posted on 2017-07-23, 00:00authored byR.H. Jacobs, J.L. Orr, J.R. Gowins, E.E. Forbes, S.A. Langenecker
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
Funding
Funded by 5T32MH016434 (PI Peterson supporting RHJ), and
UL1TR00050 (to UIC Center for Clinical and Translational Science supporting RHJ). SAL was
supported by NIH MH091811 and MH101487 and EEF was supported through NIH MH093605
and DA033612. The content is solely the responsibility of the authors and does not necessarily
represent the official views of the funding bodies.
History
Publisher Statement
This is the author’s version of a work that was accepted for publication in Journal of Affective Disorders . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Affective Disorders, 2015. 175: 494-506. DOI: 10.1016/j.jad.2015.01.038.