| Summary: | Major Depressive Disorder (MDD) poses significant clinical challenges due to its complicated and elusive pathophysiology. This thesis seeks to fill research gaps in understanding MDD by investigating peripheral metabolites and lipids as potential biomarkers which could be used to improve diagnosis and prognosis of this debilitating mental health condition. Small molecules, such as amino acids, that are involved in energy homeostasis are of particular interest to investigate alongside lipids because mood disorders are strongly associated with metabolic dysfunction. A systematic review and meta-analysis was first conducted on articles reporting metabolomic analyses on amino acids and demonstrated significantly elevated citrate, alanine, and glutamate levels in peripheral biofluids from depressed people compared to healthy individuals. The investigation analyzed metabolomic and lipidomic data from UK twin populations, focusing on individuals assessed using the Hospital Anxiety and Depression Scale (HADS). Participants meeting inclusion criteria had at least one HADS score and <15% missing data. With 1,532 entries, the primary emphasis was on a two-year database, revealing insights into twin health, including 43 Normal, 35 Borderline Abnormal, and 35 Abnormal samples. This comprehensive study illuminated connections between lifestyle factors, inflammatory markers, lipidomics, and MDD severity. Positive associations between smoking and alcohol consumption underscored gender-specific implications and negative correlations between exercise and HADS suggested a protective relationship against depression, aligning with established benefits for mental well-being. Pyruvate and tyrosine were found to be positively associated with depression, while alanine and acetate were negatively associated with depression. However, none of these associations were statistically significant. Multiple regression study found positive relationships between IDLFC, SLDLL, LDLC, and HADS levels, while others exhibited significant negative associations. SLDLCE and SLDLFC were found to have the highest impact on HADS scores, highlighting the complex nature of lipid metabolism in depression. Furthermore, the inverse connection between cholesterol and cholesterol esters in small and medium LDL particles and depression severity shows that cholesterol may be a more important biomarker for major depressive disorder (MDD), regardless of lipoprotein size or density. The OPLS-DA model effectively distinguished normal and abnormal mental health categories, highlighting the discriminative potential of metabolite biomarkers. Conspicuously, "AcAce" led with a VIP score of 2.24, followed by "BOHBut" (1.89) and "Ala" (1.75), emphasizing their impact. The lipid analysis illuminated complex shifts in cholesterol metabolism and lipid species, accentuating their crucial role in the pathophysiology of depression. "FreeC" stood out with a VIP score of 1.04, indicating its significant predictive influence, while "SM" closely followed with a score of 1.03. Additionally, "IDLFC" and "LLDLL" demonstrated remarkable importance, with VIP scores of 1.02 and 1.01, respectively, capturing essential data patterns. Comorbidity was found to influence lipid profiles, emphasizing the need to consider psychiatric conditions in research and clinical practice. Collectively, these findings contribute valuable insights into the multifaceted nature of MDD, proposing potential new biomarkers that may refine diagnostic accuracy and deepen our comprehension of this complex disorder.
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