Note: Descriptions are shown in the official language in which they were submitted.
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BLOOD-BASED SCREEN FOR DETECTING NEUROLOGICAL DISEASES IN PRIMARY
CARE SETTINGS
STATEMENT OF FEDERALLY FUNDED RESEARCH
[0001] This invention was made with government support under AG054073,
AG051848, AG058252,
AG016574, and AG058537 awarded by The National Institutes of Health. The
government has
certain rights in the invention.
FIELD OF INVENTION
[0002] The present invention relates in general to the field of screening,
detecting and discriminating
between neurological diseases within primary care settings, and more
particularly, to biomarkers for the
.. detection, screening, and discriminating patients with neurological
diseases.
Background of the Invention
[0003] Without limiting the scope of the invention, its background is
described in connection with
neurological diseases.
[0004] The detection and evaluation of disease conditions has progressed
greatly as a result of the
sequencing of the human genome and the availability of bioinformatics tools.
One such system is taught
in United States Patent No. 8,430,816, issued to Avinash, et al., for a system
and method for analysis of
multiple diseases and seventies. Briefly, these inventors teach a data
processing technique that includes a
computer-implemented method for accessing reference deviation maps for a
plurality of disease types.
The reference deviation maps may include subsets of maps associated with
severity levels of respective
disease types and a disease severity score may be associated with each
severity level. The method is said
to also include selecting patient severity levels for multiple disease types
based on the subsets of
reference deviation maps. Also, the method may include automatically
calculating a combined patient
disease severity score based at least in part on the disease severity scores
associated with the selected
patient severity levels, and may include outputting a report based at least in
part on the combined patient
disease severity score.
[0005] Another such invention, is taught in United States Patent No.
8,008,025, issued to Zhang and
directed to biomarkers for neurodegenerative disorders. Briefly, this inventor
teaches methods for
diagnosing neurodegenerative disease, such as Alzheimer's Disease, Parkinson's
Disease, and dementia
with Lewy body disease by detecting a pattern of gene product expression in a
cerebrospinal fluid sample
and comparing the pattern of gene product expression from the sample to a
library of gene product
expression pattern known to be indicative of the presence or absence of a
neurodegenerative disease. The
methods are also said to provide for monitoring neurodegenerative disease
progression and assessing the
effects of therapeutic treatment. Also provided are kits, systems and devices
for practicing the subject
methods.
[0006] United States Patent Application Publication No. 2013/0012403, filed by
Hu is directed to
Compositions and Methods for Identifying Autism Spectrum Disorders. This
application is directed to
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microRNA chips having a plurality of different oligonucleotides with
specificity for genes associated
with autism spectrum disorders. The invention is said to provide methods of
identifying microRNA
profiles for neurological and psychiatric conditions including autism spectrum
disorders, methods of
treating such conditions, and methods of identifying therapeutics for the
treatment of such neurological
and psychiatric conditions.
[0007] Yet another application is United States Patent Application Publication
No. 2011/0159527, filed
by Schlossmacher, et al., for Methods and Kits for Diagnosing
Neurodegenerative Disease. Briefly, the
application is said to teach methods and diagnostic kits for determining
whether a subject may develop or
be diagnosed with a neurodegenerative disease. The method is said to include
quantitating the amount of
alpha-synuclein and total protein in a cerebrospinal fluid (CSF) sample
obtained from the subject and
calculating a ratio of alpha-synuclein to total protein content; comparing the
ratio of alpha-synuclein to
total protein content in the CSF sample with the alpha-synuclein to total
protein content ratio in CSF
samples obtained from healthy neurodegenerative disease-free subjects; and
determining from the
comparison whether the subject has a likelihood to develop neurodegenerative
disease or making a
diagnosis of neurodegenerative disease in a subject. It is said that a
difference in the ratio of alpha-
synuclein to total protein content indicates that the subject has a likelihood
of developing a
neurodegenerative disease or has developed a neurodegenerative disease.
SUMMARY OF THE INVENTION
[0008] In one embodiment, the present invention includes a method for
detecting biomarkers within a
primary care setting comprising: measuring a level of four or more biomarkers
selected from Ill, IL7,
TNFa, IL5, IL6, CRP, IL10, TNC, ICAM1, FVII, 1309, TNFR1, A2M, TARC,
adiponectin, MIP1,
eotaxin3, sVCAM1, TPO, FABP, IL18, B2M, SAA, PPY, DJ1, and a-synuclein in a
sample separated
from a human subject in the primary care setting with neurological disease
with a nucleic acid, an
immunoassay or an enzymatic activity assay. In one aspect, the first four
biomarkers are used in the
analysis, namely, Ill, IL7, TNFa, and IL5. In one aspect, the neurological
disease is selected from the
group consisting of Alzheimer's Disease, Parkinson's Disease, Down's syndrome,
Frontotemporal
dementia, Dementia with Lewy Bodies. In another aspect, the neurological
disease is selected from the
group consisting of Alzheimer's Disease or Parkinson's Disease. In another
aspect, the neurological
disease is selected from the group consisting of Alzheimer's Disease or
Dementia with Lewy Bodies. In
another aspect, the neurological disease is selected from the group consisting
of Parkinson's Disease or
Dementia with Lewy Bodies. In another aspect, the neurological disease is
selected from the group
consisting of Alzheimer's Disease, Parkinson's Disease, or Dementia with Lewy
Bodies. In another
aspect, the method detects 5, 6, 7, 8, 9, 10, 11, 12, or 13 biomarkers of
neurological diseases. In another
aspect, the sample is serum or plasma. In another aspect, the method further
comprises the step of
obtaining the following parameters: patient age, and a neurocognitive
screening tests, wherein the
combination of four or more biomarkers (e.g., serum- or plasma-based, age and
the neurocognitive
screening tests) are at least 90% accurate in a primary care setting for the
determination of Alzheimer's
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disease when compared to a control subject that does not have a neurological
disease or disorder. In
another aspect, a profile comprises age, and one or more biomarkers selected
from sVCAM1, IL5, B2M,
IL6, ILL adiponexin, Eotaxin, MIP1 and IL10. In one aspect, the first four
biomarkers are used in the
analysis, namely, sVCAM1, IL5, B2M, and IL6. In another aspect, a profile
comprises NFL, PPY,
FABP3, IL18, IL7, TARC, TPO, a-syn, Eotaxin3 and IL5, and further comprises
Ab40, Ab42, tau, alpha-
syn, and NfL. In another aspect, a profile for identifying Dementia with Lewy
Bodies from other
neurodegenerative diseases comprises sVCAM1, IL5, B2M, and IL6, and may
further comprise one or
more biomarkers selected from ILL adiponexin, Eotaxin, MIP1 and IL10. In
another aspect, a profile for
identifying Parkinson's disease from other neurodegenerative diseases
comprises ICAM1, VCAM1,
A1342, and B2M, and may further comprise one or more biomarkers selected from
Tenacin C, A1340,
TNF-a, PPY, TARC, and IL6. In another aspect, the method further comprises the
step of determining
one or more of the following parameters: sleep disturbance (yes/no), visual
hallucinations (yes/no),
psychiatric/personality changes (yes/no), age, neurocognitive screening, and
four or more biomarkers for
the accurate detection and discrimination between neurodegenerative diseases.
In another aspect, the
level of expression identified by nucleic acid, an immunoassay or an enzymatic
activity assay is selected
from fluorescence detection, chemiluminescence detection,
electrochemiluminescence detection and
patterned arrays, reverse transcriptase-polymerase chain reaction, antibody
binding, fluorescence
activated sorting, detectable bead sorting, antibody arrays, microarrays,
enzymatic arrays, receptor
binding arrays, allele specific primer extension, target specific primer
extension, solid-phase binding
arrays, liquid phase binding arrays, fluorescent resonance transfer, or
radioactive labeling. In another
aspect, the method is used to screen for at least one of mild AD (CDR global
score <=1.0) with an overall
accuracy of 94, 95, 96, 97, 98, 99 or 100 % (sensitivity (SN), specificity
(SP) of (SN=0.94, SP=0.83)), or
very early AD (CDR global score = 0.5), with an overall accuracy of 91, 92,
93, 94, 95, 96, 97, 98, 99, or
100% (SN=0.97, SP=0.72). In another aspect, the method is used to screen in
the primary setting uses a
higher specificity than sensitivity, wherein the specificity is in the range
of 0.97 to 1.0, and the sensitivity
is in the range of 0.80 to 1Ø In another aspect, a profile for identifying
Dementia with Lewy Bodies
from other neurodegenerative diseases comprises sVCAM1, IL5, B2M, and IL6, and
may further
comprise one or more biomarkers selected from Ill, adiponexin, Eotaxin3, MIP1
and IL10 or the
biomarkers in Figure 7. In another aspect, a profile for identifying
Parkinson's disease from other
neurodegenerative diseases comprises ICAM1, VCAM1, A1342, and B2M, and may
further comprise one
or more biomarkers selected from Tenacin C, A1340, TNF-a, PPY, TARC, and IL6.
In another aspect, a
profile for identifying Parkinson's disease from negative controls comprises
NFL, PPY, FABP3, and
IL18, and may further comprise one or more biomarkers selected from IL7, TARC,
TPO, a-syn, Eotaxin3
and IL5. In another aspect, the neurological disease is determined from the
biomarkers found in Figures
7- 11, which will often be used in order.
[0009] In another embodiment, the present invention includes a method for
detecting biomarkers in a
human patient with neurological disease, the method comprising: detecting a
level of four or more
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proteins selected from IL7, TNFa, IL5, IL6, CRP, IL10, TNC, ICAM1, FVII, 1309,
TNFR1, A2M,
TARC, eotaxin3, VCAM1, TPO, FABP, IL18, B2M, SAA, PPY, DJ1, and a-synuclein by
separating the
proteins in a sample separated from a human subject in the primary care
setting with neurological disease
contained in the sample and a molecular marker by electrophoresis; contacting
the separated proteins
with four or more antibodies that each specifically bind to four or more
proteins selected from IL7,
TNFa, IL5, IL6, CRP, IL10, TNC, ICAM1, FVII, 1309, TNFR1, A2M, TARC, eotaxin3,
VCAM1, TPO,
FABP, IL18, B2M, SAA, PPY, DJ1, and a-synuclein, and thereafter with a
secondary antibody; and then
detecting the presence of IL7, TNFa, IL5, IL6, CRP, IL10, TNC, ICAM1, FVII,
1309, TNFR1, A2M,
TARC, eotaxin3, VCAM1, TPO, FABP, IL18, B2M, SAA, PPY, DJ1, and a-synuclein
according to the
.. molecular weight marker. In another aspect, a profile for identifying
Parkinson's disease from negative
controls comprises NFL, PPY, FABP3, and IL18, and may further comprise one or
more biomarkers
selected from IL7, TARC, TPO, a-syn, Eotaxin3 and IL5. In one aspect, the
first four biomarkers are
used in the analysis for detecting Alzheimer's disease, namely, IL7, TNFa,
IL5, and IL6. In one aspect,
the secondary antibody comprises a fluorescence label, chemiluminescence
label, an
electrochemiluminescence label, the separation is on a patterned array, an
antibody arrays, a fluorescent
resonance transfer label, or a radioactive label. In one aspect, the
neurological disease is selected from
the group consisting of Alzheimer's Disease, Parkinson's Disease, Down's
syndrome, Frontotemporal
dementia, Dementia with Lewy Bodies. In another aspect, the neurological
disease is selected from the
group consisting of Alzheimer's Disease or Parkinson's Disease. In another
aspect, the neurological
disease is selected from the group consisting of Alzheimer's Disease or
Dementia with Lewy Bodies. In
another aspect, the neurological disease is selected from the group consisting
of Parkinson's Disease or
Dementia with Lewy Bodies. In another aspect, the neurological disease is
selected from the group
consisting of Alzheimer's Disease, Parkinson's Disease, or Dementia with Lewy
Bodies. In another
aspect, the method detects 5, 6, 7, 8, 9, 10, 11, 12, or 13 biomarkers of
neurological diseases. In another
aspect, the sample is serum or plasma. In another aspect, the method further
comprises the step of
obtaining the following parameters: patient age, and a neurocognitive
screening tests, wherein the
combination of four or more biomarkers, age and the neurocognitive screening
tests) are at least 90%
accurate in a primary care setting for the determination of Alzheimer's
disease when compared to a
control subject that does not have a neurological disease or disorder. In
another aspect, a profile
comprises age, sVCAM1, IL5, B2M, IL6, ILl, adiponexin, Eotaxin, MIP1 and IL10.
In one aspect, the
first four biomarkers are used in the analysis, namely, sVCAM1, IL5, B2M, and
IL6. In another aspect, a
profile comprises NFL, PPY, FABP3, IL18, IL7, TARC, TPO, a-syn, Eotaxin3 and
IL5, and further
comprises Ab40, Ab42, tau, alpha-syn, and NfL. In another aspect, a profile
for identifying Dementia
with Lewy Bodies from other neurodegenerative diseases comprises sVCAM1, IL5,
B2M, and IL6, and
may further comprise one or more biomarkers selected from Ill, adiponexin,
Eotaxin, MIP1 and IL10.
