Pharmacogenomics in diabetes: outcomes of thiamine therapy in TRMA syndrome
Bibliographic record
Abstract
AIMS/HYPOTHESIS: ) in a cohort of individuals with TRMA-related diabetes. METHODS: We studied 32 individuals with biallelic SLC19A2 mutations identified by Sanger or next generation sequencing. Clinical details were collected through a follow-up questionnaire. RESULTS: We identified 24 different mutations, of which nine are novel. The onset of the first TRMA symptom ranged from birth to 4 years (median 6 months [interquartile range, IQR 3-24]) and median age at diabetes onset was 10 months (IQR 5-27). At presentation, three individuals had isolated diabetes and 12 had asymptomatic hyperglycaemia. Follow-up data was available for 15 individuals treated with thiamine for a median 4.7 years (IQR 3-10). Four patients were able to stop insulin and seven achieved better glycaemic control on lower insulin doses. These 11 patients were significantly younger at diabetes diagnosis (p = 0.042), at genetic testing (p = 0.01) and when starting thiamine (p = 0.007) compared with the rest of the cohort. All patients treated with thiamine became transfusion-independent and adolescents achieved normal puberty. There were no additional benefits of thiamine doses >150 mg/day and no reported side effects up to 300 mg/day. CONCLUSIONS/INTERPRETATION: In TRMA syndrome, diabetes can be asymptomatic and present before the appearance of other features. Prompt recognition is essential as early treatment with thiamine can result in improved glycaemic control, with some individuals becoming insulin-independent. DATA AVAILABILITY: SLC19A2 mutation details have been deposited in the Decipher database ( https://decipher.sanger.ac.uk/ ).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".