Association of arginase I or nitric oxide-related factors with job strain in healthy workers

Estimating Group Stress Level by Measuring Body Motion

Understanding employee stress has become a key issue for top management for corporate growth and risk reduction. So far, annual employee satisfaction surveys (ESs) have been conducted to assess the soundness of an organization. However, since it is difficult to collect questionnaires quantitatively and continuously, there is a need for a practical method that can be used to frequently measure group stress levels with a small burden on employees. We propose such a method and evaluated four combinations of approaches, using activity/rest duration distributions from body motion data and generating estimation models on an individual/group basis. The optimal result was obtained when modeling was made on a group basis by using the activity duration distribution (r = 0.928, p < 0.001, estimation error: 1.36%), making it possible to assess the degree of the stress of employees quantitatively and easily, and this showed the possibility of this method being useful as a management guide for companies.

Plasma Arginase-1 Level Is Associated with the Mental Status of Outpatients with Chronic Liver Disease

While plasma arginase-1 has been suggested as a biomarker of mental status in healthy individuals, it has not been evaluated in patients with chronic liver disease. This cross-sectional study investigated the utility of plasma arginase-1 for screening mental status in patients with chronic liver disease. This study included outpatients with chronic liver disease who underwent regular check-ups at Okayama University Hospital between September 2018 and January 2019. In addition to the standard blood tests, the plasma arginase-1 level was analyzed. The patients’ mental status was assessed using the Japanese version of the General Health Questionnaire-28 (GHQ-28). The associations between mental status and various parameters, including plasma arginase-1, were investigated using logistic regression analysis. Among 114 participating patients, 8 were excluded, comprising 6 with insufficient blood samples for plasma arginase-1 measurement and 2 with incomplete questionnaires. Multivariate binomial logistic regression analysis revealed that plasma arginase-1 was significantly and negatively associated with the GHQ-total score, especially somatic symptoms. Therefore, plasma arginase-1 may be a useful biomarker for assessing the mental status of outpatients with chronic liver disease.

Blood metabolic signatures of hikikomori, pathological social withdrawal

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.