Sertraline-Induced Hypoglycemia
Bibliographic record
Abstract
OBJECTIVE: To report a case of hypoglycemia that occurred in a patient treated with the selective serotonin-reuptake inhibitor, sertraline. CASE SUMMARY: An 82-year-old white woman with mild cardiovascular disease and no history of glucose intolerance was seen in the emergency department for a presyncopal episode associated with a blood glucose of 32 mg/dL as measured by the ambulance attendant. She had similar symptoms the day before. Despite repeated administration of oral and intravenous glucose, the patient had recurrent episodes of hypoglycemia and was hospitalized for four days. She had started taking sertraline 50 mg once daily for mild depression 25 days prior to presentation. Other medications included furosemide 20 mg/d, ramipril 5 mg/d, clopidogrel 75 mg/d, nitroglycerin patch 0.4 mg/h, and lorazepam 1 mg taken occasionally for agitation. She had never been prescribed any oral hypoglycemic agents. Serum sertraline and desmethylsertraline concentrations measured two, three, and four days after discontinuing sertraline were within the expected range, but the rate of decline was consistent with a moderately prolonged half-life. DISCUSSION: Sertraline has been shown to blunt postprandial hyperglycemia in rats and to potentiate the hypoglycemic effects of sulfonylurea agents in humans. It has not been reported to cause hypoglycemia independently, but in this case, a nondiabetic patient experienced multiple episodes of hypoglycemia that resolved after discontinuation of sertraline. CONCLUSIONS: This report and another implicating fluoxetine in a case of hypoglycemia suggest that healthcare professionals should consider these medications among the possible causes of hypoglycemia occurring in patients receiving selective serotonin-reuptake inhibitors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".