Plasma biomarkers of depressive symptoms in older
SE Arnold1,2, SX Xie3, Y-Y Leung4, L-S Wang4, MA Kling1, X Han3, EJ Kim4, DA Wolk2, DA Bennett5, A Chen-Plotkin2, M Grossman2,
W Hu6, VM-Y Lee4, R Scott Mackin7, JQ Trojanowski4, RS Wilson5and LM Shaw4, for the Alzheimer’s Disease Neuroimaging
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
psychiatricillness,antidepressantor other psychotropic medicine, apolipoprotein Egenotype,body mass index, serum glucose
and cerebrospinal fluid (CSF) s 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 (B80%) 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.
Translational Psychiatry (2012) 2, e65; doi:10.1038/tp.2011.63; published online 3 January 2012
The prevalence and incidence of clinically significant depres-
sive symptoms increase with advancing age, especially
among those with physical illness, cognitive decline and
functional disability.1–3In community-dwelling seniors, the
prevalence of major depression is B10%, whereas the rate
for ‘minor’, ‘subsyndromal’ or ‘subthreshold’ depression is
B30%, but may be as high as 48% among those 475 years.
Subsyndromal depression is not benign as it carries clinically
significant disability, poorer quality of life and higher health-
The pathophysiology of late-life depression is complex,
and a host of central nervous system and peripheral systemic
mechanismsmay play primary
Age-associated neurological illnesses such as Alzheimer’s
disease (AD), stroke and Parkinson’s disease are well-known
risk factors and may be associated with depression due
to disruption of neural circuitries that mediate the experience
and expression of negative emotions and behaviors by
their defined neuropathological lesions and neurochemical
changes.5–7Cardiovascular disease, inflammatory condi-
tions, cancer, metabolic and endocrine dysfunction also
increase with age and are highly associated with depression.
They are commonly thought to exert their effect via circulating
factors such as inflammatory markers produced in the course
of these chronic disease processes, although evidence to
support this is still controversial.8–11Numerous hypothesis-
based studies of depression in adulthood and late life have
identified associations with glucocorticoids and other stress
hormones,12insulin resistance,13inflammatory cytokines and
chemokines14and trophic factors15–17that may be activated
with normal and abnormal aging processes and/or in
response to illness, injury and other stressors, although the
data in older adults for all of these are relatively scarce.
Whether alterations in these various age- and depression-
associated factors are causative of depression or conse-
quences thereof and how they interact are not established.
The identification and validation of biomarkers in psychiatry
has been challenging but the task remains important to both
better understand the pathophysiology of depression and
better classify and treat this multifactorial syndrome in an era
of personalized medicine. Here, we used data from a large
multianalyte biochemical panel in plasma samples from older
adult participants in the Alzheimer’s Disease Neuroimaging
Initiative (ADNI) in a discovery analysis to identify peripheral
Received 9 May 2011; revised 1 November 2011; accepted 1 November 2011
1Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA;2Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA;
3Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA;4Department of Pathology and Laboratory Medicine, University of
Pennsylvania, Philadelphia, PA, USA;5Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA;6Department of Neurology, Emory
University, Atlanta, GA, USA and7Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA
Correspondence: Dr SE Arnold, Penn Memory Center, 3615 Chestnut Street, Philadelphia, PA 19104, USA.
8Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the
investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.
A complete listing of ADNI investigators can be found at: http://adni.loni.ucla.edu/wp-content/uploads/how_to_apply/ADNI_Authorship_List.pdf
Keywords: Alzheimer’s disease neuroimaging initiative; biochemical biomarker; geriatric depression; mild cognitive impairment
Citation: Transl Psychiatry (2012) 2, e65, doi:10.1038/tp.2011.63
& 2012 Macmillan Publishers Limited All rights reserved 2158-3188/12
biochemical biomarkers that associate with depressive
symptoms. Our findings provide further support for a role of
several previously identified proteins in negative affect states
and identify novel candidates for validation and investigation
in future studies and other cohorts.
Subjects and methods
Description of the ADNI. The ADNI is a large, multicenter,
longitudinal neuroimaging and biomarker study, launched in
2004,18–20with a primary goal to characterize cognitive
functioning and AD in older adults with longitudinal clinical
assessments, multiple modality neuroimaging and blood,
urine and cerebrospinal fluid (CSF) molecular and bio-
chemical biomarkers. Participants received baseline and
periodic physical and neurological examinations, standar-
dized neuropsychological assessments, apolipoprotein E
(APOE) genotyping and they provided biofluid samples
throughout the study.21,22Imaging (magnetic resonance
imaging and for a subset,18F-fluorodeoxyglucose positron
emission tomography and
positron emission tomography) is performed at baseline
and at regular intervals thereafter.20The procedures for this
study were approved by institutional review boards of all
participating institutions. All subjects gave written, informed
consent for all procedures before participation.
11C-Pittsburgh compound B
Participants. The total ADNI cohort includes 819 older
adults, 55 to 90 years old, who met entry criteria for a clinical
diagnosis of normal cognition, amnestic mild cognitive
impairment (MCI) or probable AD.19Participants with AD
met National Institute of Neurological and Communication
Association criteria for probable AD and had a Mini-Mental
State Examination score between 20 and 26 and a global
Clinical Dementia Rating (CDR) of 0.5 or 1.0 and, therefore,
were only mildly impaired at baseline. Inclusion criteria for
participants with MCI included memory complaint and
abnormal memory score on the Wechsler Memory Scale,
Mini-Mental Sate Examination score between 24 and 30
and a CDR of 0.5 with a Memory Box score of at least 0.5.
