Amino Acid-Fatty Acid Profile as a Novel Predictive Method in the Assessment of Diagnosis and Treatment Efficacy of Anxiety-Related Disorders and Mood Disorders.

Major depressive disorder (MDD) and anxiety disorders are increasingly understood as conditions involving complex metabolic dysregulation across multiple biological domains. This review aimed to synthesize current clinical and translational evidence on amino acid metabolism, lipid metabolism and short-chain fatty acids (SCFAs) as potential biomarkers, and components of integrative metabolic profiling in these disorders. A structured narrative approach was applied, focusing on studies assessing metabolomic alterations, their clinical correlates and their potential role in patient stratification, and treatment response. The available evidence indicates that amino acid disturbances, particularly within the tryptophan-kynurenine pathway, represent the most consistent and clinically interpretable findings. Lipid-related alterations, especially involving long-chain polyunsaturated fatty acids, provide complementary insights into membrane function, inflammation and neuroplasticity. In contrast, SCFAs appear to function as context-dependent markers rather than robust standalone biomarkers, with their clinical relevance depending on biological matrix, metabolic context and host-microbiota interactions. Importantly, most studies assess individual metabolites rather than integrated metabolic profiles, limiting their interpretability within a metabolomic framework. Overall, current evidence supports a shift toward integrative biomarker models that combine metabolic data with selected molecular and clinical parameters. Future research should focus on standardized, reproducible profiling approaches to enable biologically informed stratification and personalized treatment strategies.