Low levels of cerebrospinal fluid complement 3 and factor H predict faster cognitive decline in mild cognitive impairment
Alzheimer's Research & Therapy
Low levels of cerebrospinal fluid complement 3 and factor H predict faster cognitive decline in mild cognitive impairment
Jon B Toledo 0 2
Ané Korff 0 1
Leslie M Shaw 2
John Q Trojanowski 2
Jing Zhang 1
0 Equal contributors
1 Department of Pathology, University of Washington School of Medicine , HMC Box 359635, 325 9th Avenue, Seattle, WA 98104 , USA
2 Pathology & Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine , Philadelphia, PA , USA
Introduction: Alzheimer's disease (AD) is characterized by the deposition of tau and amyloid in the brain. Although the core cerebrospinal fluid (CSF) AD biomarkers amyloid β peptide 1-42 (Aβ1-42), total tau (t-tau) and phosphorylated tau 181 (p-tau181) show good diagnostic sensitivity and specificity, additional biomarkers that can aid in preclinical diagnosis or better track disease progression are needed. Activation of the complement system, a pivotal part of inflammation, occurs at very early stages in the AD brain. Therefore, CSF levels of complement proteins that could be linked to cognitive and structural changes in AD may have diagnostic and prognostic value. Methods: Using xMAP® technology based assays we measured complement 3 (C3) and factor H (FH) in the CSF of 110 controls (CN), 187 mild cognitive impairment (MCI) and 92 AD subjects of the AD Neuroimaging Initiative (ADNI) at baseline. All ADNI participants underwent clinical follow-up at 12 month intervals and MCI subjects had additional visits at 6 and 18 months. The association between CSF biomarkers and different outcome measures were analyzed using Cox proportional hazard models (conversion from MCI to AD), logistic regression models (classification of clinical groups) and mixed-effects models adjusted for age, gender, education, t-tau/Aβ1-42 and APOE ε4 presence (baseline and longitudinal association between biomarkers and cognitive scores). Results: Although no association was found between the complement proteins and clinical diagnosis or cognitive measures, lower levels of C3 (β = −0.12, p = 0.041) and FH (β = −0.075, p = 0.041) were associated with faster cognitive decline in MCI subjects as measured by the AD Assessment Scale-cognitive subscale (ADAS-Cog) test. Furthermore, lower FH levels were associated with larger lateral ventricular volume (p = 0.024), which is indicative of brain atrophy. Conclusions: Our study confirms a lack of suitability of CSF C3 and FH as diagnostic biomarkers of AD, but points to their modest potential as prognostic biomarkers and therapeutic targets in cognitively impaired patients.
Alzheimer’s disease (AD) affects an estimated 34 million
people worldwide, a number that is predicted to triple
by 2050 due to the aging population [
intensive research and the identification of several promising
drug candidates in preclinical studies, a neuroprotective
treatment remains a major unmet need. Possible reasons
for the failure of disease-modifying drug clinical trials
include our inability to diagnose AD before substantial
neuronal damage has occurred, as well as track disease
progression and treatment response [
]. In this regard,
sensitive and specific biomarkers are urgently needed.
The cerebrospinal fluid (CSF) biomarkers amyloid β
peptide 1–42 (Aβ1–42), which correlates inversely with
plaque pathology, total tau (t-tau), which is hypothesized
to reflect neuroaxonal degeneration, and phosphorylated
tau (p-tau181), which may correlate with tangle pathology,
have recently been incorporated into the National Institute
on Aging guidelines for AD diagnosis [
]. With regard
to temporal dynamics, the decrease in CSF Aβ1–42
seems to be an early event, reaching a plateau before the
onset of dementia and remaining relatively unchanged
thereafter. The increase in CSF tau occurs after Aβ1–42
changes, but still in the preclinical stage of the disease
and does not change appreciably over time in
cognitively impaired subjects [
]. Therefore, although these
core biomarkers show good sensitivity and specificity
for the diagnosis of AD, additional biomarkers that can
aid in the early diagnosis of dementia or can better track
disease progression will improve the design and
interpretation of clinical trials.
