White Matter Tract Integrity in Alzheimer's Disease vs. Late Onset Bipolar Disorder and Its Correlation with Systemic Inflammation and Oxidative Stress Biomarkers

Background: Late Onset Bipolar Disorder (LOBD) is the development of Bipolar Disorder (BD) at an age above 50 years old. It is often difficult to differentiate from other aging dementias, such as Alzheimer's Disease (AD), because they share cognitive and behavioral impairment symptoms.Objectives: We look for WM tract voxel clusters showing significant differences when comparing of AD vs. LOBD, and its correlations with systemic blood plasma biomarkers (inflammatory, neurotrophic factors, and oxidative stress).Materials: A sample of healthy controls (HC) (n = 19), AD patients (n = 35), and LOBD patients (n = 24) was recruited at the Alava University Hospital. Blood plasma samples were obtained at recruitment time and analyzed to extract the inflammatory, oxidative stress, and neurotrophic factors. Several modalities of MRI were acquired for each subject,Methods: Fractional anisotropy (FA) coefficients are obtained from diffusion weighted imaging (DWI). Tract based spatial statistics (TBSS) finds FA skeleton clusters of WM tract voxels showing significant differences for all possible contrasts between HC, AD, and LOBD. An ANOVA F-test over all contrasts is carried out. Results of F-test are used to mask TBSS detected clusters for the AD > LOBD and LOBD > AD contrast to select the image clusters used for correlation analysis. Finally, Pearson's correlation coefficients between FA values at cluster sites and systemic blood plasma biomarker values are computed.Results: The TBSS contrasts with by ANOVA F-test has identified strongly significant clusters in the forceps minor, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, and cingulum gyrus. The correlation analysis of these tract clusters found strong negative correlation of AD with the nerve growth factor (NGF) and brain derived neurotrophic factor (BDNF) blood biomarkers. Negative correlation of AD and positive correlation of LOBD with inflammation biomarker IL6 was also found.Conclusion: TBSS voxel clusters tract atlas localizations are consistent with greater behavioral impairment and mood disorders in LOBD than in AD. Correlation analysis confirms that neurotrophic factors (i.e., NGF, BDNF) play a great role in AD while are absent in LOBD pathophysiology. Also, correlation results of IL1 and IL6 suggest stronger inflammatory effects in LOBD than in AD

Gene co-expression architecture in peripheral blood in a cohort of remitted first-episode schizophrenia patients

A better understanding of schizophrenia subtypes is necessary to stratify the patients according to clinical attributes. To explore the genomic architecture of schizophrenia symptomatology, we analyzed blood co-expression modules and their association with clinical data from patients in remission after a first episode of schizophrenia. In total, 91 participants of the 2EPS project were included. Gene expression was assessed using the Clariom S Human Array. Weighted-gene co-expression network analysis (WGCNA) was applied to identify modules of co-expressed genes and to test its correlation with global functioning, clinical symptomatology, and premorbid adjustment. Among the 25 modules identified, six modules were significantly correlated with clinical data. These modules could be clustered in two groups according to their correlation with clinical data. Hub genes in each group showing overlap with risk genes for schizophrenia were enriched in biological processes related to metabolic processes, regulation of gene expression, cellular localization and protein transport, immune processes, and neurotrophin pathways. Our results indicate that modules with significant associations with clinical data showed overlap with gene sets previously identified in differential gene-expression analysis in brain, indicating that peripheral tissues could reveal pathogenic mechanisms. Hub genes involved in these modules revealed multiple signaling pathways previously related to schizophrenia, which may represent the complex interplay in the pathological mechanisms behind the disease. These genes could represent potential targets for the development of peripheral biomarkers underlying illness traits in clinical remission stages after a first episode of schizophrenia

The polygenic basis of relapse after a first episode of schizophrenia

Little is known about genetic predisposition to relapse. Previous studies have linked cognitive and psychopathological (mainly schizophrenia and bipolar disorder) polygenic risk scores (PRS) with clinical manifestations of the disease. This study aims to explore the potential role of PRS from major mental disorders and cognition on schizophrenia relapse. 114 patients recruited in the 2EPs Project were included (56 patients who had not experienced relapse after 3 years of enrollment and 58 patients who relapsed during the 3-year follow-up). PRS for schizophrenia (PRS-SZ), bipolar disorder (PRS-BD), education attainment (PRS-EA) and cognitive performance (PRS-CP) were used to assess the genetic risk of schizophrenia relapse.Patients with higher PRS-EA, showed both a lower risk (OR=0.29, 95% CI [0.11–0.73]) and a later onset of relapse (30.96± 1.74 vs. 23.12± 1.14 months, p=0.007. Our study provides evidence that the genetic burden of neurocognitive function is a potentially predictors of relapse that could be incorporated into future risk prediction models. Moreover, appropriate treatments for cognitive symptoms appear to be important for improving the long-term clinical outcome of relapse

Discrimination between Alzheimer’s Disease and Late Onset Bipolar Disorder using multivariate analysis

textbf{Background} Late Onset Bipolar Disorder (LOBD) is often difficultto distinguish from degenerative dementias, such as Alzheimer Disease(AD), due to comorbidities and common cognitive symptoms. Moreover,LOBD prevalence in the elder population is not negligible and it isincreasing. Both pathologies share pathophysiological features relatedto neuroinflammation. Improved means to differentiate between LOBDand AD in elder subjects will help to select the best personalizedtreatment. textbf{Objective} The aim of this study textcolor{red}{was}to assess the relative significance of clinical observations, neuropsychologicaltests, and textcolor{red}{specific} textcolor{red}{blood plasma}biomarkers (inflammatory and neurotrophic), separately and combined,in the textcolor{red}{differential diagnosis} of LOBD versus AD.The textcolor{red}{significance} assessment textcolor{red}{was}carried out evaluating the accuracy achieved by classification basedcomputer aided diagnosis (CAD) systems based on these variables. textbf{Materials}A sample of healthy controls (HC) (n=26), AD patients (n=37), andLOBD patients (n=32) textcolor{red}{was} recruited at the Alava UniversityHospital. Clinical observations, neuropsychological tests, and plasmabiomarkers textcolor{red}{were} obtained at recruitment time. textbf{Methods}We appltextcolor{red}{ied} multivariate machine learning classificationmethods to discriminate subjects from HC, AD and LOBD populationsin the study. We analyzetextcolor{red}{d} of feature sets textcolor{red}{combining}clinical observations, neuropshycological measures, and biologicalmarkers, including inflammation biomarkers. textcolor{red}{A featureset containing variables showing significative differences for eachclassification contrast was tested also.} Furthermore, a battery ofclassifier approaches textcolor{red}{were} applied, encompassinglinear and non-linear Support Vector Machines (SVM), Random Forests(RF), Classification and regression trees (CART), and their performancetextcolor{red}{was} evaluated in a leave-one-out textcolor{red}{(LOO)}cross-validation scheme. Post-hoc analysis of Gini index in CART classifiersprovided a measure of each variable importance. textbf{Results}Welch's t-test found one biomarker (Malondialdehyde)with significative differences (p<0.001) in LOBD vs. AD contrast.Classification results with the best features are as follows: Discriminationof HC vs. AD patients reaches accuracy 97.21%, AUC 98.17%. Discriminationof LOBD vs. AD patients reaches accuracy 90.26%, AUC 89.57%. Discriminationof HC vs LOBD patients achieves accuracy 95.76%, AUC 88.46%.} textbf{Conclusions}It is feasible to build CAD systems for discrimination among LOBDand AD textcolor{red}{on the basis of a reduced set of clinical variables}to assist the clinician in this difficult differentia