BDNF and BMI effects on brain structures of bipolar offspring: results from the global mood and brain science initiative.

OBJECTIVE: To compare brain-derived neurotrophic factor (BDNF) levels between offspring of individuals with bipolar disorders (BD) and healthy controls (HCs) and investigate the effects of BDNF levels and body mass index (BMI) on brain structures. METHOD: Sixty-seven bipolar offspring and 45 HCs were included (ages 8-28). Structural images were acquired using 3.0 Tesla magnetic resonance imaging. Serum BDNF levels were measured using enzyme-linked immunosorbent assay. Multivariate and univariate analyses of covariance were conducted. RESULTS: Significantly higher BDNF levels were observed among bipolar offspring, relative to HCs (P > 0.025). Offspring status moderated the association between BDNF and BMI (F1 =4.636, P = 0.034). After adjustment for relevant covariates, there was a trend for a significant interaction of group and BDNF on neuroimaging parameters (Wilks'λ F56,94 =1.463, P = 0.052), with significant effects on cerebellar white matter and superior and middle frontal regions. Brain volume and BDNF were positively correlated among HCs and negatively correlated among bipolar offspring. Interactions between BDNF and BMI on brain volumes were non-significant among HCs (Wilks'λ F28,2 =2.229, P = 0.357), but significant among bipolar offspring (Wilks'λ F28,12 =2.899, P = 0.028). CONCLUSION: Offspring status and BMI moderate the association between BDNF levels and brain structures among bipolar offspring, underscoring BDNF regulation and overweight/obesity as key moderators of BD pathogenesis.

U-shaped relationship between depression and body mass index in the Korean adults.

BACKGROUND: Although a number of studies have examined the relationship between depression and obesity, it is still insufficient to establish the specific pattern of relationship between depression and body mass index (BMI) categories. Thus, this study was aimed to investigate the relationship between depression and BMI categories. METHODS: A cross-sectional study was conducted for a cohort of 159,390 Korean based on Kangbuk Samsung Health Study (KSHS). Study participants were classified into 5 groups by Asian-specific cut-off of BMI (18.5, 23, 25 and 30kg/m2). The presence of depression was determined by Center for Epidemiologic Studies-Depression scales (CES-D)≥16 and≥25. The adjusted odd ratios (ORs) for depression were evaluated by multiple logistic regression analysis, in which independent variable was 5 categories of BMI and dependent variable was depression. Subgroup analysis was conducted by gender and age. RESULTS: When normal group was set as a reference, the adjusted ORs for depression formed U-shaped pattern of relationship with BMI categories [underweight: 1.31 (1.14-1.50), overweight: 0.94 (0.85-1.04), obese group: 1.01 (0.91-1.12), severe obese group: 1.28 (1.05-1.54)]. This pattern of relationship was more prominent in female and young age group than male and elderly subgroup. BMI level with the lowest likelihood of depression was 18.5kg/m2 to 25kg/m2 in women and 23kg/m2 to 25kg/m2 in men. CONCLUSIONS: There was a U-shaped relationship between depression and BMI categories. This finding suggests that both underweight and severe obesity are associated with the increased risk for depression.

Repurposing of Anti-Diabetic Agents for the Treatment of Cognitive Impairment and Mood Disorders.

Impairments in cognitive function represent a consistent, non-specific, and clinically significant feature in metabolic, mood, and dementing disorders. The foregoing observation is instantiated by evidence demonstrating that these disorders share pathophysiological mechanisms including, but not limited to, aberrant insulin signaling, inflammation, and glucocorticoid activity. Moreover, these mechanisms have been consistently reported to increase vulnerability to and/or exacerbate impairments in cognitive function. Notwithstanding evidence suggesting a bidirectional relationship between disturbances in the metabolic milieu, mood, and increased risk for dementia, efficacious treatments that target cognitive impairments in these populations do not presently exist. Taken together, it is proposed that anti-diabetic agents may aid the management of mood disorders and future risk for dementia through disease modification by targeting underlying pathophysiological mechanisms (e.g., aberrant metabolic function) rather than focusing solely on symptom mitigation. The current aim is to provide a brief narrative review of extant studies that report on the potential neurotherapeutic effects of anti-diabetic agents on disturbances in mood and impairments in cognitive function.

Inflammation, neurotrophism and oxidative stress and childhood psychopathology in a large community sample.

OBJECTIVE: To investigate the association between peripheral biomarkers and child psychopathology in a large community sample. METHOD: A total of 625 aged 6- to 13-year old subjects were recruited from a community school-based study. Psychopathology was assessed using the Child Behaviour Checklist (CBCL). Psychiatric diagnosis was evaluated using the Development and Well-Being Assessment. The following biomarkers were examined in peripheral blood: brain-derived neurotrophic factor, cytokines (IL-2, IL-4, IL-6, IL-10, IL-17, IFN-g, and TNF-α), chemokines (eotaxin/CCL11, IP-10, MCP-1), cytokine receptors (sTNFR1 and sTNFR2), and the oxidative stress marker TBARS. RESULTS: We found significant associations between sTNFR2, eotaxin/CCL11 and CBCL total score, as well as with specific dimensions of psychopathology. There were different patterns of association between these biomarkers and psychological and behavioural symptoms in children with and without a mental disorder. TBARS, IL-6 and MCP-1 were more specific to some clusters of symptoms in children with a psychiatric diagnosis. CONCLUSION: Our data support the potential use of biomarkers, especially those involved in immune-inflammatory pathways, in investigating neurodevelopmental psychopathology. Their association with different dimensions of symptoms might be of useful when analyzing illness severity and clusters of symptoms within specific disorders.