Development and validation of the 25â item Hikikomori Questionnaire (HQâ 25)

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146508/1/pcn12691_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146508/2/pcn12691.pd

Low-Density Lipoprotein Cholesterol Is a Possible Blood Biomarker of Schizoid Personality Traits among Females

Lower serum levels of low-density lipoprotein cholesterol (LDL-C) have been suggested to indicate higher suicide risk and various psychiatric symptoms. Previously, we reported that lower serum LDL-C levels are associated with loneliness, social phobia, isolated life with little social support, and lower trust in others among young non-clinical females. Thus, we hypothesize that schizoid personality traits may be associated with lower serum LDL-C. We here verified this hypothesis using non-clinical data and clinical data with schizophrenia. Using the database from the Midlife in Japan (MIDJA), a cohort of residents living in Tokyo, we analyzed whether schizoid-related interpersonal characteristics were associated with LDL-C. In addition, we assessed the association between blood biomarkers including LDL-C and schizoid personality traits in 101 adult non-clinical volunteers. Finally, we evaluated the interaction between LDL-C and social decision making of patients with schizophrenia. In female non-clinical volunteers, serum LDL-C level was a predictive factor and negatively correlated with schizoid personality traits. Female patients with schizophrenia, whose serum LDL-C levels were lower, tended not to trust other females. The present findings suggest that LDL-C may influence schizoid personality traits in females, which provide a basis for further investigation into the biological aspects of schizoid personality disorder

Development and validation of the 25‐item Hikikomori Questionnaire (HQ‐25)

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146508/1/pcn12691_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146508/2/pcn12691.pd

Blood biomarkers of Hikikomori, a severe social withdrawal syndrome

Abstract Hikikomori, a severe form of social withdrawal syndrome, is a growing social issue in Japan and internationally. The pathophysiology of hikikomori has not yet been elucidated and an effective treatment remains to be established. Recently, we revealed that avoidant personality disorder is the most common comorbidity of hikikomori. Thus, we have postulated that avoidant personality is the personality underpinning hikikomori. First, we herein show relationships between avoidant personality traits, blood biomarkers, hikikomori-related psychological features, and behavioural characteristics assessed by a trust game in non-hikikomori volunteers. Avoidant personality traits were negatively associated with high-density lipoprotein cholesterol (HDL-C) and uric acid (UA) in men, and positively associated with fibrin degeneration products (FDP) and high sensitivity C-reactive protein (hsCRP) in women. Next, we recruited actual individuals with hikikomori, and compared avoidant personality traits, blood biomarkers, and psychological features between individuals with hikikomori and age-matched healthy controls. Individuals with hikikomori had higher avoidant personality scores in both sexes, and showed lower serum UA levels in men and lower HDL-C levels in women compared with healthy controls. This is the first report showing possible blood biomarkers for hikikomori, and opens the door to clarify the underlying biological pathophysiology of hikikomori

Plasma Metabolites Predict Severity of Depression and Suicidal Ideation in Psychiatric Patients-A Multicenter Pilot Analysis

<div><p>Evaluating the severity of depression (SOD), especially suicidal ideation (SI), is crucial in the treatment of not only patients with mood disorders but also psychiatric patients in general. SOD has been assessed on interviews such as the Hamilton Rating Scale for Depression (HAMD)-17, and/or self-administered questionnaires such as the Patient Health Questionnaire (PHQ)-9. However, these evaluation systems have relied on a person’s subjective information, which sometimes lead to difficulties in clinical settings. To resolve this limitation, a more objective SOD evaluation system is needed. Herein, we collected clinical data including HAMD-17/PHQ-9 and blood plasma of psychiatric patients from three independent clinical centers. We performed metabolome analysis of blood plasma using liquid chromatography mass spectrometry (LC-MS), and 123 metabolites were detected. Interestingly, five plasma metabolites (3-hydroxybutyrate (3HB), betaine, citrate, creatinine, and gamma-aminobutyric acid (GABA)) are commonly associated with SOD in all three independent cohort sets regardless of the presence or absence of medication and diagnostic difference. In addition, we have shown several metabolites are independently associated with sub-symptoms of depression including SI. We successfully created a classification model to discriminate depressive patients with or without SI by machine learning technique. Finally, we produced a pilot algorithm to predict a grade of SI with citrate and kynurenine. The above metabolites may have strongly been associated with the underlying novel biological pathophysiology of SOD. We should explore the biological impact of these metabolites on depressive symptoms by utilizing a cross species study model with human and rodents. The present multicenter pilot study offers a potential utility for measuring blood metabolites as a novel objective tool for not only assessing SOD but also evaluating therapeutic efficacy in clinical practice. In addition, modification of these metabolites by diet and/or medications may be a novel therapeutic target for depression. To clarify these aspects, clinical trials measuring metabolites before/after interventions should be conducted. Larger cohort studies including non-clinical subjects are also warranted to clarify our pilot findings.</p></div

Classification and regression models for predicting suicidality and a grade of suicidal ideation in depressive patients.

<p>(<b>A</b>) Receiver-operating curve based on ten kinds of logistic regression models for classifying suicidality in depressive patients. Based on mixed data containing three independent data sets (Data set-1, Data set-2, and Data set-3), 10 kinds of distinct training data are created and subjected to building classification models discriminating between the depressive patients who have a sub-scale of suicidal ideation (HAMD_SI: HAMD-17_11> = 1) and not (HAMD_SI = 0) (details are described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0165267#pone.0165267.s001" target="_blank">S1 Table</a>). Curves in red denote highly predictive models for test data set (true rate >0.7). (<b>B</b>) Significant correlation (R = 0.22, p = 0.028) between scored HAMD_SI and predictive scores of multiple linear regression model, based on standardized intensities of plasma citrate and kynurenine. A fitted linear regression line was depicted with 95% confidence area (gray shaded). Parameters of the model were described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0165267#pone.0165267.s002" target="_blank">S2 Table</a>.</p

Plasma metabolome-based PLS regression model for PHQ-9 and HAMD-17 prediction.

<p>(A) For the medication-free plasma sample set of 26 patients, we conducted LC-MS-based metabolome analysis of water-soluble metabolites in plasma. Based on the 123 kinds of metabolites, the multivariate data sets were centered, scaled to Pareto and subjected to a multivariate statistical analysis using the SIMCA P+ ver. 14.0 software program (Umetrics, Sweden). The partial-least-squares (PLS) PHQ-9-regression model was created to identify the metabolites that associated with the severity of depression (SOD). The x-axis indicates the observed score of PHQ-9, while the y-axis indicates the predicted value of PHQ-9. (B) The HAMD-17-regression model was created based on the same data set 1. The x-axis indicates the observed score of HAMD-17, while the y-axis indicates the predicted value of HAMD-17. (C) Plasma samples from 23 medicated MDD patients (Data set 2) were subjected to LC-MS metabolome analysis and a HAMD-17-regression model was created as in (B). (D) Plasma samples of 41 patients from the third data set including both the medicated and non-medicated plasma samples, which were diagnosed with MDD (n = 27, black) and with bipolar disorder (n = 14, gray), were subjected to LC-MS metabolome/HAMD-17 regression analysis. R², the square of Spearman’s correlation coefficient; RMSEE, root mean square error of estimation; RMSEcv, root mean square error of cross validation.</p