LEDA LEME TALIB
Projetos de Pesquisa
Unidades Organizacionais
Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina
LIM/27 - Laboratório de Neurociências, Hospital das Clínicas, Faculdade de Medicina
LIM/27 - Laboratório de Neurociências, Hospital das Clínicas, Faculdade de Medicina
7 resultados
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- Decision tree-based classification as a support to diagnosis in the Alzheimer's disease continuum using cerebrospinal fluid biomarkers: insights from automated analysis(2022) COSTA, Alana; PAIS, Marcos; LOUREIRO, Julia; STELLA, Florindo; RADANOVIC, Marcia; GATTAZ, Wagner; FORLENZA, Orestes; TALIB, LedaObjective: Cerebrospinal fluid (CSF) biomarkers add accuracy to the diagnostic workup of cognitive impairment by illustrating Alzheimer's disease (AD) pathology. However, there are no universally accepted cutoff values for the interpretation of AD biomarkers. The aim of this study is to determine the viability of a decision-tree method to analyse CSF biomarkers of AD as a support for clinical diagnosis. Methods: A decision-tree method (automated classification analysis) was applied to concentrations of AD biomarkers in CSF as a support for clinical diagnosis in older adults with or without cognitive impairment in a Brazilian cohort. In brief, 272 older adults (68 with AD, 122 with mild cognitive impairment [MCI], and 82 healthy controls) were assessed for CSF concentrations of A beta(1-42), total-tau, and phosphorylated-tau using multiplexed Luminex assays; biomarker values were used to generate decision-tree algorithms (classification and regression tree) in the R statistical software environment. Results: The best decision tree model had an accuracy of 74.65% to differentiate the three groups. Cluster analysis supported the combination of CSF biomarkers to differentiate AD and MCI vs. controls, suggesting the best cutoff values for each clinical condition. Conclusion: Automated analyses of AD biomarkers provide valuable information to support the clinical diagnosis of MCI and AD in research settings.
- Functional Connectome Analysis in Mild Cognitive Impairment: Comparing Alzheimer's Disease Continuum and Suspected Non-Alzheimer Pathology(2022) LOURENCO, Rafael Brandes; CAMPOS, Brunno Machado de; RIZZI, Liara; SOUZA, Milene Sakzenian de; FORLENZA, Orestes Vicente; JOAQUIM, Helena Giroud; TALIB, Leda Leme; CENDES, Fernando; BALTHAZAR, Marcio Luiz FigueredoIntroduction: Research in brain resting-state functional connectivity (FC) analysis in mild cognitive impairment (MCI) has conflicting results. This work intends to find differences in resting-state FC of 2 groups of MCI subjects due to Alzheimer's disease (MCI-AD) continuum or to suspected non-Alzheimer pathology (MCI-SNAP). Materials and Methods: Ninety-two subjects older than 55 years were enrolled. MCI and controls were grouped using clinical dementia rating and neuropsychological data. Cerebrospinal fluid biomarkers were collected from MCI subjects, resulting in 32 MCI-AD, 25 MCI-SNAP, and 35 controls. A region of interest (ROI)-to-ROI analysis was carried out looking at inter- and intranetwork interactions selecting the following networks: default mode network (DMN), salience network (SN), visuospatial network (VN), and executive network. Pearson correlation coefficients, converted to Z-scores, were compared by T-tests with alpha set to 0.05, and false discovery rate corrected. Results: Groups were similar in age, education, and demographic measures, there were no differences in neuropsychological data between the MCI groups. The ROI-to-ROI analysis of MCI-AD versus MCI-SNAP showed no differences. MCI-AD versus controls showed decreased FC between ROIs of the SN and between ROIs from SN and VN. MCI-SNAP versus controls showed increased FC between an ROI of DMN and VN. Discussion: SN, DMN, and VN are multimodal networks with high value/high cost and may be more vulnerable to AD pathogenic processes. SN and VN were affected in the MCI-AD group, with maintained anticorrelation between DMN and VN. This may indicate subthreshold DMN dysfunction. The result in MCI-SNAP, although discrete, reflects a rearrangement of brain FC, as DMN and VN are expected to be anticorrelated. More research is necessary to confirm these findings.