In another aspect, a profile for identifying Parkinson's disease from other
neurodegenerative diseases
comprises ICAM1, VCAM1, A1342, and B2M, and may further comprise one or more
biomarkers
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selected from Tenacin C, A1340, TNF-a, PPY, TARC, and IL6. In another aspect,
the method further
comprises the step of determining one or more of the following parameters:
sleep disturbance (yes/no),
visual hallucinations (yes/no), psychiatric/personality changes (yes/no), age,
neurocognitive screening,
and four or more biomarkers for the accurate detection and discrimination
between neurodegenerative
diseases. In another aspect, the level of expression identified by nucleic
acid, an immunoassay or an
enzymatic activity assay is selected from fluorescence detection,
chemiluminescence detection,
electrochemiluminescence detection and patterned arrays, reverse transcriptase-
polymerase chain
reaction, antibody binding, fluorescence activated sorting, detectable bead
sorting, antibody arrays,
microarrays, enzymatic arrays, receptor binding arrays, allele specific primer
extension, target specific
primer extension, solid-phase binding arrays, liquid phase binding arrays,
fluorescent resonance transfer,
or radioactive labeling. In another aspect, the method is used to screen for
at least one of mild AD (CDR
global score <=1.0) with an overall accuracy of 94, 95, 96, 97, 98, 99 or 100
% (sensitivity (SN),
specificity (SP) of (SN=0.94, SP=0.83)), or very early AD (CDR global score =
0.5), with an overall
accuracy of 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% (SN=0.97, SP=0.72). In
another aspect, the
method is used to screen in the primary setting uses a higher specificity than
sensitivity, wherein the
specificity is in the range of 0.97 to 1.0, and the sensitivity is in the
range of 0.80 to 1Ø In another
aspect, a profile for identifying Dementia with Lewy Bodies from other
neurodegenerative diseases
comprises sVCAM1, IL5, B2M, and IL6, and may further comprise one or more
biomarkers selected
from Ill, adiponexin, Eotaxin, MIP1 and IL10. In another aspect, a profile for
identifying Parkinson's
disease from other neurodegenerative diseases comprises ICAM1, VCAM1, A1342,
and B2M, and may
further comprise one or more biomarkers selected from Tenacin C, A1340, TNF-a,
PPY, TARC, and IL6.
In another aspect, a profile for identifying Parkinson's disease from negative
controls comprises NFL,
PPY, FABP3, and IL18, and may further comprise one or more biomarkers selected
from IL7, TARC,
TPO, a-syn, Eotaxin3 and IL5. In another aspect, the neurological disease is
determined from the
biomarkers found in Figures 7 - 11, which will often be used in order.
100101 In another embodiment, the present invention includes a method of
selecting subjects for a
clinical trial to evaluate a candidate drug believed to be useful in treating
neurological diseases, the
method comprising: measuring a level of four or more biomarkers selected from
IL7, TNFa, IL5, IL6,
CRP, IL10, TNC, ICAM1, FVII, 1309, TNFR1, A2M, TARC, eotaxin3, VCAM1, TPO,
FABP, IL18,
B2M, SAA, PPY, DJ1, and a-synuclein in a sample separated from a human subject
in the primary care
setting with neurological disease with a nucleic acid, an immunoassay or an
enzymatic activity assay; and
determining if the subject should participate in the clinical trial based on
the results of the identification
of the neurodegenerative disease profile of the subject obtained from the step
(a), wherein the subject is
only selected if the neurodegenerative disease profile if the candidate drug
is likely to be useful in
treating the neurological disease. In one aspect, the first four biomarkers
are used in the analysis, namely,
IL7, TNFa, IL5, and IL6. In another aspect, a profile for identifying Dementia
with Lewy Bodies from
other neurodegenerative diseases comprises sVCAM1, IL5, B2M, and IL6, and may
further comprise one
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or more biomarkers selected from ILL adiponexin, Eotaxin, MIP1 and IL10. In
another aspect, a profile
for identifying Parkinson's disease from other neurodegenerative diseases
comprises ICAM1, VCAM1,
A1342, and B2M, and may further comprise one or more biomarkers selected from
Tenacin C, A1340,
TNF-a, PPY, TARC, and IL6. In another aspect, a profile for identifying
Parkinson's disease from
negative controls comprises NFL, PPY, FABP3, and IL18, and may further
comprise one or more
biomarkers selected from IL7, TARC, TPO, a-syn, Eotaxin3 and IL5. In another
aspect, the neurological
disease is determined from the biomarkers found in Figures 7 - 11, which will
often be used in order.
[0011] In another embodiment, the present invention includes a method of
evaluating the effect of a
treatment for a neurological disease, the method comprising: treating a
patient for a neurological disease;
measuring a level of four or more biomarkers selected from IL7, TNFa, IL5,
IL6, CRP, IL10, TNC,
ICAM1, FVII, 1309, TNFR1, A2M, TARC, eotaxin3, VCAM1, TPO, FABP, IL18, B2M,
SAA, PPY,
DJ1, and a-synuclein in a sample separated from a human subject in the primary
care setting with
neurological disease with a nucleic acid, an immunoassay or an enzymatic
activity assay; and
determining if the treatment reduces the expression of the one or more
biomarkers that is statistically
significant as compared to any reduction occurring in the second subset of
patients that have not been
treated or from a prior sample obtained from the patient, wherein a
statistically significant reduction
indicates that the treatment is useful in treating the neurological disease.
In one aspect, the first four
biomarkers are used in the analysis, namely, IL7, TNFa, IL5, and IL6. In
another aspect, a profile for
identifying Dementia with Lewy Bodies from other neurodegenerative diseases
comprises sVCAM1, IL5,
B2M, and IL6, and may further comprise one or more biomarkers selected from
Ill, adiponexin,
Eotaxin, MIP1 and IL10. In another aspect, a profile for identifying
Parkinson's disease from other
neurodegenerative diseases comprises ICAM1, VCAM1, A1342, and B2M, and may
further comprise one
or more biomarkers selected from Tenacin C, A1340, TNF-a, PPY, TARC, and IL6
In another aspect, a
profile for identifying Parkinson's disease from negative controls comprises
NFL, PPY, FABP3, and
IL18, and may further comprise one or more biomarkers selected from IL7, TARC,
TPO, a-syn, Eotaxin3
and IL5. In another aspect, the neurological disease is determined from the
biomarkers found in Figures 7
- 11, which will often be used in order.
[0012] In one embodiment, the present invention includes a method and/or
apparatus for screening for
neurological disease within a primary care setting comprising: obtaining a
blood test sample from a
subject in the primary care setting; measuring two or more biomarkers in the
blood sample selected from
IL7, TNFa, IL5, IL6, CRP, IL10, TNC, ICAM1, FVII, 1309, TNFR1, A2M, TARC,
eotaxin3, VCAM1,
TPO, FABP, IL18, B2M, SAA, PPY, DJ1, and/or a-synuclein; comparing the level
of the one or a
combination of biomarkers with the level of a corresponding one or combination
of biomarkers in a
normal blood sample; measuring an increase in the level of the two or more
biomarkers in the blood test
sample in relation to that of the normal blood sample, which indicates that
the subject is likely to have a
neurological disease; identifying the neurological disease based on the two
biomarkers measured; and
selecting a course of treatment for the subject based on the neurological
disease predicted. In one aspect,
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at least one of the biomarker measurements is obtained by a method selected
from the group consisting of
immunoassay and enzymatic activity assay. In one aspect, the first four
biomarkers are used in the
analysis, namely, IL7, TNFa, IL5, and IL6. In another aspect, the method
further comprises advising the
individual or a primary health care practitioner of the change in calculated
risk. In another aspect, the
method further comprises advising the individual or a primary health care
practitioner of the change in
calculated risk. In another aspect, the method uses 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, or 13 biomarkers to
distinguish between neurological diseases. In another aspect, the isolated
biological sample is serum or
plasma. In another aspect, the sample is a serum sample and upon the initial
determination of a
neurological disease within the primary care clinic, providing that primary
care provider with information
.. regarding the specific type of specialist referral appropriate for that
particular blood screen finding and
directing the individual to a specialist for that neurological disease and
treatment in accordance therewith.
In another aspect, the neurological diseases are selected from Alzheimer's
Disease, Parkinson's Disease,
Down's syndrome, Frontotemporal dementia, Dementia with Lewy Bodies, and
neurodegenerative
disease. In another aspect, the method further comprises the step of refining
the analysis by including the
following parameters: patient age, and a neurocognitive screening tests,
wherein the combination of two
or more serum-based markers, age and the neurocognitive screening tests are at
least 90% accurate in a
primary care setting for the determination of Alzheimer's disease when
compared to a control subject that
does not have a neurological disease or disorder. In another aspect, the
method further comprises the step
of determining one or more of the following parameters: sleep disturbance
(yes/no), visual hallucinations
(yes/no), psychiatric/personality changes (yes/no), age, neurocognitive
screening, and two or more
serum-based markers for the accurate detection and discrimination between
neurodegenerative diseases.
In another aspect, the level of expression of the various proteins is measured
by at least one of
fluorescence detection, chemiluminescence detection, electrochemiluminescence
detection and patterned
arrays, reverse transcriptase-polymerase chain reaction, antibody binding,
fluorescence activated sorting,
detectable bead sorting, antibody arrays, microarrays, enzymatic arrays,
receptor binding arrays, allele
specific primer extension, target specific primer extension, solid-phase
binding arrays, liquid phase
binding arrays, fluorescent resonance transfer, or radioactive labeling. In
another aspect, the method is
used to screen for at least one of mild AD (CDR global score <=1.0) with an
overall accuracy of 94, 95,
96, 97, 98, 99 or 100% (sensitivity (SN), specificity (SP) of (SN=0.94,
SP=0.83)), or very early AD
(CDR global score = 0.5), with an overall accuracy of 91, 92, 93, 94, 95, 96,
97, 98, 99, or 100%
(SN=0.97, SP=0.72). In another aspect, the method is used to screen in the
primary setting used a higher
specificity than sensitivity, wherein the specificity is in the range of 0.97
to 1.0, and the sensitivity is in
the range of 0.80 to 1Ø In another aspect, a profile for identifying
Dementia with Lewy Bodies from
other neurodegenerative diseases comprises sVCAM1, IL5, B2M, and IL6, and may
further comprise one
or more biomarkers selected from IL 1, adiponexin, Eotaxin, MIP1 and IL10. In
another aspect, a profile
for identifying Parkinson's disease from other neurodegenerative diseases
comprises ICAM1, VCAM1,
A1342, and B2M, and may further comprise one or more biomarkers selected from
Tenacin C, A1340,
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TNF-a, PPY, TARC, and IL6. In another aspect, a profile for identifying
Parkinson's disease from
negative controls comprises NFL, PPY, FABP3, and IL18, and may further
comprise one or more
biomarkers selected from IL7, TARC, TPO, a-syn, Eotaxin3 and IL5. In another
aspect, the neurological
disease is determined from the biomarkers found in Figures 7 ¨ 11, which will
often be used in order.
[0013] Another embodiment of the present invention includes a method and
apparatus for distinguishing
between one or more neurological disease states; the method comprising:
obtaining from at least one
biological sample isolated from an individual suspected of having a
neurological disease measurements
of biomarkers comprising the biomarkers IL-7 and TNFa; adding the age of the
subject and the results
from one or more neurocognitive screening tests from the subject (clock
drawing, verbal fluency, list
learning, sleep disturbances, visual hallucinations, behavioral disturbances,
motor disturbances);
calculating the individual's risk for developing the neurological disease from
the output of a model,
wherein the inputs to the model comprise the measurements of the two
biomarkers, the subject's age and
the results from one or more cognitive tests, and further wherein the model
was developed by fitting data
from a longitudinal study of a selected population of individuals and the
fitted data comprises levels of
the biomarkers, the subject's age and the results from one or more cognitive
tests and neurological
disease in the selected population of individuals; and comparing the
calculated risk for the individual to a
previously calculated risk obtained from at least one earlier sample from the
individual. In one aspect, at
least one of the biomarker measurements is obtained by a method selected from
at least one of
fluorescence detection, chemiluminescence detection, electrochemiluminescence
detection and patterned
arrays, reverse transcriptase-polymerase chain reaction, antibody binding,
fluorescence activated sorting,
detectable bead sorting, antibody arrays, microarrays, enzymatic arrays,
receptor binding arrays, allele
specific primer extension, target specific primer extension, solid-phase
binding arrays, liquid phase
binding arrays, fluorescent resonance transfer, or radioactive labeling. In
another aspect, two or more of
the methods for biomarker measurement are used to cross-validate the
neurological disease. In another
aspect, the method further comprises advising the individual or a health care
practitioner of the change in
calculated risk. In another aspect, the method further comprises advising the
individual or a health care
practitioner of the change in calculated risk. In another aspect, the
biomarkers further comprise one or
more biomarkers selected from IL7, TNFa, IL5, IL6, CRP, IL10, TNC, ICAM1,
FVII, 1309, TNFR1,
A2M, TARC, eotaxin3, VCAM1, TPO, FABP, IL18, B2M, SAA, PPY, DJ1, and/or a-
synuclein. In one
aspect, the first four biomarkers are used in the analysis, namely, IL7, TNFa,
IL5, and IL6. In another
aspect, the method uses 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13 biomarkers to
distinguish the neurological
disease. In another aspect, the isolated biological sample is serum or plasma.
In another aspect, the
sample is a serum sample and upon the initial determination of a neurological
disease, directing the
individual to a specialist for that neurological disease. In another aspect,
the neurological diseases are
selected from Alzheimer's Disease, Down's syndrome, Frontotemporal dementia,
Dementia with Lewy
Bodies, Parkinson's Disease, and dementia. In another aspect, the method is
used to exclude one or more
neurological diseases selected from Alzheimer's Disease, Down's syndrome,
Frontotemporal dementia,
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Dementia with Lewy Bodies, Parkinson's Disease, and dementia. In another
aspect, the method is used
to screen in the primary setting used a higher specificity than sensitivity,
wherein the specificity is in the
range of 0.97 to 1.0, and the sensitivity is in the range of 0.80 to 1Ø
[0014] In another embodiment, the present invention also includes a method of
performing a clinical
trial to evaluate a candidate drug believed to be useful in treating
neurological diseases, the method
comprising: (a) measuring an two or more biomarkers selected from IL7, TNFa,
IL5, IL6, CRP, IL10,
TNC, ICAM1, FVII, 1309, TNFR1, A2M, TARC, eotaxin3, VCAM1, TPO, FABP, IL18,
B2M, SAA,
PPY, DJ1, and/or a-synuclein from one or more blood samples obtained from
patients suspected of
having a neurological disease, the patient's age, and results from one or more
neurocognitive screening
tests of the patient; (b) administering a candidate drug to a first subset of
the patients, and a placebo to a
second subset of the patients; (c) repeating step (a) after the administration
of the candidate drug or the
placebo; and (d) determining if the candidate drug reduces the expression of
the one or more biomarkers
that is statistically significant as compared to any reduction occurring in
the second subset of patients,
wherein a statistically significant reduction indicates that the candidate
drug is useful in treating the
neurological disease. In another aspect, the method further comprises the
steps of obtaining one or more
additional blood samples from the patient after a predetermined amount of time
and comparing the levels
of the biomarkers from the one or more additional samples to determine disease
progression. In another
aspect, the method further comprises the steps of treating the patient for a
pre-determined period of time,
obtaining one or more additional blood samples from the patient after the
predetermined amount of time
and comparing the levels of the biomarkers from the one or more additional
samples to determine disease
progression. In one aspect, the first four biomarkers are used in the
analysis, namely, IL7, TNFa, IL5,
and IL6. In another aspect, a profile for identifying Dementia with Lewy
Bodies from other
neurodegenerative diseases comprises sVCAM1, IL5, B2M, and IL6, and may
further comprise one or
more biomarkers selected from ILL adiponexin, Eotaxin, MIP1 and IL10. In
another aspect, a profile for
identifying Parkinson's disease from other neurodegenerative diseases
comprises ICAM1, VCAM1,
A1342, and B2M, and may further comprise one or more biomarkers selected from
Tenacin C, A1340,
TNF-a, PPY, TARC, and IL6. In another aspect, a profile for identifying
Parkinson's disease from
negative controls comprises NFL, PPY, FABP3, and IL18, and may further
comprise one or more
biomarkers selected from IL7, TARC, TPO, a-syn, Eotaxin3 and IL5. In another
aspect, the neurological
disease is determined from the biomarkers found in Figures 7 - 11, which will
often be used in order.
