Blood Fatty Acid Profile as a Predictor of Antidepressant Efficacy—A Prospective Cohort Pilot Study Protocol

Background/Objectives: Despite the availability of multiple pharmacological treatment options, up to one-third of patients with depressive disorders fail to achieve adequate symptom relief following first-line antidepressant therapy, representing a major unmet clinical need. Fatty acids—including short-chain (SCFAs), medium-chain (MCFAs), and long-chain polyunsaturated fatty acids (PUFAs)—are increasingly implicated in depression pathogenesis through neuroinflammation, gut–brain axis signaling, and neurotransmitter metabolism, but their potential as predictors of antidepressant response remains largely unexplored. The primary aim is to evaluate whether baseline fatty acid profiles can predict pharmacological antidepressant treatment efficacy. The secondary objective is to assess the association between blood fatty acid profile and clinical presentation of depressive disorders. Methods: Sixty adults diagnosed with depressive disorders (ICD-11) at the initiation of a new antidepressant treatment will be recruited from psychiatric settings. Fasting blood samples collected at baseline will undergo gas chromatographic analysis of fatty acid methyl esters (FAMEs) and GC-MS quantification of SCFAs (acetic, propionic, and butyric acids). Clinical outcomes will be assessed at baseline and at 3, 6, and 12 weeks using the Beck Depression Inventory-II (BDI-II) and the Depression Anxiety Stress Scales (DASS-42). The primary endpoint is the change in BDI-II total score from baseline to week 6. Treatment response, defined as a ≥50% reduction in BDI-II total score at week 6, and remission, defined as a BDI-II score ≤12 at week 6, will be examined as secondary outcomes. Dietary habits, physical activity, quality of life, and anthropometric parameters will be collected as potential confounders. Discussion: This study is among the first prospective investigations to comprehensively characterize the circulating fatty acid spectrum as potential predictors of antidepressant outcomes. Findings may support identification of metabolic phenotypes of depression and contribute to personalized treatment strategies.