Breakthrough Discovery: Objective Biological Markers for Clinical Depression Unveiled
A group of researchers from Skoltech, the University of Sharjah, and Al-Farabi Kazakh National University has made an exciting breakthrough in the field of clinical depression. They have discovered objective biological markers that will revolutionize the way this condition is diagnosed. Their study, published in the journal Neurobiology of Stress, showcases the potential of these markers to make diagnostic criteria more objective and reliable.
Clinical depression, also known as major depressive disorder, is a significant cause of disability globally. In fact, it is projected to become the leading cause of disability by 2030. However, despite affecting an estimated 280 million people worldwide, diagnosing clinical depression remains a challenge.
At present, mental disorders are diagnosed through patient interviews, questionnaires, and assessment scales. Unfortunately, these methods can be subjective, as different practitioners may interpret the results differently. This is why there is a pressing need for reliable biomarkers—objective indicators that can identify a predisposition to mental disorders or track disease progression.
The research team aimed to find such biomarkers that are both reliable and accessible. They explored multimodal data, which provides a comprehensive view of the patient from different perspectives. This included MRI examinations, electroencephalography, blood tests, genotyping, and transcriptome analysis.
According to Assistant Professor Maxim Sharaev, co-author of the study and Head of the Research Group at the Applied AI Center, We assume that the era of simple biomarkers is coming to an end. Now, one criterion is not enough to diagnose a disease. We need a combination of markers that are easier to find through machine learning. By using machine learning models, the team was able to identify integrative biomarkers based on different types of data.
The researchers focused on transcriptome analysis, a field that examines gene expression in cells. By studying the gene expression patterns of 170 patients with clinical depression and 121 healthy individuals, they successfully discovered several biomarkers using bioinformatics and machine learning methods.
To ensure the objectiveness of their findings, the team compared the results of two analyses and identified the most important genes. They then validated these results using a new sample and conducting lab tests on saliva samples. When analyzing the gene expression in different regions of the human brain using the Allen Brain Atlas, they confirmed that these genes are indeed expressed in the relevant areas.
In the future, the team plans to refine and expand the set of genes for screening and rapid diagnostic procedures. They aim to achieve this by utilizing saliva tests, making the diagnosis easier and less invasive. Sharaev believes that this research has the potential to revolutionize the way clinical depression is diagnosed, thereby benefiting both patients and clinical psychiatrists.
The study sheds light on the significance of using artificial intelligence (AI) in combination with bioinformatics to gain a deeper understanding of complex diseases like major depressive disorder. By identifying potential non-invasive diagnostic biomarkers, researchers can provide invaluable insights into the molecular mechanisms involved in the condition.
This breakthrough in understanding clinical depression marks a major step forward in the field. The discovery of objective biological markers brings hope that in the near future, diagnosing this debilitating condition will become more accurate, efficient, and accessible. As scientists continue to unravel the intricacies of mental health disorders, the integration of AI and advanced technologies will undoubtedly play a pivotal role in transforming patient care and improving outcomes.
