P5 medicine and justice: the future is now

International Journal of Legal Medicine -     

Cortisol,salivary alpha-amylase and children’s perceptions of their social networks

In recent years there has been a growing interest in the use of social network analysis in biobehavioral research. Despite the well-established importance of social relationships in influencing human behavior and health, little is known about how children’s perception of their immediate social relationships correlates with biological parameters of stress. In this study we explore the association between two measures of children’s personal social networks, perceived network size and perceived network density, with two biomarkers of stress, cortisol and salivary alpha-amylase. Forty children (mean age = 8.30, min age = 5, and max age = 12) were interviewed to collect information about their friendships and three samples of saliva were collected. Our results show that children characterized by a lower pre-interview cortisol concentration and a lower salivary alpha-amylase reactivity to the interview reported the highest density of friendships. We discuss this result in light of the multisystem approach to the study of children’s behavioral outcomes, emphasizing that future work of this kind is needed in order to understand the cognitive and biological mechanisms underlying children’s and adolescents’ social perceptual biases.     

Clinical utility of novel biomarkers for cardiovascular disease risk stratification

Over the past few decades, a number of coronary artery disease (CAD) and cardiovascular disease (CVD) risk factors have been identified. The predictive power of “conventional” risk factors have been validated by observational, prospective and intervention studies. Nevertheless, all attempts to exactly predict the individual risk for CAD have failed, biased by a large number of incorrectly risk-classified subjects. To improve cardiovascular (CV) risk prediction, a large number of genetic and/or non-genetic biomarkers have been discovered and tested against the “classical” risk factors for their power to predict CV risk. Only few of them had a significant improvement over the predictive models. In this paper, the most investigated biomarkers will be discussed and the evidence of their use as predictors of CV will be questioned.