Consensus recommendations for the diagnosis, treatment and follow‐up of inherited methylation disorders
Bibliographic record
Abstract
Inherited methylation disorders are a group of rarely reported, probably largely underdiagnosed disorders affecting transmethylation processes in the metabolic pathway between methionine and homocysteine. These are methionine adenosyltransferase I/III, glycine N-methyltransferase, S-adenosylhomocysteine hydrolase and adenosine kinase deficiencies. This paper provides the first consensus recommendations for the diagnosis and management of methylation disorders. Following search of the literature and evaluation according to the SIGN-methodology of all reported patients with methylation defects, graded recommendations are provided in a structured way comprising diagnosis (clinical presentation, biochemical abnormalities, differential diagnosis, newborn screening, prenatal diagnosis), therapy and follow-up. Methylation disorders predominantly affect the liver, central nervous system and muscles, but clinical presentation can vary considerably between and within disorders. Although isolated hypermethioninemia is the biochemical hallmark of this group of disorders, it is not always present, especially in early infancy. Plasma S-adenosylmethionine and S-adenosylhomocysteine are key metabolites for the biochemical clarification of isolated hypermethioninemia. Mild hyperhomocysteinemia can be present in all methylation disorders. Methylation disorders do not qualify as primary targets of newborn screening. A low-methionine diet can be beneficial in patients with methionine adenosyltransferase I/III deficiency if plasma methionine concentrations exceed 800 μmol/L. There is some evidence that this diet may also be beneficial in patients with S-adenosylhomocysteine hydrolase and adenosine kinase deficiencies. S-adenosylmethionine supplementation may be useful in patients with methionine adenosyltransferase I/III deficiency. Recommendations given in this article are based on general principles and in practice should be adjusted individually according to patient's age, severity of the disease, clinical and laboratory findings.
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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 | 0.035 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".