Diet quality and associations with lactate and metabolic syndrome in bipolar disorder.

BACKGROUND: Nutrition is largely affected in bipolar disorder (BD), however, there is a lack of understanding on the relationship between dietary categories, BD, and the prevalence of metabolic syndrome. The objective of this study is to examine dietary trends in BD and it is hypothesized that diets with increased consumption of seafood and high-fiber carbohydrates will be correlated to improved patient outcomes, and a lower frequency of metabolic syndrome. METHODS: This retrospective cohort study includes two French cohorts. The primary cohort, FACE-BD, includes 268 stable BD patients. The second cohort, I-GIVE, includes healthy controls, both stable and acute BD and schizophrenia patients. Four dietary categories were assessed: meat, seafood, low-fiber and high-fiber carbohydrates. Dietary data from two food frequency questionnaires were normalized using min-max scaling and assessed using various statistical analyses. RESULTS: In our primary cohort, the increased high-fiber carbohydrate consumption was correlated to lower prevalence of metabolic syndrome and improved mood. Low-fiber carbohydrate consumption is associated with higher BMI, while higher seafood consumption was correlated to improved mood and delayed age of onset. Results were not replicated in our secondary cohort. LIMITATIONS: Our populations were small and two different dietary questionnaires were used; thus, results were used to examine similarities in trends. CONCLUSIONS: Overall, various dietary trends were associated with metabolic syndrome, BMI, lactate, mood and age of onset. Improving our understanding of nutrition in BD can provide mechanistic insight, clinically relevant nutritional guidelines for precision medicine and ultimately improve the quality of lives for those with BD.

Immune-based Machine learning prediction of diagnosis and illness state in schizophrenia and bipolar disorder.

BACKGROUND: Schizophrenia and bipolar disorder frequently face significant delay in diagnosis, leading to being missed or misdiagnosed in early stages. Both disorders have also been associated with trait and state immune abnormalities. Recent machine learning-based studies have shown encouraging results using diagnostic biomarkers in predictive models, but few have focused on immune-based markers. Our main objective was to develop supervised machine learning models to predict diagnosis and illness state in schizophrenia and bipolar disorder using only a panel of peripheral kynurenine metabolites and cytokines. METHODS: The cross-sectional I-GIVE cohort included hospitalized acute bipolar patients (n = 205), stable bipolar outpatients (n = 116), hospitalized acute schizophrenia patients (n = 111), stable schizophrenia outpatients (n = 75) and healthy controls (n = 185). Serum kynurenine metabolites, namely tryptophan (TRP), kynurenine (KYN), kynurenic acid (KA), quinaldic acid (QUINA), xanthurenic acid (XA), quinolinic acid (QUINO) and picolinic acid (PICO) were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS), while V-plex Human Cytokine Assays were used to measure cytokines (interleukin-6 (IL-6), IL-8, IL-17, IL-12/IL23-P40, tumor necrosis factor-alpha (TNF-ɑ), interferon-gamma (IFN-γ)). Supervised machine learning models were performed using JMP Pro 17.0.0. We compared a primary analysis using nested cross-validation to a split set as sensitivity analysis. Post-hoc, we re-ran the models using only the significant features to obtain the key markers. RESULTS: The models yielded a good Area Under the Curve (AUC) (0.804, Positive Prediction Value (PPV) = 86.95; Negative Prediction Value (NPV) = 54.61) for distinguishing all patients from controls. This implies that a positive test is highly accurate in identifying the patients, but a negative test is inconclusive. Both schizophrenia patients and bipolar patients could each be separated from controls with a good accuracy (SCZ AUC 0.824; BD AUC 0.802). Overall, increased levels of IL-6, TNF-ɑ and PICO and decreased levels of IFN-γ and QUINO were predictive for an individual being classified as a patient. Classification of acute versus stable patients reached a fair AUC of 0.713. The differentiation between schizophrenia and bipolar disorder yielded a poor AUC of 0.627. CONCLUSIONS: This study highlights the potential of using immune-based measures to build predictive classification models in schizophrenia and bipolar disorder, with IL-6, TNF-ɑ, IFN-γ, QUINO and PICO as key candidates. While machine learning models successfully distinguished schizophrenia and bipolar disorder from controls, the challenges in differentiating schizophrenic from bipolar patients likely reflect shared immunological pathways by the both disorders and confounding by a larger state-specific effect. Larger multi-centric studies and multi-domain models are needed to enhance reliability and translation into clinic.

Mitochondrial Biomarkers and Metabolic Syndrome in Bipolar Disorder.

