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Enregistrement W4414741178 · doi:10.1093/clinchem/hvaf086.089

A-090 Comparison of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) Assays, with or without pyridoxal-5-phosphate, on various fibrosis scores

2025· article· en· W4414741178 sur OpenAlexaff
S. Bello, Sean T. Campbell, Kayode Balogun

Notice bibliographique

RevueClinical Chemistry · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueLiver Disease Diagnosis and Treatment
Établissements canadiensSinai Health System
Organismes subventionnairesnon disponible
Mots-clésAlanine aminotransferaseAlanine transaminaseCofactorLiver function testsLiver functionEnzymeFibrosisAlanineAspartate Aminotransferases

Résumé

récupéré en direct d'OpenAlex

Abstract Background The most frequently ordered tests to assess hepatocellular injury are aspartate aminotransferase (AST) and alanine aminotransferase (ALT). Conventional enzymatic methods for quantifying AST and ALT concentrations involve the transfer of an L-aspartate or L-alanine group, respectively, to 2-oxoglutarate. The coupled oxidation reaction with NADH is then measured spectrophotometrically at a wavelength of 340 nm. Pyridoxal-5-phosphate (PLP) is a cofactor for both ALT and AST and is necessary for their enzymatic activity. Conventional ALT and AST assays produced by most vendors do not include PLP. Thus, utilizing these assays in individuals with PLP deficiency may yield spurious results. The International Federation of Clinical Chemistry recommends adding PLP to the reaction to saturate the enzyme. Consequently, more vendors are reformulating their assays to include PLP. It is known that the addition of PLP leads to higher ALT and AST results. This assay modification and subsequent positive bias may have clinical implications, particularly in settings where liver function tests are used for non-invasive risk stratification of liver fibrosis. The aim of this study is to investigate the effect of utilizing ALT and AST results obtained from assays with and without PLP on non-invasive markers of fibrosis, including FibroScan, Fibrosis-4 (FIB-4) score, NAFLD fibrosis score (NFS), and the Aspartate Aminotransferase to Platelet Ratio Index (APRI). Methods Banked serum samples from outpatients on routine clinic visits were utilized for the study. Patients with clinical conditions such as hepatitis and liver diseases, which could potentially confound the results, were excluded. ALT and AST concentrations without PLP were measured using the Abbott Alinity conventional assay, while the Abbott activated assays were used to measure ALT and AST concentrations with PLP. Liver fibrosis risk stratification calculations were conducted using the FIB-4, NFS, and APRI scores. Other variables required for calculating the scores were obtained from patient charts. The two liver enzyme methods were compared using Deming regression analysis, Bland-Altman plots, and analysis of variance. Categorical variables were analyzed using the Chi-square test. Results A total of 259 patients (47% female, 53% male) were included in the study. The patients* ages ranged from 3 to 89 years, with a mean age of 55 years. The correlation coefficient for method comparison between assays with and without PLP was >0.99 for both ALT and AST, with biases of 16.6% and 13.3%, respectively. ALT and AST assays with PLP showed significantly higher concentrations than assays without PLP (P<0.05). Additionally, discrepancies were observed in liver fibrosis risk stratification, particularly in the FIB-4 score, with 12 discordant risk classifications between the two assays. However, no statistically significant differences were noted between the assays for NFS and APRI. Conclusion Our findings show that the Abbott ALT and AST assays with PLP yielded higher concentrations compared to the conventional Abbott Alinity assays without PLP, leading to discordant FIB-4 scores. However, no differences were observed for NFS and APRI scores. As liver blood tests increasingly contribute to non-invasive fibrosis assessment algorithms, clinicians and laboratorians need to evaluate the implications of assay reformulations on clinically relevant indices.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,445
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,034
Tête enseignante GPT0,368
Écart entre enseignants0,334 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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