Polyunsaturated fatty acids (PUFAs), especially long-chain PUFAs (LCPUFAs), are crucial for both the structural and functional integrity of cells. PUFAs have been reported to be insufficient in schizophrenia, and the resulting cell membrane impairments have been hypothesized as an etiological mechanism. However, the impact of PUFA deficiencies on the onset of schizophrenia remain uncertain. We investigated the associations between PUFAs consumption and schizophrenia incidence rates through correlational analyses and conducted Mendelian randomization analyses to reveal the causal effects. Using dietary PUFA consumption and national schizophrenia incidence rates in 24 countries, we found that incidence rates of schizophrenia were inversely correlated with arachidonic acid (AA) and ω-6 LCPUFA consumption (r_AA = −0.577, p < 0.01; r_{ω-6 LCPUFA} = −0.626, p < 0.001). Moreover, Mendelian randomization analyses revealed that genetically predicted AA and gamma-linolenic acid (GLA) were protective factors against schizophrenia (OR_AA = 0.986, OR_GLA = 0.148). In addition, no significant relationships were observed between schizophrenia and docosahexaenoic acid (DHA) or other ω-3 PUFAs. These findings show that the deficiencies of ω-6 LCPUFAs, especially AA, are associated with schizophrenia risk, which sheds novel insight into the etiology of schizophrenia and a promising diet supplementation for the prevention and treatment of schizophrenia.

Adolescent depression is a globally concerned mental health issue, the pathophysiological mechanisms of which remain elusive. Membrane lipids play a crucial role in brain development and function, potentially serving as a crossroad for the abnormalities in neurotransmitters, neuroendocrine, inflammation, oxidative stress, and energy metabolism observed in depressed adolescents. The primary aim of this study was to investigate the erythrocyte membrane lipid profile in adolescent depression. A total of 2838 erythrocyte membrane lipids were detected and quantified in 81 adolescents with depression and 67 matched healthy adolescents using ultra-high performance liquid chromatography-mass spectrometry. Depressed adolescents exhibited significantly different membrane lipid characteristics compared to healthy controls. Specifically, the levels of cholesterol, sphingomyelins, and ceramides were increased, while ether lipids were decreased in patients. Moreover, the patients showed reduced polyunsaturated fatty acids and elevated lipophilic index in membrane, suggesting diminished membrane fluidity. The higher oxidized membrane lipids and plasma malondialdehyde were observed in adolescent depression, indicating the presence of oxidative stress. Importantly, membrane lipid damage was associated with more severe depressive symptoms and worse cognitive function in patients. In addition, reduced polyunsaturated fatty acids and membrane fluidity may be partly responsible for the blunted niacin skin flushing response found in depressed adolescents. In conclusion, our results reveal impaired erythrocyte membrane lipid homeostasis in adolescents with depression, which may implicate membrane dysfunction in the brain. These findings offer new insights into the underlying molecular mechanisms of adolescent depression, highlighting the potential of counteracting membrane damage as a promising avenue for future therapeutic interventions.

Adolescent depression is a significant global health challenge, with many patients responding inadequately to antidepressant treatments. Omega-3 polyunsaturated fatty acids (ω3 PUFAs) have been proposed as a potential adjunctive treatment, but their precise mechanisms remain poorly understood. This study aimed to explore the mechanisms through which ω3 PUFAs exert their antidepressant effects and to identify potential biomarkers for their therapeutic response. A comprehensive assessment of plasma metabolomic and erythrocyte membrane lipidomic was performed on 51 depressed adolescents who were randomly assigned to received either ω3 PUFAs plus paroxetine (n = 27) or paroxetine alone (n = 24) for 12 weeks. Following ω3 PUFA supplementation, phospholipid metabolism emerged as the most significantly altered pathway. ω3 PUFAs markedly influenced the composition of membrane fatty acids, significantly increasing the ω3 PUFA content, decreasing the ω6/ω3 PUFA ratio, and increasing membrane fluidity. Notably, ω3 PUFAs reduced lipid peroxidation in both plasma and cell membranes while enhancing antioxidant capacity in the membranes. Moreover, alterations in phospholipids and membrane function were significantly correlated with improvements in depressive symptoms and cognitive function. Importantly, ω3 PUFA supplementation resulted in greater improvement in clinical symptoms compared to the non-supplemented group exclusively in the subgroup with high baseline oxidative damage levels. This study suggests that ω3 PUFAs promoted phospholipid integration and alleviated oxidative stress, which may account for their antidepressant effects. Lipid oxidation biomarkers could help identify patients likely to benefit from ω3 PUFA supplementation. These findings advance our understanding of the mechanism and clinical application of ω3 PUFAs in treating adolescent depression.