Neuroinflammation is generally recognized to be a
major component of AD [
]. However, whether it is a
cause, a contributing factor, or merely a consequence of
neurodegeneration is unclear. Clinical and experimental
evidence supports the involvement of inflammatory
changes in the early stages of AD before the
appearance of amyloid plaques [
], as well as in the
progression of neurodegeneration [
]. If this is indeed the
case, biomarkers that reflect the inflammatory process
in AD hold promise for both early diagnosis and
tracking of disease progression.
The complement system is a pivotal part of the immune
system and inflammatory processes. Depending on the
trigger, complement can be activated via the classical,
alternative, or lectin pathways. All three pathways
culminate in the formation of complement convertases,
which results in the proteolytic cleavage of complement 3
(C3) and, later in the cascade, complement 5. The resulting
active fragments act as proinflammatory and chemotactic
anaphylatoxins, opsonins allowing phagocytosis, or anchors
for the assembly of the membrane attack complex. The
complement system is kept under tight control by
soluble and membrane-bound regulators, including factor
H (FH), which inhibits C3 convertases in the alternative
pathway, and complement 1 inhibitor, which inhibits
several proteases of the classical and lectin pathways
]. The final outcome therefore depends on the balance
between complement activation and inhibition, and
dysregulation of this balance may contribute to
neuroinflammation and disease [
]. As expected, complement
activation has been shown to occur in the AD brain [
even at very early stages of the disease [
Furthermore, genome-wide association studies have identified
AD-associated variants at the complement receptor 1
], which correlate with a greater Aβ plaque
burden and age-related cognitive decline . Polymorphisms
in the FH gene have also been linked to susceptibility to
], although there are conflicting reports [
In a previous cross-sectional study, we found that CSF
levels of C3 and FH were significantly increased in AD
patients compared with controls (CN) and that the
increase correlated significantly with lower Mini-Mental
State Examination (MMSE) scores in AD patients. In the
current study, we attempt to validate these observations
in a large, independent cohort of well-characterized
subjects. In addition, we extend the previous analysis
by including patients diagnosed with mild cognitive
impairment (MCI) and by analyzing additional clinical
and neuroimaging data. Finally, we explore the
prognostic potential of CSF C3 and FH levels by analyzing
longitudinally collected clinical data.
Data used in the current study were downloaded on 27
July 2013 from the Alzheimer’s Disease Neuroimaging
Initiative (ADNI) database [
]. Ethics approval was
obtained for each institution involved (see Acknowledgements
and Additional file 1). The ADNI is conducted according
to Good Clinical Practice guidelines, the Declaration of
Helsinki, US 21CFR Part 50 – Protection of Human
Subjects and Part 56 – Institutional Review Boards,
and pursuant to state and federal Health Insurance
Portability and Accountability Act regulations. Written
informed consent is obtained from all subjects and/or
authorized representatives and study partners before
protocol-specific procedures are carried out. Institutional
review boards were constituted according to applicable
State and Federal requirements for each participating
location. The protocols were submitted to the appropriate
boards and their written unconditional approval obtained
and submitted to Regulatory Affairs at the ADNI
Coordinating Center prior to commencement of the study.
The ADNI Coordinating Center supplied relevant data
for investigators to submit to their hospital/university/
independent institutional review boards for protocol
review and approval. Verification of institutional review
board unconditional approval of the protocol and the
written informed consent statement with written
information to be given to the participants and/or their
authorized representatives and the study partners were
transmitted and validated by the ADNI Coordinating
Center in order to obtain approval for shipment of study
supplies to study sites. The ADNI has previously been
described extensively [
]. Criteria for the different
diagnostic groups can be found in the ADNI procedures manual
] (see also Additional file 2). In the current study,
389 ADNI 1 [
] subjects (110 CN, 187 MCI subjects and
92 AD subjects) had C3, FH, Aβ1–42, t-tau and p-tau181
measured in their CSF samples at baseline.
