From genotype to phenotype: Early prediction of disease severity in argininosuccinic aciduria
Bibliographic record
Abstract
Argininosuccinic aciduria (ASA) is an inherited urea cycle disorder and has a highly variable phenotypic spectrum ranging from individuals with lethal hyperammonemic encephalopathy, liver dysfunction, and cognitive deterioration, to individuals with a mild disease course. As it is difficult to predict the phenotypic severity, we aimed at identifying a reliable disease prediction model. We applied a biallelic expression system to assess the functional impact of pathogenic argininosuccinate lyase (ASL) variants and to determine the enzymatic activity of ASL in 58 individuals with ASA. This cohort represented 42 ASL gene variants and 42 combinations in total. Enzymatic ASL activity was compared with biochemical and clinical endpoints from the UCDC and E-IMD databases. Enzymatic ASL activity correlated with peak plasma ammonium concentration at initial presentation and with the number of hyperammonemic events (HAEs) per year of observation. Individuals with ≤9% of enzymatic activity had more severe initial decompensations and a higher annual frequency of HAEs than individuals above this threshold. Enzymatic ASL activity also correlated with the cognitive outcome and the severity of the liver disease, enabling a reliable severity prediction for individuals with ASA. Thus, enzymatic activity measured by this novel expression system can serve as an important marker of phenotypic severity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".