Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods
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 focu...
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2018
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oai:doaj.org-article:1f5f4b7d0ff14717814b4720f3836f622021-12-02T15:08:10ZPotential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods10.1038/s41598-018-33110-w2045-2322https://doaj.org/article/1f5f4b7d0ff14717814b4720f3836f622018-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-33110-whttps://doaj.org/toc/2045-2322Abstract 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.Taku ObaraMami IshikuroGen TamiyaMasao UekiChizuru YamanakaSatoshi MizunoMasahiro KikuyaHirohito MetokiHiroko MatsubaraMasato NagaiTomoko KobayashiMachiko KamiyamaMikako WatanabeKazuhiko KakutaMinami OuchiAki KuriharaNaru FukuchiAkihiro YasuharaMasumi InagakiMakiko KagaShigeo KureShinichi KuriyamaNature PortfolioarticleAutism Spectrum DisorderPyridoxine ResponsivenessSelectable Phenotypic VariationPlasma Glutamine LevelsConducted Hypothesis TestingMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-7 (2018) |
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DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Autism Spectrum Disorder Pyridoxine Responsiveness Selectable Phenotypic Variation Plasma Glutamine Levels Conducted Hypothesis Testing Medicine R Science Q |
| spellingShingle |
Autism Spectrum Disorder Pyridoxine Responsiveness Selectable Phenotypic Variation Plasma Glutamine Levels Conducted Hypothesis Testing Medicine R Science Q Taku Obara Mami Ishikuro Gen Tamiya Masao Ueki Chizuru Yamanaka Satoshi Mizuno Masahiro Kikuya Hirohito Metoki Hiroko Matsubara Masato Nagai Tomoko Kobayashi Machiko Kamiyama Mikako Watanabe Kazuhiko Kakuta Minami Ouchi Aki Kurihara Naru Fukuchi Akihiro Yasuhara Masumi Inagaki Makiko Kaga Shigeo Kure Shinichi Kuriyama Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods |
| description |
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. |
| format |
article |
| author |
Taku Obara Mami Ishikuro Gen Tamiya Masao Ueki Chizuru Yamanaka Satoshi Mizuno Masahiro Kikuya Hirohito Metoki Hiroko Matsubara Masato Nagai Tomoko Kobayashi Machiko Kamiyama Mikako Watanabe Kazuhiko Kakuta Minami Ouchi Aki Kurihara Naru Fukuchi Akihiro Yasuhara Masumi Inagaki Makiko Kaga Shigeo Kure Shinichi Kuriyama |
| author_facet |
Taku Obara Mami Ishikuro Gen Tamiya Masao Ueki Chizuru Yamanaka Satoshi Mizuno Masahiro Kikuya Hirohito Metoki Hiroko Matsubara Masato Nagai Tomoko Kobayashi Machiko Kamiyama Mikako Watanabe Kazuhiko Kakuta Minami Ouchi Aki Kurihara Naru Fukuchi Akihiro Yasuhara Masumi Inagaki Makiko Kaga Shigeo Kure Shinichi Kuriyama |
| author_sort |
Taku Obara |
| title |
Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods |
| title_short |
Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods |
| title_full |
Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods |
| title_fullStr |
Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods |
| title_full_unstemmed |
Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods |
| title_sort |
potential identification of vitamin b6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods |
| publisher |
Nature Portfolio |
| publishDate |
2018 |
| url |
https://doaj.org/article/1f5f4b7d0ff14717814b4720f3836f62 |
| work_keys_str_mv |
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1718388237472366592 |