People with significant current psychiatric illness were
ineligible to participate in ADNI and thus only participants with
no more than mild depression, anxiety or other symptoms
were enrolled. Specific exclusion criteria for all participants
included major mood disorder within a year of screening, a
score of X6 on the Geriatric Depression Scale (GDS),23,24
history of schizophrenia, alcohol or substance abuse within
the past 2 years, and for AD and MCI participants, any
psychosis, agitation or behavior problems. For a review and
more details on inclusion and exclusion criteria, see Mueller
et al.25and http://www.adni-info.org/.
The multi-analyte biochemical panel assays used here
were conducted in a subsample of 566 ADNI participants,
including 396 with MCI, 112 with AD and 58 with normal
cognition. Subsampling was necessary because of budgetary
constraints. Samples were selected to enrich for MCI cases
and cases with additional biomarker end points (for example,
CSF amyloid-b and -t and amyloid neuroimaging) to be used
in separate analyses. Table 1 presents demographic and
clinical characteristics of the sample used here.
Assessments of depression and cognition. The primary
outcome measure ofdepressive
short version of the GDS.26This 15-item self report form
has demonstrated robust validity for depression in older
adults,24,26including those with MCI,27although its reliability
may diminish in people with dementia.28One item in the GDS
asks if the subject feels he/she has more memory problems
than most. Given the frequency of memory complaints in
ADNI, this item was excluded from the total score that was
used in analyses here.
The ADNI protocol includes a comprehensive assessment
of cognitive functioning with particular emphasis on domains
relevant to AD. A full description is available at http://
data from the baseline and 12-month assessments were
used. To control for potentially confounding effects of
cognitive syndrome on outcome measures of depression in
participants with AD, MCI and normal cognition, the CDR
Sum-of-Boxes score was included as a covariate in statistical
analyses.29The CDR is a structured interview assessing
functional status in the domains of memory, orientation,
judgment and problem solving, home and hobbies and
symptoms was the
Plasma sampling and biochemical multi-analyte panel.
Morning fasting plasma samples were obtained at baseline
and 12-month assessments. Whole-blood samples were
obtained in EDTA tubes, placed immediately on ice, spun
down for plasma aliquoting and frozen at ?801C, per ADNI
lab standard operating protocols as reviewed recently.30
Plasma samples from the ADNI biospecimen repository at
the University of Pennsylvania were sent to Rules-Based
Medicine (RBM, Austin, TX, USA) for measurement of 190
protein analytes with a multiplex immunoassay panel. This
platform to measure proteins previously reported to be altered
in cancer, cardiovascular disease, metabolic disorders and
inflammation. RBM also included additional analytes believed
to be involved in cell signaling and previously reported to
change in patients with AD.31RBM has attempted to validate
each of the 190 analytes up to CLIA (Clinical Laboratory
Improvement Amendment) standards, but the assays them-
selves are not CLIA approved. Each analyte has an individual
standard curve with between 6 and 8 reference standards.
Each plate is run with three levels of quality control (QC)
measures (low, medium and high dilutions). Each analyte has
a validation report, including a dynamic range in young adults.
Additional QC measures for almost all analytes were
employed specifically for the ADNI plasma analysis by testing
blank human plasmas spiked with extracts of cell cultures
expressing the individual analytes. Samples from the total
ADNI plasma cohort were run on 15 plates.
APOE genotyping. DNA was extracted from blood using
commercial reagents (FlexiGene, Qiagen, Valencia, CA,
USA). Two single-nucleotide polymorphisms (rs7412 and
rs429358) in APOE were typed using allelic discrimination
Biomarkers of depressive symptoms in older adults
SE Arnold et al
assays with TaqMan reagents (Applied Biosystems, Foster
City, CA, USA) on an ABI 7500. The APOE genotypes (e2, e3
and e4) were assigned by incorporating the genotyping
results into an algorithm.
Statistical analyses and machine learning analyses.
Analyses were conducted
Multiplex’ data set available for download at https://ida.
loni.ucla.edu/login.jsp?project¼ADNI. This data set contains
the cleaned, quality-controlled data as described in the ADNI
statistical analysis plan. Missing, ‘LOW’ or outlier values
were imputed respectively as the mean of the nonmissing
values, half the least detectable dose, or by a nearest
neighbor or similar algorithm. Analytes for which missing/
’LOW’ values were 425% of all samples were excluded,
resulting in valid data for 146 of the total 190 analytes on the
Linear regression models evaluated the association of
levels of each protein to GDS scores at baseline and month
12. Mixed-effect models examined if each protein is asso-
ciated with change of depression symptoms by examining the
interaction between follow-up time and each protein.32This
statistical procedure accounts for the correlations that are
due to repeated measurements of depression symptoms in
the same patient. All models adjusted for age, sex, education
and CDR Sum-of-Boxes score as covariates. Analyses were
conducted in SPSS Statistics 19 (IBM, Somers, NY, USA),
SAS version 9.2 (SAS Institute, Cary, NC, USA) and JMP
8.0.1 (SAS). Secondary analyses incorporated terms for past
depression or other psychiatric illness, antidepressant use
and APOE genotype. All statistical tests were two sided. We
did not conduct multiple testing adjustments except lowering
the significance level to 0.01 and the ‘trend’ level to 0.05
because the current analyses are discovery in nature.
