Paroxetine-induced increase in LDL cholesterol levels
Bibliographic record
Abstract
Paroxetine is widely prescribed because it has the indication for multiple psychiatric disorders. Our objective was to assess the effect of short-term administration of paroxetine on low-density lipoprotein cholesterol (LDL-C) levels in both healthy controls (HCs) and in patients with panic disorder (PD). Blood samples for measurement of LDL-C were collected atbaseline, after 8 weeks of paroxetine administration and post-discontinuation in 24 male HCs and nine male patients suffering from PD, for a total of 33 subjects. Paroxetine treatment, both in HCs and PD patients, induced a mean 9% increase per subject in LDL-C that normalized post-discontinuation, suggesting causality. The National Cholesterol Education Program (NCEP) guidelines suggest that this paroxetine-induced increase in LDL-C is clinically significant but would not warrant therapeutic intervention in this population selected to be at low cardiovascular risk. However, the increase in LDL-C levels raised above the threshold of 2.7 mmol/L (100 mg/dL) in 36% of our low-risk subjects. The LDL-C increase in this subgroup would be associated with a minor increase in coronary heart disease (CHD) risk. A similar 9% paroxetine-induced increase in LDL-C observed in the large number of psychiatric patients suffering from comorbid established CHD would be detrimental from a cardiovascular perspective and would oppose the new NCEP therapeutic goals of decreasing LDL-C levels by 30-40% in high and moderately high-risk patients. It is possible that longer treatment duration and use of higher doses of paroxetine would lead to a greater increase in LDL-C.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".