U.S. patent application number 11/148595 was filed with the patent office on 2006-05-04 for methods and compositions for diagnosis, stratification, and monitoring of alzheimer's disease and other neurological disorders in body fluids.
Invention is credited to Sandip Ray, Anton Wyss-Coray.
Application Number | 20060094064 11/148595 |
Document ID | / |
Family ID | 37498785 |
Filed Date | 2006-05-04 |
United States Patent
Application |
20060094064 |
Kind Code |
A1 |
Ray; Sandip ; et
al. |
May 4, 2006 |
Methods and compositions for diagnosis, stratification, and
monitoring of alzheimer's disease and other neurological disorders
in body fluids
Abstract
The inventors have discovered a collection of proteinaceous
biomarkers ("AD biomarkers) which can be measured in peripheral
biological fluid samples to aid in the diagnosis of
neurodegenerative disorders, particularly Alzheimer's disease and
mild cognitive impairment (MCI). The invention further provides
methods of identifying candidate agents for the treatment of
Alzheimer's disease by testing prospective agents for activity in
modulating AD biomarker levels.
Inventors: |
Ray; Sandip; (San Francisco,
CA) ; Wyss-Coray; Anton; (Belmont, CA) |
Correspondence
Address: |
MORRISON & FOERSTER LLP
755 PAGE MILL RD
PALO ALTO
CA
94304-1018
US
|
Family ID: |
37498785 |
Appl. No.: |
11/148595 |
Filed: |
June 8, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10993813 |
Nov 19, 2004 |
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11148595 |
Jun 8, 2005 |
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60523796 |
Nov 19, 2003 |
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60566783 |
Apr 30, 2004 |
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60566782 |
Apr 30, 2004 |
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Current U.S.
Class: |
435/7.2 |
Current CPC
Class: |
C12Q 1/6883 20130101;
G01N 33/6896 20130101; G01N 2800/2821 20130101; G01N 2800/28
20130101; G01N 2800/60 20130101 |
Class at
Publication: |
435/007.2 |
International
Class: |
G01N 33/53 20060101
G01N033/53; G01N 33/567 20060101 G01N033/567 |
Claims
1. A method of aiding diagnosis of Alzheimer's disease ("AD"),
comprising comparing a measured level of at least one AD diagnosis
biomarker in a biological fluid sample from an individual to a
reference level for the biomarker, wherein the AD diagnosis
biomarker is selected from the group consisting of GCSF; IFN-g;
IGFBP-1; BMP-6; BMP-4; Eotaxin-2; IGFBP-2; TARC; RANTES; ANG; PARC;
Acrp30; AgRP(ART); TIMP-1; TIMP-2; ICAM-1; TRAIL R3; uPAR; IGFBP-4;
LEPTIN(OB); PDGF-BB; EGF; BDNF; NT-3; NAP-2; IL-1ra; MSP-a; SCF;
TGF-b3; TNF-b; MIP-1d; IL-3; FGF-6; IL-6 R; sTNF RII; AXL; bFGF;
FGF-4; CNTF; MCP-1; MIP-1b; TPO; VEGF-B; IL-8; FAS; EGF-R.
2. The method of claim 1, comprising comparing the measuring level
of at least two AD diagnosis biomarkers to a reference level for
the biomarkers.
3. The method of claim 1, comprising comparing the measured level
of at least three AD diagnosis biomarkers to a reference level for
the biomarkers.
4. The method of claim 1, comprising comparing the measured level
of at least four AD diagnosis biomarkers to a reference level for
the biomarkers.
5. The method of claim 1, comprising comparing the measured level
of at least five AD diagnosis biomarkers to a reference level for
the biomarkers.
6. The method of claim 1, wherein comparing the measured value to a
reference value for each AD diagnosis biomarker measured comprises
calculating the fold difference between the measured value and the
reference value.
7. A method of aiding diagnosis of Alzheimer's disease ("AD"),
comprising: comparing a measured level of at least one AD diagnosis
biomarker in a biological sample fluid sample from an individual to
a reference level for the biomarker wherein the AD diagnosis
biomarker is selected from the group consisting BTC; SDF-1; MCP-2;
IFN-gamma; IGFBP-4; IGF-1 SR; IL-8; GM-CSF; and ANG-2.
8. The method of claim 7, comprising comparing the measuring level
of at least two AD diagnosis biomarkers to a reference level for
the biomarkers.
9. The method of claim 7, comprising comparing the measured level
of at least three AD diagnosis biomarkers to a reference level for
the biomarkers.
10. The method of claim 7, comprising comparing the measured level
of at least four AD diagnosis biomarkers to a reference level for
the biomarkers.
11. The method of claim 7, comprising comparing the measured level
of at least five AD diagnosis biomarkers to a reference level for
the biomarkers.
12. The method of claim 7 wherein the at least one AD diagnosis
biomarker is selected from the group consisting of biomarkers
IFN-gamma and IL-8.
13. A method of aiding diagnosis of Alzheimer's disease ("AD"),
comprising: comparing a measured level of at least one AD diagnosis
biomarker in a biological sample fluid sample from an individual to
a reference level for the biomarker wherein the AD diagnosis
biomarker is selected from the group consisting of TNF RII;
MSP-alpha; uPAR; TPO; MIP-1beta; VEGF-beta; FAS; MCP-1; NAP-2;
ICAM-1; TRAIL R3; PARC; ANG; IL-3; MIP-1delta; IFN-gamma; IL-8; and
FGF-6.
14. The method of claim 13, comprising comparing the measuring
level of at least two AD diagnosis biomarkers to a reference level
for the biomarkers.
15. The method of claim 13, comprising comparing the measured level
of at least three AD diagnosis biomarkers to a reference level for
the biomarkers.
16. The method of claim 13, comprising comparing the measured level
of at least four AD diagnosis biomarkers to a reference level for
the biomarkers.
17. The method of claim 13, comprising comparing the measured level
of at least five AD diagnosis biomarkers to a reference level for
the biomarkers.
18. A method of aiding diagnosis of Alzheimer's disease ("AD"),
comprising: comparing a measured level of at least one AD diagnosis
biomarker in a biological sample fluid sample from an individual to
a reference level for the biomarker wherein the AD diagnosis
biomarker is selected from the group consisting of lymphotactin and
IL-11.
19. The method of claim 7 or claim 13, wherein said biological
fluid sample is a peripheral biological fluid sample.
20. A method for monitoring progression of Alzheimer's disease (AD)
in an AD patient, comprising comparing a measured level of at least
one AD diagnosis biomarker in a biological sample fluid sample from
an individual to a reference level for the biomarker wherein the AD
diagnosis biomarker is selected from the group consisting of
lymphotactin and IL-11.
21. The method of claim 20, wherein said reference level is a level
obtained from a biological fluid sample from the same AD patient at
an earlier point in time.
22. The method of claim 20 wherein the biological fluid sample is a
peripheral biological fluid sample.
23. A method for stratifying Alzheimer's disease (AD) in an
individual, comprising: comparing a measured level of at least one
AD diagnosis biomarker in a biological sample from the individual
with a reference level for the at least one biomarker, wherein the
at least one biomarker is selected from the group consisting of
lymphotactin and IL-11.
24. The method of claim 23 wherein said biological fluid sample is
a peripheral fluid sample.
25. A kit for use in aiding in the diagnosis of AD, wherein the kit
comprises at least one reagent specific for at least one AD
diagnosis marker, wherein said at least one AD diagnosis biomarker
is selected from the group consisting of BTC; SDF-1; MCP-2;
IFN-gamma; IGFBP-4; IGF-1SR; IL-8; GM-CSF; and ANG-2, and
instructions for carrying out a method of aiding in the diagnosis
of AD.
26. The kit of claim 25 wherein the at least one AD diagnosis
biomarker is selected from the group consisting of biomarkers
IFN-gamma and IL-8.
27. A kit for use in aiding in the diagnosis of AD, wherein the kit
comprises at least one reagent specific for at least one AD
diagnosis marker, wherein said at least one AD diagnosis biomarker
is selected from the group consisting of sTNF RII; MSP-alpha; uPAR;
TPO; MIP-1beta; VEGF-beta; FAS; MCP-1; NAP-2; ICAM-1; TRAIL R3;
PARC; ANG; IL-3; MIP-1delta; IFN-gamma; IL-8; and FGF-6, and
instructions for carrying out a method of aiding in the diagnosis
of AD.
28. A kit for use in aiding in the diagnosis of AD, wherein the kit
comprises at least one reagent specific for at least one AD
diagnosis marker, wherein said at least one AD diagnosis biomarker
is selected from the group consisting of the biomarkers
lymphotactin and IL-11, and instructions for carrying out a method
of aiding in the diagnosis of AD.
29. The kit of claim 28 wherein the reagent specific for the AD
diagnosis biomarker is an antibody, or fragment thereof, that is
specific for the AD diagnosis biomarker.
30. The kit of claim 28 further comprising at least one biomarker
for normalizing data.
31. The kit of claim 30 wherein said biomarker for normalizing data
is selected from the group consisting of TGF-beta and
TGF-beta3.
32. A method for identifying at least one biomarker useful for the
diagnosis of a neurological disease, comprising: obtaining measured
values from a set of peripheral biological fluid samples for a
plurality of biomarkers, wherein said set of peripheral biological
fluid samples is divisible into subsets on the basis of a
neurological disease; comparing the measured values from each
subset for at least one biomarker; and identifying at least one
biomarker for which the measured values are significantly different
between the subsets.
33. The method of claim 32 wherein the neurological disease is
AD.
34. A method of aiding diagnosis of a neurodegenerative disease
comprising obtaining measured values of one or more biomarkers
shown in Table 12A-12B with a q-value % of less than 1.5, and
comparing the measured value to a reference value.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation in part application of
U.S. patent application Ser. No. 10/993,813, filed Nov. 19, 2004
which claims benefit of U.S. Provisional Patent Application No.
60/523,796, filed Nov. 19, 2003, U.S. Provisional Patent
Application Ser. No. 60/566,783, filed Apr. 30, 2004, and U.S.
Provisional Patent Application No. 60/566,782, filed Apr. 30, 2004,
all of which are incorporated by reference herein in their
entireties.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] Not applicable.
REFERENCE TO A COMPACT DISK APPENDIX
[0003] Not applicable
BACKGROUND OF THE INVENTION
[0004] An estimated 4.5 million Americans have Alzheimer's Disease
("AD"). By 2050, the estimated range of AD prevalence will be 11.3
million to 16 million. Currently, the societal cost of AD to the
U.S. is $100 billion per year, including $61 billion borne by U.S.
businesses. Neither Medicare nor most private health insurance
covers the long-term care most patients need.
[0005] Alzheimer's Disease is a neurodegenerative disease of the
central nervous system associated with progressive memory loss
resulting in dementia. Two pathological characteristics are
observed in AD patients at autopsy: extracellular plaques and
intracellular tangles in the hippocampus, cerebral cortex, and
other areas of the brain essential for cognitive function. Plaques
are formed mostly from the deposition of amyloid beta ("A.beta."),
a peptide derived from amyloid precursor protein ("APP").
Filamentous tangles are formed from paired helical filaments
composed of neurofilament and hyperphosphorylated tau protein, a
microtubule-associated protein. It is not clear, however, whether
these two pathological changes are only associated with the disease
or truly involved in the degenerative process. Late-onset/sporadic
AD has virtually identical pathology to inherited
early-onset/familial AD (FAD), thus suggesting common pathogenic
pathways for both forms of AD. To date, genetic studies have
identified three genes that cause autosomal dominant, early-onset
AD, amyloid precursor protein ("APP"), presenilin 1 ("PS1"), and
presenilin 2 ("PS2"). A fourth gene, apolipoprotein E ("ApoE"), is
the strongest and most common genetic risk factor for AD, but does
not necessarily cause it. All mutations associated with APP and PS
proteins can lead to an increase in the production of A.beta.
peptides, specifically the more amyloidogenic form, A.beta..sub.42.
In addition to genetic influences on amyloid plaque and
intracellular tangle formation, environmental factors (e.g.,
cytokines, neurotoxins, etc.) may also play important roles in the
development and progression of AD.
[0006] The main clinical feature of AD is a progressive cognitive
decline leading to memory loss. The memory dysfunction involves
impairment of learning new information which is often characterized
as short-term memory loss. In the early (mild) and moderate stages
of the illness, recall of remote well-learned material may appear
to be preserved, but new information cannot be adequately
incorporated into memory. Disorientation to time is closely related
to memory disturbance.
[0007] Language impairments are also a prominent part of AD. These
are often manifest first as word finding difficulty in spontaneous
speech. The language of the AD patient is often vague, lacking in
specifics and may have increased automatic phrases and cliches.
Difficulty in naming everyday objects is often prominent. Complex
deficits in visual function are present in many AD patients, as are
other focal cognitive deficits such as apraxia, acalculia and
left-right disorientation. Impairments of judgment and problems
solving are frequently seen.
[0008] Non-cognitive or behavioral symptoms are also common in AD
and may account for an event larger proportion of caregiver burden
or stress than the cognitive dysfunction. Personality changes are
commonly reported and range from progressive passivity to marked
agitation. Patients may exhibit changes such as decreased
expressions of affection. Depressive symptoms are present in up to
40%. A similar rate for anxiety has also been recognized. Psychosis
occurs in 25%. In some cases, personality changes may predate
cognitive abnormality.
[0009] Currently, the primary method of diagnosing AD in living
patients involves taking detailed patient histories, administering
memory and psychological tests, and ruling out other explanations
for memory loss, including temporary (e.g., depression or vitamin
B.sub.12 deficiency) or permanent (e.g., stroke) conditions. These
clinical diagnostic methods, however, are not foolproof.
[0010] One obstacle to diagnosis is pinpointing the type of
dementia; AD is only one of seventy conditions that produce
dementia. Because of this, AD cannot be diagnosed with complete
accuracy until after death, when autopsy reveals the disease's
characteristic amyloid plaques and neurofibrillary tangles in a
patient's brain. In addition, clinical diagnostic procedures are
only helpful after patients have begun displaying significant,
abnormal memory loss or personality changes. By then, a patient has
likely had AD for years.
[0011] Given the magnitude of the public health problem posed by
AD, considerable research efforts have been undertaken to elucidate
the etiology of AD as well as to identify biomarkers (secreted
proteins or metabolites) that can be used to diagnose and/or
predict whether a person is likely to develop AD. Because AD the
CNS is relatively isolated from the other organs and systems of the
body, most research (in regards to both disease etiology and
biomarkers) has focused on events, gene expression, biomarkers,
etc. within the central nervous system. With regards to biomarkers,
the proteins amyloid beta and tau are probably the most well
characterized. Research has shown that cerebrospinal fluid ("CSF")
samples from AD patients contain higher than normal amounts of tau,
which is released as neurons degenerate, and lower than normal
amounts of beta amyloid, presumably because it is trapped in the
brain in the form of amyloid plaques. Because these biomarkers are
released into CSF, a lumbar puncture (or "spinal tap") is required
to obtain a sample for testing.
[0012] A number of U.S. patents have been issued relating to
methods for diagnosing AD, including U.S. Pat. Nos. 4,728,605,
5,874,312, 6,027,896, 6,114,133, 6,130,048, 6, 210, 895, 6,358,681,
6,451,547, 6,461,831, 6,465,195, 6,475,161, and 6,495,335.
Additionally, a number of reports in the scientific literature
relate to certain biochemical markers and their
correlation/association with AD, including Fahnestock et al., 2002,
J. Neural. Transm. Suppl. 2002(62):241-52; Masliah et al., 1195,
Neurobiol. Aging 16(4):549-56; Power et al., 2001, Dement. Geriatr.
Cogn. Disord. 12(2):167-70; and Burbach et al., 2004, J. Neurosci.
24(10):2421-30. Additionally, Li et al. (2002, Neuroscience
113(3):607-15) and Sanna et al. (2003, J. Clin. Invest.
111(2):241-50) have investigated Leptin in relation to memory and
multiple sclerosis, respectively.
[0013] All patents and publications cited herein are incorporated
by reference in their entirety.
BRIEF SUMMARY OF THE INVENTION
[0014] The inventors have discovered a collection of biochemical
markers, present in the blood of individuals, including from the
serum or plasma of individuals, which are altered in individuals
with Alzheimer's Disease ("AD"). Accordingly, these biomarkers ("AD
diagnosis biomarkers") may be used to assess cognitive function, to
diagnose or aid in the diagnosis of AD and/or to measure
progression of AD in AD patients. AD diagnosis markers may be used
individually or in combination for diagnosing or aiding in the
diagnosis of AD. The invention provides methods for the diagnosis
of AD or aiding the diagnosis of AD in an individual by measuring
the amount of one or more AD diagnosis biomarkers in a biological
fluid sample, such as a peripheral biological fluid sample from the
individual and comparing the measured amount with a reference value
for each AD diagnosis biomarker measured. The information thus
obtained may be used to aid in the diagnosis or to diagnose AD in
the individual. Accordingly, the present invention provides a
method of aiding diagnosis of Alzheimer's disease ("AD"),
comprising comparing a measured level of at least one AD diagnosis
biomarker in a biological fluid sample from an individual to a
reference level for the biomarker, wherein the AD diagnosis
biomarker is selected from the group consisting of GCSF; IFN-g;
IGFBP-1; BMP-6; BMP-4; Eotaxin-2; IGFBP-2; TARC; RANTES; ANG; PARC;
Acrp30; AgRP(ART); TIMP-1; TIMP-2; ICAM-1; TRAIL R3; uPAR; IGFBP-4;
LEPTIN(OB); PDGF-BB; EGF; BDNF; NT-3; NAP-2; IL-1ra; MSP-a; SCF;
TGF-b3; TNF-b; MIP-1d; IL-3; FGF-6; IL-6 R; sTNF RII; AXL; bFGF;
FGF4; CNTF; MCP-1; MIP-1b; TPO; VEGF-B; IL-8; FAS; EGF-R. In some
examples, the AD diagnosis biomarker is selected from the group
consisting of basic fibroblast growth factor (bFGF); BB homodimeric
platelet derived growth factor (PDGF-BB); brain derived
neurotrophic factor (BDNF); epidermal growth factor (EGF),
fibroblast growth factor 6 (FGF-6), interleukin-3 (IL-3), soluble
interleukin-6 receptor (sIL-6R), leptin (also known as ob),
macrophage inflammatory protein-1 delta (MIP-1.delta.), macrophage
stimulating protein alpha chain (MSP-.alpha.), neurotrophin-3
(NT-3), neutrophil activating peptide-2 (NAP-2), RANTES, soluble
tumor necrosis factor receptor-2 (sTNF RII), stem cell factor
(SCF), thrombopoietin (TPO), tissue inhibitor of metalloproteases-1
(TIMP-1), tissue inhibitor of metalloproteases-2 (TIMP-2),
transforming growth factor-beta 3 (TGF-.beta.3), and tumor necrosis
factor beta (TNF-.beta.). In other examples, the AD diagnosis
marker is selected from the group consisting of BDNF, sIL-6R, IL-8,
leptin, MIP-1.delta., PDGF-BB, and TIMP-1. In yet other examples,
the AD diagnosis marker is selected from the group consisting of
sIL-6R, IL-8, and TIMP-1. In further examples, the AD diagnosis
marker is selected from the group consisting of BDNF, MIP-1.delta.,
and TIMP-1. In additional examples, the AD diagnosis marker is
selected from the group consisting of BDNF, PDGF-BB, leptin and
RANTES. In additional examples, the AD diagnosis marker comprises
BDNF, PDGF-BB, leptin and RANTES. In additional examples, the
method comprises comparing the measuring level of at least two AD
diagnosis biomarkers to a reference level for the biomarkers. In
additional examples, the method comprises comparing the measuring
level of at least three AD diagnosis biomarkers to a reference
level for the biomarkers. In further examples, the method comprises
comparing the measuring level of at least four AD diagnosis
biomarkers to a reference level for the biomarkers. In additional
examples, comparing the measured level to a reference level for
each AD diagnosis biomarker measured comprises calculating the fold
difference between the measured level and the reference level. In
some examples, a method further comprises comparing the fold
difference for each AD diagnosis biomarker measured with a minimum
fold difference level. In some examples, the method further
comprises the step of obtaining a value for the comparison of the
measured level to the reference level. Provided herein are computer
readable formats comprising the values obtained by the method as
described herein.
[0015] Provided herein are methods of aiding diagnosis of
Alzheimer's disease ("AD"), comprising comparing a measured level
of at least four AD diagnosis biomarkers, wherein said biomarkers
comprise BDNF, PDGF-BB, leptin and RANTES, in a biological fluid
sample from an individual to a reference level for each AD
diagnosis biomarker. In some examples, AD is diagnosed when BDNF is
decreased at least about 20% as compared to a reference level of
BDNF. In other examples, AD is diagnosed when Leptin is decreased
at least about 25% as compared to a reference level of Leptin. In
additional examples, AD is diagnosed when RANTES is decreased at
least about 16% as compared to a reference level of RANTES. In
further examples, severe AD is diagnosed when PDGF-BB is decreased
at least about 85% as compared to a reference level of PDGF-BB. In
yet further examples, the biological fluid sample is a peripheral
biological fluid sample.
[0016] Provided herein are methods for monitoring progression of
Alzheimer's disease (AD) in an AD patient, comprising: comparing a
measured level of at least one AD diagnosis biomarker in a
biological fluid sample from an individual to a reference level for
the biomarker, wherein the AD diagnosis biomarker is selected from
the group consisting of GCSF; IFN-g; IGFBP-1; BMP-6; BMP-4;
Eotaxin-2; IGFBP-2; TARC; RANTES; ANG; PARC; Acrp30; AgRP(ART);
TIMP-1; TIMP-2; ICAM-1; TRAIL R3; uPAR; IGFBP-4; LEPTIN(OB);
PDGF-BB; EGF; BDNF; NT-3; NAP-2; IL-1ra; MSP-a; SCF; TGF-b3; TNF-b;
MIP-1d; IL-3; FGF-6; IL-6 R; sTNF RII; AXL; bFGF; FGF4; CNTF;
MCP-1; MIP-1b; TPO; VEGF-B; IL-8; FAS; EGF-R. In some examples, the
AD diagnosis biomarker is selected from the group consisting of
basic fibroblast growth factor (bFGF); BB homodimeric platelet
derived growth factor (PDGF-BB); brain derived neurotrophic factor
(BDNF); epidermal growth factor (EGF), fibroblast growth factor 6
(FGF-6), interleukin-3 (IL-3), soluble interleukin-6 receptor
(sIL-6R), leptin (also known as ob), macrophage inflammatory
protein-1 delta (MIP-1.delta.), macrophage stimulating protein
alpha chain (MSP-.alpha.), neurotrophin-3 (NT-3), neutrophil
activating peptide-2 (NAP-2), RANTES, soluble tumor necrosis factor
receptor-2 (sTNF RII), stem cell factor (SCF), thrombopoietin
(TPO), tissue inhibitor of metalloproteases-1 (TIMP-1), tissue
inhibitor of metalloproteases-2 (TIMP-2), transforming growth
factor-beta 3 (TGF-.beta.3), and tumor necrosis factor beta
(TNF-.beta.). In other examples, the AD diagnosis marker is
selected from the group consisting of BDNF, PDGF-BB, leptin and
RANTES.
[0017] The inventors have also discovered methods of identifying
individuals with mild cognitive deficit (MCI), a clinically
recognized disorder considered distinct from AD in which cognition
and memory are mildly deficient. The inventors have found that the
biomarker RANTES is decreased in individuals with MCI. Individuals
with MCI can be distinguished from those with AD by measuring
biomarkers which are reduced in AD patients, but not those
individuals with MCI (e.g., Leptin). Accordingly, the invention
provides methods for diagnosing or aiding in the diagnosis of MCI
by obtaining a measured value for the level of RANTES in a
peripheral biological fluid sample and comparing that measured
value against a reference value. In certain embodiments, such
methods include obtaining a measuring value for Leptin levels in
the peripheral biological fluid sample and comparing that measured
level against a reference value. The information thus obtained may
be used to aid in the diagnosis or to diagnose MCI in the
individual.
[0018] Further, the inventors have discovered methods of
stratifying AD patients (i.e., sorting individuals with a probable
diagnosis of AD or diagnosed with AD into different classes of AD)
by obtaining measured values for brain derived neurotrophic factor
(BDNF) and BB-homodimer platelet derived growth factor (PDGF-BB)
levels in a peripheral biological fluid sample from an AD patient.
The measured levels of these two biomarkers are compared with
reference values. The information thus obtained may be used to aid
in stratification of the AD diagnosis (or probable AD diagnosis) of
the individual. Accordingly, the present invention provides methods
for stratifying Alzheimer's disease (AD) in an individual,
comprising comparing measured values for brain derived neurotrophic
factor (BDNF) and BB homodimeric platelet derived growth factor
(PDGF-BB) levels in a biological fluid sample from said patient
with reference values for BDNF and PDGF-BB. In some examples, the
biological fluid sample is a peripheral fluid sample, including
blood, serum or plasma. In other examples, the method further
comprises comparing measured values for leptin and Rantes levels
with reference values for leptin and Rantes, wherein reference
values for BDNF, PDGF-BB, leptin and Rantes are for samples from
individuals with MMSE scores from 25 to 28, wherein an increase in
leptin and PDGF-BB levels and wherein levels of BDNF and RANTES
stay substantially the same indicate mild AD as indicated by an
MMSE score of 20-25. In additional examples, the method further
comprises comparing measured values for leptin and Rantes levels
with reference values for leptin and Rantes, wherein reference
values for BDNF, PDGF-BB, leptin and Rantes are for samples from
individuals with MMSE scores from 20-25, wherein a decrease in
Rantes, BDNF, and PDGF levels and wherein levels of Leptin stays
substantially the same indicate moderate AD as indicated by an MMSE
score of 10-20.
[0019] In one aspect, the invention provides methods of aiding in
the diagnosis of Alzheimer's disease ("AD") by obtaining a measured
level of at least one AD diagnosis biomarker in a peripheral
biological fluid sample from an individual, where the AD diagnosis
biomarker is from the group consisting of basic fibroblast growth
factor (bFGF), BB homodimeric platelet derived growth factor
(PDGF-BB), brain derived neurotrophic factor (BDNF), epidermal
growth factor (EGF), fibroblast growth factor 6 (FGF-6),
interleukin-3 (IL-3), soluble interleukin-6 receptor (sIL-6R),
Leptin (also known as ob), macrophage inflammatory protein-1 delta
(MIP-16), macrophage stimulating protein alpha chain (MSP-.alpha.),
neurotrophin-3 (NT-3), neutrophil activating peptide-2 (NAP-2),
RANTES, soluble tumor necrosis factor receptor-2 (sTNF RII), stem
cell factor (SCF), thrombopoietin (TPO), tissue inhibitor of
metalloproteases-1 (TIMP-1), tissue inhibitor of metalloproteases-2
(TIMP-2), transforming growth factor-beta 3 (TGF-.beta.3), and
tumor necrosis factor beta (TNF-.beta.), and comparing the measured
level to the reference level. In some embodiments, measured levels
are obtained for at least two, three, four, or five AD diagnosis
biomarkers. In some embodiments, the comparison of the measured
value and the reference value includes calculating a fold
difference between the measured value and the reference value. In
some embodiments the measured value is obtained by measuring the
level of the AD diagnosis biomarker(s) in the sample, while in
other embodiments the measured value is obtained from a third
party. Also provided are methods of aiding in the diagnosis of
Alzheimer's disease ("AD") by comparing a measured level of at
least one AD diagnosis biomarker in a peripheral biological fluid
sample from an individual with a reference level. Further provided
are methods of aiding in the diagnosis of Alzheimer's disease
("AD") by measuring a level of at least one AD diagnosis biomarker
in a peripheral biological fluid sample from an individual, wherein
a decrease as compared to a reference level suggests a diagnosis of
AD.
[0020] In another aspect, the invention provides methods for aiding
in the diagnosis of mild cognitive impairment (MCI) by obtaining a
measured level for RANTES in a peripheral biological fluid sample
from an individual, and comparing the measured level to a reference
level. In some embodiments, the method for aiding in the diagnosis
of MCI also includes obtaining a measured value for Leptin in the
peripheral biological fluid sample and comparing measured value for
Leptin to a reference level. In certain embodiments, the measured
value is obtained by measuring the level of RANTES (and/or Leptin)
in the sample, while in other embodiments, the measured value(s) is
obtained from a third party. Also provided are methods of aiding in
the diagnosis of mild cognitive impairment (MCI) by comparing a
measured level for RANTES, and optionally Leptin, in a peripheral
biological fluid sample from an individual with a reference level.
Further provided are methods for aiding in the diagnosis of MCI by
measuring a level for RANTES, and optionally Leptin, in a
peripheral biological fluid sample from an individual, wherein a
reduction in the RANTES level as compared to a reference level
suggests a diagnosis of MCI (in embodiments in which Leptin in
measured, a Leptin level that is equal to or greater than the
reference level also suggests MCI).
[0021] In a further aspect, the invention provides methods for
monitoring progression of Alzheimer's disease (AD) in an AD patient
by obtaining a measured value for Leptin in a peripheral biological
fluid sample; and comparing said measured value for Leptin with a
reference value. In certain embodiments, the measured value is
obtained by measuring the level of Leptin in the sample to produce,
while in other embodiments, the measured value is obtained from a
third party. Also provided are methods for monitoring progression
of AD in an AD patient by comparing a measured value for Leptin in
a peripheral biological fluid sample with a reference value.
Further provided are methods for monitoring progression of AD in an
AD patient by measuring a level for Leptin in a peripheral
biological fluid sample, wherein a decrease in Leptin as compared
with a reference value suggests progression (increased severity) of
the AD. In some examples, the invention provides methods for
monitoring progression of Alzheimer's disease (AD) in an AD patient
by obtaining a measured value for Lymphotactin and/or IL-11 in a
peripheral biological fluid sample; and comparing said measured
value for Leptin with a reference value.
[0022] In another aspect, the invention provides methods for
stratifying AD in an AD patient. In some embodiments,
stratification between mild and more advanced AD is carried out by
obtaining a measured value for brain derived neurotrophic factor
(BDNF) levels in a peripheral biological fluid sample from an AD
patient, and comparing the measured value with reference values for
BDNF. In other embodiments, stratification between mild, moderate,
and severe AD is carried out by obtaining levels for BDNF and BB
homodimeric platelet derived growth factor (PDGF-BB), and comparing
the measured levels with reference levels for BDNF and PDGF-BB. In
certain embodiments, the measured value is obtained by measuring
the level(s) of BDNF (and PDGF-BB) in the sample to produce the
measured value(s), while in other embodiments, the measured
value(s) is obtained from a third party. Also provided are methods
for stratifying AD in an AD patient by comparing a BDNF (and,
optionally, PDGF-BB) level in a peripheral biological fluid sample
from an AD patient with a reference value for BDNF (and PDGF-BB
when appropriate). Further provided are methods for stratifying AD
in an AD patient by measuring a BDNF level (and, optionally, a
PDGF-BB level) in a peripheral biological fluid sample, wherein a
low level of BDNF (as compared to a reference value) suggests mild
AD, a high level of BDNF (as compared to a reference value)
suggests more advanced AD, a high level of BDNF and a low level of
PDGF-BB (as compared to reference values) suggests moderate AD, and
a high level of BDNF and a high level of PDGF-BB (as compared to
reference values) suggests severe AD. In another aspect, the
invention provides methods for stratifying AD in an AD patient. In
some examples, stratification between mild and more advanced AD is
carried out by obtaining a measured value for Lymphotactin and/or
IL-11 levels in a peripheral biological fluid sample from an AD
patient, and comparing the measured value with reference value for
Lymphotactin and/or IL-11.
[0023] In some embodiments, the peripheral biological fluid sample
is a blood sample. In certain embodiments the peripheral biological
fluid sample is a plasma sample. In other embodiments, the
peripheral biological fluid sample is a serum sample.
[0024] In yet another aspect, the invention provides methods of
identifying candidate agents for treatment of Alzheimer's Disease
by assaying a prospective candidate agent for activity in
modulating an AD biomarker, where the AD biomarker is from the
group consisting of basic fibroblast growth factor (bFGF), BB
homodimeric platelet derived growth factor (PDGF-BB), brain derived
neurotrophic factor (BDNF), epidermal growth factor (EGF),
fibroblast growth factor 6 (FGF-6), interleukin-3 (IL-3), soluble
interleukin-6 receptor (sIL-6R), Leptin (also known as ob),
macrophage inflammatory protein-1 delta (MIP-1.delta.), macrophage
stimulating protein alpha chain (MSP-.alpha.), neurotrophin-3
(NT-3), neutrophil activating peptide-2 (NAP-2), RANTES, soluble
tumor necrosis factor receptor-2 (sTNF RII), stem cell factor
(SCF), thrombopoietin (TPO), tissue inhibitor of metalloproteases-1
(TIMP-1), tissue inhibitor of metalloproteases-2 (TIMP-2),
transforming growth factor-beta 3 (TGF-.beta.3), tumor necrosis
factor beta (TNF-.beta.). Provided herein are methods of
identifying a candidate agent for treatment of Alzheimer's Disease,
comprising: assaying a prospective candidate agent for activity in
modulating an AD biomarker, said AD biomarker selected from the
group consisting of GCSF; IFN-g; IGFBP-1; BMP-6; BMP-4; Eotaxin-2;
IGFBP-2; TARC; RANTES; ANG; PARC; Acrp30; AgRP(ART); TIMP-1;
TIMP-2; ICAM-1; TRAIL R3; uPAR; IGFBP-4; LEPTIN(OB); PDGF-BB; EGF;
BDNF; NT-3; NAP-2; IL-1ra; MSP-a; SCF; TGF-b3; TNF-b; MIP-1d; IL-3;
FGF-6; IL-6 R; sTNF RII; AXL; bFGF; FGF4; CNTF; MCP-1; MIP-1b; TPO;
VEGF-B; IL-8; FAS; EGF-R. In some examples, the AD biomarkers are
selected from the group consisting of BDNF, PDGF-BB, Leptin and
RANTES.
[0025] In a further aspect, the invention provides kits for
diagnosing Alzheimer's disease (AD) including at least one reagent
specific for an AD diagnosis marker, where the AD diagnosis
biomarker is from the group consisting of basic fibroblast growth
factor (bFGF), BB homodimeric platelet derived growth factor
(PDGF-BB), brain derived neurotrophic factor (BDNF), epidermal
growth factor (EGF), fibroblast growth factor 6 (FGF-6),
interleukin-3 (IL-3), soluble interleukin-6 receptor (sIL-6R),
Leptin (also known as ob), macrophage inflammatory protein-1 delta
(MIP-1), macrophage stimulating protein alpha chain (MSP-.alpha.),
neurotrophin-3 (NT-3), neutrophil activating peptide-2 (NAP-2),
RANTES, soluble tumor necrosis factor receptor-2 (sTNF RII), stem
cell factor (SCF), thrombopoietin (TPO), tissue inhibitor of
metalloproteases-1 (TIMP-1), tissue inhibitor of metalloproteases-2
(TIMP-2), transforming growth factor-beta 3 (TGF-.beta.3), tumor
necrosis factor beta (TNF-.beta.), and instructions for carrying
out a method of aiding in the diagnosis of AD described herein.