Consequently, there is a chance of false discovery because
of the number of statistical tests performed.
in the ‘ADNIPlasmaQC
Finally, to assess the utility of such multiplex immuno-
assays as biomarker classifiers of depressive symptoms, we
applied a machine learning ensemble classification method,
Random Forests (using R package RandomForest, version
cc_home.htm). The samples were dichotomized into those
with depressive symptoms (GDS X2) versus those without
depressive symptoms (GDS¼0), with GDS¼1 serving as a
buffer. Analytes were first sorted by z-score (derived from
variable importance score) obtained from Random Forests.
The higher the score, the more significant the analyte is for
discriminating between samples with and without depressive
symptoms. The first selected analyte has the highest
importance. This is then combined with all remaining analytes
to find the second analyte, which in combination with the
first one gives the best Out-of-Bag error estimation (used
for deriving accuracy). Finally, to gauge the robustness of
fivefold cross-validation procedure to the analytes selected
by Random Forests. In each of the five validations, 80% of
the subjects were chosen for training and the remaining 20%
were used for testing; the partition was done randomly while
preserving the ratio of samples from the two categories. The
procedure was repeated three times.
Of the 566 ADNI participants for whom at least baseline
plasmas were analyzed, 165 (29.2%) had depressive
symptoms (GDS 2–5) at baseline. In the subsequent year,
there were some generally modest changes in GDS scores
with a small increase in the proportion of participants with
depressive symptoms (32.1% with GDS X2 at month 12).
This included 21 participants (4.1%) whose GDS scores
increased into a more significant category of syndromal
depression (X6, range 6–12).
Table 1 Demographic and clinical data for ADNI plasma biomarker cohort
Variable (at baseline)
Sex (% female)
APOE genotype (% e4+)
74.8 (7.5, 54.8–90)a
15.6 (3.0, 4–20)
0.63 (0.70, 0–4)
75.1 (5.8, 62–90)
15.6 (2.7, 8–20)
0.64 (0.79, 0–3)
74.6 (7.4, 54.8–89.6)
15.7 (3.0, 4–20)
0.62 (0.70, 0–4)
74.8 (8.1, 54.8–88.8)
15.1 (3.2, 4–20)
0.65 (0.68, 0–3)
Current or past psychiatric
Antidepressant use (%)
1.0 (1.2, 0–5)0.8 (1.2, 0–4) 1.1 (1.3, 0–5)1.1 (1.2, 0–5) F(2,563)¼1.71
CDR Sum of Boxes
26.5 (2.4, 20–30)
12.3 (5.8, 1.7–42.7)
3.0 (1.6, 1–10)
28.9 (1.2, 25–30)
6.2 (2.8, 1.7–14.3)
1.0 (0.2, 1–2)
27.0 (1.8, 23–30)
11.5 (4.4, 2–27.7)
2.6 (0.9, 1–6)
23.59 (1.9, 20–27)
18.3 (6.4, 8.67–42.67)
5.3 (1.6, 2–10)
Abbreviations: AD, Alzheimer’s disease; ADAS-Cog, Alzheimer’s Disease Assessment Scale-Cognitive Subscale; ADNI, Alzheimer’s Disease Neuroimaging
Initiative; APOE, apolipoprotein E; CDR, Clinical Dementia Rating; MCI, mild cognitive impairment; MMSE, Mini-Mental Sate Examination.
aMean (s.d., range).
Biomarkers of depressive symptoms in older adults
SE Arnold et al
There were no significant differences in age, gender or
education among the normal, MCI and mild AD diagnostic
groups at baseline. As expected, these groups differed in
frequency of APOE genotype and in measures of cognition.
Interestingly, although the diagnostic groups did not differ in
GDS scores at baseline, there were highly significant
differences in the frequency of past psychiatric illness, with
MCI and AD participants respectively having B1.5-fold and
2.5-fold higher frequencies than those categorized as normal.
strengths of associations of analytes with depressive symp-
toms at baseline and at month 12. The top analytes are
presented in Table 2 and statistics for the associations of all
146 plasma analytes with the main outcomes of interest are
presented in the Supplementary Table S1. Associations were
forms of insulin, pregnancy-associated plasma protein A
(PAPPA), and vascular endothelial growth factor (VEGF).
To examine potentially confounding effects of other vari-
ables in the associations of the top analytes, we used
separate linear regression models with additional terms for
past history of psychiatric illness, antidepressant use, use of
any psychotropic medicine, modified Hachinski Ischemic
Score,33APOE genotype, body mass index and serum
glucose (baseline only). The associations were essentially
the same (data not shown).
We also tested whether depressive symptoms were
related to molecular signatures of AD pathology and whether
these moderated the significant associations of depressive
analyte panel. We found no associations between baseline
p-t181 (F¼2.03, d.f.¼5, 351, P¼0.15), or Ab (F¼0.89,
d.f.¼5, 351, P¼0.35), and no modifying effects.
Random Forest analyses were performed as a complemen-
classify participants into those with (GDS X2) or without
(GDS¼0) depressive symptoms. The set of analytes was first
selected based on z-scores obtained from Random Forests.
The final sets of analytes were then selected with the aim of
obtaining the best classification accuracy. Two sets of
analytesfrom the multi-
experiments were performed on each of the baseline and
month-12 data. At baseline, 140 people with depressive
symptoms were compared with 225 with none. At month 12,
158 people had depressive symptoms whereas 210 had none.
At baseline, the best set of analytes achieved an overall 75.4%
accuracy in classifying presence or absence of depressive
At month 12, accuracy was 80.9% with a sensitivity of 59.5%
and a specificity of 94.2%. The analytes selected by Random
Forests at each assessment are presented in Table 3.