- Use of a Bayesian Network Model to predict psychiatric illness in individuals with 'at risk mental states' from a general population cohort(2022) LOCH, Alexandre Andrade; ARA, Anderson; HORTENCIO, Lucas; MARQUES, Julia Hatagami; TALIB, Leda Leme; ANDRADE, Julio Cesar; SERPA, Mauricio Henriques; SANCHEZ, Luciano; ALVES, Tania Maria; BILT, Martinus Theodorus van de; ROESSLER, Wulf; GATTAZ, Wagner FaridThe 'at risk mental state' (ARMS) paradigm has been introduced in psychiatry to study prodromal phases of schizophrenia. With time it was seen that the ARMS state can also precede mental disorders other than schizophrenia, such as depression and anxiety. However, several problems hamper the paradigm's use in preventative medicine, such as varying transition rates across studies, the use of non-naturalistic samples, and the multifactorial nature of psychiatric disorders. To strengthen ARMS predictive power, there is a need for a holistic model incorporating-in an unbiased fashion-the small-effect factors that cause mental disorders. Bayesian networks, a probabilistic graphical model, was used in a populational cohort of 83 ARMS individuals to predict conversion to psychiatric illness. Nine predictors-including state, trait, biological and environmental factors-were inputted. Dopamine receptor 2 polymorphism, high private religiosity, and childhood trauma remained in the final model, which reached an 85.51% (SD = 0.1190) accuracy level in predicting conversion. This is the first time a robust model was produced with Bayesian networks to predict psychiatric illness among at risk individuals from the general population. This could be an important tool to strengthen predictive measures in psychiatry which should be replicated in larger samples to provide the model further learning.
- Plasmatic endocannabinoids are decreased in subjects with ultra-high risk of psychosis(2022) JOAQUIM, Helena P. G.; COSTA, Alana C.; PEREIRA, Cicero A. C.; TALIB, Leda L.; V, Martinus M. Bilt; LOCH, Alexandre A.; GATTAZ, Wagner F.The onset of frank psychosis is usually preceded by a prodromal phase characterized by attenuated psychotic symptoms. Currently, research on schizophrenia prodromal phase (ultra-high risk for psychosis [UHR]) has focused on the risk of developing psychosis, on the transition to full blown psychosis and on its prediction. Neurobiological differences between UHR individuals who fully recover (remitters) versus those who show persistent/progressive prodromal symptoms (nonremitters) have been little explored. The endocannabinoid system constitutes a neuromodulatory system that plays a major role in brain development, synaptic plasticity, emotional behaviours and cognition. It comprises two cannabinoid receptors (CB1/CB2), two endocannabinoid ligands, arachidonylethanolamide (AEA) and 2-arachidonoylglycerol (2AG) along with their inactivation enzymes. Despite much evidence that the endocannabinoid system is imbalanced during psychosis, very little is known about it in UHR. Therefore, we aimed to quantify the plasma endocannabinoid levels in UHR and healthy controls (HC) and verify if these metabolites could differentiate between remitters and nonremitters. Circulating concentrations of AEA (p = .003) and 2AG (p < .001) were lower in UHR when compared with HC, with no difference between remitters and nonremitters. Regarding clinical evolution, it was observed that out of 91 UHRs initially considered, 16 had psychiatric complaints (3 years of follow-up). Considering those subjects, there were weak correlations between clinical parameters and plasma concentrations of endocannabinoids. Our results suggest that the endocannabinoids are imbalanced before frank psychosis and that changes can be seen in plasma of UHR individuals. These molecules proved to be potential biomarkers to identify individuals in the prodromal phase of psychosis.
- COX-2 pathway is upregulated in ultra-high risk individuals for psychosis(2022) PEREIRA, Cicero A. C.; COSTA, Alana C.; JOAQUIM, Helena P. G.; TALIB, Leda L.; BILT, Martinus T. van de; LOCH, Alexandre A.; GATTAZ, Wagner F.Background The identification of Ultra-High Risk (UHR) individuals is thought to be useful for early intervention to improve psychosis outcomes. However, transition rates vary widely, and there is an effort to make these criteria more specific and accurate. Neuroinflammation has been discussed in the pathophysiology of psychosis. The metabolism of eicosanoids is a key process in inflammatory states. Therefore, we investigated whether the study of the inflammatory COX-2 pathway through the quantification of the eicosanoid levels can be a useful approach for the characterisation of UHR individuals. Methods One hundred and twenty-two individuals were included in this study (67 UHR and 55 controls) based on performance on the Prodromal Questionnaire. UHR status was assessed by Structured Interview for Prodromal Syndromes (SIPS). We determined the levels of Prostaglandin F-2 alpha (PGF(2 alpha)), Prostaglandin E-2 (PGE(2)), and Thromboxane B-2 (TxB(2)) in plasma using ELISA assays. Results Concentrations of PGE(2) and TxB(2) were increased in UHR compared to controls (p = 0.01 and p < 0.05, respectively). PGE(2) and PGF(2 alpha) levels were correlated to negative symptoms (p < 0.01 and p < 0.05), whereas TxB(2) correlated with positive symptoms (p = 0.05) as assessed by the SIPS. Conclusions Our findings suggest that overactivation of the COX-2 pathway may be related to an increased risk for psychosis. However, our data do not allow us to draw conclusions related to the cause-effect mechanisms. Future studies should determine whether the levels of the eicosanoids have a predictive value for the transition of UHR to frank psychosis.
