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WO2011143574A2 - Marqueurs biologiques plasmatiques pour le diagnostic de la maladie d'alzheimer - Google Patents

Marqueurs biologiques plasmatiques pour le diagnostic de la maladie d'alzheimer Download PDF

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Publication number
WO2011143574A2
WO2011143574A2 PCT/US2011/036457 US2011036457W WO2011143574A2 WO 2011143574 A2 WO2011143574 A2 WO 2011143574A2 US 2011036457 W US2011036457 W US 2011036457W WO 2011143574 A2 WO2011143574 A2 WO 2011143574A2
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WO
WIPO (PCT)
Prior art keywords
disease
alzheimer
individual
biomarkers
age
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PCT/US2011/036457
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English (en)
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WO2011143574A3 (fr
Inventor
John Q. Trojanowski
Virginia M.Y. Lee
Leslie M. Shaw
Holly D. Soares
Eve H. Pickering
Andrew Kuhn
William Tzu-Lung Hu
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The Trustees Of The University Of Pennsylvania
Pfizer Inc.
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Application filed by The Trustees Of The University Of Pennsylvania, Pfizer Inc. filed Critical The Trustees Of The University Of Pennsylvania
Publication of WO2011143574A2 publication Critical patent/WO2011143574A2/fr
Publication of WO2011143574A3 publication Critical patent/WO2011143574A3/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • G01N33/6896—Neurological disorders, e.g. Alzheimer's disease
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/28—Neurological disorders
    • G01N2800/2814—Dementia; Cognitive disorders
    • G01N2800/2821—Alzheimer

Definitions

  • Alzheimer's disease is a progressive neurodegenerative disorder estimated to affect 27 million people worldwide with numbers doubling every 20 years (Prince et al, 2009 Alzheimer's Disease International 1-92). Although symptoms of Alzheimer's disease manifest early as deficits in memory and other cognitive domains, pathological data show neuropathological features of Alzheimer's disease, including amyloid plaques and neurofibrillary tangles, occur well before the onset of dementia (Sawa et al, 2009 N Engl J Med. 360(22):2302-2309). Research focused upon cognitive performance in individuals who have progressed to dementia have further specified a pre-demented stage characterized by deficits in memory and executive function (Petersen et al, 2008 CNS Spectr. 13(l):45-53).
  • Alzheimer's disease a progressive neurodemented stage of Alzheimer's disease
  • a stereotypical biomarker signature supporting the notion that Alzheimer's disease can be diagnosed prior to the onset of dementia and that clinical studies focused upon dementia prevention are feasible (Craig-Schapiro et al, 2009 Neurobiol Dis. 35(2): 128-140).
  • DSM-IV-TR Diagnostic and Statistical manual of Mental Disorders
  • NINCDS-ADRDA National Institute of Neurological Disorders and Stroke - Alzheimer's Disease and Related Disorders
  • NINCDS-ADRDA are similar, but specify that onset is gradual and other systemic or neural diseases known to impair cognition have been excluded (McKhann et al., 1984 Neurology 34(7): 939-944).
  • Most clinical studies in Alzheimer's disease utilize either NINCDS-ADRDA or DSM-IV criteria to enroll mild to moderate stage individuals.
  • individuals with dementia are already in mid to late neuropathological stages of Alzheimer's disease.
  • Alzheimer's disease-related neuropathology is reversible once dementia manifests. Indeed, recent studies have implied that amelioration of plaque load may occur following therapeutic treatment, but such clearance may not be associated with clinical benefit once the underlying pathology is significant.
  • symptomatic individuals at risk of progressing to dementia do exhibit a stereotypical biomarker phenotype.
  • symptomatic, and in some cases non-symptomatic, individuals at risk of progressing to dementia exhibit decreased levels of CSF amyloid beta peptide 42 ( ⁇ 42) and elevated levels of tau and phosphorylated tau (Mattsson et al, 2009 Jama 302(4):385-393; Shaw et al, 2009 Ann Neurol. 65(4):403-413; Sunderland et al, 2003 Jama 289(16):2094-2103; Visser et al, 2009 Lancet Neurol.
  • CSF and imaging biomarkers hold great promise as tools to enable early Alzheimer's disease drug development, they are problematic as diagnostic tools for the general practitioner.
  • CSF procedures in many countries are considered highly invasive and PET imaging techniques are expensive and, in some countries, not widely available.
  • Simple cognitive tests offer an alternative screening approach, but require time that is often not reimbursed by many health care systems. In the setting of a general practitioner where time constraints with individuals are an issue, cognitive tests are often not brief enough to accommodate a standard six-ten minute individual interview.
  • a blood test would be a simple route for screening, and, if positive, could be used in combination with more extensive cognitive testing followed by referral to a specialist for more confirmatory CSF and imaging testing that would enable early diagnosis.
  • a simple blood test would offer a tractable option towards screening individuals at risk.
  • the present invention fills these needs by providing among other things, novel biomarkers and combinations of biomarkers useful for diagnosing Alzheimer's disease, as well as methods and kits for using the biomarkers to diagnose Alzheimer's disease.
  • the present invention provides a composition for detecting Alzheimer's
  • the biological sample is a plasma sample.
  • the biomarkers for Alzheimer's disease comprise Cortisol, pancreatic polypeptide, osteopontin, IGF BP2, and resistin.
  • the biomarkers for Alzheimer's disease comprise resistin, pancreatic polypeptide, ApoD, G-CSF, MlPlbeta, ApoE, NrCAM, MMP2, Cortisol, PDGF-BB, MMP1, and 1-309.
  • the biomarkers for Alzheimer's disease comprise Alpha- 1 Microglobulin, Angiopoietin-2, Apolipoprotein E, Beta-2 Microglobulin, Chemokine (C-X-C motif) ligand 13 (CXCL13) also known as B lymphocyte chemoattractant (BLC), Cortisol, E-Selectin, FAS, Fatty Acid Binding Protein,
  • Hepatocyte Growth Factor IGF BP-2, Interleukin 10, NT-Pro-BNP (Brain natriuretic peptide co-secreted along with a 76 amino acid N-terminal fragment), Osteopontin, Pancreatic Polypeptide, PAPP-A, Resistin, Stem Cell Factor, Tenascin C,
  • Thrombomodulin tissue inhibitor of metalloproteinases-1 (TIMP-1), vascular cell adhesion molecule- 1 (VCAM-1), Vascular endothelial growth factor (VEGF), and Von Willebrand Factor.
  • TRIP-1 tissue inhibitor of metalloproteinases-1
  • VCAM-1 vascular cell adhesion molecule- 1
  • VEGF Vascular endothelial growth factor
  • Von Willebrand Factor Von Willebrand Factor
  • the biomarkers include one or more biomarkers selected from Table 2.
  • the reagent for detecting a plurality of biomarkers for Alzheimer's Disease in a biological sample from the individual is an antibody. In one embodiment, the reagent is on a solid support.
  • the invention provides a method of diagnosing Alzheimer's Disease in an individual, the method comprising analyzing a biological sample from an individual to determine the level(s) of one or more biomarkers for Alzheimer's Disease in the sample, wherein the one or more biomarkers are selected from Table 2; and comparing the level(s) of the one or more biomarkers in the sample to Alzheimer's Disease-positive and/or Alzheimer's Disease-negative reference levels of the one or more biomarkers in order to diagnose whether the individual has Alzheimer's Disease.
  • the method further comprises analyzing age, gender, and ApoE genotype (e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4) of the individual in order to diagnose whether the individual has Alzheimer's Disease.
  • ApoE genotype e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4
  • the levels of the biomarkers, age, gender, and ApoE genotype are incorporated into an algorithm to diagnose whether the individual has Alzheimer's Disease.
  • the algorithm is selected from the group consisting of Logistic Regression, Boosted Tree Models, Flexible Discriminant Analysis (FDA), K- Nearest Neighbors (KNN), Naive Bayes, Partial Least Squares (PLS), Randon Forests, Shrunken Centroids, Sparse Partial Least Squares, Support Vector Machines approaches, and any combination thereof.
  • the Alzheimer's Disease diagnosed is selected from the group consisting of late onset Alzheimer's disease, early onset Alzheimer's disease, familial Alzheimer's disease, and sporadic Alzheimer's disease.
  • the biological sample is selected from the group consisting of whole blood, a blood component, CSF, urine, and any combination thereof.
  • the present invention provides a method for determining whether dementia in an individual is associated with AD, the method comprising analyzing a biological sample from an individual to determine the level(s) of Cortisol, pancreatic polypeptide, osteopontin, IGF BP2, and resistin in the sample, and comparing the level(s) of Cortisol, pancreatic polypeptide, osteopontin, IGF BP-2, and resistin in a corresponding reference sample to determine whether dementia in an individual is associated with AD.
  • the dementia is associated with AD.
  • the method further comprises analyzing age, gender, and ApoE genotype (e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4) of the individual in order to determine the presence or status of dementia in the individual.
  • ApoE genotype e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4
  • the biological sample from the individual is analyzed to determine the level(s) of one or more biomarkers selected from Table 2; and comparing the level(s) of the one or more biomarkers in the sample to Alzheimer's Disease-positive and/or Alzheimer's Disease-negative reference levels of the one or more biomarkers in order to determine the presence or status of Alzheimer's Disease or other types of dementia in the individual.
  • the levels of Cortisol, pancreatic polypeptide, osteopontin, IGF BP2, resistin biomarkers, age, gender, and ApoE genotype are incorporated into an algorithm in order to determine the presence or status of Alzheimer's Disease or other types of dementia in the individual.
  • the present invention also includes a method for diagnosing Alzheimer's disease in an individual, the method comprising: measuring the level of one or more biomarkers selected from Table 2 in a plasma sample from the individual, and comparing the level of the biomarkers in the plasma sample of the individual to positive control reference values and optionally to negative control reference values, wherein levels of the biomarkers in the plasma sample of the individual resemble the positive control reference values and do not resemble the negative control reference values are an indication that the individual has Alzheimer's disease.
  • the method further comprises analyzing age, gender, and ApoE genotype (e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4) of the test individual in order to diagnose whether the individual has Alzheimer's Disease.
  • the level of the biomarkers, age, gender, and ApoE genotype are incorporated into an algorithm to diagnose whether the individual has Alzheimer's Disease.
  • the present invention includes a method of determining risk of an individual developing Alzheimer's disease, the method comprising: measuring the level of one or more biomarkers selected from Table 2 in a plasma sample of an individual, and comparing the level of the biomarkers in the plasma sample of the individual to positive and optionally to negative control values, wherein levels of the biomarkers in the plasma sample of the individual that resemble the positive control and do not resemble the negative control are an indication that the individual is at risk of developing Alzheimer's disease.
  • the method further comprises analyzing age, gender, and ApoE genotype (e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4) of the individual in order to determine risk of an individual of developing Alzheimer's disease.
  • ApoE genotype e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4
  • the level of biomarkers, age, gender, and ApoE genotype are incorporated into an algorithm in order to determine risk of an individual of developing Alzheimer's disease.
  • the invention includes a method for monitoring Alzheimer's disease in an individual, the method comprising: measuring the level of one or more biomarkers selected from Table 2 in a plasma sample of an individual at a first time, and comparing the level of the biomarkers in the plasma sample of the individual at the first time to a level of the biomarkers in a plasma sample of the individual at the second time.
  • the method further comprises analyzing age, gender, and ApoE genotype (e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4) of the individual in order to monitor Alzheimer's disease in the individual.
  • ApoE genotype e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4
  • the level of the biomarkers, age, gender, and ApoE genotype are incorporated into an algorithm in order to monitor Alzheimer's disease in the individual.
  • the invention provides a method of assessing increased risk of developing Alzheimer's disease, the method comprising: obtaining a first sample of plasma from an individual blood at a first time, assessing the level of a biomarker for Alzheimer's disease in the first plasma sample to obtain a baseline level, obtaining a second sample of plasma from the individual at a second time to obtain a second level, assessing the level of the biomarker in the second plasma sample to obtain a second level, wherein if the second level is greater in the case of a biomarker that is over-expressed in AD or lower in the case of a biomarker that is under-expressed in AD, the individual is at an increased risk of developing Alzheimer's disease.
