Background: Major depressive disorder (MDD) is a neuropsychiatric disorder caused by multiple factors. Although there are clear guidelines for the diagnosis of MDD, the direct and objective diagnostic methods remain inadequate thus far.
Methods: This study aims to discover peripheral biomarkers in patients with MDD and promote the diagnosis of MDD. Plasma samples of healthy controls (HCs, = 52) and patients with MDD ( = 38) were collected, and then, metabolism analysis was performed using ultrahigh-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). Heatmap analysis was performed to identify the different metabolites. Meanwhile, receiver operating characteristic (ROC) curves of these differential metabolites were generated.
Results: Six differential metabolites were found by LC-MS/MS analysis. Three of these were increased, including L-aspartic acid (Asp), diethanolamine, and alanine. Three were decreased, including O-acetyl-L-carnitine (LAC), cystine, and fumarate. In addition, LAC, Asp, fumarate, and alanine showed large areas under the curve (AUCs) by ROC analysis.
Conclusion: The study explored differences in peripheral blood between depressed patients and HCs. These results indicated that differential metabolites with large AUCs may have the potential to be promising biomarkers for the diagnosis of MDD.
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http://dx.doi.org/10.3389/fpsyt.2021.810302 | DOI Listing |
Patterns (N Y)
December 2024
Medical Robot Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
This study developed an artificial intelligence (AI) system using a local-global multimodal fusion graph neural network (LGMF-GNN) to address the challenge of diagnosing major depressive disorder (MDD), a complex disease influenced by social, psychological, and biological factors. Utilizing functional MRI, structural MRI, and electronic health records, the system offers an objective diagnostic method by integrating individual brain regions and population data. Tested across cohorts from China, Japan, and Russia with 1,182 healthy controls and 1,260 MDD patients from 24 institutions, it achieved a classification accuracy of 78.
View Article and Find Full Text PDFClin Transplant
January 2025
Department of Cardiovascular Surgery, Başkent University Faculty of Medicine, Ankara, Turkey.
Introduction: End-stage heart failure (ESHF) remains a significant challenge despite optimal treatment, with heart transplantation (HTx) being the gold standard of care. Mechanical circulatory support (MCS) devices such as left ventricular assist devices (LVADs) are increasingly used for temporary or permanent treatment. Psychiatric comorbidities are common in patients with ESHF and may affect treatment outcomes, but the relationship between sociodemographic, clinical, and psychiatric characteristics remains unclear.
View Article and Find Full Text PDFBMC Psychiatry
January 2025
Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Capital Medical University, Beijing, China.
Background: It is important to timely capture the fluctuation of the symptoms related to major depressive disorder (MDD). However, most conventionally used assessment tools for MDD symptoms are not designed for real-time assessment. The Immediate Mood Scaler (IMS) is suitable for the real-time evaluation of the mood of patients with MDD.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!