Clinical assessment and cognitive profile
The same neuropsychological testing battery was applied
to all subjects in the ADNI, with visits scheduled every
12 months, except for the MCI subjects who had additional
visits at 6 and 18 months. Tests included the MMSE, the
Alzheimer’s Disease Assessment Scale – cognitive subscale
(ADAS-Cog), the clock drawing test, the Rey Auditory
Verbal Learning Test, Digit Span forward and backward,
category fluency, the trail-making test, the digit symbol
substitution test, the Boston naming test and the logical
memory test. Finally, we further characterized the
cognitive profile of each subject using the summary composite
executive function and memory measures developed by
Gibbons and colleagues [
] and Crane and colleagues
]. These measures summarize into a score factors from
different tests that belong to the same cognitive domain,
giving each test a specific loading and accounting for
the difficulty of the different variations of the tests; for
example, the different word lists available for the Rey
Auditory Verbal Learning Test.
Cerebrospinal fluid sample collection and analysis
CSF samples were obtained in the morning after an
overnight fast at ADNI baseline visits. Lumbar puncture
was performed with a 20-gauge or 24-gauge spinal needle
as described previously [
] (for more details including
sample handling and storage, see Additional file 2).
CSF C3 and FH levels were measured using an xMAP
technology-based multiplex human neurodegenerative
kit (HNDG1-36 K; Millipore, Billercia, MA, USA)
according to the manufacturer’s overnight protocol with minor
modifications. A detailed protocol can be found on the
ADNI website [
]. Aβ1–42, t-tau and p-tau181 were
measured using the multiplex xMAP Luminex platform
(Luminex Corp, Austin, TX, USA) with an Innogenetics
kit (INNO-BIA AlzBio3; Innogenetics, Ghent, Belgium)
according to the manufacturer’s protocol [
hemoglobin was measured using an ELISA kit from
Bethyl Lab Inc. (Montgomery, TX, USA) according to
the manufacturer’s instructions. Rules-based medicine
(RBM; Austin, TX, USA) evaluated CSF samples using
a multiplex Human DiscoveryMAP™ panel consisting
of 159 analytes (including C3) on a Luminex 100
platform (Luminex Corp, Austin, TX, USA). More details
on the methods used in the ADNI CSF Proteome study
can be found on the ADNI website [
]. For details
regarding each assay’s performance, see Additional file 2.
Magnetic resonance imaging acquisition and processing
Acquisition of 1.5-T magnetic resonance imaging (MRI)
data for the ADNI 1 subjects followed a previously
described standardized protocol that included sagittal
volumetric three-dimensional magnetization prepared
rapid acquisition gradient recalled echo (MPRAGE),
with variable resolution around the target of 1.2 mm
isotropically. The scans had gone through certain correction
methods such as gradwarp, B1 calibration, N3 correction
and (in-house) skull-stripping (for details see [
images were processed with a freely available pipeline 
(for software see [
]). Briefly, images were segmented
into three tissue types: grey matter, white matter and
CSF. After high-dimensional image warping to an atlas,
regional volumetric maps for grey matter, white matter
and CSF were created – referred to herein as regional
volumetric analysis of brain images, which are used for
voxel-based analysis and group comparisons of regional
tissue atrophy, as well as for constructing an index of AD
brain morphology. We tested for differences in ventricular
volume in our primary analysis and for associations with
the different regions of interest in a secondary analysis.
For the analyses included in the descriptive table (Table 1),
one-way analyses of variance were used for quantitatively
normally distributed variables and the data are presented as
the mean (standard deviation). Kruskall–Wallis tests were
used for quantitative non-normally distributed variables
and the data are presented as the median (first quartile to
third quartile). Chi-square tests were applied for qualitative
variables and the data are presented as the percentage of
counts. For further analyses, distributions of the variables
and residuals were tested and power transformations
applied if necessary. C3, FH and the C3/FH ratio were
standardized to compare effect sizes across analytes.
Previous studies suggest that blood contamination of
CSF can significantly affect CSF concentrations of certain
]. We therefore first tested whether hemoglobin
levels were associated with complement biomarker levels in
CSF, using a model that included gender, age,
apolipoprotein E epsilon 4 allele presence (APOE ε4) and clinical
diagnosis as covariates. CSF levels of FH (Figure S1a in
Additional file 3) but not of C3 were significantly associated
with hemoglobin. This association disappeared after
exclusion of samples with hemoglobin levels > 1,500 ng/ml
(14 CN, 24 MCI subjects, nine AD subjects) (Figure S1b
in Additional file 3). All further analyses were therefore
performed on the remaining 342 subjects. Exclusion of
the 47 subjects with high hemoglobin levels did not
significantly change the difference between diagnostic
groups for any of the variables reported in Table 1.
