Pyruvate Kinase (PK) Protein and Enzyme Levels in the Diagnosis and Clinical Phenotype of PK Deficiency
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».