[0015] In another embodiment, the present invention also includes a method of
selecting subjects for a
clinical trial to evaluate a candidate drug believed to be useful in treating
neurological diseases, the
method comprising: (a) measuring an two or more biomarker selected from IL7,
TNFa, IL5, IL6, CRP,
IL10, TNC, ICAM1, FVII, 1309, TNFR1, A2M, TARC, eotaxin3, VCAM1, TPO, FABP,
IL18, B2M,
SAA, PPY, DJ1, and/or a-synuclein in a blood samples obtained from the
subject, the patient's age and
the results from one or more neurocognitive screening tests to determine a
neurodegenerative disease
profile; and (b) determining if the subject should participate in the clinical
trial based on the results of the
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identification of the neurodegenerative disease profile of the subject
obtained from the step (a), wherein
the subject is only selected if the neurodegenerative disease profile if the
candidate drug is likely to be
useful in treating the neurological disease. In one aspect, the first four
biomarkers are used in the
analysis, namely, IL7, TNFa, IL5, and IL6.
[0016] In another embodiment, the present invention also includes a method of
evaluating the effect of a
treatment for a neurological disease, the method comprising: treating a
patient for a neurological disease;
measuring two or more biomarkers from a blood samples obtained from patients
suspected of having a
neurological disease, the patient's age, and results from one or more
cognitive tests of the patient; and
determining if the treatment reduces the expression of the one or more
biomarkers that is statistically
significant as compared to any reduction occurring in the second subset of
patients that have not been
treated or from a prior sample obtained from the patient, wherein a
statistically significant reduction
indicates that the treatment is useful in treating the neurological disease.
[0017] In another embodiment, the present invention also includes a method of
aiding diagnosis of
neurological diseases, comprising: obtaining a blood sample from a human
individual; comparing
normalized measured levels of IL-7 and TNFa biomarkers from the individual's
blood sample to a
reference level of each neurological disease diagnosis biomarker; wherein the
group of neurological
disease diagnosis biomarkers comprises IL-7 and TNFa; and obtaining the
patient's age and results from
one or more cognitive tests of the patient; wherein the reference level of
each neurological disease
diagnosis biomarker comprises a normalized measured level of the neurological
disease diagnosis
biomarker from one or more blood samples of human individuals without
neurological disease ; and
wherein levels of neurological disease diagnosis biomarkers greater than the
reference level of each
neurological disease diagnosis biomarker, the patient's age and the patient's
results from one or more
cognitive tests indicate a greater likelihood that the individual suffers from
neurological disease. In one
aspect, the present invention also includes a method of level of expression of
IL-7 and TNF alpha in the
blood are elevated when compared to the reference level indicates a greater
likelihood that the individual
suffers from the neurological disease. In another aspect, the method further
comprises the step of
determining the blood levels of one or more biomarkers selected from IL7,
TNFa, IL5, IL6, CRP, IL10,
TNC, ICAM1, FVII, 1309, TNFR1, A2M, TARC, eotaxin3, VCAM1, TPO, FABP, IL18,
B2M, SAA,
PPY, DJ1, and/or a-synuclein. In another aspect, the method uses 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, or 13
biomarkers to distinguish the neurological disease. In another aspect, the
levels of CRP and IL10 are
lower when compared to the reference level indicates a greater likelihood that
the individual suffers from
the neurological disease. In another aspect, the method further comprises the
steps of obtaining one or
more additional blood samples from the patient after a predetermined amount of
time and comparing the
levels of the biomarkers from the one or more additional samples to determine
disease progression. In
another aspect, the isolated blood sample is serum sample. In another aspect,
the blood sample is a serum
sample and upon the initial determination of a neurological disease, directing
the individual to a specialist
for that neurological disease. In another aspect, the neurological diseases
are selected from Alzheimer's
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Disease, Parkinson's Disease, and dementia. In another aspect, the method is
used to screen in the
primary setting used a higher specificity than sensitivity, wherein the
specificity is in the range of 0.97 to
1.0, and the sensitivity is in the range of 0.80 to 1Ø
[0018] In another embodiment, the present invention also includes a rapid-
screening kit for aiding
diagnosis of a neurological disease in a primary care setting, comprising: one
or more reagents for
detecting the level of expression of IL-7 and TNFa in a blood sample obtained
from a human individual,
and one or more neurological screening test sheets; and instructions for
comparing normalized measured
levels of the IL-7 and TNFa biomarkers from the individual's blood sample to a
reference level, the
patient's age and the patient's results from the neurological screening tests;
wherein the reference level of
each neurological disease diagnosis biomarker comprises a normalized measured
level of the
neurological disease diagnosis biomarker from one or more blood samples of
human individuals without
neurological disease; and wherein levels of neurological disease diagnosis
biomarkers less than the
reference level of each neurological disease diagnosis biomarker indicate a
greater likelihood that the
individual suffers from neurological disease, wherein the test is at least 90%
accurate. In another aspect,
the level of expression of IL-7 and TNF alpha in the blood are elevated when
compared to the reference
level indicates a greater likelihood that the individual suffers from the
neurological disease. In another
aspect, the kit further comprises one or more reagents for detecting the level
of expression markers
selected from IL7, TNFa, IL5, IL6, CRP, IL10, TNC, ICAM1, FVII, 1309, TNFR1,
A2M, TARC,
eotaxin3, VCAM1, TPO, FABP, IL18, B2M, SAA, PPY, DJ1, and/or a-synuclein. In
another aspect, the
levels of CRP and IL10 are lower when compared to the reference level
indicates a greater likelihood that
the individual suffers from the neurological disease. In another aspect, the
sample is a serum sample and
upon the initial determination of a neurological disease, directing the
individual to a specialist for that
neurological disease. In another aspect, the neurological diseases are
selected from Alzheimer's Disease,
Down's syndrome, Frontotemporal dementia, Dementia with Lewy Bodies,
Parkinson's Disease, and
dementia. In another aspect, the level of expression of the various proteins
is measured at least one of the
nucleic acid, the protein level, or functionally at the protein level. In
another aspect, the level of
expression of the various proteins is measured by at least one of fluorescence
detection,
chemiluminescence detection, electrochemiluminescence detection and patterned
arrays, reverse
transcriptase-polymerase chain reaction, antibody binding, fluorescence
activated sorting, detectable bead
sorting, antibody arrays, microarrays, enzymatic arrays, receptor binding
arrays, allele specific primer
extension, target specific primer extension, solid-phase binding arrays,
liquid phase binding arrays,
fluorescent resonance transfer, or radioactive labeling. In another aspect, a
profile for identifying
Dementia with Lewy Bodies from other neurodegenerative diseases comprises
sVCAM1, IL5, B2M, and
IL6, and may further comprise one or more biomarkers selected from Ill,
adiponexin, Eotaxin, MIP1
and IL10. In another aspect, a profile for identifying Parkinson's disease
from other neurodegenerative
diseases comprises ICAM1, VCAM1, A1342, and B2M, and may further comprise one
or more
biomarkers selected from Tenacin C, A1340, TNF-a, PPY, TARC, and IL6. In
another aspect, a profile for
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identifying Parkinson's disease from negative controls comprises NFL, PPY,
FABP3, and IL18, and may
further comprise one or more biomarkers selected from IL7, TARC, TPO, a-syn,
Eotaxin3 and IL5. In
another aspect, the neurological disease is determined from the biomarkers
found in Figures 7 - 11,
which will often be used in order.
[0019] In another embodiment, the present invention also includes a method of
determining one or more
neurological disease profiles that best matches a patient profile, comprising:
(a) comparing, on a suitably
programmed computer, the level of expression of IL-7 and TNFa in a blood
sample from a patient
suspected of having one or more neurological diseases with reference profiles
in a reference database to
determine a measure of similarity between the patient profile and each the
reference profiles; (b)
identifying, on a suitably programmed computer, a reference profile in a
reference database that best
matches the patient profile based on a maximum similarity among the measures
of similarity determined
in step (a); and (c) outputting to a user interface device, a computer
readable storage medium, or a local
or remote computer system; or displaying, the maximum similarity or the
disease of the disease cell
sample of the reference profile in the reference database that best matches
the patient profile. In one
aspect, the method further comprises the step of determining the level of
expression of one or more
markers from a blood sample further selected from, in order, IL5, IL6, CRP,
IL10, TNC, ICAM1, FVII,
1309, TNFR1, A2M, TARC, eotaxin3, VCAM1, TPO, FABP, IL18, B2M, SAA, PPY, DJ1,
and/or a-
synuclein. In another aspect, the method uses 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
or 13 biomarkers to
distinguish the neurological disease. In another aspect, the method is used to
screen in the primary
setting used a higher specificity than sensitivity, wherein the specificity is
in the range of 0.97 to 1.0, and
the sensitivity is in the range of 0.80 to 1Ø In one aspect, the first four
biomarkers are used in the
analysis, namely, IL7, TNFa, IL5, and IL6. In another aspect, a profile for
identifying Dementia with
Lewy Bodies from other neurodegenerative diseases comprises sVCAM1, IL5, B2M,
and IL6, and may
further comprise one or more biomarkers selected from ILL adiponexin, Eotaxin,
MIP1 and IL10. In
another aspect, a profile for identifying Parkinson's disease from other
neurodegenerative diseases
comprises ICAM1, VCAM1, A1342, and B2M, and may further comprise one or more
biomarkers
selected from Tenacin C, A1340, TNF-a, PPY, TARC, and IL6. In another aspect,
the method further
comprising the step of determining the level of expression of one or more
markers from a blood sample
selected from IL7, TNFa, IL5, IL6, CRP, IL10, TNC, ICAM1, FVII, 1309, TNFR1,
A2M, TARC,
eotaxin3, VCAM1, TPO, FABP, IL18, B2M, SAA, PPY, DJ1, and/or a-synuclein. In
another aspect, the
method further comprises measuring 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13
biomarkers to distinguish the
neurological disease. In another aspect, the method further comprises
screening a patient in a primary
setting used a higher specificity than sensitivity, wherein the specificity is
in the range of 0.97 to 1.0, and
the sensitivity is in the range of 0.80 to 1Ø In another aspect, the method
further comprises using four
biomarkers for identifying Dementia with Lewy Bodies from other
neurodegenerative diseases comprises
sVCAM1, IL5, B2M, and IL6, and may further comprise one or more biomarkers
selected from Ill,
adiponexin, Eotaxin, MIP1 and IL10. In another aspect, the method further
comprises using four
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biomarkers for identifying Parkinson's disease from other neurodegenerative
diseases comprises ICAM1,
VCAM1, AB42, and B2M, and may further comprise one or more biomarkers selected
from Tenacin C,
AB40, TNF-a, PPY, TARC, and IL6. In another aspect, the method further
comprises using four
biomarkers for identifying Alzheimer's disease selected from IL7, TNFa, IL5,
and IL6. In another aspect,
a profile for identifying Parkinson's disease from negative controls comprises
NFL, PPY, FABP3, and
IL18, and may further comprise one or more biomarkers selected from IL7, TARC,
TPO, a-syn, Eotaxin3
and IL5. In another aspect, the neurological disease is determined from the
biomarkers found in Figures 7
¨ 11, which will often be used in order.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] For a more complete understanding of the features and advantages of the
present invention,
reference is now made to the detailed description of the invention along with
the accompanying figures
and in which:
[0021] Figure 1 shows data from the Neurodegenerative Panel 1 that assays
THPO, FABP3, PPY, IL18,
and 1309 on an MSD platform from two control participants in duplicate. As can
be seen, the assays are
highly reliable;
[0022] Figure 2 is a box Plot of Random Forest Risk Scores for AD vs. normal
controls (NC);
[0023] Figure 3 is a receiver operation characteristic (ROC) plot of serum
biomarker profile;
[0024] Figure 4 is a Gini Plot from Random Forest Biomarker Model;
[0025] Figure 5 is a receiver operation characteristic (ROC) plot of serum
biomarker profile; and
[0026] Figure 6 highlights the importance of the relative profiles in
distinguishing between
neurodegenerative diseases. The relative profiles across disease states
varied.
[0027] Figure 7 shows a ROC curve and variable importance plot for Step 1 ¨
discriminating Lewy body
disease from normal controls.
[0028] Figure 8 shows a ROC curve and variable importance plot.
[0029] Figure 9 shows a ROC Curve and Variable Importance Plot for Proteomic
Profile for Detecting
Neurodegenerative Disease.
[0030] Figure 10 shows a ROC Curve and Variable Importance Plot for Proteomic
Profile for
Distinguishing PD from Other Neurodegenerative Diseases.
[0031] Figure 11 shows a support vector machines (SVM) importance score for
Proteomic Profile for
Distinguishing PD from Other Neurodegenerative Diseases.
DESCRIPTION OF THE INVENTION
[0032] While the making and using of various embodiments of the present
invention are discussed in
detail below, it should be appreciated that the present invention provides
many applicable inventive
concepts that can be embodied in a wide variety of specific contexts. The
specific embodiments
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discussed herein are merely illustrative of specific ways to make and use the
invention and do not delimit
the scope of the invention.
