Biomarkers of Intergenerational Risk for Depression: A Review of Mechanisms in Longitudinal High-Risk (LHR) Studies
journal contributionposted on 23.07.2017 by R.H. Jacobs, J.L. Orr, J.R. Gowins, E.E. Forbes, S.A. Langenecker
Any type of content formally published in an academic journal, usually following a peer-review process.
Background: Longitudinal research is critical for understanding the biological mechanisms underlying the development of depression. Researchers recruit high-risk cohorts to understand how risk is transmitted from one generation to the next. Biological measurements have been incorporated into these longitudinal high-risk (LHR) studies in order to illuminate mechanistic pathways. Methods: To frame our review, we first present heritability estimates along the gene-byenvironment continuum as a foundation. We then offer a Biomarkers of Intergenerational Risk for Depression (BIRD) model to describe the multiple hits individuals at risk receive and to allow for greater focus on the interactive effects of markers. BIRD allows for the known multifinality of pathways towards depression and considers the context (i.e., environment) in which these mechanisms emerge. Next, we review the extant LHR cohort studies that have assessed central nervous system (electroencephalography (EEG), neuroimaging), endocrine (hypothalamic-pituitary-adrenal axis (HPA)/cortisol), autonomic (startle, heart rate), genetic, sleep, and birth characteristics. Results: Results to date, in conjunction with the proposed model, point towards several pathways of discovery in understanding mechanisms, providing clear direction for future research examining potential endophenotypes. Limitations: Our review is based on relatively narrow inclusion and exclusion criteria. As such, many interesting studies were excluded, but this weakness is offset by strengths such as the increased reliability of findings. Conclusions: Blanket prevention programs are inefficient and plagued by low effect sizes due to low rates of actual conversion to disorder. The inclusion of biomarkers of risk may lead to enhanced program efficiency by targeting those with greatest risk