Abstract
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.
Highlights
Depression is a state of low mood and demotivated condition that affects a person’s feelings, cognition and, behaviors [1]
Digging up blood metabolites to predict severity of depression (SOD), we collected blood plasma of psychiatric patients from three independent clinical research centers; Kyushu University (26 medicationfree patients with depressive mood), Osaka University (23 medicated patients who diagnosed with major depressive disorders (MDD)), and NCNP/NCNP Biobank (41 medicated/nonmedicated patients who diagnosed with Major depressive disorder (MDD) (27 patients) and bipolar disorder (14 patients)) (Fig 1)
We found that each model showed a fairly good correlation with either value R2 = 0.24 (PHQ-9) and R2 = 0.263 (HAMD-17) and the influential metabolites were listed almost as common (Table 1)
Summary
Depression is a state of low mood and demotivated condition that affects a person’s feelings, cognition and, behaviors [1]. Severe depressive mood is recognized to be a high risk factor of suicide [3, 4], evaluating the severity of depression (SOD), especially suicidal ideation (SI), is crucial in psychiatric and primary care settings [5]. Some persons may not express depressive mood at all in order to hide internal turmoil, and others may hyperbolically show their depressed mood to obtain a sick role. These difficult situations have resulted in confusion in clinical practice [10, 11]. No significant blood biomarkers to predict SOD have existed until now To resolve this limitation, objective SOD evaluation methods have been warranted
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