Finally,asa third approachto identify candidatebiomarkers
that may be pathophysiologically relevant to depressive
symptoms, we used mixed-effects models to discern baseline
plasma analytes that were associated with change in depres-
sive symptoms from baseline through month 24. Adjusted for
Table 2 Analytes associated with depressive symptomsa
Baseline Month 12
Analyte at baselinea
Statisticsb(d.f.¼5,560) Analyte at month 12a
Hepatocyte growth factor
Pregnancy-associated plasma protein A
Pulmonary and activation-regulated chemokine F¼6.10, P¼0.0138
Vascular endothelial growth factor
Tamm–Horsfall urinary glycoprotein
Hepatocyte growth factor
Vascular endothelial growth factor
Monokine induced by g-interferon
Pregnancy-associated plasma protein A F¼5.44, P¼0.020
Trefoil factor 3
aAnalytes with Po0.05 are presented in rank order of P-values.
bLinear regression models with adjustment for age, gender, education and Clinical Dementia Rating (CDR) Sum of Boxes.
Table 3 Top analytes for discriminating between subjects with and without
depressive symptoms using random forest models
Brain-derived neurotrophic factor
AXL receptor tyrosine kinase
stimulating factor 1
Myeloid progenitor inhibitory
plasma protein A
Tumor necrosis factor
von Willebrand factor
aThe analytes are in alphabetical order.
Biomarkers of depressive symptoms in older adults
SE Arnold et al
age, gender, education and CDR Sum of Boxes, the rate of
change of depression symptoms was associated with three
baseline analytes at the Po0.01 level, including angiopoietin
2,FAS ligand receptor and fatty acid-binding protein, and nine
at the Po0.05 level, including HGF (see Supplementary
Table S1 for statistical results for all analytes).
Our data identified candidate plasma biomarkers of depres-
sive symptoms in older adults, which may prove useful in
investigations into the pathophysiology of negative affect
across the lifespan. Previous biomarker studies of depression
have focused on contemporaneous conceptualizations of
depression pathophysiology.12–17,34–36Here, we used an
unbiased multiplex discovery interrogation of 146 known
plasma analytes in a large cohort of well-characterized
subjects. In particular, HGF, insulins, PAPPA and VEGF
were identified as among the more robust and interesting
HGF (also known as scatter factor and hematopoietin A) is
a 103kDa heterodimeric protein first identified as a regenera-
tion factor for hepatocytes after liver injury.37It is a multi-
functional trophic factor that signals through a tyrosine kinase
signaling cascade after binding to MET, its proto-oncogenic
neuronal cells, whereas MET is especially highly expressed
in neurons. During embryogenesis, HGF is a neural inducer,
an interneuron motogen, an axonal chemoattractant, an
angiogenic factor and a neuroprotective survival factor.38,39
Its expression continues in adulthood and it is induced by
ischemic injury40and other disease processes, including
AD.41HGF enhances long-term potentiation,42improves
or attenuates ischemia-related memory deficits43and Ab-
induced behavioral impairment,44and has anxiolytic proper-
whereas its suppression increases anxiety and
depressive behaviors in rodents.46To our knowledge, only
two studies have investigated HGF in relation to depression in
humans, one of which found significantly lower serum HGF
levels among 26 people with major depression compared with
19 controls,47whereas the other study found no difference in
personality disorder.48Higher serum levels of HGF have also
been found in those patients with panic disorder who best
responded to antidepressant medication.49To this literature,
we add our finding of an increase in plasma HGF levels with
increasing depressive symptoms in older adults. Given the
generally salutary effects of HGF, we speculate that such
an increase may represent a compensatory mechanism in
the setting of other depression-inducing pathophysiological
Depressive symptoms were positively associated with
levelsofinsulinpolypeptides, insupportofa growingliterature
linking insulin resistance to depression.13,50–53The nature of
this association is complex as some evidence indicates a
for subsequent development of type 2 diabetes mellitus,54
whereas the presence of type 2 diabetes mellitus increases
risk for incident depression.52One mechanism that has been
proposed to account for insulin resistance in depression is
byneurons and non-
dysregulation of the hypothalamic–pituitary–adrenal axis with
abnormal circulating levels of glucocorticoids.12,55Others
have invoked the effects on insulin signaling by inflammatory
cytokines14and some trophic factors15–17that may also be
activated in depression. Conversely, insulin activates diverse,
multifunctional signaling cascades (for example, Akt and
glycogen synthase kinase 3) that are believed to play
important roles in mood.56,57
As further support for an insulin link to depression, we
observed an association of PAPPA with depressive symp-
toms. PAPPA is a secreted metalloproteinase that cleaves
insulin-like growth factor-binding proteins, increasing their
the brain have not been studied.
VEGF was also found to be elevated in relation to
depressive symptoms in the ADNI cohort. Previous studies
have reported VEGF gene polymorphisms associated with
major depression,59,60increased leukocyte VEGF mRNA
expression in refractory depression61and increased serum or
third one.63VEGF (specifically VEGF-A measured here) is a
232-amino-acid protein with multiple isoforms that bind to
three high-affinity receptor tyrosine kinases. It is widely
expressed and is best known for its growth factor activity in
angiogenesis, vasculogenesis and endothelial cell growth.64
Analogous to insulin, after binding to its receptors, VEGF
activates numerous downstream signaling proteins including
protein kinase C, phospholipase C-g, phosphatidylinositol
3-kinase, Akt and mammalian target of rapamycin. VEGF
increases the permeability of the blood–brain barrier,65
facilitates neurogenesis and proliferation of neurons in the
adult hippocampus,66plays a role in synaptic neurotransmis-
sion67and synaptic plasticity in hippocampus-dependent
learning and memory68and has anxiolytic and antidepressant
activity in rodent models.66
ADNI study design as well as its weaknesses. Foremost, the
ADNI study incorporates an extraordinary array of biomarker
approaches that characterize many aspects of the functional,
anatomic, physiological, biochemical and molecular health
of the aging brain. It is longitudinal, thus allowing both
cross-sectional and dynamic monitoring of phenomenology
and biology that may better elucidate disease processes.