[0033] To facilitate the understanding of this invention, a number of terms
are defined below. Terms
defined herein have meanings as commonly understood by a person of ordinary
skill in the areas relevant
to the present invention. Terms such as "a", "an" and "the" are not intended
to refer to only a singular
entity, but include the general class of which a specific example may be used
for illustration. The
terminology herein is used to describe specific embodiments of the invention,
but their usage does not
delimit the invention, except as outlined in the claims.
[0034] As used herein, the phrase "primary care clinic", "primary care
setting", "primary care provider"
are used interchangeably to refer to the principal point of
contact/consultation for patients within a health
care system and coordinates with specialists that the patient may need.
[0035] As used herein, the phrase "specialist" refers to a medical practice or
practitioner that specializes
in a particular disease, such as neurology, psychiatry or even more
specifically movement disorders or
memory disorders.
[0036] As used herein, the following abbreviations are used and can include
mammalian version of these
genes but in certain embodiments the genes are human genes: IL7 - interleukin-
7, TNFa ¨tumor necrosis
factor alpha, IL5 - interleukin-5, IL6- interleukin-6, CRP- C-reactive
protein, IL10 - interleukin-10, TNC-
Tenascin C, ICAM1 ¨intracellular adhesion molecule 1, FVII ¨ factor VII, 1309 -
chemokine (C-C motif)
ligand 1, TNFR1 - tumor necrosis factor receptor 1, A2M ¨ alpha-2-
microglobulin, TARC - Chemokine
(C-C Motif) Ligand 17, eotaxin3, VCAM1 - Vascular Cell Adhesion Molecule 1,
TPO ¨
Thrombopoietin, FABP3 - fatty acid binding protein 3, IL18- interleukin-18,
B2M ¨ beat-2-
microglobulin, SAA ¨ serum amyloid Al cluster, PPY - pancreatic polypeptide,
DJ1 - Parkinson Protein
7, a-synuclein.
[0037] As used herein, the phrase "neurological disease" refers to a disease
or disorder of the central
nervous system and many include, e.g., neurodegenerative disorders such as AD,
Parkinson's disease,
mild cognitive impairment (MCI) and dementia and neurological diseases include
multiple sclerosis,
neuropathies. The present invention will find particular use in detecting AD
and for distinguishing the
same, as an initial or complete screen, from other neurodegenerative disorders
such as Parkinson's
Disease, Frontotemporal dementia, Dementia with Lewy Bodies, and Down's
syndrome.
[0038] 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).
[0039] As used herein, the terms "Parkinson's disease patient", and
"individual diagnosed with
Parkinson's disease" all refer to an individual who has been diagnosed with PD
or has been given a
diagnosis of Parkinson's disease.
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[0040] As used herein, the terms "Frontotemporal dementia", and "individual
diagnosed with
frontotemporal dementia" all refer to an individual who has been diagnosed
with FTD or has been given a
diagnosis of FTD.
[0041] As used herein, the term "Dementia with Lewy bodies" (DLB), and
"individual diagnosed with
DLB" all refer to an individual who has been diagnosed with DLB or has been
given a diagnosis of DLB.
[0042] As used herein, the term "Down's syndrome" (DS), and "individual
diagnosed with Down's
syndrome" all refer to an individual who has been diagnosed with DS or has
been given a diagnosis of
DS.
[0043] As used herein, the phrase "neurological disease biomarker" refers to a
biomarker that is a
neurological disease diagnosis biomarker.
[0044] As used herein, the term "neurological disease biomarker protein",
refers to any of: a protein
biomarkers or substances that are functionally at the level of a protein
biomarker.
[0045] As used herein, methods for "aiding diagnosis" refer to methods that
assist in making a clinical
determination regarding the presence, or nature, of the neurological disease
(e.g., AD, PD, DLB, FTD,
DS or MCI), and may or may not be conclusive with respect to the definitive
diagnosis. Accordingly, for
example, a method of aiding diagnosis of neurological disease can comprise
measuring the amount of one
or more neurological disease biomarkers in a blood sample from an individual.
[0046] 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.).
[0047] 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.
[0048] As used herein, "biological fluid sample" refers to a wide variety of
fluid sample types obtained
from an individual and can be used in a diagnostic or monitoring assay.
Biological fluid sample include,
e.g., blood, cerebral spinal fluid (CSF), urine and other liquid samples of
biological origin. Commonly,
the samples are treatment with stabilizing reagents, solubilization, or
enrichment for certain components,
such as proteins or polynucleotides, so long as they do not interfere with the
analysis of the markers in
the sample.
[0049] As used herein, a "blood sample" refers to a biological sample derived
from blood, preferably
peripheral (or circulating) blood. A blood sample may be, e.g., whole blood,
serum or plasma. In certain
embodiments, serum is preferred as the source for the biomarkers as the
samples are readily available and
often obtained for other sampling, is stable, and requires less processing,
thus making it ideal for
locations with little to refrigeration or electricity, is easily
transportable, and is commonly handled by
medical support staff.
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[0050] As used herein, a "normal" individual or a sample from a "normal"
individual refers to
quantitative data, qualitative data, or both from an individual who has or
would be assessed by a
physician as not having a disease, e.g., a neurological disease. Often, a
"normal" individual is also age-
matched within a range of 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 years with the
sample of the individual to be
assessed.
100511 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: (1) elimination of one or more
symptoms of AD, (2)
reduction of one or more symptoms of AD, (3) stabilization of the symptoms of
AD (e.g., failure to
progress to more advanced stages of AD), and (4) delay in onset of one or more
symptoms of AD delay
in progression (i.e., worsening) of one or more symptoms of AD; and (5) delay
in progression (i.e.,
worsening) of one or more symptoms of AD.
[0052] As used herein, the term "fold difference" refers to a numerical
representation of the magnitude
difference between a measured value and a reference value, e.g., an AD
biomarker, a Parkinson's
biomarker, a dementia biomarker, or values that allow for the differentiation
of one or more of the
neurological diseases. Typically, 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%).
[0053] 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. Generally, a reference
value is based on an individual
sample value, such as for example, a value obtained from a sample from the
individual with e.g., a
neurological disease such as AD, Parkinson's Disease, or dementia, preferably
at an earlier point in time,
or a value obtained from a sample from an neurological disease patient other
than the individual being
tested, or a "normal" individual, that is an individual not diagnosed with AD,
Parkinson's Disease, or
dementia. The reference value can be based on a large number of samples, such
as from AD patients,
Parkinson's Disease patients, dementia patients, or normal individuals or
based on a pool of samples
including or excluding the sample to be tested.
[0054] As used herein, the phrase "a predetermined amount of time" is used to
describe the length of
time between measurements that would yield a statistically significant result,
which in the case of disease
progression for neurological disease can be 7 days, 2 weeks, one month, 3
months, 6 months, 9 months, 1
year, 1 year 3 months, 1 year 6 months, 1 year 9 months, 2 years, 2 years 3
months, 2 years 6 months, 2
years 9 months, 3, 4, 5, 6, 7, 8, 9 or even 10 years and combinations thereof
[0055] As used herein, the phrases "neurocognitive screening tests", or
"cognitive test" are used to
describe one or more tests known to the skilled artisan for measuring
cognitive status or impairment and
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can include but is not limited to: a 4-point clock drawing test, an verbal
fluency test, trail making test, list
learning test, and the like. The skilled artisan will recognize and know how
these tests can be modified,
how new tests that measure similar cognitive function can be developed and
implemented for use with
the present invention.
[0056] The differential diagnosis of neurodegenerative diseases is difficult,
yet of critical importance for
clinical treatment and management as well as for designing therapeutic and
prevention trials (1-4). In
order for patients to be referred to specialty clinics for advanced
assessments and treatment
implementation, an appropriate referral is normally required from primary care
providers. However,
prior work demonstrates that the assessment and management of
neurodegenerative diseases is poor in
primary care settings5-8 with inappropriate medications frequently
administered (9). Given that the
average physician visit duration in an ambulatory setting for those age 65+ is
approximately 18 minutes
(10), primary care providers are in desperate need for a rapid and cost-
effective method for screening
neurological illness within their geriatric patients so appropriate referrals
to a specialist can be made as
warranted.
[0057] The availability of blood-based screening tools that can be implemented
within primary care
clinic settings has significant implications. From a clinical standpoint,
while fewer than half of
physicians surveyed believed screenings for neurodegenerative disease was
important, the vast majority
of the general public and caregivers believed such screenings were vitally
important (11). Additionally,
the average physician visit is less than 20 minutes for elderly patients in an
ambulatory setting (10),
severely limiting the time available for even brief neurological and cognitive
assessments. Therefore,
primary care providers are in desperate need of a method for determining which
patients should be
referred to a specialist for advanced clinical evaluation of possible
neurodegenerative disease. While a
tremendous amount of work has been completed demonstrating the utility of
advanced neuroimaging
techniques (MRI, fMRI, DTI, PET) in diagnosing neurodegenerative diseases,
they are cost prohibitive as
the first step in a multi-stage diagnostic process. Due to cost and access, it
has been proposed that blood-
based biomarkers "will most likely be the prerequisite to future sensitive
screening of large populations"
at risk for neurodegenerative disease and the baseline in a diagnostic flow
approach (12). For example,
PET amyloid-beta (A13) scans were recently FDA approved for use in the
diagnostic process of
Alzheimer's disease. If PET A13 imaging were made available at even $1,000 per
exam (less than a third
to one tenth of the actual cost) and only 1 million elders were screened
annually within primary care
settings (there are 40 million Americans age 65+), the cost would be Si
billion (U.S. dollars) annually for
neurodegenerative screening. If a blood-based screener were made available at
$100/person, the cost
would be $100 million annually. If 15% tested positive and went on to PET A13
imaging ($150 million),
the cost savings of this screen ¨ follow-up procedure would be $750 million
dollars annually screening
less than one fortieth of those who actually need annual screening.
[0058] A blood-based tool can easily fit the role as the first step in the
multi-stage diagnostic process for
neurodegenerative diseases with screen positives being referred to specialist
for confirmatory diagnosis
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and treatment initiation. In fact, this is the process already utilized for
the medical fields of cancer,
cardiology, infectious disease and many others.
[0059] While application of specialty clinic-based screens to primary care
settings seems straight
forward, this is not the case and no prior procedures will work within primary
care settings as
demonstrated below. The ability to implement blood-based screenings as the
first step in a multi-stage
diagnostic process is critical, yet very complicated due to substantially
lower base rates of disease
presence as compared to specialty clinics13 and this lower base rate has a
tremendous impact on the
predictive accuracy of test results.
[0060] Example 1. Screening patients for neurodegenerative diseases.
[0061] Another substantial advancement comes from the current procedure.
Specifically, the procedure
can also be utilized for screening patients prior to entry into a clinical
trial. A major impediment to
therapeutic trials aimed at preventing, slowing progression, and/or treating
AD is the lack of biomarkers
available for detecting the disease14,15. The validation of a blood-based
screening tool for AD could
significantly reduce the costs of such trials by refining the study entry
process. If imaging diagnostics
(e.g., Al3 neuroimaging) are required for study entry, only positive screens
on the blood test would be
referred for the second phase of screening (i.e., PET scan), which would
drastically reduce the cost for
identification and screening of patients. The new methods for screening of the
present invention facilitate
recruitment, screening, and/or selection of patients from a broader range of
populations and/or clinic
settings, thereby offering underserved patient populations the opportunity to
engage in clinical trials,
which has been a major limitation to the majority of previously conducted
trials16.
[0062] The present inventors provide for the first time, data that
demonstrates the following: a novel
procedure can detect and discriminate between neurodegenerative diseases with
high accuracy. The
current novel procedure which can be utilized for implementation as the first
line screen within primary
care settings that leads to specific referrals to specialist providers for
disease confirmation and initiation
of treatment.
[0063] Methods. Neurodegenerative disease patients. AD and Control Patients.
Non-fasting serum
samples from the 300 TARCC participants (150 AD cases, 150 controls) were
analyzed. Additionally,
200 plasma samples (100 AD cases and 100 controls), from the same subject
group were analyzed. The
methodology of the TARCC protocol has been described elsewhere21,22. Briefly,
each participant
undergoes an annual standardized assessment at one of the five participating
TARCC sites that includes a
medical evaluation, neuropsychological testing, and a blood draw. Diagnosis of
AD is based on
NINCDS-ADRDA criteria23 and controls performed within normal limits on
psychometric testing.
Institutional Review Board approval was obtained at each site and written
informed consent is obtained
for all participants.
[0064] Non-AD Patients. Down's Samples. Serum samples were obtained from 11
male patients
diagnosed with Down's syndrome (DS) from the Alzheimer's Disease Cooperative
Studies core at the
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University of California San Diego (UCSD). Parkinson's disease Samples. Serum
samples from 49
patients (28 males and 21 females) diagnosed with Parkinson's disease (PD)
came from the University of
Texas Southwestern Medical Center (UTSW) Movement Disorders Clinic. Dementia
with Lewy Bodies
(DLB) and Frontotemporal dementia (FTD) Samples. Serum samples from 11 DLB and
19 FTD samples
.. were obtained from the UTSW Alzheimer's Disease Coordinating Center (ADCC).
[0065] Serum sample collection. TARCC and UTSW ADC serum samples were
collected as follows:
(1) non-fasting serum samples was collected in 10 ml tiger-top tubes, (2)
allowed to clot for 30 minutes at
room temperature in a vertical position, (3) centrifuged for 10 minutes at
1300 x g within one hour of
collection, (4) 1.0 ml aliquots of serum were transferred into cryovial tubes,
(5) FreezerworksTM barcode
labels were firmly affixed to each aliquot, and (6) samples placed into -80 C
freezer for storage until use
in an assay. Down's syndrome serum samples were centrifuged at 3000rpm for 10
minutes prior to
aliquoting and storage in a -80 C freezer.
[0066] Plasma: (1) non-fasting blood was collected into 10 ml lavender-top
tubes and gently invert 10-
12 times, (2) centrifuge tubes at 1300 x g for 10 minutes within one hour of
collection, (3) transfer 1 ml
aliquots to cryovial tubes, (4) affix FreezerworksTM barcode labels, and (5)
placed in -80 C freezer for
storage.
