Successful Treatment of Statin Resistant Hypercholesterolemia by an Inhibitor of Cholesterol Absorption, Ezetimibe
Bibliographic record
Abstract
HMG-CoA reductase inhibitors (statins) a re frequently prescribed against hypercholesterolemia, and these agents successfully suppress levels of serum LDL-cholesterol in most cases. We experienced a case of hypercholesterolemia resistant to statins, but well responsive to an inhibitor of cholesterol absorption in the intestine, ezetimibe. The case was a 58-year-old, non-obese female with persistent high-levels of LDL-cholesterol (LDL-Cho) around 200 (193 - 204) mg /dl even after administration of statins, pitavastatin or rosuvastatin. An inhibitor of cholesterol absorption, ezetimibe was added to rosuvastatin, which resulted in lowering serum LDL-Cho levels to 90 mg/dl. The actual reduction rate of LDL-Cho was 5.4% by rosuvastatin alone, and this rate was up to 53.4% by adding ezetimibe. These results suggest that enhanced choleste rol absorption, rather than cholesterol synthesis, caused hypercholesterolemia in this case. Cholesterol absorption is accelerated in some diseases or conditions, such as diabetes mellitus and obesity, both of which were not identified in this case. Unknown genetic or acquired abnormalities in the intestinal cholesterol-transporting system may be involved in developing hypercholesterolemia. Hence, suppression of cholesterol absorption is a considerable option for hypercholesterolemia resistant to statins. doi:10.4021/jmc124w
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".