Yeeun Yun

1. Major depressive disorder (MDD) is the most common mood disorder affecting physical health and is diagnosed when symptoms persist for more than 2 weeks. Its symptoms include distinct mood swings...

Major depressive disorder (MDD) is a multifactorial and highly recurrent illness. Patients are typically required to take antidepressants for 6–12 months following a single depressive episode, even if symptoms are alleviated. However, some individuals are reluctant to use these medications or adhere to prescribed dosing instructions. Therefore, this study aimed to identify biomarkers that can predict MDD recurrence, thus facilitating early detection of relapses and providing strong clinical justification for sustained antidepressant therapy. Serum samples were collected from participants, categorized into two groups: major depressive disorder with a single episode (MDD-S) and recurrent episodes (MDD-R) groups. Candidate biomarkers were identified via targeted tandem mass spectrometry, with features extracted using MassHunter Profinder v10.0 and Mass Profiler Professional software v 15.1 (p < 0.05, fold-change ≥ 1.2). After spectral matching, hypoxanthine was finally identified. Subsequently, a multiple reaction monitoring analysis, a targeted metabolomics technique, was performed to validate hypoxanthine. This analysis confirmed that hypoxanthine levels were significantly lower in the MDD-R group than in the MDD-S group. Validation results demonstrated that this difference was independent of sex and the presence or absence of antidepressant treatment. Moreover, receiver operating characteristic analysis confirmed that the MDD-S and MDD-R groups were significantly distinct (Area Under the Curve = 0.87, sensitivity = 80%, specificity = 80%). In conclusion, this study identified hypoxanthine as a biomarker for predicting MDD recurrence, confirming lower hypoxanthine levels in patients with recurrent MDD than in those with single-episode MDD. Hypoxanthine is a key intermediate in purine metabolism and is known to be involved in the regulation of neuronal energy homeostasis and oxidative stress; these processes are increasingly recognized as being closely linked to the pathophysiology of major depressive disorder. This biomarker may enable the identification of patients at a high risk of recurrence, thereby facilitating the development of personalized treatment strategies.

Major depressive disorder (MDD) is a mood disorder that causes serious functional impairment. Existing diagnostic methods rely on subjective assessments because of its complex and heterogeneous pathophysiology; therefore, development of objective biomarkers is urgently needed. To control the high heterogeneity of MDD, a pairwise design in which depressed and remitted states of the same patient were paired to minimize the influence of intrinsic factors was introduced, and serum metabolite changes between states were analyzed using non‐targeted and targeted metabolomics approaches. State‐based biomarkers that could be used for objective diagnosis of MDD were identified, and their clinical applicability was validated in an expanded study group. L‐Glutamine was selected because it showed a tendency to increase during the remitted state. Through multiple reaction monitoring‐based quantitative verification, L‐glutamine levels significantly increased in the remitted state compared with those in the depressed state, regardless of the influence of drug treatment, proving its potential as a diagnostic marker. This study identified differences in serum metabolites in patients with MDD using a metabolomic approach; L‐glutamine could be used as a promising biomarker to distinguish between depressive and remissive states. These results can contribute to the precise diagnosis of MDD and establishment of personalized treatment strategies.

Objective: Spontaneous remission may influence the outcome of clinical trials and evaluation of antidepressant efficacy, as it is associated with placebo effects and false remission rates. However, the characteristics of spontaneous remission and its biological mechanisms remain poorly understood. This study aimed to explore the metabolic signatures and underlying biological mechanisms of spontaneous remission using metabolomics. Methods: This study conducted untargeted and targeted metabolomic analyses in a discovery cohort (n = 16) comprising patients with major depressive disorder (MDD) and those who were spontaneously remitted without medication. Findings were validated in an independent cohort (n = 185), comprising drug-treated patients and healthy controls. Results: Acetylcarnitine levels were significantly increased in spontaneous remission compared to depression, whereas glycerophosphocholine levels were decreased in spontaneous remission. Both metabolites showed the highest concentration in the control group, followed by the remission group, and the lowest concentration in the depression group, regardless of medication status. These changes suggest alterations in mitochondrial and membrane lipid metabolism. Conclusion: Altered levels in acetylcarnitine and glycerophosphocholine may reflect key pathogenic mechanisms of MDD. These findings offer new insight into spontaneous remission as a distinct clinical subtype of depression and may highlight the potential of these metabolites as biomarkers for treatment monitoring in MDD.Keywords: Depressive disorder, major; Remission, spontaneous; Metabolomics; Acetylcarnitine; Glycerophosphocholine

