Correlation of BDNF , VEGF , TNF ‐α, and S100B with cognitive impairments in chronic, medicated schizophrenia patients

Cognitive impairment is a prominent cause of disability in schizophrenia. Although antipsychotic drugs can rescue the psychotic symptoms, the cognitive impairments persist, with no treatment available. Alterations of BDNF, VEGF, TNF‐α, and S100B have been linked to cognitive impairment in several neurological disorders. However, it remains unclear whether their levels are correlated with the cognitive functions of schizophrenia patients. Forty‐one chronic, medicated schizophrenia patients were included in this study. Enzyme‐linked, immunosorbent assays were used to measure the serum concentrations of BDNF, VEGF, TNF‐α, and S100B. Associations between serum protein levels and various domains of the cognitive functions of the schizophrenia patients were observed. We found significant, positive correlations between serum BDNF and the processing speed and attention levels of the patients. Serum VEGF was also positively correlated with their memory and learning functions. In contrast, serum S100B and TNF‐α were negatively correlated with the processing speed and attention of the schizophrenia patients. The findings warrant further investigation of these molecules as potential prognostic markers or treatment targets for cognitive impairment in schizophrenia patients.

Associations between serum protein levels and domains of the cognitive functions of the schizophrenia patients were investigated. The results show 1) positive correlations between serum BDNF and the processing speed and attention levels, 2) positive correlations between serum VEGF and the memory and learning functions, and 3) negative correlation between serum S100B and TNF‐α and the processing speed and attention.

A Longitudinal Study of Changes in Cognition Among Older Thais: Studying From the Kanchanaburi Demographic Surveillance System

Objectives: To examine the impacts of changes in social determinants of health (SDH) toward changes in cognition. Methods: Longitudinal data came from the Kanchanaburi Demographic Surveillance System (KDSS) collected in 2007 and 2011. Cognitive impairment was measured by category fluency and delayed recall. Generalized estimating equation (GEE) was used to investigate changes in cognition by taking SDH and other variables including age, gender, marital status, education, and depression into the model. Results: GEE revealed longitudinal effects of wealth index and working status against cognition. Older Thais living with richest wealth index (odds ratio [OR] = 0.54; 95% confidence interval [CI] = 0.31-0.94) and still being employed (OR = 0.65; 95% CI = 0.47-0.89) were less likely to have cognitive impairment. Discussion: Poorer wealth index and being unemployed were served as a risk factor for cognitive impairment over time. Increasing age was still the major risk for cognitive impairment.