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  • Title: Association of Lipid Peroxidation and Brain-Derived Neurotrophic Factor with Executive Function in Adolescent Bipolar Disorder.
    Author: Newton DF, Naiberg MR, Andreazza AC, Scola G, Dickstein DP, Goldstein BI.
    Journal: Psychopharmacology (Berl); 2017 Feb; 234(4):647-656. PubMed ID: 27957714.
    Abstract:
    BACKGROUND: Executive dysfunction is common and impairing in youth bipolar disorder (BD), and oxidative stress (OS) and brain-derived neurotrophic factor (BDNF) have been implicated in executive deficits of adult BD. This study aimed to determine the association between OS and executive dysfunction in BD adolescents and the influence of BDNF on this association. METHODS: Serum levels of lipid hydroperoxides (LPH) and 4-hydroxy-2-nonenal (4-HNE) and BDNF levels were measured in 29 BD and 25 control adolescents. The intra-extra-dimensional (IED) set-shifting task assessed executive function. Lower IED scores indicated better performance. High and low BDNF subgroups were defined by median split. RESULTS: IED Z-scores were impaired in the BD group compared to controls, whereas biomarker levels were not significantly different between groups. LPH-BDNF correlations were significantly different between BD and controls (Z = 2.046, p = 0.041). In high BDNF BD subjects, LPH was significantly positively correlated with IED completed stage trials (ρ = 0.755, p = 0.001) and pre-extra-dimensional shift errors (ρ = 0.588, p = 0.017). Correlations were opposite in controls. In a linear model, LPH, BDNF, and the LPH-BDNF interaction each significantly explained variance of IED total trials (adjusted) (model r 2 = 0.187, F = 2.811, p = 0.035). CONCLUSIONS: There is a negative association between LPH and executive function in BD adolescents, which may be modulated by BDNF. LPH and BDNF may be useful biomarkers of executive function in BD. These findings highlight the importance of examining multiple peripheral biomarkers in relation to cognitive functions in BD adolescents. Future studies should explore these factors in longitudinal designs to determine the directionality of observed associations.
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