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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".