Associations between CSF complement biomarkers and
age, gender and APOE ε4 presence were tested in linear
regression models. FH and C3, but not C3/FH, were
associated with both age and gender. None of the CSF
complement biomarkers showed a significant association with
APOE ε4 presence (Table S1 in Additional file 4).
To test the classification accuracy of the analytes, we
split the sample into a discovery set (67%) and a validation
set (33%), stratifying by clinical diagnosis. To train a
classifier and cross-validate the cutoff values in the discovery set,
the subjects were further randomly split 10 times to form
training (67%) versus test (33%) sets. The cutoff values of
the model were selected in the discovery set using accuracy
and the kappa index as performance metrics [
The obtained logistic regression model was then applied
to the validation set and the sensitivity, specificity and the
area under the curve of the receiver operating
characteristic curve were obtained .
A Cox hazards model, with age, gender, t-tau/Aβ1–42
ratio, APOE ε4 presence and education as covariates was
used to study the conversion of MCI to AD for different
CSF biomarkers. Standardized values (mean = 0, standard
deviation = 1) were used for the biomarker values in order
to compare the effect size of the association.
We analyzed the cross-sectional and longitudinal
association between CSF biomarkers and different
outcome measures using mixed-effects models [
mixed-effects model is an extension of a linear regression
model that allows calculation of the mean trajectory of
biomarker values for each group as well as the estimation
of each patient’s trajectory. The mixed-effects model takes
into account within-subject correlations from repeated
measurements of biomarker values in the same subjects
and for missing data points. Age, gender, education, APOE
ε4 presence, t-tau/Aβ1–42 ratio and clinical diagnosis at
baseline were included as fixed effects. We include an
intercept, follow-up time and squared follow-up time in weeks
as random effects. Our model specified the intercept and
the regression coefficient for the follow-up time as random
effects such that subjects have a unique intercept and slope
characterizing their individual trajectories. An interaction
between time and clinical diagnosis, between time and
t-tau/Aβ1–42 ratio, and between time and the studied
CSF biomarker was also included to assess whether these
biomarkers were associated with the longitudinal change.
A significant interaction between clinical diagnosis and
time, for example, would indicate that the slope of change
during follow-up is different in CN and MCI subjects.
To plot the data we calculated the 25th, 50th and 75th
percentile biomarker values for the MCI group and
estimated the predicted changes based on the coefficients of
the corresponding mixed-effects model (for the variables
included in the model, median and mode values were used
for quantitative and categorical predictors).
For the MRI analysis, a mixed-effects model was
used, with a nested term inside subjects for the left and
right sides for each region of interest. Age, gender and
t-tau/Aβ1–42 ratio, clinical diagnosis and intracranial
volume were included as fixed effects.
Statistical tests were two-sided and significance was
set at P < 0.05. In the case of multiple comparisons, the
Benjamini–Hochberg correction and the Holms method
was applied when a large and lower number of
comparisons, respectively, had been performed. Analyses
were performed using R v. 3.0.1 [
A total of 389 ADNI 1 participants with CSF C3, FH,
Aβ1–42, t-tau and p-tau181 as well as MRI data were
included in the current study. Clinical and demographic
characteristics of the studied subjects are summarized in
Table 1. As expected, the clinical groups differed in gender,
MMSE, ADAS-Cog, APOE ε4 presence and CSF Aβ1–42,
t-tau and p-tau181. The data for CSF Aβ1–42, t-tau and
p-tau181 in the AD subjects are in line with published
cutoff values from autopsy confirmed cases using the
AlzBio3 kit [
]. However, no differences in CSF C3 or
FH levels or the C3/FH ratio were noted between the
diagnostic groups. As discussed earlier, all subsequent
analysis were performed on the 342 subjects whose CSF
samples had hemoglobin levels ≤ 1,500 ng/ml. Exclusion
of subjects with hemoglobin levels > 1,500 ng/ml (14 CN,
24 MCI subjects, nine AD subjects) did not seem to result
in any selection bias (Table 1).