  • the biomarker is one or more biomarkers selected from Table 2.
  • the method further comprises analyzing age, gender, and ApoE genotype (e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4) of the individual in order to assess whether the individual is at an increased risk of developing Alzheimer's disease.
  • ApoE genotype e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4
  • the level of the biomarkers, age, gender, and ApoE genotype are incorporated into an algorithm in order to assess whether the individual is at an increased risk of developing Alzheimer's disease.
  • the invention includes a method of assessing the likelihood that a pharmaceutical agent is efficacious in treating Alzheimer's disease in an individual, the method comprising: obtaining a first sample of plasma from an individual in the absence of a pharmaceutical agent, assessing the level of a biomarker for Alzheimer's disease in the first plasma sample to obtain a baseline level, administering the pharmaceutical agent to the individual, obtaining a second sample of plasma from the individual after administration of the pharmaceutical agent, assessing the level of the biomarker for Alzheimer's disease in the second plasma sample to obtain a treated level, wherein the likelihood that the pharmaceutical agent treats Alzheimer's disease is increased if the treated level is lower than the baseline level in the case of a biomarker that is over- expressed in AD and higher than the baseline level in the case of a biomarker that is under-expressed in AD.
  • the biomarker is one or more biomarkers selected from Table 2.
  • the method further comprises analyzing age, gender, and ApoE genotype (e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4) of the individual in order to assess the likelihood that the pharmaceutical agent is efficacious in treating
  • the levels of the biomarkers, age, gender, and ApoE genotype are incorporated into an algorithm in order to assess the likelihood that the pharmaceutical agent is efficacious in treating Alzheimer's disease in the individual.
  • the invention includes a method of diagnosing Alzheimer's Disease in an individual, comprising obtaining a plasma sample from the individual; assaying levels of at least five biomarkers in the plasma sample, wherein the at least five biomarkers comprise: Cortisol, IGF BP2, pancreatic polypeptide, resistin, and ApoE; and comparing the levels of each of the at least five biomarkers to non-AD controls, wherein a statistically significant increase in the levels of Cortisol, IGF BP2, pancreatic polypeptide, and resistin, and a statistically significant decrease in the level of ApoE provides a positive diagnosis of AD in the individual.
  • the invention includes a method of diagnosing Alzheimer's Disease in an individual, comprising obtaining a plasma sample from the individual; assaying levels of at least five biomarkers in the plasma sample, wherein the at least five biomarkers comprise: Cortisol, IGF BP2, pancreatic polypeptide, resistin, and osteopontin; and comparing the levels of each of the at least five biomarkers to non-AD controls, wherein a statistically significant increase in the levels of Cortisol, IGF BP2, pancreatic polypeptide, osteopontin and resistin provides a positive diagnosis of AD in the individual.
  • the method further comprises conducting one or more cognitive tests on the individual to confirm the positive diagnosis of AD.
  • the method further comprises obtaining a CSF sample from the individual to confirm the positive diagnosis of AD.
  • the invention provides a method for screening to identify individuals at increased risk of developing AD, the method comprising obtaining a plasma sample from the individual; assaying levels of at least five biomarkers in the plasma sample, wherein the at least five biomarkers comprise: Cortisol, IGF BP2, pancreatic polypeptide, resistin, and ApoE; and comparing the levels of each of the at least five biomarkers to non-AD controls, wherein a statistically significant increase in the levels of Cortisol, IGF BP2, pancreatic polypeptide, and resistin, and a statistically significant decrease in the level of ApoE provides an identification of an individual at increased risk of developing AD.
  • the invention provides a method for screening to identify individuals at increased risk of developing AD, the method comprising obtaining a plasma sample from the individual; assaying levels of at least five biomarkers in the plasma sample, wherein the at least five biomarkers comprise: Cortisol, IGF BP2, pancreatic polypeptide, resistin, and osteopontin; and comparing the levels of each of the at least five biomarkers to non- AD controls, wherein a statistically significant increase in the levels of Cortisol, IGF BP2, pancreatic polypeptide, osteopontin and resistin provides an identification of an individual at increased risk of developing AD.
  • the invention provides a method of diagnosing Alzheimer's Disease in an individual, comprising obtaining a plasma sample from the individual; assaying levels of at least five biomarkers in the plasma sample, wherein the at least five biomarkers comprise: Cortisol, IGF BP2, pancreatic polypeptide, resistin, and ApoE; and determining whether, relative to non-AD controls, the levels of the at least five biomarkers provide a signature of a positive diagnosis of AD in the individual, wherein the signature comprises: a statistically significant increase in the levels of each of Cortisol, IGF BP2, pancreatic polypeptide, and resistin, and a statistically significant decrease in the level of ApoE.
  • the invention provides a method of diagnosing Alzheimer's Disease in an individual, comprising obtaining a plasma sample from the individual; assaying levels of at least five biomarkers in the plasma sample, wherein the at least five biomarkers comprise: Cortisol, IGF BP2, pancreatic polypeptide, resistin, and osteopontin; and determining whether, relative to non-AD controls, the levels of the at least five biomarkers provide a signature of a positive diagnosis of AD in the individual, wherein the signature comprises: a statistically significant increase in the levels of each of Cortisol, IGF BP2, pancreatic polypeptide, osteopontin and resistin.
  • the invention provides a kit for detecting Alzheimer's Disease in an individual with at least 80% sensitivity, comprising a reagent for detecting a plurality of biomarkers for Alzheimer's Disease in a biological sample from the individual.
  • the reagent is capable of detecting biomarkers of
  • Alzheimer's disease in a plasma sample Alzheimer's disease in a plasma sample.
  • the biomarkers of Alzheimer's disease comprise Cortisol, pancreatic polypeptide, osteopontin, IGF BP2, and resistin.
  • the biomarkers for Alzheimer's disease comprise resistin, pancreatic polypeptide, ApoD, G-CSF, MlPlbeta, ApoE, NrCAM, MMP2, Cortisol, PDGF-BB, MMP1, and 1-309.
  • the biomarkers for Alzheimer's disease comprise Alpha- 1 Microglobulin, Angiopoietin-2, Apolipoprotein E, Beta-2 Microglobulin, Chemokine (C-X-C motif) ligand 13 (CXCL13) also known as B lymphocyte chemoattractant (BLC), Cortisol, E-Selectin, FAS, Fatty Acid Binding Protein,
  • Hepatocyte Growth Factor IGF BP-2, Interleukin 10, NT-Pro-BNP (Brain natriuretic peptide co-secreted along with a 76 amino acid N-terminal fragment), Osteopontin, Pancreatic Polypeptide, PAPP-A, Resistin, Stem Cell Factor, Tenascin C,
  • Thrombomodulin tissue inhibitor of metalloproteinases-1 (TIMP-1), vascular cell adhesion molecule- 1 (VCAM-1), Vascular endothelial growth factor (VEGF), and Von Willebrand Factor.
  • TRIP-1 tissue inhibitor of metalloproteinases-1
  • VCAM-1 vascular cell adhesion molecule- 1
  • VEGF Vascular endothelial growth factor
  • Von Willebrand Factor Von Willebrand Factor
  • the biomarkers include one or more biomarkers selected from Table 2.
  • the reagent is an antibody.
  • the reagent is on a solid support.
  • the kit further comprises an instruction manual.
  • Figure 1 is an image depicting Test set ROC curves for each model.
  • the solid black dot indicates the default cutoff of 0.50 and the square indicates the alternate cutoff to get sensitivity closest to 0.80.
  • PLS Partial Least Square Model
  • Figure 2 is a series of graphs depicting expression of analytes in CSF Autopsy Confirmed Samples. Graphs represent means and error bars represent SEM.
  • Figure 3 is a graph depicting ROC curves using logistic regression of baseline (age, gender and ApoE genotype) vs Rules-Based Medicine (RBM) analytes alone versus baseline plus 24 RBM analytes.
  • Age, gender, ApoE genotype e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4
  • alphal microglobulin e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4
  • Apolipoprotein E e2/e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4
  • alphal microglobulin e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4
  • alphal microglobulin e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/
  • Osteopontin Pancreatic polypeptide, PAPP-A, Resistin, Stem Cell Factor, Tenascin C, Thrombomodulin, TIMP-1, VCAM, VEGF and von Willebrand Factor.
  • Figure 4A is an image demonstrating a correlation matrix analysis in the Alzheimer's disease samples exhibiting a high degree of correlation amongst the tested analytes.
  • Figure 4B is a chart demonstrating high correlation of the analytes with each other and that there is high correlation with other analytes on the panel.
  • NT-proBNP showed high correlation with IGFBP2 and beta2 microglobulin suggesting these analytes may be linked in an AD specific pathophysiological pathway.
  • analytes that exhibit high correlation can be interchanged with other highly correlated analytes and still obtain similar differentiation performance.
  • Figure 5 is an image demonstrating that plasma levels of ApoE, CRP and IL-15 differ dependent upon genetic ApoE allele status independent of diagnosis. Levels of ApoE4 are lowest in subjects with one or more E4 allele(s) and highest in subjects with one or more E2 allele(s).
  • Figure 6 is an image depicting graphical summary of the training and test set estimates for sensitivity and specificity
  • Figure 7 is an image depicting test set sensitivity and specificity profiles across various cutoffs for each model.
  • Figure 8 is an image depicting the overall distribution of PLS variables importance scores.
  • Figure 9 is an image depicting the correlation matrix for the top 15 predictors.
  • Figure 10 is an image depicting the training set data points for the top 15 predictors.
  • the present invention provides a method for diagnosing, monitoring and/or staging neurological disorders comprising the use of a multivariate and/or univariate approach to identify a neurological disorder in an individual.
  • the present invention relates generally to diagnostic methods and markers, prognostic methods and markers, and therapy evaluators for neurodegenerative disorders, such as Alzheimer's Disease.
  • the markers of the invention are useful for detecting early stage Alzheimer's disease.
  • the method comprises the step of obtaining a sample of plasma from the individual's blood, and assessing the level of markers of Alzheimer's Disease in the plasma sample.
  • the present invention relates to markers of Alzheimer's Disease, methods for diagnosis of Alzheimer's Disease, methods of determining predisposition to Alzheimer's Disease, methods of monitoring
  • the invention further provides methods for permitting refinement of disease diagnosis, disease risk prediction, and clinical management of individuals associated with a neurodegenerative disorder.
  • Alzheimer's disease markers of the invention represent a plasma based panel for assessing Alzheimer's disease that can be used for determining the disease state or disease risk.
  • the detection of the selective markers of the invention in individuals, or samples obtained therefrom permits refinement of disease diagnosis, disease risk prediction, and clinical management of individuals being treated with agents that are associated with Alzheimer's disease.
  • the Alzheimer's disease markers of the invention include one or more of the markers shown in Table 2.
  • age, gender, and ApoE genotype are additional factors that are considered in identifying an individual for Alzheimer's disease.
  • markers of AD includes at least
  • markers of AD includes at least resistin, e3/e3, pancreatic polypeptide, e3/e4, ApoD, G-CSF, MlPlbeta, ApoE, NrCAM, MMP2, Cortisol, PDGF-BB, e2/e3, MMP 1, and 1-309.
  • markers of AD includes at least
  • CXCL13 Chemokine (C-X-C motif) ligand 13 also known as B lymphocyte chemoattractant (BLC), Cortisol, E-Selectin, FAS, Fatty Acid Binding Protein,
  • Hepatocyte Growth Factor IGF BP-2, Interleukin 10, NT-Pro-BNP (Brain natriuretic peptide co-secreted along with a 76 amino acid N-terminal fragment), Osteopontin, Pancreatic Polypeptide, PAPP-A, Resistin, Stem Cell Factor, Tenascin C,
  • the plasma based panel for assessing Alzheimer's disease comprises one or more of the following markers: Alpha-1 Microglobulin, Angiopoietin-2, Apolipoprotein E, Beta-2 Microglobulin, Chemokine (C-X-C motif) ligand 13 (CXCL13) also known as B lymphocyte chemoattractant (BLC), Cortisol, E- Selectin, FAS, Fatty Acid Binding Protein, Hepatocyte Growth Factor, IGF BP-2,
  • Interleukin 10 Interleukin 10, NT-Pro-BNP (Brain natriuretic peptide co-secreted along with a 76 amino acid N-terminal fragment), Osteopontin, Pancreatic Polypeptide, PAPP-A, Resistin, Stem Cell Factor, Tenascin C, Thrombomodulin, tissue inhibitor of metalloproteinases- 1 (TIMP-1), vascular cell adhesion molecule- 1 (VCAM-1), Vascular endothelial growth factor (VEGF), and Von Willebrand Factor.