Future analyses will investigate the relationships of bio-
chemical biomarkers of depressive symptoms identified here
in relation to other neuroimaging and longitudinal outcome
On the other hand, the principal outcome of interest of
the ADNI is cognitive functioning, not emotional functioning,
and its clinical assessments of mood and other psychiatric
symptoms are limited. The assessment of depressive
symptoms is by the interviewer-administered GDS with the
research participant, and thus may lack the depth and
accuracy of a structured psychiatric interview or observer
ratings. Although the GDS is among the most well-validated
and widely used scales for depressive symptoms in the
elderly, its sensitivity wanes in people with dementia in
comparison with other ‘gold-standard’ expert rater-based
scales of depression in dementia.27As well, ADNI was
designed to recruit older adults who are typical of participants
Biomarkers of depressive symptoms in older adults
SE Arnold et al
in clinical trials of AD and MCI. These focus on cognitive
function unconfounded by depression or other illnesses that
are common in general older populations. Indeed, eligibility in
ADNI was restricted to participants with no active psychiatric
illness and no more than very mild depressive symptoms at
baseline. Despite these limitations, the sample size and
expanding range of depressive symptoms over time did allow
us to discern candidate biomarkers of depressive symptoms
that are also biologically plausible. However, given the
exclusion of subjects with any clinically significant depression
at the time of enrollment in ADNI, it is important to recognize
that our analyses may have failed to detect depression-
related analytes that would be found in people with more
severe symptoms or that our findings may be specific only to
subsyndromal depressive symptoms in older adults and not
major or minor depression in general.
There have been very few biomarker discovery investiga-
tions of psychiatric disorders that employ multi-analyte panels
such as ours. Simon et al.69tested a chronic inflammation
hypothesis of major depressive disorder using a 22-plex
cytokine/chemokine panel on a Luminex platform in 49
patients and 49 controls and found significant differences in
most of the analytes, thus supporting their hypothesis. More
recently, Domenici et al.70used a 79-plex analyte panel from
RBMto profile plasmas from 245 patientswith recurrent major
depressive disorder, 229 patients with schizophrenia and
254 nonpsychiatric controls. Many of the analytes in their
panel were also measured in ours. In univariate analyses,
the analyte that showed the greatest difference between
depressed and control groups was insulin.
There is substantial evidence that higher levels of inflam-
matory biomarkers, such as tumor necrosis factor-a, various
interleukins and transforming growth factor-b are present in
major depression as well as AD.71,72Our analyses did not find
these inflammatory cytokines. However, it is important to
again note the differences in subjects used in these studies,
where ADNI specifically excludes participants with major
There is growing recognition that chronic psychological
distress is a risk factor for cognitive decline and aging-related
dementias.73–76Consonant with this, we observed a 41.5-
fold higher rate of past psychiatric illness among ADNI
participants with MCI at baseline and an almost 2.5-fold
higher rate in those with AD compared with those with normal
cognition. The mechanism(s) by which depression and other
facets of psychological distress increase dementia risk is not
clear. Overall, post-mortem clinicopathological studies have
not found associations between psychological distress and
the defining pathologies of neurodegenerative dementias
such as Ab plaque or neurofibrillary t tangle densities,
a-synuclein Lewy bodies or cerebral infarcts.77,78Here as
well, we saw no association between CSF t or Ab levels (as
proxies for brain AD pathology) and depressive symptoms.
Thus, it will be important to further investigate the candidate
this study for their relationship to subsequent cognitive
decline, to neuroimaging evidence of atrophy and other
evidence of neurodegeneration and to post-mortem neuro-
pathological findings as these become available.
Conflict of interest
The authors declare no conflict of interest.
Acknowledgements. 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. Data collection and sharing for this project was funded by
theAlzheimer’s DiseaseNeuroimaging Initiative(ADNI;NationalInstitutesofHealth
Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the
NationalInstitute of Biomedical Imagingand 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. Private sector contributions to ADNI are
and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the
University of California, San Diego. 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
1. MeeksTW,VahiaIV,Lavretsky H,KulkarniG,JesteDV.Atunein‘‘aminor’’can‘‘bmajor’’:
a review of epidemiology, illness course, and public health implications of subthreshold
depression in older adults. J Affect Disord 2010; 129: 126–142.
2. Thielke SM, Diehr P, Unutzer J. Prevalence, incidence, and persistence of major
depressive symptoms in the Cardiovascular Health Study. Aging Ment Health 2010; 14:
3. Dozeman E, van Marwijk HW, van Schaik DJ, Stek ML, van der Horst HE, Beekman AT
etal. Highincidence ofclinically relevant depressive symptoms in vulnerable persons of75
years or older living in the community. Aging Ment Health 2010; 14: 828–833.
4. Lyness JM, Heo M, Datto CJ, Ten Have TR, Katz IR, Drayer R et al. Outcomes of minor
and subsyndromal depression among elderly patients in primary care settings. Ann Intern
Med 2006; 144: 496–504.
a common neuropathological cascade? J Alzheimers Dis 2011; 23: 177–193.
