Regional Genetic Heterogeneity Among Patients with Pyruvate Kinase Deficiency
Notice bibliographique
Résumé
Background: Pyruvate kinase (PK) deficiency is a rare, congenital hemolytic anemia caused by homozygous or compound heterozygous mutations in the PKLR gene, with a prevalence of 3.2-8.5 individuals per million in Western populations. Over 350 pathogenic variants in the PKLR gene have been reported, with the majority being missense substitutions. Although their frequency differs across regions and populations, the geographic distribution of PKLR variants has not yet been fully investigated. Aim: To evaluate the PKLR genotype distribution by region among patients (pts) with PK deficiency in the merged real-world database of the Peak Registry (NCT03481738) and PK Deficiency Natural History Study (NHS; NCT02053480). Methods: The NHS was a global, longitudinal, observational study that enrolled pts with PK deficiency from 2014-2017 (study completed May 2020). The Peak Registry is an ongoing global registry which was initiated in 2018 to build on the NHS with expanded pt recruitment and geographic reach, broader data collection, and longer pt follow-up. Comparable data from these two studies were integrated to form a merged database providing enlarged sample size and prolonged follow-up from pts who participated in both studies. This analysis included pts with available genotype data from the NHS and Peak Registry, including those who were siblings; a large Amish cohort of pts carrying the homozygous p.R479H variant was excluded to avoid reporting bias. Genotype classifications were aligned between the two studies. Data were extracted from the merged database of NHS and Peak (data cutoff date: 13May2022) and summarized descriptively by region. Regions were defined as: Northern (N) Europe (Denmark, Ireland, Netherlands, UK); Central (C) Europe (Czech Republic, Germany, Switzerland); Southern (S) Europe (France, Italy, Spain, Portugal); Asia (S Korea, Thailand, Turkey); N America (Canada, US). Results: 362 pts were included in the analysis (N Europe, N=59; S Europe, N=62; C Europe, N=51; Asia, N=23; N America, N=167), 97 (27%) of whom were siblings. Most pts (72%) had compound heterozygous PKLR variants (Table). Homozygous mutations were most common among pts from Asia (15/23; 65%) and were least common among pts from S Europe (8/62; 13%). Almost half of pts from N Europe (49%) had a compound heterozygous missense/missense (M/M) genotype, while a similar proportion from S Europe (44%) had a compound heterozygous missense/non-missense (M/NM) genotype. A total of 724 PKLR variants were identified, including 197 unique variants, 26 of which were newly identified. Most were missense mutations (76%), followed by nonsense (10%), splice site (7%), and frameshift (5%) mutations. The majority of mutated alleles in the cohort were single nucleotide variants (90%), followed by small deletions (4%), large deletions (4%), and insertions (2%). The amino acid variant with the highest observed frequency was p.Arg510Gln, both overall (158/724; 22%), and in N Europe (31/118; 26%), C Europe (27/102; 26%), and N America (92/334; 28%), but it was not observed in Asia (0/46; 0%). The second most common variant overall, p.Arg486Trp (67/724; 9%), was the most frequent in S Europe (31/124; 25%). When stratified by genotype, p.Arg510Gln was the most common missense mutation in homozygous pts in each region (40-67%) except in Asia. In Asia, p.Thr157Ala, p.Lys541Asn, and p.Arg194Pro were the most common mutations overall (4/46; 9%), and also the most common missense mutations in homozygous pts (4/28; 14%). One hundred and seventy-four unique variants were observed in compound heterozygotes. In S Europe (27/102; 26%) and Asia (2/10; 20%), p.Arg486Trp was the most frequently observed missense mutation in compound heterozygote pts. Overall and in all regions except for Asia (where it was absent), p.Glu241Ter was the most common non-missense mutation in compound heterozygote pts (32/254; 13%); this was p.Gly540Ter (3/6; 50%) in Asia. Conclusions: This analysis, which used the largest real-world data source for pts with PK deficiency, confirms the genetic heterogeneity of the disease, showing that the mutation frequency in PKLR varies substantially based on geographic distribution and that the identification of novel genetic variants is relatively common in PK deficiency.
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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 ».