Combination Therapy with Pyridoxine and Arginine Supplementations along with a Lysine-Restricted Diet in Individuals with Pyridoxine-Dependent Epilepsy: A Comprehensive Systematic Review
Bibliographic record
Abstract
Background: Pyridoxine-dependent epilepsy (PDE) is identified as a rare neurometabolic disease marked by biallelic pathogenic mutations of the ALDH7A1 gene. A combination therapy involving pyridoxine, arginine supplementation (AS), and a lysine-restricted diet (LRD) was frequently reported to effectively improve PDE through reducing neurotoxic lysine metabolites, improving seizure management, and enhancing neurodevelopmental outcomes. Objectives: The study sought to investigate the effects of mono-(pyridoxine), dual- (pyridoxine combined with AS or LRD), and triple-therapy approaches in individuals diagnosed with PDE. Methods: An extensive search was carried out across international databases, comprising Scopus, Embase, Web of Science, PubMed, and Google Scholar, to find relevant publications published before 12 November, 2024. The methodological quality assessment of chosen articles was evaluated utilizing the Newcastle-Ottawa Scale and the Joanna Briggs Institute tool. Results: Among 2097 studies reviewed, 38 met inclusion criteria, covering treatment methods for individuals with PDE including monotherapy (22 articles), dual therapy (9 articles), and triple therapy (7 articles). The results indicated that pyridoxine monotherapy is a highly effective first-line treatment in PDE that improves seizure control with minimal cognitive decline. Combining pyridoxine with an LRD or AS targets metabolic issues, reducing neurotoxic metabolites and enhancing cognitive and motor functions. Early triple therapy, within the first 6 months of life, exhibited significant benefits for seizure management and cognitive performance in patients with PDE. Conclusions: In summary, administration of pyridoxine is highly effective, particularly when combined with AS and an LRD. Triple therapy illustrates promise for improved seizure control and cognitive function, especially when initiated early. Further research is warranted.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".