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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".