  • TRIP-1 tissue inhibitor of metalloproteinases- 1
  • VCAM-1 vascular cell adhesion molecule- 1
  • VEGF Vascular endothelial growth factor
  • Von Willebrand Factor Von Willebrand Factor
  • age, gender, ApoE genotype (e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4) are additional factors that are considered in evaluating the diagnosis of an individual for Alzheimer's disease.
  • the invention includes a method comprising obtaining a sample from an individual, and assessing the level of one or more of Alpha-1 Microglobulin,
  • Angiopoietin-2 Apolipoprotein E, Beta-2 Microglobulin, BLC, Cortisol, E-Selectin, FAS, Fatty Acid Binding Protein, Hepatocyte Growth Factor, IGF BP-2, Interleukin 10, NT-Pro-BNP, Osteopontin, Pancreatic Polypeptide, PAPP-A, Resistin, Stem Cell Factor, Tenascin C, Thrombomodulin, TIMP-1, VCAM-1, VEGF, and Von Willebrand Factor in the sample.
  • the invention should not be limited to only these markers disclosed herein (e.g., Table 2) because a skilled artisan when armed with the present disclosure would be able identify additional markers that can be used as indicators for Alzheimer's disease.
  • a test sample and a control sample can be subjected to any commercially available panel comprising a plurality of markers and analyzed according to the statistic models disclosed herein to identify markers associated with AD.
  • the disclosure presented here demonstrates a high degree of correlation amongst certain markers for identifying AD in an individual.
  • a skilled artisan when armed with the present disclosure, particularly Figures 4A and 4B, would recognize that analytes that exhibit high correlation can be interchanged with other highly correlated analytes and still obtain similar differentiation performance.
  • NT-proBNP showed high correlation with IGFBP2 and beta2 microglobulin suggesting these analytes may be linked in an AD specific pathophysiological pathway.
  • markers that are associated with an AD specific pathophysiological pathway can be interchangeable. Accordingly, correlation amongst the markers of the invention provides means to identify other related markers associated with the specific pathophysiological pathway as being indicators for Alzheimer's disease.
  • biomarkers may be used in the methods disclosed herein. That is, the disclosed methods may include the determination of the level(s) of one biomarker, two or more biomarkers, three or more biomarkers, four or more biomarkers, five or more biomarkers, six or more biomarkers, seven or more biomarkers, eight or more biomarkers, nine or more biomarkers, ten or more biomarkers, fifteen or more biomarkers, etc., including a combination of all of the biomarkers in each or all of Tables 2, 3, 7, 8, 9, 10, 1 1, 12 or any fraction thereof.
  • the biomarkers are used in combination with other factors that predict for AD. Such factors include but are not limited to age, gender, ApoE genotype (e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4).
  • an immunoassay is used for the assessment of a marker level.
  • a luminex technology multiplex immunoassay is used to assess the marker level.
  • a method of diagnosing Alzheimer's disease in an individual comprises the steps of obtaining a first sample of plasma from the individual at a first time; assessing the level of Alzheimer's disease markers using plasma based panel of the invention in the first plasma sample to obtain a baseline level; obtaining a second sample of plasma from the individual at a second time and assessing the level of
  • Alzheimer's disease markers in the second plasma sample to obtain a second level. If the second level is significantly altered compared to the baseline level, the individual is at an increased risk of developing or having Alzheimer's disease. In one embodiment, the second level is also compared to a reference population of an individual's without Alzheimer's disease; if the second level is significantly altered compared to the level derived from a reference population, the individual is at an increased risk of developing or having Alzheimer's disease.
  • the invention provides methods of monitoring a plasma panel of particular markers of Alzheimer's disease to evaluate the progress of a therapeutic treatment of Alzheimer's disease.
  • the invention provides methods for selecting a patient that is most likely to respond to treatment.
  • the invention also provides methods for screening an individual to determine if the individual is at increased risk of having Alzheimer's disease. Individuals found to be at increased risk can be given appropriate therapy and monitored using the methods of the invention.
  • an element means one element or more than one element.
  • “About” as used herein when referring to a measurable value such as an amount, a temporal duration, and the like, is meant to encompass variations of ⁇ 20% or ⁇ 10%, more preferably ⁇ 5%, even more preferably ⁇ 1%, and still more preferably ⁇ 0.1% from the specified value, as such variations are appropriate to perform the disclosed methods.
  • abnormal when used in the context of organisms, tissues, cells or components thereof, refers to those organisms, tissues, cells or components thereof that differ in at least one observable or detectable characteristic (e.g., age, treatment, time of day, etc.) from those organisms, tissues, cells or components thereof that display the "normal” (expected) respective characteristic. Characteristics which are normal or expected for one cell or tissue type, might be abnormal for a different cell or tissue type.
  • antibody refers to an immunoglobulin molecule which is able to specifically bind to a specific epitope on an antigen.
  • Antibodies can be intact immunoglobulins derived from natural sources or from recombinant sources and can be immunoreactive portions of intact immunoglobulins.
  • the antibodies in the present invention may exist in a variety of forms including, for example, polyclonal antibodies, monoclonal antibodies, intracellular antibodies
  • synthetic antibody an antibody which is generated using recombinant DNA technology, such as, for example, an antibody expressed by a bacteriophage as described herein.
  • the term should also be construed to mean an antibody which has been generated by the synthesis of a DNA molecule encoding the antibody and which DNA molecule expresses an antibody protein, or an amino acid sequence specifying the antibody, wherein the DNA or amino acid sequence has been obtained using synthetic DNA or amino acid sequence technology which is available and well known in the art.
  • the term “heavy chain antibody” or “heavy chain antibodies” comprises immunoglobulin molecules derived from camelid species, either by immunization with a peptide and subsequent isolation of sera, or by the cloning and expression of nucleic acid sequences encoding such antibodies.
  • the term “heavy chain antibody” or “heavy chain antibodies” further encompasses immunoglobulin molecules isolated from an animal with heavy chain disease, or prepared by the cloning and expression of VH (variable heavy chain immunoglobulin) genes from an animal.
  • an “immunoassay” refers to any binding assay that uses an antibody capable of binding specifically to a target molecule to detect and quantify the target molecule.
  • an antibody which recognizes an specific antigen, but does not substantially recognize or bind other molecules in a sample.
  • an antibody that specifically binds to an antigen from one species may also bind to that antigen from one or more species. But, such cross-species reactivity does not itself alter the classification of an antibody as specific.
  • an antibody that specifically binds to an antigen may also bind to different allelic forms of the antigen. However, such cross reactivity does not itself alter the classification of an antibody as specific.
  • the terms “specific binding” or “specifically binding”, can be used in reference to the interaction of an antibody, a protein, or a peptide with a second chemical species, to mean that the interaction is dependent upon the presence of a particular structure (e.g., an antigenic determinant or epitope) on the chemical species; for example, an antibody recognizes and binds to a specific protein structure rather than to proteins generally. If an antibody is specific for epitope "A”, the presence of a molecule containing epitope A (or free, unlabeled A), in a reaction containing labeled "A” and the antibody, will reduce the amount of labeled A bound to the antibody.
  • a particular structure e.g., an antigenic determinant or epitope
  • biomarker in the context of the present invention encompasses, without limitation, proteins, nucleic acids, and metabolites, together with their polymorphisms, mutations, variants, modifications, subunits, fragments, protein- ligand complexes, and degradation products, protein-ligand complexes, elements, related metabolites, and other analytes or sample-derived measures. Biomarkers can also include mutated proteins or mutated nucleic acids. Biomarkers also encompass non-blood borne factors or non-analyte physiological markers of health status, such as clinical parameters, as well as traditional laboratory risk factors. Biomarkers also include any calculated indices created mathematically or combinations of any one or more of the foregoing measurements, including temporal trends and differences.
  • the term “data” in relation to one or more biomarkers, or the term “biomarker data” generally refers to data reflective of the absolute and/or relative abundance (level) of a product of a biomarker in a sample.
  • the term “dataset” in relation to one or more biomarkers refers to a set of data representing levels of each of one or more biomarker products of a panel of biomarkers in a reference population of subjects.
  • a dataset can be used to generate a formula/classifier of the invention. According to one embodiment the dataset need not comprise data for each biomarker product of the panel for each individual of the reference population.
  • the "dataset" when used in the context of a dataset to be applied to a formula can refer to data representing levels of products of each biomarker for each individual in one or more reference populations, but as would be understood can also refer to data representing levels of products of each biomarker for 99%, 95%, 90%, 85%, 80%, 75%, 70% or less of the individuals in each of said one or more reference populations and can still be useful for purposes of applying to a formula.
  • “Differentially increased expression” or “up regulation” refers to biomarker product levels which are at least 10% or more, for example, 20%, 30%, 40%, or 50%, 60%, 70%, 80%, 90% higher or more, and/or 1.1 fold, 1.2 fold, 1.4 fold, 1.6 fold, 1.8 fold higher or more, than a control.
  • “Differentially decreased expression” or “down regulation” refers to biomarker product levels which are at least 10% or more, for example, 20%, 30%, 40%, or 50%, 60%, 70%, 80%, 90% lower or less, and/or 0.9 fold, 0.8 fold, 0.6 fold, 0.4 fold, 0.2 fold, 0.1 fold or less lower than a control.
  • a “formula,” “algorithm,” or “model” is any mathematical equation, algorithmic, analytical or programmed process, or statistical technique that takes one or more continuous or categorical inputs (herein called “parameters”) and calculates an output value, sometimes referred to as an "index” or “index value.”
  • “formulas” include sums, ratios, and regression operators, such as coefficients or exponents, biomarker value transformations and normalizations (including, without limitation, those normalization schemes based on clinical parameters, such as gender, age, or ethnicity), rules and guidelines, statistical classification models, and neural networks trained on historical populations.
  • Alzheimer's disease markers and other biomarkers are linear and non-linear equations and statistical classification analyses to determine the relationship between levels of Alzheimer's disease markers detected in a subject sample.
  • structural and synactic statistical classification algorithms, and methods of risk index construction utilizing pattern recognition features, including established techniques such as cross-correlation, Principal Components Analysis (PCA), factor rotation, Logistic Regression (LogReg), Linear Discriminant Analysis (LDA), Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Random Forest (RF), Partial Least Squares, Sparse Partial Least Squares, Flexible Discriminant Analysis, Recursive Partitioning Tree (RPART), as well as other related decision tree classification techniques, Nearest Shrunken Centroids (SC)", stepwise model selection procedures, Kth-Nearest Neighbor, Boosting or Boosted Tree, Decision Trees, Neural Networks, Bayesian Networks, Support Vector Machines, and Hidden Markov Models, among others.
  • PCA Principal Components Analysis
  • LogReg Line
  • an “analyte”, as used herein refers to any substance or chemical constituent that is undergoing analysis.
  • an "analyte” can refer to any atom and/or molecule; including their complexes and fragment ions. The term may refer to a single component or a set of components. In the case of biological
  • molecules/macromolecules such analytes include but are not limited to: polypeptides, polynucleotides, proteins, peptides, antibodies, DNA, RNA, carbohydrates, steroids, and lipids, and any detectable moiety thereof, e.g. immunologically detectable fragments.
  • a “disease” is a state of health of an animal wherein the animal cannot maintain homeostasis, and wherein if the disease is not ameliorated then the animal's health continues to deteriorate.