6. Rodriguez-Oroz MC, Jahanshahi M, Krack P, Litvan I, Macias R, Bezard E et al. Initial
clinical manifestations of Parkinson’s disease: features and pathophysiological
mechanisms. Lancet Neurol 2009; 8: 1128–1139.
7. Fang J, Cheng Q. Etiological mechanisms of post-stroke depression: a review. Neurol Res
2009; 31: 904–909.
8. Flicker L. Cardiovascular risk factors, cerebrovascular disease burden, and healthy brain
aging. Clin Geriatr Med 2010; 26: 17–27.
9. Bremmer MA, Beekman AT, Deeg DJ, Penninx BW, Dik MG, Hack CE et al. Inflammatory
markers in late-life depression: results from a population-based study. J Affect Disord
2008; 106: 249–255.
10. Penninx BW, Kritchevsky SB, Yaffe K, Newman AB, Simonsick EM, Rubin S et al.
Inflammatory markers and depressed mood in older persons: results from the Health,
Aging and Body Composition study. Biol Psychiatry 2003; 54: 566–572.
11. Forti P, Rietti E, Pisacane N, Olivelli V, Mariani E, Chiappelli M et al. Blood inflammatory
proteins and risk of incident depression in the elderly. Dement Geriatr Cogn Disord 2010;
12. Bremmer MA, Deeg DJ, Beekman AT, Penninx BW, Lips P, Hoogendijk WJ. Major
depression in late life is associated with both hypo- and hypercortisolemia. Biol Psychiatry
2007; 62: 479–486.
13. Amato L, Paolisso G, Cacciatore F, Ferrara N, Canonico S, Rengo F et al. Non-insulin-
dependent diabetes mellitus is associated with a greater prevalence of depression in the
14. Lee KS, Chung JH, Lee KH, Shin MJ, Oh BH, Lee SH et al. Simultaneous measurement of
23 plasma cytokines in late-life depression. Neurol Sci 2009; 30: 435–438.
15. Diniz BS, Teixeira AL, Talib LL, Mendonca VA, Gattaz WF, Forlenza OV. Serum brain-
derived neurotrophic factor level is reduced in antidepressant-free patients with late-life
depression. World J Biol Psychiatry 2010; 11: 550–555.
16. Shi Y, You J, Yuan Y, Zhang X, Li H, Hou G. Plasma BDNF and tPA are associated with
late-onset geriatric depression. Psychiatry Clin Neurosci 2010; 64: 249–254.
17. Lang UE, Hellweg R, Gallinat J. BDNF serum concentrations in healthy volunteers are
Biomarkers of depressive symptoms in older adults
SE Arnold et al
18. Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack C, Jagust W et al. The Alzheimer’s
disease neuroimaging initiative. Neuroimaging Clin N Am 2005; 15: 869–877, xi–xii..
of the Alzheimer’s Disease Neuroimaging Initiative: progress and plans. Alzheimers
Dement 2010; 6: 239–246.
20. Weiner MW, Aisen PS, Jack Jr CR, Jagust WJ, Trojanowski JQ, Shaw L et al. The
Alzheimer’s disease neuroimaging initiative: progress report and future plans. Alzheimers
Dement 2010; 6: 202–211, e207.
21. Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, Harvey DJ et al.
Alzheimer’s Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology
2010; 74: 201–209.
22. Shaw LM. PENN Biomarker Core of the Alzheimer’s Disease Neuroimaging Initiative.
Neurosignals 2008; 16: 19–23.
23. Yesavage JA, Brink TL, Rose TL, Lum O, Huang V, Adey M et al. Development and
validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res
1982; 17: 37–49.
24. Almeida OP, Almeida SA. Short versions of the geriatric depression scale: a study of their
validity for the diagnosis of a major depressive episode according to ICD-10 and DSM-IV.
Int J Geriatr Psychiatry 1999; 14: 858–865.
25. Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack CR, Jagust W et al. Ways toward an
early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative
(ADNI). Alzheimers Dement 2005; 1: 55–66.
26. Friedman B, Heisel MJ, Delavan RL. Psychometric properties of the 15-item geriatric
depression scale in functionally impaired, cognitively intact, community-dwelling elderly
primary care patients. J Am Geriatr Soc 2005; 53: 1570–1576.
the geriatric depression scale a reliable screening tool for depressive symptoms in elderly
patients with cognitive impairment? Int J Geriatr Psychiatry 2009; 24: 556–562.
28. Burke WJ, Roccaforte WH, Wengel SP. The short form of the geriatric depression scale:
a comparison with the 30-item form. J Geriatr Psychiatry Neurol 1991; 4: 173–178.
29. Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules.
Neurology 1993; 43: 2412–2414.
30. Trojanowski JQ, Vandeerstichele H, Korecka M, Clark CM, Aisen PS, Petersen RC et al.
Update on the biomarker core of the Alzheimer’s Disease Neuroimaging Initiative subjects.
Alzheimers Dement 2010; 6: 230–238.
31. Ray S, Britschgi M, Herbert C, Takeda-Uchimura Y, Boxer A, Blennow K et al.
Classification and prediction of clinical Alzheimer’s diagnosis based on plasma signaling
proteins. Nat Med 2007; 13: 1359–1362.
32. Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics 1982; 38:
33. Rosen WG, Terry RD, Fuld PA, Katzman R, Peck A. Pathological verification of ischemic
score in differentiation of dementias. Ann Neurol 1980; 7: 486–488.
