Plasma lipidomics for the differential diagnosis of psychotic and mood disorders: a systematic review
I. Mangas Palma1, J. Paupério1, L. Ferreira1, R. Moreira1
1Hospital Universitário de São João, Serviço de Psiquiatria, Porto, Portugal
Background: Schizophrenia and mood disorders affect millions of individuals worldwide and impose a substantial burden. Although classified as distinct diagnostic entities, these disorders show considerable clinical overlap, and shared genetic risk factors further blur diagnostic boundaries. Consequently, symptom-based diagnostic systems remain vulnerable to misclassification, particularly in early or complex presentations.
There is growing interest in lipids as peripheral biomarkers in psychiatric disorders. Lipidomic studies have consistently reported alterations in serum and plasma lipid profiles, including reductions in ether phospholipids in schizophrenia and changes in acylcarnitines and ceramides in mood disorders. However, the extent to which these alterations can discriminate between psychotic and mood disorders remains unclear, as most studies have compared single diagnostic groups with healthy controls. Consequently, the discriminative value of lipidomic signatures across these categories has yet to be synthesised.
There is growing interest in lipids as peripheral biomarkers in psychiatric disorders. Lipidomic studies have consistently reported alterations in serum and plasma lipid profiles, including reductions in ether phospholipids in schizophrenia and changes in acylcarnitines and ceramides in mood disorders. However, the extent to which these alterations can discriminate between psychotic and mood disorders remains unclear, as most studies have compared single diagnostic groups with healthy controls. Consequently, the discriminative value of lipidomic signatures across these categories has yet to be synthesised.
Aims & Objectives: This systematic review aims to synthesise current evidence on the use of lipidomic profiling for the differential diagnosis of schizophrenia and other psychotic disorders versus mood disorders.
Method: A systematic literature search was conducted in PubMed. Additionally, the reference lists of all eligible articles were reviewed to identify further relevant studies. Studies were included if they involved human participants, applied lipidomic analyses, and directly compared individuals with schizophrenia and/or other psychotic disorders with individuals diagnosed with bipolar disorder and/or major depressive disorder. Data extraction focused on study design, sample characteristics, lipidomic analysis, statistical methods, and lipid species associated with diagnostic differentiation.
Results: Five studies met the inclusion criteria and were included in the systematic review, encompassing sample sizes ranging from 90 to over 1,500 participants. Across studies, between several dozen and more than one hundred lipid species were reported as significantly altered.
The majority of discriminative lipids belonged to glycerophospholipids, sphingolipids and glycerolipids. Psychotic disorders were more consistently associated with reductions in ether phospholipids and sphingomyelins, as well as alterations in polyunsaturated fatty acid–containing triglycerides. In contrast, mood disorders exhibited comparatively distinct patterns in triglyceride composition and specific sphingolipid subclasses. Although substantial transdiagnostic overlap was observed, multiple studies identified subsets of lipids showing opposing patterns between psychotic and affective disorders.
Multivariate classification models demonstrated moderate to high diagnostic performance. Receiver operating characteristic analyses reported areas under the curve ranging approximately from 0.80 to above 0.90 for differentiating psychotic disorders from mood disorders, with sensitivity and specificity values generally exceeding 70% in higher-performing models. In larger cohorts, lipidomic panels composed of approximately 10–20 lipid species were sufficient to achieve stable discrimination between diagnostic groups.
The majority of discriminative lipids belonged to glycerophospholipids, sphingolipids and glycerolipids. Psychotic disorders were more consistently associated with reductions in ether phospholipids and sphingomyelins, as well as alterations in polyunsaturated fatty acid–containing triglycerides. In contrast, mood disorders exhibited comparatively distinct patterns in triglyceride composition and specific sphingolipid subclasses. Although substantial transdiagnostic overlap was observed, multiple studies identified subsets of lipids showing opposing patterns between psychotic and affective disorders.
Multivariate classification models demonstrated moderate to high diagnostic performance. Receiver operating characteristic analyses reported areas under the curve ranging approximately from 0.80 to above 0.90 for differentiating psychotic disorders from mood disorders, with sensitivity and specificity values generally exceeding 70% in higher-performing models. In larger cohorts, lipidomic panels composed of approximately 10–20 lipid species were sufficient to achieve stable discrimination between diagnostic groups.
Discussion & Conclusions: Overall, the findings indicate that lipidomic profiling holds potential as a complementary tool for the differential diagnosis of psychotic and mood disorders. While shared lipid alterations support transdiagnostic biological mechanisms, disorder-specific lipid patterns suggest that lipidomics may contribute to improved diagnostic precision. Nonetheless, heterogeneity in study design, sample characteristics, and lipidomic methodologies currently limits direct comparability and clinical translation.
Future research should prioritise larger, well-characterised cohorts, standardised lipidomic methodologies, and longitudinal designs to validate robust lipid-based biomarkers. Integrating lipidomic data with clinical assessment may ultimately enhance diagnostic accuracy and support more personalised treatment approaches.
Future research should prioritise larger, well-characterised cohorts, standardised lipidomic methodologies, and longitudinal designs to validate robust lipid-based biomarkers. Integrating lipidomic data with clinical assessment may ultimately enhance diagnostic accuracy and support more personalised treatment approaches.
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