  • a “disorder” in an animal is a state of health in which the animal is able to maintain homeostasis, but in which the animal's state of health is less favorable than it would be in the absence of the disorder. Left untreated, a disorder does not necessarily cause a further decrease in the animal's state of health.
  • Alzheimer's disease and “Alzheimer's disease” refer to a neurodegenerative disorder and encompass familial Alzheimer's disease and sporadic Alzheimer's disease.
  • familial Alzheimer's disease refers to familial Alzheimer's disease and sporadic Alzheimer's disease.
  • familial Alzheimer's disease refers to familial Alzheimer's disease and sporadic Alzheimer's disease.
  • Alzheimer's disease associated with genetic factors i.e., inheritance is demonstrated
  • sporadic Alzheimer's disease refers to Alzheimer's disease that is not associated with prior family history of the disease.
  • Symptoms indicative of Alzheimer's disease in human subjects typically include, but are not limited to, mild to severe dementia, progressive impairment of memory (ranging from mild forgetfulness to disorientation and severe memory loss), poor visual spatial skills, personality, changes, poor impulse control, poor judgment, distrust of others, increased stubbornness, restlessness, poor planning ability, poor decision making, and social withdrawal.
  • patients lose the ability to use language and communicate, and require assistance in personal hygiene, eating and dressing, and are eventually bedridden.
  • Hallmark pathologies within brain tissue include extracellular neuritic amyloid plaques, neurofibrillary tangles, neurofibrillary degeneration, granulovascular neuronal degeneration, synaptic loss, and extensive neuronal cell death.
  • “Increased risk of developing Alzheimer's disease” is used herein to refer to an increase in the likelihood or possibility of developing Alzheimer's disease. This risk can be assessed relative to an individual's own risk, or with respect to a reference population that does not have clinical evidence of Alzheimer's disease. The reference population may be representative of the individual with regard to approximate age, age group and/or gender.
  • Delaying development of Alzheimer's disease refers to a prolonging of the time to the development of Alzheimer's disease and/or delay in the rate of increased extent of Alzheimer's disease.
  • Alzheimer's disease refers to a decrease in the severity of Alzheimer's disease.
  • Alzheimer's patient As used herein, the terms "Alzheimer's patient”, “Alzheimer's disease patient”, and “individual diagnosed with Alzheimer's disease” all refer to an individual who has been diagnosed with Alzheimer's disease or has been given a probable diagnosis of Alzheimer's Disease.
  • An "individual with mild Alzheimer's disease” is an individual who has been diagnosed with Alzheimer's disease or has been given a diagnosis of probable Alzheimer's disease. In some instances, an "individual with mild Alzheimer's disease” has either been assessed with the Mini-Mental State Examination (MMSE) (referenced in Folstein et al, J. Psychiatr. Res 1975; 12: 1289-198) and scored 22-27 or would achieve a score of 22-27 upon MMSE testing.
  • MMSE Mini-Mental State Examination
  • An "individual with moderate Alzheimer's disease” is an individual who has been diagnosed with Alzheimer's disease or has been given a diagnosis of probable Alzheimer's disease. In some instances, an "individual with moderate Alzheimer's disease” has either been assessed with the MMSE and scored 16-21 or would achieve a score of 16-21 upon MMSE testing.
  • An "individual with severe Alzheimer's disease” is an individual who has been diagnosed with Alzheimer's disease or has been given a diagnosis of probable
  • Alzheimer's disease In some instances, an "individual with severe Alzheimer's disease" has either been assessed with the MMSE and scored 12-15 or would achieve a score of 12-15 upon MMSE testing.
  • the "level" of one or more biomarkers means the absolute or relative amount or concentration of the biomarker in the sample.
  • detection means assessing the presence, absence, quantity or amount (which can be an effective amount) of either a given substance within a clinical or subject-derived sample, including the derivation of qualitative or quantitative concentration levels of such substances, or otherwise evaluating the values or categorization of a subject's clinical parameters.
  • a “reference level” of a biomarker means a level of the biomarker that is indicative of a particular disease state, phenotype, or lack thereof, as well as combinations of disease states, phenotypes, or lack thereof.
  • a “positive" reference level of a biomarker means a level that is indicative of a particular disease state or phenotype.
  • a “negative” reference level of a biomarker means a level that is indicative of a lack of a particular disease state or phenotype.
  • an "Alzheimer's Disease-positive reference level" of a biomarker means a level of a biomarker that is indicative of a positive diagnosis of Alzheimer's Disease in a subject
  • an "Alzheimer's Disease-negative reference level" of a biomarker means a level of a biomarker that is indicative of a negative diagnosis of Alzheimer's Disease in a subject.
  • an "Alzheimer's Disease-positive reference level" of a biomarker means a level of a biomarker that is indicative of a positive diagnosis of Alzheimer's Disease in a subject
  • an "Alzheimer's Disease-negative reference level" of a biomarker means a level of a biomarker that is indicative of a negative diagnosis of Alzheimer's Disease in a subject.
  • an "Alzheimer's Disease-positive reference level" of a biomarker means a level of a biomarker that is indicative of a positive diagnosis of Alzheimer's Disease in a subject
  • Alzheimer's-Disease-progression-positive reference level of a biomarker means a level of a biomarker that is indicative of progression of Alzheimer's Disease in a subject
  • an "Alzheimer's-Disease-regression-positive reference level" of a biomarker means a level of a biomarker that is indicative of regression of the Alzheimer's Disease.
  • a “reference level" of a biomarker may be an absolute or relative amount or concentration of the biomarker, a presence or absence of the biomarker, a range of amount or concentration of the biomarker, a minimum and/or maximum amount or concentration of the biomarker, a mean amount or concentration of the biomarker, and/or a median amount or concentration of the biomarker; and, in addition, “reference levels” of combinations of biomarkers may also be ratios of absolute or relative amounts or concentrations of two or more biomarkers with respect to each other.
  • Appropriate positive and negative reference levels of biomarkers for a particular disease state, phenotype, or lack thereof may be determined by measuring levels of desired biomarkers in one or more appropriate subjects, and such reference levels may be tailored to specific populations of subjects (e.g., a reference level may be age-matched so that comparisons may be made between biomarker levels in samples from subjects of a certain age and reference levels for a particular disease state, phenotype, or lack thereof in a certain age group). Such reference levels may also be tailored to specific techniques that are used to measure levels of biomarkers in biological samples, where the levels of biomarkers may differ based on the specific technique that is used. Such reference levels may also be tailored to specific statistical models used to analyze levels of biomarkers in biological samples, where the levels of biomarkers may differ based on the specific statistical model that is used.
  • a "detector molecule” is a molecule that may be used to detect a compound of interest.
  • Non-limiting examples of a detector molecule are molecules that bind specifically to a compound of interest, such as, but not limited to, an antibody, a cognate receptor, and a small molecule.
  • an “effective amount” or “therapeutically effective amount” of a compound is that amount of compound which is sufficient to provide a beneficial effect to the individual to which the compound is administered.
  • An “effective amount” of a delivery vehicle is that amount sufficient to effectively bind or deliver a compound.
  • an "instructional material” includes a publication, a recording, a diagram, or any other medium of expression which can be used to communicate the usefulness of a compound, composition, vector, or delivery system of the invention in the kit for effecting alleviation of the various diseases or disorders recited herein.
  • the instructional material can describe one or more methods of alleviating the diseases or disorders in a cell or a tissue of a individual.
  • the instructional material of the kit of the invention can, for example, be affixed to a container which contains the identified compound, composition, vector, or delivery system of the invention or be shipped together with a container which contains the identified compound, composition, vector, or delivery system.
  • the instructional material can be shipped separately from the container with the intention that the instructional material and the compound be used cooperatively by the recipient.
  • microarray refers broadly to both “DNA microarrays” and “DNA chip(s),” and encompasses all art-recognized solid supports, and all art-recognized methods for affixing nucleic acid molecules thereto or for synthesis of nucleic acids thereon.
  • patient refers to any animal, or cells thereof whether in vitro or in situ, amenable to the methods described herein.
  • the patient, subject or individual is a human.
  • protein typically refers to large polypeptides.
  • sample or “biological sample” as used herein means a biological material isolated from an individual.
  • the biological sample may contain any biological material suitable for detecting the desired biomarkers, and may comprise cellular and/or non-cellular material obtained from the individual.
  • a “therapeutic” treatment is a treatment administered to an individual who exhibits signs of pathology, for the purpose of diminishing or eliminating those signs.
  • treat means reducing the frequency with which symptoms are experienced by an individual or subject or administering an agent or compound to reduce the frequency with which symptoms are experienced.
  • “treat” and “treating” are not limited to the case where the subject (e.g. patient) is cured and the disease is eradicated. Rather, the present invention also contemplates treatment that merely reduces symptoms, improves (to some degree) and/or delays disease progression.
  • treatment also refers to the alleviation, amelioration, and/or stabilization of symptoms, as well as delay in progression of symptoms of a particular disorder.
  • treatment of Alzheimer's disease includes any one or more of: elimination of one or more symptoms of Alzheimer's disease, reduction of one or more symptoms of Alzheimer's disease, stabilization of the symptoms of Alzheimer's disease (e.g., failure to progress to more advanced stages of Alzheimer's disease), and delay in progression (i.e., worsening) of one or more symptoms of Alzheimer's disease.
  • treating a disease or disorder means reducing the frequency with which a symptom of the disease or disorder is experienced by an individual.
  • Disease and disorder are used interchangeably herein.
  • ranges throughout this disclosure, various aspects of the invention can be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range and, when appropriate, partial integers of the numerical values within ranges. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 2.7, 3, 4, 5, 5.3, and 6. This applies regardless of the breadth of the range. Description
  • Alzheimer's disease is a progressive neurodegenerative disorder characterized by memory and cognitive impairments and other non-cognitive behavioral symptoms. Age is the strongest risk factor, wherein almost 50% of people over the age of 85 are affected. Early-onset Alzheimer's disease (EOAD) is associated with genetic mutations in amyloid precursor protein (APP), presenilin 1 (PSE 1) and presenilin 2
  • APP amyloid precursor protein
  • PSE 1 presenilin 1
  • presenilin 2 presenilin 2
  • LOAD sporadic or late-onset Alzheimer's disease
  • LOAD sporadic or late-onset Alzheimer's disease
  • genetic factors may account for as much as 80% of the disease risk associated with LOAD (Gatz et al. (2006) Arch. Gen. Psychiatry 63(2): 168-174).
  • monogenic mutations cause EOAD
  • the only extensively validated susceptibility gene for LOAD is the apolipoprotein E ( ⁇ - ⁇ 4) allele
  • the present invention provides novel Alzheimer's disease biomarkers present in a biological sample of an individual.
  • the biological sample is plasma.
  • the level of these biomarkers allow a more accurate diagnosis or prognosis of Alzheimer's disease in individuals that are at risk for Alzheimer's disease, that exhibit no clinical signs of Alzheimer's disease, or that exhibit minor clinical signs of Alzheimer's disease.
  • the measurement of the biomarkers of the invention allows the monitoring of Alzheimer's disease, such that a comparison of biomarker levels facilitates an evaluation of disease progression in individuals that have been diagnosed with Alzheimer's disease, or that do not yet exhibit any clinical signs of Alzheimer's disease.
  • the Alzheimer's disease biomarkers of the invention may be used in concert with known Alzheimer's disease biomarkers such that a more accurate diagnosis or prognosis of Alzheimer's disease may be made.
  • the invention provides a screening tool to detect Alzheimer's disease in an individual.
  • the screening tool includes a examining a multiplexed plasma panel to differentiate Alzheimer's disease from age-matched controls.
  • the screening tool encompasses a multivariate based approach combined a statistical model to predict Alzheimer's disease in an individual.
  • the multivariate approach useful in the invention encompasses the use of a multiplexed plasma panel in combination with age, gender and ApoE genetic status (6 co- variates e2/e2; e2/e3; e2/e4; e3/e4; e3/e3; e4/e4) for determining Alzheimer's disease in an individual.
  • the screening tool of the invention can differentiate Alzheimer's disease from age-matched controls with high sensitivity suitable for preliminary screening of individuals who might be eligible for clinical trial enrollment in early Alzheimer's disease studies.