Provided herein are kits for use in the methods as disclosed
herein, comprising at least one reagent specific for at least one
AD diagnosis marker, said at least one AD diagnosis biomarker
selected from the group consisting of GCSF; IFN-g; IGFBP-1; BMP-6;
BMP-4; Eotaxin-2; IGFBP-2; TARC; RANTES; ANG; PARC; Acrp30;
AgRP(ART); TIMP-1; TIMP-2; ICAM-1; TRAIL R3; uPAR; IGFBP-4;
LEPTIN(OB); PDGF-BB; EGF; BDNF; NT-3; NAP-2; IL-1ra; MSP-a; SCF;
TGF-b3; TNF-b; MIP-1d; IL-3; FGF-6; IL-6 R; sTNF RII; AXL; bFGF;
FGF-4; CNTF; MCP-1; MIP-1b; TPO; VEGF-B; IL-8; FAS; EGF-R and
instructions for carrying out methods provided herein.
Additionally, provided herein are sets of reference values for AD
diagnosis biomarkers comprising BDNF, PDGF-BB, Leptin and RANTES
and set of reagents specific for AD diagnosis biomarkers, wherein
said biomarkers comprise BDNF, PDGF-BB, Leptin and RANTES.
[0026] In another aspect, the invention provides kits for
identifying individuals with mild cognitive impairment (MCI)
including at least one reagent specific for RANTES; and
instructions for carrying out method of aiding in the diagnosis of
MCI described herein. In certain embodiments, kits for identifying
individuals with MCI may also include a reagent specific for
Leptin.
[0027] In yet another aspect, the invention provides kits for
monitoring progression of Alzheimer's disease (AD) in AD patients
including at least one reagent specific for Leptin; and
instructions for carrying out a method of monitoring AD progression
described herein.
[0028] In a further aspect, the invention provides kits for
stratifying an Alzheimer's disease (AD) patients including at least
one reagent specific for brain derived neurotrophic factor (BDNF),
at least one reagent specific for BB homodimeric platelet derived
growth factor (PDGF-BB), and instructions for carrying out a method
of stratifying an AD patient described herein. In yet further
examples, kits for use in the methods as described herein, comprise
AD diagnosis markers are selected from the group consisting of
BDNF, PDGF-BB, leptin and RANTES. In further examples of kits for
use in the methods as disclosed herein, the reagent specific for
the AD diagnosis biomarker is an antibody, or fragment thereof,
that is specific for said AD diagnosis biomarker. In further
examples kits for use in the methods disclosed herein further
comprise at least one reagent specific for a biomarker that
measures sample characteristics.
[0029] Provided herein are surfaces comprising attached thereto, at
least one reagent specific for each AD diagnosis biomarker in a set
of AD diagnosis biomarkers, wherein said set of AD diagnosis
biomarkers comprises BDNF, PDGF-BB, leptin and RANTES. Provided
herein are surfaces comprising attached thereto, at least one
reagent specific for each AD diagnosis biomarker in a set of AD
diagnosis biomarkers, wherein said set of AD diagnosis biomarkers
consists of BDNF, PDGF-BB, leptin and RANTES; and at least one
reagent specific for a biomarker that measures sample
characteristics. In further examples, provided herein are surfaces
wherein said reagent specific for said AD diagnosis biomarker is an
antibody, or fragment thereof, that is specific for said AD
diagnosis biomarker.
[0030] Provided herein are combinations comprising the surfaces as
described herein having attached thereto at least one reagent
specific for each AD diagnosis biomarker and a peripheral
biological fluid sample from an individual. In some examples, the
individual is at least 60, 65, 70, 75, 80, or 85 years of age.
[0031] Provided herein are methods for obtaining values for the
comparison of the measured level to the reference level of
biological fluid samples. The present invention provides computer
readable formats comprising the values obtained by the methods
described herein.
[0032] Provided herein are methods of aiding diagnosis of
Alzheimer's disease ("AD"), comprising comparing a measured level
of at least one AD diagnosis biomarker selected from the group
consisting of the biomarkers listed in Tables 9A1-9A2 and 9B in a
biological fluid sample from an individual to a reference level for
each AD diagnosis biomarker. In some examples, provided herein are
methods of aiding diagnosis of Alzheimer's disease ("AD"),
comprising comparing a measured level of at least one AD diagnosis
biomarker in a biological fluid sample from an individual to a
reference level for the biomarker, wherein the AD diagnosis
biomarker is selected from the group consisting of BTC; SDF-1;
MCP-2; IFN-gamma; IGFBP-4; IGF-1 SR; IL-8; GM-CSF; and ANG-2. In
some examples, provided herein are methods that comprise comparing
a measured level of at least two, three, four or five AD diagnosis
biomarker in a biological fluid sample from an individual to a
reference level for the biomarker, wherein the AD diagnosis
biomarker is selected from the group consisting of BTC; SDF-1;
MCP-2; IFN-gamma; IGFBP-4; IGF-1 SR; IL-8; GM-CSF; and ANG-2. In
some examples, the at least one AD diagnosis biomarker is selected
from the group consisting of biomarkers IFN-gamma and IL-8. In
other examples, provided herein are methods of aiding diagnosis of
Alzheimer's disease ("AD"), comprising comparing a measured level
of at least one AD diagnosis biomarker in a biological fluid sample
from an individual to a reference level for the biomarker, wherein
the AD diagnosis biomarker is selected from the group consisting of
sTNF RII; MSP-alpha; uPAR; TPO; MIP-1beta; VEGF-beta; FAS; MCP-1;
NAP-2; ICAM-1; TRAIL R3; PARC; ANG; IL-3; MIP-1delta; IFN-gamma;
IL-8; and FGF-6. Provided herein are methods of aiding diagnosis of
Alzheimer's disease ("AD"), comprising comparing a measured level
of at least one AD diagnosis biomarker in a biological fluid sample
from an individual to a reference level for the biomarker, wherein
the AD diagnosis biomarker is selected from the group consisting of
lymphotactin and IL-11. In some examples, the biological fluid
sample is a peripheral biological fluid sample. In additional
examples, the biological fluid sample is plasma. Provided herein
are methods of aiding diagnosis of a neurodegenerative disease
comprising obtaining measured values of one or more biomarkers
shown in Table 12A-12B with a q-value % of less than 1.5, and
comparing the measured value to a reference value.
[0033] Provided herein are methods for monitoring progression of
Alzheimer's disease (AD) in an AD patient, comprising comparing a
measured level of at least one AD diagnosis biomarker in a
biological fluid sample from an individual to a reference level for
the biomarker, wherein the AD diagnosis biomarker is selected from
the group consisting of the biomarkers listed in Tables 9A1-9A2 and
9B. In some examples, the reference level is a level obtained from
a biological fluid sample from the same AD patient at an earlier
point in time. In other examples, the biological fluid sample is a
peripheral biological fluid sample. In yet additional examples, the
biological fluid sample is plasma. In further examples, the at
least one AD diagnosis biomarker is selected from the group
consisting of BTC; SDF-1; MCP-2; IFN-gamma; IGFBP-4; IGF-1SR; IL-8;
GM-CSF; and ANG-2. In additional examples, the at least one AD
diagnosis biomarker is selected from the group consisting of
IFN-gamma and IL-8. In further examples, the at least one AD
diagnosis biomarker is selected from the group consisting of
biomarkers sTNF RII; MSP-alpha; uPAR; TPO; MIP-1beta; VEGF-beta;
FAS; MCP-1; NAP-2; ICAM-1; TRAIL R3; PARC; ANG; IL-3; MIP-1delta;
IFN-gamma; IL-8; and FGF-6. In additional examples, provided herein
are methods for monitoring progression of Alzheimer's disease (AD)
in an AD patient, comprising, comparing a measured level of at
least one AD diagnosis biomarker in a biological fluid sample from
an individual to a reference level for the biomarker, wherein the
AD diagnosis biomarker is selected from the group consisting of
lymphotactin and IL-11.
[0034] Provided herein are methods for stratifying Alzheimer's
disease (AD) in an individual, comprising, comparing measured
levels for at least one biomarker in a biological fluid sample from
an individual to a reference level for the biomarker, wherein the
AD diagnosis biomarker is selected from the group consisting of
lymphotactin and IL-11. In some examples, the biological fluid
sample is a peripheral fluid sample. In other examples, the
biological fluid sample is plasma.
[0035] Provided herein are methods of identifying a candidate
agent(s) for treatment of Alzheimer's Disease, comprising: assaying
a prospective candidate agent for activity in modulating at least
one AD diagnosis biomarker in a biological fluid sample from an
individual, wherein the AD diagnosis biomarker is selected from the
group consisting of the biomarkers listed in Tables 9A1-9A2 and 9B.
In some examples, the at least one AD diagnosis biomarker is
selected from the group consisting of biomarkers BTC; SDF-1; MCP-2;
IFN-gamma; IGFBP-4; IGF-1 SR; IL-8; GM-CSF; and ANG-2. In other
examples, the at least one AD diagnosis biomarker is selected from
the group consisting of biomarkers IFN-gamma and IL-8. In further
examples, the at least one AD diagnosis biomarker is selected from
the group consisting of biomarkers sTNF RII; MSP-alpha; uPAR; TPO;
MIP-1beta; VEGF-beta; FAS; MCP-1; NAP-2; ICAM-1; TRAIL R3; PARC;
ANG; IL-3; MIP-1delta; IFN-gamma; IL-8; and FGF-6. In additional
examples, the at least one AD diagnosis biomarker is selected from
the group consisting of biomarkers lymphotactin and IL-11. In some
examples, the assay is performed in vivo.
[0036] In additional examples, provided herein are kits for use in
the methods as described herein, such as for example, aiding in the
diagnosis of AD or diagnosing AD comprising, at least one reagent
specific for at least one AD diagnosis marker, wherein said at
least one AD diagnosis biomarker is selected from the group
consisting of the biomarkers listed in Tables 9A1-9A2 and 9B, and
instructions for carrying out the method, such as for example,
aiding in the diagnosis of AD or diagnosing AD. In some examples of
kits as described herein, the at least one AD diagnosis biomarker
is selected from the group consisting of BTC; SDF-1; MCP-2;
IFN-gamma; IGFBP-4; IGF-1SR; IL-8; GM-CSF; and ANG-2. In other
examples, the at least one AD diagnosis biomarker is selected from
the group consisting of IFN-gamma and IL-8. In further examples,
the at least one AD diagnosis biomarker is selected from the group
consisting of sTNF RII; MSP-alpha; uPAR; TPO; MIP-1beta; VEGF-beta;
FAS; MCP-1; NAP-2; ICAM-1; TRAIL R3; PARC; ANG; IL-3; MIP-1delta;
IFN-gamma; IL-8; and FGF-6. In further examples, the at least one
AD diagnosis biomarker is selected from the group consisting of
lymphotactin and IL-11. In further examples, a kit comprises at
least one reagent specific for each of at least two AD diagnosis
markers; at least one reagent specific for each of at least three
AD diagnosis markers; at least one reagent specific for each of at
least four AD diagnosis markers, or at least one reagent specific
for each of at least five AD diagnosis markers. In further
examples, the reagent specific for the AD diagnosis biomarker is an
antibody, or fragment thereof, that is specific for said AD
diagnosis biomarker. In further examples, the kit detects common
variants of the biomarkers listed in Tables 9A1-9A2 and 9B, wherein
a common variant indicates a protein that is expressed in at least
5 or more of the population in industrialized nations. In further
examples, a kit for use in the methods disclosed herein further
comprises a biomarker for normalizing data. In some examples, the
biomarker for normalizing data is selected from the group
consisting of TGF-beta and TGF-beta3.
[0037] Provided herein are surfaces comprising attached thereto, at
least one reagent specific for an AD diagnosis biomarker selected
from the group consisting of the biomarkers listed in Table 7,
wherein the AD diagnosis marker is characterized by the following
criteria: Correlation: greater than 90% (r=0.9 to r=0.99) with the
biomarker clusters 0-8 listed in Tables 9A1-9A2 and 9B; P-value
less than 0.001 up to 0.05; Fold change greater than 20%; and a
Score greater than 1 (for markers that increase) or less than 1
(for markers that decrease). Provided herein are combinations
comprising a surface and a peripheral biological fluid sample from
an individual. In some examples, the individual is at least 60, 65,
70, 75, 80, or 85.
[0038] Provided herein are methods for identifying at least one
biomarker useful for the diagnosis of a neurological disease,
comprising, obtaining measured values from a set of peripheral
biological fluid samples for a plurality of biomarkers, wherein
said set of peripheral biological fluid samples is divisible into
subsets on the basis of a neurological disease; comparing the
measured values from each subset for at least one biomarker; and
identifying at least one biomarker for which the measured values
are significantly different between the subsets. In some examples,
neurological disease is AD.
BRIEF DESCRIPTION OF THE DRAWINGS
[0039] FIGS. 1A-1C show ELISA results for 3 proteins, FIG. 1A BDNF;
FIG. 1B Leptin; and FIG. 1C RANTES, selected from the list from
Table 3 shown herein in the Examples. 95 plasma samples from
individuals having AD and having mean MMSE scores of 20, and mean
age of 74, was compared to plasma sample from 88 age-matched
controls having mean MMSE score of 30. Non-parametric, unpaired t
tests comparing the mean concentration of each protein was used to
determine statistical significance (p-value).
[0040] FIG. 2 shows a Cell Bar Chart for concentration of BDNF in
plasma. (Cell Bar Chart Grouping Variable(s): stage Error
Bars:.+-.1 Standard error(s) Inclusion criteria: Sparks from Center
All)
[0041] FIG. 3 shows BDNF in control vs AD for male and female.
(Cell Bar Chart Grouping Variable(s): Disease Split By: sex Error
Bars:.+-.1 Standard Error(s) Row exclusion: Center All)
[0042] FIG. 4 shows RANTES concentration in plasma. (Cell Bar Chart
Grouping Variable(s): stage Error Bars: .+-.1 Standard Error(s) Row
exclusion: Center All)
[0043] FIG. 5 shows concentration of Leptin in plasma. (Cell Bar
Chart Grouping Variable(s): stage Error Bars: .+-.1 Standard
Error(s) Row exclusion: Center All)
[0044] FIG. 6 shows PDGF-BB concentration in plasma. (Cell Bar
Chart Grouping Variable(s): stage Error Bars: .+-.1 Standard
Error(s) Row exclusion: Center All)
[0045] FIG. 7 shows BDNF concentration in plasma. (Cell Bar Chart
Grouping Variable(s): stage Error Bars: .+-.1 Standard Error(s) Row
exclusion: Center All)
DETAILED DESCRIPTION OF THE INVENTION
[0046] Inflammation and injury responses are invariably associated
with neuron degeneration in AD, Parkinson's Disease (PD),
frontotemporal dementia, cerebrovascular disease, multiple
sclerosis, and neuropathies. The brain and CNS are not only
immunologically active in their own accord, but also have complex
peripheral immunologic interactions. Fiala et al. (1998 Mol Med.
July; 4(7):480-9) has shown that in Alzheimer's disease,
alterations in the permeability of the blood-brain barrier and
chemotaxis, in part mediated by chemokines and cytokines, may
permit the recruitment and transendothelial passage of peripheral
cells into the brain parenchyma. A paradigm of the blood-brain
barrier was constructed utilizing human brain endothelial and
astroglial cells with the anatomical and physiological
characteristics observed in vivo. This model was used to test the
ability of monocytes/macrophages to transmigrate when challenged by
A beta 1-42 on the brain side of the blood-brain barrier model. In
that model A beta 1-42 and monocytes on the brain side potentiated
monocyte transmigration from the blood side to the brain side. In
some individuals, circulating monocytes/macrophages, when recruited
by chemokines produced by activated microglia and macrophages,
could add to the inflammatory destruction of the brain in
Alzheimer's disease.
[0047] The inventors assert that the monitoring for relative
concentrations of many secreted markers measured simultaneously in
the serum is a more sensitive method for monitoring the progression
of disease than the absolute concentration of any single
biochemical markers have been able to achieve. A composite or array
embodying the use of 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100,
110, 120, 130, 140, 150, 160, 170, 180, 190, 200 markers in Table 7
simultaneously, consisting of antibodies bound to a solid support
or protein bound to a solid support, for the detection of
inflammation and injury response markers associated with neuron
degeneration in AD, PD, frontotemporal dementia, cerebrovascular
disease, multiple sclerosis, and neuropathies.
[0048] The inventors have discovered a collection of biochemical
markers (collectively termed "AD biomarkers") useful for diagnosis
of AD, aiding in diagnosis of AD, monitoring AD in AD patients
(e.g., tracking disease progression in AD patients, which may be
useful for tracking the effect of medical or surgical therapy in AD
patients), stratifying AD patients, and diagnosing or aiding in the
diagnosis of mild cognitive impairment (MCI) as well as diagnosing
or aiding in the diagnosis of cognitive impairment. The AD
biomarkers are present in biological fluids of individuals. In some
examples, the AD biomarkers are present in peripheral biological
fluids (e.g., blood) of individuals, allowing collection of samples
by procedures that are relatively non-invasive, particularly as
compared to the lumbar puncture procedure commonly used to collect
cerebrospinal fluid samples.
[0049] Definitions
[0050] As used herein, the terms "Alzheimer's patient", "AD
patient", and "individual diagnosed with AD" all refer to an
individual who has been diagnosed with AD or has been given a
probable diagnosis of Alzheimer's Disease (AD).
[0051] As used herein, the phrase "AD biomarker" refers to a
biomarker that is an AD diagnosis biomarker.
[0052] The term "AD biomarker polynucleotide", as used herein,
refers to any of: a polynucleotide sequence encoding a AD
biomarker, the associated trans-acting control elements (e.g.,
promoter, enhancer, and other gene regulatory sequences), and/or
mRNA encoding the AD biomarker.
[0053] As used herein, methods for "aiding diagnosis" refer to
methods that assist in making a clinical determination regarding
the presence, or nature, of the AD or MCI, and may or may not be
conclusive with respect to the definitive diagnosis. Accordingly,
for example, a method of aiding diagnosis of AD can comprise
measuring the amount of one or more AD biomarkers in a biological
sample from an individual.
[0054] As used herein, the term "stratifying" refers to sorting
individuals into different classes or strata based on the features
of a neurological disease. For example, stratifying a population of
individuals with Alzheimer's disease involves assigning the
individuals on the basis of the severity of the disease (e.g.,
mild, moderate, advanced, etc.).
[0055] As used herein, the term "predicting" refers to making a
finding that an individual has a significantly enhanced probability
of developing a certain neurological disease.
[0056] As used herein, the phrase "neurological disease" refers to
a disease or disorder of the central nervous system. Neurological
diseases include multiple sclerosis, neuropathies, and
neurodegenerative disorders such as AD, Parkinson's disease,
amyotrophic lateral sclerosis (ALS), mild cognitive impairment
(MCI) and frontotemporal dementia.
[0057] As used herein, "biological fluid sample" encompasses a
variety of fluid sample types obtained from an individual and can
be used in a diagnostic or monitoring assay. The definition
encompasses blood, cerebral spinal fluid (CSF), urine and other
liquid samples of biological origin. The definition also includes
samples that have been manipulated in any way after their
procurement, such as by treatment with reagents, solubilization, or
enrichment for certain components, such as proteins or
polynucleotides.
[0058] As used herein, the term "peripheral biological fluid
sample" refers to a biological fluid sample that is not derived
from the central nervous system (i.e., is not a CSF sample) and
includes blood samples and other biological fluids not derived from
the CNS.
[0059] A "blood sample" is a biological sample which is derived
from blood, preferably peripheral (or circulating) blood. A blood
sample may be, for example, whole blood, plasma or serum.
[0060] An "individual" is a mammal, more preferably a human.
Mammals include, but are not limited to, humans, primates, farm
animals, sport animals, rodents and pets.
[0061] A "Normal" individual or sample from a "Normal" individual
as used herein for quantitative and qualitative data refers to an
individual who has or would be assessed by a physician as not
having AD or MCI, and has an Mini-Mental State Examination (MMSE)
(referenced in Folstein et al., J. Psychiatr. Res 1975;
12:1289-198) score or would achieve a MMSE score in the range of
25-30. A "Normal" individual is generally age-matched within a
range of 5 to 10 years, including but not limited to an individual
that is age-matched, with the individual to be assessed.
[0062] An "individual with mild AD" is an individual who (a) has
been diagnosed with AD or has been given a diagnosis of probable
AD, and (b) has either been assessed with the Mini-Mental State
Examination (MMSE) (referenced in Folstein et al., J. Psychiatr.
Res 1975; 12:1289-198) and scored 22-27 or would achieve a score of
22-27 upon MMSE testing. Accordingly, "mild AD" refers to AD in a
individual who has either been assessed with the MMSE and scored
22-27 or would achieve a score of 22-27 upon MMSE testing.
[0063] An "individual with moderate AD" is an individual who (a)
has been diagnosed with AD or has been given a diagnosis of
probable AD, and (b) has either been assessed with the MMSE and
scored 16-21 or would achieve a score of 16-21 upon MMSE testing.
Accordingly, "moderate AD" refers to AD in a individual who has
either been assessed with the MMSE and scored 16-21 or would
achieve a score of 16-21 upon MMSE testing.
[0064] An "individual with severe AD" is an individual who (a) has
been diagnosed with AD or has been given a diagnosis of probable
AD, and (b) has either been assessed with the MMSE and scored 12-15
or would achieve a score of 12-15 upon MMSE testing. Accordingly,
"severe AD" refers to AD in a individual who has either been
assessed with the MMSE and scored 12-15 or would achieve a score of
12-15 upon MMSE testing.
[0065] As used herein, the term "treatment" refers to the
alleviation, amelioration, and/or stabilization of symptoms, as
well as delay in progression of symptoms of a particular disorder.
For example, "treatment" of AD includes any one or more of:
elimination of one or more symptoms of AD, reduction of one or more
symptoms of AD, stabilization of the symptoms of AD (e.g., failure
to progress to more advanced stages of AD), and delay in
progression (i.e., worsening) of one or more symptoms of AD.
[0066] As used herein, the phrase "fold difference" refers to a
numerical representation of the magnitude difference between a
measured value and a reference value for an AD biomarker. Fold
difference is calculated mathematically by division of the numeric
measured value with the numeric reference value. For example, if a
measured value for an AD biomarker is 20 nanograms/milliliter
(ng/ml), and the reference value is 10 ng/ml, the fold difference
is 2 (20/10=2). Alternatively, if a measured value for an AD
biomarker is 10 nanograms/milliliter (ng/ml), and the reference
value is 20 ng/ml, the fold difference is 10/20 or -0.50 or
-50%).
[0067] As used herein, a "reference value" can be an absolute
value; a relative value; a value that has an upper and/or lower
limit; a range of values; an average value; a median value, a mean
value, or a value as compared to a particular control or baseline
value. A reference value can be based on an individual sample
value, such as for example, a value obtained from a sample from the
individual with AD, MCI or cognitive impairment, but at an earlier
point in time, or a value obtained from a sample from an AD patient
other than the individual being tested, or a "normal" individual,
that is an individual not diagnosed with AD. The reference value
can be based on a large number of samples, such as from AD patients
or normal individuals or based on a pool of samples including or
excluding the sample to be tested.
[0068] As used herein, "a", "an", and "the" can mean singular or
plural (i.e., can mean one or more) unless indicated otherwise.
[0069] Methods of the Invention
[0070] Methods for Identifying Biomarkers
[0071] The invention provides methods for identifying one or more
biomarkers useful for diagnosis, aiding in diagnosis, stratifying,
assessing risk, monitoring, and/or predicting a neurological
disease. In certain aspects of the invention, levels of a group of
biomarkers are obtained for a set of peripheral biological fluid
samples from one or more individuals. The samples are selected such
that they can be segregated into one or more subsets on the basis
of a neurological disease (e.g., samples from normal individuals
and those diagnosed with amyotrophic lateral sclerosis or samples
from individuals with mild Alzheimer's disease and those with
severe Alzheimer's disease and/or other neurological diseases, such
as neurodegenerative diseases). The measured values from the
samples are compared to each other to identify those biomarkers
which differ significantly amongst the subsets. Those biomarkers
that vary significantly amongst the subsets may then be used in
methods for aiding in the diagnosis, diagnosis, stratification,
monitoring, and/or prediction of neurological disease. In other
aspects of the invention, measured values for a set of peripheral
biological fluid samples from one or more individuals (where the
samples can be segregated into one or more subsets on the basis of
a neurological disease) are compared, wherein biomarkers that vary
significantly are useful for aiding in the diagnosis, diagnosis,
stratification, monitoring, and/or prediction of neurological
disease. In further aspects of the invention, levels of a set of
peripheral biological fluid samples from one or more individuals
(where the samples can be segregated into one or more subsets on
the basis of a neurological disease) are measured to produced
measured values, wherein biomarkers that vary significantly are
useful for aiding in the diagnosis, diagnosis, stratification,
monitoring, and/or prediction of neurological disease.
[0072] The instant invention utilizes a set of peripheral
biological fluid samples, such as blood samples, that are derived
from one or more individuals. The set of samples is selected such
that it can be divided into one or more subsets on the basis of a
neurological disease. The division into subsets can be on the basis
of presence/absence of disease, stratification of disease (e.g.,
mild vs. moderate), or subclassification of disease (e.g.,
relapsing/remitting vs. progressive relapsing). Biomarkers measured
in the practice of the invention may be any proteinaceous
biological marker found in a peripheral biological fluid sample.
Tables 7 and 8 contain a collection of exemplary biomarkers.
Additional biomarkers are described herein in the Examples.
[0073] Accordingly, the invention provides methods identifying one
or more biomarkers which can be used to aid in the diagnosis, to
diagnose, detect, stratify, and/or predict neurological diseases
such as neurodegenerative disorders. The methods of the invention
are carried out by obtaining a set of measured values for a
plurality of biomarkers from a set of peripheral biological fluid
samples, where the set of peripheral biological fluid samples is
divisible into at least two subsets in relation to a neurological
disease, comparing said measured values between the subsets for
each biomarker, and identifying biomarkers which are significantly
different between the subsets.
[0074] The process of comparing the measured values may be carried
out by any method known in the art, including Significance Analysis
of Microarrays, Tree Harvesting, CART, MARS, Self Organizing Maps,
Frequent Item Set, or Bayesian networks.
[0075] In one aspect, the invention provides methods for
identifying one or more biomarkers useful for the diagnosis of a
neurological disease by obtaining measured values from a set of
peripheral biological fluid samples for a plurality of biomarkers,
wherein the set of peripheral biological fluid samples is divisible
into subsets on the basis of a neurological disease, comparing the
measured values from each subset for at least one biomarker; and
identifying at least one biomarker for which the measured values
are significantly different between the subsets. In some
embodiments, the comparing process is carried out using
Significance Analysis of Microarrays. In certain embodiments, the
neurodegenerative disease is from the group consisting of
Alzheimer's disease, Parkinson's disease, Huntington's disease, and
amyotrophic lateral sclerosis (ALS).
[0076] In another aspect, the invention provides methods for
identifying at least one biomarker useful for aiding in the
diagnosis of a neurological disease by obtaining measured values
from a set of peripheral biological fluid samples for a plurality
of biomarkers, wherein the set of peripheral biological fluid
samples is divisible into subsets on the basis of a neurological
disease, comparing the measured values from each subset for at
least one biomarker; and identifying biomarkers for which the
measured values are significantly different between the
subsets.
[0077] In a further aspect, the invention provides methods for
identifying at least one biomarker useful for the stratification of
a neurological disease by obtaining measured values from a set of
peripheral biological fluid samples for a plurality of biomarkers,
wherein the set of peripheral biological fluid samples is divisible
into subsets on the basis of strata of a neurological disease,
comparing the measured values from each subset for at least one
biomarker; and identifying biomarkers for which the measured values
are significantly different between the subsets.
[0078] In another aspect, the invention provides methods for
identifying at least one biomarker useful for the monitoring of a
neurological disease by obtaining measured values from a set of
peripheral biological fluid samples for a plurality of biomarkers,
wherein the set of peripheral biological fluid samples is divisible
into subsets on the basis of strata of a neurological disease,
comparing the measured values from each subset for at least one
biomarker; and identifying biomarkers for which the measured values
are significantly different between the subsets. In other examples,
the measured values are obtained from peripheral biological fluid
samples of varying sources.
[0079] In yet another aspect, the invention provides methods for
identifying at least one biomarker useful for the prediction of a
neurological disease by obtaining measured values from a set of
peripheral biological fluid samples for a plurality of biomarkers,
wherein the set of peripheral biological fluid samples is divisible
into subsets on the basis of a neurological disease, comparing the
measured values from each subset for at least one biomarker; and
identifying biomarkers for which the measured values are
significantly different between the subsets. In other examples, the
measured values are obtained from peripheral biological fluid
samples of varying sources.
[0080] Methods of Assessing Cognitive Function
[0081] Provided herein are methods for assessing cognitive
function, assessing cognitive impairment, diagnosing or aiding
diagnosis of cognitive impairment by obtaining measured levels of
one or more AD diagnosis biomarkers in a biological fluid sample
from an individual, such as for example, a peripheral biological
fluid sample from an individual, and comparing those measured
levels to reference levels. Reference to "AD diagnosis markers" "AD
biomarker" and "Biomarker" (used interchangeably herein) are terms
of convenience to refer to the markers described herein and their
use, and is not intended to indicate the markers are only used to
diagnose AD. As this disclosure makes clear, these biomarkers are
useful for, for example, assessing cognitive function, assessing
MCI, assessing risk of developing AD, stratifying AD, etc. AD
biomarkers include but are not limited to secreted proteins or
metabolites present in a person's biological fluids (that is, a
biological fluid sample), such as for example, blood, including
whole blood, plasma or serum; urine; cerebrospinal fluid; tears;
and saliva. Biological fluid samples encompass clinical samples,
and also includes serum, plasma, and other biological fluids. A
blood sample may include, for example, various cell types present
in the blood including platelets, lymphocytes, polymorphonuclear
cells, macrophages, erythrocytes.
[0082] As described herein, assessment of results can depend on
whether the data were obtained by the qualitative or quantitative
methods described herein and/or type of reference point used. For
example, as described in Example 4, qualitative measurement of AD
biomarker levels relative to another reference level, which may be
relative to the level of another AD biomarker, may be obtained. In
other methods described herein, such as in Example 7, quantitative
or absolute values, that is protein concentration levels, in a
biological fluid sample may be obtained. "Quantitative" result or
data refers to an absolute value (see Example 7), which can include
a concentration of a biomarker in pg/ml or ng/ml of molecule to
sample. An example of a quantitative value is the measurement of
concentration of protein levels directly for example by ELISA.
"Qualitative" result or data provides a relative value which is as
compared to a reference value. In some examples herein (Example 4),
qualitative measurements are assessed by signal intensity on a
filter. In some examples herein, multiple antibodies specific for
AD biomarkers are attached to a suitable surface, e.g. as slide or
filter. As described herein in Examples 11 and 12, qualitative
assessment of results may include normalizing data. In this
disclosure, various sets of biomarkers are described. It is
understood that the invention contemplates use of any of these
sets, any one or more members of the sets, as well as markers
comprising the sets.
[0083] In one aspect, the present invention provides methods of
aiding diagnosis of Alzheimer's disease ("AD") and diagnosing AD,
by obtaining measured levels of one or more AD diagnosis biomarkers
in a biological fluid sample from an individual, such as for
example, a peripheral biological fluid sample from an individual,
and comparing those measured levels to reference levels. In some
examples, a peripheral biological fluid sample is plasma.
[0084] In some examples, the AD diagnosis biomarkers are selected
from the group shown in Table 7. In some examples, the AD diagnosis
biomarkers are selected from the group GCSF; IFN-g; IGFBP-1; BMP-6;
BMP-4; Eotaxin-2; IGFBP-2; TARC; RANTES; ANG; PARC; Acrp30;
AgRP(ART); TIMP-1; TIMP-2; ICAM-1; TRAIL R3; uPAR; IGFBP-4;
LEPTIN(OB); PDGF-BB; EGF; BDNF; NT-3; NAP-2; IL-1ra; MSP-a; SCF;
TGF-b3; TNF-b; MIP-1d; IL-3; FGF-6; IL-6 R; sTNF RII; AXL; bFGF;
FGF4; CNTF; MCP-1; MIP-1b; TPO; VEGF-B; IL-8; FAS; and EGF-R. In
some examples, the AD diagnosis biomarker(s) is/are selected from
the group shown in Table 8. Additionally, Tables 9A1-9A2 and 9B
provide a listing of biomarkers (clustered by methods as described
herein) in order of highest ranked biomarker to lowest ranked
biomarker within each cluster based on score value) that are
significantly increased (9A1-9A2) or decreased (9B) in AD compared
to age-matched normal controls plus other non-AD forms of
neurodegeneration, such as for example PD and PN (that is, as
compared to all controls). Generally, a significant increase in a
biomarker as compared to an appropriate control is indicative of
AD, and a significant decrease in a biomarker as compared to an
appropriate control is indicative of AD. The columns from left to
right in Tables 9A1-9A2 and 9B are Biomarker name, Score(d); Fold
change; q-value(%); and cluster number. Any one or more of the
biomarkers listed in Tables 9A1-9A2 and 9B, that is, reagents
specific for the biomarker, can be used in the methods disclosed
herein, such as for example, for aiding in the diagnosis of or
diagnosing AD. In some examples, any one or more of the biomarkers
listed in Tables 9A1-9A2 and 9B can be used to diagnose AD as
distinguished from other non-AD neurodegenerative diseases or
disorders, such as for example PD and PN.
[0085] Tables 10A1-10A2 and 10B provide a listing of biomarkers
(not clustered and in order of highest ranked biomarker to lowest
ranked biomarker based on score value) that are significantly
increased (10A1-10A2) or decreased (10B) in AD compared to healthy
age-matched controls. The columns from left to right in Tables
10A1-10A2 and 10B, Tables 11A1-11A2 and 11B, and Tables 12A-12B are
Biomarker name, Score(d); Fold change; and q-value(%). Based on
Tables 10A1-10A2 and 10B, identified biomarkers that are
significantly increased in AD as compared to healthy age-matched
controls include (in descending order based on score): BTC; ANG-2;
MIF; IGFBP-6; spg130; CTACK; IGFBP3; MIP-1a; TRAIL R4; IL-12 p40;
AR; NT-4; VEGF-D; OSM; OST; IL-11; sTNF R1; I-TAC; Eotaxin; TECK;
PIGF; bNGF; Lymphotactin; MIP-3b; HCC-4; ICAM-3; DTK; IL-1 RI;
IGF-1 SR; GRO; GITR-Light; HGF; IL-1R4/ST; IL-2 Ra; ENA-78; and
FGF-9. Based on Tables 10A1-10A2 and 10B, identified biomarkers
that are significantly decreased in AD as compared to healthy
age-matched controls include (in descending order based on score):
MCP-2; M-CSF; MCP-3; MDC; MCP-4; IL-1b; IL-4; IL-1a; BLC; CKb8-1;
IL-2; IL-15; MIP3a; MIG; SCF; IL-6; IL-16; Eotaxin-3; 1-309;
TGF-beta; TGF-alpha; GDNF; LIGHT; SDF; IFG-1; Fractalkine; IL-5;
Fit-3 ligand; GM-CSF; and GCP-2. Any one or more of the biomarkers
listed in Tables 10A1-10A2 and 10B, that is, reagents specific for
the biomarker, can be used in the methods disclosed herein, such as
for example, for aiding in the diagnosis of or diagnosing AD. In
some examples, biomarkers are selected for use in methods disclosed
herein, such as for example, for aiding in the diagnosis of or
diagnosing AD that have a p-value of equal to or less than 0.05,
(or a q-value (%) of equal to or less than 5.00). For Table
10A1-10A2 (biomarkers increased or positively correlated)
biomarkers GRO, GITR-Light, IGFBP, HGF, IL-1R4/ST, IL-2Ra, ENA-78,
and FGF-9 have a P-value of greater than 0.05. Accordingly, in some
examples, positively correlated biomarkers for use in the methods
as disclosed herein, such as for example, for aiding in the
diagnosis of or diagnosing AD are selected from the group
consisting of biomarkers listed in Table 10A1-10A2, excluding
biomarkers GRO, GITR-Light, IGFBP, HGF, IL-1R4/ST, IL-2Ra, ENA-78,
and FGF-9. For Table 10B (biomarkers decreased or negatively
correlated) biomarkers BMP-4, Fit-3 ligand, GM-CSF, IGFBP4, GCP-2,
and TARC have a p-value of greater than 0.05. Accordingly, in some
examples, negatively correlated biomarkers for use in the methods
as disclosed herein, such as for example, for aiding in the
diagnosis of or diagnosing AD are selected from the group
consisting of biomarkers listed in Table 10B, excluding biomarkers
BMP-4, Fit-3 ligand, GM-CSF, IGFBP4, GCP-2, and TARC.
