A SNP of NPC1L1 Affects Cholesterol Absorption in Japanese
Bibliographic record
Abstract
AIM: Ezetimibe is known to target Niemann-Pick Type C1 Like1 (NPC1L1), a key protein in intestinal cholesterol absorption, and thus to decrease serum LDL-cholesterol (LDL-C) levels. The response of serum LDL-C levels to ezetimibe was reported to differe among NPC1L1 haplotypes.We analyzed NPC1L1 genotypes in Japanese and investigated differences in markers of cholesterol synthesis/absorption among the genotypes. METHODS: Blood samples were collected from 42 adult volunteers to measure markers of cholesterol synthesis (lathosterol) and absorption (sitosterol and campesterol) by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Based on a study by Hegele RA et al. in Canada, we selected three SNPs (1735 C>G, 25342 A>C and 27677 T>C (numbers relative to the transcription start site)) and analyzed them using PCR-RFLP. RESULTS: The frequencies of genotypes were as follows: 1735 C/G (46%)>C/C (35%)>G/G (19%), 25342 A/A (97%)>A/C (3%)>C/C (0%) and 27677 T/T (97%)>T/C (3%)>C/C (0%). Serum campesterol levels were significantly higher in the 1735 G/G group than 1735 C/G+C/C group, but lathosterol levels showed no significant differences between the genotypes. CONCLUSION: Our results revealed differences in the frequency of the NPC1L1 polymorphism between Japanese and Canadians. In Japanese, the 1735 G/G group showed enhanced cholesterol absorption from the intestine, as compared to the 1735 C/G+C/C group.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".