  • the disclosure presented herein also includes additional markers associated with increased or decreased risk of Alzheimer's disease as shown in Table 2.
  • the present invention relates partly on the discovery of biomarkers that can differentiate Alzheimer's disease from controls and identify individuals at risk of progressing to dementia.
  • a two tiered strategy was employed to find the optimal discriminatory panel of biomarkers.
  • multivariate approaches were utilized to identify top plasma analytes that contributed to maximal differentiation using a training/test set approach.
  • plasma analytes were compared to expression in CSF from individuals with diagnosis confirmed by autopsy analysis.
  • a top list of analytes was identified based upon 1) performance in multivariate models, 2) expression in CSF from autopsy confirmed individuals and 3) upon linkage to disease by pathway analysis.
  • An objective was to determine whether a plasma based signature with a sensitivity of at least 80% could be achieved. In some instances, the objective was to determine whether a plasma based signature with a sensitivity of at least 80% and a specificity of at least 80% could be achieved.
  • a test with reasonable (at least 80%) sensitivity and specificity (Consensus report of the Working Group on:
  • a biomarker is an organic biomolecule which is differentially present in a sample taken from an individual of one phenotypic status (e.g., having a disease) as compared with an individual of another phenotypic status (e.g., not having the disease).
  • a biomarker is differentially present between the two individuals if the mean or median expression level of the biomarker in the different individuals is calculated to be statistically significant.
  • Biomarkers alone or in combination, provide measures of relative risk that an individual belongs to one phenotypic status or another. Therefore, they are useful as markers for diagnosis of disease, therapeutic effectiveness of a drug, and drug toxicity.
  • the invention provides methods for identifying one or more biomarkers which can be used to aid in the diagnosis, to diagnose, detect, predict neurological diseases such as neurodegenerative disorders.
  • the methods of the invention are carried out by obtaining a set of measured values for a plurality of biomarkers from a biological sample derived from a test individual, obtaining a set of measured values for a plurality of biomarkers from a biological sample derived from a control individual, comparing the measured values for each biomarker between the test and control sample, and identifying biomarkers which are significantly different between the test value and the control value also referred to as a reference value.
  • the process of comparing a measured value and a reference value can be carried out in any convenient manner appropriate to the type of measured value and reference value for the AD biomarker at issue.
  • “measuring” can be performed using quantitative or qualitative measurement techniques, and the mode of comparing a measured value and a reference value can vary depending on the
  • the levels may be compared by visually comparing the intensity of the colored reaction product, or by comparing data from densitometric or spectrometric measurements of the colored reaction product (e.g., comparing numerical data or graphical data, such as bar charts, derived from the measuring device).
  • data from densitometric or spectrometric measurements of the colored reaction product e.g., comparing numerical data or graphical data, such as bar charts, derived from the measuring device.
  • the measured values used in the methods of the invention will most commonly be quantitative values (e.g., quantitative
  • measured values are qualitative.
  • the comparison can be made by inspecting the numerical data, by inspecting representations of the data (e.g., inspecting graphical representations such as bar or line graphs).
  • a measured value is generally considered to be substantially equal to or greater than a reference value if it is at least about 95% of the value of the reference.
  • a measured value is considered less than a reference value if the measured value is less than about 95% of the reference value.
  • a measured value is considered more than a reference value if the measured value is at least more than about 5% greater than the reference value.
  • the process of comparing may be manual (such as visual inspection by the practitioner of the method) or it may be automated.
  • an assay device such as a luminometer for measuring chemiluminescent signals
  • a separate device e.g., a digital computer
  • Automated devices for comparison may include stored reference values for the AD biomarker(s) being measured, or they may compare the measured value(s) with reference values that are derived from contemporaneously measured reference samples.
  • the methods of the invention utilize "simple" or “binary" comparison between the measured level(s) and the reference level(s) (e.g., the comparison between a measured level and a reference level determines whether the measured level is higher or lower than the reference level).
  • a comparison showing that the measured value for the biomarker is altered from the reference value indicates or suggests a diagnosis of AD.
  • the process of comparing the measured values to identify indicators for disease state can be carried out using multiple marker analysis approach including but not limited to Logistic Regression, Boosted Tree Models, Flexible Discriminant Analysis (FDA), K-Nearest Neighbors (KNN), Naive Bayes,
  • FDA Flexible Discriminant Analysis
  • KNN K-Nearest Neighbors
  • Naive Bayes Naive Bayes
  • PLS Partial Least Squares
  • Random Forests Random Forests
  • Shrunken Centroids Sparse Partial Least Squares
  • Support Vector Machines approaches.
  • a biomarker is typically a protein, found in a bodily fluid, whose level varies with disease state and may be readily quantified. The quantified level may then be compared to a known value. The comparison may be used for several different purposes, including but not limited to, diagnosis of Alzheimer's disease, prognosis of Alzheimer's disease, and monitoring treatment of Alzheimer's disease.
  • biomarkers have been identified as associated with Alzheimer's disease.
  • biomarkers identified as being indicators of Alzheimer's disease include, but is not limited, to Alpha- 1 Microglobulin, Angiopoietin-2, Apolipoprotein E, Beta-2 Microglobulin, BLC, Cortisol, E-Selectin, FAS, Fatty Acid Binding Protein, Hepatocyte Growth Factor, IGF BP-2, Interleukin 10, NT-Pro-BNP, Osteopontin, Pancreatic Polypeptide, PAPP-A, Resistin, Stem Cell Factor, Tenascin C,
  • Thrombomodulin TIMP-1, VCAM-1, VEGF, Von Willebrand Factor, and any combination thereof.
  • age, gender, and ApoE genotype are additional factors that are considered in evaluating the diagnosis of an individual for Alzheimer's disease.
  • the invention provides compositions for use in the methods of the invention.
  • the composition comprises a reagent that detects and/or quantitates an Alzheimer Disease biomarker.
  • the composition comprises a panel of reagents, each of which detects and/or quantitates a different Alzheimer Disease biomarker.
  • the Alzheimer Disease biomarker is an Alzheimer Disease biomarker shown in Table 2.
  • the composition comprises two or more reagents, each of which detects and/or quantitates a different one of two or more, three or more, four or more or five or more Alzheimer Disease biomarkers.
  • the compositions comprises a panel of reagents each of which detects and/or quantitates a different one of 24 Alzheimer Disease biomarkers shown in Table 2.
  • the composition comprises a panel of reagents for the detection and/or quantification of Cortisol, pancreatic polypeptide, osteopontin, IGF BP2, and resistin.
  • the composition comprises a panel of reagents for the detection and/or quantification of resistin, pancreatic polypeptide, ApoD, G-CSF, MlPlbeta, ApoE, NrCAM, MMP2, Cortisol, PDGF-BB,
  • the composition comprises a panel of reagents for the detection and/or quantification of Alpha- 1 Microglobulin, Angiopoietin-2, Apolipoprotein E, Beta-2 Microglobulin, Chemokine (C-X-C motif) ligand 13 (CXCL13) also known as B lymphocyte chemoattractant (BLC), Cortisol, E-Selectin, FAS, Fatty Acid Binding Protein, Hepatocyte Growth Factor, IGF BP-2, Interleukin 10, NT-Pro-BNP (Brain natriuretic peptide co-secreted along with a 76 amino acid N-terminal fragment), Osteopontin, Pancreatic Polypeptide, PAPP-A, Resistin, Stem Cell Factor, Tenascin C, Thrombomodulin, tissue inhibitor of metalloproteinases-1 (TIMP-1), vascular cell adhesion molecule- 1 (VCAM-1), Vas
  • the combination of biomarkers associated with assessing Alzheimer's disease is collectively presented on a detectable medium referred to as a panel or plasma based panel.
  • a detectable medium referred to as a panel or plasma based panel.
  • Each of the biomarkers identified above may be used in concert with other biomarkers. For instance, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 10 or more, 15 or more, 20 or more, 30 or more, 40 or more, (and any integer value in between) Alzheimer's disease biomarkers may be used in concert with other, and with other known or to be known biomarkers for
  • the composition is an array of two or more detection reagents, e.g., antibodies and/or oligonucleotides each of which binds to a different one of two Alzheimer Disease biomarker proteins or nucleic acids, respectively.
  • the Alzheimer Disease biomarkers are proteins and the array contains antibodies sufficient to measure a statistically significant alteration in Alzheimer Disease biomarker expression compared to a reference value.
  • the invention provides a kit with at least two detection reagents, each of which binds to a different one of two Alzheimer Disease biomarker proteins, metabolites, or other analytes.
  • a. detection reagent is immobilized on a solid matrix such as a porous strip or bead to form at least one Alzheimer Disease biomarker detection site,
  • assessing cognitive function assessing cognitive impairment, diagnosing or aiding diagnosis of cognitive impairment by obtaining measured levels of one or more AD diagnosis biomarkers in a biological sample from an individual, such as for example, a plasma sample from an individual, and comparing those measured levels to reference levels.
  • the levels of Alzheimer's disease biomarkers of the invention may be assessed in several different biological samples, for example bodily fluids.
  • bodily fluids include whole blood, plasma, serum, bile, lymph, pleural fluid, semen, saliva, sweat, urine, and CSF.
  • the bodily fluid is selected from the group of whole blood, plasma, and serum.
  • the bodily fluid is whole blood.
  • the bodily fluid is plasma.
  • the bodily fluid is serum.
  • the bodily fluid is obtained from the individual using conventional methods in the art. For instance, one skilled in the art knows how to draw blood and how to process it in order to obtain serum and/or plasma for use in the method. Generally speaking, the method preferably maintains the integrity of the biomarkers of the invention such that it can be accurately quantified in the bodily fluid. Methods for collecting blood or fractions thereof are well known in the art. For example, see US Patent No. 5,286,262, which is hereby incorporated by reference in its entirety.
  • a bodily fluid may be obtained from any mammal known to suffer from Alzheimer's disease or that can be used as a disease model for Alzheimer's disease.
  • the mammal is a rodent. Examples of rodents include mice, rats, and guinea pigs.
  • the mammal is a primate. Examples of primates include monkeys, apes, and humans.
  • the mammal is a human.
  • the individual has no clinical signs of Alzheimer's disease.
  • the individual has mild clinical signs of Alzheimer's disease.
  • the individual may be at risk for Alzheimer's disease.
  • the individual has been diagnosed with Alzheimer's disease.
  • Assessment of biomarker levels may encompass assessment of the level of protein concentration or the level of enzymatic activity of the biomarker, wherever applies. In either case, the level is quantified such that a value, an average value, or a range of values is determined. In one embodiment, the level of protein concentration of the Alzheimer's disease biomarker is quantified.
  • kits include ProteoQwestTM Colohmetric Western Blotting Kits (Sigma-Aldrich, Co.), QuantiProTM bicinchoninic acid (BCA) Protein Assay Kit (Sigma-Aldrich, Co.), FluoroProfileTM Protein Quantification Kit (Sigma-Aldrich, Co.), the Coomassie Plus - The Better Bradford Assay (Pierce Biotechnology, Inc.), and the Modified Lowry Protein Assay Kit (Pierce Biotechnology, Inc.).
  • the protein concentration is measured using a luminex based multiplex immunoassay panel.
  • the invention should not be limited to any particular assay for assessing the level of a biomarker of the invention. That is, any currently known assay used to detect protein levels and assays to be discovered in the future can be used to detect the biomarkers of the invention.
  • assessing the level of a protein involves the use of a detector molecule for the biomarker.
  • Detector molecules can be obtained from commercial vendors or can be prepared using conventional methods available in the art.
  • Exemplary detector molecules include, but are not limited to, an antibody that binds specifically to the biomarker, a naturally-occurring cognate receptor, or functional domain thereof, for the biomarker, and a small molecule that binds specifically to the biomarker.
  • the level of a biomarker is assessed using an antibody.