[0086] Tables 11A1-11A2 and 11B provide a listing of biomarkers
(not clustered and in order of highest ranked biomarker to lowest
ranked biomarker based on score value) that are significantly
increased (11A1-11A2) or decreased (11B) in AD compared to
age-matched degenerative controls. Based on Tables 11A1-11A2 and
11B, identified biomarkers that are significantly increased in AD
as compared to age-matched other non-AD neurodegenerative controls
include (in descending order based on score): TRAIL R4; Eotaxin;
IL-12 p40; BTC-1; MIF; OST; MIP-1a; sTNF R1; IL-1; Lymphotactin;
NT-4; VEFG-D; HGF; IGFBP3; IGFBP-1; OSM; IL-1R1; PIGF; IGF-1 SR;
CCL-28; IL-2 Ra; IL-12 p70; GRO; IGFBP-6; IL-17; CTACK; I-TAC;
ICAM-3; ANG-2; MIP-3b; FGF-9; HCC-4; IL-1R4/ST; GITR; and DTK.
Based on Tables 11A1-11A2 and 11B, identified biomarkers that are
significantly decreased in AD as compared to age-matched other
non-AD neurodegenerative controls include (in descending order
based on score): MCP-2; M-CSF; MCP-3; MDC; MCP-4; IL-1b; IL-4;
IL-1a; BLC; CKb8-1; IL-2; IL-15; MIP3a; MIG; SCF; IL-6; IL-16;
Eotaxin-3; 1-309; TGF-beta; TNF-alpha; GDNF; LIGHT; SDF-1; IFG-1;
Fractalkine; IL-5; Fit-3 Ligand; GM-CSF; and GCP-2. Any one or more
of the biomarkers listed in Tables 11A1-11A2 and 11B, that is,
reagents specific for the biomarkers, can be used in the methods
disclosed herein, such as for example, for aiding in the diagnosis
of or diagnosing AD. For Table 11A1-11A2 (biomarkers increased or
positively correlated) biomarkers IL-1ra, IL-2ra, PARC, FAS, IL-12
p70, NAP-2, GRO, NT-3, IGFBP-6, TIMP-1, IL-17, IGFBP-2, CTACK,
I-TAC, ICAM-3, ANG-2, FGF-4, MIP-3b, FGF-9, HCC-4, IL-1R4/ST, ANG,
GITR, DTK, IL-6 R, EGF-R have a p-value of greater than 0.05.
Accordingly, in some examples, positively correlated biomarkers for
use in the methods as disclosed herein for aiding in the diagnosis
of or diagnosing AD are selected from the group consisting of
biomarkers listed in Table 11A1-11A2, excluding biomarkers IL-1ra,
IL-2ra, PARC, FAS, IL-12 p70, NAP-2, GRO, NT-3, IGFBP-6, TIMP-1,
IL-17, IGFBP-2, CTACK, I-TAC, ICAM-3, ANG-2, FGF-4, MIP-3b, FGF-9,
HCC-4, IL-1R4/ST, ANG, GITR, DTK, IL-6 R, EGF-R. For Table 11B
(biomarkers decreased or negatively correlated) biomarkers IL-1a,
MCP-2, IGFBP-4, spg130, SDF-1, M-CSF, MIP-1d, IL-10, GM-CSF, TNF-a,
MDC, FGF-6, TNF-b, IFN-gamma, and GDNF have a p-value of less than
0.05. Accordingly, in some examples, negatively correlated
biomarkers for use in the methods as disclosed herein, such as for
example, for aiding in the diagnosis of or diagnosing AD are
selected from the group consisting of biomarkers IL-1a, MCP-2,
IGFBP-4, spg130, SDF-1, M-CSF, MIP-1d, IL-10, GM-CSF, TNF-a, MDC,
FGF-6, TNF-b, IFN-gamma, and GDNF that have a p-value of less than
0.05. It is contemplated that biomarkers having a p-value of
greater than 0.05 may also be used in the methods as described
herein as long as appropriate controls are used. In some examples,
methods comprise the use of at least one biomarker having a p-value
of greater than 0.05 along with at least one biomarker having a
p-value of less than 0.05.
[0087] Tables 12A-12B provide a listing of biomarkers (not
clustered and in order of highest ranked biomarker to lowest ranked
biomarker based on score value) that are significantly increased
(12A) or decreased (12B) in AD plus other non-AD neurodegenerative
controls with reference to age matched controls. Any one or more of
the biomarkers listed in Tables 12A-12B, that is, reagents specific
for the biomarker, can be used in the methods disclosed herein,
such as for example, for aiding in the diagnosis of or diagnosing
neurodegenerative diseases, including AD. In further examples, the
AD diagnosis biomarker is selected from Lymphotactin and IL-11 and
in other examples, comprises Lymphotactin and IL-11. In further
examples, an AD diagnosis markers is selected from the group
consisting of BTC; SDF-1; MCP-2; IFN-gamma; IGFBP4; IGF-1SR; IL-8;
GM-CSF; and ANG-2, as described in the Examples. In further
examples, an AD diagnosis marker is selected from the group
consisting of IFN-gamma and IL-8, as described in the Examples. In
yet other examples, an AD diagnosis biomarker is selected from the
group consisting of biomarkers sTNF RII; MSP-alpha; uPAR; TPO;
MIP-1beta; VEGF-beta; FAS; MCP-1; NAP-2; ICAM-1; TRAIL R3; PARC;
ANG; IL-3; MIP-1 delta; IFN-gamma; IL-8; and FGF-6, as described in
the Examples. In further examples, an AD diagnosis biomarker is
selected from the group consisting of BDNF, PDGF-BB, Leptin and
RANTES. As shown herein in the examples, quantitative Leptin and
BDNF levels have a statistically significant positive correlation
with MMSE scores; quantitative PDGF-BB levels have a statistically
significant negative correlation with MMSE scores in men; and
quantitative RANTES levels have a statistically significant
positive correlation with PDGF-BB and BDNF. In some examples, the
AD diagnosis biomarkers for use in methods of aiding diagnosis of
Alzheimer's disease ("AD") and diagnosing AD include two or more of
the following 4 biomarkers: BDNF, PDGF-BB, Leptin and RANTES. In
further examples, the AD diagnosis biomarkers for use in methods of
aiding diagnosis of Alzheimer's disease ("AD") and diagnosing AD
comprise Leptin and RANTES; Leptin and BDNF; Leptin and PDGF-BB;
Leptin, RANTES and BDNF; Leptin, RANTES and PDGF-BB; Leptin, BDNF
and PDGF-BB; RANTES and BDNF; RANTES and PDGF-BB; RANTES, BDNF, and
PDGF-BB; BDNF and PDGF-BB; or Leptin, RANTES, BDNF and PDGF-BB. In
some examples, the AD diagnosis markers for use in methods of
aiding diagnosis of AD or diagnosing AD comprise Leptin, RANTES,
BDNF and PDGF-BB. In other examples, the AD diagnosis markers for
use in methods of aiding diagnosis of AD or diagnosing AD consist
essentially of or consist of Leptin, RANTES, BDNF and PDGF-BB.
[0088] In some examples, provided herein are methods of aiding
diagnosis of neurological disease, such as neurodegenerative
disease, and diagnosing neurological disease, such as
neurodegenerative disease, by obtaining measured levels of one or
more AD diagnosis biomarkers shown in Tables 12A-12B (biomarkers
that are increased or decreased, respectively) in neurodegenerative
controls compared to healthy age-matched controls) in a biological
fluid sample from an individual, such as for example, a peripheral
biological fluid sample from an individual, and comparing those
measured levels to reference levels. Such methods may be used for
example, as an initial screening for neurological disease. In some
examples, methods for aiding diagnosis of AD and/or diagnosing AD
as described herein may be used before or concurrently with methods
for aiding diagnosis of neurological disease and/or diagnosing
neurological disease or after, for example, as a secondary screen.
Additionally or alternatively, methods of aiding diagnosis of AD or
diagnosing AD and/or distinguishing AD from other non-AD
neurological diseases may comprise obtaining measured levels of one
or more AD diagnosis biomarkers shown in Tables 9A1-9A2 and 9B in a
biological fluid sample from an individual, such as for example, a
peripheral biological fluid sample from an individual, and
comparing those measured levels to reference levels. In some
examples, a peripheral biological fluid sample is plasma.
[0089] Methods of assessing cognitive function, aiding diagnosis of
AD and diagnosing AD as described herein may comprise any of the
following steps of obtaining a biological fluid sample from an
individual, measuring the level of at least one AD diagnosis
biomarker in the sample and comparing the measured level to an
appropriate reference; obtaining measured levels of at least one AD
diagnosis biomarker in a sample and comparing the measured level to
an appropriate reference; comparing measured levels of at least one
AD diagnosis biomarker obtained from a sample to an appropriate
reference; measuring the level of at least one AD diagnosis
biomarker in a sample; measuring the level of at least one AD
diagnosis biomarker in a sample and comparing the measured level to
an appropriate reference; diagnosing AD based on comparison of
measured levels to an appropriate reference; or obtaining a
measured value for at least one AD diagnosis biomarker in a sample.
Comparing a measured level of an AD diagnosis biomarker to a
reference level or obtaining a measured value for an AD diagnosis
biomarker in a sample may be performed for 1, 2, 3, 4, 5, 6, 7, 8,
9, 10, or more AD diagnosis biomarker(s). The present invention
also provides methods of evaluating results of the analytical
methods described herein. Such evaluation generally entails
reviewing such results and can assist, for example, in advising
regarding clinical and/or diagnostic follow-up and/or treatment
options. The present invention also provides methods for assessing
a biological fluid sample for an indicator of any one or more of
the following: cognitive function and/or impairment; MCI; AD;
extent of AD, such as, for example, mild, moderate, severe;
progression of AD; by measuring the level of or obtaining the
measured level of or comparing a measured level of an AD diagnosis
biomarker as described herein. Methods of assessing cognitive
impairment includes the ADAS-COG, which is generally accepted to be
equivalent to MMSE scoring.
[0090] Provided herein are methods for assessing the efficacy of
treatment modalities in individuals, or population(s) of
individuals, such as from a single or multiple collection
center(s), subject to impaired cognitive function and/or diagnosed
with AD comprising anyone of the following steps: obtaining a
biological fluid sample from the individual(s) subject to
treatment; measuring the level of at least one AD diagnosis
biomarker in the sample and comparing the measured level to an
appropriate reference, which in some examples is a measured level
of the biomarker in a fluid sample obtained from the individual(s)
prior to treatment; obtaining measured levels of at least one AD
diagnosis biomarker in a sample from the individual(s) and
comparing the measured level to an appropriate reference; comparing
measured levels of at least one AD diagnosis biomarker obtained
from a sample from the individual(s) to an appropriate reference;
measuring the level of at least one AD diagnosis biomarker in a
sample from the individual(s); measuring the level of at least one
AD diagnosis biomarker in a sample from the individual(s) and
comparing the measured level to an appropriate reference;
diagnosing efficacy of treatment based on comparison of measured
levels to an appropriate reference; or obtaining a measured value
for at least one AD diagnosis biomarker in a sample. Measured
levels of at least one AD diagnosis biomarker may be obtained once
or multiple times during assessment of the treatment modality.
[0091] For methods of diagnosing AD as described herein, the
reference level is generally a predetermined level considered
`normal` for the particular AD diagnosis biomarker (e.g., an
average level for age-matched individuals not diagnosed with AD or
an average level for age-matched individuals diagnosed with
neurological disorders other than AD and/or healthy age-matched
individuals), although reference levels which are determined
contemporaneously (e.g., a reference value that is derived from a
pool of samples including the sample being tested) are also
contemplated. Also provided are methods of aiding in the diagnosis
of Alzheimer's disease ("AD") by comparing a measured level of at
least one AD diagnosis biomarker in a biological fluid sample, such
as, for example, a peripheral biological fluid sample from an
individual with a reference level. Further provided are methods of
aiding in the diagnosis of Alzheimer's disease ("AD") by measuring
a level of at least one AD diagnosis biomarker in a biological
fluid sample, such as, for example, a peripheral biological fluid
sample from an individual. For the AD diagnosis biomarkers
disclosed herein, a measurement for a marker which is below or
above the reference level suggests (i.e., aids in the diagnosis of)
or indicates a diagnosis of AD.
[0092] In another aspect, the invention provides methods of
identifying individuals with mild cognitive impairment (MCI), by
obtaining a quantitative measured level for RANTES in a biological
fluid sample, such as, for example, a peripheral biological fluid
sample from an individual, and comparing that level to a reference
level. Generally, the reference level for RANTES is a predetermined
level considered `normal` for RANTES, and may be an age-matched
normal level for RANTES, although reference levels which are
determined contemporaneously (e.g., a reference value that is
derived from a pool of samples including the sample being tested)
are also contemplated. Also provided are methods of aiding in the
diagnosis of MCI by comparing a quantitative measured level for
RANTES in a biological fluid sample, such as, for example, a
peripheral biological fluid sample from an individual with a
reference level. Further provided are methods for aiding in the
diagnosis of MCI by measuring a level for RANTES in a biological
fluid sample, such as, for example, a peripheral biological fluid
sample from an individual. A finding that the quantitative level of
RANTES is low (below the reference level) in the biological fluid
sample, such as, for example, the peripheral biological fluid
sample from the individual suggests (i.e., aids in the diagnosis
of) or indicates a diagnosis of MCI. In certain embodiments, such
methods further include measuring, obtaining, and/or comparing the
quantitative level of Leptin in the biological fluid sample, such
as, for example, a peripheral biological sample. When both RANTES
and Leptin levels are utilized, a finding that the quantitative
RANTES level is low while the quantitative Leptin level is not
(i.e., is substantially the same as or higher than the Leptin
reference value) suggests (i.e., aids in the diagnosis of) or
indicates a diagnosis of MCI. Accordingly the present invention
provides methods for aiding in the diagnosis of mild cognitive
impairment (MCI), comprising comparing a measured level for RANTES
in a biological fluid sample obtained from an individual to a
reference level. In some examples, the methods further comprise
comparing a measured value for leptin in the biological fluid
sample obtained from the individual to a reference level. In yet
other examples, the methods further comprises measuring a level for
leptin in said biological fluid sample, thereby producing said
measured value for leptin. In yet other examples, the methods
comprise measuring a level for RANTES in said biological fluid
sample, thereby producing said measured value for RANTES. In yet
other examples, the biological fluid sample is a peripheral fluid
sample.
[0093] In a further aspect, the invention provides methods of
monitoring progression of AD in an AD patient. As shown in Example
7, the inventors have found that quantitative levels of RANTES are
decreased in AD patients with Questionable AD (MMSE=25-28); and
that quantitative levels of RANTES are decreased in AD patients
with mild AD (MMSE=20-25), and RANTES levels decrease further as
the severity of the AD intensifies. An individual with
"Questionable AD" as used herein for quantitative data (also called
absolute measurement) is an individual who (a) has been diagnosed
with AD or has been given a diagnosis of probable AD, and (b) has
either been assessed with the Mini-Mental State Examination (MMSE)
(referenced in Folstein et al., J. Psychiatr. Res 1975;
12:1289-198) and scored 25-28 or would achieve a score of 25-28
upon MMSE testing. Accordingly, "Questionable AD" refers to AD in a
individual having scored 25-28 on the MMSE and or would achieve a
score of 25-28 upon MMSE testing. The reference level may be a
predetermined level considered `normal` for the particular RANTES
(e.g., an average level for age-matched individuals not diagnosed
with AD or MCI), or may be a historical reference level for the
particular patient (e.g., a RANTES level that was obtained from a
sample derived from the same individual, but at an earlier point in
time). Reference levels which are determined contemporaneously
(e.g., a reference value that is derived from a pool of samples
including the sample being tested) are also contemplated.
Accordingly, the invention provides methods for monitoring
progression of AD in an AD patient by obtaining a quantitative
value for RANTES from a biological fluid sample, such as for
example, a peripheral biological fluid sample and comparing
measured value to a reference value. Also provided are methods for
monitoring progression of AD in an AD patient by comparing a
measured value for leptin in a biological fluid sample, such as for
example, a peripheral biological fluid sample with a reference
value. Further provided are methods for monitoring progression of
AD in an AD patient by measuring a level for leptin in a biological
fluid sample, such as for example, a peripheral biological fluid
sample. A decrease in the measured value indicates or suggests
(diagnoses or suggests a diagnosis) progression (e.g., an increase
in the severity) of AD in the AD patient.
[0094] In a further aspect, the inventors have found that
quantitative Leptin levels are decreased in AD patients with
Questionable AD; and that the quantitative levels of Leptin are
decreased in AD patients with mild AD, and quantitative Leptin
levels decrease further as the severity of the AD intensifies; and
the quantitative levels of Leptin are positively correlated with
MMSE scores (as described in Example 7). The reference level may be
a predetermined level considered `normal` for the particular Leptin
(e.g., an average level for age-matched individuals not diagnosed
with AD or MCI), or may be a historical reference level for the
particular patient (e.g., a Leptin level that was obtained from a
sample derived from the same individual, but at an earlier point in
time). Quantitative reference levels which are determined
contemporaneously (e.g., a reference value that is derived from a
pool of samples including the sample being tested) are also
contemplated. Accordingly, the invention provides methods for
monitoring progression of AD in an AD patient by obtaining a
quantitative measured value for Leptin from a biological fluid
sample, such as for example, a peripheral biological fluid sample
and comparing measured value to a reference value. Also provided
are methods for monitoring progression of AD in an AD patient by
comparing a measured value for Leptin in a biological fluid sample,
such as for example, a peripheral biological fluid sample with a
reference value. Further provided are methods for monitoring
progression of AD in an AD patient by measuring a level for Leptin
in a biological fluid sample, such as for example, a peripheral
biological fluid sample. A decrease in the quantitative measured
value indicates or suggests (diagnoses or suggests a diagnosis)
progression (e.g., an increase in the severity) of AD in the AD
patient.
[0095] The inventors have found that quantitative BDNF levels are
decreased in AD patients with mild AD, and that the quantitative
BDNF levels in women are correlated with MMSE scores and BDNF
levels decrease further as the severity of the AD intensifies (as
described in Example 7). The reference level may be a predetermined
level considered `normal` for the particular BDNF (e.g., an average
level for age-matched individuals not diagnosed with AD or MCI), or
may be a historical reference level for the particular patient
(e.g., a BDNF level that was obtained from a sample derived from
the same individual, but at an earlier point in time). Reference
levels which are determined contemporaneously (e.g., a reference
value that is derived from a pool of samples including the sample
being tested) are also contemplated. Accordingly, the invention
provides methods for monitoring progression of AD in an AD patient
by obtaining a quantitative measured value for BDNF from a
biological fluid sample, such as for example, a peripheral
biological fluid sample and comparing measured value to a reference
value. Also provided are methods for monitoring progression of AD
in an AD patient by comparing a quantitative measured value for
BDNF in a biological fluid sample, such as for example, a
peripheral biological fluid sample with a reference value. Further
provided are methods for monitoring progression of AD in an AD
patient by measuring a level for BDNF in a biological fluid sample,
such as for example, a peripheral biological fluid sample.
Generally speaking, a decrease in the measured value indicates or
suggests (diagnoses or suggests a diagnosis) progression (e.g., an
increase in the severity) of AD in the AD patient.
[0096] The inventors have found that quantitative PDGF-BB levels
are decreased in AD patients with Questionable AD; that PDGF-BB
levels are decreased in Questionable AB compared to Mild AD; and
that the MMSE scores for male AD patients are negatively correlated
with PDGF-BB levels (as described in Example 7). The reference
level may be a predetermined level considered `normal` for the
PDGF-BB (e.g., an average level for age-matched male individuals
not diagnosed with AD or MCI), or may be a historical reference
level for the particular patient (e.g., a PDGF-BB level that was
obtained from a sample derived from the same male individual, but
at an earlier point in time). Reference levels which are determined
contemporaneously (e.g., a reference value that is derived from a
pool of samples including the sample being tested) are also
contemplated. Accordingly, the invention provides methods for
monitoring progression of AD in an AD patient by obtaining a
measured value for PDGF-BB from a biological fluid sample from a
male, such as for example, a peripheral biological fluid sample and
comparing measured value to a reference value. Also provided are
methods for monitoring progression of AD in an AD patient by
comparing a measured value for PDGF-BB in a biological fluid
sample, such as for example, a peripheral biological fluid sample
with a reference value. Further provided are methods for monitoring
progression of AD in an AD patient by measuring a level for PDGF-BB
in a biological fluid sample, such as for example, a peripheral
biological fluid sample. A decrease in the measured value indicates
or suggests (diagnoses or suggests a diagnosis) progression (e.g.,
an increase in the severity) of AD in the AD patient.
[0097] Additionally, the invention provides methods of stratifying
individuals diagnosed with (or having a probable diagnosis of) AD.
The inventors have found that analysis of the levels of BDNF, or
BDNF and PDGF-BB in biological fluid samples, such as, peripheral
biological fluid samples provides information as to the severity of
the AD in the AD patient from whom the peripheral biological fluid
sample is derived. The reference values for BDNF and PDGF-BB used
in these aspects of the invention are most commonly obtained from a
population of AD patients other than the AD patient who is the
source of the sample being tested (e.g., a mean or median value
derived from a large number of AD patients), although reference
levels for BDNF and PDGF-BB which are determined contemporaneously
(e.g., a reference values that is derived from a pool of samples
including the sample being tested) are also contemplated.
Accordingly, the invention provides methods of stratifying AD
patients into mild, and more advanced (e.g., moderate and severe)
stages of AD ("staging") by obtaining a measured level for BDNF,
and comparing the measured value with a reference value for BDNF.
Accordingly, the invention provides methods of stratifying AD in an
AD patient by obtaining a measured value for BDNF, and, optionally,
PDGF-BB, in a biological fluid sample, such as a peripheral
biological fluid sample, and comparing the measured level to a
reference level. The invention also provides methods of stratifying
AD in an AD patient by comparing a measured value for BDNF, and,
optionally, PDGF-BB, in a biological fluid sample, such as a
peripheral biological fluid sample with a reference value. The
invention further provides methods of stratifying AD in an AD
patient by measuring BDNF and, optionally, PDGF-BB, in a biological
fluid sample, such as a peripheral biological fluid sample. As
described in Example 4, and under the experimental conditions
disclosed in Example 4 which provide qualitative results, samples
which have BDNF levels lower than the reference level suggest or
indicate mild AD, while samples with BDNF levels higher than the
reference level suggest more advanced AD (i.e., moderate or severe
AD). Amongst those samples with BDNF levels higher than the
reference level, those also having PDGF-BB levels below the
reference level suggest or indicate moderate AD, while those
samples also having PDGF-BB levels above the reference level
suggest or indicate severe AD. It has been found that for
Questionable AD (MMSE score in the range of 25-28) the levels of
Leptin and PDGF-BB increase significantly whereas BDNF and RANTES
do not change significantly. It has been found that from Mild AD
(MMSE score in the range of 20-25) to Moderate AD (MMSE score in
the range of 10-20) the level of LEPTIN does not decline whereas
the levels for RANTES, BDNF and PDGF-BB declines. Accordingly, in
some embodiments (as defined by the above MMSE scores from Example
7), Mild AD is indicated in quantitative assays when the levels of
Leptin and/or PDGF-BB increase significantly whereas BDNF and
RANTES do not change significantly as compared to Questionable AD
as a reference. Accordingly, in some embodiments, (as defined by
the above MMSE scores from Example 7), Moderate AD is indicated
when Leptin does not decline whereas the levels for RANTES, BDNF
and PDGF declines as compared to Mild AD as a reference.
Accordingly, provided herein are methods comprising comparing
measured values for RANTES and Leptin levels in a biological fluid
sample from said patient with reference values for RANTES and
Leptin; comparing measured values for brain derived neurotrophic
factor (BDNF), Leptin, and RANTES, levels in a biological fluid
sample from said patient with reference values for BDNF, Leptin,
and RANTES; comparing measured values for Leptin and BB homodimeric
platelet derived growth factor (PDGF-BB) levels in a biological
fluid sample from said patient with reference values for Leptin and
PDGF-BB. Accordingly, the present invention provides methods for
stratifying Alzheimer's disease (AD) in an individual, comprising
comparing measured values for brain derived neurotrophic factor
(BDNF) and BB homodimeric platelet derived growth factor (PDGF-BB)
levels in a biological fluid sample from said patient with
reference values for BDNF and PDGF-BB. In some examples, the
methods further comprise comparing measured values for leptin and
Rantes levels with reference values for leptin and Rantes, wherein
reference values for BDNF, PDGF-BB, leptin and Rantes are for
samples from individuals with MMSE scores from 25 to 28, wherein an
increase in leptin and PDGF-BB levels and wherein levels of BDNF
and RANTES stay substantially the same indicate mild AD as
indicated by an MMSE score of 20-25. The present invention also
provides methods of further comprising comparing measured values
for leptin and Rantes levels with reference values for leptin and
Rantes, wherein reference values for BDNF, PDGF-BB, leptin and
Rantes are for samples from individuals with MMSE scores from
20-25, wherein a decrease in Rantes, BDNF, and PDGF levels and
wherein levels of Leptin stays substantially the same indicate
moderate AD as indicated by an MMSE score of 10-20. Additional
biomarkers useful in methods for stratifying AD as described herein
in an individual include Lymphotactin and IL-11. An AD biomarker
that stays "substantially the same" means that there is not a
significant change, and that the values stay about the same. In
some embodiments, substantially the same is a change less than any
of about 12%, 10%, 5%, 2%, 1%. In some embodiments, a significant
change means not statistically significant using standard methods
in the art. The methods described above are also applicable to
methods for assessing progression of AD. It is understood that the
cognitive function indicated by the markers herein can be by other
measurements with results or indicia that corresponds to
approximately the same level of cognitive function as the MMSE
scores provided herein.
[0098] The present invention also provides methods of aiding
diagnosis of Alzheimer's disease ("AD"), comprising comparing a
measured level of at least one AD diagnosis biomarker in a
biological fluid sample from an individual to a reference level for
the biomarker for each biomarker measured, wherein the at least one
AD diagnosis biomarker is selected from Table 7 and has a
statistically significant positive correlation with MMSE scores
that is comparable to BDNF and/or Leptin correlation with MMSE
scores, and wherein the at least one AD diagnosis biomarker is not
statistically correlated with age. An AD diagnosis biomarker that
has a statistically significant positive correlation with MMSE
scores that is comparable to BDNF and/or leptin correlation with
MMSE scores means that the biomarker is an AD diagnosis marker. In
some examples, the AD diagnosis biomarker is selected from the
group of biomarkers consisting of GCSF; IFN-g; IGFBP-1; BMP-6;
BMP-4; Eotaxin-2; IGFBP-2; TARC; RANTES; ANG; PARC; Acrp30;
AgRP(ART); TIMP-1; TIMP-2; ICAM-1; TRAIL R3; uPAR; IGFBP-4;
LEPTIN(OB); PDGF-BB; EGF; BDNF; NT-3; NAP-2; IL-1ra; MSP-a; SCF;
TGF-b3; TNF-b; MIP-1d; IL-3; FGF-6; IL-6 R; sTNF RII; AXL; bFGF;
FGF4; CNTF; MCP-1; MIP-1b; TPO; VEGF-B; IL-8; FAS; EGF-R and in
other examples is selected from the group of biomarkers consisting
of basic fibroblast growth factor (bFGF); BB homodimeric platelet
derived growth factor (PDGF-BB); brain derived neurotrophic factor
(BDNF); epidermal growth factor (EGF), fibroblast growth factor 6
(FGF-6), interleukin-3 (IL-3), soluble interleukin-6 receptor
(sIL-6R), leptin (also known as ob), macrophage inflammatory
protein-1 delta (MIP-16), macrophage stimulating protein alpha
chain (MSP-.alpha.), neurotrophin-3 (NT-3), neutrophil activating
peptide-2 (NAP-2), RANTES, soluble tumor necrosis factor receptor-2
(sTNF RII), stem cell factor (SCF), thrombopoietin (TPO), tissue
inhibitor of metalloproteases-1 (TIMP-1), tissue inhibitor of
metalloproteases-2 (TIMP-2), transforming growth factor-beta 3
(TGF-.beta.3), and tumor necrosis factor beta (TNF-.beta.).
Additional biomarkers are provided in Table 8. Additionally, Tables
9A1-9A2 and 9B provide a listing of biomarkers (clustered by
methods as described herein) in order of highest ranked biomarker
to lowest ranked biomarker within each cluster based on score
value) that are significantly increased (9A1-9A2) or decreased (9B)
in AD compared to age-matched normal controls plus other non-AD
forms of neurodegeneration, such as for example PD and PN (that is,
as compared to all controls). The columns from left to right in
Tables 9A1-9A2 and 9B are Biomarker name, Score(d); Fold change;
q-value(%); and cluster number. Any one or more of the biomarkers
listed in Tables 9A1-9A2 and 9B, that is, reagents specific for the
biomarker, can be used in the methods disclosed herein, such as for
example, methods for aiding in the diagnosis of or diagnosing AD.
In some examples, any one or more of the biomarkers listed in
Tables 9A1-9A2 and 9B can be used to diagnose AD. In some examples,
any one or more of the biomarkers listed in Tables 9A1-9A2 and 9B
can be used to diagnose AD as distinguished from other non-AD
neurodegenerative diseases or disorders, such as for example PD and
PN.
[0099] Tables 10A1-10A2 and 10B provide a listing of biomarkers
(not clustered and in order of highest ranked biomarker to lowest
ranked biomarker based on score value) that are significantly
increased (10A1-10A2) or decreased (10B) in AD compared to healthy
age-matched controls. The columns from left to right in Tables
10A1-10A2 and 10B, Tables 11A1-11A2 and 11B, and Tables 12A-12B are
Biomarker name, Score(d); Fold change; and q-value(%). Based on
Tables 10A1-10A2 and 10B, identified biomarkers that are
significantly increased in AD as compared to healthy age-matched
controls include (in descending order based on score): BTC; ANG-2;
MIF; IGFBP-6; spg130; CTACK; IGFBP3; MIP-1a; TRAIL R4; IL-12 p40;
AR; NT-4; VEGF-D; OSM; OST; IL-11; sTNF R1; I-TAC; Eotaxin; TECK;
PIGF; bNGF; Lymphotactin; MIP-3b; HCC-4; ICAM-3; DTK; IL-1 RI;
IGF-1 SR; GRO; GITR-Light; HGF; IL-1R4/ST; IL-2 Ra; ENA-78; and
FGF-9. Based on Tables 10A1-10A2 and 10B, identified biomarkers
that are significantly decreased in AD as compared to healthy
age-matched controls include (in descending order based on score):
MCP-2; M-CSF; MCP-3; MDC; MCP-4; IL-1b; IL-4; IL-1a; BLC; CKb8-1;
IL-2; IL-15; MIP3a; MIG; SCF; IL-6; IL-16; Eotaxin-3; 1-309;
TGF-beta; TGF-alpha; GDNF; LIGHT; SDF; IFG-1; Fractalkine; IL-5;
Fit-3 ligand; GM-CSF; and GCP-2. Any one or more of the biomarkers
listed in Tables 10A1-10A2 and 10B, that is, reagents specific for
the biomarker, can be used in the methods disclosed herein, such as
for example, for aiding in the diagnosis of or diagnosing AD. In
some examples, biomarkers are selected for use in methods disclosed
herein, such as for example, for aiding in the diagnosis of or
diagnosing AD that have a p-value of equal to or less than 0.05,
(or a q-value (%) of equal to or less than 5.00). For Table
10A1-10A2 (biomarkers increased or positively correlated)
biomarkers GRO, GITR-Light, IGFBP, HGF, IL-1R4/ST, IL-2Ra, ENA-78,
and FGF-9 have a P-value of greater than 0.05. Accordingly, in some
examples, positively correlated biomarkers for use in the methods
as disclosed herein for aiding in the diagnosis of or diagnosing AD
are selected from the group consisting of biomarkers listed in
Table 10A1-10A2, excluding biomarkers GRO, GITR-Light, IGFBP, HGF,
IL-1R4/ST, IL-2Ra, ENA-78, and FGF-9. For Table 10B (biomarkers
decreased or negatively correlated) biomarkers BMP-4, Fit-3 ligand,
GM-CSF, IGFBP-4, GCP-2, and TARC have a p-value of greater than
0.05. Accordingly, in some examples, negatively correlated
biomarkers for use in the methods as disclosed herein for aiding in
the diagnosis of or diagnosing AD are selected from the group
consisting of biomarkers listed in Table 10B, excluding biomarkers
BMP-4, Fit-3 ligand, GM-CSF, IGFBP-4, GCP-2, and TARC.
[0100] Tables 11A1-11A2 and 11B provide a listing of biomarkers
(not clustered and in order of highest ranked biomarker to lowest
ranked biomarker based on score value) that are significantly
increased (11A1-11A2) or decreased (11B) in AD compared to
age-matched degenerative controls. Based on Tables 11A1-11A2 and
11B, identified biomarkers that are significantly increased in AD
as compared to age-matched other non-AD neurodegenerative controls
include (in descending order based on score): TRAIL R4; Eotaxin;
IL-12 p40; BTC-1; MIF; OST; MIP-1a; sTNF R1; IL-11; Lymphotactin;
NT-4; VEFG-D; HGF; IGFBP3; IGFBP-1; OSM; IL-1R1; PIGF; IGF-1 SR;
CCL-28; IL-2 Ra; IL-12 p70; GRO; IGFBP-6; IL-17; CTACK; I-TAC;
ICAM-3; ANG-2; MIP-3b; FGF-9; HCC-4; IL-1R4/ST; GITR; and DTK.