34. Carroll BJ, Martin FI, Davies B. Resistance to suppression by dexamethasone of plasma
11-O.H.C.S. levels in severe depressive illness. Br Med J 1968; 3: 285–287.
35. Belmaker R, Beckmann H, Goodwin F, Murphy D, Pollin W, Buchsbaum M et al.
Relationships between platelet and plasma monoamine oxidase, plasma dopamine-beta-
hydroxylase, and urinary 3-methoxy-4-hydroxy phenylglycol. Life Sci 1975; 16: 273–280.
36. Karege F, Perret G, Bondolfi G, Schwald M, Bertschy G, Aubry JM. Decreased serum
brain-derived neurotrophic factor levels in major depressed patients. Psychiatry Res 2002;
37. Nakamura T, Mizuno S. The discovery of hepatocyte growth factor (HGF) and its
significance for cell biology, life sciences and clinical medicine. Proc Jpn Acad Ser B Phys
Biol Sci 2010; 86: 588–610.
38. Jung W, Castren E, Odenthal M, Vande Woude GF, Ishii T, Dienes HP et al. Expression
and functional interaction of hepatocyte growth factor-scatter factor and its receptor c-met
in mammalian brain. J Cell Biol 1994; 126: 485–494.
39. Powell EM, Campbell DB, Stanwood GD, Davis C, Noebels JL, Levitt P. Genetic disruption
of cortical interneuron development causes region- and GABA cell type-specific deficits,
epilepsy, and behavioral dysfunction. J Neurosci 2003; 23: 622–631.
40. Nagayama T, Nagayama M, Kohara S, Kamiguchi H, Shibuya M, Katoh Y et al. Post-
ischemic delayed expression of hepatocyte growth factor and c-Met in mouse brain
following focal cerebral ischemia. Brain Res 2004; 999: 155–166.
41. Fenton H, Finch PW, Rubin JS, Rosenberg JM, Taylor WG, Kuo-Leblanc V et al.
Hepatocyte growth factor (HGF/SF) in Alzheimer’s disease. Brain Res 1998; 779:
42. Akimoto M, Baba A, Ikeda-Matsuo Y, Yamada MK, Itamura R, Nishiyama N et al.
Hepatocyte growth factor as an enhancer of nmda currents and synaptic plasticity in the
hippocampus. Neuroscience 2004; 128: 155–162.
43. Date I, Takagi N, Takagi K, Kago T, Matsumoto K, Nakamura T et al. Hepatocyte growth
factor improved learning and memory dysfunction of microsphere-embolized rats.
J Neurosci Res 2004; 78: 442–453.
Abeta-induced cognitive impairment by ultrasound-mediated gene transfer of HGF in a
mouse model. Gene Therapy 2008; 15: 561–571.
45. Isogawa K, Akiyoshi J, Kodama K, Matsushita H, Tsutsumi T, Funakoshi H et al.
Anxiolytic effect of hepatocyte growth factor infused into rat brain. Neuropsychobiology
2005; 51: 34–38.
46. Wakatsuki M, Akiyoshi J, Ichioka S, Tanaka Y, Tsuru J, Matsushita H et al. Administration
of antisense DNA for hepatocyte growth factor causes an depressive and anxiogenic
response in rats. Neuropeptides 2007; 41: 477–483.
47. RussoAJ.Decreasedserum hepatocytegrowth factor(HGF) inindividualswithdepression
correlates with severity of disease. Biomark Insights 2010; 5: 63–67.
48. Kahl KG, Bens S, Ziegler K, Rudolf S, Kordon A, Dibbelt L et al. Angiogenic factors in
patients with current major depressive disorder comorbid with borderline personality
disorder. Psychoneuroendocrinology 2009; 34: 353–357.
49. Kanehisa M, Ishitobi Y, Ando T, Okamoto S, Maruyama Y, Kohno K et al. Serum
hepatocyte growth factor levels and the effects of antidepressants in panic disorder.
Neuropeptides 2010; 44: 431–435.
50. Okamura F, Tashiro A, Utumi A, Imai T, Suchi T, Tamura D et al. Insulin resistance in
patients with depression and its changes during the clinical course of depression: minimal
model analysis. Metabolism 2000; 49: 1255–1260.
51. Pouwer F, Beekman AT, Nijpels G, Dekker JM, Snoek FJ, Kostense PJ et al. Rates and
risks for co-morbid depression in patients with Type 2 diabetes mellitus: results from a
community-based study. Diabetologia 2003; 46: 892–898.
52. Pan A, Lucas M, Sun Q, van Dam RM, Franco OH, Manson JE et al. Bidirectional
association between depression and type 2 diabetes mellitus in women. Arch Intern Med
2010; 170: 1884–1891.
53. Weber-Hamann B, Gilles M, Lederbogen F, Heuser I, Deuschle M. Improved
insulin sensitivity in 80 nondiabetic patients with MDD after clinical remission in a
double-blind, randomized trial of amitriptyline and paroxetine. J Clin Psychiatry 2006; 67:
54. Knol MJ, Twisk JW, Beekman AT, Heine RJ, Snoek FJ, Pouwer F. Depression as a risk
factor for the onset of type 2 diabetes mellitus. A meta-analysis. Diabetologia 2006; 49:
55. Vogelzangs N, Suthers K, Ferrucci L, Simonsick EM, Ble A, Schrager M et al.
Hypercortisolemic depression is associated with the metabolic syndrome in late-life.
Psychoneuroendocrinology 2007; 32: 151–159.