  • non-limiting exemplary methods for assessing the level of a biomarker in a biological sample include various immunoassays, for example, immunohistochemistry assays, immunocytochemistry assays, ELISA, capture ELISA, sandwich assays, enzyme immunoassay, radioimmunoassay, fluorescent immunoassay, and the like, all of which are known to those of skill in the art. See e.g. Harlow et ah, 1988, Antibodies: A Laboratory Manual, Cold Spring Harbor, New York; Harlow et ah, 1999, Using
  • Antibodies A Laboratory Manual, Cold Spring Harbor Laboratory Press, NY. The generation of polyclonal antibodies is accomplished by inoculating the desired animal with an antigen and isolating antibodies which specifically bind the antigen therefrom.
  • Monoclonal antibodies directed against one of the biomarkers identified herein may be prepared using any well known monoclonal antibody preparation procedures, such as those described, for example, in Harlow et al. (1988, In: Antibodies, A Laboratory Manual, Cold Spring Harbor, NY) and in Tuszynski et al. (1988, Blood, 72: 109-1 15). Human monoclonal antibodies may be prepared by the method described in U.S. patent publication 2003/0224490. Monoclonal antibodies directed against a biomarker such as EGF are generated from mice immunized with the biomarker using standard procedures as referenced herein.
  • a biomarker may be purified from a biological source that endogenously comprises the biomarker, or from a biological source recombinantly-engineered to produce or over-produce the biomarker, using conventional methods known in the art.
  • Exemplary nucleic acid and protein sequences for the eleven biomarkers described herein are readily available in public sequence databases, such as National Library of Medicine's genetic sequence database GenBank® (Benson et al, 2008, Nucleic Acids Research, 36(Database issue):D25-30).
  • GenBank® National Library of Medicine's genetic sequence database GenBank® (Benson et al, 2008, Nucleic Acids Research, 36(Database issue):D25-30).
  • antibodies are generated against the human homologs of each of the eleven biomarkers for practicing the methods using a biological sample from a human individual diagnosed with Parkinson's Disease.
  • Nucleic acid encoding the monoclonal antibody obtained using the procedures described herein may be cloned and sequenced using technology which is available in the art, and is described, for example, in Wright et al. (1992, Critical Rev. Immunol. 12(3,4): 125-168) and the references cited therein. Further, the antibody useful in the practice of the invention may be "humanized” using the technology described in Wright et al, (supra) and in the references cited therein, and in Gu et al. (1997,
  • a cDNA library is first obtained from mRNA which is isolated from cells, e.g., the hybridoma, which express the desired protein to be expressed on the phage surface, e.g., the desired antibody.
  • cDNA copies of the mRNA are produced using reverse transcriptase.
  • immunoglobulin fragments are obtained by PCR and the resulting DNA is cloned into a suitable bacteriophage vector to generate a bacteriophage DNA library comprising DNA specifying immunoglobulin genes.
  • a suitable bacteriophage vector to generate a bacteriophage DNA library comprising DNA specifying immunoglobulin genes.
  • the procedures for making a bacteriophage library comprising heterologous DNA are well known in the art and are described, for example, in Sambrook et al. (2001, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY).
  • Bacteriophage which encode the desired antibody may be engineered such that the protein is displayed on the surface thereof in such a manner that it is available for binding to the antigen against which the antibody is directed.
  • Bacteriophage which express a specific antibody are incubated in the presence of the antigen, for instance, antigen immobilized on a resin or surface, the bacteriophage will bind to the antigen.
  • Bacteriophage which do not express the antibody will not bind to the antigen.
  • panning techniques are well known in the art and are described for example, in Wright et al, (supra).
  • a cDNA library is generated from mRNA obtained from a population of antibody -producing cells.
  • the mRNA encodes rearranged immunoglobulin genes and thus, the cDNA encodes the same.
  • Amplified cDNA is cloned into M13 expression vectors creating a library of phage which express human Fab fragments on their surface. Phage which display the antibody of interest are selected by antigen binding and are propagated in bacteria to produce soluble human Fab
  • Fab molecules comprise the entire Ig light chain, that is, they comprise both the variable and constant region of the light chain, but include only the variable region and first constant region domain (CHI) of the heavy chain.
  • Single chain antibody molecules comprise a single chain of protein comprising the Ig Fv fragment.
  • An Ig Fv fragment includes only the variable regions of the heavy and light chains of the antibody, having no constant region contained therein.
  • Phage libraries comprising scFv DNA may be generated following the procedures described in Marks et al, 1991, J. Mol. Biol.
  • phage display libraries in which the heavy and light chain variable regions may be synthesized such that they include nearly all possible specificities (Barbas, 1995, Nature Medicine 1 :837-839; de Kruif et al, 1995, J. Mol. Biol. 248:97-105) may also be used to prepare an antibody useful in the practice of the invention.
  • chromatography e.g., HPLC, gas chromatography, liquid chromatography
  • mass spectrometry e.g., MS, MS-MS
  • a chromatography medium comprising a cognate receptor for the biomarker or a small molecule that binds to the biomarker can be used to substantially isolate the biomarker from the biological sample.
  • Small molecules that bind specifically to a biomarker can be identified using conventional methods in the art, for instance, screening of compounds using combinatorial library methods known in the art, including biological libraries, spatially-addressable parallel solid phase or solution phase libraries, synthetic library methods requiring deconvolution, the "one-bead one- compound” library method, and synthetic library methods using affinity chromatography selection.
  • the level of substantially isolated protein can be quantitated directly or indirectly using a conventional technique in the art such as spectrometry, Bradford protein assay, Lowry protein assay, biuret protein assay, or bicinchoninic acid protein assay, as well as immunodetection methods.
  • the level of enzymatic activity of the biomarker if such biomarker has an enzymatic activity maybe quantified.
  • enzyme activity may be measured by means known in the art, such as measurement of product formation, substrate degradation, or substrate concentration, at a selected point(s) or time(s) in the enzymatic reaction.
  • There are numerous known methods and kits for measuring enzyme activity For example, see US Patent No. 5,654, 152. Some methods may require purification of the Alzheimer's disease biomarker prior to measuring the enzymatic activity of the biomarker.
  • a pure biomarker constitutes at least about 90%, preferably, 95% and even more preferably, at least about 99% by weight of the total protein in a given sample.
  • Alzheimer's disease biomarkers of the invention may be purified according to methods known in the art, including, but not limited to, ion-exchange chromatography, size-exclusion chromatography, affinity chromatography, differential solubility, differential centrifugation, and HPLC. Determination of the Status of Alzheimer's Disease
  • the present invention is based on biomarker profiles or signatures determined for biological samples from individuals diagnosed with Alzheimer's Disease as well as from one or more other groups of control individuals (e.g., healthy control subjects not diagnosed with Alzheimer's Disease).
  • the profile for Alzheimer's Disease was compared to the profile for biological samples from the one or more other groups of control individual.
  • Those molecules differentially present, including those molecules differentially present at a level that is statistically significant, in the profile of Alzheimer's Disease samples as compared to another group (e.g., healthy control subjects not diagnosed with Alzheimer's Disease) were identified as biomarkers to distinguish those groups.
  • the biomarkers are discussed in more detail elsewhere herein.
  • AD biomarker levels relative to another reference level which may be relative to the level of another AD biomarker, may be obtained according to an appropriate statistical model disclosed herein.
  • high correlation amongst many biomarkers was observed to provide an indicator of disease state when analyzed using an appropriate statistical model.
  • the reference level used for comparison with the measured level for a AD biomarker may vary, depending on aspect of the invention being practiced, as will be understood from the foregoing discussion.
  • the "reference level” is typically a predetermined reference level, such as an average of levels obtained from a population that is not afflicted with AD, but in some instances, the reference level can be a mean or median level from a group of individuals including AD patients.
  • the predetermined reference level is derived from (e.g., is the mean or median of) levels obtained from an age-matched population.
  • the age-matched population comprises individuals with non-AD neurodegenerative disorders.
  • the reference level may be a historical reference level for the particular patient (e.g., a biomarker level that was obtained from a sample derived from the same individual, but at an earlier point in time).
  • the predetermined reference level is derived from (e.g., is the mean or median of) levels obtained from an age-matched population.
  • Age-matched populations are ideally the same age as the individual being tested, but approximately age-matched populations are also acceptable. Approximately age-matched populations may be within 1, 2, 3, 4, or 5 years of the age of the individual tested, or may be groups of different ages which encompass the age of the individual being tested. Approximately age-matched populations may be in 2, 3, 4, 5, 6, 7, 8, 9, or year increments (e.g. a "5 year increment" group which serves as the source for reference values for a 62 year old individual might include 58-62 year old individuals, 59-63 year old individuals, 60-64 year old individuals, 61-65 year old individuals, or 62-66 year old individuals.
  • the level(s) of the one or more biomarkers may be compared to
  • Alzheimer's Disease-positive and/or Alzheimer's Disease-negative reference levels using various techniques, including a simple comparison (e.g., a manual comparison) of the level(s) of the one or more biomarkers in the biological sample to Alzheimer's Disease- positive and/or Alzheimer's Disease-negative reference levels.
  • the level(s) of the one or more biomarkers in the biological sample may also be compared to Alzheimer's Disease- positive and/or Alzheimer's Disease-negative reference levels using one or more statistical analyses.
  • Statistical models useful in the present invention includes but are not limited to Logistic Regression, Boosted Tree Models, Flexible Discriminant Analysis (FDA), K-Nearest Neighbors (KNN), Na ' ive Bayes, Partial Least Squares (PLS), Random Forests, Shrunken Centroids, Sparse Partial Least Squares and Support Vector Machines approaches.
  • the statistical model is Partial Least Squares.
  • the AD biomarkers of the invention include one or more of the biomarkers shown in Table 2.
  • the combination of biomarkers associated with assessing Alzheimer's disease is collectively presented on a detectable medium referred to as a panel or plasma based panel.
  • Each of the biomarkers identified above may be used in concert with other biomarkers. For instance, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 10 or more, 15 or more, 20 or more, 30 or more, 40 or more, (and any integer value in between) Alzheimer's disease biomarkers may be used in concert with other, and with other known or to be known biomarkers for Alzheimer's disease.
  • age, gender, and ApoE genotype are additional factors that are considered in identifying an individual for Alzheimer's disease. Therefore, in some instances, predictors for AD in an individual are selected from the group consisting of Alpha- 1 Microglobulin,
  • Angiopoietin-2 Apolipoprotein E, Beta-2 Microglobulin, BLC, Cortisol, E-Selectin, FAS, Fatty Acid Binding Protein, Hepatocyte Growth Factor, IGF BP-2, Interleukin 10, NT-Pro-BNP, Osteopontin, Pancreatic Polypeptide, PAPP-A, Resistin, Stem Cell Factor, Tenascin C, Thrombomodulin, TIMP-1, VCAM-1, VEGF, Von Willebrand Factor, age, gender, ApoE genotype (e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4), and any combination thereof.
  • ApoE genotype e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4
  • predictors of AD in an individual are selected from the group consisting of Resistin, e3/e3, Pancreatic Polypeptide, e3/e4, ApoD, G-CSF, MlPlbeta, ApoE, NrCAM, MMP2, Cortisol, PDGF-BB, e2/e3, MMP 1, 1-309, and any combination thereof.
  • predictors of AD in an individual are selected from the group consisting of Cortisol, Pancreatic Polypeptide, osteopontin, IGF BP2, Resistin, and any combination thereof.
  • a plasma based signature for Alzheimer's disease can be detected in plasma and that signature can differentiate AD from healthy controls and other forms of dementia.
  • the signature for Alzheimer's disease includes the combination of biomarkers disclosed herein.
  • the signature for Alzheimer's disease is a combination of biomarkers and predictors of Alzheimer's disease disclosed herein.
  • the biomarkers of the invention in combination with other factors such as age, gender, ApoE genotype (e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4), can improve diagnostic and screening accuracy.
  • the biomarkers can also be combined with cognitive tests such as a simple memory test to improve diagnostic and screening accuracy.
  • the biomarkers of the invention can be combined with additional confirmatory CSF and imaging testing.
  • Tables 2 provide a listing of biomarkers that are useful for identifying an individual with AD.
