According to the World Health Organization, depression is now the leading cause of disability, globally affecting more than 300 million people worldwide. Depression is a condition that results from interactions between biological, psychological and social factors. Although depression can become manifest at any age, it often first emerges during adolescence. Identifying the risk factors for depression before clinically confounding symptoms arise is crucial for targeted and effective prevention strategies.
A study now published by a team from the Max Planck Institute of Psychiatry in Munich, and the Ludwig-Maximilians-Universitaet (LMU) in Munich Medical Center, in collaboration with researchers at Emory University, (Atlanta, USA), the University of Coimbra (Portugal), and the University of Helsinki (Finland), brings us a step closer to being able to prevent depression in children and adolescents. The authors have used a relatively new method of calculating the genetic risk of depression. Traditional genetic studies focus on one genetic difference at a time and determine its statistical association with risk of disease. In this study, information derived from many genetic variants associated with depression, which had been identified in a sample of over 460,000 adults, was used to create a score that reflects the aggregated genetic risk for depression, also known as a polygenic risk score. Individually, these variants have little impact on risk, but when taken together they can reveal an otherwise hidden disease risk, thus providing a much clearer picture. The method has already been successfully used to quantify genetic risk for many common diseases, such as heart disease or diabetes.
The study appears in the American Journal of Psychiatry, the journal most widely read by psychiatrists and mental-health professionals. Thorhildur Halldorsdottir, first author of the study, explains how it was done in more detail: “The score was first calculated from genetic data obtained from a very large number of adults with depression. This parameter was then evaluated in smaller cohorts of children and adolescents to determine whether it could predict depression and symptoms of depression in this age group.” In addition, she investigated the impact of an environmental factor — childhood abuse — which has been found to predict depression. “We also looked to see how a history of childhood abuse affected the risk. We found that both the polygenic risk score and exposure to childhood abuse were informative in identifying young people at risk for depression.”
Elisabeth Binder, Director of the Max Planck Institute and Head of the Department in which this research was carried out, summarizes the findings as follows: “This is the first study to show that the polygenic risk score calculated from adults with depression can be used to identify children who are at risk of developing depression, before any clinical symptoms have emerged.”
Effective psychological and pharmacological interventions for depression are already well known. A combination of such interventions has been found to be most effective. Unfortunately, application of these measures is not feasible within the sphere of public health, in part due to lack of resources. Gerd Schulte-Körne, Director and Chair of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy at the LMU Medical Center, and joint leader of the study, adds: “By applying the findings of studies like this one, it should be possible in future to target young people who are at greatest risk for depression, i.e., those with a high polygenic risk score and/or a history of childhood abuse, for these effective interventions.”
Binder concludes: “There is still a lot of work that needs to be done to perfect the early identification of young people at risk for depression. However, identifying which children are more likely to go on to develop depression would give us the opportunity to implement effective prevention strategies and reduce the huge health burden associated with depression.”
Materials provided by Ludwig-Maximilians-Universität München. Note: Content may be edited for style and length.