[0067] Human serum assays. All samples were assayed in duplicate via a multi-
plex biomarker assay
platform using electrochemiluminescence (ECL) on the SECTOR Imager 2400A from
Meso Scale
Discovery (MSD; www.mesoscale.com). The MSD platform has been used extensively
to assay
biomarkers associated with a range of human disease including AD (24-28). ECL
technology uses labels
that emit light when electrochemically stimulated, which improves sensitivity
of detection of many
analytes at very low concentrations. ECL measures have well-established
properties of being more
sensitive and requiring less volume than conventional ELISAs (26), the gold
standard for most assays.
The markers assayed were from a previously generated and cross-validated AD
algorithm (17,19,29) and
included: fatty acid binding protein (FABP3), beta 2 microglobulin, pancreatic
polypeptide (PPY),
sTNFR1, CRP, VCAM1, thrombopoeitin (THPO), a2 macroglobulin (A2M), exotaxin 3,
tumor necrosis
factor a, tenascin C, IL-5, IL6, IL7, IL10, IL18, 1309, Factor VII, TARC, SAA,
and ICAM1, a-synuclein.
Figure 1 illustrates the reliability of the MSD assay of the present
invention.
[0068] Statistical Analyses. Analyses were performed using R (V 2.10)
statistical software (30) and
IBM 5P5519. Chi square and t-tests were used to compare case versus controls
for categorical variables
(APOE E4 allele frequency, gender, race, ethnicity, presence of cardiovascular
risk factors) and
continuous variables (age, education, Mini Mental State Exam [MMSE] and
clinical dementia rating sum
of boxes scores [CDR-SB]), respectively. The biomarker data was transformed
using the Box-Cox
transformation. The random forest (RF) prediction model was performed using R
package randomForest
(V 4.5)(31), with all software default settings. The ROC (receiver operation
characteristic) curves were
analyzed using R package AUC (area under the curve) was calculated using R
package DiagnosisMed (V
0.2.2.2). The sample was randomly divided into training and test samples
separately for serum and
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plasma markers. The RF model was generated in the training set and then
applied to the test sample.
Logistic regression was used to combine demographic data (i.e. age, gender,
education, and APOE4
presence [yes/no]) with the RF risk score as was done in the present
inventors' prior work (17,19,29,32).
Clinical variables were added to create a more robust diagnostic algorithm
given the prior work
documenting a link between such variables and cognitive dysfunction in AD (33-
36). In order to further
refine the algorithm, the biomarker risk score was limited to the smallest set
of markers that retained
optimal diagnostic accuracy as a follow-up analysis. For the second aim of
these studies, support vector
machines (SVM) analysis was utilized for multi-classification of all
diagnostic groups. A random sample
of data from 100 AD cases and controls utilized in the first set of analyses
(AD n=51; NC n=49) was
selected and combined with serum data from 11 DS, 49 PD, 19 FTD and 11 DLB
cases along with 12
additional normal controls (NC) (62 total NCs). The SVM analyses were run on
the total combined
sample with five-fold cross-validation. SVM is based on the concept of
decision planes that define
decision boundaries and is primarily a method that performs classification
tasks by constructing
hyperplanes in a multidimensional space that separates cases of different
class labels. An SVM-based
method was used with five-fold cross-validation to develop the classifier for
the combined samples, and
then applied the classifier to predict the combined samples.
[0069] Results. As with prior work from the present inventors, the AD patients
were significantly older
(p<0.001), achieved fewer years of education (p<0.001), scored lower on the
MMSE (p<0.001) and
higher on the CDR-SB (p<0.001) (see Table 1). There was no significant
difference between groups in
terms of gender or presence of dyslipidemia, diabetes, or hypertension. The AD
group had significantly
more APOE4 carriers while the NC group had significantly more individuals who
were classified as
obese (BMI>=30).
[0070] Table 1. Demographic Characteristics of Cohort
AD (N=150) Control (N=150) P-value
Gender (male) 35% 31% 0.46
Age (years) 78.0(8.2) 70.6(8.9) <0.001
57-94 52-90
Education (years) 14.0 (3.4) 15.6(2.7) <0.001
0-22 10-23
APOE4 presence (yes/no) 61% 26% <0.001
Hispanic Ethnicity 5% 5% 0.61
Race (non-Hispanic white) 95% 97% 0.49
MMSE 19.2(6.1) 29.4(0.9) <0.001
1-30 26-30
CDR-SB 7.8(4.4) 0.0(0.04) <0.001
1-18 0-1
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Hypertension (% yes) 56% 59% 0.73
Dy slipidemia (% yes) 53% 56% 0.49
Diabetes (% yes) 12% 13% 0.60
Obese (% yes) 13% 24% 0.04
When the serum-based RF biomarker profile from the ECL assays was applied to
the test sample, the
obtained sensitivity (SN) was 0.90, specificity (SP) was 0.90 and area under
the ROC curve (AUC) was
0.96 (See Figures 2 and 3, and Table 2).
[0071] Table 2: Statistical results for AD biomarker sensitivity and
specificity and area under the
receiver operating characteristic curve (AUC).
AUC Sensitivity (95% CI) Specificity
(95% CI)
Serum Biomarker alone 0.96 0.90 (0.81,0.95) 0.90 (0.82, 0.95)
Clinical variables alone 0.85 0.77 (0.66, 0.85) 0.82 (0.72,
0.89)
Biomarkers + Clinical variables 0.98 0.95 (0.87, 0.98) 0.90 (0.81,
0.95)
Abbreviated Biomarker Profile 0.95 0.88 (0.79, 0.94) 0.92 (0.83,
0.96)
(8 proteins)
Abbreviated Biomarker Profile 0.98 0.92 (0.84, 0.96) 0.94 (0.87,
0.98)
(8 proteins) + Clinical Variables
Plasma Biomaker alone 0.76 0.65 (0.46, 0.74) 0.79 0.69, 0.95)
[0072] Figure 3 shows a ROC plot for a serum biomarker profile using 21 serum
biomarkers. The
plasma-based algorithm yielded much lower accuracy estimates of SN, SP, and
AUC of 0.65, 0.79, and
0.76, respectively. Therefore, the remaining analyses focused solely on serum.
Inclusion of age, gender,
education and APOE4 into the algorithm with the RF biomarker profile increased
SN, SP, and AUC to
0.95, 0.90, and 0.98, respectively (Table 2). Next the RF was re-run to
determine the optimized
algorithm with the smallest number of serum biomarkers. Using only the top 8
markers from the
biomarker profile (see Figure 4) yielded a SN, SP, and AUC of 0.88, 0.92 and
0.95, respectively (see
Figure 5 and Table 2). The addition of age, gender, education and APOE4
genotype increased SN, SP,
and AUC to 0.92, 0.94, and 0.98, respectively.
[0073] Figure 4 shows a Gini Plot from Random Forest Biomarker Model
demonstrating variable
importance and differential expression. Figure 5 shows a ROC plot using only
the top 8 biomarkers for
the AD algorithm.
[0074] For the SVM multi-classifier analyses to determine if the AD blood-
based biomarker profiles
could be utilized to discriminate AD from other neurological diseases,
analyses were conducted on
protein assays from 203 participants (AD n=51, PD n=49, DS n=11, FTD n=19, DLB
n=11, NC n=62).
Demographic characteristics of this sample are provided in Table 3.
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[0075] Table 3: Demographic characteristics of a second cohort for
multivariate classification
AD PD DS FTD DLB NC
N=51 N=49 N=11 N=19 N=11 N=61
Age 78.0 (9.0) 68(9.6) 52(2.0) 65.8(8.8)
75.6(4.5) 70(9.0)
Education 15.0 (3.0) 14.8(3.2) 14.8(2.8)
16.2 (2.7)
Gender 22 M; 29 F 28 M; 21 F 52M 14 M; 5 F 8 M; 3F
23 M; 38 F
Note: information not available regarding education for PD and DS cases.
Abbreviations: AD,
Alzheimer's disease. PD, Parkinson's disease. DS, Down's syndrome. FTD,
Frontotemporal
dementia. DLB, Lewy Body dementia. NC, normal controls.
[0076] Figure 6 highlights the importance of the relative profiles in
distinguishing between
neurodegenerative diseases. The relative profiles across disease states
varied. For example, A2M and
FVII are disproportionately elevated in DLB and FTD whereas TNFa is
disproportionately elevated in
AD and lowest in PD and DLB whereas PPY is lowest in PD and highest in DLB.
Using the SVM-based
algorithm, biomarker profiles combining all proteins were created to
simultaneously classify all
participants. Surprisingly, the overall accuracy of the SVM was 100% (SN=1.0,
SP=1.0) with all of the
individuals being correctly classified within their respective
categorizations.
[0077] Implementing the blood screen in a community-based setting. The 1998
Consensus Report of the
Working Group on: "Molecular and Biochemical Markers of Alzheimer's Disease"37
provided guidelines
regarding the minimal acceptable performance standards of putative biomarkers
for AD. It was stated
that sensitivity (SN) and specificity (SP) should be no less than 0.80 with
positive predictive value (PPV)
of 80% or more, with PPV approaching 90% being best. The report also states
that a "high negative
predictive value [NPV] would be extremely useful." The PI and bioinformatics
team on this grant have
extensive experience calculating diagnostic accuracy statistics, including PPV
and NPV17-20'38-43. The
important difference between SN/SP and PPV/NPV is that the latter are
prediction accuracy statistics (i.e.
how correct is a clinician when diagnosing a patient based on the test).
PPV/NPV are dependent on base
rates of disease presence44. With regards to AD, it is estimated that the base
rate of disease presence in
the community is 11% of those age 65 and above as compared to 50% or more in
specialty clinic
settings. PPV and NPV are based on Bayesian statistics and calculated as
outlined here:
ppv (SN x BR)
(SN x BR) + RI - SP.) x RCI
(SP x RC)
NPV
(SP :x RC) RI - SN) BR
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PPV = positive predictive value, SN = sensitivity, BR = base rate, RC =
remaining cases, NPV = negative
predictive value, SP = specificity. In an 8-protein screen or algorithm, when
SP was held at 0.98, SN fell
to 0.86. Applying PPV and NPV calculations with an estimated base rate of AD
of 11% within the
community , the screen and/or algorithm of the present invention is very
accurate and can be used within
a community-based setting, that is, at the primary point-of-care. This is in
comparison to the minimal
requirements to be acceptable based on the 1998 Consensus Report where PPV was
less than 35% (see
Table 4).
Table 4: Diagnostic Accuracy of Blood- Base Rate = 11%
Based Screen for Alzheimer's disease in
Primary Care Settings
SN SP PPV NPV
Current Novel Procedure p0.86 10.98 p0.84 p0.98
I
i
1998 Consensus Report minimal guidelines37 0.80 0.80 0.33 0.97
Our Prior work17 0.94 0.84 .42 .99
Our Prior wore 0.89 0.85 0.42 0.98
Our Prior work19 0.75 0.91 0.50 0.97
AIBL study 45 0.85 0.85 0.41 0.98
Peptoid approach46 0.94 0.94 0.66 0.99
Laske and colleagues47 0.94 0.80 0.37 0.99
BR = base rate, SN = sensitivity, SP=specificity, PPV = positive predictive
value,
NPV=negative predictive value
[0078] The findings from the present inventors' prior work as well as that
from other research groups
have also been included for comparison. As is clearly illustrated from above,
the current novel procedure
is the only procedure that can possibly be utilized in primary care settings
in order to have an acceptable
accuracy in referrals to specialty clinics. With the exception of the peptoid
approach, no other efforts
would be better than chance (i.e., 50%) when indicating to a primary care
provider that a specialty
referral would be needed.
Table 5: Diagnostic Accuracy of Base Rate = 11%
Blood-Based Screen for
Neurodegenerative Diseases in
Primary Care Settings
SN P PPV NPV
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Current Novel Procedure I 1.0 I 1.0 I 1.0 I 1.0
I
I
1998 Consensus Report minimal 0.80 0.80 0.33 0.97
guideline s37
BR = base rate, SN = sensitivity, SP=specificity, PPV = positive predictive
value, NPV=negative
predictive value
[0079] The current approach is 100% at identifying neurodegenerative diseases
via the use of overall
profiles. Given the very low prevalence of these diseases in the general
population, the high accuracy is
needed for appropriate referrals to specialist to be made by the primary care
practitioners.
[0080] Combining specific biomarkers with select cognitive testing. The
inventors have demonstrated
that molecular profiles could be generated for neuropsychological test
performance, and that these
profiles accounted for upwards of 50% of the variance in test scores48. It was
further demonstrated that
specific serum-based biomarkers and select cognitive testing can be combined
to refine the assessment
process and increase diagnostic accuracy. In one example, only the top 2
markers were selected from the
serum-algorithm (TNFa and IL7), in conjunction with a single, easy-to-
administer cognitive test (in this
example a 4-point clock drawing test, but other short and easy tests can be
used, e.g., verbal fluency, trail
making, list learning, and the like). When these 3 items were combined into a
single logistic regression,
92% accuracy was found (SN=0.94, SP=0.90) in distinguishing all AD (n=150)
from NC (n=150). When
the sample was restricted only to mild AD (CDR global score <=1.0), an overall
accuracy of 94%
(SN=0.94, SP=0.83) was found. Lastly, and importantly, the sample was
restricted only to very early AD
(CDR global score = 0.5), which resulted in an overall accuracy of 91%
(SN=0.97, SP=0.72). These
findings clearly demonstrate the possibility of combining specific biomarkers
with select cognitive
testing to refine the overall algorithm.
[0081] In summary, the current approach: (1) is highly accurate at detecting
Alzheimer's disease; (2) is
highly accurate at detecting and discriminating between neurodegenerative
diseases; (3) can be
implemented within primary care settings as the first step in a multi-stage
diagnostic process; and (4) the
combination of specific serum biomarkers and select neurocognitive screening
assessments can refine the
screening process with excellent accuracy.
[0082] Table 6 shows the selection of the specialist for referral, and hence
the course of treatment, based
on the results of the screen of the two or more biomarkers measured at the
primary care center or point of
care.