  • certain biomarkers are increases and other biomarkers are decreased in AD compared to age-matched normal controls (Tables 10, 11, and 12.
  • a significant increase in a biomarker as compared to an appropriate control is indicative of AD
  • a significant decrease in a biomarker as compared to an appropriate control is indicative of AD.
  • correlation between biomarkers provide an indication of AD in an individual.
  • any one or more of the biomarkers listed in Table 2 can be used to identify AD in an individual as distinguished from other non-AD individuals.
  • the biomarkers of the invention can be used in diagnostic tests to assess the status of Alzheimer's disease in an individual, e.g., to diagnose Alzheimer's disease or to assess the degree of Alzheimer's disease in the individual.
  • the phrase "Alzheimer's disease status" includes any distinguishable manifestation of the disease, including non- Alzheimer's disease, e.g., normal or non-demented.
  • disease status includes, without limitation, the presence or absence of Alzheimer's disease (e.g., Alzheimer's disease v. non-Alzheimer's disease), the risk of developing disease, the stage of the disease, the progress of disease (e.g., progress of disease or remission of disease over time) and the effectiveness or response to treatment of disease. Based on this status, further procedures may be indicated, including additional diagnostic tests or therapeutic procedures or regimens.
  • ROC receiver operated characteristic
  • diagnostic tests that use the biomarkers of the invention exhibit a sensitivity and specificity of at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98% and about 100%.
  • screening tools of the present invention exhibits a high sensitivity of at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98% and about 100%.
  • screening tools should exhibit high sensitivity, but specificity can be low.
  • Diagnostics should have high sensitivity and specificity.
  • biomarkers While individual biomarkers are useful diagnostic biomarkers, it has been found that a combination of biomarkers can provide greater predictive value of a particular status than single biomarkers alone. Specifically, the detection of a plurality of biomarkers in a sample can increase the sensitivity and/or specificity of the test.
  • a combination of at least two biomarkers is sometimes referred to as a "biomarker profile” or “biomarker fingerprint.”
  • biomarker profile or “biomarker fingerprint.”
  • biomarker fingerprint A combination of at least two biomarkers.
  • Examples of Alzheimer's disease biomarkers of the invention include, but are not limited, to Alpha- 1 Microglobulin, Angiopoietin-2, Apolipoprotein E, Beta-2 Microglobulin, BLC, Cortisol, E-Selectin, FAS, Fatty Acid Binding Protein, Hepatocyte Growth Factor, IGF BP-2, Interleukin 10, NT-Pro-BNP, Osteopontin, Pancreatic Polypeptide, PAPP-A, Resistin, Stem Cell Factor, Tenascin C,
  • Thrombomodulin TIMP-1, VCAM-1, VEGF, Von Willebrand Factor, and any combination thereof.
  • age, gender, and ApoE genotype are additional factors that are considered in evaluating the diagnosis of an individual for Alzheimer's disease.
  • the methods disclosed herein using the biomarkers listed in the tables presented herein may be used in combination with clinical diagnostic measures of Alzheimer's Disease and/or other neurodegenerative diseases. Combinations with clinical diagnostics may facilitate the disclosed methods, or confirm results of the disclosed methods (for example, facilitating or confirming diagnosis, monitoring progression or regression, and/or determining predisposition to Alzheimer's Disease).
  • Determining Alzheimer's disease status typically involves classifying an individual into one of two or more groups based on the results of the diagnostic test.
  • the diagnostic tests described herein can be used to classify an individual into a number of different states.
  • the invention provides methods for determining the presence or absence of Alzheimer's disease in an individual (status: Alzheimer's disease v. non-Alzheimer's disease).
  • the presence or absence of Alzheimer's disease is determined by measuring the relevant biomarker or biomarkers in samples obtained from individuals and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular risk level.
  • the invention provides methods for determining the risk of developing disease in an individual.
  • Biomarker amounts or patterns are characteristic of various risk states, e.g., high, medium or low.
  • the risk of developing Alzheimer's disease is determined by measuring the relevant biomarker or biomarkers in sample obtained from individuals and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular risk level.
  • the invention provides methods for determining the stage of Alzheimer's disease in an individual.
  • Each stage of the disease can be characterized by the amount of a biomarker or relative amounts of a set of biomarkers (i.e., a pattern) that are found in a sample obtained from the individual.
  • the stage of Alzheimer's disease is determined by measuring the relevant biomarker or biomarkers and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular stage.
  • the invention provides methods for determining the course of Alzheimer's disease in an individual.
  • Disease course refers to changes in disease status over time, including disease progression (worsening) and disease regression (improvement).
  • the amounts or relative amounts (e.g., the pattern) of the biomarkers changes.
  • levels of various biomarkers of the present invention increase with progression of disease.
  • this method involves measuring the level of one or more biomarkers in an individual at two or more different time points, e.g., a first time and a second time, and comparing the change in amounts. The course of disease is determined based on these comparisons.
  • the levels of various biomarkers of the invention decreases with disease progression.
  • the level of one or more biomarkers in a sample from an individual is measured at two or more different time points, e.g., a first time and a second time, and the change in levels, if any is assessed. The course of disease is determined based on these comparisons.
  • changes in the rate of disease progression may be monitored by measuring the level of one or more biomarkers at different times and calculating the rate of change in biomarker levels.
  • the ability to measure disease state or rate of disease progression is important for drug treatment studies where the goal is to slow down or arrest disease progression using therapy.
  • Additional embodiments of the invention relate to the communication of the results or diagnoses or both to technicians, physicians or patients, for example.
  • computers are used to communicate results or diagnoses or both to interested parties, e.g., physicians and their patients.
  • the methods of the invention further comprise managing individual treatment based on their disease status.
  • Such management includes the actions of the physician or clinician subsequent to determining Alzheimer's disease status. For example, if a physician makes a diagnosis of Alzheimer's disease, then a certain regime of treatment, such as prescription or administration of the therapeutic compound might follow. Alternatively, a diagnosis of non-Alzheimer's disease might be followed by further testing to determine any other diseases that might the patient might be suffering from. Also, if the test is inconclusive with respect to Alzheimer's disease status, further tests may be called for.
  • a diagnosis based on the presence or absence or relative levels in the biological sample of an individual of the relevant biomarkers disclosed herein is communicated to the individual as soon as possible after the diagnosis is obtained.
  • the present invention provides a method of assessing efficacy of a treatment of Alzheimer's disease in a patient comprising: a) determining a baseline level of biomarkers in a first sample obtained from the patient before receiving the treatment; b) determining the level of same biomarkers in a second sample obtained from the patient after receiving the treatment; wherein an alteration in the levels of the biomarkers in the post-treatment sample is correlated with a positive treatment outcome.
  • the experiments disclosed herein were designed to develop an assay to identify universal accepted biomarkers for diagnosing, screening, monitoring and staging neurodegenerative diseases such as Alzheimer's disease that are fast, more accurate, and less expensive.
  • the results presented herein demonstrate that a diagnostic assay has been developed that can detect among other things early onset of Alzheimer's disease.
  • Detection of early onset of Alzheimer's disease is believed to increase the success rate of the individual being successfully treated for Alzheimer's disease.
  • the diagnostic method of the present invention can be applied to subjects who have been previously diagnosed with Alzheimer's disease, those who are suspected of having Alzheimer's disease, and those at risk of developing Alzheimer's disease.
  • patients diagnosed with dementia in particular, those patients who were previously clinically normal, are suitable subjects.
  • the present invention be limited to use with any particular subject types.
  • the subject is a human subject.
  • the subject is selected from the group consisting of subjects displaying pathology resulting from Alzheimer's disease, subjects suspected of displaying pathology resulting from Alzheimer's disease, and subjects at risk of displaying pathology resulting from Alzheimer's disease.
  • the Alzheimer's disease diagnosed using the method of the present invention is selected from the group consisting of late onset Alzheimer's disease, early onset Alzheimer's disease, familial Alzheimer's disease and sporadic Alzheimer's disease.
  • EOAD Early-onset Alzheimer's disease
  • EOAD is a rare form of Alzheimer's disease in which individuals are diagnosed with the disease before age 65. Less than 10% of all Alzheimer's disease patients have EOAD. Younger individuals who develop Alzheimer's disease exhibit more of the brain abnormalities that are normally associated with Alzheimer's disease.
  • EOAD is usually familial and follows an autosomal dominant inheritance pattern.
  • mutations in three genes including amyloid precursor protein (APP) on chromosome 21, presenilin 1 (PSEN1) on chromosome 14 and presenilin 2 (PSEN2) on chromosome 1 have been identified in families with EOAD . Mutations in the APP, PSEN1 and PSEN2 genes account for about 50% of the disease .
  • APP amyloid precursor protein
  • PSEN1 presenilin 1
  • PSEN2 presenilin 2
  • Late-onset Alzheimer's disease is the most common form of Alzheimer's disease, accounting for about 90% of cases and usually occurring after age 65.
  • LOAD strikes almost half of all individuals over the age of 85 and may or may not be hereditary. It is a complex and multifactorial disease with the possible involvement of several genes. Genome-wide linkage or linkage disequilibrium studies on LOAD have provided informative data for the existence of multiple putative genes for Alzheimer's disease on several chromosomes, with the strongest evidence on chromosomes 12, 10, 9 and 6. LOAD cases tend to be sporadic, wherein there is no family history of the disease. Genetic susceptibility at multiple genes and interaction between these genes as well as environmental factors are most likely responsible for the etiology of LOAD.
  • Twin data on incident cases indicates that almost 80% of the LOAD risk is attributable to genetic factors.
  • the Apolipoprotein E (APOE) gene on chromosome 19ql3 has been identified as a strong risk factor for LOAD.
  • the ⁇ - ⁇ 4 allele has been established as a strong susceptibility marker that accounts for nearly 30% of the risk in late-onset Alzheimer's disease.
  • three variants of APOE, encoded by codons 1 12 and 158, have been found to modify the risk of LOAD.
  • the effect of the ⁇ - ⁇ 4 allele is dose related, wherein one or two copies of the ⁇ - ⁇ 4 allele are associated with 3-fold or 15-fold risk, respectively.
  • the effect of the ⁇ - ⁇ 4 allele on Alzheimer's disease risk appears to decline with increasing age (M. Ilyas Kamboh (2004), supra).
  • a profile of biomarkers for Alzheimer's disease can be detected in plasma and that profile can differentiate AD from healthy controls and other forms of dementia.
  • the profiles for Alzheimer's disease includes the biomarkers disclosed herein.
  • the profile for Alzheimer's disease is a combination of biomarkers and other factors of Alzheimer's disease disclosed herein.
  • the biomarkers of the invention in combination with other factors such as age, gender, ApoE genotype (e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4), can improve diagnostic and screening accuracy.
  • the biomarkers can also be combined with cognitive tests such as a simple memory test to improve diagnostic and screening accuracy.
  • the biomarkers of the invention can be combined with additional confirmatory CSF and imaging testing.
  • biomarkers of the invention can be combined with existing criteria for dementia to improve diagnostic and screening accuracy of
  • Dementia is the decline of memory and other cognitive functions in comparison with the patient's previous level of function as determined by a history of decline in performance and by abnormalities noted from clinical examination and neuropsychological tests.
  • a diagnosis of dementia cannot be made when consciousness is impaired by delirium, drowsiness, stupor, or coma or when other clinical abnormalities prevent adequate evaluation of mental status.
  • Dementia is a diagnosis based on behavior and cannot be determined by computerized tomography, electroencephalography, or other laboratory instructions, although specific causes of dementia may be identified by these means.
  • the biomarkers of the invention can be combined with existing criteria Alzheimer's disease.
  • a clinical diagnosis of probable Alzheimer's disease can be made with confidence if there is a typical insidious onset of dementia with progression and if there are no other systemic or brain diseases that could account for the progressive memory and other cognitive deficits.
  • disorders that must be excluded are manic depressive disorder, Parkinson's disease, multiinfarct dementia, and drug intoxication; less commonly encountered disorders that may cause dementia include thyroid disease, pernicious anemia, luetic brain disease and other chronic infections of the nervous system, subdural hematoma, occult hydrocephalus, Huntington's disease, Creutzfeldt- Jakob disease, and brain tumors.