In linear regression analysis, a strong, positive
correlation between C3 and FH (rpartial = 0.81, P < 0.0001) was
present after adjusting for age and gender (Figure S2 in
Additional file 3). In age, gender and APOE ε4 presence
adjusted models, FH was significantly associated with t-tau
but not with p-tau181 or Aβ1–42. C3 was not associated with
Aβ1–42, t-tau or p-tau181 (Table S2 in Additional file 4).
Linear regression models adjusted for age, gender and
APOE ε4 presence revealed no association of C3, FH or
C3/FH with clinical diagnosis (Table S3 in Additional file 4).
A lack of contribution from C3 and FH in classifying
different clinical groups was further confirmed by the
fact that the addition of C3, FH or C3/FH did not
improve the performance of t-tau/Aβ1–42 in classifying AD
subjects versus CN or MCI subjects versus CN (Table 2).
The diagnostic utility of C3 and or FH was therefore not
Finally, with regard to disease severity C3, FH and C3/
FH did not associate with baseline ADAS-cog, MMSE,
memory summary or executive function summary scores
of AD (Table 3) or MCI (Table 4) subjects in
mixedeffects models adjusted for age, gender, education, APOE
ε4 presence and t-tau/Aβ1–42 ratio.
Of the 160 MCI subjects included in the analysis, 79
converted to AD with a median follow-up time of
158 weeks. A Cox hazards model with age at baseline
(hazard ratio = 1.01, P = 0.59), gender (hazard ratio = 1.05,
P = 0.84), t-tau/Aβ1–42 ratio (hazard ratio = 1.49, P = 0.002),
APOE ε4 presence (hazard ratio = 1.13, P = 0.63) and
education (hazard ratio = 1.02, P = 0.70) as covariates was used
to test the association of the CSF complement biomarkers
with conversion of MCI to AD. A weak association
between lower levels of C3 (hazard ratio = 0.62, Punadj = 0.046)
and increased conversion was lost after adjustment for
multiple comparisons (Padj = 0.14).
Longitudinal ADAS-Cog, MMSE, memory summary and
executive function summary scores were next analyzed
against baseline CSF C3 and FH levels. Follow-up of ADNI
1 patients with a baseline diagnosis of AD was
discontinued at an earlier time point than baseline MCI patients
(mean follow-up was 98.3 weeks for the AD group and
184 weeks for the MCI group). Owing to this imbalance
in the number of visits, we analyzed the AD subjects and
the MCI subjects separately. All of the following analyses
were adjusted for age, gender, APOE ε4 presence,
education and t-tau/Aβ1–42 ratio. In the analysis of the AD
group, none of the complement biomarkers were
associated with changes in ADAS-Cog, MMSE, memory
summary, or executive function summary scores
during follow-up (Table 3). In the MCI subjects, lower
levels of both C3 and FH were associated with an increase
(more severe cognitive impairment) in ADAS-Cog scores
during follow-up (biomarker × time interaction, Table 4;
Figure 1a,b). The C3/FH ratio showed no association
with longitudinal ADAS-Cog score changes. Finally,
none of the CSF complement biomarkers showed a
significant association with MMSE, memory summary, or
executive function summary scores, although there
was a trend for an association of lower C3 levels with a
decline in memory summary score during follow-up
(biomarker × time interaction, Table 4; Figure 1c,d,e,f ).
Correlations of complement 3 and factor H with other
A subset (n = 256) of the ADNI 1 subjects included in
the study also had RBM CSF data available. The RMB
CSF panel included C3, but not FH. We explored the
association between our CSF C3 measurements and the
RBM C3 levels and found a strong correlation between
the two (r = 0.79, P < 0.0001), indicating that the two
immunoassays performed similarly. As expected, when we
repeated the diagnostic and prognostic analyses described
above using C3 levels obtained by the RBM assay, very
similar, if not identical, results were found, especially with
regard to the association between lower levels of C3 and
faster cognitive decline of MCI subjects.
