Single nucleotide polymorphisms in ABCG5 and ABCG8 are associated with changes in cholesterol metabolism during weight loss
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".