Based on Tables 11A1-11A2 and 11B, identified biomarkers that are
significantly decreased in AD as compared to age-matched other
non-AD neurodegenerative controls include (in descending order
based on score): MCP-2; M-CSF; MCP-3; MDC; MCP-4; IL-1b; IL-4;
IL-1a; BLC; CKb8-1; IL-2; IL-15; MIP3a; MIG; SCF; IL-6; IL-16;
Eotaxin-3; I-309; TGF-beta; TNF-alpha; GDNF; LIGHT; SDF-1; IFG-1;
Fractalkine; IL-5; Fit-3 Ligand; GM-CSF; and GCP-2. Any one or more
of the biomarkers listed in Tables 11A1-11A2 and 11B, that is,
reagents specific for the biomarkers, can be used in the methods
disclosed herein, such as for example, for aiding in the diagnosis
of or diagnosing AD. For Table 11A1-11A2 (biomarkers increased or
positively correlated) biomarkers IL-1ra, IL-2ra, PARC, FAS, IL-12
p70, NAP-2, GRO, NT-3, IGFBP-6, TIMP-1, IL-17, IGFBP-2, CTACK,
I-TAC, ICAM-3, ANG-2, FGF4, MIP-3b, FGF-9, HCC-4, IL-1R4/ST, ANG,
GITR, DTK, IL-6 R, EGF-R have a p-value of greater than 0.05.
Accordingly, in some examples, positively correlated biomarkers for
use in the methods as disclosed herein, such as for example, for
aiding in the diagnosis of or diagnosing AD are selected from the
group consisting of biomarkers listed in Table 11A1-11A2, excluding
biomarkers IL-1ra, IL-2ra, PARC, FAS, IL-12 p70, NAP-2, GRO, NT-3,
IGFBP-6, TIMP-1, IL-17, IGFBP-2, CTACK, I-TAC, ICAM-3, ANG-2,
FGF-4, MIP-3b, FGF-9, HCC-4, IL-1R4/ST, ANG, GITR, DTK, IL-6 R,
EGF-R. For Table 11B (biomarkers decreased or negatively
correlated) biomarkers IL-1a, MCP-2, IGFBP-4, spg130, SDF-1, M-CSF,
MIP-1d, IL-10, GM-CSF, TNF-a, MDC, FGF-6, TNF-b, IFN-gamma, and
GDNF have a p-value of less than 0.05. Accordingly, in some
examples, negatively correlated biomarkers for use in the methods
as disclosed herein for aiding in the diagnosis of or diagnosing AD
are selected from the group consisting of biomarkers IL-1a, MCP-2,
IGFBP-4, spg130, SDF-1, M-CSF, MIP-1d, IL-10, GM-CSF, TNF-a, MDC,
FGF-6, TNF-b, IFN-gamma, and GDNF that have a p-value of less than
0.05.
[0101] Tables 12A-12B provide a listing of biomarkers (not
clustered and in order of highest ranked biomarker to lowest ranked
biomarker based on score value) that are significantly increased
(12A) or decreased (12B) in AD plus other non-AD degenerative
controls with reference to age matched controls. Any one or more of
the biomarkers listed in Tables 12A-12B, that is, reagents specific
for the biomarker, can be used in the methods disclosed herein,
such as for example, for aiding in the diagnosis of or diagnosing
neurological diseases, including AD. In further examples, the AD
diagnosis biomarker is selected from Lymphotactin and IL-11 and in
other examples, comprise Lymphotactin and IL-11. In further
examples, an AD diagnosis markers is selected from the group
consisting of BTC; SDF-1; MCP-2; IFN-gamma; IGFBP4; IGF-1SR; IL-8;
GM-CSF; and ANG-2, as described in the Examples. In further
examples, an AD diagnosis marker is selected from the group
consisting of IFN-gamma and IL-8, as described in the Examples. In
yet other examples, an AD diagnosis biomarker is selected from the
group consisting of biomarkers sTNF RII; MSP-alpha; uPAR; TPO;
MIP-1beta; VEGF-beta; FAS; MCP-1; NAP-2; ICAM-1; TRAIL R3; PARC;
ANG; IL-3; MIP-1 delta; IFN-gamma; IL-8; and FGF-6, as described in
the Examples.
[0102] The results of the comparison between the measured value(s)
and the reference value(s) are used to diagnose or aid in the
diagnosis of AD or MCI, to stratify AD patients according to the
severity of their disease, or to monitor progression of AD in an AD
patient. Accordingly, if the comparison indicates a difference
(that is, an increase or decrease) between the measured value(s)
and the reference value(s) that is suggestive/indicative of AD or
MCI, then the appropriate diagnosis is aided in or made.
Conversely, if the comparison of the measured level(s) to the
reference level(s) does not indicate differences that suggest or
indicate a diagnosis of AD or MCI, then the appropriate diagnosis
is not aided in or made. Likewise, when comparison of a measured
level for Leptin in a sample derived from an AD patient is
decreased in comparison to the reference value, diagnosis of
progression of the patient's AD is made or aided in. Similarly,
when the comparison of levels of BDNF and PDGF-BB levels in a
sample obtained from an AD patient indicates or suggests a
particular stage of AD, the diagnosis of the particular stage of AD
(mild, moderate or severe) is aided in or made.
[0103] As will be understood by those of skill in the art, when, in
the practice of the AD diagnosis methods of the invention (i.e.,
methods of diagnosing or aiding in the diagnosis of AD), more than
one AD diagnosis biomarker is used but the markers do not
unanimously suggest or indicate a diagnosis of AD, the `majority`
suggestion or indication (e.g., when the method utilizes five AD
diagnosis biomarkers, 3 of which suggest/indicate AD, the result
would be considered as suggesting or indicating a diagnosis of AD
for the individual) is considered the result of the assay. However,
in some embodiments in which measured values for at least two AD
diagnosis biomarkers are obtained and one of the measured values is
for Leptin, the measured value for Leptin must be less than the
reference value to indicate or suggest a diagnosis of AD. As will
be appreciated by one of skill in the art, methods disclosed herein
may include the use of any of a variety of biological markers
(which may or may not be AD markers) to determine the integrity
and/or characteristics of the biological sample(s). For example,
Leptin levels, which are generally higher in females, may be
measured as a marker of gender.
[0104] In certain embodiments of the invention, levels for AD
biomarkers are obtained from an individual at more than one time
point. Such "serial" sampling is well suited for the aspects of the
invention related to monitoring progression of AD in an AD patient.
Serial sampling can be performed on any desired timeline, such as
monthly, quarterly (i.e., every three months), semi-annually,
annually, biennially, or less frequently. The comparison between
the measured levels and the reference level may be carried out each
time a new sample is measured, or the data relating to levels may
be held for less frequent analysis.
[0105] As will be understood by those of skill in the art,
biological fluid samples including peripheral biological fluid
samples are usually collected from individuals who are suspected of
having AD, or developing AD or MCI. The invention also contemplates
samples from individuals for whom cognitive assessment is desired.
Alternatively, individuals (or others involved in for example
research and/or clinicians may desire such assessments without any
indication of AD, suspected AD, at risk for AD. For example, a
normal individual may desire such information. Such individuals are
most commonly 65 years or older, although individuals from whom
biological fluid samples, such as peripheral biological fluid
samples are taken for use in the methods of the invention may be as
young as 35 to 40 years old, when early onset AD or familial AD is
suspected.
[0106] The invention also provides methods of screening for
candidate agents for the treatment of AD and/or MCI by assaying
prospective candidate agents for activity in modulating AD
biomarkers. The screening assay may be performed either in vitro
and/or in vivo. Candidate agents identified in the screening
methods described herein may be useful as therapeutic agents for
the treatment of AD and/or MCI.
[0107] The probability P that the composite is more predictive than
any subset of markers present in the composite can be expressed
mathematically as: P=1-(1-P.sub.1)(1-P.sub.2)(1-P.sub.3) . . .
(1-P.sub.n)
[0108] Where the probability P.sub.1, P.sub.2, P.sub.n represent
the probability of individual marker being able to predict clinical
phenotypes, and where 1-P.sub.n represents the complement of that
probability. Any subset of the composite, will always therefore
have a smaller value for P.
[0109] In accordance with a further embodiment of the present
invention, the relative concentrations in serum, CSF, or other
fluids of the biomarkers cited in Table 7, and other Tables
described herein, as a composite, or collective, or any subset of
such a composite, composed of 5 (five) or more elements is more
predictive than the absolute concentration of any individual marker
in predicting clinical phenotypes, disease detection,
stratification, monitoring, and treatment of AD, PD, frontotemporal
dementia, cerebrovascular disease, multiple sclerosis, and
neuropathies.
[0110] AD Diagnosis Biomarkers
[0111] Immune mechanisms are an essential part of the host defense
system and typically feature prominently in the inflammatory
response. A growing number of studies are discovering intriguing
links between the immune system and the CNS. For example, it has
become clear that the CNS is not entirely sheltered from immune
surveillance and that various immune cells can traverse the
blood-brain barrier. Invading leukocytes can attack target antigens
in the CNS or produce growth factors that might protect neurons
against degeneration (Hohlfeld et al., 2000, J. Neuroimmunol. 107,
161-166). These responses are elicited through a variety of protein
mediators, including but not limited to cytokines, chemokines,
neurotrophic factors, collectins, kinins, and acute phase proteins
in the immune and inflammatory systems, in intercellular
communication across neurons, glial cells, endothelial cells and
leukocytes. Without being bound by theory, it is hypothesized that
the cytokines, chemokines, neurotrophic factors, collectins,
kinins, and acute phase proteins listed in Table 7 are
differentially expressed in serum associated with neurodegenerative
and inflammatory diseases such as Alzheimer's, Parkinson's disease,
Multiple Sclerosis, and neuropathies. Cytokines are a heterogeneous
group of polypeptide mediators that have been associated with
activation of numerous functions, including the immune system and
inflammatory responses. Peripheral cytokines also penetrate the
blood-brain barrier directly via active transport mechanisms or
indirectly via vagal nerve stimulation. Cytokines can act in an
autocrine manner, affecting the behavior of the cell that releases
the cytokine, or in a paracrine manner, affecting the behavior of
adjacent cells. Some cytokines can act in an endocrine manner,
affecting the behavior of distant cells, although this depends on
their ability to enter the circulation and on their half-life. The
cytokine families include, but are not limited to, interleukins
(IL-1 alpha, IL-1 beta, ILIra and IL-2 to IL-18), tumor necrosis
factors (TNF-alpha and TNF-beta), interferons (INF-alpha, beta and
gamma), colony stimulating factors (G-CSF, M-CSF, GM-CSF, IL-3 and
some of the other ILs), and growth factors (EGF, FGF, PDGF, TGF
alpha, TGF betas, BMPs, GDFs, CTGF, and ECGF).
[0112] The inventors have discovered a collection of biochemical
markers present in peripheral bodily fluids that may be used to
assess cognitive function, including diagnose or aid in the
diagnosis of AD. These "AD diagnosis markers" include, but are not
limited to GCSF; IFN-g; IGFBP-1; BMP-6; BMP-4; Eotaxin-2; IGFBP-2;
TARC; RANTES; ANG; PARC; Acrp30; AgRP(ART); TIMP-1; TIMP-2; ICAM-1;
TRAIL R3; uPAR; IGFBP-4; LEPTIN(OB); PDGF-BB; EGF; BDNF; NT-3;
NAP-2; IL-1ra; MSP-a; SCF; TGF-b3; TNF-b MIP-1d; IL-3; FGF-6; IL-6
R; sTNF RII; AXL; bFGF; FGF4; CNTF; MCP-1; MIP-1b; TPO; VEGF-B;
IL-8; FAS; EGF-R. In other examples, these "AD diagnosis
biomarkers" are: basic fibroblast growth factor (bFGF), BB
homodimeric platelet derived growth factor (PDGF-BB), brain derived
neurotrophic factor (BDNF), epidermal growth factor (EGF),
fibroblast growth factor 6 (FGF-6), interleukin-3 (IL-3), soluble
interleukin-6 receptor (sIL-6R), Leptin (also known as ob),
macrophage inflammatory protein-1 delta (MIP-16), macrophage
stimulating protein alpha chain (MSP-.alpha.), neurotrophin-3
(NT-3), neutrophil activating peptide-2 (NAP-2), RANTES, soluble
tumor necrosis factor receptor-2 (sTNF RII), stem cell factor
(SCF), thrombopoietin (TPO), tissue inhibitor of metalloproteases-1
(TIMP-1), tissue inhibitor of metalloproteases-2 (TIMP-2),
transforming growth factor-beta 3 (TGF-.beta.3), tumor necrosis
factor beta (TNF-.beta.). In other examples, the AD diagnosis
markers include one or more of Leptin, RANTES, PDFG-BB and
BDNF.
[0113] Additionally, Tables 9A 1-9A2 and 9B provide a listing of
biomarkers (clustered by methods as described herein) in order of
highest ranked biomarker to lowest ranked biomarker within each
cluster based on score value) that are significantly increased
(9A1-9A2) or decreased (9B) in AD compared to age-matched normal
controls plus other non-AD forms of neurodegeneration, such as for
example PD and PN (that is, as compared to all controls). The
columns from left to right in Tables 9A1-9A2 and 9B are Biomarker
name, Score(d); Fold change; q-value(%); and cluster number. Any
one or more of the biomarkers listed in Tables 9A1-9A2 and 9B, that
is, reagents specific for the biomarker, can be used in the methods
disclosed herein, such as for example, for aiding in the diagnosis
of or diagnosing AD. In some examples, any one or more of the
biomarkers listed in Tables 9A1-9A2 and 9B can be used to diagnose
AD as distinguished from other non-AD neurodegenerative diseases or
disorders, such as for example PD and PN.
[0114] Tables 10A1-10A2 and 10B provide a listing of biomarkers
(not clustered and in order of highest ranked biomarker to lowest
ranked biomarker based on score value) that are significantly
increased (10A1-10A2) or decreased (10B) in AD compared to healthy
age-matched controls. The columns from left to right in Tables
10A1-10A2 and 10B, Tables 11A1-11A2 and 11B, and Tables 12A-12B are
Biomarker name, Score(d); Fold change; and q-value(%). Based on
Tables 10A1-10A2 and 10B, identified biomarkers that are
significantly increased in AD as compared to healthy age-matched
controls include (in descending order based on score): BTC; ANG-2;
MIF; IGFBP-6; spg130; CTACK; IGFBP3; MIP-1a; TRAIL R4; IL-12 p40;
AR; NT-4; VEGF-D; OSM; OST; IL-11; sTNF R1; I-TAC; Eotaxin; TECK;
PIGF; bNGF; Lymphotactin; MIP-3b; HCC-4; ICAM-3; DTK; IL-1 RI;
IGF-1 SR; GRO; GITR-Light; HGF; IL-1R4/ST; IL-2 Ra; ENA-78; and
FGF-9. Based on Tables 10A1-10A2 and 10B, identified biomarkers
that are significantly decreased in AD as compared to healthy
age-matched controls include (in descending order based on score):
MCP-2; M-CSF; MCP-3; MDC; MCP-4; IL-1b; IL-4; IL-1a; BLC; CKb8-1;
IL-2; IL-15; MIP3a; MIG; SCF; IL-6; IL-16; Eotaxin-3; 1-309;
TGF-beta; TGF-alpha; GDNF; LIGHT; SDF; IFG-1; Fractalkine; IL-5;
Fit-3 ligand; GM-CSF; and GCP-2. Any one or more of the biomarkers
listed in Tables 10A1-10A2 and 10B, that is, reagents specific for
the biomarker, can be used in the methods disclosed herein, such as
for example, for aiding in the diagnosis of or diagnosing AD. In
some examples, biomarkers are selected for use in methods disclosed
herein for aiding in the diagnosis of or diagnosing AD that have a
p-value of equal to or less than 0.05, (or a q-value (%) of equal
to or less than 5.00). For Table 10A1-10A2 (biomarkers increased or
positively correlated) biomarkers GRO, GITR-Light, IGFBP, HGF,
IL-1R4/ST, IL-2Ra, ENA-78, and FGF-9 have a P-value of greater than
0.05. Accordingly, in some examples, positively correlated
biomarkers for use in the methods as disclosed herein for aiding in
the diagnosis of or diagnosing AD are selected from the group
consisting of biomarkers listed in Table 10A1-10A2, excluding
biomarkers GRO, GITR-Light, IGFBP, HGF, IL-1R4/ST, IL-2Ra, ENA-78,
and FGF-9. For Table 10B (biomarkers decreased or negatively
correlated) biomarkers BMP-4, Fit-3 ligand, GM-CSF, IGFBP-4, GCP-2,
and TARC have a p-value of greater than 0.05. Accordingly, in some
examples, negatively correlated biomarkers for use in the methods
as disclosed herein for aiding in the diagnosis of or diagnosing AD
are selected from the group consisting of biomarkers listed in
Table 10B, excluding biomarkers BMP-4, Fit-3 ligand, GM-CSF,
IGFBP-4, GCP-2, and TARC.
[0115] Tables 11A1-11A2 and 11B provide a listing of biomarkers
(not clustered and in order of highest ranked biomarker to lowest
ranked biomarker based on score value) that are significantly
increased (11A1-11A2) or decreased (11B) in AD compared to
age-matched degenerative controls. Based on Tables 11A1-11A2 and
11B, identified biomarkers that are significantly increased in AD
as compared to age-matched other non-AD neurodegenerative controls
include (in descending order based on score): TRAIL R4; Eotaxin;
IL-12 p40; BTC-1; MIF; OST; MIP-1a; sTNF R1; IL-11; Lymphotactin;
NT-4; VEFG-D; HGF; IGFBP3; IGFBP-1; OSM; IL-1RI; PIGF; IGF-1 SR;
CCL-28; IL-2 Ra; IL-12 p70; GRO; IGFBP-6; IL-17; CTACK; I-TAC;
ICAM-3; ANG-2; MIP-3b; FGF-9; HCC-4; IL-1R4/ST; GITR; and DTK.
Based on Tables 11A1-11A2 and 11B, identified biomarkers that are
significantly decreased in AD as compared to age-matched other
non-AD neurodegenerative controls include (in descending order
based on score): MCP-2; M-CSF; MCP-3; MDC; MCP-4; IL-1b; IL-4;
IL-1a; BLC; CKb8-1; IL-2; IL-15; MIP3a; MIG; SCF; IL-6; IL-16;
Eotaxin-3; 1-309; TGF-beta; TNF-alpha; GDNF; LIGHT; SDF-1; IFG-1;
Fractalkine; IL-S; Fit-3 Ligand; GM-CSF; and GCP-2. Any one or more
of the biomarkers listed in Tables 11A1-11A2 and 11B, that is,
reagents specific for the biomarkers, can be used in the methods
disclosed herein, such as for example, for aiding in the diagnosis
of or diagnosing AD. For Table 11A1-11A2 (biomarkers increased or
positively correlated) biomarkers IL-1ra, IL-2ra, PARC, FAS, IL-12
p70, NAP-2, GRO, NT-3, IGFBP-6, TIMP-1, IL-17, IGFBP-2, CTACK,
I-TAC, ICAM-3, ANG-2, FGF4, MIP-3b, FGF-9, HCC-4, IL-1R4/ST, ANG,
GITR, DTK, IL-6 R, EGF-R have a p-value of greater than 0.05.
Accordingly, in some examples, positively correlated biomarkers for
use in the methods as disclosed herein for aiding in the diagnosis
of or diagnosing AD are selected from the group consisting of
biomarkers listed in Table 11A1-11A2, excluding biomarkers IL-1ra,
IL-2ra, PARC, FAS, IL-12 p70, NAP-2, GRO, NT-3, IGFBP-6, TIMP-1,
IL-17, IGFBP-2, CTACK, I-TAC, ICAM-3, ANG-2, FGF4, MIP-3b, FGF-9,
HCC-4, IL-1R4/ST, ANG, GITR, DTK, IL-6 R, EGF-R. For Table 11B
(biomarkers decreased or negatively correlated) biomarkers IL-1a,
MCP-2, IGFBP-4, spg130, SDF-1, M-CSF, MIP-1d, IL-10, GM-CSF, TNF-a,
MDC, FGF-6, TNF-b, IFN-gamma, and GDNF have a p-value of less than
0.05. Accordingly, in some examples, negatively correlated
biomarkers for use in the methods as disclosed herein for aiding in
the diagnosis of or diagnosing AD are selected from the group
consisting of biomarkers IL-1a, MCP-2, IGFBP-4, spg130,
SDF-1.mu.M-CSF, MIP-1d, IL-10, GM-CSF, TNF-a, MDC, FGF-6, TNF-b,
IFN-gamma, and GDNF that have a p-value of less than p equal to or
less than 0.05.
[0116] Tables 12A-12B provide a listing of biomarkers (not
clustered and in order of highest ranked biomarker to lowest ranked
biomarker based on score value) that are significantly increased
(12A) or decreased (12B) in AD plus other non-AD degenerative
controls with reference to age matched controls. Any one or more of
the biomarkers listed in Tables 12A-12B, that is, reagents specific
for the biomarker, can be used in the methods disclosed herein,
such as for example, for aiding in the diagnosis of or diagnosing
neurological diseases, including AD. In further examples, the AD
diagnosis biomarker is selected from Lymphotactin and IL-11 and in
other examples, comprise Lymphotactin and IL-11. In further
examples, an AD diagnosis markers is selected from the group
consisting of BTC; SDF-1; MCP-2; IFN-gamma; IGFBP4; IGF-1 SR; IL-8;
GM-CSF; and ANG-2, as described in the Examples. In further
examples, an AD diagnosis marker is selected from the group
consisting of IFN-gamma and IL-8, as described in the Examples. In
yet other examples, an AD diagnosis biomarker is selected from the
group consisting of biomarkers sTNF RII; MSP-alpha; uPAR; TPO;
MIP-1beta; VEGF-beta; FAS; MCP-1; NAP-2; ICAM-1; TRAIL R3; PARC;
ANG; IL-3; MIP-1delta; IFN-gamma; IL-8; and FGF-6, as described in
the Examples.
[0117] The AD diagnosis biomarkers discovered by the inventors are
all known molecules. Brain derived neurotrophic factor (BDNF) is
described in, for example Rosenthal et al., 1991, Endocrinology
129(3):1289-94. Basic fibroblast growth factor (bFGF) is described
in, for example Abraham et al., 1986, EMBO J. 5(10):2523-28.
Epidermal growth factor (EGF) is described in, for example Gray et
al., 1983, Nature 303(5919):722-25. Fibroblast growth factor 6
(FGF-6) is described in, for example Marics et al., 1989, Oncogene
4(3):335-40. Interleukin-3 (IL-3) is described in, for example Yang
et al., 1986, Cell 47(1):3-10. Soluble interleukin-6 receptor
(sIL-6R) is described in, for example, Taga et al., 1989, Cell
58(3):573-81. Leptin (also known as "ob") is described in, for
example Masuzaki et al. 1995, Diabetes 44(7):855-58. Macrophage
inflammatory protein-1 delta (MIP-16) is described in, for example
Wang et al., 1998, J. Clin. Immunol. 18(3):214-22. Macrophage
stimulating protein alpha chain (MSP-.alpha.) is described in, for
example, Yoshimura et al., 1993, J. Biol. Chem. 268 (21), 15461-68,
and Yoshikawa et al., 1999, Arch. Biochem. Biophys. 363(2):356-60.
Neutrophil activating peptide-2 (NAP-2) is described in, for
example Walz et al., 1991, Adv. Exp. Med. Biol. 305:39-46.
Neurotrophin-3 (NT-3) is described in, for example Hohn et al.,
1990, Nature 344(6264):339-41. BB homodimeric platelet derived
growth factor (PDGF-BB) is described in, for example Collins et
al., 1985, Nature 316(6030):748-50. RANTES is described in, for
example Schall et al., 1988, J. Immunol. 141(3):1018-25. Stem cell
factor (SCF) is described in, for example Zseboet al., 1990, Cell
63(1):213-24. Soluble tumor necrosis factor receptor-2 (sTNF RII)
is described in, for example Schall et al., 1990, Cell
61(2):361-70. Transforming growth factor-beta 3 (TGF-.beta.3) is
described in, for example ten Dijke et al., 1988, Proc. Natl. Acad.
Sci. U.S.A. 85 (13):4715-19. Tissue inhibitor of metalloproteases-1
(TIMP-1) is described in, for example, Docherty et al., 1985,
Nature 318(6041):66-69 and Gasson et al., 1985, Nature
315(6022):768-71. Tissue inhibitor of metalloproteases-2 (TIMP-2)
is described in, for example, Stetler-Stevenson et al., 1190, J.
Biol. Chem. 265(23): 13933-38. Tumor necrosis factor beta
(TNF-.beta.) is described in, for example Gray et al., 1984, Nature
312(5996):721-24. Thrombopoietin (TPO) is described in, for
example, Foster et al., 1994, Proc. Natl. Acad. Sci. U.S.A.
91(26):13023-27.
[0118] Although the inventors have found acceptable levels of
sensitivity and specificity with single AD diagnosis biomarkers for
practice of the AD diagnosis methods, the effectiveness (e.g.,
sensitivity and/or specificity) of the methods of the AD diagnosis
methods of the instant invention are generally enhanced when at
least two AD diagnosis biomarkers are utilized. In some examples,
the methods of the AD diagnosis methods of the instant invention
are generally enhanced when at least four AD diagnosis biomarkers
are utilized. Multiple AD diagnosis biomarkers may be selected from
the AD diagnosis biomarkers disclosed herein by a variety of
methods, including "q value" and/or by selecting for cluster
diversity. AD diagnosis biomarkers may be selected on the basis of
"q value", a statistical value that the inventors derived when
identifying the AD diagnosis biomarkers (see Table 3 in Example 1).
"q values" for selection of AD diagnosis biomarkers range from less
than about 0.0001 to about 0.05 and in some examples, range from
about 0.01 to about 0.05. Alternately (or additionally), AD
diagnosis biomarkers may be selected to preserve cluster diversity
of selected proteins or sample diversity. The inventors have
separated the AD diagnosis biomarkers into a number of clusters
(see Table 1). Additional clusters of AD diagnosis markers are
found in Tables 9A1-9A2 and 9B. Here the clusters are formed by
qualitative measurements for each biomarker which are most closely
correlated. As used herein, "correlate" or "correlation" is a
simultaneous change in value of two numerically valued random
variables such as MMSE scores and quantitative protein
concentrations or qualitative protein concentrations. As used
herein "discriminate" or "discriminatory" is refers to the
quantitative or qualitative difference between two or more samples
for a given variable. The cluster next to such a cluster is a
cluster that is most closely correlated with the cluster. The
correlations between biomarkers and between clusters can
represented by a hierarchical tree generated by unsupervised
clustering using a public web based software called wCLUTO
available at: cluto.ccgb.umn.edu/cgi-bin/wCluto/wCluto.cgi. If more
than one AD diagnosis biomarker is selected for testing, in some
examples, the AD diagnosis biomarkers selected are at least
partially diverse (i.e., the AD diagnosis biomarkers represent at
least two different clusters, for example, a set of AD diagnosis
biomarkers comprising Leptin, BDNF and/or PDGF-BB from cluster 4 in
Table 1 and RANTES from cluster 3 of Table 1), and in some
instances the AD diagnosis biomarkers are completely diverse (i.e.
no two of the selected AD diagnosis biomarkers are from the same
cluster). Accordingly, the invention provides a number of different
embodiments for diagnosing or aiding in the diagnosis of AD.
TABLE-US-00001 TABLE 1 Cluster Biomarker 0 bFGF 1 TPO 2 FGF-6 IL-3
sIL-6 R MIP-1d sTNF RII TNF-b 3 RANTES TIMP-1 TIMP-2 4 BDNF EGF
LEPTIN(OB) MSP-.alpha. NAP-2 NT-3 PDGF-BB SCF TGF-b3
[0119] In some embodiments, the level of a single AD diagnosis
biomarker in a peripheral biological fluid sample is obtained and
the measured level is compared to a reference level to diagnose or
aid in diagnosing AD. In certain embodiments where measured level
for a single AD diagnosis biomarker is obtained for the practice of
the invention, the measured level is for RANTES in the peripheral
biological fluid sample.
[0120] In other embodiments, the levels of at least two AD
diagnosis biomarkers in a peripheral biological fluid sample are
obtained and compared to reference levels for each of the markers.
Accordingly, the invention provides methods for diagnosing and/or
aiding in the diagnosis of AD by measuring the levels of at least
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, or 20 AD diagnosis
biomarkers and comparing the measured levels with reference levels.
Exemplary embodiments utilize 2, 3, 4, or 5 AD diagnosis
biomarkers. In some embodiments, provided herein are methods for
diagnosing and/or aiding in the diagnosis of AD by measuring the
levels of at least Leptin, RANTES, BDGF, and PDGF-BB. In other
examples, provided herein are methods, such as for example, for
diagnosing and/or aiding in the diagnosis of AD by measuring the
levels of at least one biomarker selected from the group consisting
of Lymphotactin and IL-11. In other examples, biomarkers comprise
Lymphotactin and IL-11. In further examples, an AD diagnosis
markers is selected from the group consisting of BTC; SDF-1; MCP-2;
IFN-gamma; IGFBP-4; IGF-1SR; IL-8; GM-CSF; and ANG-2, as described
in the Examples. In further examples, an AD diagnosis marker is
selected from the group consisting of IFN-gamma and IL-8, as
described in the Examples. In yet other examples, an AD diagnosis
biomarker is selected from the group consisting of biomarkers sTNF
RII; MSP-alpha; uPAR; TPO; MIP-1beta; VEGF-beta; FAS; MCP-1; NAP-2;
ICAM-1; TRAIL R3; PARC; ANG; IL-3; MIP-1delta; IFN-gamma; IL-8; and
FGF-6, as described in the Examples. For those embodiments which
utilize more than one AD diagnosis biomarker (i.e., those
embodiments in which measured values are obtained for more than one
AD diagnosis biomarker), exemplary combinations of AD diagnosis
biomarkers shown in Table 3 include (1) Leptin in combination with
any of the other AD diagnosis biomarkers (i.e., Leptin and BDNF,
Leptin and bFGF, Leptin and EGF, Leptin and FGF-6, Leptin and IL-3,
Leptin and sIL-6R, Leptin and MIP-1.delta., Leptin and MSP-.alpha.,
Leptin and NAP-2, Leptin and NT-3, Leptin and PDGF-BB, Leptin and
RANTES, Leptin and SCF, Leptin and sTNR RII, Leptin and
TGF-.beta.3, Leptin and TIMP-1, Leptin and TIMP-2, Leptin and
TNF-.beta., and Leptin and TPO), (2) RANTES in combination with any
of the other AD diagnosis biomarkers (i.e., RANTES and BDNF, RANTES
and bFGF, RANTES and EGF, RANTES and FGF-6, RANTES and IL-3, RANTES
and sIL-6R, RANTES and Leptin, RANTES and MIP-1, RANTES and
MSP-.alpha., RANTES and NAP-2, RANTES and NT-3, RANTES and PDGF-BB,
RANTES and SCF, RANTES and sTNR RII, RANTES and TGF-.beta.3, RANTES
and TIMP-1, RANTES and TIMP-2, RANTES and TNF-.beta., and RANTES
and TPO); (3) PDGF-BB and any of the other AD diagnosis biomarkers
(i.e., PDGF-BB and BDNF, PDGF-BB and bFGF, PDGF-BB and EGF, PDGF-BB
and FGF-6, PDGF-BB and IL-3, PDGF-BB and sIL-6R, PDGF-BB and
Leptin, PDGF-BB and MIP-1.delta., PDGF-BB and MSP-.alpha., PDGF-BB
and NAP-2, PDGF-BB and NT-3, PDGF-BB and RANTES, PDGF-BB and SCF,
PDGF-BB and sTNR RII, PDGF-BB and TGF-.beta.3, PDGF-BB and TIMP-1,
PDGF-BB and TIMP-2, PDGF-BB and TNF-.beta., and PDGF-BB and TPO);
(4) BDNF in combination with any of the other AD diagnosis
biomarkers (i.e., BDNF and bFGF, BDNF and EGF, BDNF and FGF-6, BDNF
and IL-3, BDNF and sIL-6R, BDNF and Leptin, BDNF and MIP-16, BDNF
and MSP-.alpha., BDNF and NAP-2, BDNF and NT-3, BDNF and PDGF-BB,
BDNF and RANTES, BDNF and SCF, BDNF and sTNR RII, BDNF and
TGF-.beta.3, BDNF and TIMP-1, BDNF and TIMP-2, BDNF and TNF-.beta.,
and BDNF and TPO); (5) RANTES, PDGF-BB, and NT-3; (6) Leptin,
PDGF-BB, and RANTES; (7) BDNF, PDGF-BB, and RANTES; (8) BDNF,
Leptin, and RANTES; (9) BDNF, Leptin, and PDGF-BB; (10) PDGF-BB,
EGF, and NT-3; (11) PDGF-BB, NT 3, and Leptin; (12) BDNF, Leptin,
PDGF-BB, RANTES; and (13) RANTES, PDGF-BB, NT-3, EGF, NAP-2, and
Leptin. Additional exemplary combinations of AD diagnosis
biomarkers include (14) Leptin in combination with any of the other
AD diagnosis biomarkers disclosed herein (i.e., Leptin and GCSF,
Leptin and IFN-.gamma., Leptin and IGFBP-1, Leptin and BMP-6,
Leptin and BMP-4, Leptin and Eotaxin-2, Leptin and IGFBP-2, Leptin
and TARC, Leptin and ANG, Leptin and PARC, Leptin and Acrp30,
Leptin and AgRP(ART), Leptin and ICAM-1, Leptin and TRAIL R3,
Leptin and uPAR, Leptin and IGFBP-4, Leptin and IL-1 Ra, Leptin and
AXL, Leptin and FGF-4, Leptin and CNTF, Leptin and MCP-1, Leptin
and MIP1b, Leptin and VEGF-B, Leptin and IL-8, Leptin and FAS and
Leptin and EGF-R), (15) RANTES in combination with any of the other
AD diagnosis biomarkers disclosed herein (i.e., RANTES and GCSF,
RANTES and IFN-.gamma., RANTES and IGFBP-1, RANTES and BMP-6,
RANTES and BMP-4, RANTES and Eotaxin-2, RANTES and IGFBP-2, RANTES
and TARC, RANTES and ANG, RANTES and PARC, RANTES and Acrp30,
RANTES and AgRP(ART), RANTES and ICAM-1, RANTES and TRAIL R3,
RANTES and uPAR, RANTES and IGFBP-4, RANTES and IL-1 Ra, RANTES and
AXL, RANTES and FGF-4, RANTES and CNTF, RANTES and MCP-1, RANTES
and MIP1b, RANTES and VEGF-B, RANTES and IL-8, RANTES and FAS and
RANTES and EGF-R), (16) PDGF-BB in combination with any of the
other AD diagnosis biomarkers disclosed herein (i.e., PDGF-BB and
GCSF, PDGF-BB and IFN-.gamma., PDGF-BB and IGFBP-1, PDGF-BB and
BMP-6, PDGF-BB and BMP-4, PDGF-BB and Eotaxin-2, PDGF-BB and
IGFBP-2, PDGF-BB and TARC, PDGF-BB and ANG, PDGF-BB and PARC,
PDGF-BB and Acrp30, PDGF-BB and AgRP(ART), PDGF-BB and ICAM-1,
PDGF-BB and TRAIL R3, PDGF-BB and uPAR, PDGF-BB and IGFBP4, PDGF-BB
and IL-1 Ra, PDGF-BB and AXL, PDGF-BB and FGF-4, PDGF-BB and CNTF,
PDGF-BB and MCP-1, PDGF-BB and MIP1b, PDGF-BB and VEGF-B, PDGF-BB
and IL-8, PDGF-BB and FAS and PDGF-BB and EGF-R), (17) BDNF in
combination with any of the other AD diagnosis biomarkers disclosed
herein (i.e., BDNF and GCSF, BDNF and IFN-.gamma., BDNF and
IGFBP-1, BDNF and BMP-6, BDNF and BMP-4, BDNF and Eotaxin-2, BDNF
and IGFBP-2, BDNF and TARC, BDNF and ANG, BDNF and PARC, BDNF and
Acrp30, BDNF and AgRP(ART), BDNF and ICAM-1, BDNF and TRAIL R3,
BDNF and uPAR, BDNF and IGFBP-4, BDNF and IL-1Ra, BDNF and AXL,
BDNF and FGF4, BDNF and CNTF, BDNF and MCP-1, BDNF and MIP1b, BDNF
and VEGF-B, BDNF and IL-8, BDNF and FAS and BDNF and EGF-R).