In patients with major depressive disorder, various functional areas are impaired, negatively impacting the quality of life. Remission can restore pre-depression functions; however, some patients may still have residual impairments. Distinguishing between near-normal recovery and residual impairment helps identify those at a high risk of relapse risk and helps tailor treatment. Accordingly, we aimed to discover and validate biomarkers that distinguish between near-normal recovery and residual impairment in remission states through serum proteome analysis. Pooled serum and individual serum samples from three groups (depression status, remission status with residual impairment, and remission status with normal recovery) were analyzed using liquid chromatography-tandem mass spectrometry. The combination of four proteins—antithrombin-III, serum amyloid A4 protein, C1q subcomponent subunit B, and serum amyloid P-component—was selected as a candidate biomarker. The trend of protein changes suggests complement C1q subcomponent subunit B and serum amyloid P-component as potential biomarkers for distinguishing remission from residual impairment. Changes in complement C1q subcomponent subunit B and serum amyloid P-component suggest that the complement system and inflammation-related immune mechanisms are associated with residual impairment in remittent major depressive disorder.

Background: Major depressive disorder (MDD) exhibits a recurrence rate of up to 70%. Frequent recurrence can lead to chronic depression, which has considerable personal and societal consequences. This study aims to identify a serum protein biomarker to predict MDD recurrence and progression to chronicity. Methods: Serum samples from the MDD with single episode group (MDD-S), MDD with recurrence group (MDD-R), and a healthy control group were collected. Non-targeted analysis of the serum proteome was conducted using liquid chromatography–tandem mass spectrometry. Statistically significant common proteins when comparing the three groups were chosen. The selected marker candidates were subsequently validated through multiple response monitoring (MRM), incorporating a healthy control, MDD-S, MDD-R(2) (two episodes), and MDD-R(> 2) (more than two episodes) groups. Results: L-selectin levels showed an upward trend in the MDD-R group compared to the healthy control and MDD-S groups. MRM validation revealed a decreased tendency for L-selectin in the MDD-R(> 2) group, indicative of a chronic state, versus the healthy control and MDD-S groups. The receiver operating characteristic analysis highlighted L-selectin as the chosen biomarker due to its classification efficacy for the MDD-R(> 2) group. Conclusion: L-selectin emerged as a predictive biomarker for MDD recurrence and its potential evolution into chronic depression. This marker offers insights into changes in leukocyte-mediated inflammatory responses characteristic of chronic depression. Consequently, it may forecast the transition from acute to chronic inflammation in depressive patients.

Purpose: Depressive mood is a major psychiatric symptom that causes serious disturbances in daily life. Unlike physical symptoms, psychiatric symptoms are more difficult to evaluate objectively. Therefore, we aimed to discover biomarkers that reflect changes in serum protein metabolism during a clinical depressive mood. Methods: Serum protein profiling was conducted in participants who were not experiencing a current depressive episode (healthy individuals and patients in remission). Serum proteins were identified and quantified using liquid chromatography–tandem mass spectrometry. Differentially expressed proteins with a p-value < 0.05 were selected, and candidate biomarkers were verified using multiple reaction monitoring analysis for absolute quantification. Results: Apolipoprotein A-IV levels were lower in the group with a current episode of depression than in the remission and healthy control groups. Further, fibronectin levels were also lower in the group with a current episode of depression than in the healthy control group but not in the remission group. Conclusion: We found that apolipoprotein A-IV-mediated inflammation is involved in clinical depressive moods, possibly by inducing neurological changes in the brain. Therefore, apolipoprotein A-IV and fibronectin levels may be explored as potentially novel biomarkers for detecting a current episode of depression.