Blood metabolic signatures of hikikomori, pathological social withdrawal.
COVID-19
Hikikomori
acylcarnitine
plasma metabolome
social isolation
Journal
Dialogues in clinical neuroscience
ISSN: 1958-5969
Titre abrégé: Dialogues Clin Neurosci
Pays: England
ID NLM: 101238198
Informations de publication
Date de publication:
2021
2021
Historique:
entrez:
21
7
2022
pubmed:
22
7
2022
medline:
23
7
2022
Statut:
epublish
Résumé
A severe form of pathological social withdrawal, 'hikikomori,' has been acknowledged in Japan, spreading worldwide, and becoming a global health issue. The pathophysiology of hikikomori has not been clarified, and its biological traits remain unexplored. Drug-free patients with hikikomori ( Long-chain acylcarnitine levels were remarkably higher in patients with hikikomori; bilirubin, arginine, ornithine, and serum arginase were significantly different in male patients with hikikomori. The discriminative random forest model was highly performant, exhibiting an area under the ROC curve of 0.854 (confidential interval = 0.648-1.000). To predict hikikomori severity, a partial least squares PLS-regression model was successfully created with high linearity and practical accuracy. In addition, blood serum uric acid and plasma cholesterol esters contributed to the stratification of cases. These findings reveal the blood metabolic signatures of hikikomori, which are key to elucidating the pathophysiology of hikikomori and also useful as an index for monitoring the treatment course for rehabilitation.
Sections du résumé
Background
A severe form of pathological social withdrawal, 'hikikomori,' has been acknowledged in Japan, spreading worldwide, and becoming a global health issue. The pathophysiology of hikikomori has not been clarified, and its biological traits remain unexplored.
Methods
Drug-free patients with hikikomori (
Results
Long-chain acylcarnitine levels were remarkably higher in patients with hikikomori; bilirubin, arginine, ornithine, and serum arginase were significantly different in male patients with hikikomori. The discriminative random forest model was highly performant, exhibiting an area under the ROC curve of 0.854 (confidential interval = 0.648-1.000). To predict hikikomori severity, a partial least squares PLS-regression model was successfully created with high linearity and practical accuracy. In addition, blood serum uric acid and plasma cholesterol esters contributed to the stratification of cases.
Conclusions
These findings reveal the blood metabolic signatures of hikikomori, which are key to elucidating the pathophysiology of hikikomori and also useful as an index for monitoring the treatment course for rehabilitation.
Identifiants
pubmed: 35860171
doi: 10.1080/19585969.2022.2046978
pii: 2046978
pmc: PMC9286746
doi:
Substances chimiques
Uric Acid
268B43MJ25
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
14-28Informations de copyright
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Déclaration de conflit d'intérêts
The authors have no conflicts of interest to declare related to this study.
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