Ecstasy (MDMA)-induced hyponatraemia is associated with genetic variants in <i>CYP2D6</i> and <i>COMT</i>
Bibliographic record
Abstract
We hypothesised that genetically determined poor metabolism of 3,4-methylene dioxymetamphetamine (MDMA) due either to the presence of CYP2D6 genotypes giving absent or low CYP2D6 enzyme activity, or a COMT genotype predicting low COMT enzyme activity would be associated with a greater degree of MDMA-induced reduction in plasma sodium and osmolality than other genotypes at these genes following consumption of 'ecstasy' tablets by clubbers. Of the 48 subjects who returned to the test site post-clubbing, 30 provided samples for measurement of vasopressin (AVP), plasma sodium, urea and plasma and urine osmolality. Genotyping was performed for functional variants in CYP2D6 (n = 29) and COMT (Val158Met, n = 30). In subjects with urinary MDMA detected post-clubbing, there was a significant association between change in plasma osmolality (p = 0.009) and in plasma sodium (p = 0.012) and CYP2D6 genotypic category. Individuals with the low-activity but readily inhibitable CYP2D6 extensive metaboliser/intermediate metaboliser (EM/IM) genotype showed greater reductions in these measures than all other CYP2D6 genotypic categories. COMT low-activity genotypes (Met/Met and Val/Met) were also significantly associated with reductions in plasma osmolality (p = 0.028) and in plasma sodium (p = 0.003). On conservative Bonferroni correction for two independent genes, the CYP2D6 and COMT plasma sodium findings remain significant. The relatively high frequency of the low-activity CYP2D6 and COMT genotypes in the population warrants further attention, since consumption of free water following ingestion of MDMA in these individuals may trigger dilutational hyponatraemia and increased risk of syndrome of inappropriate antidiuretic hormone secretion.
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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 | 0.000 | 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 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".