Cumulative effects of bullying and racial discrimination on adolescent health in Australia

This study examined how cumulative exposure to racial discrimination and bullying victimization influences the health of Australian adolescents (n=2802) aged 10-11 years (19.3% visible ethnic minorities (non-White, non-Indigenous); 2.6% Indigenous) using data from 3 waves (2010-2014) of the nationally representative Longitudinal Study of Australian Children (LSAC). Cumulative exposure to racial discrimination and bullying victimization had incremental negative effects on socioemotional difficulties. Higher accumulated exposure to both stressors across time was associated with increased BMI z-scores, and risk of overweight/obesity. Studies that examine exposure to single risk factors such as bullying victimization or racial discrimination at 1 time point only are likely to miss key determinants of health for adolescents from stigmatized racial/ethnic backgrounds and under-estimate their stressor burden

Identification of a possible proteomic Biomarker in Parkinson’s Disease: Discovery and Replication in Blood, brain and CSF

Biomarkers to aid diagnosis and delineate the progression of Parkinson’s disease are vital for targeting treatment in the early phases of the disease. Here, we aim to discover a multi-protein panel representative of Parkinson’s and make mechanistic inferences from protein expression profiles within the broader objective of finding novel biomarkers. We used aptamer-based technology (SomaLogic®) to measure proteins in 1599 serum samples, 85 cerebrospinal fluid samples and 37 brain tissue samples collected from two observational longitudinal cohorts (the Oxford Parkinson’s Disease Centre and Tracking Parkinson’s) and the Parkinson’s Disease Brain Bank, respectively. Random forest machine learning was performed to discover new proteins related to disease status and generate multi-protein expression signatures with potential novel biomarkers. Differential regulation analysis and pathway analysis were performed to identify functional and mechanistic disease associations. The most consistent diagnostic classifier signature was tested across modalities [cerebrospinal fluid (area under curve) = 0.74, P = 0.0009; brain area under curve = 0.75, P = 0.006; serum area under curve = 0.66, P = 0.0002]. Focusing on serum samples and using only those with severe disease compared with controls increased the area under curve to 0.72 (P = 1.0 × 10(−4)). In the validation data set, we showed that the same classifiers were significantly related to disease status (P < 0.001). Differential expression analysis and weighted gene correlation network analysis highlighted key proteins and pathways with known relationships to Parkinson’s. Proteins from the complement and coagulation cascades suggest a disease relationship to immune response. The combined analytical approaches in a relatively large number of samples, across tissue types, with replication and validation, provide mechanistic insights into the disease as well as nominate a protein signature classifier that deserves further biomarker evaluation