title: Plasma biomarkers of depressive symptoms in older adults.
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) τ and 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.
authors: Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA. steven.arnold@uphs.upenn.edu
authors: S E
authors: SE
authors: Arnold
authors: S X
authors: SX
authors: Xie
authors: Y-Y
authors: YY
authors: Leung
authors: L-S
authors: LS
authors: Wang
authors: M A
authors: MA
authors: Kling
authors: X
authors: X
authors: Han
authors: E J
authors: EJ
authors: Kim
authors: D A
authors: DA
authors: Wolk
authors: D A
authors: DA
authors: Bennett
authors: A
authors: A
authors: Chen-Plotkin
authors: M
authors: M
authors: Grossman
authors: W
authors: W
authors: Hu
authors: V M-Y
authors: VM
authors: Lee
authors: R Scott
authors: RS
authors: Mackin
authors: J Q
authors: JQ
authors: Trojanowski
authors: R S
authors: RS
authors: Wilson
authors: L M
authors: LM
authors: Shaw
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000097:blood* / Q000175:diagnosis*; D001185:Artificial Intelligence; D015415:Biomarkers / Q000097:blood*; D003866:Depressive Disorder / Q000097:blood* / Q000175:diagnosis*; D005260:Female; D005500:Follow-Up Studies; D017228:Hepatocyte Growth Factor / Q000097:blood; D006801:Humans; D007328:Insulin / Q000097:blood; D008297:Male; D008875:Middle Aged; D010446:Peptide Fragments / Q000097:blood; D011266:Pregnancy-Associated Plasma Protein-A / Q000378:metabolism; D012016:Reference Values; D013223:Statistics as Topic; D042461:Vascular Endothelial Growth Factor A / Q000097:blood
chemical_list: D015415:Biomarkers; C066855:HGF protein, human; D007328:Insulin; D010446:Peptide Fragments; C467484:VEGFA protein, human; D042461:Vascular Endothelial Growth Factor A; D017228:Hepatocyte Growth Factor; D011266:Pregnancy-Associated Plasma Protein-A