[0121] Measuring Levels of AD Biomarkers
[0122] There are a number of statistical tests for identifying
biomarkers which vary significantly between the subsets, including
the conventional t test. However, as the number of biomarkers
measured increases, it is generally advantageous to use a more
sophisticated technique, such as SAM (see Tusher et al., 2001,
Proc. Natl. Acad. Sci. U.S.A. 98(9):5116-21). Other useful
techniques include Tree Harvesting (Hastie et al., Genome Biology
2001, 2:research0003.1-0003.12), Self Organizing Maps (Kohonen,
1982b, Biological Cybernetics 43(1):59-69), Frequent Item Set
(Agrawal et al., 1993 "Mining association rules between sets of
items in large databases." In Proc. of the ACM SIGMOD Conference on
Management of Data, pages 207--216, Washington, D.C., May 1993),
Bayesian networks (Gottardo, Statistical analysis of microarray
data, A Bayesian approach. Biostatistics (2001), 1, 1, pp 1-37),
and the commercially available software packages CART and MARS.
[0123] The SAM technique assigns a score to each biomarker on the
basis of change in expression relative to the standard deviation of
repeated measurements. For biomarkers with scores greater than an
adjustable threshold, the algorithm uses permutations of the
repeated measurements to estimate the probability that a particular
biomarker has been identified by chance (calculated as a
"q-value"), or a false positive rate which is used to measure
accuracy. The SAM technique can be carried out using publicly
available software called Significance Analysis of Microarrays (see
www-stat class.stanford.edu/.about.tibs/clickwrap/sam.html).
[0124] A biomarkers is considered "identified" as being useful for
aiding in the diagnosis, diagnosis, stratification, monitoring,
and/or prediction of neurological disease when it is significantly
different between the subsets of peripheral biological samples
tested. Levels of a biomarker are "significantly different" when
the probability that the particular biomarker has been identified
by chance is less than a predetermined value. The method of
calculating such probability will depend on the exact method
utilizes to compare the levels between the subsets (e.g., if SAM is
used, the q-value will give the probability of misidentification,
and the p value will give the probability if the t test (or similar
statistical analysis) is used). As will be understood by those in
the art, the predetermined value will vary depending on the number
of biomarkers measured per sample and the number of samples
utilized. Accordingly, predetermined value may range from as high
as 50% to as low as 20, 10, 5, 3, 2, or 1%.
[0125] As described herein, the level of at least one AD diagnosis
biomarker is measured in a biological sample from an individual.
The AD biomarker level(s) may be measured using any available
measurement technology that is capable of specifically determining
the level of the AD biomarker in a biological sample. The
measurement may be either quantitative or qualitative, so long as
the measurement is capable of indicating whether the level of the
AD biomarker in the peripheral biological fluid sample is above or
below the reference value.
[0126] The measured level may be a primary measurement of the level
a particular biomarker a measurement of the quantity of biomarker
itself (quantitative data, such as in Example 7), such as by
detecting the number of biomarker molecules in the sample) or it
may be a secondary measurement of the biomarker (a measurement from
which the quantity of the biomarker can be but not necessarily
deduced (qualitative data, such as Example 4), such as a measure of
enzymatic activity (when the biomarker is an enzyme) or a measure
of mRNA coding for the biomarker). Qualitative data may also be
derived or obtained from primary measurements.
[0127] Although some assay formats will allow testing of peripheral
biological fluid samples without prior processing of the sample, it
is expected that most peripheral biological fluid samples will be
processed prior to testing. Processing generally takes the form of
elimination of cells (nucleated and non-nucleated), such as
erythrocytes, leukocytes, and platelets in blood samples, and may
also include the elimination of certain proteins, such as certain
clotting cascade proteins from blood. In some examples, the
peripheral biological fluid sample is collected in a container
comprising EDTA. See Example 12 for additional sample collection
procedures. Commonly, AD biomarker levels will be measured using an
affinity-based measurement technology. "Affinity" as relates to an
antibody is a term well understood in the art and means the extent,
or strength, of binding of antibody to the binding partner, such as
an AD diagnosis biomarker as described herein (or epitope thereof).
Affinity may be measured and/or expressed in a number of ways known
in the art, including, but not limited to, equilibrium dissociation
constant (K.sub.D or K.sub.d), apparent equilibrium dissociation
constant (K.sub.D' or K.sub.d'), and IC.sub.50 (amount needed to
effect 50% inhibition in a competition assay; used interchangeably
herein with "I.sub.50"). It is understood that, for purposes of
this invention, an affinity is an average affinity for a given
population of antibodies which bind to an epitope. Values of
K.sub.D' reported herein in terms of mg IgG per ml or mg/ml
indicate mg Ig per ml of serum, although plasma can be used.
[0128] Affinity-based measurement technology utilizes a molecule
that specifically binds to the AD biomarker being measured (an
"affinity reagent," such as an antibody or aptamer), although other
technologies, such as spectroscopy-based technologies (e.g.,
matrix-assisted laser desorption ionization-time of flight, or
MALDI-TOF, spectroscopy) or assays measuring bioactivity (e.g.,
assays measuring mitogenicity of growth factors) may be used.
[0129] Affinity-based technologies include antibody-based assays
(immunoassays) and assays utilizing aptamers (nucleic acid
molecules which specifically bind to other molecules), such as
ELONA. Additionally, assays utilizing both antibodies and aptamers
are also contemplated (e.g., a sandwich format assay utilizing an
antibody for capture and an aptamer for detection).
[0130] If immunoassay technology is employed, any immunoassay
technology which can quantitatively or qualitatively measure the
level of a AD biomarker in a biological sample may be used.
Suitable immunoassay technology includes radioimmunoassay,
immunofluorescent assay, enzyme immunoassay, chemiluminescent
assay, ELISA, immuno-PCR, and western blot assay.
[0131] Likewise, aptamer-based assays which can quantitatively or
qualitatively measure the level of a AD biomarker in a biological
sample may be used in the methods of the invention. Generally,
aptamers may be substituted for antibodies in nearly all formats of
immunoassay, although aptamers allow additional assay formats (such
as amplification of bound aptamers using nucleic acid amplification
technology such as PCR (U.S. Pat. No. 4,683,202) or isothermal
amplification with composite primers (U.S. Pat. Nos. 6,251,639 and
6,692,918).
[0132] A wide variety of affinity-based assays are known in the
art. Affinity-based assays will utilize at least one epitope
derived from the AD biomarker of interest, and many affinity-based
assay formats utilize more than one epitope (e.g., two or more
epitopes are involved in "sandwich" format assays; at least one
epitope is used to capture the marker, and at least one different
epitope is used to detect the marker).
[0133] Affinity-based assays may be in competition or direct
reaction formats, utilize sandwich-type formats, and may further be
heterogeneous (e.g., utilize solid supports) or homogenous (e.g.,
take place in a single phase) and/or utilize or
immunoprecipitation. Most assays involve the use of labeled
affinity reagent (e.g., antibody, polypeptide, or aptamer); the
labels may be, for example, enzymatic, fluorescent,
chemiluminescent, radioactive, or dye molecules. Assays which
amplify the signals from the probe are also known; examples of
which are assays which utilize biotin and avidin, and
enzyme-labeled and mediated immunoassays, such as ELISA and ELONA
assays. Herein, the examples referred to as "quantitative data" the
biomarker concentrations were obtained using ELISA. Either of the
biomarker or reagent specific for the biomarker can be attached to
a surface and levels can be measured directly or indirectly.
[0134] In a heterogeneous format, the assay utilizes two phases
(typically aqueous liquid and solid). Typically an AD
biomarker-specific affinity reagent is bound to a solid support to
facilitate separation of the AD biomarker from the bulk of the
biological sample. After reaction for a time sufficient to allow
for formation of affinity reagent/AD biomarker complexes, the solid
support or surface containing the antibody is typically washed
prior to detection of bound polypeptides. The affinity reagent in
the assay for measurement of AD biomarkers may be provided on a
support (e.g., solid or semi-solid); alternatively, the
polypeptides in the sample can be immobilized on a support or
surface. Examples of supports that can be used are nitrocellulose
(e.g., in membrane or microtiter well form), polyvinyl chloride
(e.g., in sheets or microtiter wells), polystyrene latex (e.g., in
beads or microtiter plates), polyvinylidine fluoride, diazotized
paper, nylon membranes, activated beads, glass and Protein A beads.
Both standard and competitive formats for these assays are known in
the art. Accordingly, provided herein are complexes comprising at
least one AD diagnosis biomarker bound to a reagent specific for
the biomarker, wherein said reagent is attached to a surface. Also
provided herein are complexs comprising at least one AD diagnosis
biomarker bound to a reagent specific for the biomarker, wherein
said biomarker is attached to a surface.
[0135] Array-type heterogeneous assays are suitable for measuring
levels of AD biomarkers when the methods of the invention are
practiced utilizing multiple AD biomarkers. Array-type assays used
in the practice of the methods of the invention will commonly
utilize a solid substrate with two or more capture reagents
specific for different AD biomarkers bound to the substrate a
predetermined pattern (e.g., a grid). The peripheral biological
fluid sample is applied to the substrate and AD biomarkers in the
sample are bound by the capture reagents. After removal of the
sample (and appropriate washing), the bound AD biomarkers are
detected using a mixture of appropriate detection reagents that
specifically bind the various AD biomarkers. Binding of the
detection reagent is commonly accomplished using a visual system,
such as a fluorescent dye-based system. Because the capture
reagents are arranged on the substrate in a predetermined pattern,
array-type assays provide the advantage of detection of multiple AD
biomarkers without the need for a multiplexed detection system.
[0136] In a homogeneous format the assay takes place in single
phase (e.g., aqueous liquid phase). Typically, the biological
sample is incubated with an affinity reagent specific for the AD
biomarker in solution. For example, it may be under conditions that
will precipitate any affinity reagent/antibody complexes which are
formed. Both standard and competitive formats for these assays are
known in the art.
[0137] In a standard (direct reaction) format, the level of AD
biomarker/affinity reagent complex is directly monitored. This may
be accomplished by, for example, determining the amount of a
labeled detection reagent that forms is bound to AD
biomarker/affinity reagent complexes. In a competitive format, the
amount of AD biomarker in the sample is deduced by monitoring the
competitive effect on the binding of a known amount of labeled AD
biomarker (or other competing ligand) in the complex. Amounts of
binding or complex formation can be determined either qualitatively
or quantitatively.
[0138] The methods described in this patent may be implemented
using any device capable of implementing the methods. Examples of
devices that may be used include but are not limited to electronic
computational devices, including computers of all types. When the
methods described in this patent are implemented in a computer, the
computer program that may be used to configure the computer to
carry out the steps of the methods may be contained in any computer
readable medium capable of containing the computer program.
Examples of computer readable medium that may be used include but
are not limited to diskettes, CD-ROMs, DVDs, ROM, RAM, and other
memory and computer storage devices. The computer program that may
be used to configure the computer to carry out the steps of the
methods may also be provided over an electronic network, for
example, over the internet, world wide web, an intranet, or other
network.
[0139] In one example, the methods described in this patent may be
implemented in a system comprising a processor and a computer
readable medium that includes program code means for causing the
system to carry out the steps of the methods described in this
patent. The processor may be any processor capable of carrying out
the operations needed for implementation of the methods. The
program code means may be any code that when implemented in the
system can cause the system to carry out the steps of the methods
described in this patent. Examples of program code means include
but are not limited to instructions to carry out the methods
described in this patent written in a high level computer language
such as C++, Java, or Fortran; instructions to carry out the
methods described in this patent written in a low level computer
language such as assembly language; or instructions to carry out
the methods described in this patent in a computer executable form
such as compiled and linked machine language.
[0140] Complexes formed comprising AD biomarker and an affinity
reagent are detected by any of a number of known techniques known
in the art, depending on the format of the assay and the preference
of the user. For example, unlabelled affinity reagents may be
detected with DNA amplification technology (e.g., for aptamers and
DNA-labeled antibodies) or labeled "secondary" antibodies which
bind the affinity reagent. Alternately, the affinity reagent may be
labeled, and the amount of complex may be determined directly (as
for dye- (fluorescent or visible), bead-, or enzyme-labeled
affinity reagent) or indirectly (as for affinity reagents "tagged"
with biotin, expression tags, and the like). Herein the examples
provided referred to as "qualitative data" filter based antibody
arrays using chemiluminesense were used to obtain measurements for
biomarkers.
[0141] As will be understood by those of skill in the art, the mode
of detection of the signal will depend on the exact detection
system utilized in the assay. For example, if a radiolabeled
detection reagent is utilized, the signal will be measured using a
technology capable of quantitating the signal from the biological
sample or of comparing the signal from the biological sample with
the signal from a reference sample, such as scintillation counting,
autoradiography (typically combined with scanning densitometry),
and the like. If a chemiluminescent detection system is used, then
the signal will typically be detected using a luminometer. Methods
for detecting signal from detection systems are well known in the
art and need not be further described here.
[0142] When more than one AD biomarker is measured, the biological
sample may be divided into a number of aliquots, with separate
aliquots used to measure different AD biomarkers (although division
of the biological sample into multiple aliquots to allow multiple
determinations of the levels of the AD biomarker in a particular
sample are also contemplated). Alternately the biological sample
(or an aliquot therefrom) may be tested to determine the levels of
multiple AD biomarkers in a single reaction using an assay capable
of measuring the individual levels of different AD biomarkers in a
single assay, such as an array-type assay or assay utilizing
multiplexed detection technology (e.g., an assay utilizing
detection reagents labeled with different fluorescent dye
markers).
[0143] It is common in the art to perform `replicate` measurements
when measuring biomarkers. Replicate measurements are ordinarily
obtained by splitting a sample into multiple aliquots, and
separately measuring the biomarker(s) in separate reactions of the
same assay system. Replicate measurements are not necessary to the
methods of the invention, but many embodiments of the invention
will utilize replicate testing, particularly duplicate and
triplicate testing.
[0144] Reference Levels
[0145] The reference level used for comparison with the measured
level for a AD biomarker may vary, depending on aspect of the
invention being practiced, as will be understood from the foregoing
discussion. For AD diagnosis methods, the "reference level" is
typically a predetermined reference level, such as an average of
levels obtained from a population that is not afflicted with AD or
MCI, but in some instances, the reference level can be a mean or
median level from a group of individuals including AD patients. In
some instances, the predetermined reference level is derived from
(e.g., is the mean or median of) levels obtained from an
age-matched population. In some examples disclosed herein, the
age-matched population comprises individuals with non-AD
neurodegenerative disorders. See Examples 11 and 12.
[0146] For MCI diagnosis methods (i.e., methods of diagnosing or
aiding in the diagnosis of MCI), the reference level is typically a
predetermined reference level, such as an average of levels
obtained from a population that is not afflicted with AD or MCI,
but in some instances, the reference level can be a mean or median
level from a group of individuals including MCI and/or AD patients.
In some instances, the predetermined reference level is derived
from (e.g., is the mean or median of) levels obtained from an
age-matched population.
[0147] For AD monitoring methods (e.g., methods of diagnosing or
aiding in the diagnosis of AD progression in an AD patient), the
reference level may be a predetermined level, such as an average of
levels obtained from a population that is not afflicted with AD or
MCI, a population that has been diagnosed with MCI or AD, and, in
some instances, the reference level can be a mean or median level
from a group of individuals including MCI and/or AD patients.
Alternately, the reference level may be a historical reference
level for the particular patient (e.g., a Leptin level that was
obtained from a sample derived from the same individual, but at an
earlier point in time). In some instances, the predetermined
reference level is derived from (e.g., is the mean or median of)
levels obtained from an age-matched population.
[0148] For AD stratification methods (i.e., methods of stratifying
AD patients into mild, moderate and severe stages of AD), the
reference level is normally a predetermined reference level that is
the mean or median of levels from a population which has been
diagnosed with AD or MCI (preferably a population diagnosed with
AD) In some instances, the predetermined reference level is derived
from (e.g., is the mean or median of) levels obtained from an
age-matched population.
[0149] Age-matched populations (from which reference values may be
obtained) are ideally the same age as the individual being tested,
but approximately age-matched populations are also acceptable.
Approximately age-matched populations may be within 1, 2, 3, 4, or
5 years of the age of the individual tested, or may be groups of
different ages which encompass the age of the individual being
tested. Approximately age-matched populations may be in 2, 3, 4, 5,
6, 7, 8, 9, or 10 year increments (e.g. a "5 year increment" group
which serves as the source for reference values for a 62 year old
individual might include 58-62 year old individuals, 59-63 year old
individuals, 60-64 year old individuals, 61-65 year old
individuals, or 62-66 year old individuals).
[0150] Comparing Levels of AD Biomarkers
[0151] The process of comparing a measured value and a reference
value can be carried out in any convenient manner appropriate to
the type of measured value and reference value for the AD biomarker
at issue. As discussed above, `measuring` can be performed using
quantitative or qualitative measurement techniques, and the mode of
comparing a measured value and a reference value can vary depending
on the measurement technology employed. For example, when a
qualitative colorimetric assay is used to measure AD biomarker
levels, the levels may be compared by visually comparing the
intensity of the colored reaction product, or by comparing data
from densitometric or spectrometric measurements of the colored
reaction product (e.g., comparing numerical data or graphical data,
such as bar charts, derived from the measuring device). However, it
is expected that the measured values used in the methods of the
invention will most commonly be quantitative values (e.g.,
quantitative measurements of concentration, such as nanograms of AD
biomarker per milliliter of sample, or absolute amount). In other
examples, measured values are qualitative. As with qualitative
measurements, the comparison can be made by inspecting the
numerical data, by inspecting representations of the data (e.g.,
inspecting graphical representations such as bar or line
graphs).
[0152] A measured value is generally considered to be substantially
equal to or greater than a reference value if it is at least 95% of
the value of the reference value (e.g., a measured value of 1.71
would be considered substantially equal to a reference value of
1.80). A measured value is considered less than a reference value
if the measured value is less than 95% of the reference value
(e.g., a measured value of 1.7 would be considered less than a
reference value of 1.80). A measured value is considered more than
a reference value if the measured value is at least more than 5%
greater than the reference value (e.g., a measured value of 1.89
would be considered more than a reference value of 1.80).
[0153] The process of comparing may be manual (such as visual
inspection by the practitioner of the method) or it may be
automated. For example, an assay device (such as a luminometer for
measuring chemiluminescent signals) may include circuitry and
software enabling it to compare a measured value with a reference
value for an AD biomarker. Alternately, a separate device (e.g., a
digital computer) may be used to compare the measured value(s) and
the reference value(s). Automated devices for comparison may
include stored reference values for the AD biomarker(s) being
measured, or they may compare the measured value(s) with reference
values that are derived from contemporaneously measured reference
samples.
[0154] In some embodiments, the methods of the invention utilize
`simple` or `binary` comparison between the measured level(s) and
the reference level(s) (e.g., the comparison between a measured
level and a reference level determines whether the measured level
is higher or lower than the reference level). For AD diagnosis
biomarkers, a comparison showing that the measured value for the
biomarker is lower than the reference value indicates or suggests a
diagnosis of AD. For methods relating to the diagnosis of MCI, a
comparison showing that measured value for RANTES is lower than the
reference value indicates or suggests a diagnosis of AD. In those
embodiments relating to diagnosis of MCI which additionally utilize
a measured value for Leptin, a comparison showing that RANTES is
less than the reference value while Leptin is substantially equal
to or greater than the reference level suggests or indicates a
diagnosis of MCI.
[0155] As described herein, biological fluid samples may be
measured quantitatively (absolute values) or qualitatively
(relative values). The respective AD biomarker levels for a given
assessment may or may not overlap. As described herein, for some
embodiments, qualitative data indicate a given level of cognitive
impairment (mild, moderate or severe AD) (which can be measured by
MMSE scores) and in other embodiments, quantitative data indicate a
given level of cognitive impairment. As shown in Example 4 and
under the conditions provided in Example 4 (qualitative data), in
those embodiments relating to stratification of AD, a comparison
which shows BDNF levels lower than the reference level suggests or
indicates mild AD, while a comparison which shows BDNF levels
higher than the reference level suggests more advanced AD (i.e.,
moderate or severe AD), and amongst those samples with BDNF levels
higher than the reference level, those also having PDGF-BB levels
below the reference level suggest or indicate moderate AD, while
those samples also having PDGF-BB levels above the reference level
suggest or indicate severe AD. In those embodiments relating to
stratification of AD shown in Example 7 (quantitative data), a
comparison which shows BDNF levels lower than the reference level
where the reference level is Normal suggests or indicates mild AD,
while a comparison which shows BDNF levels lower than the reference
level where the reference level is Mild AD suggests more advanced
AD (i.e., moderate, severe AD), while those samples with leptin
levels equal to the reference level where the reference level is
Mild AD, those having RANTES levels below the reference level
suggest or indicate moderate AD, while those samples with leptin
levels equal to the reference level where the reference level is
Moderate AD those having PDGF-BB, RANTES, or BDNF levels lower than
the reference level suggest or indicate severe AD.
[0156] However, in certain aspects of the invention, the comparison
is performed to determine the magnitude of the difference between
the measured and reference values (e.g., comparing the `fold` or
percentage difference between the measured value and the reference
value). A fold difference that is about equal to or greater than
the minimum fold difference disclosed herein suggests or indicates
a diagnosis of AD, MCI, progression from MCI to AD, or progression
from mild AD to moderate AD, as appropriate to the particular
method being practiced. A fold difference can be determined by
measuring the absolute concentration of a protein and comparing
that to the absolute value of a reference, or a fold difference can
be measured by the relative difference between a reference value
and a sample value, where neither value is a measure of absolute
concentration, and/or where both values are measured
simultaneously. A fold difference and be in the range of 10% to
95%. An ELISA measures the absolute content or concentration of a
protein from which a fold change is determined in comparison to the
absolute concentration of the same protein in the reference. An
antibody array measures the relative concentration from which a
fold change is determined. Accordingly, the magnitude of the
difference between the measured value and the reference value that
suggests or indicates a particular diagnosis will depend on the
particular AD biomarker being measured to produce the measured
value and the reference value used (which in turn depends on the
method being practiced). Tables 2A-2B list minimum fold difference
values for AD biomarkers for use in methods of the invention which
utilize a fold difference in making the comparison between the
measured value and the reference value. In those embodiments
utilizing fold difference values, a fold difference of about the
fold difference indicated in Table 2A suggests a diagnosis of AD,
wherein the fold change is a negative value. For example, as
described herein, BDNF levels (as measured by ELISA) are decreased
in AD patients with mild AD, and BDNF levels decrease further as
the severity of the AD intensifies. As shown in Table 6, a BDNF
fold change of -46% means a reduction of BDNF levels by 46%. As
shown in Table 2A, for qualitative measurements using antibodies, a
BDNF fold change of 0.60 means a reduction in BDNF levels by about
60%. Table 2B provides additional information regarding fold
changes. TABLE-US-00002 TABLE 2A Fold Change (as negative value or
Biomarker decrease) BDNF 0.60 bFGF 0.75 EGF 0.60 FGF-6 0.70 IL-3
0.80 sIL-6 R 0.75 Leptin 0.55 MIP-1.delta. 0.60 MSP-.alpha. 0.80
NAP-2 0.75 NT-3 0.75 PDGF-BB 0.60 RANTES 0.75 SCF 0.80 sTNF RII
0.75 TGF-.beta.3 0.80 TIMP-1 0.75 TIMP-2 0.80 TNF-.beta. 0.70 TPO
0.75
[0157] TABLE-US-00003 TABLE 2B Relative Fold Absolute Fold Protein
Change (n = 51) q-value Change (n = 187) p-value MIP-1d -0.54291
0.0165 PDGF-BB -0.53687 0.0165 -0.135 0.891 LEPTIN(OB) -0.47625
0.0165 -0.357 0.0018 IL-6 R -0.6763 0.0165 BDNF -0.53628 0.0165
-0.355 0.0006 TIMP-1 -0.71622 0.0165 RANTES -0.68299 0.0165 -0.184
0.0144 EGF -0.56182 0.0165 TIMP-2 -0.75011 0.0165 NAP-2 -0.67257
0.0165 sTNF RII -0.70029 0.0165 TNF-b -0.64998 0.0165 TPO -0.71405
0.0165 FGF-6 -0.66467 0.0165 NT-3 -0.69805 0.0165 bFGF -0.67351
0.0165 IL-3 -0.75802 0.0165 SCF -0.73041 0.0165 TGF-b3 -0.76912
0.0165 MSP-a -0.76466 0.0165
[0158] As will be apparent to those of skill in the art, when
replicate measurements are taken for the biomarker(s) tested, the
measured value that is compared with the reference value is a value
that takes into account the replicate measurements. The replicate
measurements may be taken into account by using either the mean or
median of the measured values as the "measured value."
[0159] Screening Prospective Agents for AD Biomarker Modulation
Activity
[0160] The invention also provides methods of screening for
candidate agents for the treatment of AD and/or MCI by assaying
prospective candidate agents for activity in modulating AD
biomarkers. The screening assay may be performed either in vitro
and/or in vivo. Candidate agents identified in the screening
methods described herein may be useful as therapeutic agents for
the treatment of AD and/or MCI.
[0161] The screening methods of the invention utilize the AD
biomarkers described herein and AD biomarker polynucleotides as
"drug targets." Prospective agents are tested for activity in
modulating a drug target in an assay system. As will be understood
by those of skill in the art, the mode of testing for modulation
activity will depend on the AD biomarker and the form of the drug
target used (e.g., protein or gene). A wide variety of suitable
assays are known in the art.
[0162] When the AD biomarker protein itself is the drug target,
prospective agents are tested for activity in modulating levels or
activity of the protein itself. Modulation of levels of an AD
biomarker can be accomplished by, for example, increasing or
reducing half-life of the biomarker protein. Modulation of activity
of an AD biomarker can be accomplished by increasing or reducing
the availability of the AD biomarker to bind to its cognate
receptor(s) or ligand(s).
[0163] When an AD biomarker polynucleotide is the drug target, the
prospective agent is tested for activity in modulating synthesis of
the AD biomarker. The exact mode of testing for modulatory activity
of a prospective agent will depend, of course, on the form of the
AD biomarker polynucleotide selected for testing. For example, if
the drug target is an AD biomarker polynucleotide, modulatory
activity is typically tested by measuring either mRNA transcribed
from the gene (transcriptional modulation) or by measuring protein
produced as a consequence of such transcription (translational
modulation). As will be understood by those in the art, many assay
formats will utilize a modified form of the AD biomarker gene where
a heterologous sequence (e.g., encoding an expression marker such
as an enzyme or an expression tag such as oligo-histidine or a
sequence derived from another protein, such as myc) is fused to (or
even replaces) the sequence encoding the AD biomarker protein. Such
heterologous sequence(s) allow for convenient detection of levels
of protein transcribed from the drug target.
[0164] Prospective agents for use in the screening methods of the
invention may be chemical compounds and/or complexes of any sort,
including both organic and inorganic molecules (and complexes
thereof). As will be understood in the art, organic molecules are
most commonly screened for AD biomarker modulatory activity. In
some situations, the prospective agents for testing will exclude
the target AD biomarker protein.
[0165] Screening assays may be in any format known in the art,
including cell-free in vitro assays, cell culture assays, organ
culture assays, and in vivo assays (i.e., assays utilizing animal
models of AD and MCI). Accordingly, the invention provides a
variety of embodiments for screening prospective agents to identify
candidate agents for the treatment of AD and/or MCI.
[0166] In some embodiments, prospective agents are screened to
identify candidate agents for the treatment of AD and/or MCI in a
cell-free assay. Each prospective agent is incubated with the drug
target in a cell-free environment, and modulation of the AD
biomarker is measured. Cell-free environments useful in the
screening methods of the invention include cell lysates
(particularly useful when the drug target is an AD biomarker gene)
and biological fluids such as whole blood or fractionated fluids
derived therefrom such as plasma and serum (particularly useful
when the AD biomarker protein is the drug target). When the drug
target is an AD biomarker gene, the modulation measured may be
modulation of transcription or translation. When the drug target is
the AD biomarker protein, the modulation may of the half-life of
the protein or of the availability of the AD biomarker protein to
bind to its cognate receptor or ligand.
[0167] In other embodiments, prospective agents are screened to
identify candidate agents for the treatment of AD and/or MCI in a
cell-based assay. Each prospective agent is incubated with cultured
cells, and modulation of target AD biomarker is measured. In
certain embodiments, the cultured cells are astrocytes, neuronal
cells (such as hippocampal neurons), fibroblasts, or glial cells.
When the drug target is an AD biomarker gene, transcriptional or
translational modulation may be measured. When the drug target is
the AD biomarker protein, the AD biomarker protein is also added to
the assay mixture, and modulation of the half-life of the protein
or of the availability of the AD biomarker protein to bind to its
cognate receptor or ligand is measured.
[0168] Further embodiments relate to screening prospective agents
to identify candidate agents for the treatment of AD and/or MCI in
organ culture-based assays. In this format, each prospective agent
is incubated with either a whole organ or a portion of an organ
(such as a portion of brain tissue, such as a brain slice) derived
from a non-human animal and modulation of the target AD biomarker
is measured. When the drug target is an AD biomarker gene,
transcriptional or translational modulation may be measured. When
the drug target is the AD biomarker protein, the AD biomarker
protein is also added to the assay mixture, and modulation of the
half-life of the protein or of the availability of the AD biomarker
protein to bind to its cognate receptor is measured.
[0169] Additional embodiments relate to screening prospective
agents to identify candidate agents for the treatment of AD and/or
MCI utilizing in vivo assays. In this format, each prospective
agent is administered to a non-human animal and modulation of the
target AD biomarker is measured. Depending on the particular drug
target and the aspect of AD and/or MCI treatment that is sought to
be addressed, the animal used in such assays may either be a
"normal" animal (e.g., C57 mouse) or an animal which is a model of
AD or MCI. A number of animal models of AD are known in the art,
including the 3.times.Tg-AD mouse (Caccamo et al., 2003, Neuron
39(3):409-21), mice over expressing human amyloid beta precursor
protein (APP) and presenilin genes (Westaway et al., 1997, Nat.
Med. 3(1):67-72), and others (see Higgins et al., 2003, Behav.
Pharmacol. 14(5-6):419-38). When the drug target is an AD biomarker
gene, transcriptional or translational modulation may be measured.
When the drug target is the AD biomarker protein, modulation of the
half-life of the target AD biomarker or of the availability of the
AD biomarker protein to bind to its cognate receptor or ligand is
measured. The exact mode of measuring modulation of the target AD
biomarker will, of course, depend on the identity of the AD
biomarker, the format of the assay, and the preference of the
practitioner. A wide variety of methods are known in the art for
measuring modulation of transcription, translation, protein
half-life, protein availability, and other aspects which can be
measured. In view of the common knowledge of these techniques, they
need not be further described here.
[0170] Kits
[0171] The invention provides kits for carrying out any of the
methods described herein. Kits of the invention may comprise at
least one reagent specific for an AD biomarker, and may further
include instructions for carrying out a method described herein.
Kits may also comprise AD biomarker reference samples, that is,
useful as reference values. Kits comprise any biomarker and/or sets
of biomarkers as described herein. "AD diagnosis markers" for use
in kits provided herein include, but are not limited to GCSF;
IFN-g; IGFBP-1; BMP-6; BMP-4; Eotaxin-2; IGFBP-2; TARC; RANTES;
ANG; PARC; Acrp30; AgRP(ART); TIMP-1; TIMP-2; ICAM-1; TRAIL R3;
uPAR; IGFBP-4; LEPTIN(OB); PDGF-BB; EGF; BDNF; NT-3; NAP-2; IL-1ra;
MSP-a; SCF; TGF-b3; TNF-b MIP-1d; IL-3; FGF-6; IL-6 R; sTNF RII;
AXL; bFGF; FGF-4; CNTF; MCP-1; MIP-1b; TPO; VEGF-B; IL-8; FAS;
EGF-R. In other examples, "AD diagnosis biomarkers" for use in kits
provided herein include but are not limited to basic fibroblast
growth factor (bFGF), BB homodimeric platelet derived growth factor
(PDGF-BB), brain derived neurotrophic factor (BDNF), epidermal
growth factor (EGF), fibroblast growth factor 6 (FGF-6),
interleukin-3 (IL-3), soluble interleukin-6 receptor (sIL-6R),
Leptin (also known as ob), macrophage inflammatory protein-1 delta
(MIP-1.delta.), macrophage stimulating protein alpha chain
(MSP-.alpha.), neurotrophin-3 (NT-3), neutrophil activating
peptide-2 (NAP-2), RANTES, soluble tumor necrosis factor receptor-2
(sTNF RII), stem cell factor (SCF), thrombopoietin (TPO), tissue
inhibitor of metalloproteases-1 (TIMP-1), tissue inhibitor of
metalloproteases-2 (TIMP-2), transforming growth factor-beta 3
(TGF-.beta.3), tumor necrosis factor beta (TNF-.beta.). In other
examples, kits comprise any one, two, three or four of the AD
diagnosis markers Leptin, RANTES, PDFG-BB and BDNF. In other
examples, "AD diagnosis biomarkers" for use in kits provided herein
include but are not limited to at least one biomarker selected from
the group consisting of the biomarkers listed in Tables 9A1-9A2 and
9B that are significantly increased (9A1-9A2) or decreased (9B) in
AD compared to age-matched normal controls plus other non-AD forms
of neurodegeneration, such as for example PD and PN (that is, as
compared to all controls). In some examples, any one or more of the
biomarkers listed in Tables 9A1-9A2 and 9B, that is reagents
specific for the biomarkers, can be used in kits for use in the
methods as disclosed herein, including for example, methods to
diagnose AD, or to diagnose AD as distinguished from other non-AD
neurodegenerative diseases or disorders, such as for example PD and
PN.
[0172] Tables 10A1-10A2 and 10B provide a listing of biomarkers
that are significantly increased (10A1-10A2) or decreased (10B) in
AD compared to healthy age-matched controls. Any one or more of the
biomarkers listed in Tables 10A1-10A2 and 10B, that is, reagents
specific for the biomarker, can be used in kits for use in the
methods disclosed herein, such as for example, for aiding in the
diagnosis of or diagnosing AD. In some examples, biomarkers are
selected for use in methods disclosed herein, for aiding in the
diagnosis of or diagnosing AD that have a p-value of equal to or
less than 0.05, (or a q-value (%) of equal to or less than 5.00).
Tables 11A1-11A2 and 11B provide a listing of biomarkers that are
significantly increased (11A1-11A2) or decreased (11B) in AD
compared to age-matched degenerative controls. Any one or more of
the biomarkers listed in Tables 11A1-11A2 and 11B, that is,
reagents specific for the biomarker, can be used in kits for use in
the methods disclosed herein, such as for example, for aiding in
the diagnosis of or diagnosing AD.