Screen Result Specialist Referral
Alzheimer's Disease Memory Disorders Specialist
Parkinson's Disease Movement Disorders Specialist
Serum Screen in Dementia with Lewy Specialty Clinic for DLB
patients
Primary Care Setting _____________________________________________________
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Bodies
Frontotemporal Specialty Clinic for FTD
patients and
Dementia inclusion of psychiatry
Down's syndrome Neurodevelopmental disease
specialist
and genetic testing/counseling
[0083] Example 2. Proteomic Signature for dementia with Lewy bodies
[0084] The inventors sought to determine if a proteomic profile approach
developed to detect
Alzheimer's disease would distinguish patients with Lewy body disease from
normal controls, and if it
would distinguish dementia with Lewy bodies (DLB) from Parkinson's disease
(PD).
[0085] Stored plasma samples were obtained 145 patients (DLB n=57, PD without
dementia n=32,
normal controls n=56) enrolled from patients seen in the Behavioral Neurology
or Movement Disorders
clinics at the Mayo Clinic, Florida. Proteomic assays were conducted and
analyzed using the protocols
above.
[0086] The proteomic profile described herein distinguished the DLB-PD group
from controls with a
diagnostic accuracy of 0.97, sensitivity of 0.91 and specificity of 0.86. In
second step, the proteomic
profile distinguished the DLB from PD groups with a diagnostic accuracy of
0.92, sensitivity of 0.94 and
specificity of 0.88.
[0087] Lewy Body disease is the second most common neurodegenerative disease
and clinically may
present with dementia as Dementia with Lewy bodies (DLB), or without dementia
as Parkinson's disease
(PD). DLB was first characterized as a dementia by Kosaka [1] and
operationalized diagnostic criteria
were initially put forth by McKeith [2] in 1992. Patients who meet consensus
criteria for DLB commonly
have Lewy-related pathology [3] at autopsy, and in a large dementia autopsy
series [4], 25% were found
to have Lewy-related pathology. The core clinical features of DLB include
parkinsonism, fluctuating
cognition, fully formed visual hallucinations and a history of probable REM
behavior disorder. [5, 61 [7]
There is a subset of patients with Lewy-related pathology who are often not
recognized clinically as
having DLB [8], in large part because of concomitant Alzheimer (AD) related
pathology. Further, the
more extensive the tau pathology the harder it is to recognize the DLB
phenotype. Multimodality imaging
helps to distinguish DLB from AD, but it is an expensive and less viable
method for disease detection
methods in community samples [9]. Therefore, a front-line, minimally invasive
and cost-effective
screening method would be of tremendous value to the field.
[0088] A major impediment to the development of treatments and clinical trials
for neurodegenerative
diseases is the lack of sensitive and easily-obtained diagnostic biomarkers
[10-14]. The search for
biomarkers with diagnostic and prognostic utility in neurodegenerative
diseases has grown exponentially,
with the majority of work focusing on neuroimaging [15-18] and cerebrospinal
fluid (CSF)
methodologies [11, 15, 17-191. Some new promising evidence suggests that CSF
may yield a potential
CA 03129836 2021-08-10
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biomarker for a-synuclein but replication with a large sample will be needed
[20]. While advanced
imaging and CSF methods have tremendous potential as confirmatory diagnostic
biomarkers of
neurodegenerative diseases, accessibility and cost barriers preclude these
from being utilized as the first
step in this process [12, 13, 211. Reliable biomarkers of DLB could have many
uses, including early and
pre-clinical diagnosis, tracking disease progression, and identifying disease
endophenotypes [14, 211. In
addition, the advancement of biomarkers may serve to pave the road toward a
precision medicine
approach to identifying surrogates for therapeutic outcome measures and for
the development of disease-
modifying treatments [22].
[0089] There are no currently validated biomarkers for DLB [23]. It has been
proposed that biological
markers of the clinical conditions associated with DLB should be "cheap,
reliable and reproducible, and
make use of biological samples that are easy to obtain"(pg. 1) [13]. Blood-
based biomarkers would fulfill
these proposed criteria. Additionally, it has been proposed that proteomic
biomarker profiling is a
promising method for discovering DLB biomarkers [21, 231 because a battery of
markers covering a
range of biological processes may be required to address the needs of such
complex disorders [24]. In
fact, profiling analytes associated with multiple disease may highlight novel
biological pathways for
therapeutic interventions in the dementia syndromes[251. The inventors' work
on blood-based
biomarkers of Alzheimer's disease (AD) and PD has consistently shown that a
multi-marker approach
identifying biomarker profiles of disease presence can yield excellent results
[26-28]. The inventors'
blood-based biomarker profile provides a cost- and time-effective method for
establishing a rapidly
scalable multi-tiered neurodiagnostic process [29, 301 for detecting
neurodegenerative disease, including
DLB. With this initial screening approach, appropriate referrals can be made
for subsequent specialty
exanimations and confirmatory diagnostic biomarkers (imaging, CSF), following
the multi-stage models
used for diagnosing cancer [31]. For example, Groveman et al [20] recently
demonstrated the accuracy of
a rapid and ultra-sensitive seed amplification technique for detection of a-
synuclein. In the current
proposed context, a blood-based screening tool can be utilized to rule out the
vast majority of patients
who do not need to undergo lumbar puncture for biomarker confirmatory
diagnostics. This approach can
also be readily adopted to clinical trials thereby (1) increasing access to
broader numbers of patients and
(2) significantly reducing screening costs into such novel trials.
[0090] In the work described hereinabove, the inventors generated and cross-
validated the AD proteomic
profile across platforms[26, 321, cohorts[26, 28, 29, 33, 341, species (human,
mouse)[321, tissue (brain,
serum, plasma)[321 and ethnicities (non-Hispanic white, Mexican American)[26,
351, which is currently
being prospectively tested in primary care settings. This same approach was
highly accurate in
discriminating PD from AD. Here the inventors further shows that the proteomic
profile approach to
detecting AD [29, 321 is successful in (1) detecting neurodegenerative disease
due to synucleinopathy
(DLB and PD vs controls) and (2) discriminating amongst neurodegenerative
disease due to
synucleinopathy (i.e. DLB vs PD). This study was conducted by examination of
plasma samples from the
Mayo Clinic, Jacksonville. Following the methods described above, the
inventors also examined the
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impact of demographic factors (age, gender, education) on the proteomic
profile. Here the inventors
utilized the same described above beginning with the discovery phase by using
a multi-step approach to
determine if this approach can further differentiate neurode generative
disease and discriminate DLB from
PD.
[0091] Subjects. The study sample included 145 patients (DLB n=57, PD n=32,
normal control n=56)
seen though the Alzheimer's Disease Research Center (ADRC) and the Movement
Disorders Center at
the Mayo Clinic, Florida. All participants underwent a neurologic examination,
a Mini-Mental State
Examination (MMSE) and diagnosis was based on recent criteria [5, 361. The DLB
patients also
underwent, neuropsychological testing, had pathologic confirmation of diffuse
or transitional Lewy body
disease., and were specifically selected for this study if they had a
documented response to cholinesterase
inhibitors based the work described above showing that DLB cases who respond
to these medications are
less likely to have imaging-based AD comorbid pathology [18]. Normal controls
were recruited through
the ADRC and were all cognitively normal based on neuropsychological testing.
All PD-dementia (PDD)
cases were not included in this study.
[0092] Proteomics. Blood samples were collected per the NACC ¨ Alzheimer's
Center guidelines,
which also align with the recent guidelines published by an international
working group[37]. Briefly,
non-fasting sample was collected in an EDTA tube from participants while
seated using a 21g needle,
gently inverted 5-10 times and centrifuged at 2000 x g for 10min before being
aliquoted into cryovial
(polypropylene) tubes and stored at -80 C. All processing was completed
within a two-hour timeframe.
.. Samples remained in storage until shipped to the O'Bryant laboratory for
assay. Plasma samples were
assayed via a multi-plex biomarker assay platform using
electrochemiluminescence (ECL) lab using the
QuickPlex from Meso Scale Discovery per the inventors' previously published
methods using
commercially available kits [29, 321. The MSD platform has been used
extensively to assay biomarkers
associated with a range of human diseases including AD [38-41]. ECL technology
uses labels that emit
.. light when electronically stimulated, which improves the sensitivity of
detection of many analytes at very
low concentrations. ECL measures have well established properties of being
more sensitive and
requiring less volume than conventional ELISAs [40], the gold standard for
most assays. The inventors
recently reported the analytic performance of each of these markers for >1,300
samples across multiple
cohorts and diagnoses (normal cognition, MCI, AD) [29]. The assays are
reliable and, in the inventors'
experience with these assays, again show excellent spiked recovery, dilution
linearity, coefficients of
variation, as well as detection limits. Inter- and intra-assay variability has
been excellent. Internal QC
protocols are implemented in addition to manufacturing protocols including
assaying consistent controls
across batches and assay of pooled standards across lots. To further improve
assay performance, assay
preparation was automated using a customized Hamilton Robotics StarPlus
system. A total of 500 1 of
plasma was utilized to assay the following markers (including CV and lowest
level of detection) with
CVs and LLODs calculated from this automated system using the MSD plates:
fatty acid binding protein
(CV=2.2 LLOD=13,277pg/mL), beta 2 microglobulin (CV=7.4, LLOD=32.5pg/mL),
pancreatic
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polypeptide (CV=4.1, LLOD=390pg/mL), CRP (CV=2.4; LLOD=2.41pg/mL), ICAM-1
(CV=4.6;
LLOD=1.8pg/mL), thrombopoeitin (CV=2.2; LLOD=33.1pg/mL), a2 macroglobulin
(CV=2.8;
LLOD=5886pg/mL), exotaxin 3 (CV=18.74 LLOD=3.25pg/mL), tumor necrosis factor a
(CV=3.5;
LLOD=0.077pg/mL), tenascin C (CV=3.7; LLOD=17pg/mL), interleukin (IL)-5
(CV=12.1;
LLOD=0.108pg/mL), IL6 (CV=4.6; LLOD=0.081pg/mL), IL7 (CV=12.3;
LLOD=0.206pg/mL), IL10
(CV=6. 7; LLOD=0. 071p g/mL), IL18 (CV=3 .1; LLOD=6. 07p g/mL), 1309 (CV=6.9;
LLOD=1.22pg/mL),
Factor VII (CV=2.7; LLOD=49.9pg/mL), VCAM 1 (CV=2.3; LLOD=6.13pg/mL), TARC
(CV=5.9;
LLOD=0.21pg/mL) SAA (CV=4.4; LLOD=19pg/mL). As can be seen, analytic
performance was
excellent with the average CVs across all plates for each analyte being well
below standard research use
only assays; all CVs<10 and 62% were <5%.
[0093] Statistical Analysis. Statistical analyses were conducted using the R
(V 3.3.3) statistical software
[42], SPSS 24 (IBM) and SAS. Support vector machine (SVM) analyses were
conducted to create
proteomic profiles specifically for control versus Lewy Body Disease and then
DLB vs PD. SVM is
based on the concept of decision planes that define decision boundaries and is
primarily a classifier
method that performs classification tasks by constructing hyperplanes in a
multidimensional space that
separates cases of different class labels. Diagnostic accuracy was calculated
via receiver operating
characteristic (ROC) curves. First, SVM analyses were utilized to discriminate
controls from Lewy Body
Disease (i.e. DLB/PD) with resulting diagnostic accuracy statistics generated
(Step 1). Next, SVM
analysis was restricted only to those with Lewy Body Disease to discriminate
DLB from PD (Step 2) with
resulting diagnostic accuracy statistics generated. This two-step process was
utilized to allow for the
overall algorithm to be more robust and avoid multi-level analyses
simultaneously, which reduces risk for
error and sample over-identification. Additionally, as described hereinabove,
the inventors have
demonstrated that the overall profile differs amongst neurodegenerative
diseases and, therefore, the
multi-step process capitalizes on these overall proteomic profile
fluctuations. Lastly, samples from n=53
AD cases were analyzed to provide preliminary analyses on a three-step
approach to (1) detect
neurodegenerative disease (Alzheimer's disease [AD]/DLB/PD) from controls, (2)
discriminate dementia
(AD/DLB) from PD and (3) discriminate AD from DLB. These AD cases were also
evaluated and
clinically diagnosed by the Mayo ADRC. Demographic characteristics of the AD
cases are provided in
Table 7.
[0094] Descriptive statistics of the sample are provided in Table 7. The PD
group was younger and
included more females than the other two groups. As expected, the DLB group
had lower scores on the
MMSE.
[0095] Table 7: Demographic characteristics of the cohort
DLB PD Normal Control AD
Mean(SD) Mean(SD) Mean(SD) Mean(SD)
57 32 56 53
Age; 76.03(6.23) 67.06(11.58)
76.16(6.07) 76.12(5.95)
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mean(sd)
Education 14.73(3.56) 15.74(2.49)
14.47(2.87) 13.68(3.25)
mean(sd)
Gender (%M) 76.0 68.8 74.5 74.2
MMSE score 21.13(6.8) 28.04(1.64)
18.30(5.97)
mean(sd)
DLB = Dementia with Lewy bodies, PD = Parkinson's disease,
MMSE = Mini Mental State Examination
[0096] For the SVM-analyses, a two-step analytic approach was taken. First,
the SVM-profile was used
to differentiate Lewy Body disease (DLB and PD) from controls. Second, the SVM-
analysis was used to
differentiate DLB from PD. This two-step approach was utilized as shown above
to show that a
proteomic profile can be highly accurate in detecting "neurodegenerative
disease" in general [29] and
therefore, this analyses for discriminating amongst neurodegenerative diseases
refines the analysis further
without contamination of normal controls in the analytics.
[0097] In Step 1, the SVM-based proteomic profile was highly accurate in
detecting Lewy Body disease
(DLB and PD) as compared to normal controls. The overall AUC of the proteomic
profile was 0.94 with
a sensitivity (SN) of 0.99 and specificity (SP) of 0.64. As with the
inventors' prior work, inclusion of
demographic variables (age, gender, education) increased the overall accuracy
somewhat with an overall
AUC was 0.97 with an decreased SN to 0.91 but increased SP to 0.86. Table 8
shows all of the correct
and incorrect predictions while the variable importance plot and ROC curve are
presented in Figure 7.
