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Record 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 on OpenAlexaff
S. Bello, Sean T. Campbell, Kayode Balogun

Bibliographic record

VenueClinical Chemistry · 2025
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsSinai Health System
Fundersnot available
KeywordsAlanine aminotransferaseAlanine transaminaseCofactorLiver function testsLiver functionEnzymeFibrosisAlanineAspartate Aminotransferases

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.368
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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