[0173] Tables 12A-12B provide a listing of biomarkers that are
significantly increased (12A) or decreased (12B) in AD plus other
non-AD degenerative controls with reference to age matched
controls. Any one or more of the biomarkers listed in Tables
12A-12B, that is, reagents specific for the biomarker, can be used
in kits for use in the methods disclosed herein, such as for
example, for aiding in the diagnosis of or diagnosing
neurodegenerative diseases, including AD. In further examples, kits
comprise reagents specific for Lymphotactin and/or IL-11; and/or
reagents specific for BTC; SDF-1; MCP-2; IFN-gamma; IGFBP4; IGF-1
SR; IL-8; GM-CSF; and/or ANG-2; and/or reagents specific for
IFN-gamma and/or IL-8, and/or reagents specific for sTNF RII;
MSP-alpha; uPAR; TPO; MIP-1beta; VEGF-beta; FAS; MCP-1; NAP-2;
ICAM-1; TRAIL R3; PARC; ANG; IL-3; MIP-1delta; IFN-gamma; IL-8;
and/or FGF-6. In additional examples, a kit comprises at least one
AD diagnosis biomarker for use in normalizing data from
experiments. In some examples, a kit comprises at least one of
TGF-beta and TGF-beta 3 for use in normalizing data and in other
examples, a kit comprises both TGF-beta and TGF-beta 3 for use in
normalizing data. More commonly, kits of the invention comprise at
least two different AD biomarker-specific affinity reagents, where
each reagent is specific for a different AD biomarker. In some
embodiments, kits comprise at least 3, at least 4, at least 5, at
least 6, at least 7, at least 8, at least 9, or at least 10
reagents specific for an AD biomarker. In some embodiments, the
reagent(s) specific for an AD biomarker is an affinity reagent.
[0174] Kits comprising a single reagent specific for an AD
biomarker will generally have the reagent enclosed in a container
(e.g., a vial, ampoule, or other suitable storage container),
although kits including the reagent bound to a substrate (e.g., an
inner surface of an assay reaction vessel) are also contemplated.
Likewise, kits including more than one reagent may also have the
reagents in containers (separately or in a mixture) or may have the
reagents bound to a substrate.
[0175] In some embodiments, the AD biomarker-specific reagent(s)
will be labeled with a detectable marker (such as a fluorescent dye
or a detectable enzyme), or be modified to facilitate detection
(e.g., biotinylated to allow for detection with a avidin- or
streptavidin-based detection system). In other embodiments, the AD
biomarker-specific reagent will not be directly labeled or
modified.
[0176] Certain kits of the invention will also include one or more
agents for detection of bound AD biomarker-specific reagent. As
will be apparent to those of skill in the art, the identity of the
detection agents will depend on the type of AD biomarker-specific
reagent(s) included in the kit, and the intended detection system.
Detection agents include antibodies specific for the AD
biomarker-specific reagent (e.g., secondary antibodies), primers
for amplification of an AD biomarker-specific reagent that is
nucleotide based (e.g., aptamer) or of a nucleotide `tag` attached
to the AD biomarker-specific reagent, avidin- or
streptavidin-conjugates for detection of biotin-modified AD
biomarker-specific reagent(s), and the like. Detection systems are
well known in the art, and need not be further described here.
Accordingly, provided herein are kits for identifying an individual
with mild cognitive impairment (MCI), comprising at least one
reagent specific for RANTES; and instructions for carrying out the
method. In some examples, the kits further comprise a reagent
specific for leptin. In other examples, provided herein are kits
for monitoring progression of Alzheimer's disease (AD) in an AD
patient, comprising at least one reagent specific for leptin; and
instructions for carrying out the method. Also provided herein are
kits for stratifying an Alzheimer's disease (AD) patient,
comprising at least one reagent specific for brain derived
neurotrophic factor (BDNF); at least one reagent specific for BB
homodimeric platelet derived growth factor (PDGF-BB); and
instructions for carrying out the method.
[0177] A modified substrate or other system for capture of AD
biomarkers may also be included in the kits of the invention,
particularly when the kit is designed for use in a sandwich-format
assay. The capture system may be any capture system useful in an AD
biomarker assay system, such as a multi-well plate coated with an
AD biomarker-specific reagent, beads coated with an AD
biomarker-specific reagent, and the like. Capture systems are well
known in the art and need not be further described here.
[0178] In certain embodiments, kits for use in the methods
disclosed herein include the reagents in the form of an array. The
array includes at least two different reagents specific for AD
biomarkers (each reagent specific for a different AD biomarker)
bound to a substrate in a predetermined pattern (e.g., a grid).
Accordingly, the present invention provides arrays comprising "AD
diagnosis markers" including, but not limited to GCSF; IFN-g;
IGFBP-1; BMP-6; BMP-4; Eotaxin-2; IGFBP-2; TARC; RANTES; ANG; PARC;
Acrp30; AgRP(ART); TIMP-1; TIMP-2; ICAM-1; TRAIL R3; uPAR; IGFBP-4;
LEPTIN(OB); PDGF-BB; EGF; BDNF; NT-3; NAP-2; IL-1ra; MSP-a; SCF;
TGF-b3; TNF-b MIP-1d; IL-3; FGF-6; IL-6 R; sTNF RII; AXL; bFGF;
FGF4; CNTF; MCP-1; MIP-1b; TPO; VEGF-B; IL-8; FAS; EGF-R. In other
examples, "AD diagnosis biomarkers" include but are not limited to
basic fibroblast growth factor (bFGF), BB homodimeric platelet
derived growth factor (PDGF-BB), brain derived neurotrophic factor
(BDNF), epidermal growth factor (EGF), fibroblast growth factor 6
(FGF-6), interleukin-3 (IL-3), soluble interleukin-6 receptor
(sIL-6R), Leptin (also known as ob), macrophage inflammatory
protein-1 delta (MIP-1.delta.), macrophage stimulating protein
alpha chain (MSP-.alpha.), neurotrophin-3 (NT-3), neutrophil
activating peptide-2 (NAP-2), RANTES, soluble tumor necrosis factor
receptor-2 (sTNF RII), stem cell factor (SCF), thrombopoietin
(TPO), tissue inhibitor of metalloproteases-1 (TIMP-1), tissue
inhibitor of metalloproteases-2 (TIMP-2), transforming growth
factor-beta 3 (TGF-.beta.3), tumor necrosis factor beta
(TNF-.beta.). In other examples, arrays comprise any one, two,
three or four of the AD diagnosis markers Leptin, RANTES, PDFG-BB
and BDNF. Other examples of markers and sets of markers are
described herein. The localization of the different AD
biomarker-specific reagents (the "capture reagents") allows
measurement of levels of a number of different AD biomarkers in the
same reaction. Kits including the reagents in array form are
commonly in a sandwich format, so such kits may also comprise
detection reagents. Normally, the kit will include different
detection reagents, each detection reagent specific to a different
AD biomarker. The detection reagents in such embodiments are
normally reagents specific for the same AD biomarkers as the
reagents bound to the substrate (although the detection reagents
typically bind to a different portion or site on the AD biomarker
target than the substrate-bound reagents), and are generally
affinity-type detection reagents. As with detection reagents for
any other format assay, the detection reagents may be modified with
a detectable moiety, modified to allow binding of a separate
detectable moiety, or be unmodified. Array-type kits including
detection reagents that are either unmodified or modified to allow
binding of a separate detectable moiety may also contain additional
detectable moieties (e.g., detectable moieties which bind to the
detection reagent, such as labeled antibodies which bind unmodified
detection reagents or streptavidin modified with a detectable
moiety for detecting biotin-modified detection reagents).
[0179] The instructions relating to the use of the kit for carrying
out the invention generally describe how the contents of the kit
are used to carry out the methods of the invention. Instructions
may include information as sample requirements (e.g., form,
pre-assay processing, and size), steps necessary to measure the AD
biomarker(s), and interpretation of results.
[0180] Instructions supplied in the kits of the invention are
typically written instructions on a label or package insert (e.g.,
a paper sheet included in the kit), but machine-readable
instructions (e.g., instructions carried on a magnetic or optical
storage disk) are also acceptable. In certain embodiments,
machine-readable instructions comprise software for a programmable
digital computer for comparing the measured values obtained using
the reagents included in the kit.
[0181] The following Examples are provided to illustrate the
invention, but are not intended to limit the scope of the invention
in any way.
EXAMPLES
Example 1
AD Diagnosis Biomarkers
[0182] We compared plasma protein expression levels for 120
proteins in 32 cases of serum collected from patients with
Alzheimer's Disease (with a mean age of 74) to 19 cases of serum
collected from control subjects (also with mean age of 74).
Alzheimer's Disease subjects were clinically diagnosed with AD by a
neurologist, and had Mini Mental State Exam (MMSE) scores ranging
from 26-14.
[0183] Plasma samples were assayed using a sandwich-format ELISA on
a nitrocellulose filter substrate. Plasma samples were diluted 1:10
in phosphate buffer and incubated with the capture substrate (a
nitrocellulose membrane spotted with capture antibodies). The
samples were incubated with the capture substrate for two hours at
room temperature, then decanted from the capture substrate. The
substrate was washed twice with 2 ml of washing buffer
(1.times.PBS; 0.05% Tween-20) at room temp, then incubated with
biotinylated detection antibodies for two hours at room
temperature. The capture antibody solution was decanted and the
substrate was washed twice for 5 min with washing buffer. The
washed substrate was then incubated with horseradish
peroxidase/streptavidin conjugate for 45 minutes, at which time the
conjugate solution was decanted and the membranes were washed with
washing buffer twice for 5 minutes. The substrate was transferred
onto a piece of filter paper, incubated in enhanced
chemiluminescence (ECL) Detection Buffer solution purchased from
Raybiotech, Inc. Chemiluminescence was detected and quantified with
a chemiluminescence imaging camera. Signal intensities were
normalized to standard proteins blotted on the substrate and used
to calculate relative levels of biomarkers. In other examples,
signal intensities were normalized to the median and used to
calculate relative levels of biomarkers. Measured levels of any
individual biomarkers can be normalized by comparing the level to
the mean or median measured level of two or more biomarkers from
the same individual.
[0184] Relative biomarker levels in plasma are compared between
control and AD groups revealing 46 discriminatory biomarkers: GCSF;
IFN-g; IGFBP-1; BMP-6; BMP-4; Eotaxin-2; IGFBP-2; TARC; RANTES;
ANG; PARC; Acrp30; AgRP(ART); TIMP-1; TIMP-2; ICAM-1; TRAIL R3;
uPAR; IGFBP-4; LEPTIN(OB); PDGF-BB; EGF; BDNF; NT-3; NAP-2; IL-1ra;
MSP-a; SCF; TGF-b3; TNF-b MIP-1d; IL-3; FGF-6; IL-6 R; sTNF RII;
AXL; bFGF; FGF4; CNTF; MCP-1; MIP-1b; TPO; VEGF-B; IL-8; FAS;
EGF-R. An unsupervised clustering (that is, the clustering
algorithm does not know which cases are AD and which are normal) of
the 46 discriminatory markers results in the clustering of the
samples into 2 groups or clusters, a cluster of control samples,
and a cluster of AD samples. Sensitivity was calculated as the
number of correctly classed AD samples in the AD cluster/total
number of AD samples, which is 29/32 or 90.6%. Specificity was
calculated as total number of correctly classed control samples in
the control cluster/total number of controls, which is
(14/19=73.6%).
[0185] Biomarker levels were compared between control and AD
groups, revealing 20 biomarkers (shown in Table 3) that are
differentially regulated (each is decreased in AD as compared to
control) between the two groups. Statistical analysis was performed
to find the probability that the finding of differential levels was
in error (the "q" value) for any one biomarker. Biomarkers with
differential levels and associated q values (shown as percentage
values) are shown in Table 3 (fold change indicates the fold change
between levels in control vs. AD samples). Sensitivity was
calculated as number of AD samples in AD cluster/total number of AD
samples, which is 29/32 or 90.6%. Specificity was calculated as
total correctly predicted AD/total predicted AD (29/34=85%).
TABLE-US-00004 TABLE 3 Fold Change (as negative value or q-value
Qualitative Biomarker decrease) (%) Brain derived neurotrophic
factor (BDNF) 0.536 1.656 Basic fibroblast growth factor (bFGF)
0.673 1.656 Epidermal growth factor (EGF) 0.561 1.656 Fibroblast
growth factor-6 (FGF-6) 0.664 1.656 Interleukin-3 (IL-3) 0.758
1.656 Soluble interleukin-6 receptor (sIL-6 R) 0.676 1.656 Leptin
(also known as OB) 0.476 1.656 Macrophage inflammatory protein
1-delta 0.542 1.656 (MIP-1.delta.) MSP-a 0.764 1.656 NAP-2 0.672
1.656 Neurotrophin-3 (NT-3) 0.698 1.656 Platelet derived growth
factor, BB dimer 0.536 1.656 (PDGF-BB) RANTES 0.682 1.656 Stem cell
factor (SCF) 0.730 1.656 sTNF RII 0.700 1.656 Transforming growth
factor beta-3 (TGF-.beta.3) 0.769 1.656 Tissue inhibitor of
metalloproteases-1 (TIMP-1) 0.716 1.656 Tissue inhibitor of
metalloproteases-2 (TIMP-2) 0.750 1.656 Tumor necrosis factor beta
(TNF-.beta.) 0.649 1.656 TPO 0.714 1.656
Example 2
Decision Trees from AD Diagnosis Marker Data
[0186] Upon further analysis of the data from example 1, two
different decision trees were formulated for diagnosis of AD using
AD diagnosis biomarkers.
[0187] The first decision tree utilizes sIL-6R, IL-8, and TIMP-1
levels. The rules which make up the decision tree are: (1) If
sIL-6R.ltoreq.5.18 and IL-8 is .ltoreq.0.957, the indication is
normal; (2) if sIL-6R.ltoreq.5.18 and IL-8>0.957, the indication
is AD; (3) if sIL-6R>5.18 and TIMP-1.ltoreq.7.978, the
indication is AD; and (4) if sIL-6R>5.18 and TIMP-1 is
>7.978, the indication is normal, wherein the values expressed
are relative concentrations.
[0188] Accuracy of this decision tree was measured using 10-fold
cross-validation testing feature in CART to generate
misclassification rates for learning samples and testing samples.
Sensitivity was calculated from the testing scores as number of AD
samples correctly predicted as AD/total number of AD samples
(29/32=0.906). Specificity was calculated from the testing scores
as total correctly predicted cases of AD/total number of cases
predicted AD (29/33=0.878).
[0189] A second decision tree was formulating using BDNF, TIMP-1
and MIP-16 levels. The rules which make up the decision tree are:
(1) if BDNF>4.476, the indication is normal; (2) if
BDNF.ltoreq.4.476 and TIMP-1.ltoreq.8.942, the indication is AD;
(3) if BDNF.ltoreq.4.476, TIMP-1>8.942, and
MIP-1.delta..ltoreq.1.89, the indication is AD; and (4) if
BDNF.ltoreq.4.476, TIMP-1>8.942, and MIP-1.delta.>1.89, the
indication is normal. Accuracy of this decision tree was measured
using 10-fold cross-validation testing feature in CART to generate
misclassification rates for learning samples and testing samples.
Sensitivity was calculated from the testing scores as number of AD
samples correctly predicted as AD/total number of AD samples
(0.875). Specificity was calculated from the testing scores as
total correctly predicted cases of AD/total number of cases
predicted AD (0.82).
Example 3
Diagnosis of MCI
[0190] Levels of RANTES and Leptin were measured in 18 samples from
control subjects (mean age=74) and 6 samples from patients
diagnosed with mild cognitive impairment (MCI). MCI patients had
been clinically diagnosed by a neurologist, and had an AULT-A7
score of less than 5 and Mini Mental State Exam (MMSE) scores
ranging from 30-28. Control subjects had an AULT-A7 score greater
than or equal to 5 and MMSE score ranging from 30-28.
[0191] RANTES and Leptin levels were measured using an ELISA kit
from R&D systems according to the manufacturer's instructions.
The raw ELISA expressions values were normalized by dividing each
value by the median of all the samples. Analysis of the data showed
(a) Leptin is not decreased in MCI patients as compared to control
subjects (in the six MCI samples, Leptin was actually 11% higher
than the control subjects), and (b) a bimodal distribution of
RANTES, where MCI patients had RANTES levels of between 1.043 and
1.183 (levels from control subjects were either.ltoreq.1.043 or
>1.183). However, closer inspection of the data led us to
believe that those control subjects with RANTES.ltoreq.1.043 had
been incorrectly classified as normal (and should have been
diagnosed as MCI).
[0192] Reclassification of control subjects with
RANTES.ltoreq.1.043 as MCI patients allows the creation of a simple
rule: if RANTES.ltoreq.1.183 and Leptin>=0.676, the indication
is MCI. Sensitivity and specificity, calculated as described in
Example 2, were 83.3% and 88.88%, respectively.
Example 4
Monitoring and Stratification of AD Patients
[0193] Levels of RANTES, Leptin, PDGF-BB, and BDNF were measured in
serum samples collected from 36 patients diagnosed with Alzheimer's
Disease. (mean age of 74) using ELISA kits from R&D systems
according to the manufacturer's instructions. The raw ELISA
expressions values were normalized by dividing each value by the
median of all the samples. The samples were grouped into three
classes on the basis of MMSE score: Class 1 (mild AD), MMSE 27-22;
Class 2 (moderate AD), MMSE 21-16; and Class 3 (severe AD), MMSE
15-12.
[0194] Upon analysis of the ELISA data, we formulated a decision
tree using BDNF and PDGF-BB. The rules which make up the decision
tree are: (1) if BDNF.ltoreq.0.626, the indication is mild AD; (2)
if BDNF>0.626 and PDGF-BB.ltoreq.0.919, the indication is
moderate AD; and (3) if BDNF>0.626 and PDGF-BB>0.919, the
indication is severe AD. The values expressed are relative
concentrations that have been normalized to the median. Average
normalized levels for Leptin were: Class I=0.886; class II=0.757;
class III=0.589. Average normalized levels for BDNF were: Class
I=0.595; class II=0.956; class III=1.23. When applied to a set of
"test" data, the decision tree produced 58%, 47%, and 57% percent
correct stratification of the test samples into mild, moderate, and
severe categories.
Example 5
Four Discriminatory Markers
[0195] The absolute concentrations in plasma of only 4
discriminatory markers, BDNF, PDGF-BB, LEPTIN, and RANTES measured
by ELISA was used to classify samples. ELISA kits were purchased
from R&D Systems, and measurements were obtained according to
manufacturer recommendations. For example for RANTES, the following
protocol was followed.
[0196] 1. Add 50 .mu.L standards, specimens or controls to
appropriate wells.
[0197] 2. Add 50 .mu.L anti-RANTES Biotin Conjugate to each
well.
[0198] 3. Incubate wells at 37.degree. C. for 1 hour.
[0199] 4. Aspirate and wash wells 4.times. with Working Wash
Buffer.
[0200] 5. Add 100 .mu.L Streptavidin-HRP Working Conjugate to each
well.
[0201] 6. Incubate for 30 minutes at room temperature.
[0202] 7. Aspirate and wash wells 4.times. with Working Wash
Buffer.
[0203] 8. Add 100 .mu.L of Stabilized Chromogen to each well.
[0204] 9. Incubate at room temperature for 30 minutes in the
dark.
[0205] 10. Add 100 .mu.L of Stop Solution to each well.
[0206] 11. Read absorbance at 450 nm.
[0207] Following the above protocol, an unsupervised clustering of
BDNF, PDGF-BB, LEPTIN, and RANTES was performed using the publicly
available web based clustering software wCLUTO at
cluto.ccgb.umn.edu/cgi-bin/wCluto/wCluto.cgi. Here the clustering
of the 4 proteins resulted in the clustering of the samples into 2
groups or clusters, a cluster of control samples and a cluster of
AD samples. Sensitivity was calculated as the number of correctly
classed AD samples in the AD cluster/total number of AD samples,
which is 21/24 or 87.5%. Specificity was calculated as total number
of correctly classed control samples in the control cluster/total
number of controls, which is 20/24=83.3%.
[0208] Additionally, absolute biomarker levels in plasma (as
measured by ELISA) for BDNF, PDGF-BB, and LEPTIN, were correlated
with MMSE scores (range 12-30). AD could be identified in MMSE
scores in a range of 12-28 and control samples were identified in
MMSE scores in the range of 25-30. Table 4 shows the correlations
and their statistical significance (p-value). The upper and lower
correlations show whether the upper end of the range of MMSE scores
and biomarker concentrations or the lower end of the range of MMSE
scores and biomarker concentrations are more correlated. Therefore,
the correlations show that higher levels of BDNF and Leptin are
significantly correlated with better MMSE scores, and that increase
in the concentration of BDNF and Leptin from a reference point or
an earlier collection is an indication of improvement in cognition
as measured by MMSE. Simultaneously, or by itself, the lower the
levels of PDGF-BB in men is significantly correlated with better
MMSE scores, and a decrease in the concentration of PDGF-BB in male
sample compared to an earlier collection in that male, is an
indication of improvement in cognition as measured by MMSE.
[0209] The results show (Table 4) the correlation between the
plasma concentration of 3 discriminatory proteins for AD to the
MMSE score of the subjects and the correlation between
concentrations of proteins that are discriminatory for AD. There
was no correlation between MMSE score and Age among AD subjects and
there was no correlation between Age and the concentration of BDNF,
PDGF-BB, or LEPTIN in plasma among AD subjects. The p-values show
that the correlations are statistically significant. The count
shows the number of cases. BDNF has a statistically significant
positive correlation with MMSE scores. PDGF-BB has a statistically
significant negative correlation with MMSE scores in men. LEPTIN
has a statistically significant positive correlation with MMSE
scores. This experiment demonstrates that plasma concentrations for
PDGF-BB, LEPTIN, and BDNF can be used to monitor the progression of
cognitive decline. TABLE-US-00005 TABLE 4 95% 95% Correlation Count
Z-value P-value Lower Upper BDNF to MMSE 0.184 165 2.373 0.0176
0.032 0.328 BDNF to MMSE (Females) 0.229 91 2.18 0.0289 0.024 0.415
PDGF-BB to MMSE (Males) -0.207 74 -1.769 0.0768 -0.416 0.023 LEPTIN
to MMSE 0.193 164 2.478 0.0132 0.041 0.336 BDNF to PDGF-BB 0.700
181 11.575 0.0001 0.617 0.768 PDGF-BB to RANTES 0.563 181 8.5
0.0001 0.454 0.655 BDNF to RANTES 0.714 181 11.9 0.0001 0.634
0.779
[0210] Controls and AD cases were age matched, and had a mean age
of 74. The mean MMSE score for AD cases (n=24) was 20, while the
mean MMSE score for Control cases (n=24) was 30. Classification of
the samples was performed with unsupervised clustering of protein
concentration. The total accuracy of classification was 85.4%. This
results demonstrated that plasma protein concentrations for BDNF,
PDGF-BB, LEPTIN, and RANTES, as measured by ELISA can be used to
accurately discriminate between AD and controls.
Example 6
Validation of Mean Protein Concentrations in AD and Controls by
ELISA
[0211] Protein concentrations for proteins, LEPTIN, BDNF and
RANTES, in plasma samples of AD (n=95) to age matched Controls
(n=88) are shown in FIGS. 1A-1C. One of the four proteins we
measured was Brain Derived Neurotrophic Factor (BDNF). The mean
concentration of BDNF in AD plasma was 8.1 ng/ml (SE+/-0.4)
compared to the mean of control plasma 10.8 ng/ml (SE+/-0.68) and
the difference was found to be extremely statistically significant
(p-value=0.0006). We also found that the concentrations of BDNF
were lower in other forms of dementia (5.74 ng/ml, n=20) than AD.
The mean concentration of a second protein Leptin in AD plasma was
found to be 10.9 ng/ml (SE+/-1.06) compared to the mean of control
plasma 17.4 ng/ml (SE+/-1.8) and the difference was found to be
statistically very significant (p-value=0.0018). The mean
concentration of a third protein Rantes in AD plasma was found to
be 66.3 ng/ml (SE+/-2.4) compared to control samples 74.5 ng/ml
(SE+/-3.2) and the difference was found to be statistically
significant (p-value=0.0403). No difference in the means of
concentrations for RANTES, PDGF-BB, and BDNF were observed among AD
subjects with MMSE scores=/>20 (n=54) and those <20
(n=41).
Example 7
Absolute Biomarker Concentrations in Plasma
[0212] Additionally, absolute biomarker concentrations in plasma
were measured for BDNF, and mean concentrations for Controls was
compared to MCI (Mild Cognitive Impairment), MMSE 25-28, MMSE
20-25, and MMSE 10-20. For the purposes of this experiment, the
index used in the following example is: questionable AD is =MMSE
score in the range of 25-28; mild AD=MMSE score in the range of
20-25; and moderate AD=MMSE score in the range of 10-20 and severe
AD=MMSE score in the range of 10-20. For the purpose of Example 7,
all individuals assessed as having Questionable AD were diagnosed
by a physician as having AD. The FIG. 2 shows that mean
concentrations of BDNF in plasma for MMSE 25-28; MMSE 20-25; MMSE
10-20 are significantly lower than the mean concentration in
Controls (Normal, mean age 74) and the mean concentration of BDNF
in MCI is significantly higher than in Controls and all cases of
AD. FIG. 2. TABLE-US-00006 Unpaired t-test for BDNF plasma Grouping
Variable: stage Hypothesized Difference = 0 Inclusion criteria:
Sparks from CenterAll Mean Diff. DF t-Value P-Value MCI, mild
6349.252 47 3.050 .0038 MCI, moderate 6828.574 31 2.651 .0125 MCI,
normal 3961.358 86 1.442 .1529 MCI, questionable 7547.218 17 2.550
.0207 mild, moderate 479.322 68 .460 .6467 mild, normal -2387.894
123 -2.270 .0250 mild, questionable 1197.966 54 .969 .3369
moderate, normal -2867.216 107 -2.175 .0319 moderate, questionable
718.644 38 .475 .6372 normal, questionable 3585.860 93 1.993
.0492
[0213] TABLE-US-00007 Group Info for BDNF plasma Grouping Variable:
stage Inclusion criteria: Sparks from CenterAll Count Mean Variance
Std. Dev. Std. Err MCI 6 14879.833 85932530.967 9269.980 3784.454
mild 43 8530.581 15299257.963 3911.427 596.487 moderate 27 8051.259
22317487.815 4724.139 909.161 normal 82 10918.476 39478328.993
6283.178 693.861 question- 13 7332.615 15122872.923 3888.814
1078.563 able
[0214] Additionally, absolute concentrations of BDNF, in plasma
samples collected from four separate Alzheimer's Centers was
compared for gender differences in mean concentrations between AD
(Females) and Control (Females) and AD (Males) and Control (Males).
FIG. 3 shows that there is 40% difference in the concentration of
BDNF in AD Females compared to Control Females and the difference
is highly statistically significant (p-value=0.004). The difference
in the mean concentration of BDNF for all AD cases compared to all
Control case was found to be extremely statistically significant
(p-value=0.0006). TABLE-US-00008 Unpaired t-test for BDNF plasma
Grouping Variable: Disease Split By: sex Hypothesized Difference =
0 Row exclusion: CenterAll Mean Diff. DF t-Value P-Value AD,
Control: Total -2974.140 187 -3.482 .0006 AD, Control: F -3939.353
87 -2.924 .0044 AD, Control: M -1348.601 92 -1.165 .2469
[0215] Results for totals may not agree with results for individual
cells because of missing values for split variables. TABLE-US-00009
Group Info for BDNF plasma Grouping Variable: Disease Split By: sex
Row exclusion: CenterAll Count Mean Variance Std. Dev. Std. Err AD:
106 5596.113 24323422.844 4931.878 479.026 Total AD: F 38 5775.921
25121499.318 5012.135 813.076 AD: M 62 5396.774 24336564.079
4933.210 626.518 Control: 83 8570.253 46322420.606 6806.058 747.062
Total Control: 51 9715.275 50173107.603 7083.298 991.860 F Control:
32 6745.375 36011373.274 6000.948 1060.828 M
[0216] Results for totals may not agree with results for individual
cells because of missing values for split variables.
[0217] Additionally, absolute biomarker concentrations in plasma
were measured for RANTES in plasma samples collected from four
different Alzheimer's Centers, and mean concentrations for Controls
were compared to MCI (Mild Cognitive Impairment), MMSE 25-28; (MMSE
20-25; MMSE 10-20; and MMSE 10-20. The index is described above.
The mean differences between Mild AD compared to Moderate AD, Mild
AD compared to Normal, Mild AD compared to Severe AD, Moderate AD
compared to Normal, Questionable AD compared to Normal, Normal to
Severe AD were all found to be statistically significant. FIG. 4.
TABLE-US-00010 Unpaired t-test for RANTES ELISA Grouping Variable:
stage Hypothesized Difference = 0 Row exclusion: CenterAll Mean
Diff. DF t-Value P-Value MCI, mild 84.789 64 .007 .9945 MCI,
moderate 12454.688 51 1.042 .3022 MCI, normal -10422.892 106 -.866
.3884 MCI, questionable 9682.438 29 .682 .5007 MCI, severe
50349.200 10 1.647 .1305 mild, moderate 12369.899 97 1.814 .0728
mild, normal -10507.681 152 -1.775 .0780 mild, questionable
9597.649 75 1.081 .2830 mild, severe 50264.411 56 2.031 .0470
moderate, normal -22877.580 139 -3.606 .0004 moderate, questionable
-2772.250 62 -.315 .7535 moderate, severe 37894.512 43 1.647 .1069
normal, questionable 20105.330 117 2.353 .0203 normal, severe
60772.092 98 2.395 .0185 questionable, severe 40666.762 21 1.624
.1192
[0218] TABLE-US-00011 Group Info for RANTES ELISA Grouping
Variable: stage Row exclusion: CenterAll Count Mean Variance Std.
Dev. Std. Err MCI 10 54919.200 1729660285.733 41589.185 13151.655
mild 56 54834.411 1203622609.701 34693.265 4636.082 mod- 43
42464.512 1036226732.256 32190.476 4909.002 erate normal 98
65342.092 1275358885.672 35712.167 3607.474 ques- 21 45236.762
1201710117.890 34665.691 7564.674 tion- able severe 2 4570.000
2976800.000 1725.341 1220.000
[0219] Additionally, absolute biomarker concentrations in plasma
were measured for Leptin in plasma samples collected from four
different Alzheimer's Centers, and mean concentrations for Controls
were compared to MCI (Mild Cognitive Impairment); MMSE 25-28; MMSE
20-25; MMSE 10-20; and MMSE 10-20. The mean differences between
Questionable AD compared to MCI, Mild AD compared to Normal, Mild
AD compared to Questionable AD, Questionable AD compared to Normal,
and Moderate AD compared to Normal were all found to be
statistically significant. FIG. 5. TABLE-US-00012 Unpaired t-test
for Leptin ELISA Grouping Variable: stage Hypothesized Difference =
0 Row exclusion: CenterAll Mean Diff. DF t-Value P-Value MCI, mild
4164.889 64 1.338 .1856 MCI, moderate 4707.044 51 1.061 .2939 MCI,
normal -650.092 105 -.123 .9022 MCI, questionable 7793.348 29 2.000
.0550 MCI, severe 8187.800 10 .739 .4767 mild, moderate 542.155 97
.272 .7860 mild, normal -4814.981 151 -2.117 .0359 mild,
questionable 3628.458 75 1.897 .0617 mild, severe 4022.911 56 .734
.4661 moderate, normal -5357.136 138 -1.963 .0516 moderate,
questionable 3086.303 62 1.085 .2822 moderate, severe 3480.756 43
.403 .6892 normal, questionable 8443.439 116 2.368 .0195 normal,
severe 8837.892 97 .778 .4383 questionable, severe 394.452 21 .078
.9383
[0220] TABLE-US-00013 Group Info for Leptin ELISA Grouping
Variable: stage Row exclusion: CenterAll Count Mean Variance Std.
Dev. Std. Err MCI 10 15727.300 225300738.678 15010.021 4746.585
mild 56 11562.411 58790550.756 7667.500 1024.613 moderate 43
11020.256 145797834.909 12074.677 1841.371 normal 97 16377.392
255125297.032 15972.642 1621.776 question- 21 7933.952 47833192.348
6916.154 1509.229 able severe 2 7539.500 16125520.500 4015.659
2839.500
[0221] Additionally, absolute biomarker concentrations in plasma
were measured for PDGF-BB in plasma samples collected from four
different Alzheimer's Centers, and mean concentrations for Controls
were compared to MCI (Mild Cognitive Impairment); MMSE 25-28; MMSE
20-25; MMSE 10-20; and MMSE 10-20. The mean differences between
Questionable AD compared to Mild AD, Mild AD compared to Severe AD,
Moderate AD compared to Severe AD, Normal compared to Questionable
AD, and Normal to Severe AD were all found to be statistically
significant. FIG. 6. TABLE-US-00014 Unpaired t-test for PDGF-BB
ELISA Grouping Variable: stage Hypothesized Difference = 0 Row
exclusion: CenterAll Mean Diff. DF t-Value P-Value MCI, mild
-62.275 58 -.286 .7756 MCI, moderate 81.595 44 .411 .6831 MCI,
normal -42.865 103 -.210 .8343 MCI, questionable 191.571 28 .810
.4246 MCI, severe 637.000 9 1.072 .3117 mild, moderate 143.869 86
1.285 .2023 mild, normal 19.410 145 .199 .8426 mild, questionable
253.846 70 1.812 .0742 mild, severe 699.275 51 1.745 .0871
moderate, normal -124.459 131 -1.201 .2320 moderate, questionable
109.977 56 .869 .3885 moderate, severe 555.405 37 1.716 .0945
normal, questionable 234.436 115 1.767 .0799 normal, severe 679.865
96 1.696 .0931 questionable, severe 445.429 21 1.278 .2153
[0222] TABLE-US-00015 Group Info for PDGF-BB ELISA Grouping
Variable: stage Row exclusion: CenterAll Count Mean Variance Std.
Dev. Std. Err MCI 9 731.000 650139.000 806.312 268.771 mild 51
793.275 315391.883 561.598 78.639 moderate 37 649.405 204231.470
451.920 74.295 normal 96 773.865 318171.171 564.067 57.570
questionable 21 539.429 233024.657 482.726 105.340 severe 2 94.000
648.000 25.456 18.000
[0223] Additionally, absolute biomarker concentrations in plasma
were measured for BDNF in plasma samples collected from four
different Alzheimer's centers, and means concentrations for
Controls were compared to MCI (Mild Cognitive Impairment),
Questionable AD (MMSE 25-28), Mild differences between MCI compared
to Moderate AD, MCI compared to Questionable AS, Mild AD to Normal,
Mild AD to sever AD, Moderate to Normal, Normal to Questionable AD,
and Normal to Severe were all found to be statistically
significant. FIG. 7. TABLE-US-00016 Unpaired t-test for BDNF plasma
Grouping Variable: stage Hypothesized Difference = 0 Row exclusion:
CenterAll Mean Diff. DF t-Value P-Value MCI, mild 2819.186 64 1.433
.1568 MCI, moderate 4071.016 51 1.877 .0663 MCI, normal 124.278 106
.053 .9578 MCI, questionable 4535.757 29 1.806 .0813 MCI, severe
8660.400 10 1.202 .2570 mild, moderate 1251.831 97 1.262 .2098
mild, normal -2694.908 152 -2.638 .0092 mild, questionable 1716.571
75 1.447 .1520 mild, severe 5841.214 56 1.726 .0898 moderate,
normal -3946.739 139 -3.431 .0008 moderate, questionable 464.741 62
.360 .7199 moderate, severe 4589.384 43 1.265 .2128 normal,
questionable 4411.480 117 2.868 .0049 normal, severe 8536.122 98
1.781 .0781 questionable, severe 4124.643 21 1.321 .2006
[0224] TABLE-US-00017 Group Info for BDNF plasma Grouping Variable:
stage Row exclusion: CenterAll Count Mean Variance Std. Dev. Std.