- Differences in structural and functional default mode network connectivity in amyloid positive mild cognitive impairment: a longitudinal study(2022) MAGALHAES, Thamires Naela Cardoso; GERBELLI, Christian Luiz Baptista; PIMENTEL-SILVA, Luciana Ramalho; CAMPOS, Brunno Machado de; REZENDE, Thiago Junqueira Ribeiro de; RIZZI, Liara; JOAQUIM, Helena Passarelli Giroud; TALIB, Leda Leme; FORLENZA, Orestes Vicente; CENDES, Fernando; BALTHAZAR, Marcio Luiz FigueredoPurpose Default mode network (DMN) has emerged as a potential biomarker of Alzheimer's disease (AD); however, it is not clear whether it can differentiate amnestic mild cognitive impairment with altered amyloid (aMCI-A beta +) who will evolve to AD. We evaluated if structural and functional connectivity (FC), hippocampal volumes (HV), and cerebrospinal fluid biomarkers (CSF-A beta(42), p-Tau, and t-Tau) can differentiate aMCI-A beta + converters from non-converters. Methods Forty-eight individuals (18 normal controls and 30 aMCI subjects in the AD continuum - with altered A beta(42) in the CSF) were followed up for an average of 13 months. We used MultiAtlas, (UFC)-C-2, and Freesurfer software to evaluate diffusion tensor imaging, FC, and HV, respectively, INNOTEST (R) kits to measure CSF proteins, and neuropsychological tests. Besides, we performed different MANOVAs with further univariate analyses to differentiate groups. Results During follow-up, 8/30 aMCI-A beta + converted (26.6%) to AD dementia. There were no differences in multivariate analysis between groups in CSF biomarkers (p = 0.092) or at DMN functional connectivity (p = 0.814). aMCI-A beta + converters had smaller right HV than controls (p = 0.013), and greater right cingulum parahippocampal bundle radial diffusivity than controls (p < 0.001) and non-converters (p = 0.036). Conclusion In this exploratory study, structural, but not functional, DMN connectivity alterations may differentiate aMCI-A beta + subjects who converted to AD dementia.
- Cholinergic drug downregulates annexin A3 in platelets of Alzheimer’s Disease patients(2022) PARK, L. Y.; GATTAZ, W. F.; JOAQUIM, H. P. G.; TALIB, L. L.Background: Investigation of the expression of annexin A3 (ANXA3) platelets of Alzheimer´s Disease patients pre and post three-month treatment with cholinesterase inhibitors and compared to healthy controls. Method: The sample consisted of healthy controls (n=24) and AD drug-naïve patients (n=34), diagnosed according to DSM-IV and specificized according to NINCDS-ADRDA diagnostic criteria. After three months of treatment with cholinesterase inhibitors, platelets were extracted from blood samples of the same AD patients (n=34). The Cambridge cognitive test (CAMCOG) was used as a measure of global cognitive performance, however, scores on both tests were not used to establish the patients' diagnosis. The method of Western Blotting sample analysis was performed through primary antibodies against ANXA3 and secondary antibody, anti-rabbit IgG-HRP peroxidase. Student's t-test or Mann-Whitney, Pearson's Chi-square tests were used for numerical variables, Kolmogorov-Smirnov test were used to analyze a normal distribution and Wilcoxon test to longitudinal variables. Spearman’s rank correlation coefficient was performed for association between ANXA3 levels and clinical parameters. Result: There was no difference in ANXA3 expression between AD patients before treatment and healthy controls (p=0.926). However, after three months with anticholinergic treatment the AD patients present a decrease in ANXA3 expressions (p=0.012). Conclusion: ANXA3 levels on platelets samples of patients diagnosed with AD decrease before an evident improvement of the clinical manifestation of the disease. © 2022 the Alzheimer's Association.