Pyruvate Kinase (PK) Protein and Enzyme Levels in the Diagnosis and Clinical Phenotype of PK Deficiency
Bibliographic record
Abstract
Background: Pyruvate Kinase (PK) deficiency is caused by mutations in the PKLR gene leading to chronic hereditary non-spherocytic hemolytic anemia. Diagnostic evaluation can be challenging with falsely normal PK enzyme activity levels in chronically transfused patients, in those with profound reticulocytosis, or in patients with mutations with unusual biochemical properties. Genetic testing can also be complex, as up to 20% of affected patients have new PKLR variants and up to 10% of patients have variants not routinely detected through exon sequencing. The phenotypic spectrum is broad, and biomarkers of clinical severity would be helpful for both prognosis and monitoring. Aims: To describe the correlation of PK enzyme activity, red cell PK (PK-R) protein level, and red cell metabolites [adenosine triphosphate (ATP), 2,3-diphosphoglycerate (2,3-DPG)] with clinical phenotype. To estimate the sensitivity of PK enzyme activity to diagnose PK deficiency in a cohort of patients with genetically confirmed PK deficiency. Methods: Blood samples were collected from a subset of 41 patients enrolled in the PK Deficiency Natural History Study, all with two confirmed PKLR mutations and no red cell transfusions for ≥3 months prior to the blood sample. PK and hexokinase (HK) enzyme activity testing were performed using standard biochemical assays (Beutler 1985) at the Stanford Red Blood Cell Special Studies Laboratory (normal ranges: PK activity: 3.2-6.5 EU/gm Hb, HK activity: 0.14-0.37 EU/gm Hb). Baseline PK-R protein was quantitated by antibody-based capture and detection using a Meso Scale assay and the signal normalized to a control sample without PK deficiency. ATP and 2,3-DPG concentrations (μg/ml) in blood were analyzed using qualified tandem mass spectrometry methods and then normalized for individual hemoglobin (Hb) values (ATP and 2,3-DPG concentrations were converted to g/dL and divided by Hb (g/dL)). Spearman correlation coefficients were calculated to estimate the correlation between continuous variables, and Wilcoxon rank-sum testing was used to assess the association of continuous and binary variables. Results: The median age was 25.3 years (range 1.4-60.4) and 80% of patients were splenectomized (Table). The mean PK enzyme activity level was 1.1 EU/gm Hb with 90% (37/41) of patients with low PK activity. Normal PK activity was observed in 4 patients, 3 of whom had high HK activity and a low PK/HK ratio (normal mean PK/HK ratio 15.6, range: 8.7-22.5), with a sensitivity of 98% (40/41) when both PK activity and PK/HK ratio were used together. There were no correlations between PK enzyme activity and clinical severity indicators, including transfusion status (p=0.3), splenectomy status (p=0.4), or post-splenectomy Hb (r=0.109). Normalized ATP levels were significantly correlated with normalized 2,3-DPG levels (r=0.93) and higher in ever transfused (median=0.0022, range=0.0011-0.0029) compared with never transfused patients (median=0.0012 , range=0.0008-0.0020), (p=0.004). PK-R protein level was inversely correlated with the total number of transfusions prior to enrollment (r= -0.53) and was higher in never transfused (median=40.1%, range=9.8-73.9%) compared with ever transfused patients (median=7.7%, range=0.4%-15.1%), (p=0.0014). Conclusions: In this cohort with a molecularly confirmed diagnosis, PK deficiency was detected by enzyme testing with 98% sensitivity by utilizing both the PK activity and the PK/HK ratio. Although these assays cannot distinguish between heterozygotes and affected patients, the combination of PK activity and the PK/HK ratio is useful as a screening test to determine which patients require genetic testing. There was no correlation between PK enzyme activity and clinical phenotype. PK-R protein and ATP levels were inversely correlated with clinical severity and may be a helpful marker of disease phenotype. Disclosures Al-Samkari: Agios: Consultancy, Research Funding; Moderna: Consultancy; Dova: Consultancy, Research Funding. Glader:Agios Pharmaceuticals, Inc: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Chonat:Alexion: Other: advisory board; Agios Pharmaceuticals, Inc.: Other: advisory board. Thompson:bluebird bio, Inc.: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Baxalta: Research Funding. Kuo:Agios: Consultancy; Alexion: Consultancy, Honoraria; Apellis: Consultancy; Bioverativ: Other: Data Safety Monitoring Board; Bluebird Bio: Consultancy; Celgene: Consultancy; Novartis: Consultancy, Honoraria; Pfizer: Consultancy. Ravindranath:Agios Pharmaceuticals, Inc.: Other: I am site PI on several Agios-sponsored studies, Research Funding. Rothman:Pfizer: Consultancy, Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Agios: Honoraria, Research Funding. Kwiatkowski:Celgene: Consultancy; Terumo: Research Funding; Imara: Consultancy; Apopharma: Research Funding; Novartis: Research Funding; bluebird bio, Inc.: Consultancy, Research Funding; Agios: Consultancy. Kung:Agios Pharmaceuticals, Inc: Employment, Other: Stakeholder. Kosinski:Agios Pharmaceuticals, Inc: Employment, Other: Stakeholder. London:United Therapeutics: Consultancy; ArQule, Inc: Consultancy. Grace:Novartis: Research Funding; Agios Pharmaceuticals, Inc: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".