Err MCI 10 9511.900 96113654.322 9803.757 3100.220 mild 56 6692.714
22509096.208 4744.375 633.994 moderate 43 5440.884 25765123.534
5075.936 774.073 normal 98 9387.622 45504479.969 6745.701 681.419
question- 21 4976.143 18681976.129 4322.265 943.196 able severe 2
851.500 63724.500 252.437 178.500
[0225] It has been found that for Questionable AD (MMSE score in
the range of 25-28) the levels of Leptin and PDGF-BB increase
significantly whereas BDNF and RANTES do not change significantly.
It has been found that from Mild AD (MMSE score in the range of
20-25) to Moderate AD (MMSE score in the range of 10-20) the level
of LEPTIN does not decline whereas the levels for RANTES, BDNF and
PDGF-BB declines.
Example 8
[0226] In an attempt to identify proteins that are altered in the
peripheral immune system in AD, expression levels of 120 cytokines,
chemokines, and growth factors in plasma from 32 AD patients and 19
nondemented age-matched controls were measured using spotted
antibody microarrays on filters. Statistical analysis identified 20
proteins as significantly different between AD and controls. Six of
them including brain derived neurotrophic factor (BDNF) and NT-3,
and PDGF-BB, EGF, FGF-6, bFGF, TGF-b3 have known neurotrophic
activity and were significantly reduced in AD plasma. BDNF levels
correlated with better cognitive function in the mini mental state
exam (MMSE). BDNF measurements in plasma from two hundred AD cases
and controls using commercial sandwich ELISA showed a highly
significant 25% reduction in AD cases. Consistent with the array
data, reduced plasma BDNF levels were associated with impaired
memory function. BDNF is critical for neuronal maintenance,
survival, and function. Without being bound by theory decreased
blood levels of neurotrophins and BDNF may be linked with
neurodegeneration and cognitive dysfunction in AD.
Example 9
Additional Biomarkers
[0227] Additionally, qualitative biomarker levels for GDNF, SDF-1,
IGFBP3, FGF-6, TGF-b3, BMP-4, NT-3, EGF, BDNF, IGFBP-2 were
correlated with MMSE scores (range 12-30) for AD (MMSE range 12-28)
and control samples (MMSE range 25-30). Table 5 shows the
correlations and their statistical significance (p-value). The
upper and lower correlations show whether the upper end of the
range of MMSE Scores and biomarker concentrations or the lower end
of the range of MMSE scores and biomarker concentrations are more
correlated. A negative correlation means that MMSE scores increase
with decreasing levels of biomarker and vice versa. A positive
correlation mean that MMSE scores increase with increasing levels
of biomarker. TABLE-US-00018 TABLE 5 95% 95% Correlation Count
Z-value P-value Lower Upper GDNF to MMSE -0.258 42 -1.646 0.0997
-0.521 0.05 SDF-1 to MMSE -0.363 42 -2.375 0.0175 -0.601 -0.066
IGFBP-3 to MMSE 0.293 42 1.886 0.0593 -0.012 0.548 FGF-6 to MMSE
0.471 42 3.192 0.0014 0.195 0.687 TGF-b3 to MMSE 0.317 42 2.049
0.0405 0.014 0.566 BMP-4 to MMSE 0.294 42 1.845 0.0583 -0.011 0.545
NT-3 to MMSE 0.327 42 2.118 0.0342 0.025 0.574 EGF to MMSE 0.409 42
2.711 0.0067 0.12 0.634 BDNF to MMSE 0.464 42 3.139 0.0017 0.187
0.673 IGFBP-2 to MMSE (Females) 0.498 24 2.5 0.0123 0.118 0.75
Example 10
[0228] This example shows Table 6, a Summary of Quantitative
Markers for Identification and Stratification of AD. TABLE-US-00019
TABLE 6 Plasma % Difference References Samples BioMarker in Samples
p-value Normal Questionable AD BDNF -46% 0.0049 Normal Questionable
AD Leptin -52% 0.0195 Normal Questionable AD RANTES -31% 0.0203
Normal Questionable AD PDGF-BB -30% 0.0799 Normal Mild AD BDNF -29%
0.0092 Normal Mild AD Leptin -29% 0.0359 Normal Mild AD RANTES -16%
0.0780 Normal Moderate AD BDNF -42% 0.0008 Normal Moderate AD
Leptin -33% 0.0359 Normal Moderate AD RANTES -35% 0.0004 Normal
Severe AD BDNF -90% 0.0781 Normal Severe AD RANTES -93% 0.0185
Normal Severe AD PDGF-BB -89% 0.0931 Questionable AD Mild AD Leptin
45% 0.0617 Questionable AD Mild AD PDGF-BB 46% 0.0742 Mild AD
Moderate AD RANTES -23% 0.0780 Mild AD Severe AD BDNF -87% 0.0898
Mild AD Severe AD RANTES -92% 0.0470 Mild AD Severe AD PDGF-BB -88%
0.0871 Questionable AD MCI BDNF 91% 0.0813 Questionable AD MCI
Leptin 98% 0.0550 MCI Mild AD BDNF -42% 0.0038
[0229] Accordingly, the present invention provides methods of
aiding diagnosis of Alzheimer's disease ("AD"), comprising
comparing a measured level of at least 4 AD diagnosis biomarkers,
wherein said biomarkers comprise BDNF, PDGF-BB, Leptin and RANTES,
in a biological fluid sample from an individual to a reference
level for each AD diagnosis biomarker. Accordingly, methods are
provided in which BDNF decreased at least about 10%, about 15%,
about 20%, about 25% or about 30% as compared to a reference level
of BDNF, indicates cognitive impairment, such as for example, an
indication of AD. Accordingly, methods are provided in which Leptin
decreased at least about 10%, about 15%, about 20%, about 25% or
about 30% as compared to a reference level of Leptin, indicates
cognitive impairment, such as for example, an indication of AD.
Accordingly, methods are provided in which RANTES decreased at
least about 5%, about 10%, or about 15% as compared to a reference
level of RANTES, indicates cognitive impairment, such as for
example, an indication of AD. Accordingly, methods are provided in
which PDGF-BB decreased at least about 80%, about 85% or about 90%
as compared to a reference level of PDGF-BB, indicates cognitive
impairment, such as for example, an indication of severe AD.
TABLE-US-00020 TABLE 7 Protein Protein Alternate names Class ID
alpha-1 acid glycoprotein acute phase alpha-1 antitrypsin acute
phase Ceruloplasmin acute phase Haptoglobin acute phase Hemopexin
acute phase Hemoxygenase acute phase plasminogen activator
inhibitor-1 PAI-1 acute phase serum amyloid A SAA acute phase serum
amyloid P SAP acute phase 4-11313 ligand 4-1BBL/CD137L apoptosis
P41273 BAFF TALL-1 apoptosis Q9Y275 soluble TRAIL receptor 3 TRAIL
sR3/TNFR S10C apoptosis 014755 soluble TRAIL receptor 4 TRAIL
sR4/TNFR S10D apoptosis Q9UBN6 TNF-related death ligand 1a
TRDL-1a/APRIL apoptosis AF046888 TNFSF-14 LIGHT apoptosis 043557
TRAIL Apo2L apoptosis P50591 BCA-1 BLC chemokine 043927 CCL-28
CCK-1 chemokine cutaneous T cell attracting chemokine CTACK, CCL27
chemokine Qgz1X0 ENA-78 chemokine P42830 Eotaxin-1 chemokine P51671
Eotaxin-2 MPIF-2 chemokine 000175 Eotaxin-3 CCL26 chemokine Q9Y258
Fractalkine neurotactin chemokine P78423 Granulocyte chemotactic
protein 2 GCP-2 chemokine P80162 GRO alpha MGSA chemokine P09341
GRO beta MIP-2alpha chemokine P19875 GRO gamma MIP-2beta chemokine
P19876 haemoinfiltrate CC chemokine 1 HCC-1 chemokine Q16627
haemoinfiltrate CC chemokine 4 HCC-4/CCL16 chemokine 015476 I-309
TCA-3/CCL-1 chemokine P22362 IFNgamma inducible protein-10 IP-10
chemokine P02778 IFN-inducible T cell alpha chemokine I-TAC/CXCL11
chemokine AF030514 interleukin-8 IL-8/NAP-1 chemokine P10145
leucocyte cell-derived chemotaxin-2 LECT2 chemokine Lungkine
CXCL-15/WECHE chemokine Lymphotactin Lptn/ATAC chemokine P47992
MIP- 1alpha/ pLD78/ macrophage inflammatory protein 1alpha CCL3
chemokine P10147 macrophage inflammatory protein 1beta
MIP-1beta/ACT-2/CCL4 chemokine P13236 macrophage inflammatory
protein 1d MIP-1d/CCL15/LKN-1 chemokine macrophage inflammatory
protein 1gamma MIP-1gamma/CCL9/MIP- chemokine 3alpha/CCL20/
macrophage inflammatory protein 3alpha LARC chemokine P78556
macrophage inflammatory protein 3beta MIP-3beta/ELC/CCL19 chemokine
Q99731 macrophage-derived chemokine MDC/STCP-1 chemokine 000626
monocyte chemoattractant protein-1 MCP-1/CCL2 chemokine P13500
monocyte chemoattractant protein-2 MCP-2/CCL8 chemokine P78388
monocyte chemoattractant protein-3 MCP-3/CCL7 chemokine P80098
monocyte chemoattractant protein-4 MCP-4/CCL13 chemokine Q99616
monocyte chemoattractant protein-5 MCP-5/CCL12 chemokine monokine
induced by IFN gamma MIG chemokine Q07325 mucosa-associated
chemokine MEC chemokine AF266504 Myeloid progenitor inhibitory
factor MPIF/CKbeta8/CCL23 chemokine platelet basic protein
PBP/CTAP-III/NAP-2 chemokine P02775 platelet factor 4 PF-4/CXCL4
chemokine P02776 pulmonary activation regulated chemokine
PARC/CCL18/MIP-4 chemokine RANTES CCL5 chemokine P13501 secondary
lymphoid tissue chemokine SLC/6Ckine chemokine 000585 stromal cell
derived factor 1 SDF-1/CXCL12 chemokine P48061 thymus activation
regulated chemokine TARC/CCL17 chemokine Q92583 thymus expressed
chemokine TECK/CCL25 chemokine 015444 Clq collectin mannose binding
lectin MBL collectin surfactant protein A SP-A collectin surfactant
protein D SP-D collectin C1 inhibitor complement C3a complement Cob
binding protein C4BP complement C5a complement complement C3 C3
complement complement C5 C5 complement complement C8 C8 complement
complement C9 C9 complement decay accelerating factor DAF
complement Factor H complement membrane inhibitor of reactive lysis
MIRL/CD59 complement Properdin complement soluble complement
receptor 1 sCR1 complement soluble complement receptor 2 sCR2
complement cardiotrophin-1 CT-1 cytokine Q16619 CD27 cytokine
P26842 CD27L CD70 cytokine P32970 CD30 Ki-1 cytokine P28908 CD30L
TNFSF8 cytokine P32971 CD40L TRAP/CD154 cytokine P29965 interferon
alpha IFNalpha cytokine P01562 interferon beta IFNbeta cytokine
P01574 interferon gamma IFNgamma cytokine P01579 interferon omega
IFNomega cytokine P05000 interferon-sensitive gene 15 ISG-15
cytokine P05161 Leptin OB cytokine P41159 leukemia inhibitory
factor LIF/CNDF cytokine P15018 Lymphotoxin LT/TNF beta cytokine
P01374 macrophage colony stimulating factor M-CSF/CSF-1 cytokine
P09603 macrophage stimulating protein-alpha MSPalpha/HGF1 cytokine
P26927 macrophage stimulating protein-beta MSPbeta/HGF1 cytokine
P26927 migration inhibition factor MIF/GIF cytokine P14174
oncostatin M OSM cytokine P13725 RANKL TRANCE/TNFSF-11 cytokine
014788 soluble IL6 R complex sIL6RC (gp130 + sIL6R) cytokine
soluble Fas ligand sCD95L cytokine P48023 TNF type I receptor
TNF-RI p55 cytokine P19438 TNF type II receptor TNF-R p75 cytokine
P20333 TNFSF-18 GITRL/AITRL cytokine 095852 tumor necrosis factor
alpha TNF-alpha/Apo3L/DR3-L/ cytokine P01375 TNFSF-12 TWEAK
cytokine 043508 acidic fibroblast growth factor aFGF growth factor
P05230 activin beta A growth factor P08476 agouti related protein
AGRP growth factor AAB52240 Amphiregulin AR/SDGF growth factor
P15514 angiopoietin-like factor ALF growth factor basic fibroblast
growth factor bFGF growth factor P09038 Betacellulin growth factor
P35070 bone morphogenic protein 2 BMP2 growth factor P12643 bone
morphogenic protein 4 BMP4 growth factor bone morphogenic protein 5
BMP5 growth factor bone morphogenic protein 6 BMP6 growth factor
bone morphogenic protein 7 BMP7 growth factor cripto-1 CRGF growth
factor epidermal growth factor EGF growth factor P01133
Erythropoietin Epo growth factor fibroblast growth factor 17 FGF-17
growth factor fibroblast growth factor 18 FGF-18 growth factor
fibroblast growth factor 19 FGF-19 growth factor fibroblast growth
factor 2 FGF-2 growth factor fibroblast growth factor 4 FGF-4
growth factor fibroblast growth factor 6 FGF-6 growth factor
fibroblast growth factor 7 FGF-7/KGF growth factor fibroblast
growth factor 8 FGF-8 growth factor fibroblast growth factor 9
FGF-9 growth factor Flt3 ligand Flt L growth factor P49771
Follistatin FSP growth factor Granulocyte colony stimulating factor
G-CSF growth factor P09919 granulocyte/macrophage CSF GM-CSF growth
factor P04141 growth and differentiation factor 11 GDF-11 growth
factor growth and differentiation factor 15 GDF-15 growth factor
growth arrest specific gene 6 Gas-6 growth factor heparin-binding
epidermal growth factor HB-EGF growth factor Q99075. hepatocyte
growth factor HGF/SF growth factor P14210 hepatopoietin A HPTA/HRG
alpha/ growth factor neuregulin heregulin alpha NDF/HRG
beta/neuregulin/ growth factor heregulin beta NDF growth factor IGF
binding protein-1 IGFBP-1 growth factor IGF binding protein-2
IGFBP-2 growth factor IGF binding protein-3 IGFBP-3 growth factor
IGF binding protein-4 IGFBP-4 growth factor inhibin A growth factor
inhibin B growth factor insulin-like growth factor IA IGF-IA growth
factor P01343 insulin-like growth factor IB IGF-IB growth factor
P05019 insulin-like growth factor II IGF-II growth factor P01344
macrophage galatose-specific lectin 1 MAC-1 growth factor Neuritin
growth factor Neurturin growth factor orexin A growth factor
Osteonectin SPARC growth factor Osteoprotegrin TNFRSF11B growth
factor placenta growth factor PGIF growth factor platelet derived
growth factor alpha PDGF-A growth factor P04085 platelet derived
growth factor beta PDGF-B growth factor P01127 pregnancy zone
protein growth factor Prolactin PRL growth factor P01236 sensory
and motor neuron-derived factor SMDF growth factor soluble GM-CSF
receptor sGM-CSF R growth factor P15509 stem cell factor
SLF/SCF/kit ligand/MGF growth factor P21583 Thrombopoietin
TPO/c-MPL ligand growth factor P40225 thymic stromal lymphoprotein
TSLP growth factor Thymopoietin Tpo growth factor transforming
growth factor alpha TGF-alpha growth factor P01135 transforming
growth factor beta 1 TGF-beta1 growth factor P01137 transforming
growth factor beta 2 TGF-beta2 growth factor P08112 transforming
growth factor beta 3 TGF-beta3 growth factor P10600 vascular
endothelial growth factor VEGF growth factor P15692 interleukin-1
receptor antagonist ILiRa interleukin P18510 interleukin-10 IL-10
interleukin P22301 interleukin-11 IL-11 interleukin P20809
interleukin-12p35 IL-12p35 interleukin P29459 interleukin-12p40
IL-12p40 interleukin P29460 interleukin-13 IL-13 interleukin P35225
interleukin-14 IL-14 interleukin L15344 interleukin-15 IL-15
interleukin P40933 interleukin-16 IL-16 interleukin Q14005
interleukin-17 IL-17 interleukin Q16552 interleukin-18 IL-18
interleukin Q14116 interleukin-1alpha IL-1al.pha interleukin P01583
interleukin-1beta IL-1beta interleukin P01584 interleukin-2 IL-2
interleukin P01585 interleukin-3 IL-3 interleukin P08700
interleukin-4 IL-4 interleukin P05112 interleukin-5 IL-5
interleukin P05113 interleukin-6 IL-6 interleukin P05231
interleukin-7 IL-7 interleukin P13232 interleukin-9 IL-9
interleukin P15248 soluble interleukin-1 receptor I sILIR/CD121a
interleukin P14778 soluble interleukin-1 receptor II sIL1R/CD121b
interleukin P27930 soluble interleukin-2 receptor IL-2R/CD25
interleukin P01589 soluble interleukin-5 receptor sIL-5R/CD126
interleukin Q01344 soluble interleukin-6 receptor sIL-6R/CD126
interleukin P08887 soluble interleukin-7 receptor sIL-7R/CD127
interleukin P16871 soluble interleukin-9 receptor sIL-9R
interleukin PQ01113 AD7C NTP neuronal AF010144 alpha synuclein
neuronal AAH13293 GAP-43 neuronal Neurofilament neuronal
Synaptogamin neuronal Synaptophysin neuronal tau P199 neuronal
brain derived neurotrophic factor BDNF neurotrophin P23560 ciliary
neurotrophic factor CNTF neurotrophin P26441 glial derived
neurotrophic factor GDNF neurotrophin P39905 nerve growth factor
NGF neurotrophin P01138 neurotrophin 3 NT-3 neurotrophin P20783
neurotrophin 4 NT-4 neurotrophin P34130 soluble CNTF receptor
sCNTFR neurotrophin P26992 alpha2-macroglobulin alpha 2M others
Alzheimer associated protein ALZAS others amyloid beta protein
Abeta 1-x others apolipoprotein A apoA others apolipoprotein B apoB
others
apolipoprotein D apoD others apolipoprotein E apoE others
apolipoprotein J apoD/clusterin others C reactive protein CRP
others clara cell protein CC16 others glial fibrillary acidic
protein GFAP others Melanotransferrin others soluble transferring
receptor TfR others Thrombomodulin others Thrombospondin Tsp others
tissue transglutaminase others Transferrin others alpha
1-antichymotrypsin ACT protease NP001076 Clr protease Cls protease
complement C2 C2 protease Factor B protease Factor D adipsin
protease FactorI protease Kallikrein protease MBL-associated serine
protease 1 MASP-1 protease MBL-associated serine protease 2 MASP-2
protease Neuroserpin protease AAH18043 secretory leukocyte protease
inhibitor SLPI protease Angiogenin vascular Angiostatin vascular
P00747 Endostatin vascular Endothelin vascular soluble E selectin s
E selectin vascular vascular endothelial growth inhibitor VEGI
vascular
Example 11
[0230] This example describes methods useful for measuring the
levels of AD biomarkers and/or analyzing data regarding
measurements of the levels of AD biomarkers and/or correlating data
based on the measurements of the levels of AD biomarkers and/or
identifying AD biomarkers by analyzing and/or correlating data
based on the measurements of the levels of AD biomarkers obtained
from biological samples from subjects across different test
centers. These methods are also applicable to biological samples
obtained from an individual and/or single collection center. The
methods are designed to minimize or reduce test center variability
resulting from collection procedures and/or storage and handling
conditions. This example, along with Example 12, provides methods
for identifying additional biomarkers that are useful in the
detection of AD, including markers which provide a high degree of
sensitivity (calculated as the number of AD samples in the AD
cluster divided by the total number of AD samples used in the
experiment) and specificity (calculated as the number of controls
in the control cluster divided by total number of controls used in
the experiment for diagnosing AD), as well as identifying such
biomarkers.
[0231] Collection procedures as well as storage and handling
conditions can introduce variability in the concentration of
biomarkers measured in biological samples, such as plasma, of AD
and Control Subjects. This in turn could cause misclassification of
subjects without appropriate normalization and/or standardization
and/or controls. For example, protein concentrations may be
affected, in part, by whether a particular plasma sample is
platelet rich or platelet poor. In general, plasma samples that are
platelet rich will have greater quantitative levels of many
biomarkers, while samples that are platelet poor will have reduced
quantitative levels of many biomarkers (as compared to appropriate
controls, for example population controls). For example, the
concentration of BDNF, which is tightly held within platelets, was
measured as a surrogate for platelet degranulation and therefore
the release of BDNF from platelets. It was observed that carefully
prepared platelet poor plasma has a concentration of BDNF that is
equivalent to 10 pg/ml whereas platelet rich preparations of plasma
can have concentrations as high as 20 ng/ml. The correlation of
BDNF measured by ELISA and BDNF measured by spotted filter antibody
array has an r=0.679, with p<0.0001. The samples used in the
experimental design were prepared in a manner such that they did
not include platelet poor preparation of BDNF, as these are not
representative of plasma collection in common practice.
[0232] In some examples, plasma is used as the biological sample
for the methods disclosed herein rather than serum. Plasma was used
in the methods of Example 1, and Examples 11-14. This is due, in
part, to the variables involved in the blood clotting process used
to make serum. These variables may lead to varying degrees of
proteolysis of biomarkers contained in the serum. Also, if plasma
is used, there is less chance of inadvertently removing a protein
of interest. If large amounts of fibrinogen or albumin do present a
problem, there are depletion kits publicly available to deplete the
plasma of these proteins, although if this is done, associated
proteins may be removed as well. If depletion kits are used,
appropriate controls to monitor removal of the associated proteins
may be used in the methods.
[0233] Sterile blood collection tubes that are pre-loaded with
protease inhibitors, as well as a self-contained system for
removing red blood cells and platelets are publicly available. See
for example, the Beckton Dickenson Company product lists at:
bd.com/vacutainer/products/venous/ordering_info_tubes.asp.
[0234] The protocol below is one illustrative example of sample
collection procedures.
[0235] Becton Dickenson BD P100 tubes are stored at 4.degree. C.,
until use. A full 8.5 mL of blood is collected to produce about
2.5-3 mL of plasma. Immediately after collection, the tube is
inverted 8-10 times to mix the protease inhibitors and
anticoagulent with the blood sample. The tube is placed in wet ice
before centrifuging. (Centrifugation should be done within 30
minutes of collection). The tubes are centrifuged at 2000-3000 RCF
at 4.degree. C. for 15 min. (See BD P100 package insert for
converting rpm to RCF). Do not exceed 3000 g, or 10,000 RCF.
[0236] Within 30 minutes of centrifugation, the plasma is
transferred in 1-mL aliquots to pre-labeled Fisherbrand 4-mL
self-standing cryovials (Fisher Scientific # 0566966) and
immediately placed on dry ice. Aliquots are frozen at -80.degree.
C. until used. (Avoid freeze-thaw cycles). To remove
microplatelets, the plasma is transferred to a different centrifuge
tube, and is centrifuged at 12,000 g at 4.degree. C. for 15
min.
[0237] The objective of this experiment, in part, was to determine
methods, including identification of appropriate controls, for use
in analyzing data that minimize individual variations in the immune
response and variations produced by collection and storage
conditions while identifying AD subjects with a high degree of
specificity and sensitivity.
[0238] The methods used in the experiments were the same as
described herein in Example 1 with filter based antibody arrays
consisting of 120 antibodies specific for the proteins, that is
biomarkers, listed in Table 8. In some previous experiments using
filter based antibody arrays of 120 antibodies specific for the
biomarkers listed in Table 8 (the designation of ".sub.--1" after
each biomarker name in Tables 8, 9A1-9A2 and 9B, 10A1-10A2 and 10B,
11A1-11A2 and 11B, and 12A-12B is a function of the program and is
not part of the name of each biomarker) when a signal was not
detectable, it was not clear if this was a false negative result
(for example, due to problems with the use of certain of the
reagents) or a true negative result. In the following experiments,
due to improvements made by the manufacturer of the reagents
(RayBiotech), it was determined that a signal could be detected for
all of the 120 proteins screened using the antibody arrays. This
improvement in reagents resulted in identification of additional
biomarkers (as shown in Example 12) for use in the methods as
disclosed herein, such as for example, in methods for aiding in the
diagnosis of and/or diagnosing AD, which biomarkers may or may not
have been detectable in previous experiments.
[0239] In this experiment, the levels of the 120 biomarkers listed
in Table 8 were measured for biological samples collected at five
different Alzheimer's centers (n=34, mean age=74, Mean MMSE=20)
including 16 samples collected 1.5 yrs apart from 8 subjects with
AD, who were later confirmed by autopsy to have AD, were compared
to controls, for example, age matched controls collected from two
centers (n=17) and other non-AD neurodegenerative age-matched
controls (n=16) consisting of 4 subjects diagnosed with Parkinson's
disease, and 12 subjects diagnosed with peripheral neuropathy.
Power calculations show that 10 samples of autopsy confirmed AD
samples are necessary to have an Alpha of 0.001 and power of
0.999.
[0240] Experimental data for all 120 biomarkers were extracted
using Imagene software licensed from Biodiscovery. The extracted
data was then normalized to the positive control for the experiment
spotted on the blot. An example of a positive control is IgG. The
data for each individual biomarker was then normalized to the
median concentration of all 120 proteins measured by the antibody
array. The Significance analysis of microarrays (SAM) was used to
determine significance of each biomarker. This method for
normalizing data extracted from a blot experiment minimizes or
reduces variability due to the fact that individual samples can
have slightly higher or lower levels of proteins based on the
individual's immune response status. Following the determination of
significance using SAM, the biomarkers with p-values less than or
equal to 0.1% (53 markers) were used for cluster analysis to
classify AD from controls. (See Tables 13A (biomarkers that are
positively correlated) and 13B (biomarkers that are negatively
correlated for the markers listed that have a p-value % of about
0.1). All biomarkers with p-values less than or equal to 5% (Tables
9A1-9A2 and 9B) were all used in cluster analysis to classify
samples as AD based on the controls used. Results of analysis of
extracted data that were normalized as described above are
disclosed in Example 12 and Tables 13A-13B (unclustered, and in
order of highest ranked biomarker to lowest ranked biomarker,
significantly increased (13A) or decreased (13B) in AD compared to
age-matched normal controls plus other non-AD forms of
neurodegeneration, such as PD an PN (that is, as compared to all
controls). The columns from left to right for Tables 13A-13B are
biomarker Name, Score (d), fold change and p-value (%). Tables
9A1-9A2 and 9B as described in Example 12 show an additional
analysis of data for biomarkers having a p-value of greater than
0.1% and less than 5%.
Example 12
[0241] This example describes methods for identifying AD biomarkers
that are either increased or decreased in individuals diagnosed
with AD compared to healthy age matched controls and/or
neurodegenerative age matched controls that are non-AD, that is,
non-AD neurodegenerative controls, such as Parkinson's Disease
(PD), and peripheral neuropathy (PN). This is important because AD
is a neurodegenerative disease, and it is advantageous to identify
biomarker patterns of neurodegeneration associated with AD, in
terms of identification of biomarkers that are either decreased or
increased with respect to an appropriate control(s), that are
unique to AD and/or distinguishable from other non-AD forms of
neurodegeneration, such as for example PD and PN, in the same age
group, as well as with respect to healthy age-matched controls.
[0242] Previous experiments (see Example 1) determined that any one
or more of the following biomarkers could be used for the detection
of AD: GCSF; IFN-g; IGFBP-1; BMP-6; BMP-4; Eotaxin-2; IGFBP-2;
TARC; RANTES; ANG; PARC; Acrp30; AgRP(ART); TIMP-1; TIMP-2; ICAM-1;
TRAIL R3; uPAR; IGFBP-4; LEPTIN(OB); PDGF-BB; EGF; BDNF; NT-3;
NAP-2; IL-1ra; MSP-a; SCF; TGF-b3; TNF-b; MIP-1d; IL-3; FGF-6; IL-6
R; sTNF RII; AXL; bFGF; FGF4; CNTF; MCP-1; MIP-1b; TPO; VEGF-B;
IL-8; FAS; EGF-R. Based upon the experimental conditions and
analysis described in Example 11, additional biomarkers useful for
detecting AD were identified. The measured values for the
biomarkers from Table 8 were subjected to hierarchical clustering
based on classification of samples with normalized concentration
surveyed. Based upon the clustering analysis, the proteins were
segregated into 9 classes of similarities based on correlation.
Biomarkers with greater than a 5% p value (%) were eliminated from
the analysis. Sensitivity of the classification is calculated as
the number of AD samples in the AD cluster divided by the total
number of AD samples used in the experiment (in this case
31/34=91%). Specificity is calculated as the number of controls in
the control cluster divided by total number of controls used in the
experiment (in this case 31/33=94%).
[0243] Tables 13A-13B provide a listing of biomarkers as described
in Example 11. Tables 9A1-9A2 and 9B provide a listing of
biomarkers (clustered by methods as described above) in order of
highest ranked biomarker to lowest ranked biomarker within each
cluster based on score value) that are significantly increased
(9A1-9A2) or decreased (9B) in AD compared to age-matched normal
controls plus other non-AD forms of neurodegeneration, such as for
example PD and PN (that is, as compared to all controls). The
columns from left to right for Table 9A1-A2 and 9B are: biomarker
name; Score(d); Fold change; q-value(%) and cluster number.
Significance analysis of microarrays is discussed in for example
Tusher et al., 2001, PNAS, vol. 98:5116. Any one or more of the
biomarkers listed in Table 9A1-A2 and 9B can be used in the methods
disclosed herein, such as for examples, methods for aiding in the
diagnosis of or diagnosing AD. As described herein, multiple AD
diagnosis biomarkers may be selected from the AD diagnosis
biomarkers disclosed in Tables 9A1-9A2 and 9B by selecting for
cluster diversity. The highest ranked biomarkers from each of the 9
clusters shown in Tables 9A1-9A2 and 9B (both positively correlated
and negatively correlated) are: BTC (cluster 0); SDF-1 (cluster 1);
MCP-2 (cluster 2); IFN-gamma (cluster 3); IGFBP4 (cluster 4);
IGF-1SR (cluster 5); IL-8 (cluster 6); GM-CSF (cluster 7); and
ANG-2 (cluster 8). In some examples, biomarkers for use in the
methods disclosed herein, such as for example, methods for aiding
in the diagnosis of AD or diagnosing AD, include at least one
marker selected from the group consisting of BTC; SDF-1; MCP-2;
IFN-gamma; IGFBP4; IGF-1SR; IL-8; GM-CSF; and ANG-2 or at least one
marker from Tables 13A-13B. In some examples, additional biomarkers
for use in the methods disclosed herein, such as for example,
methods for aiding in the diagnosis of AD or diagnosing AD, include
biomarkers that correlate with one or more of BTC; SDF-1; MCP-2;
IFN-gamma; IGFBP-4; IGF-1SR; IL-8; GM-CSF; and ANG-2, that is, such
biomarkers that have a Correlation: greater than 90% (r=0.9 to
r=0.99); and a P-value less than 0.001 up to 0.05.
[0244] In some examples, biomarkers for use in the methods
disclosed herein, such as for example, methods for aiding in the
diagnosis of AD or diagnosing AD include two or more markers
selected from the group consisting of BTC; SDF-1; MCP-2; IFN-gamma;
IGFBP4; IGF-1SR; IL-8; GM-CSF; and ANG-2. In some examples,
biomarkers for use in the methods disclosed herein, such as for
example, methods for aiding in the diagnosis of AD or diagnosing AD
include markers comprising BTC; SDF-1; MCP-2; IFN-gamma; IGFBP-4;
IGF-1SR; IL-8; GM-CSF; and ANG-2. In other examples, the top ranked
2, 3, 4, or 5 biomarkers from one or more clusters represented in
Tables 9A1-9A2 and 9B are selected for use in the methods as
disclosed herein.
[0245] Tables 10A1-10A2 and 10B provide a listing of biomarkers
(not clustered and in order of highest ranked biomarker to lowest
ranked biomarker based on score value) that are significantly
increased (10A1-10A2) or decreased (10B) in AD compared to healthy
age-matched controls. The columns from left to right in Tables
10A1-10A2 and 10B, Tables 11A1-11A2 and 11B, and Tables 12A-12B are
Biomarker name, Score(d); Fold change; and q-value(%). Based on
Tables 10A1-10A2 and 10B, identified biomarkers that are
significantly increased in AD as compared to healthy age-matched
controls include, but are not limited to (in descending order based
on score): BTC; ANG-2; MIF; IGFBP-6; spg130; CTACK; IGFBP3; MIP-1a;
TRAIL R4; IL-12 p40; AR; NT-4; VEGF-D; OSM; OST; IL-11; sTNF R1;
I-TAC; Eotaxin; TECK; PIGF; bNGF; Lymphotactin; MIP-3b; HCC-4;
ICAM-3; DTK; IL-1 RI; IGF-1 SR; GRO; GITR-Light; HGF; IL-1R4/ST;
IL-2 Ra; ENA-78; and FGF-9. Based on Tables 10A1-10A2 and 10B,
identified biomarkers that are significantly decreased in AD as
compared to healthy age-matched controls include, but are not
limited to (in descending order based on score): MCP-2; M-CSF;
MCP-3; MDC; MCP-4; IL-1b; IL-4; IL-1a; BLC; CK b8-1; IL-2; IL-15;
MIP3a; MIG; SCF; IL-6; IL-16; Eotaxin-3; 1-309; TGF-beta;
TGF-alpha; GDNF; LIGHT; SDF; IFG-1; Fractalkine; IL-5; Fit-3
ligand; GM-CSF; and GCP-2. Any one or more of the biomarkers listed
in Tables 10A1-10A2 and 10B can be used in the methods disclosed
herein, such as for example, for aiding in the diagnosis of or
diagnosing AD.
[0246] Tables 11A1-11A2 and 11B provide a listing of biomarkers
(not clustered and in order of highest ranked biomarker to lowest
ranked biomarker based on score value) that are significantly
increased (11A1-11A2) or decreased (11B) in AD compared to
age-matched degenerative controls. Based on Tables 11A1-11A2 and
11B, identified biomarkers that are significantly increased in AD
as compared to age-matched other non-AD neurodegenerative controls
include, but are not limited to (in descending order based on
score): TRAIL R4; Eotaxin; IL-12 p40; BTC-1; MIF; OST; MIP-1a; sTNF
R1; IL-11; Lymphotactin; NT-4; VEFG-D; HGF; IGFBP3; IGFBP-1; OSM;
IL-1R1; PIGF; IGF-1 SR; CCL-28; IL-2 Ra; IL-12 p70; GRO; IGFBP-6;
IL-17; CTACK; I-TAC; ICAM-3; ANG-2; MIP-3b; FGF-9; HCC-4;
IL-1R4/ST; GITR; and DTK. Based on Tables 11A1-11A2 and 11B,
identified biomarkers that are significantly decreased in AD as
compared to age-matched other non-AD neurodegenerative controls
include, but are not limited to (in descending order based on
score): MCP-2; M-CSF; MCP-3; MDC; MCP-4; IL-1b; IL-4; IL-1a; BLC;
CKb8-1; IL-2; IL-15; MIP3a; MIG; SCF; IL-6; IL-16; Eotaxin-3;
1-309; TGF-beta; TNF-alpha; GDNF; LIGHT; SDF-1; IFG-1; Fractalkine;
IL-5; Fit-3 Ligand; GM-CSF; and GCP-2. Any one or more of the
biomarkers listed in Tables 11A1-11A2 and 11B can be used in the
methods disclosed herein, such as for example, methods for aiding
in the diagnosis of or diagnosing AD.
