Long-term prognosis of 35 patients with methionine adenosyltransferase deficiency based on newborn screening in China
Bibliographic record
Abstract
Methionine adenosyltransferase deficiency (MATD) is a rare metabolic disorder caused by mono- or biallelic MAT1A mutations that are not yet well understood. Of the 4,065,644 neonates screened between November 2010 and December 2021, 35 individuals have been diagnosed with an estimated incidence of 1: 116,161 by a cutoff value of methionine 82.7 μmol/L and follow-up over 11 years. MATD patients with autosomal recessive (AR) type had higher clinical and genetic heterogeneity than those with autosomal dominant (AD) type. Fifteen unrelated AD patients harbored one well-known dominant variant, c.791 G>A or c.776 C>T, and were clinically unaffected with a mean plasma methionine (Met) value <300 μmol/L. Twenty AR cases have unique genotypes and presented a wide range of clinical abnormalities from asymptomatic to white matter lesions. Of them, 10 AR patients displayed severe manifestations, such as verbal difficulty, motor delay, development delay, and white matter lesions, with mean Met >500 μmol/L and thereby were treated with a methionine-restricted diet alone or in combination with betaine, folate, or vitamin B6, and were healthy finally. Neurological abnormalities were evidenced in two patients (P16 and P27) with Met values >800 μmol/L by MRI scan. Neurological abnormalities were reversed here by liver transplantation or by the determination of S-adenosylmethionine supplementation. Additionally, 38 variants of MAT1A were distributed within patients and carriers, of which 24 were novel and mostly predicted to be damaged. Our findings with an extensive clinical and genetic dataset provided new insights into its diagnosis and treatment and will be helpful for its optimal management in the future.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".