 were found to be associated with the response of cholesterol metabolism to weight loss. The evidence for associations between SNPs in ABCG5/G8 and various parameters of cholesterol metabolism indicates the potential effectiveness of establishing genetic screening tools to determine optimal lipid-lowering treatment routes for individuals during weight 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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.040
GPT teacher head0.316
Teacher spread0.276 · 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 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".

Quick stats

Citations36
Published2007
Admission routes1
Has abstractyes

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