The Impact of Isotretinoin on Lipid Profile: a Systematic Review
Bibliographic record
Abstract
Background: Isotretinoin, or 13-cis retinoic acid, is prescribed to treat moderate to severe recalcitrant nodulocystic acne that remains untreated with other drugs, such as antibiotics. The adverse effect profile of isotretinoin has raised concerns since studies have reported a substantial elevation in the low-density lipoprotein (LDL)/high-density lipoprotein (HDL) ratio, indicating the risk of cardiovascular diseases after a long-term course of isotretinoin. Assessing the impact of isotretinoin on lipid profile markers in acne patients was the aim of this systematic review. Method: Various databases, such as PubMed, Google Scholar, SCOPUS, Embase, and Web of Science, were comprehensively searched to identify relevant clinical studies. Ten articles out of 256 were selected based on the inclusion and exclusion criteria by two independent reviewers. Each review was thoroughly evaluated using Assessment of the Methodological Quality of Systematic Reviews and the Newcastle-Ottawa Scale. Key results: A decrease in HDL was observed, whereas total cholesterol, triglyceride, and LDL levels were notably elevated. However, most changes in lipid profile parameters are non-progressive, and their clinical significance is poorly understood. Liver enzyme levels, including aspartate transaminase and alanine transaminase, were altered to a lesser degree. Conclusion: Long-term use of isotretinoin is associated with mild alterations in the lipid profile, resulting in increased total cholesterol, triglyceride, and LDL levels. However, since lipid alterations vary depending on factors such as the population studied, dosage, and duration of isotretinoin treatment, regular monitoring of the lipid profile along with low density lipoprotein is recommended to avoid potential risk factors of isotretinoin on lipid metabolism and, thereafter, on the cardiovascular system, but is not deemed paramount.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".