[0247] Tables 12A-12B provide a listing of biomarkers (not
clustered and in order of highest ranked biomarker to lowest ranked
biomarker based on score value) that are significantly increased
(12A) or decreased (12B) in AD plus other non-AD neurodegenerative
controls with reference to age matched controls. Any one or more of
the biomarkers listed in Tables 12A-12B can be used in the methods
disclosed herein, such as for example, methods for aiding in the
diagnosis of or diagnosing neurodegenerative diseases, including
AD. In other examples, the top ranked 2, 3, 4, or 5 biomarkers
listed in Tables 12A-12B are selected for use in the methods as
disclosed herein. In some examples, additional biomarkers for use
in the methods disclosed herein, such as for example, methods for
aiding in the diagnosis of AD or diagnosing AD, include biomarkers
that correlate with the top ranked 1, 2, 3, 4, or 5 biomarkers
listed in Tables 12A-12B, that is, such biomarkers that have a
Correlation: greater than 90% (r=0.9 to r=0.99); and a P-value less
than 0.001 up to 0.05.
[0248] As will be understood by the skilled artisan, biomarkers
disclosed herein in the Examples and Tables can be selected for use
in the methods disclosed herein depending on the type of
measurement desired. For example, any one or more of the markers
selected from the group consisting of the markers listed in Table 7
and/or Table 8 can be used to aid in the diagnosis of AD or for
diagnosing AD. In some examples, biomarkers from Table 7 and/or
Table 8 are selected for use in the methods disclosed herein based
on the following criteria: Correlation: greater than 90% (r=0.9 to
r=0.99); P-value less than 0.001 up to 0.05; Fold change greater
than 20%; and a Score greater than 1 (for markers that increase,
that is, that are positively correlated) or less than 1 (for
markers that decrease, that is, that are negatively
correlated).
[0249] In other examples, one or more markers selected from the
group consisting of GCSF; IFN-g; IGFBP-1; BMP-6; BMP-4; Eotaxin-2;
IGFBP-2; TARC; RANTES; ANG; PARC; Acrp30; AgRP(ART); TIMP-1;
TIMP-2; ICAM-1; TRAIL R3; uPAR; IGFBP-4; LEPTIN(OB); PDGF-BB; EGF;
BDNF; NT-3; NAP-2; IL-1ra; MSP-a; SCF; TGF-b3; TNF-b; MIP-1d; IL-3;
FGF-6; IL-6 R; sTNF RII; AXL; bFGF; FGF-4; CNTF; MCP-1; MIP-1b;
TPO; VEGF-B; IL-8; FAS; and EGF-R can be used in the methods
disclosed herein, such as, for example, to aid in the diagnosis of
AD or for the diagnosis of AD. In other examples, one or more
biomarkers selected from Tables 12A-12B can be used to aid in the
detection of general neurodegenerative disorders (including AD)
and/or to diagnose neurodegenerative disorders generally while one
or more biomarkers selected from Tables 9A1-9A2 and 9B can be used
to aid in the diagnosis of AD or to diagnose AD and/or distinguish
AD from other non-AD neurodegenerative diseases. In other examples,
one or more biomarkers from Tables 10A1-10A2 and 10B or Tables
11A1-11A2 and 11B can be used to aid in the diagnosis of AD or to
diagnose AD.
[0250] In addition to the biomarkers identified above, additional
biomarkers can be identified by the methods described herein and
methods known in the art. The parameters for selection of
additional biomarkers are as follows: [0251] Correlation: greater
than 90% (r=0.9 to r=0.99); [0252] P-value less than 0.001 up to
0.05; [0253] Fold change greater than 20%; and [0254] a Score
greater than 1 (for markers that increase) or less than 1 (for
markers that decrease).
Example 13
[0255] This example provides the biomarkers for aiding in the
diagnosis of or diagnosing AD identified in two different
experiments (single collection center and multi-collection center)
as being significant.
[0256] Additional biomarkers, sTNF RII; MSP-alpha; uPAR; TPO;
MIP-1beta; VEGF-beta; FAS; MCP-1; NAP-2; ICAM-1; TRAIL R3; PARC;
ANG; IL-3; MIP-1delta; IFN-gamma; IL-8; and FGF-6 were identified
as significant in both the experiment from a single collection
center (see Example 1) and the multi-test center experiment
(Examples 11-12) that was normalized as described in Examples
11-12. Of these 18 biomarkers, two, IFN-gamma and IL-8, also appear
in Tables 9A1-9A2 and 9B as the highest ranked biomarker from
cluster 3 and cluster 6, respectively. Accordingly, biomarkers for
use in the methods of the present invention for aiding in the
diagnosis of or diagnosing AD include IFN-gamma and/or IL-8. It was
found that the following two biomarkers were useful as
normalization controls in the methods of the present invention for
aiding in the diagnosis of or diagnosing AD: TGF-beta and
TGF-beta3. Accordingly, biomarkers for use in the methods of the
present invention, such as for example, for aiding in the diagnosis
of or diagnosing AD include TGF-beta and/or TGF-beta3 as
normalization controls. TABLE-US-00021 TABLE 8 List of Biomarkers
ANG_1 BDNF_1 BLC_1 BMP-4_1 BMP-6_1 CK b8-1_1 CNTF_1 EGF_1 Eotaxin_1
Eotaxin-2_1 Eotaxin-3_1 FGF-6_1 FGF-7_1 Fit-3 Ligand_1
Fractalkine_1 GCP-2_1 GDNF_1 GM-CSF_1 I-309_1 IFN-g_1 IGF-1_1
IGFBP-1_1 IGFBP-2_1 IGFBP-4_1 IL-10_1 IL-13_1 IL-15_1 IL-16_1
IL-1a_1 IL-1b_1 IL-1ra_1 IL-2_1 IL-3_1 IL-4_1 IL-5_1 IL-6_1 IL-7_1
LEPTIN(OB)_1 LIGHT_1 MCP-1_1 MCP-2_1 MCP-3_1 MCP-4_1 M-CSF_1 MDC_1
MIG_1 MIP-1d_1 MIP-3a_1 NAP-2_1 NT-3_1 PARC_1 PDGF-BB_1 RANTES_1
SCF_1 SDF-1_1 TARC_1 TGF-b_1 TGF-b3_1 TNF-a_1 TNF-b_1 Acrp30_1
AgRP(ART)_1 ANG-2_1 AR_1 AXL_1 bFGF b-NGF_1 BTC_1 CCL-28_1 CTACK_1
DTK_1 EGF-R_1 ENA-78_1 FAS_1 FGF-4_1 FGF-9_1 GCSF_1 GITR_1
GITR-Light_1 GRO_1 GRO-a_1 HCC-4_1 HGF_1 ICAM-1_1 ICAM-3_1 IGF-1 SR
IGFBP3_1 IGFBP-6_1 IL-1 RI_1 IL-11_1 IL-12 p40_1 IL-12 p70_1
IL-17_1 IL-1R4/ST2_1 IL-2 Ra_1 IL-6 R_1 IL-8_1 I-TAC_1
Lymphotactin_1 MIF_1 MIP-1a_1 MIP-1b_1 MIP-3b_1 MSP-a_1 NT-4_1
OSM_1 OST_1 PIGF_1 spg130_1 sTNF RI_1 sTNF RII_1 TECK_1 TIMP-1_1
TIMP-2_1 TPO_1 TRAIL R3_1 TRAIL R4_1 uPAR_1 VEGF-B_1 VEGF-D_1
[0257] TABLE-US-00022 TABLE 9A1 Name Score(d) Fold Change q-value
(%) Cluster BTC_1 5.280599 2.30404 0.102881 0 TRAIL R4.sub.--
4.18957 4.38847 0.102881 0 MIF_1 3.78626 2.46763 0.102881 0
MIP-1a_1 3.671968 2.04509 0.102881 0 sTNF RII.sub.-- 3.57664
1.81136 0.102881 0 MSP-a_1 3.532718 2.23649 0.102881 0 OST_1
3.519536 2.85493 0.102881 0 uPAR_1 3.42578 3.10753 0.102881 0 TPO_1
3.260328 2.04533 0.102881 0 NT-4_1 3.182778 2.48474 0.102881 0
MIP-1b_1 3.119065 2.07252 0.102881 0 NAP-2_1 2.970365 1.51262
0.102881 0 ICAM-1_1 2.949073 1.6633 0.102881 0 IGFBP3_1 2.868921
1.68668 0.102881 0 TRAIL R3.sub.-- 2.808197 1.85516 0.102881 0
Eotaxin_1 2.747874 2.23776 0.102881 0 VEGF-B_1 2.73066 1.94657
0.102881 0 PARC_1 2.703205 1.59801 0.102881 0 sTNF RI_1 2.628389
2.27051 0.102881 0 PIGF_1 2.59266 2.46572 0.102881 0 OSM_1 2.548107
1.79103 0.102881 0 ANG_1 2.527071 1.38167 0.102881 0 FAS_1 2.522175
1.42939 0.102881 0 VEGF-D_1 2.453761 3.08586 0.102881 0 Acrp30_1
2.277494 2.1151 0.102881 0 TIMP-1_1 1.815742 1.3765 0.102881 0
TIMP-2_1 1.768441 1.37666 0.102881 0 MIP-3b_1 1.516186 1.55797
0.290698 0 RANTES.sub.-- 1.482515 1.29415 0.290698 0 EGF-R_1
1.461975 1.24406 0.362319 0 CCL-28_1 1.332609 2.09378 0.362319 0
GCSF_1 1.248565 1.39107 0.531915 0 bFGF 1.135651 1.19806 0.687285 0
b-NGF_1 1.018717 1.22647 0.948845 0 TGF-b3_1 1.000846 1.16675
0.948845 3 IGF-1 SR 2.154497 2.01788 0.102881 5 GRO_1 1.12464
1.34176 0.687285 5 FGF-9_1 0.908764 1.34736 1.257862 5 GITR-Light
0.891591 1.23962 1.323988 5 IL-8_1 4.611751 2.30142 0.102881 6
IL-12 p40.sub.-- 4.397923 2.30237 0.102881 6 IL-11_1 3.428231
3.16541 0.102881 6 Lymphotac 2.655294 1.92588 0.102881 6 IL-1 RI_1
2.299796 2.69797 0.102881 6 CTACK_1 2.166969 1.4123 0.102881 6
HGF_1 1.917834 2.11589 0.102881 6 I-TAC_1 1.761741 1.75813 0.102881
6 ICAM-3_1 1.647733 1.63994 0.102881 6 IL-2 Ra_1 1.517361 1.75028
0.290698 6 DTK_1 1.334052 1.36685 0.362319 6 IL-12 p70.sub.--
1.136177 1.52347 0.687285 6
[0258] TABLE-US-00023 TABLE 9A2 Name Score(d) Fold Change q-value
(%) Cluster IL-17_1 0.973182 1.5033 0.948845 6 ANG-2_1 2.573094
1.48217 0.102881 8 IGFBP-6_1 2.559164 1.49096 0.102881 8 IL-6 R_1
2.308765 1.42281 0.102881 8 IGFBP-1_1 1.641212 1.3909 0.102881 8
AR_1 1.388841 1.31995 0.362319 8 IGFBP-2_1 1.313148 1.18336
0.362319 8 HCC-4_1 1.301826 1.48316 0.362319 8 IL-1R4/ST 0.973381
1.28961 0.948845 8
[0259] TABLE-US-00024 TABLE 9B Name Score(d) Fold Change q-value
(%) Cluster SDF-1_1 -3.717529 0.51302 0.102881 1 TNF-a_1 -3.502517
0.52906 0.102881 1 TARC_1 -2.327413 0.47705 0.102881 1 TNF-b_1
-1.156171 0.86239 1.121795 1 MCP-2_1 -5.829911 0.25732 0.102881 2
M-CSF_1 -5.008296 0.42889 0.102881 2 IL-1a_1 -4.92065 0.29231
0.102881 2 MDC_1 -4.362592 0.48973 0.102881 2 MCP-3_1 -4.034665
0.36994 0.102881 2 BLC_1 -3.624823 0.54297 0.102881 2 MCP-4_1
-3.391387 0.33264 0.102881 2 Eotaxin-3.sub.-- -3.378874 0.50745
0.102881 2 IL-3_1 -3.292671 0.45124 0.102881 2 IL-1b_1 -3.2351
0.33216 0.102881 2 IL-16_1 -3.112419 0.26418 0.102881 2 IL-2_1
-3.091275 0.39923 0.102881 2 FGF-6_1 -2.995265 0.60629 0.102881 2
IL-15_1 -2.990886 0.2798 0.102881 2 IL-4_1 -2.909983 0.56937
0.102881 2 GDNF_1 -2.898614 0.57687 0.102881 2 I-309_1 -2.813435
0.58059 0.102881 2 MCP-1_1 -2.807517 0.60158 0.102881 2 IL-5_1
-2.533339 0.11191 0.102881 2 IGF-1_1 -2.429866 0.60042 0.102881 2
LIGHT_1 -1.739557 0.68069 0.102881 2 GCP-2_1 -1.69179 0.3493
0.102881 2 Fractalkine -1.687498 0.59612 0.102881 2 IL-1ra_1
-1.589684 0.78477 0.200803 2 Fit-3 Ligan -1.113565 0.67551 1.190476
2 IFN-g_1 -3.560171 0.58458 0.102881 3 MIP-1d_1 -3.163485 0.71538
0.102881 3 IL-6_1 -2.794102 0.48921 0.102881 3 CK b8-1_1 -2.589929
0.68946 0.102881 3 BMP-6_1 -2.434357 0.72473 0.102881 3
Eotaxin-2.sub.-- -2.356828 0.7222 0.102881 3 CNTF_1 -2.309291
0.75875 0.102881 3 MIP-3a_1 -2.029226 0.70276 0.102881 3 MIG_1
-1.894224 0.72898 0.102881 3 TGF-b_1 -1.782306 0.70401 0.102881 3
BMP-4_1 -0.922924 0.92324 1.697531 3 IGFBP-4_1 -2.630045 0.5017
0.102881 4 IL-7_1 -0.692426 0.40835 2.19697 4 PDGF-BB.sub.--
-1.153073 0.79665 1.121795 5 GM-CSF_1 -3.318119 0.16273 0.102881 7
SCF_1 -2.478851 0.6653 0.102881 7 IL-10_1 -1.864524 0.3965 0.102881
7 IL-13_1 -1.538539 NA 0.200803 7 GRO-a_1 -1.338516 0.47248
0.531915 7 FGF-7_1 -1.147464 0.55216 1.121795 7 BDNF_1 -0.877883
0.9095 1.75841 7
[0260] TABLE-US-00025 TABLE 10A1 Name Score(d) Fold Change q-value
(%) 3.015803 2.3311 0.416667 ANG-1_1 2.779311 2.0092 0.416667
2.755264 2.63872 0.416667 ICAM-1_1 2.518324 2.54462 0.416667 IL-6_1
2.163434 2.07358 0.416667 BLC_1 2.100654 2.19149 0.416667 2.033582
3.65294 0.416667 MSP-a_1 2.002596 2.39185 0.416667 STNF RII_1
1.968631 2.15344 0.416667 TIMP-2_1 1.871601 1.99706 0.416667
TRAIL-R3 1.833582 2.20251 0.416667 ANG_2 1.780639 2.07536 0.416667
IL-8_1 1.733209 2.02022 0.416667 1.650103 1.883 0.416667 MIF-1
1.643462 2.22659 0.416667 TIMP-1_1 1.583688 1.7417 0.416667
MIP-1b_1 1.57533 2.36633 0.416667 IGFBP-6_1 1.46848 1.92629
0.416667 spg130_1 1.391691 2.1923 0.416667 CTACK_1 1.34839 1.72505
0.416667 IGFBP3_1 1.338495 1.84934 0.416667 uPAR_1 1.334936 2.42069
0.416667 MIP-1a_1 1.318658 1.931 0.416667 TRAIL-R4 1.311669 1.98605
0.416667 IL-12p40.sub.-- 1.291117 1.63912 0.416667 AR_1 1.220642
2.15904 0.416667 TPO_1 1.204405 1.86455 0.416667 NT-4_1 1.179381
2.41703 0.416667 FAS_1 1.169934 1.59942 0.416667 1.148262 1.58016
0.416667 VEGF-B_1 1.135884 1.89024 0.416667 VEG-D_1 1.097408
3.07633 0.416667 OSM_1 1.024058 1.8449 0.416667 OST_1 0.984518
1.82276 0.416667 0.96755 2.26315 0.416667 STNFR1_1 0.962797 1.96913
0.416667 RANTES.sub.-- 0.94568 1.34024 0.416667 0.916484 2.27116
0.416667 Eotaxin_1 0.890839 1.46174 1.215278 TECK_1 0.882859
1.77056 1.215278 PIGF_1 0.828355 2.16487 1.215278 FGF-R_1 0.816062
1.60576 1.215278 Lymphotac 0.796052 1.41315 1.215278 MIP-3b_1
0.758506 1.55228 1.215278 0.702511 1.81688 2.5 0.655704 1.70769 2.5
0.637012 1.72939 3.012048 IGFBP-2.sub.-- 0.620817 1.2029 3.012048
0.561553 1.50254 3.633721 0.534716 1.73834 3.932584 0.513525 1.5253
3.932584
[0261] TABLE-US-00026 TABLE 10A2 Name Score(d) Fold Change q-value
(%) AoRP(ART) 0.512419 1.82258 3.932584 0.466677 1.31521 5.163043
0.45041 1.38962 5.859375 IGFBP-1_1 0.435299 1.20224 5.859375
0.403816 1.33883 6.185567 0.287572 1.22954 9.926471 IL-2 R_1
0.25742 1.2669 10.71429 0.246878 1.29573 10.71429 FGF-9_1 0.242041
1.23628 10.71429
[0262] TABLE-US-00027 TABLE 10B Name Score(d) Fold Change q-value
(%) MCP-2_1 -2.304292 0.22807 0.416667 -2.207305 0.55921 0.416667
M-CSF_1 -2.079388 0.38905 0.416667 MCP-3_1 -2.025291 0.4534
0.416667 -1.949721 0.33125 0.416667 MCP-3_1 -1.890097 0.29936
0.416667 MDC_1 -1.783743 0.44485 0.416667 MCP-4_1 -1.716191 0.24506
0.416667 IL-1b_1 -1.709073 0.25335 0.416667 BMP-6_1 -1.601608
0.60317 0.416667 IL-4_1 -1.556667 0.46009 0.416667 -1.53838 0.31159
0.416667 BLC_1 -1.506867 0.48287 0.416667 CNTF_1 -1.494671 0.6341
0.416667 CK b8-1_1 -1.477242 0.56519 0.416667 L-2 -1.464754 0.30616
0.416667 IFN-9_1 -1.374387 0.55449 0.416667 IL-15_1 -1.279379
0.22092 0.416667 Eotaxin-2.sub.-- -1.235631 0.64369 0.416667
MIP-3a_1 -1.224965 0.56046 0.416667 MIG_1 -1.169439 0.59839
0.416667 SCF_1 -1.090775 0.62327 0.416667 IL-6_1 -1.04355 0.43341
1.215278 PDGF-BB.sub.-- -1.026201 0.68948 1.215278 IL-16_1
-0.996931 0.23613 1.215278 Eotaxin-3 -0.967402 0.52064 1.215278
-0.941786 0.54744 1.215278 TGF-b_1 -0.941131 0.59424 1.215278
TNF-a_1 -0.90183 0.58157 1.623377 FGF-6_1 -0.897254 0.63694
1.623377 CDNF_1 -0.869795 0.60042 1.623377 MIP-1d_1 -0.857723
0.77094 1.623377 LIGHT_1 -0.853961 0.606 1.623377 SDF-1_1 -0.807095
0.60929 2.5 IGF-1_1 -0.746646 0.61547 3.012048 Fractalkine
-0.731016 0.51894 3.633721 BDNF_1 -0.722385 0.82491 3.633721
-0.630005 0.12006 4.532967 TGF-b_1 -0.622882 0.8205 4.532967
BMF-4_1 -0.578993 0.87844 5.319149 -0.569274 0.55604 5.319149
-0.528832 0.25808 6.565657 -0.508646 0.69375 6.565657 -0.430976
0.37597 7.5 TARC_1 -0.408834 0.59042 7.673267
[0263] TABLE-US-00028 TABLE 11A1 Name Score(d) Fold Change q-value
(%) 2.264751 NA 0.904762 1.934453 4.70062 0.904762 1.880163 3.86536
0.904762 1.792904 2.4468 0.904762 1.624 2.67095 0.904762 1.578135
2.79532 0.904762 MSP-a_1 1.541907 2.11334 0.904762 uPAR_1 1.392662
4.38083 0.904762 OST_1 1.357148 6.61147 0.904762 MIP-1a_1 1.131823
2.18476 0.904762 TPO_1 1.127049 2.28982 0.904762 TRAIL R-3 1.092119
1.61261 0.904762 TGF-b3_1 1.04397 1.99067 0.904762 sTNFR11.sub.--
1.033891 1.55451 0.904762 GCSF_1 1.024952 3.10372 0.904762 sTNFR1
1.014653 2.78772 0.904762 1.003918 5.07851 0.904762 MP-1b_1
0.996616 1.83838 0.904762 VEGF-B_1 0.94194 2.00884 0.904762
Lymphotac 0.935601 2.41527 0.904762 NT-4_1 0.923994 2.57292
0.904762 VEGF-D_1 0.898048 3.15089 0.904762 Acrp30_1 0.885692
1.51332 0.904762 HGF_1 0.849923 4.96263 0.904762 IGFBR3_1 0.792485
1.54086 0.904762 IGFBP-1_1 0.78458 1.62237 0.904762 OSM-1 0.74836
1.76423 0.904762 0.744755 6.0184 0.904762 PIGF_1 0.723609 2.81402
1.544715 GF-1 SR 0.708495 3.05733 1.544715 RANTES 0.701614 1.26004
1.544715 ICAM-1_1 0.644564 1.24206 2.753623 CCL-28_1 0.587722
5.65125 3.298611 L-1a_1 0.555953 1.3324 5.61828 L-2Ra_1 0.551415
2.80849 5.61828 PARG_1 0.518736 1.15104 5.61828 FAS_1 0.507009
1.28116 5.61828 L-12P70 0.487912 3.29805 5.61828 NAP-2_1 0.484247
1.11825 5.61828 GRO 0.461543 1.44588 5.61828 NT-3_1 0.410048
1.32477 7.6 GFBP6 0.40842 1.21894 7.6 TIMP-1_1 0.400113 1.14706 7.6
L-17_1 0.392499 2.73288 7.6 GFBP-2 0.386188 1.16272 7.6 CTACK_1
0.380916 1.19299 7.6 LTAC_1 0.370637 1.4308 7.6 CAM-3 0.338506
1.47039 8.417722 0.33537 1.14941 8.417722 0.311494 1.91614 9.104167
0.293879 1.34124 9.728916
[0264] TABLE-US-00029 TABLE 11A 2 Fold q-value Name Score(d) Change
(%) 0.293743 1.46816 9.728916 0.263286 1.29481 11.61111 0.25256
1.32988 11.61111 0.248721 1.05528 11.61111 0.247866 1.33642
11.61111 0.241137 1.25033 11.61111 0.225219 1.072 12.0471 0.193332
1.1082 13.81206
[0265] TABLE-US-00030 TABLE 11B Fold q-value Name Score(d) Change
(%) -1.425686 0.28059 0.904762 -1.212676 0.30691 0.904762 -1.208951
0.39001 0.904762 -1.199429 0.61096 0.904762 -1.153624 0.44789
0.904762 -1.111198 0.48295 0.904762 -1.070072 0.65762 0.904762
-1.009846 0.25518 1.544715 -0.958718 0.11603 1.544715 -0.934948
0.49119 1.544715 -0.869781 0.55252 2.753623 -0.846319 0.58971
2.753623 -0.842647 0.72752 2.753623 -0.831081 0.60989 2.753623
-0.790743 0.55062 3.298611 -0.749212 0.51758 5.61828 -0.64395
0.49699 5.61828 -0.635584 0.621 5.61828 -0.626812 0.59071 5.61828
-0.621813 0.407 5.61828 -0.606932 NA 5.61828 -0.602712 0.80618
5.61828 -0.535561 0.42506 7.6 -0.527429 0.48739 7.6 -0.523648
0.72671 7.6 -0.523277 0.10826 7.6 -0.519148 0.30682 7.6 -0.512085
0.61731 7.6 -0.483536 0.76083 8.417722 -0.472803 0.6455 8.417722
-0.441918 0.57236 9.728916 -0.414943 0.38371 11.61111 -0.40133
0.31787 11.61111 -0.39242 0.52574 12.0471 -0.354478 0.82923
13.81206 -0.343716 0.58707 13.85417 -0.334159 0.74801 13.85417
-0.315677 0.48289 14.21769 -0.307046 0.77313 14.21769 -0.288231
0.71595 14.39394 -0.25551 0.69456 16.77393 -0.212551 0.37996
16.77393 -0.171954 0.89232 18.09524 -0.165428 0.918 18.09524
-0.162081 0.88435 18.09524 -0.157018 0.8931 18.09524
[0266] TABLE-US-00031 TABLE 12A Fold q-value Name Score(d) Change
(%) NAP-2_1 4.267334 2.25145 0.694444 ANG_1 4.061566 1.97693
0.694444 AXL_1 3.946682 2.03097 0.694444 PARC_1 3.740647 2.53113
0.694444 ICAM-1_1 3.510347 2.38945 0.694444 IL-6 R_1 3.397778
2.02276 0.694444 spg130_1 3.297869 2.61126 0.694444 ANG-2_1
3.253421 1.98738 0.694444 AR_1 2.780729 2.195 0.694444 IGFBP-6_1
2.766085 1.81674 0.694444 TIMP-2_1 2.746738 1.96642 0.694444 sTNF
RII.sub.-- 2.70119 1.9052 0.694444 BTC_1 2.354153 1.77895 0.694444
Acrp30_1 2.292376 3.26933 0.694444 CTACK_1 2.286645 1.63476
0.694444 bFGF 2.254793 1.59862 0.694444 TIMP-1_1 2.203826 1.67415
0.694444 TRAIL R3.sub.-- 2.143125 1.93754 0.694444 MSP-a_1 2.110976
1.99091 0.694444 MIP-1b_1 2.086051 2.01983 0.694444 FAS_1 2.059914
1.48374 0.694444 IGFBP3_1 1.955992 1.63927 1.092896 TECK_1 1.799893
1.93772 1.092896 IL-8_1 1.798862 1.61555 1.092896 b-NGF_1 1.772438
1.60984 1.092896 MIF_1 1.695812 1.77156 1.092896 MIP-1a_1 1.679684
1.59738 1.092896 NT-4_1 1.61208 1.94614 1.092896 EGF-R_1 1.607028
1.36793 1.092896 I-TAC_1 1.557412 2.05114 3.196347 OSM_1 1.48
1.59379 3.196347 TPO_1 1.401631 1.53133 3.196347 VEGF-B_1 1.386749
1.58684 3.196347 VEGF-D_1 1.343569 2.40993 3.196347 uPAR_1 1.32707
1.82461 3.196347 MIP-3b_1 1.264924 1.66183 3.196347 AgRP(ART
1.184203 2.12294 4.819277 PIGF_1 1.121384 1.71402 4.819277 HCC-4_1
1.115816 1.57811 4.819277 IL-11_1 1.111969 1.67652 4.819277 DTK_1
1.089757 1.40526 4.819277 sTNF RI_1 1.083266 1.57406 4.819277
TNF-b_1 1.064988 1.18835 4.819277 ICAM-3_1 1.047944 1.5309 4.819277
RANTES.sub.-- 1.039346 1.25415 4.819277
[0267] TABLE-US-00032 TABLE 12B Fold q-value Name Score(d) Change
(%) IL-1ra_1 -5.041602 0.51632 0.694444 IL-3_1 -4.65699 0.37506
0.694444 MCP-1_1 -4.613776 0.47067 0.694444 MCP-4_1 -4.073299
0.31815 0.694444 MCP-3_1 -3.883939 0.40145 0.694444 MCP-2_1
-3.794381 0.40233 0.694444 CK b8-1_1 -3.694038 0.58898 0.694444
CNTF_1 -3.611565 0.64605 0.694444 IL-1b_1 -3.539065 0.34043
0.694444 BMP-6_1 -3.532174 0.62281 0.694444 IL-2_1 -3.525665
0.37899 0.694444 IL-4_1 -3.512443 0.51003 0.694444 M-CSF_1
-3.435204 0.5251 0.694444 MIP-3a_1 -3.223006 0.57152 0.694444 MDC_1
-3.136714 0.56385 0.694444 BLC_1 -2.977992 0.57752 0.694444 MIG_1
-2.84823 0.61226 0.694444 IL-I5_1 -2.83153 0.33554 0.694444
Eotaxin-2.sub.-- -2.466855 0.68813 0.694444 IFN-g_1 -2.339649
0.66411 0.694444 TGF-b3_1 -2.302077 0.68801 0.694444 TGF-b_1
-2.237739 0.6243 0.694444 IL-6_1 -2.232468 0.54172 0.694444 IL-16_1
-2.116464 0.41262 0.694444 IL-Ia_1 -1.926189 0.57411 0.694444
I-309_1 -1.895322 0.65572 0.694444 SCF_1 -1.888043 0.70339 0.694444
LIGHT_1 -1.703026 0.66186 1.092896 PDGF-BB.sub.-- -1.661166 0.70275
1.092896 BDNF_1 -1.610622 0.82141 1.092896 Fractalkine -1.601759
0.59002 1.092896 Eotaxin-3.sub.-- -1.528746 0.69067 1.092896 Fit-3
Ligan -1.421242 0.58491 3.196347 GCSF_1 -1.236217 0.70092 3.196347
GDNF_1 -1.233345 0.75441 3.196347 BMP-4_1 -1.194332 0.88628
3.196347 FGF-6_1 -1.183592 0.78548 3.196347 IGF-1_1 -1.132697
0.75456 4.819277 IL-5_1 -1.102411 0.44825 5.098039 TNF-a_1
-1.087972 0.779 5.098039
[0268] TABLE-US-00033 TABLE 13A Protein Fold p-value Name Score(d)
Change (%) BTC_1 5.280599 2.30404 0.106838 IL-8_1 4.611751 2.30142
0.106838 IL-12 p40_1 4.397923 2.30237 0.106838 TRAIL 4.18957
4.38847 0.106838 R4_1 MIF_1 3.78626 2.46763 0.106838 MIP-1a_1
3.671968 2.04509 0.106838 sTNF RII_1 3.57664 1.81136 0.106838
MSP-a_1 3.532718 2.23649 0.106838 OST_1 3.519536 2.85493 0.106838
IL-11_1 3.428231 3.16541 0.106838 uPAR_1 3.42578 3.10753 0.106838
TPO_1 3.260328 2.04533 0.106838 NT-4_1 3.182778 2.48474 0.106838
MIP-1b_1 3.119065 2.07252 0.106838 NAP-2_1 2.970365 1.51262
0.106838 ICAM-1_1 2.949073 1.6633 0.106838 IGFBP3_1 2.868921
1.68668 0.106838 TRAIL 2.808197 1.85516 0.106838 R3_1 Eotaxin_1
2.747874 2.23776 0.106838 VEGF-B_1 2.73066 1.94657 0.106838 PARC_1
2.703205 1.59801 0.106838 Lympho- 2.655294 1.92588 0.106838
tactin_1 sTNF RI_1 2.628389 2.27051 0.106838 PIGF_1 2.59266 2.46572
0.106838 ANG-2_1 2.573094 1.48217 0.106838 IGFBP-6_1 2.559164
1.49096 0.106838 OSM_1 2.548107 1.79103 0.106838 ANG_1 2.527071
1.38167 0.106838 FAS_1 2.522175 1.42939 0.106838
[0269] TABLE-US-00034 TABLE 13B Protein Fold p-value Name Score(d)
Change (%) MCP-2_1 -5.82991 0.25732 0.106838 M-CSF_1 -5.0083
0.42889 0.106838 IL-1a_1 -4.92065 0.29231 0.106838 MDC_1 -4.36259
0.48973 0.106838 MCP-3_1 -4.03467 0.36994 0.106838 SDF-1_1 -3.71753
0.51302 0.106838 BLC_1 -3.62482 0.54297 0.106838 IFN-g_1 -3.56017
0.58458 0.106838 TNF-a_1 -3.50252 0.52906 0.106838 MCP-4_1 -3.39139
0.33264 0.106838 Eotaxin-3_1 -3.37887 0.50745 0.106838 GM-CSF_1
-3.31812 0.16273 0.106838 IL-3_1 -3.29267 0.45124 0.106838 IL-1b_1
-3.2351 0.33216 0.106838 MIP-1d_1 -3.16349 0.71538 0.106838 IL-16_1
-3.11242 0.26418 0.106838 IL-2_1 -3.09127 0.39923 0.106838 FGF-6_1
-2.99526 0.60629 0.106838 IL-15_1 -2.99089 0.2798 0.106838 IL-4_1
-2.90998 0.56937 0.106838 GDNF_1 -2.89861 0.57687 0.106838 I-309_1
-2.81343 0.58059 0.106838 MCP-1_1 -2.80752 0.60158 0.106838 IL-6_1
-2.7941 0.48921 0.106838
Example 14
[0270] Example 14 discloses the identification of biomarkers found
to significantly correlate with MMSE scores (from 8 to 28) of AD
subjects as shown below in Table 14. Therefore, Lymphotactin and
IL-11 are useful for detection of early to mild AD and for the
staging and progression of the disease. Lymphotactin and/or IL-11
can be used alone or together with other AD biomarkers, including
those described herein in the methods disclosed herein.
Accordingly, provided herein are methods for stratifying AD as well
as monitoring the progress of AD that comprise comparing a measured
level of Lymphotactin and/or IL-11 in a biological fluid sample,
such as plasma, from an individual to a reference level for the
biomarker. TABLE-US-00035 TABLE 14 Correlation Coefficient
Hypothesized Correlation = 0 Cor- 95% 95% rela- Z- P- Low- Up- tion
Count Value Value er per MMSE, IL-11_1 .529 35 3.329 .0009 .237
.733 MMSE, Lymphotactin_1 .516 35 3.226 .0013 .220 .724 IL-11_1,
Lymphotactin_1 .488 35 3.015 .0026 .184 .706
[0271] Although the foregoing invention has been described in some
detail by way of illustration and example for purposes of clarity
of understanding, it will be apparent to those skilled in the art
that certain changes and modifications may be practiced. Therefore,
the descriptions and examples should not be construed as limiting
the scope of the invention.
* * * * *