[0098] Table 8: Diagnostic accuracy of blood test in Step 1 ¨ discriminating
control from Lewy body
disease
Table 8. Confusion Matrix for SVM-classification for
discriminating Lewy body disease from normal controls
SVM Model
Predicted DLB and PD Normal control
LBD 81 8
NC 8 48
Sensitivity 91.0%
Specificity 85.7%
AUC 0.9653
[0099] In the Step 2, the overall SVM-proteomic profile also showed good
accuracy at distinguishing
DLB from PD. In this model, the AUC was 0.84 with SN=0.95 and SN=0.68.
Inclusion of demographic
variables improved the accuracy to AUC=0.92, SN=0.94 and SP=0.88. Table 9
shows the all
classifications (correct and incorrect) while the variable importance plot and
ROC curve are presented in
Figure 8.
[0100] Next, the inventors conducted preliminary analyses on a three-step
algorithmic approach. Here the
full algorithm was applied (proteins + demographic variables). In the first
step of the model, the inventors
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sought to detect neurodegenerative disease (AD/DLB/PD) versus controls. With
an optimized SVM-risk
threshold cut-off of -0.753, the AUC was 0.96 with a SN=0.90 and SP=0.89. In
the second step, the
inventors sought to discriminate dementia (AD/DLB) from PD which yielded an
AUC=0.98, SN=0.96
and SP=0.97. In the third step, the inventors sought to discriminate amongst
dementias (DLB vs. AD) and
found an AUC=0.96, SN=0.96, SP=0.97.
[0101] Table 9 shows the diagnostic accuracy of blood test in Step 2 ¨
Discriminating between
Dementia with Lewy bodies and Parkinson's disease.
Table 9: Confusion Matrix for SVM-classification
for discriminating DLB from PD
SVM Model
Predicted DLB PD
LDB 46 5
PD 3 35
Sensitivity 93.9%
Specificity 87.5%
AUC 0.9204
[0102] The current example demonstrates, that a multi-step blood-based
proteomic profile can
accurately distinguish neurodegenerative disease due to synucleinopathy (DLB
and PD) from normal
controls (AUC=0.97) and DLB from PD (AUC = 0.92). Recent work demonstrates
that a CSF-based a-
synuclein seeding technology can also achieve strong diagnostic accuracy in
detecting neurodegenerative
disease due to synucleinopathy (93% sensitivity and 100% specificity). While
that work requires cross-
validation in larger studies, the advancement of the current work in tandem is
promising for a sensitive
and specific time- and cost-effective multi-step approach for broad-based
screening of DLB for
prospective studies, clinical trials and routine clinical practice.
[0103] While not a limitation of the present invention, by way of explanation,
the accuracy of the
approach is directly due to the differing overall profiles, which is captured
using advanced SVM-
analyses. Specifically, as can be seen from Figures 7 and 8, the variable
importance plots are different in
Step 1 versus Step 2. Therefore, by capitalizing on the complexity of the
neurodegenerative disease due
to synucleinopathy and the number of proteomics available, the inventors can
generate bioinformatics
profiles. When reviewing the variable importance plots (Figures 7 and 8), the
overall profiles for
discriminating DLB/PD from controls was different than the profile for
discriminating DLB from PD.
The top 10 markers for discriminating DLB/PD from controls were as follows:
age, sVCAM1, IL5, B2M,
IL6, ILl, Adipo, Eotaxin, MIP1 and IL10. The top 4 biomarkers are sVCAM1, IL5,
B2M, IL6, followed
by, in order, Ill, Adipo, Eotaxin, MIP1 and IL10. Not surprisingly the top
variable was age in both
models. However, the top 2 proteins in this profile were the bottom 2 in the
profile for discriminating
DLB from PD. In fact, only age, B2M, IL6, adiponectin, and eotaxin overlapped
in the top 10 markers in
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the algorithm (5 out of top 10). Overall, the profile was a mix of
inflammatory, metabolic and vascular
dysfunction, but at different levels between the categories. The inventors
have found that the AD profile
is heavily inflammatory in nature as compared to PD and controls. In fact, the
AD in adults with Down
syndrome is also heavily inflammatory in nature. Therefore, while there are
certainly disease-overlapping
pathological processes depicted in this work, the profiles are different
amongst categories. Prior work has
demonstrated that there is a range of biological dysfunction across numerous
neurodegenerative diseases.
When tau and Al3 are present in DLB, they tend to occur at far less densities
than what is typically seen in
AD. A recent study showed that in DLB, a-synuclein is a key predictor of
disease duration
independently, and synergistically with tau and Al3 [Ferman et al., 20181. It
is possible that the proteomic
profiles here are picking up on different levels of biological dysfunction due
to differing levels of a-
synuclein, amyloid and tau pathology. Further work is needed to elucidate the
pathological relevance of
these overall proteomic profiles.
[0104] As shown hereinabove, the inventors have created and validated a
proteomic signature for
detecting AD across cohorts, species (humans, mice) and tissue (serum, plasma,
brain) [26, 28, 29, 321.
Subsequently, the inventors have proposed a multi-tiered neurodiagnostic
process for detecting
neurodegenerative disease beginning in primary care clinics using blood-based
biomarkers [29, 301 which
is now being prospectively studied in primary care settings (i.e. Alzheimer's
Disease in Primary Care
[ADPC] study). The inventors have also demonstrated that the inventors' multi-
protein algorithmic
approach can discriminate AD from PD [32] as well as controls from
"neurodegenerative disease" (i.e.
AD, PD, DLB, Down Syndrome) [29]. When compared with AD, the synucleinopathy
profile and DLB
vs PD profile is different from the AD profile. Additional preliminary
analyses were provided here to
support the notion that the multi-marker, multi-step profile can also
discriminate DLB and PD from AD.
Given the sample size, these results are preliminary, but strongly supportive
of further work. Therefore,
the current work takes a significant step forward in the area of blood-
biomarkers for detecting
neurodegenerative diseases as it sets the stage for a large-scale, multi-level
proteomic-bioinformatic
model that takes into account disease-specific profiles across numerous
neurodegenerative diseases. The
current team is currently assaying large numbers of samples across disease
states in order to test this
model.
[0105] Example 3. Two-step proteomic signature for Parkinson's disease
[0106] Next, the inventors sought to further validate the proteomic profile
approach for detecting
Alzheimer's disease would detect Parkinson's disease (PD) and distinguish PD
from other
neurodegenerative diseases describe hereinabove.
[0107] Plasma samples were assayed from 150 patients of the Harvard Biomarkers
Study (PD, n=50;
other neurodegenerative diseases, n=50; healthy controls n=50) using
electrochemiluminescence and
Simoa platforms.
[0108] The first step proteomic profile distinguished neurodegenerative
diseases from controls with a
diagnostic accuracy of 0.94. The second step profile distinguished PD cases
from other
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neurodegenerative diseases with a diagnostic accuracy of 0.98. The proteomic
profile differed in step 1
versus step 2 suggesting that a multi-step proteomic profile algorithm to
detecting and distinguishing
between neurodegenerative diseases may be optimal.
[0109] This example demonstrates the utility of a multi-tiered blood-based
proteomic screening method
for detecting individuals with neurodegenerative disease and then
distinguishing PD from other
neurode generative diseases.
[0110] Parkinson's disease (PD) is the second most common neurodegenerative
disease affecting over
1% of people age 65 and over in the United States[1]. The cost of PD to
society was reported to be $23
billion annually in the U.S. in 2005[2]. Considering the estimated 15% growth
in the elderly U.S.
population during the last decade, these costs can be expected to increase
dramatically as the population
ages. Neuropathologically, PD is a progressive disorder of unknown cause
affecting multiple
neurotransmitter systems. Common non-motor features of the disease include
autonomic failure, urinary
incontinence, hallucinations, and dementia[3]. While a number of treatments
have been developed that
improve the "dopaminergic deficit", no treatment has been demonstrated to slow
the neuronal
degeneration of the substantia nigra neurons. Novel therapeutic approaches are
needed with new disease
modifying therapies (DMTs) currently being examined that may ultimately
improve patient outcomes.
[0111] A major impediment to treatment developments and clinical trials for
neurodegenerative diseases
is the lack of a sensitive, easily-obtained biomarker of disease presence[4-
81. The "cornerstone" to the
development of novel DMTs in PD is the identification and validation of
biomarkers of disease presence
and progression[9]. Over the last several decades, the search for biomarkers
that have diagnostic and
prognostic utility in neurodegenerative diseases has grown exponentially [5,
10, 111 with the majority of
work focusing on neuroimaging and cerebrospinal (CSF) methods (CSF) [5, 10-141
and increasingly
clinical-genetic algorithms[15, 161. In fact, Al3 PET scanning tracers and CSF
assays have been approved
by the Food and Drug Administration (FDA) for use in the diagnostic process
for Alzheimer's disease
(AD) and dopamine transporter single photon emission CT [DaT-SPECT][171 has
been established for
PD. Recent work suggests CSF markers may also have utility in the differential
diagnosis of
neurode generative diseases [181. While advance imaging and CSF methods have
tremendous potential as
biomarkers of PD and other neurodegenerative diseases, invasiveness,
accessibility and cost barriers
preclude these from being utilized as initial detection procedures [6, 7, 19,
201. Therefore, it has been
proposed that blood-based methods require additional investigation[21-231 and
may serve as first step in
a multi-tier detection process[6, 191 similar to the models used in
cancer[241.
[0112] There has been a surge in the search for blood-based biomarkers for
PD[25-271. Blood-based
biomarkers have potential to serve as the initial step in the neurodiagnostic
process used in large-scale
screening, in primary care settings [191, as well as screening into novel
clinical trials, the latter of which
will result in substantial cost savings to the overall trial itself As is the
case with all initial screening
tests, the goal of the first-step is to screen out those patients who should
not undergo more expensive and
invasive confirmatory diagnostic procedures[19]. This is the same model
utilized by cancer biomarkers
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that have received both regulatory and reimbursement approval[24]. The present
inventors' work on
blood-based biomarkers of Alzheimer's disease (AD) has consistently shown that
a multi-marker
approach identifying biomarker profiles of disease presence can yield
excellent results [28-30]. This
blood-based biomarker profile approach provides a cost- and time-effective
method for establishing a
rapidly scalable multi-tiered neurodiagnostic process [19, 311 for detecting
neurodegenerative disease,
including PD. With this initial screening approach, appropriate referrals can
be made for subsequent
specialty exanimations and confirmatory diagnostic biomarkers (imaging, CSF),
following the multi-
stage models used for diagnosing cancer [24].
[0113] This example expands on the validated proteomic profile approach to
detecting AD described
hereinabove, [31, 321 that is successful in (1) detecting neurodegenerative
diseases (PD and other
neurodegenerative diseases vs. controls) and (2) discriminating PD from other
neurodegenerative disease.
This study was conducted by examination of plasma samples from the Harvard
Biomarker Study (HBS).
[0114] Subjects. The study sample included 150 patients from the Harvard
Biomarker Study (HBS; PD
n=50; other neurodegenerative diseases n=50, controls n=50). The other
neurodegenerative diseases
category included AD (n=12), frontotemporal dementia (FTD n=25), progressive
supranuclear palsy
(n=7), and corticobasal degeneration (n=6)(See Table 10). HBS is a
longitudinal, case-control study that
tracks clinical phenotypes and linked biospecimens of individuals with
neurodegenerative diseases and
controls without neurologic disease. High-quality biosamples and high-
resolution clinical phenotypes are
longitudinally tracked over time. HBS was designed for the primary goal of
developing biomarkers that
track disease progression and allow go/no go decisions in phase II clinical
trials. The HBS specifically
fosters research across neurodegenerative diseases, such as the proof-of-
concept study described here.
HBS has been published extensively [15, 33-401.
[0115] Table 10: Descriptive Characteristics of the Sample
Parkinson's disease Neurodegenerative controls Healthy
controls
Total N 50 50 50
N male/female 25/25 25/25 25/25
UPDRS 49.6 23.9
Age 72.4 9.4 72.64 10.3 69.08 9.7
MMSE 26.5 3.7 20.4 6.7 29.2 1.6
PD medications 36 (72%) 0 (0%) 0 (0%)
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[0116] Proteomics. Plasma samples were assayed using two technological
platforms. The proteomic
assays were conducted using two automated systems. The
electrochemiluminescence (ECL) assays from
the work hereinabove is a previously validated AD blood screen that captured
via the multi-plex
platform, QuickPlex from Meso Scale Discovery with assay preparation performed
via automation using
the Hamilton Robotics StarPlus system. The inventors reviewed this analytic
performance for each of
these markers for >1,300 samples across multiple cohorts and diagnoses (normal
cognition, MCI, AD).
The results shows that the assays are reliable and the inventors' experience
with these assays show
excellent spiked recovery, dilution linearity, coefficients of variation, as
well as detection limits. Inter-
and intra-assay variability has been excellent. A total of 250 pi of plasma
was utilized to assay the
following markers: fatty acid binding protein, beta2-microglobulin, pancreatic
polypeptide, CRP, CAM-
1, thrombopoeitin, a2-macroglobulin, exotaxin 3, tumor necrosis factor a,
tenascin C, interleukin (IL)-5,
IL6, IL7, IL10, IL18, 1309, Factor VII, VCAM 1, TARC, SAA. With automation,
the average CVs for
these assays on >1,000 samples in the inventors' laboratory has been excellent
with nearly all having
CVs<10% and 62% having CVs<5%. Given the recent surge in the literature
examining ultra-sensitive
blood-based markers of neuropathological markers in neurodegenerative
diseases, here the Simoa assays
for A1340, A1342, tau, a-synuclein and NfL were conducted using the automated
HD-1 analyzer from
Quanterix. The performance of the assays in the inventors' laboratory from
>1,000 samples has been
excellent with all CVs<=5%.
[0117] Proteomic Profile. As shown hereinabove, the inventors have generated
and cross-validated the
AD proteomic profile across platforms[28, 321, cohorts[28, 30, 31, 41, 421,
species (human, mouse)[321,
tissue (brain, serum, plasma)[321 and ethnicities (non-Hispanic white, Mexican
American)[28, 431. A
locked-down referent cohort was created for prospective application of the AD
Blood Screen[311 and the
AD Blood Screen is currently being prospectively studied explicitly as a blood
screener for AD in
primary care (Alzheimer's Disease in Primary Care [ADPC] study; R01AG058537).
