Maleic acid is a biomarker for maleylacetoacetate isomerase deficiency; implications for newborn screening of tyrosinemia type 1
Bibliographic record
Abstract
Dried blood spot succinylacetone (SA) is often used as a biomarker for newborn screening (NBS) for tyrosinemia type 1 (TT1). However, false-positive SA results are often observed. Elevated SA may also be due to maleylacetoacetate isomerase deficiency (MAAI-D), which appears to be clinically insignificant. This study investigated whether urine organic acid (uOA) and quantitative urine maleic acid (Q-uMA) analyses can distinguish between TT1 and MAAI-D. We reevaluated/measured uOA (GC-MS) and/or Q-uMA (LC-MS/MS) in available urine samples of nine referred newborns (2 TT1, 7 false-positive), eight genetically confirmed MAAI-D children, and 66 controls. Maleic acid was elevated in uOA of 5/7 false-positive newborns and in the three available samples of confirmed MAAI-D children, but not in TT1 patients. Q-uMA ranged from not detectable to 1.16 mmol/mol creatinine in controls (n = 66) and from 0.95 to 192.06 mmol/mol creatinine in false-positive newborns and MAAI-D children (n = 10). MAAI-D was genetically confirmed in 4/7 false-positive newborns, all with elevated Q-uMA, and rejected in the two newborns with normal Q-uMA. No sample was available for genetic analysis of the last false-positive infant with elevated Q-uMA. Our study shows that MAAI-D is a recognizable cause of false-positive TT1 NBS results. Elevated urine maleic acid excretion seems highly effective in discriminating MAAI-D from TT1.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".