Since brain atrophy detected by MRI is associated with
AD severity and correlates closely with changes in cognitive
], we also studied the association between
the CSF complement biomarkers and MRI volumes at
baseline in a model adjusted for age, gender, clinical
diagnosis, t-tau/Aβ1–42 ratio and intracranial volume. There were
no associations between C3 levels or the C3/FH ratio and
MRI volumes. However, although no association with CSF
FH levels was noted when different region of interest were
analyzed independently (Table S4 in Additional file 4), low
FH values were clearly associated with increased lateral
ventricular volume in the multiple comparison-adjusted
Age, gender (male reference category), education, total tau/amyloid β peptide 1–42 ratio and apolipoprotein E epsilon 4 allele presence adjusted mixed-effects
model. P values are corrected for multiple comparisons (Holms). ADAS-Cog, Alzheimer’s disease Assessment Scale – cognitive subscale; C3, complement 3; FH,
factor H; MMSE, Mini-Mental State Examination.
Age, gender (male reference category), education, total tau/amyloid β peptide 1–42 ratio and apolipoprotein E epsilon 4 allele presence adjusted mixed-effects
model. P values are corrected for multiple comparisons (Holms). ADAS-Cog, Alzheimer’s disease Assessment Scale – cognitive subscale; C3, complement 3; FH,
factor H; MMSE, Mini-Mental State Examination.
analyses (Padj = 0.024), consistent with the argument that
low CSF FH level is associated with greater brain atrophy.
In this study, we explored the diagnostic and prognostic
value of CSF C3 and FH levels in AD and MCI. In
cross-sectional analysis, there were no significant
differences in either biomarker or in their ratio between
diagnostic groups, and nor were there any correlations
with disease severity in AD or MCI subjects as measured
by the MMSE, ADAS-Cog, memory summary score or
executive function summary score. In the longitudinal
analysis of MCI patients, however, low levels of both C3
and FH were modestly associated with an increase in
ADAS-Cog scores (more severe cognitive impairment)
and validation in an independent and longitudinal cohort
is needed. Additionally, there was a significant association
of low CSF FH levels with increased lateral ventricular
volume, which is indicative of brain atrophy and has been
shown to correlate strongly with changes in cognitive tests
]. Strengths of the current study include the use of a
large cohort of subjects that underwent detailed clinical
and neuropsychological testing and longitudinal
followup, the availability of APOE ε4 genotype data, hemoglobin
measurements to control for blood contamination of CSF,
as well as the use of RBM CSF C3 data to corroborate our
own measurements and results.
Previous studies have generally reported elevated levels
of complement components in AD CSF, although results
are inconsistent. For example, in a recent study [
using a commercially available ELISA kit, CSF C3 levels
were increased in AD patients and CN subjects
compared with stable MCI subjects, but there was no
significant difference between AD patients and CN subjects,
or between the MCI-to-AD group and any of the other
groups. Consistent with the current finding, receiver
operating characteristic analysis revealed no diagnostic
utility for CSF C3. On the other hand, in a study using
the RBM Human DiscoveryMAP™ panel on a Luminex
100 platform, CSF C3 levels were increased in
autopsyconfirmed AD cases compared with normal controls.
Furthermore, there was a significant correlation between
CSF C3 levels and MMSE scores in AD subjects, but not
in MCI subjects [
]. In contrast, a study using
twodimensional electrophoresis found no significant
difference in the average percent volume for C3b or FH in
CSF samples from AD compared with normal controls
]. In line with the RBM study discussed above, our
own earlier study found increased CSF C3 and FH levels
in AD patients compared with CN subjects, as well as
significant negative correlations between the two
complement biomarkers and MMSE scores [
]. The failure
to validate our own earlier findings in the current study
may be related to differences between the two cohorts,
including highly selected subjects in the ADNI versus
the community-based cohort in our previous study,
gender distribution and mean age, as well as the number of
AD patients included in the analysis (38 in the previous
study vs. 83 in the current study). One should also point
out that although our original study found significant
differences in CSF C3 and FH levels between diagnostic
groups, receiver operating characteristic analysis showed
that neither biomarker had acceptable sensitivity or
specificity (>60%) for classifying CN versus AD subjects. In
summary, we conclude that these studies are in
agreement regarding a lack of suitability of CSF C3 and FH as
diagnostic biomarkers of AD.
