Blood metabolic signatures of hikikomori, pathological social withdrawal

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 ( n = 42) and healthy controls ( n = 41) were recruited. Psychological assessments for the severity of hikikomori and depression were conducted. Blood biochemical tests and plasma metabolome analysis were performed. Based on the integrated information, machine-learning models were created to discriminate cases of hikikomori from healthy controls, predict hikikomori severity, stratify the cases, and identify metabolic signatures that contribute to each model. 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.

Leveraging Machine Learning to Uncover the Hidden Links between Trusting Behavior and Biological Markers

“…Acetylcarnitine is related to oxidative stress 18 , and several studies have reported both positive and negative correlations with depression 19,20 . Interestingly, acetyl carnitine is involved in the same metabolic pathway as acylcarnitine in the mitochondria, which we previously reported to be related to hikikomori 11 . There are no reports on the function of 5ALA in neurons and depression.…”

“…In general, biomarkers have been considered to predict whether an individual is healthy or in a state of MDD. Previously, we used machine learning to identify biomarkers by classifying HCs and patients with MDD 11 ; patients with MDD had higher concentrations of orotic acid and lactic acid and lower concentrations of cystine, arginine, and methionine in the blood compared to HCs. Another type of biomarkers is indicators that directly predict the participant’s answers and behaviors in the task, which can be identified by a native comparison between behavioral and blood sample data.…”

“…Meanwhile, some blood biomarkers have been shown to change in patients with psychiatric disorders. We previously investigated blood biomarkers in hikikomori patients who avoided social interaction to an extreme extent due to their inability to build a trusting relationship with other people 11 . We found that these patients had decreased blood uric acid levels 12 .…”

Translational chasm and dialogues in clinical neuroscience

“…In this issue, Setoyama et al. ( 2021 ) from Japan describe a metabolic signature profile, which if replicated has the potential significantly advance our understanding and treatment of this devastating condition. Emil Kraepelin more than 100 years ago described in his text book that patients with dementia praecox (now called schizophrenia) experience cognitive deterioration but not those with manic depressive psychosis (i.e., bipolar disorder) who predominantly experience mood changes with periods of exacerbation and remission.…”