Lucas B Rizzo1,2, Walter Swardfager3,4, Pawan Kumar Maurya1,5, Maiara Zeni Graiff1, Mariana Pedrini1, Elson Asevedo1, Ana Cláudia Cassinelli6, Moisés E Bauer7, Quirino Cordeiro6, Jan Scott8, Elisa Brietzke9, Hugo Cogo-Moreira10. 1. Interdisciplinary Laboratory of Clinical Neuroscience (LINC), Federal University of Sao Paulo (Unifesp), Sao Paulo, Brazil. 2. Department of Psychiatry, University of Tuebingen, Tuebingen, Germany. 3. Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada. 4. Department of Pharmcology and Toxicology, University of Toronto, Toronto, Ontario, Canada. 5. Amity Institute of Biotechnology, Amity University Uttar Pradesh, New Delhi, India. 6. Centro Interdisciplinar de Neuromodulação Clínica, Faculdade de Ciências Médicas, Santa Casa de São Paulo, Brazil. 7. Faculty of Biosciences, Pontifical Catholic University of Porto Alegre, Porto Alegre, Brazil. 8. Department of Academic Psychiatry, Wolfson Unit, Institute of Neuroscience, Newcastle University, Newcastle, UK. 9. Research Group in Molecular and Behavioral Neuroscience in Bipolar Disorder, Federal University of Sao Paulo (Unifesp), São Paulo, Brazil. 10. Department of Psychiatry (Psychiatry and Medical Psychology Graduate Program), Federal University of Sao Paulo (Unifesp), São Paulo, Brazil.
Abstract
OBJECTIVES: The study aims to generate an immunological age (IA) trait on the basis of immune cell differentiation parameters, and to test whether the IA is related to age and disease characteristics. METHODS: Forty-four euthymic type I bipolar disorder patients were included in this study. Five immunosenescence-related parameters were assessed: proportions of late-differentiated cells (e.g., CD3+CD8+CD28-CD27- and CD3-CD19+IgD-CD27-), and the expression of CD69, CD71, and CD152 after stimulation. Confirmatory factor analysis was applied to generate an IA trait underling the 5 measures. RESULTS: The best-fit model was constituted by 4 parameters that were each related to an underlying IA trait, with 1 cell population positively correlated (CD3+CD8+CD28-CD27- [λ = 0.544, where λ represents the loading of the parameter onto the IA trait] and 3 markers negatively correlated (CD69 [λ = -0.488], CD71 [λ = -0.833], and CD152 [λ = -0.674]). The IA trait was associated with chronological age (β = 0.360, p = .013) and the number of previous mood episodes (β = 0.426, p = .006). In a mediation model, 84% of the effect between manic episodes, and IA was mediated by body mass index. CONCLUSION: In bipolar disorder type I, premature aging of the immune system could be reliably measured using an index that validated against chronological age, which was related to adverse metabolic effects of the disease course.
OBJECTIVES: The study aims to generate an immunological age (IA) trait on the basis of immune cell differentiation parameters, and to test whether the IA is related to age and disease characteristics. METHODS: Forty-four euthymic type I bipolar disorderpatients were included in this study. Five immunosenescence-related parameters were assessed: proportions of late-differentiated cells (e.g., CD3+CD8+CD28-CD27- and CD3-CD19+IgD-CD27-), and the expression of CD69, CD71, and CD152 after stimulation. Confirmatory factor analysis was applied to generate an IA trait underling the 5 measures. RESULTS: The best-fit model was constituted by 4 parameters that were each related to an underlying IA trait, with 1 cell population positively correlated (CD3+CD8+CD28-CD27- [λ = 0.544, where λ represents the loading of the parameter onto the IA trait] and 3 markers negatively correlated (CD69 [λ = -0.488], CD71 [λ = -0.833], and CD152 [λ = -0.674]). The IA trait was associated with chronological age (β = 0.360, p = .013) and the number of previous mood episodes (β = 0.426, p = .006). In a mediation model, 84% of the effect between manic episodes, and IA was mediated by body mass index. CONCLUSION: In bipolar disorder type I, premature aging of the immune system could be reliably measured using an index that validated against chronological age, which was related to adverse metabolic effects of the disease course.
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