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Record W4293072726 · doi:10.1212/wnl.0000000000201222

Association Between Lysine Reduction Therapies and Cognitive Outcomes in Patients With Pyridoxine-Dependent Epilepsy

2022· article· en· W4293072726 on OpenAlexaff
Curtis R. Coughlin, Laura A. Tseng, Levinus A. Bok, Hans Hartmann, Emma Footitt, Pasquale Striano, Brahim Tabarki, Roelineke J. Lunsing, Sylvia Stöckler‐Ipsiroglu, Shanlea Gordon, Johan L.K. Van Hove, José E. Abdenur, Monica Boyer, Nicola Longo, Ashley Andrews, Mirian C. H. Janssen, Annemiek M. J. van Wegberg, Chitra Prasad, Asuri N. Prasad, Molly M. Lamb, Frits A. Wijburg, Sídney M. Gospe, Clara D.M. van Karnebeek

Bibliographic record

VenueNeurology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsBC Children's HospitalUniversity of British ColumbiaWestern University
FundersNational Center for Advancing Translational SciencesNational Institutes of HealthEuropean CommissionGeorgia Clinical and Translational Science AllianceVitaflo
KeywordsPyridoxineEpilepsyMedicinePsychologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Pyridoxine-dependent epilepsy (PDE-ALDH7A1) is a developmental epileptic encephalopathy characterized by seizure improvement after pyridoxine supplementation. Adjunct lysine reduction therapies (LRTs) reduce the accumulation of putative neurotoxic metabolites with the goal to improve developmental outcomes. Our objective was to examine the association between treatment with LRTs and cognitive outcomes. METHODS: Participants were recruited from within the International Registry for Patients with Pyridoxine-Dependent Epilepsy from August 2014 through March 2021. The primary outcome was standardized developmental test scores associated with overall cognitive ability. The relationship between test scores and treatment was analyzed with multivariable linear regression using a mixed-effects model. A priori, we hypothesized that treatment in early infancy with pyridoxine and LRTs would result in a normal developmental outcome. A subanalysis was performed to evaluate the association between cognitive outcome and LRTs initiated in the first 6 months of life. RESULTS: A total of 112 test scores from 60 participants were available. On average, treatment with pyridoxine and LRTs was associated with a nonsignificant increase of 6.9 points (95% CI -2.7 to 16.5) on developmental testing compared with treatment with pyridoxine alone. For the subanalysis, a total of 14 developmental testing scores were available from 8 participants. On average, treatment with pyridoxine and LRTs in the first 6 months of life was associated with a significant increase of 21.9 points (95% CI 1.7-42.0) on developmental testing. DISCUSSION: Pyridoxine and LRTs at any age was associated with mild improvement in developmental testing, and treatment in early infancy was associated with a clinically significant increase in developmental test scores. These results provide insight into the mechanism of intellectual and developmental disability in PDE-ALDH7A1 and emphasize the importance of treatment in early infancy with both pyridoxine and LRTs. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that in PDE-ALDH7A1, pyridoxine and LRTs compared with pyridoxine alone is not significantly associated with overall higher developmental testing scores, but treatment in the first 6 months of life is associated with significantly higher developmental testing scores.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.210
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations41
Published2022
Admission routes1
Has abstractyes

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