Plasma biomarkers of depressive symptoms in older adults

• Type of Material: Article
• Authors: SE Arnold, University of Pennsylvania; SX Xie, University of Pennsylvania; Y-Y Leung, University of Pennsylvania; L-S Wang, University of Pennsylvania; MA Kling, University of Pennsylvania; X Han, University of Pennsylvania; EJ Kim, University of Pennsylvania; DA Wolk, University of Pennsylvania; DA Bennett, Rush University; A Chen-Plotkin, University of Pennsylvania; M Grossman, University of Pennsylvania; William Hu, Emory University; VM-Y Lee, University of Pennsylvania; RS Mackin, University of California San Francisco; JQ Trojanowski, University of Pennsylvania; RS Wilson, Rush University; LM Shaw, University of Pennsylvania
• Language: English
• Date: 2012-01-01
• Publisher: Nature Publishing Group: Open Access Journals - Option B
• Publication Version: Final Published Version
• Copyright Statement: © 2012 Macmillan Publishers Limited. All rights reserved.
• License: Creative Commons Attribution-NonCommerical-NoDerivs 3.0 Unported
• Final Published Version (URL): http://dx.doi.org/10.1038/tp.2011.63
• Title of Journal or Parent Work: Translational Psychiatry
• ISSN: 2158-3188
• Volume: 2
• Issue: 1
• Start Page: e65
• End Page: e65
• Grant/Funding Information: The principal sources of support for this work were NIH AG10124, AG033101 and AG10161, the Marian S Ware Alzheimer's Program, Burroughs Wellcome Career Award for medical scientists, the Benaroya Fund and the Penn-Pfizer Alliance.; The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego.; Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI; National Institutes of Health Grant U01 AG024904).; Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org">www.fnih.org).; ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly, Medpace, Merck, Novartis AG, Pfizer, F Hoffman-La Roche, Schering-Plough and Synarc, as well as from non-profit partners such as the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the US Food and Drug Administration.; ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by the NIH Grants P30 AG010129, K01 AG030514, and the Dana Foundation.
• Keywords: NEUROIMAGING INITIATIVE PROGRESS; ALZHEIMERS-DISEASE; COGNITIVE IMPAIRMENT; mild cognitive impairment; HEPATOCYTE GROWTH-FACTOR; LATE-LIFE DEPRESSION; MAJOR DEPRESSION; INFLAMMATORY MARKERS; Psychiatry; Alzheimer's disease neuroimaging initiative; Science & Technology; geriatric depression; biochemical biomarker; Life Sciences & Biomedicine; ELDERLY-PATIENTS; GERIATRIC DEPRESSION; TYPE-2 DIABETES-MELLITUS
• Research Categories: Biology, Biostatistics; Health Sciences, General; Health Sciences, Epidemiology

Abstract

The pathophysiology of negative affect states in older adults is complex, and a host of central nervous system and peripheral systemic mechanisms may play primary or contributing roles. We conducted an unbiased analysis of 146 plasma analytes in a multiplex biochemical biomarker study in relation to number of depressive symptoms endorsed by 566 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) at their baseline and 1-year assessments. Analytes that were most highly associated with depressive symptoms included hepatocyte growth factor, insulin polypeptides, pregnancy-associated plasma protein-A and vascular endothelial growth factor. Separate regression models assessed contributions of past history of psychiatric illness, antidepressant or other psychotropic medicine, apolipoprotein E genotype, body mass index, serum glucose and cerebrospinal fluid (CSF) τand amyloid levels, and none of these values significantly attenuated the main effects of the candidate analyte levels for depressive symptoms score. Ensemble machine learning with Random Forests found good accuracy (∼80%) in classifying groups with and without depressive symptoms. These data begin to identify biochemical biomarkers of depressive symptoms in older adults that may be useful in investigations of pathophysiological mechanisms of depression in aging and neurodegenerative dementias and as targets of novel treatment approaches.