ArticleDialogues in clinical neuroscience2021
Blood metabolic signatures of hikikomori, pathological social withdrawal.
Article in Dialogues in clinical neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 21 citations in OpenAlex.
- Hikikomori in the urban digital era: a psychodynamic, transdiagnostic model and multimodal interventions.Current opinion in psychiatry · 2026Review
- Integrative metabolomics and machine learning identify biomarkers of adolescent social anxiety disorder.World journal of pediatrics : WJP · 2026Article
- Parental bonding and attachment in the hikikomori trajectory.Psychiatry and clinical neurosciences · 2026Article
- The flow cytometric analysis of depression focusing on modern-type depression and hikikomori: Exploring the link between subtypes of depression and immunological imbalances.Dialogues in clinical neuroscience · 2025Article
- Leveraging machine learning to uncover the hidden links between trusting behavior and biological markers.Dialogues in clinical neuroscience · 2025Article
- Global psychiatrists' opinions aboutPCN reports : psychiatry and clinical neurosciences · 2025Article
- Investigating the Overlap ofMedicina (Kaunas, Lithuania) · 2025Article
- Hikikomori: A Society-Bound Syndrome of Severe Social Withdrawal.Psychiatry and clinical psychopharmacology · 2022Review
- Translational chasm and dialogues in clinical neuroscience.Dialogues in clinical neuroscience · 2021Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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