Trial reportScientific reports2018
Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods.
Trial report in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- High-dose Vitamin-B6 reduces sensory over-responsivity.Journal of psychopharmacology (Oxford, England) · 2024Trial
- Deep-embedded clustering by relevant scales and genome-wide association study in autism.PloS one · 2025Article
- Nutritional management and autism spectrum disorder: A systematic review.World journal of clinical pediatrics · 2024Article
- Brain-gut-brain axis, nutrition, and autism spectrum disorders: a review.Translational pediatrics · 2024Review
- Review
- Role of gastrointestinal health in managing children with autism spectrum disorder.World journal of clinical pediatrics · 2023Review
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Inborn errors in the vitamin B6 salvage enzymes associated with neonatal epileptic encephalopathy and other pathologies.Biochimie · 2021Review
- Artificial intelligence powered statistical genetics in biobanks.Journal of human genetics · 2021Review
- Prospective associations of the infant gut microbiome and microbial function with social behaviors related to autism at age 3 years.Scientific reports · 2020Article
- Clustering by phenotype and genome-wide association study in autism.Translational psychiatry · 2020Article
- Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping.JMIR medical informatics · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We investigated whether machine learning methods could potentially identify a subgroup of persons with autism spectrum disorder (ASD) who show vitamin B6 responsiveness by selected phenotype variables. We analyzed the existing data from our intervention study with 17 persons. First, we focused on signs and biomarkers that have been identified as candidates for vitamin B6 responsiveness indicators. Second, we conducted hypothesis testing among these selected variables and their combinations. Finally, we further investigated the results by conducting cluster analyses with two different algorithms, affinity propagation and k-medoids. Statistically significant variables for vitamin B6 responsiveness, including combination of hypersensitivity to sound and clumsiness, and plasma glutamine level, were included. As an a priori variable, the Pervasive Developmental Disorders Autism Society Japan Rating Scale (PARS) scores was also included. The affinity propagation analysis showed good classification of three potential vitamin B6-responsive persons with ASD. The k-medoids analysis also showed good classification. To our knowledge, this is the first study to attempt to identify subgroup of persons with ASD who show specific treatment responsiveness using selected phenotype variables. We applied machine learning methods to further investigate these variables' ability to identify this subgroup of ASD, even when only a small sample size was available.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.