University of Illinois at Chicago
Biomarkers of Intergenerational Risk.pdf (1.45 MB)

Biomarkers of Intergenerational Risk for Depression: A Review of Mechanisms in Longitudinal High-Risk (LHR) Studies

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journal contribution
posted on 2017-07-23, 00:00 authored by R.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


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.


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.


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