The object of this study is test whether mitochondrial blood-based biomarkers are associated with markers of metabolic syndrome in bipolar disorder, hypothesizing higher lactate but unchanged cell-free circulating mitochondrial DNA levels in bipolar disorder patients with metabolic syndrome. In a cohort study, primary testing from the FondaMental Advanced Centers of Expertise for bipolar disorder (FACE-BD) was conducted, including 837 stable bipolar disorder patients. The I-GIVE validation cohort consists of 237 participants: stable and acute bipolar patients, non-psychiatric controls, and acute schizophrenia patients. Multivariable regression analyses show significant lactate association with triglycerides, fasting glucose and systolic and diastolic blood pressure. Significantly higher levels of lactate were associated with presence of metabolic syndrome after adjusting for potential confounding factors. Mitochondrial-targeted metabolomics identified distinct metabolite profiles in patients with lactate presence and metabolic syndrome, differing from those without lactate changes but with metabolic syndrome. Circulating cell-free mitochondrial DNA was not associated with metabolic syndrome. This thorough analysis mitochondrial biomarkers indicate the associations with lactate and metabolic syndrome, while showing the mitochondrial metabolites can further stratify metabolic profiles in patients with BD. This study is relevant to improve the identification and stratification of bipolar patients with metabolic syndrome and provide potential personalized-therapeutic opportunities.

B Cell-activating factor (BAFF): A promising trans-nosographic biomarker of inflammation and autoimmunity in bipolar disorder and schizophrenia.

Immune dysregulation is an important aspect of schizophrenia (SZ) and bipolar disorders (BD) pathophysiology, including not only inflammatory but also autoimmune process reflective of abnormal humoral immune responses. Given that B cell-activating factor (BAFF) is an integral aspect of B lymphocyte regulation, the current study investigated BAFF in SZ and BD. 255 SZ patients, 407 BD patients and 185 healthy controls (HC) were investigated across three aspects of soluble BAFF (sBAFF) by (i) comparing sBAFF circulatory levels across SZ, BD and HC, (ii) determining potential correlations between the circulating levels of sBAFF and the genotype distribution of a functionally relevant polymorphism, namely the TNFSF13B 3'UTR insertion-deletion polymorphism (GCTGT>A), (iii) analyzing relationships between both sBAFF levels and 3'UTR insertion-deletion genotypes and disease risk, patients clinical characteristics and circulating levels of potent inflammatory molecules. In addition, in subsets of patients, we also searched for possible correlations between sBAFF levels and stigma of past infectious events as well as positivity for circulating systemic autoantibodies or those directed against central nervous system (CNS) structures. Studying blood derived serum and DNA, weobserved that circulating sBAFF levels were significantly higher in SZ and BD patients, versus HC (p = 5.3*10-10and p = 4.4*10-09). Patients experiencing acute episodes, versus stable patients, in between acute episodes, exhibited higher sBAFF levels (p = 0.017).In SZ patients, positive correlations were observed between elevated sBAFF levels and: (i) elevated positive psychotic symptoms (PANSS pos), (ii) history of childhood trauma (physical abuse), and (iii) low scores on global functioning (GAF) (p = 0.024, p = 0.024, and p = 0.041).We also found that the distribution of the BAFF Ins/Del genotypes was significantly correlated with circulating sBAFF levels in SZ and BD patients (p = 0.0004). Elevated sBAFF levels were also correlated with increased levels of pro-inflammatory markers in both SZ and BD cohorts (p < 0.001). Regarding infectious stigma, only patients seropositive, versus seronegative, for herpes simplex virus (HSV)1 immunoglobulin (Ig)G antibodies exhibited a significant association with high sBAFF levels (p = 0.013). In contrast, positivity for systemic or CNS autoantibodies was significantly associated with reduced sBAFF levels, compared to patients without autoantibodies (p = 0.0017). Overall, our findings indicate that BAFF may be a promising trans-nosographic biomarker of inflammation that is likely to offer predictive, diagnostic, and prognostic tools for the management of SZ and BD. The results therefore have practicable clinical utility given the availability of immunotherapeutic treatment options including targeted monoclonal antibodies against BAFF.

Serum autoantibodies against α7-nicotinic receptors in subgroups of patients with bipolar disorder or schizophrenia: clinical features and link with peripheral inflammation.

There is growing evidence that autoantibodies (AAbs) against proteins expressed in the brain are playing an important role in neurological and psychiatric disorders. Here, we explore the presence and the role of peripheral AAbs to the α7-nicotinic acetylcholine receptor (nAChR) in inflammatory subgroups of psychiatric patients with bipolar disorder (BD) or schizophrenia (SCZ) and healthy controls. We have identified a continuum of AAb levels in serum when employing a novel ELISA technique, with a significant elevation in patients compared to controls. Using unsupervised two-step clustering to stratify all the subjects according to their immuno-inflammatory background, we delineate one subgroup consisting solely of psychiatric patients with severe symptoms, high inflammatory profile, and significantly increased levels of anti-nAChR AAbs. In this context, we have used monoclonal mouse anti-human α7-nAChR antibodies (α7-nAChR-mAbs) and shown that TNF-α release was enhanced upon LPS stimulation in macrophages pre-incubated with α7-nAChR-mAbs compared to the use of an isotype control. These findings provide a basis for further study of circulating nicotinic AAbs, and the inflammatory profile observed in patients with major mood and psychotic disorders.