Exploring Hypoglycemic Ketoacidosis in Nondiabetic Patients on Tirzepatide: Is Starvation the Culprit?
Bibliographic record
Abstract
BACKGROUND Tirzepatide is a long-acting glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) receptor agonist administered via subcutaneous injection for weight reduction and treating type 2 diabetes. CASE REPORT We report case series of hypoglycemic ketoacidosis after the use of tirzepatide to treat nondiabetic patients with obesity from Kuwait. The first case was a 29-year-old woman with a body mass index (BMI) of 32 kg/m² who developed abdominal pain and vomiting after increasing the dose to 5 mg subcutaneously in week 5 of treatment. The second case was a 34-year-old woman with a BMI of 31.3 kg/m² who presented with abdominal pain, vomiting, and diarrhea after increasing the dose to 5 mg subcutaneously. The third case was a 17-year-old girl with a BMI of 30.4 kg/m2 who presented with abdominal pain, vomiting, and diarrhea in week 5 of treatment. The fourth case was a 26-year-old woman with a BMI of 30.8 kg/m² who presented with abdominal pain, frequent loose motions, and vomiting. The median blood sugar level was <3.89 mmol/L and high anion gap metabolic acidosis with ketosis occurred. All the patients required inpatient treatment with intravenous fluid and the correction of hypoglycemia and ketosis. CONCLUSIONS Tirzepatide can induce hypoglycemic ketoacidosis in nondiabetic patients with obesity when used for weight reduction. Measuring urine and serum ketone levels in patients with gastrointestinal symptoms who are taking dual GLP-1 and GIP receptor agonists is crucial. Medical supervision is recommended when this medication is prescribed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".