Early diagnosis of MDD contributes to a rapid and effective intervention and treatment process and thus is in high demand. Structured clinical interview is currently the most dominant way to diagnose MDD and track the treatment response, where the physician determines the patient’s symptoms by verbally interacting with the patient using standardized assessment tools, which are either clinician-administered or sometimes self-rated. Nevertheless, because the diagnostic process is relatively subjective, dependent on the physician’s expertise and the patient’s cooperation, subjected to human factors, and usually time-consuming, its applicability in the population and its diagnostic effectiveness are not ideal. Precise diagnosis of MDD through biochemical tests remains challenging due to the lack of objective physiological indicators, as well as specific laboratory tests. This indicates the need to develop more effective diagnosis methods based on an in-depth understanding of MDD’s pathophysiology. While no single biomarker exists for MDD diagnosis, there is mounting evidence of multiple dysregulated contributing factors, including inflammatory cytokines, endocrine factors, growth factors, metabolic dysregulation and genetic variations in mood disorders. This knowledge could be used to filter out the associated individual parameters to MDD pathophysiology, understand the causal mechanism and develop biomarker panels that aim to profile a diverse array of hormones, cytokines, inflammatory, metabolic, and genetic markers.
Our aim is to look at all the available biological parameters along with demographic and clinical factors that are associated with MDD risk as well as treatment response. Firstly, we will start with global screening from all the available literature to create a cumulative list of all the significantly associated parameters. Next, we will look for information about these parameters from the patient data retrieved from large biobanks such as UK Biobank. Utilising the information, we will create a prediction model that aims to predict disease risk as well as treatment outcome. Lastly, we will validate the developed model using MDD samples from other populations to assess the predictive accuracy, reproducibility and generalisability of the prediction model.
Developing a predictive model for disease risk and treatment outcomes using a data-driven approach represents a significant advancement. This model could address the existing treatment gap and improve overall response rates in MDD patients. The use of a global population in the meta-analysis and the inclusion of large biobanks enhance the generalizability of the developed model. This global perspective contributes to a more inclusive understanding of MDD across diverse populations.