A limitation of our investigation is the potential
confounding effect of pharmacotherapy, because subjects
were not drug naïve at the time of CSF collection, although
the use of many central nervous system-active drugs such
as antidepressants or neuroleptics with anti-cholinergic
properties, narcotic analgesics and anti-Parkinsonian
medications were excluded. The other limitation relates
to the fact that the data are correlational without clear
mechanistic interpretation. That said, we wish to put
forward two hypotheses for discussion. First, our finding
of low levels of CSF C3 and FH in MCI patients with
accelerated cognitive decline may reflect increased
deposition of these complement biomarkers in senile plaques.
Decreased Aβ1–42 in AD CSF is hypothesized to be the
result of trapping the peptide in plaques, and C3 and
FH have both been shown to be present in Aβ plaques
]. Trapping of C3 and FH in plaques may therefore
lead to a decrease in the CSF levels of these proteins.
However, we did not find a correlation between CSF Aβ1–42 and
C3 or FH, suggesting that the observed decrease in
complement biomarkers cannot be readily explained by such a
simplistic model. An alternative hypothesis could
therefore be that the low CSF levels of C3 and FH in faster
progressors may reflect accelerated dysregulation of the
complement system in the brain. To this end, many
studies have indicated potential involvement of
complement system in AD pathogenesis, including observations
that: Aβ fibrils activate both the classical and alternative
complement pathways in vitro [
]; inhibition of C3 in a
mouse model of AD resulted in accelerated and increased
Aβ plaque deposition, as well as neurodegeneration
]; and AD mice lacking C1q (part of the complex
triggering activation of the classical pathway) had decreased
levels of activated glia in proximity to plaques, as well as
reduced neuronal injury [
] consistent with a detrimental
role for complement activation in this model. Indeed, based
on these and other studies, a hypothesis has been suggested
that classical complement activation is detrimental in
neurodegeneration, whereas alternative complement activation
is beneficial up to a certain threshold or depending on
the complement receptor CR1 genotype [
]. Thus, if
CSF levels of C3 and FH mirror their levels in the brain,
our finding of a decreased total C3 level may indicate
increased cleavage of C3 to generate more of its active
fragments at the expense of the holoprotein. This increased
activation of C3 might be due to increased activation of
the classical, lectin and/or alternative pathways, as C3 is a
joining point for all three. Decreased levels of FH will also
lead to increased cleavage of C3 via the alternative
pathway, because FH regulates this pathway at the C3
convertase level [
]. The strong correlation observed between
CSF C3 and FH in the current study supports this
hypothesis. The negative finding with regard to the longitudinal
analysis in AD patients could be secondary to the much
shorter followup in these patients and a smaller sample
size, which results in less statistical power.
In summary, our data suggest that CSF C3 and FH levels
are prognostic biomarkers of accelerated cognitive decline
in MCI, although validation in an independent cohort is
needed. Additionally, studies with repeated CSF
measurements will shed more light on the utility of CSF C3 and
FH levels as AD progression biomarkers. Finally, results
obtained in this study should encourage further
investigations exploring the mechanisms underlying complement
activation, both the classical and alternative cascades, in
AD development and progression.
Additional file 1: is an acknowledgement list for ADNI publications:
ADNI infrastructure and site investigators.
Additional file 2: is the supplemental methods, including subjects,
recruitment criteria, CSF sample collection and handling, CSF
immunoassay performance and references.
Additional file 3: is Supplemental Figures S1 and S2 showing
associations between CSF FH and hemoglobin, and CSF C3 and FH.
Additional file 4: is Supplemental Tables S1 to S4 presenting data
showing CSF C3 and FH biomarker associations.
Aβ1–42: amyloid β peptide 1–42; AD: Alzheimer’s disease; ADAS-Cog: Alzheimer’s
Disease Assessment Scale – cognitive subscale; ADNI: Alzheimer’s Disease
Neuroimaging Initiative; APOE ε4: apolipoprotein E epsilon 4 allele;
C3: complement 3; CN: controls; CSF: cerebrospinal fluid; FH: factor H; MCI: mild
cognitive impairment; MMSE: Mini-Mental State Examination; MRI: magnetic
resonance imaging; p-tau181: tau phosphorylated at threonine 181;
RBM: rules-based medicine; t-tau: total tau.
