The pyruvate kinase (PK) to hexokinase enzyme activity ratio and erythrocyte PK protein level in the diagnosis and phenotype of PK deficiency
Bibliographic record
Abstract
Diagnosis of pyruvate kinase deficiency (PKD), the most common cause of hereditary non-spherocytic haemolytic anaemia, remains challenging in routine practice and no biomarkers for clinical severity have been characterised. This prospective study enrolled 41 patients with molecularly confirmed PKD from nine North American centres to evaluate the diagnostic sensitivity of pyruvate kinase (PK) enzyme activity and PK:hexokinase (HK) enzyme activity ratio, and evaluate the erythrocyte PK (PK-R) protein level and erythrocyte metabolites as biomarkers for clinical severity. In this population not transfused for ≥90 days before sampling, the diagnostic sensitivity of the PK enzyme assay was 90% [95% confidence interval (CI) 77-97%], whereas the PK:HK ratio sensitivity was 98% (95% CI 87-100%). There was no correlation between PK enzyme activity and clinical severity. Transfusion requirements correlated with normalised erythrocyte ATP levels (r = 0·527, P = 0·0016) and PK-R protein levels (r = -0·527, P = 0·0028). PK-R protein levels were significantly higher in the never transfused [median (range) 40·1 (9·8-73·9)%] versus ever transfused [median (range) 7·7 (0·4-15·1)%] patients (P = 0·0014). The PK:HK ratio had excellent sensitivity for PK diagnosis, superior to PKLR exon sequencing. Given that the number of PKLR variants and genotype combinations limits prognostication based on molecular findings, PK-R protein level may be a useful prognostic biomarker of disease severity and merits further study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".