Results of Regression Analysis (Monetary Scores as Dependent Variable).

<p>Note:</p><p>* p<.05,</p><p>** p<.01.</p><p>Eleven participants were excluded in this analysis for failing to complete the questions.</p><p>Results of Regression Analysis (Monetary Scores as Dependent Variable).</p

The ratio of monetary score given by partner’s sex and attractiveness (** p<.01).

<p>The ratio of monetary score given by partner’s sex and attractiveness (** p<.01).</p

Trust Game Structure with the Most Extreme Cases.

<p>Trust Game Structure with the Most Extreme Cases.</p

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

Metabolites commonly associated with HAMD_SI.

<p>In three distinct cohort studies, four metabolites moderately and commonly correlate with HAMD_SI. Gray shaded denotes negatively correlation with the respective sub-scales.</p

Correlation networks of HAMD-17 sub-scale and metabolites in medication-free cohort samples (Data set 1).

<p>Red solid/dot lines, positive correlation; blue solid/dot lines, negative correlation; Black nodes, HAMD-17 subscales; blue nodes, metabolites. Edge-width scales as the correlation coefficients in absolute value. The bolder the lines, the stronger the correlation between connected nodes is.</p

Correlation between sub-scales of PHQ-9/HAMD-17 and plasma metabolites in medication-free cohort study (Data set-1).

<p>Metabolites which have moderate correlation (absolute correlation coefficient value >0.2) with reciprocally synonymous sub-scale of PHQ-9/HAMD-17 are listed. Gray shaded denotes negatively correlation with the respective sub-scales.</p

Plasma metabolites primarily contributed to the respective PLS-regression model.

<p>The variable importance in projection (VIP) denotes the degree of contribution to the PLS regression model, whose scores greater than 1.0 can be considered important in given model. Metabolites displayed in bold style represent to commonly contribute to the three independent PLS regression models, thereby suggesting strongly associated with the underlying pathophysiology of depression.</p