Background: Niacin Skin-Flushing Response (NSR) has emerged as a promising objective biomarker for the precise diagnosis of mental disorders. However, its diagnostic potential has been constrained by the limitations of traditional statistical approaches. The advent of Artificial Intelligence (AI) offers a transformative opportunity to overcome these challenges. This study presents a novel contribution to the field by establishing an open-access dataset and developing advanced AI-driven tools to enhance the diagnostic accuracy of psychiatric disorders through NSR analysis. Methods: This study introduces the world’s first open dataset specifically developed for AI studies of Niacin Skin-Flushing Response (NSR), a physiological biomarker associated with mental illnesses including depression, bipolar disorder, and schizophrenia. Leveraging this dataset, we developed an advanced Machine Learning (ML) approach designed for the broad diagnosis of mental disorders. Distinct from prior studies which are often limited to First Episode Schizophrenia and depend on specific devices, our approach champions device independence. The core of our methodology involves a novel algorithm featuring an Efficient-Unet based Deep Learning model for the precise segmentation of NSR areas. This segmentation is significantly enhanced by runtime data augmentation and trained on a robust train/validation/test dataset split. Subsequently, a Support Vector Machine (SVM) method is employed for psychiatric disorder classification utilizing feature vectors extracted from the segmentation of NSR areas with a 3-scale quantization. The SVM training incorporates 5-fold cross-validation, Synthetic Minority Over-sampling Technique (SMOTE) for managing class imbalance, and hyperparameter tuning to optimize balanced accuracy. Results: The established dataset comprises 600 high-quality NSR images from 120 individuals, encompassing a diverse cohort of healthy controls and patients with various mental illnesses. The developed AI tools offer an objective, swift, and highly accurate approach that is demonstrably independent of the diagnosed condition or the specific device used for image acquisition. Comparative results demonstrate that the ML-based diagnostic approach achieves a sensitivity ranging from 60.0 to 65.0% and a specificity from 75.0 to 88.3% across various types of illnesses, further underscoring its broad applicability and device independence. Conclusions: This research conclusively demonstrates the significant potential of advanced AI tools in achieving precise diagnosis of psychiatric disorders, potentially surpassing human capabilities in both speed and accuracy. With the provision of the proposed open dataset and the introduction of novel methodologies, this study marks substantial progress in developing an objective and accurate NSR-based screening process for a wide spectrum of psychiatric disorders. Its enhanced applicability and independence from specific devices hold profound potential to substantially advance mental health diagnostics and contribute to improved patient outcomes globally.

Many psychiatric disorders are associated with major cognitive deficits. However, it is uncertain whether these deficits develop as a result of psychiatric disorders and what shared risk factors might mediate this relationship. Here, we utilized the Mendelian randomization (MR) analysis to investigate the complex causal relationship between nine major psychiatric disorders and three cognitive phenotypes, while also examining the potential mediating role of oxidative stress as a shared biological underpinning. Schizophrenia (SZ), major depressive disorder (MDD), and attention deficit hyperactivity disorder (ADHD) showed a decreasing effect on cognitive performance, intelligence, and education, while bipolar disorder (BPD) increased educational attainment. MR-Clust results exhibit the shared genetic basis between SZ and other psychiatric disorders in relation to cognitive function. Furthermore, when oxidative stress was considered as a potential mediating factor, the associations between SZ and the three dimensions of cognition, as well as between MDD and intelligence and ADHD and intelligence, exhibited larger effect sizes than the overall. Mediation MR analysis also supported the causal effects between psychiatric disorders and cognition via oxidative stress traits, including carotene, vitamin E, bilirubin, and uric acid. Finally, summary-based MR identified 29 potential causal associations of oxidative stress genes with both cognitive performance and psychiatric disorders. Our findings highlight the importance of considering oxidative stress in understanding and potentially treating cognitive impairments associated with psychiatric conditions.

Background: Mood disorders (MDs), including depressive disorder (DD) and bipolar disorder (BD), represent a major global health burden, yet the absence of objective biomarkers has persistently hindered their accurate clinical diagnosis. The niacin skin flushing response (NSFR) has shown promise as a potential biomarker; however, inconsistencies in diagnostic efficacy and response characteristics across studies have limited its utility. This meta‐analysis aims to systematically evaluate the NSFR in MD patients to address these discrepancies. Methods: A comprehensive search of PubMed, Embase, Cochrane Library, and Scopus databases was conducted for articles published through May 2025, identifying 18 eligible case–control studies involving 1848 participants (888 MD patients and 960 HC). Random‐effects models were used to assess the degree, speed, and sensitivity of NSFR, while subgroup analyses and meta‐regression explored potential sources of heterogeneity. Results: MD patients exhibited markedly blunted NSFR, characterized by reduced degree and speed of response (standardized mean difference [SMD] = −0.59, 95% confidence interval [CI]: [−1.12; −0.05] and SMD = 0.72, 95% CI: [0.35; 1.10]), while no statistically significant difference in NSFR sensitivity was observed (SMD = 0.51, 95% CI: [−0.18; 1.20]). Subgroup analyses identified substantial heterogeneity across detection methods, geographical regions, and control sources, highlighting the need for standardized protocols. Meta‐regression analyses indicated that sample size, publication year, gender distribution, and age did not significantly affect outcomes, further strengthening the robustness of our findings. Conclusion: This study reinforces the potential of NSFR as a biomarker for MD and highlights the critical need to address heterogeneity through standardized methodologies in future research to enhance its diagnostic reliability.