Successful treatment of severe hypertriglyceridemia with icosapent ethyl in a case of congenital generalized lipodystrophy type 4
Bibliographic record
Abstract
OBJECTIVES: Congenital generalized lipodystrophy type 4 (CGL4) is a rare autosomal recessive condition with high rates of morbidity and mortality. It is a multisystem condition associated with ventricular tachyarrhythmia, congenital myopathy, hepatitis, and metabolic profile of severe hypertriglyceridemia and insulin resistance. Metreleptin is the first line treatment, however it is unavailable in several countries. Herein, we describe a unique presentation and treatment of CGL4. CASE PRESENTATION: A 16-year-old female presented with insulin resistant diabetes, and was later found to have myopathy, hypertriglyceridemia, nonalcoholic fatty liver disease, ventricular arrhythmias, and genetic confirmation of CGL4 due to homozygous change in CAVIN1 gene. She had severe hypertriglyceridemia, frequently >17 mmol/L, requiring several hospital admissions. To better control hypertriglyceridemia, in context of known congenital myopathy, we opted for treatment with icosapent ethyl, an ethyl ester of eicosapentaenoic acid (EPA), which reduces synthesis and enhances clearance of triglycerides. On this treatment, she was able to maintain stable triglyceride levels of 4 mmol/L. CONCLUSIONS: We present the first case report of a patient with CGL4, successfully treated for hypertriglyceridemia, with icosapent ethyl.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".