56. Lute BJ, Khoshbouei H, Saunders C, Sen N, Lin RZ, Javitch JA et al. PI3K signaling
supports amphetamine-induced dopamine efflux. Biochem Biophys Res Commun 2008;
57. Robertson SD, Matthies HJ, Owens WA, Sathananthan V, Christianson NS, Kennedy JP
et al. Insulin reveals Akt signaling as a novel regulator of norepinephrine transporter
trafficking and norepinephrine homeostasis. J Neurosci 2010; 30: 11305–11316.
58. Conover CA, Bale LK, Overgaard MT, Johnstone EW, Laursen UH, Fuchtbauer EM et al.
Metalloproteinase pregnancy-associated plasma protein A is a critical growth regulatory
factor during fetal development. Development 2004; 131: 1187–1194.
59. Viikki M, Anttila S, Kampman O, Illi A, Huuhka M, Setala-Soikkeli E et al. Vascular
endothelial growth factor (VEGF) polymorphism is associated with treatment resistant
depression. Neurosci Lett 2010; 477: 105–108.
60. Tsai SJ, Hong CJ, Liou YJ, Chen TJ, Chen ML, Hou SJ et al. Haplotype analysis of single
nucleotide polymorphisms in the vascular endothelial growth factor (VEGFA) gene and
antidepressant treatment response in major depressive disorder. Psychiatry Res 2009;
61. Iga J, Ueno S, Yamauchi K, Numata S, Tayoshi-Shibuya S, Kinouchi S et al. Gene
expression and association analysis of vascular endothelial growth factor in
major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2007; 31:
62. Takebayashi M, Hashimoto R, Hisaoka K, Tsuchioka M, Kunugi H. Plasma levels of
vascular endothelial growth factor and fibroblast growth factor 2 in patients with major
depressive disorders. J Neural Transm 2010; 117: 1119–1122.
63. Ventriglia M, Zanardini R, Pedrini L, Placentino A, Nielsen MG, Gennarelli M et al.
VEGF serum levels in depressed patients during SSRI antidepressant treatment.
Prog Neuropsychopharmacol Biol Psychiatry 2009; 33: 146–149.
64. Ferrara N, Gerber HP, LeCouter J. The biology of VEGF and its receptors. Nat Med 2003;
65. Rigau V, Morin M, Rousset MC, de Bock F, Lebrun A, Coubes P et al. Angiogenesis is
associated with blood-brain barrier permeability in temporal lobe epilepsy. Brain 2007;
130(Pt 7): 1942–1956.
66. Udo H, Yoshida Y, Kino T, Ohnuki K, Mizunoya W, Mukuda T et al. Enhanced adult
neurogenesis and angiogenesis and altered affective behaviors in mice overexpressing
vascular endothelial growth factor 120. J Neurosci 2008; 28: 14522–14536.
67. McCloskey DP, Croll SD, Scharfman HE. Depression of synaptic transmission by vascular
chronic seizures. J Neurosci 2005; 25: 8889–8897.
68. Cao L, Jiao X, Zuzga DS, Liu Y, Fong DM, Young D et al. VEGF links hippocampal activity
with neurogenesis, learning and memory. Nat Genet 2004; 36: 827–835.
69. Simon NM, McNamara K, Chow CW, Maser RS, Papakostas GI, Pollack MH et al.
A detailed examination of cytokine abnormalities in major depressive disorder.
Eur Neuropsychopharmacol 2008; 18: 230–233.
70. Domenici E, Wille DR, Tozzi F, Prokopenko I, Miller S, McKeown A et al. Plasma protein
biomarkers for depression and schizophrenia by multi analyte profiling of case-control
collections. PLoS One 2010; 5: e9166.
71. Dowlati Y, Herrmann N, Swardfager W, Liu H, Sham L, Reim EK et al. A meta-analysis of
cytokines in major depression. Biol Psychiatry 2010; 67: 446–457.
Biomarkers of depressive symptoms in older adults
SE Arnold et al
72. Swardfager W,LanctotK,RothenburgL,WongA,CappellJ,HerrmannN.Ameta-analysis Download full-text
of cytokines in Alzheimer’s disease. Biol Psychiatry 2010; 68: 930–941.
73. Wilson RS, Arnold SE, Schneider JA, Kelly JF, Tang Y, Bennett DA. Chronic psycho-
logical distress and risk of Alzheimer’s disease in old age. Neuroepidemiology 2006; 27:
74. Wilson RS, Barnes LL, Mendes de Leon CF, Aggarwal NT, Schneider JS, Bach J et al.
Depressive symptoms, cognitive decline, and risk of AD in older persons. Neurology 2002;
75. Yaffe K, Vittinghoff E, Lindquist K, Barnes D, Covinsky KE, Neylan T et al. Posttraumatic
stress disorder and risk of dementia among US veterans. Arch Gen Psychiatry 2010; 67:
76. Crowe M, Andel R, Pedersen NL, Gatz M. Do work-related stress and reactivity to stress
predict dementia more than 30 years later? Alzheimer Dis Assoc Disord 2007; 21: 205–209.
77. Wilson RS, Arnold SE, Schneider JA, Li Y, Bennett DA. Chronic distress, age-related
neuropathology, and late-life dementia. Psychosom Med 2007; 69: 47–53.
78. Milwain EJ, Nagy Z. Depressive symptoms increase the likelihood of cognitive impairment
in elderly people with subclinical Alzheimer pathology. Dement Geriatr Cogn Disord 2005;
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Biomarkers of depressive symptoms in older adults
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