The authors declare that they have no competing interests.
JBT, AK, LMS, JQT and JZ made substantial contributions to the conception
and design of the study and were involved in drafting the manuscript and
revising it critically for important intellectual content. AK performed the
xMAP assays. JBT, AK and JZ had full access to the entire dataset. LMS and
JQT contributed to acquisition and storage of the samples. JBT and AK
undertook the statistical analyses. All authors read and approved the final
Data collection and sharing for this project was funded by the ADNI
(National Institutes of Health Grant U01 AG024904). The ADNI is funded by
the National Institute on Aging, the National Institute of Biomedical Imaging
and Bioengineering, and through generous contributions from the following:
Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica, Inc.;
Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals,
Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company
Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd; Janssen Alzheimer
Immunotherapy Research & Development, LLC.; Johnson & Johnson
Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co.,
Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals
Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda
Pharmaceutical Company. The Canadian Institutes of Health Research is
providing funds to support ADNI clinical sites in Canada. Private-sector
contributions are facilitated by the Foundation for the National Institutes
of Health (www.fnih.org). 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. ADNI data are disseminated by the Laboratory for Neuro Imaging at
the University of California, Los Angeles. This research was also supported by
National Institutes of Health grants P30 AG010129 and K01 AG030514. The
authors’ efforts were also supported by the National Institutes of Health
(P42 ES004696-5897 and P30 ES007033-6364), the National Institute on Aging
(R01 AG033398), the National Institute of Environmental Health Sciences
(R01 ES016873 and R01 ES019277) and the National Institute of Neurological
Disorders and Stroke (R01 NS057567, P50 NS062684-6221 and U01 NS082137).
JQT is the William Maul Measey-Truman G. Schnabel, Jr, Professor of Geriatric
Medicine and Gerontology. Ethics approval was obtained from the institutional
review boards of each institution involved: Oregon Health and Science
University; University of Southern California; University of California–San Diego;
University of Michigan; Mayo Clinic, Rochester; Baylor College of Medicine;
Columbia University Medical Center; Washington University, St. Louis; University
of Alabama – Birmingham; Mount Sinai School of Medicine; Rush University
Medical Center; Wien Center; Johns Hopkins University; New York University;
Duke University Medical Center; University of Pennsylvania; University of
Kentucky; University of Pittsburgh; University of Rochester Medical Center;
University of California, Irvine; University of Texas Southwestern Medical School;
Emory University; University of Kansas, Medical Center; University of California,
Los Angeles; Mayo Clinic, Jacksonville; Indiana University; Yale University School
of Medicine; McGill Univ., Montreal-Jewish General Hospital; Sunnybrook Health
Sciences, Ontario; U.B.C. Clinic for AD & Related Disorders; Cognitive
Neurology – St. Joseph’s, Ontario; Cleveland Clinic Lou Ruvo Center for Brain
Health; Northwestern University; Premiere Research Inst (Palm Beach Neurology);
Georgetown University Medical Center; Brigham and Women’s Hospital;
Stanford University; Banner Sun Health Research Institute; Boston University;
Howard University; Case Western Reserve University; University of California,
Davis – Sacramento; Neurological Care of CNY; Parkwood Hospital; University of
Wisconsin; University of California, Irvine – BIC; Banner Alzheimer’s Institute; Dent
Neurologic Institute; Ohio State University; Albany Medical College; Hartford
Hospital, Olin Neuropsychiatry Research Center; Dartmouth-Hitchcock Medical
Center; Wake Forest University Health Sciences; Rhode Island Hospital; Butler
Hospital; UC San Francisco; Medical University South Carolina; St. Joseph’s Health
Care Nathan Kline Institute; University of Iowa College of Medicine; Cornell
University and University of South Florida: USF Health Byrd Alzheimer’s Institute.
Data used in preparation of this article were obtained from the ADNI
database (adni.loni.ucla.edu). As such, the investigators within the ADNI
contributed to the design and implementation of the 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 online (http://adni.loni.usc.edu/
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