In that prior work,
the inventors also examined the impact of demographic factors (age, gender,
education) on the proteomic
profile to ensure that the inventors' proteomic profile performs better than
demographics alone and to
determine if simple demographic characteristics that are easily obtained can
somehow add to the
algorithm. Here the inventors' utilized the same approach described above,
beginning with the discovery
phase. Specifically, the inventors sought to expand on the work described
above to determine if the same
protein analytes used in the inventors' AD Blood Test algorithm can achieve
the same sensitivity and
specificity for detecting PD.
[0118] Statistical Analysis. Statistical analyses were conducted using R (V
3.3.3) statistical software
[44] and SPSS 24 (IBM). Diagnostic accuracy was calculated via receiver
operating characteristic (ROC)
curves. First, SVM analyses were utilized to discriminate controls from
neurodegenerative disease (i.e.
PD/Other) with resulting diagnostic accuracy statistics generated (Step 1).
Next, SVM analysis was
restricted only to PD versus Other neurodegenerative disease (Step 2). SVM
analyses were conducted
with internal 5-fold cross-validation. In the work described above, the
overall proteomic profile varies
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between different neurodegenerative diseases. Therefore, the two-step approach
was used to capitalize on
these differences to increase accuracy and also to allow for the overall
algorithm to be more robust and
avoid multi-level analyses simultaneously. The latter reduces risk for error
and sample over-
identification.
[0119] Descriptive statistics of the sample are provided in Table 10. The
average age of the sample was
71.37 (SD=9.9). There were even numbers of males and females across all three
groups. An analysis of
variance showed there were no significant age differences among the
Parkinson's disease group, the
healthy control group, and the other neurodegenerative disorders group
(F(2,147)=2.04, p=.13). There
were significant group differences in Mini Mental State Exam (MMSE) score
among the three groups
(F(2,118)=39.9, p=.<.001). Tukey's HSD post-hoc analysis revealed that
Parkinson's disease participants
(M=26.5, SD=3.7) scored significantly lower than healthy controls (M=29.2,
5D=1.6), but higher than
those with other neurodegenerative diseases (M=20.4, SD=6.7).
[0120] In Step 1, the SVM-based proteomic profile was highly accurate in
detecting neurodegenerative
disease (PD and Other) as compared to normal controls. The overall AUC was
0.94 with an observed
sensitivity (SN) of 0.92 and specificity (SP) of 0.65. Table 11 shows all of
the correct and incorrect
predictions while the variable importance plot and ROC curve are presented in
Figure 1. Inclusion of
demographic factors did not significantly change the AUC.
[0121] Table 11: Accuracy of Step 1 in Detecting Neurodegenerative Diseases
SVM Model
Predicted PD/AD/FTD/Others NC
PD/AD/FTD/Others 92 17
NC 8 31
Sensitivity 92.0%
Specificity 64.6%
AUC 0.94
[0122] In the Step 2, the overall SVM-proteomic profile also showed excellent
accuracy at
distinguishing PD from other neurodegenerative diseases. In this model, the
AUC was 0.98, SN=0.94 and
SP=0.89. Table 3 shows all classifications (correct and incorrect) while the
variable importance plot and
ROC curve are presented in Figure 10. Inclusion of demographic factors did not
significantly change the
AUC.
[0123] Table 12: Classification Accuracy for Proteomic Profile for
Distinguishing PD from Other
Neurode generative Diseases
SVM Model
Predicted PD AD/FTD/Others
PD 44 7
AD/FTD/Others 3 55
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Sensitivity 93.6%
Specificity 88.7%
AUC 0.98
[0124] When reviewing the variable importance plots (Figures 9 and 10), the
overall profiles for
discriminating PD/Other neurodegenerative diseases from controls was different
than the profile for
discriminating PD from Other neurodegenerative diseases as was the case
described above. The top 10
markers for discriminating neurodegenerative diseases from controls were as
follows: NFL, PPY,
FABP3, IL18, IL7, TARC, TPO, a-syn, Eotaxin3 and IL5. However, the top 10
variables for
discriminating PD from Other neurodegenerative diseases were ICAM1, VCAM1,
A1342, B2M, Tenacin
C, A1340, TNF-a, PPY, TARC, and IL6.
[0125] Figure 11 shows a support vector machines (SVM) importance score for
proteomic profile for
distinguishing pd from other neurodegenerative diseases. While age and gender
are included in the
graph, these are not part of the biomarkers for use with the present
invention. The most important
markers in these results are: SAA, IL6, IL10, and sICAM1. These are primary
four biomarkers in this
analysis. One or more additional biomarkers can also be used in the
evaluation, in order: 1309, IL5,
sVCAM1, TARC, TNFa, PPY, Eotaxin 3, IL7, A2M, CRP, B2M, IL18, TPO, FABP3,
Factor VII, and
Tenacin C. The same analysis applies for each of the figures. The first four
biomarkers can be used to
obtain an initial determination. Further refinement of that determination can
be provided by adding each
of the one or more additional biomarkers from the graphs, with the order
provided in the graph from top
to bottom having a preference and/or greater impact on the determination.
[0126] The present work expands on the results shown hereinabove for AD,
using; (1) the inventors' AD
proteomic algorithm, (2) only the Simoa assays, and (3) all markers combined
for discriminating PD from
AD as well as PD from controls in this sample. For PD versus AD, the Simoa
assays alone yielded an
excellent SN of 1.0, but only a SP of only 0.25. The inventors' standard ECL
proteomic profile
(described hereinabove); however, yielded a superior balance of SN (also 1.0)
and SP (0.75). When the
Simoa assays were combined with the inventors' standard ECL proteomic panel,
there was a modest
increase in SP to 0.80. When distinguishing PD from controls, the Simoa assays
yielded a 5N=0.74 and
SP=0.83. The inventors' standard ECL profile yielded an improved SN=0.92 and
SP=0.90. The combined
algorithm with the inventors' ECL and Simoa assays resulted in an increases SP
to 0.94.
[0127] The current study further demonstrates that the proteomic profile
approach of the present
invention can be applied to detecting PD and distinguishing PD from other
neurodegenerative diseases. In
detecting neurodegenerative disease versus controls, the current AUC was 0.94
with an observed SN of
0.92 and SP of 0.65. When distinguishing PD from other neurodegenerative
diseases, the overall
accuracy improved to an AUC= 0.98, SN=0.94 and SP=0.89.
[0128] The identification of some of these markers as of relevance in PD is
expected. For example,
multiple inflammatory markers such as TNFa and IL6 have previously been linked
with PD[451 and
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inflammation has been shown to improve following exercise interventions in
persons with PD [46, 471.
Mollenhauer et al found FABP to be differentially expressed in PD and dementia
with Lewy bodies
(DLB) as compared to controls[48] and FABP was among the top 10 markers in
discriminating PD from
AD in the inventors' prior work (described above). Scherzer and colleagues[40]
found differential
expression of Parkinson's disease gene a-synuclein (SNCA) in PD and low SNCA
transcript abundance
predicted cognitive decline longitudinally in PD [40]. Therefore, there is
substantial extant literature to
support the underlying rationale for these markers being altered PD. However,
the prior work never
achieved the specificity and sensitivity disclosed herein.
[0129] It is important to put these SN and SP estimates into perspective
relative to the specific context of
use (COU). All first-line screening tools are designed to rule out disease,
not rule in disease given the
population base rates of disease presence. Therefore, assuming a 20%
neurodegenerative disease base
rate in the population of those age 65 and above, the SN=0.92 and SP=0.64
would yield a negative
predictive power of 0.97 with a positive predictive power of 0.39 using
Bayesian statistics for appropriate
calculations. This means that a trial would be accurate in saying that a
specific patient should not undergo
a lumbar puncture, PET scan or additional clinical evaluations 97% of the
time, thereby allowing large-
scale screening at substantially reduced cost. In Example 1, the inventors'
group shows same sorts of
calculations for AD clinical trials.
[0130] This work also provides novel data when putting the newly designed
ultra-sensitive assays of
amyloid, tau, a-synuclein and NfL in context with other proteomic markers. In
the work taught
hereinabove, the algorithm has been highly accurate in detecting both AD and
PD. Here further cross-
validate the accuracy of the approach for detecting PD in an independent
cohort (HBS). In addition, the
inventors demonstrate that adding these new markers increases the accuracy. On
the other hand, and
surprisingly, these new markers were not very accurate at detecting PD or
distinguishing PD from AD
alone. The SN of 1.0 obtained by both approaches is likely an artifact of
sample size and will not hold in
larger samples.
[0131] Overall, these results demonstrate and validate the proteomic profiles
taught herein. In one non-
limiting example, the present invention provides clinicians and companies with
a rapidly scalable tool (or
tools) that can streamline and increase access (while cost containing) to
novel clinical trials to improve
patient outcomes. The present invention allows for the rapid identification of
neurodegenerative disease
profiles from as few as 4 biomarkers, with increasing sensitivity by the
addition of one or more of the
following biomarkers. While the skilled artisan will understand from the
figures that each additional
biomarkers will add more specificity and/or sensitivity to the analysis. In
some cases the next biomarker
does not necessarily have to be used in order, but rather, can includes any of
the additional biomarkers
(after the first four) selected from the list in any combination and amount.
In addition, other factors such
as age and/or gender can also be added to the analysis, as will be known to
the artisan skilled in the
biomarker arts. It will also be known to the artist skilled in the biomarker
arts that in some cases the
expression of the biomarker may go up or down, which is relative to the
expression in a normal cell, or
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may be relative to the expression in another neurodegenerative disease, as the
case may be. A relative
biomarker expression analysis, based on the teachings in the present
specification, will be known to the
skilled artisan without undue experimentation.
[0132] A special technical effect of the present invention is the
identification and use of novel
biomarkers obtained from, for example, a retrospective analysis of patients
for which biological samples
were obtained prior to having a neurological disease, followed by a
correlation following the initiation of
disease, from which novel biomarkers have been identified having a higher
specificity and/or sensitivity
that previously obtained. This analysis can be done in a primary care setting,
which is the first contact
with a medical professional that may or may not be an expert in the
neurological arts, but from which the
patient can be directed to an expert based on the initial neurological
diseases is identified. Another
special technical effect of the present invention is, thus, being able to
initiate the correct treatment (or
avoid a treatment that is contraindicated for that specific neurological
disease), at the earliest possible
time. Yet another special technical effects is being able to have a robust
initial identification of a possible
disease without the need for expensive (CAT, MRI or other no-invasive scans),
invasive procedures such
as obtaining a neurological biopsy, or both, and to do so in a primary care
setting.
[0133] It is contemplated that any embodiment discussed in this specification
can be implemented with
respect to any method, kit, reagent, or composition of the invention, and vice
versa. Furthermore,
compositions of the invention can be used to achieve methods of the invention.
[0134] It will be understood that particular embodiments described herein are
shown by way of
illustration and not as limitations of the invention. The principal features
of this invention can be
employed in various embodiments without departing from the scope of the
invention. Those skilled in
the art will recognize, or be able to ascertain using no more than routine
experimentation, numerous
equivalents to the specific procedures described herein. Such equivalents are
considered to be within the
scope of this invention and are covered by the claims.
[0135] All publications and patent applications mentioned in the specification
are indicative of the level
of skill of those skilled in the art to which this invention pertains. All
publications and patent
applications are herein incorporated by reference to the same extent as if
each individual publication or
patent application was specifically and individually indicated to be
incorporated by reference.
[0136] The use of the word "a" or "an" when used in conjunction with the term
"comprising" in the
claims and/or the specification may mean "one," but it is also consistent with
the meaning of "one or
more," "at least one," and "one or more than one." The use of the term "or" in
the claims is used to mean
"and/or" unless explicitly indicated to refer to alternatives only or the
alternatives are mutually exclusive,
although the disclosure supports a definition that refers to only alternatives
and "and/or." Throughout
this application, the term "about" is used to indicate that a value includes
the inherent variation of error
for the device, the method being employed to determine the value, or the
variation that exists among the
study subjects.
38
CA 03129836 2021-08-10
WO 2020/168196 PCT/US2020/018297
[0137] As used in this specification and claim(s), the words "comprising" (and
any form of comprising,
such as "comprise" and "comprises"), "having" (and any form of having, such as
"have" and "has"),
"including" (and any form of including, such as "includes" and "include") or
"containing" (and any form
of containing, such as "contains" and "contain") are inclusive or open-ended
and do not exclude
additional, unrecited elements or method steps.
[0138] The term "or combinations thereof' as used herein refers to all
permutations and combinations of
the listed items preceding the term. For example, "A, B, C, or combinations
thereof' is intended to
include at least one of: A, B, C, AB, AC, BC, or ABC, and if order is
important in a particular context,
also BA, CA, CB, CBA, BCA, ACB, BAC, or CAB. Continuing with this example,
expressly included
are combinations that contain repeats of one or more item or term, such as BB,
AAA, AB, BBC,
AAABCCCC, CBBAAA, CABABB, and so forth. The skilled artisan will understand
that typically there
is no limit on the number of items or terms in any combination, unless
otherwise apparent from the
context. In certain embodiments, the present invention may also include
methods and compositions in
which the transition phrase "consisting essentially of' or "consisting of' may
also be used.
[0139] As used herein, words of approximation such as, without limitation,
"about", "substantial" or
"substantially" refer to a condition that when so modified is understood to
not necessarily be absolute or
perfect but would be considered close enough to those of ordinary skill in the
art to warrant designating
the condition as being present. The extent to which the description may vary
will depend on how great a
change can be instituted and still have one of ordinary skilled in the art
recognize the modified feature as
still having the required characteristics and capabilities of the unmodified
feature. In general, but subject
to the preceding discussion, a numerical value herein that is modified by a
word of approximation such as
"about" may vary from the stated value by at least 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12 or 15%.
[0140] All of the compositions and/or methods disclosed and claimed herein can
be made and executed
without undue experimentation in light of the present disclosure. While the
compositions and methods of
this invention have been described in terms of preferred embodiments, it will
be apparent to those of skill
in the art that variations may be applied to the compositions and/or methods
and in the steps or in the
sequence of steps of the method described herein without departing from the
concept, spirit and scope of
the invention. All such similar substitutes and modifications apparent to
those skilled in the art are
deemed to be within the spirit, scope and concept of the invention as defined
by the appended claims.
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