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Published on: November 21, 2013
Early interventions for youths at high risk for bipolar disorder: a developmental approach
Xavier Benarous1, Angèle Consoli2,3, Vanessa Milhiet2
1Service de Psychiatrie de l'Enfant et de l'Adolescent, Université Pierre et Marie Curie, Hôpital Pitié-Salpêtrière, AP-HP, 47-83, Boulevard de l'Hôpital, 75013, Paris, France. xavierbenarous@gmail.com.
Abstract:
In recent decades, ongoing research programmes on primary prevention and early identification of bipolar disorder (BD) have been developed. The aim of this article is to review the principal forms of evidence that support preventive interventions for BD in children and adolescents and the main challenges associated with these programmes. We performed a literature review of the main computerised databases (MEDLINE, PUBMED) and a manual search of the literature relevant to prospective and retrospective studies of prodromal symptoms, premorbid stages, risk factors, and early intervention programmes for BD. Genetic and environmental risk factors of BD were identified. Most of the algorithms used to measure the risk of developing BD and the early interventions programmes focused on the familial risk. The prodromal signs varied greatly and were age dependent. During adolescence, depressive episodes associated with genetic or environmental risk factors predicted the onset of hypomanic/manic episodes over subsequent years. In prepubertal children, the lack of specificity of clinical markers and difficulties in mood assessment were seen as impeding preventive interventions at these ages. Despite encouraging results, biomarkers have not thus far been sufficiently validated in youth samples to serve as screening tools for prevention. Additional longitudinal studies in youths at high risk of developing BD should include repeated measures of putative biomarkers. Staging models have been developed as an integrative approach to specify the individual level of risk based on clinical (e.g. prodromal symptoms and familial history of BD) and non-clinical (e.g. biomarkers and neuroimaging) data. However, there is still a lack of empirically validated studies that measure the benefits of using these models to design preventive intervention programmes.
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