Case Report: Diabetic ketoacidosis after co-administration of empagliflozin and probenecid
Bibliographic record
Abstract
Sodium-glucose cotransporter-2 (SGLT2) inhibitors are filtered and secreted to their primary site of action in the proximal tubule of the kidney. At this site, SGLT2 inhibitors also reduce renal elimination of ketone bodies, a finding implicated in their propensity to cause ketoacidosis. Many commonly used medications have potential to diminish renal elimination of SGLT2 inhibitors and to compound the effects of SGLT2 inhibitors on renal elimination of ketone bodies by inhibiting tubular secretion of the SGLT2 inhibitor itself and/or ketone bodies. We present a case of severe diabetic ketoacidosis (DKA) in a patient with type 2 diabetes occurring several days after co-prescription of empagliflozin and probenecid. Other than the recent introduction of empagliflozin, no cause for the DKA episode was apparent. A pharmacokinetic interaction between probenecid and empagliflozin, involving organic anion transporter 3 (OAT3), reduces proximal tubular secretion of empagliflozin and increases patient exposure to the drug. Whether or not this phenomenon is sufficient to cause severe DKA is discussed. An alternative explanation as to the DKA aetiology is proposed, wherein probenecid may compound effects of empagliflozin on renal elimination of ketone bodies. We suggest that clinicians exercise caution when prescribing SGLT2 inhibitors alongside pharmacologic inhibitors of, or competitors for, proximal tubular organic anion transporters in patients with diabetes mellitus due to the risk of severe DKA.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".