  • a diagnosis of definite Alzheimer's disease requires histopathologic confirmation.
  • a clinical diagnosis of possible Alzheimer's disease may be made in the presence of other significant diseases, particularly if, on clinical judgment, Alzheimer's disease is considered the more likely cause of the progressive dementia.
  • the clinical diagnosis of possible rather than probable Alzheimer's disease may be used if the presentation or course is somewhat aberrant.
  • the information needed to apply these criteria is obtained by standard methods of examination: the medical history; neurologic; psychiatric, and clinical examinations; neuropsychological tests; and laboratory studies.
  • biomarkers for Alzheimer's Disease described herein may also be biomarkers for neurodegenerative diseases in general.
  • Alzheimer's Disease biomarkers may be used in the methods described herein for neurodegenerative diseases in general. That is, the methods described herein with respect to Alzheimer's Disease may also be used for diagnosing (or aiding in the diagnosis of) a neurodegenerative disease, methods of monitoring progression/regression of a neurodegenerative disease, methods of assessing efficacy of compositions for treating a neurodegenerative disease, methods of screening a composition for activity in modulating biomarkers associated with a neurodegenerative disease, methods of identifying potential drug targets for neurodegenerative diseases, and methods of treating a neurodegenerative disease. Such methods could be conducted as described herein with respect to Alzheimer's Disease.
  • kits are envisaged for every method disclosed.
  • the following description of a kit useful for diagnosing Alzheimer's disease in an individual by measuring the level of a biomarker in a biological sample therefore is not intended to be limiting and should not be construed that way.
  • the kit may comprise a negative control containing a biomarker at a concentration of about the concentration of the biomarker which is present in a biological sample of an individual who does not have Alzheimer's disease or does not have increased risk for Alzheimer's disease.
  • the kit may also include a positive control containing the biomarker at a concentration of about the concentration of the biomarker which is present in a biological sample of an individual who as Alzheimer's disease or has increased risk for Alzheimer's disease.
  • the kit includes a panel of biomarkers including one or more of the biomarkers shown in Table 2.
  • the invention should not be limited to only these markers disclosed herein (e.g., Table 2) because a skilled artisan when armed with the present disclosure would be able identify additional markers that can be used as indicators for Alzheimer's disease.
  • a test sample and a control sample can be subjected to any commercially available panel comprising a plurality of markers and analyzed according to the statistic models disclosed herein to identify markers associated with AD.
  • NT-proBNP showed high correlation with IGFBP2 and beta2 microglobulin suggesting these analytes may be linked in an AD specific pathophysiological pathway.
  • analytes that exhibit high correlation can be interchanged with other highly correlated analytes and still obtain similar differentiation performance.
  • markers that are associated with an AD specific pathophysiological pathway can be interchangeable. Accordingly, correlation amongst the markers of the invention provides means to identify other related markers associated with the specific pathophysiological pathway as being indicators for
  • biomarkers in each or all of Tables 2, 3, 7, 8, 9, 10, 11, 12 or any fraction thereof can be included in the kit.
  • other factors that predict for of AD can be included in the kit. Such factors include but are not limited to ApoE genotype (e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4).
  • the kit comprises a panel comprising at least Cortisol, Pancreatic Polypeptide, osteopontin, IGF BP2, and Resistin.
  • the kit comprises a panel comprising at least Resistin, e3/e3, Pancreatic Polypeptide, e3/e4, ApoD, G-CSF, MlP lbeta, ApoE, NrCAM, MMP2, Cortisol, PDGF-BB, e2/e3, MMP 1, 1-309.
  • the kit comprises a panel comprising Alpha- 1 Microglobulin, Angiopoietin-2, Apolipoprotein E, Beta-2 Microglobulin, BLC, Cortisol, E-Selectin, FAS, Fatty Acid Binding Protein, Hepatocyte Growth Factor, IGF BP-2, Interleukin 10, NT-Pro-BNP, Osteopontin, Pancreatic Polypeptide, PAPP-A, Resistin, Stem Cell Factor, Tenascin C, Thrombomodulin, TIMP-1, VCAM-1, VEGF, and Von Willebrand Factor.
  • the kit comprises reagents to assess age, gender,
  • ApoE genotype (e2/e2, e2/e3, e2/e4, e3/e3, e3/e4 and e4/e4) of the individual.
  • the kit of the invention can be used to assess the status of Alzheimer's disease in an individual, e.g., to diagnose Alzheimer's disease or to assess the degree of Alzheimer's disease in the individual.
  • the phrase "Alzheimer's disease status" includes any distinguishable manifestation of the disease, including non-Alzheimer's disease, e.g., normal or non-demented.
  • disease status includes, without limitation, the presence or absence of Alzheimer's disease (e.g., Alzheimer's disease v. non-Alzheimer's disease), the risk of developing disease, the stage of the disease, the progress of disease (e.g., progress of disease or remission of disease over time) and the effectiveness or response to treatment of disease. Based on this status, further procedures may be indicated, including additional diagnostic tests or therapeutic procedures or regimens.
  • the kit includes an instructional material for use in the diagnosis of Alzheimer's disease in an individual.
  • the instructional material can be a publication, a recording, a diagram, or any other medium of expression which can be used to communicate the usefulness of the method of the invention in the kit for assessment of Alzheimer's disease risk in a individual.
  • the instructional material of the kit of the invention may, for example, be affixed to a container which contains other contents of the kit, or be shipped together with a container which contains the kit. Alternatively, the instructional material may be shipped separately from the container with the intention that the instructional material and the contents of the kit be used cooperatively by the recipient.
  • the experiments disclosed herein were designed to develop an assay to identify universal accepted biomarkers for diagnosing, monitoring and staging neurodegenerative diseases such as Alzheimer's disease that are fast, more accurate, and less expensive.
  • the results presented herein demonstrate that a diagnostic assay has been developed that can detect among other things early onset of Alzheimer's disease.
  • Example 1 Assessing diagnostic accuracy of a plasma based multiplexed immunoassay panel to identify Alzheimer's disease
  • prodromal Alzheimer's can be identified through the use of cognitive tests combined with cerebrospinal fluid (CSF) and imaging biomarkers.
  • CSF cerebrospinal fluid
  • Alzheimer's disease The present study examines the suitability of a multiplexed plasma panel as a screening tool to differentiate Alzheimer's disease from age-matched controls.
  • a luminex based multiplex immunoassay panel consisting of 190 analytes was utilized in plasma samples derived from the University of Pennsylvania.
  • the strategy to identify a plasma based panel included multivariate based approaches combined with examining changes in both CSF in both autopsy confirmed and non- autopsy confirmed individuals from the UPenn biobank. Final analytes were chosen based upon high performance in multivariate models and upon expression in CSF from autopsy confirmed individuals.
  • Multivariate training/test set models using all 190 analytes suggested Alzheimer's disease individuals could be differentiated from controls using analytes on the panel. Partial least square and shrunked centroid algorithms exhibited the best performance. Many of the top analytes identified in the multivariate and ANOVA analysis were also expressed in CSF obtained from autopsy confirmed individuals.
  • Multivariate and CSF results were combined to generate a final panel consisting of 24 multiplex analytes plus age, gender and ApoE genetic status (6 co-variates e2/e2; e2/e3; e2/e4; e3/e4; e3/e3; e4/e4).
  • Performance of the panel in differentiating Alzheimer's disease from controls in the entire UPenn sample set using a logistic regression analysis exhibited 80% sensitivity 80% specificity.
  • Multivariate approaches combined with CSF-plasma expression and pathway analysis were utilized to identify a 32 variate algorithm that differentiates Alzheimer's disease from age-matched controls with high sensitivity suitable for preliminary screening of individuals who might be eligible for clinical trial enrollment in early Alzheimer's disease studies.
  • Luminex xMAP panel was used for genotyping APOE nucleotides 334 T/C and 472 C/T with an ABI 7900 real time thermocylcer using DNA freshly prepared from EDTA whole blood.
  • Luminex xMAP panel was used for genotyping APOE nucleotides 334 T/C and 472 C/T with an ABI 7900 real time thermocylcer using DNA freshly prepared from EDTA whole blood.
  • the luminex technology multiplexes immunoassays on the surface of polystyrene microsphere beads.
  • the microsphere beads are loaded with a ratio of two spectrally distinct flourochromes yielding up to 100 uniquely color-coded beads.
  • the beads are coated with capture antibodies specific for the assay and run in either standard sandwich or competitive immunoassay format. Capture-antibody microspheres are incubated with blocking solution and diluted plasma sample or calibration controls for one hour. Beads are rinsed and biotinylated detection reagent added.
  • Streptavidin-phycoerthyrin is then added to each well and incubated for 60 minutes. Following wash steps, beads are resuspended in reading solution and read on the luminex instrument. Details of each assay procedure are provided by RBM in validation reports that are compliant with CLIA standards. Some of the assays have defined a lower limit of quantitation. For the purposes of the present study, the lower limit of detection was utilized. Validation details of all the assays are available from Rules Based Medicine (http://www.rulesbasedmediciiie.com). Plasma Collection Methods
  • analytes that had more than 10% missing values were excluded from the analysis. Values that were reported as LOW (below the lowest assay limit which is defined by RBM as a value below the lowest calibrator for the individual assay) were imputed by taking the reported low detection limit for individual assays (LDD) and dividing by 2. Individual analyte distributions were tested for normalcy using Box-Cox analysis. If necessary, analytes were log transformed and checked again for normalcy. The majority of analytes exhibited non-normal distributions and were log transformed. For purposes of the univariate analysis, multidimensional scaling and Mahalanobis distances were used to detect outliers.
  • Outliers were defined as 5 standard deviations beyond the mean and were replaced with the 5 STD value. A major concern in the analysis was control of type I error rate due to relatively large number of plasma proteins in the multiplex panel. False discovery (FDR) corrections were applied to p-values to account for the multiple comparisons. ANOVA models including diagnosis, age, gender and ApoE4 allele status were utilized in the final model. ApoE allele status was classified into 6 subcategories; e2/e2, e2/e3, e2/e4, e3/e3, e3/e4, e4/e4.
  • Table 1 summarizes demographic characteristics of the UPenn sample set. Two hundred and sixty six plasma samples were shipped for multiplex analysis. Of the 266, 92 were from Alzheimer's disease, 126 were from healthy elderly controls and 48 were from individuals with other forms of dementia. Control and dementia individuals from the UPenn cohort were significantly younger than Alzheimer's disease. Thus age was included as a co-variate in all models. Amongst the groups, females were more highly represented, but there were no significant differences in gender ratios between the groups. Gender was also included as a co-variate in all the models. The prevalence of ApoE4 allele in the Alzheimer's disease group was 63%, very similar to what was observed in general Alzheimer's disease population.
  • Table 2 summarizes all the analytes included in the RBM panel. Light gray highlights those analytes that had more than 10% missing or had more than 10% LOW values. Multivariate approaches were combined with CSF expression patterns for feature selection. The top 24 performers are highlighted in dark gray (Table 2).
  • the multivariate analyses in this context are the predictive models that were performed (e.g. Random forest, PLC, etc.) for the goal of predicting the outcome.
  • Table 2 Analyte and final model summary. Analytes with more than 10% missing or had more than 10% LOW values (highlighted in light gray). Final panel (highlighted in dark grey) were selected based upon multivariate results and upon expression from auto sy confirmed individuals.
  • Multivariate strategies included a shrunken centroid algorithm developed for predictive analysis of microarrays (PAM) (Tusher et al, 2001 Proc Natl Acad Sci U S A. 98(9):5116-5121), FDA, KNN, logistic regression, Naive Bayes, partial least squares, random forest, sparse partial least square, boosted trees, linear discriminant analysis (data not shown), principal components (data not shown) and support vector machine (Vapnik et al, 1999 IEEE Trans Neural Netw. 10(5):988-999). ROC curves from the analysis are shown in Figure 1.
  • Table 3 Summary of analyte levels in UPenn Plasma and ANOVA Results: Age and gender included as co-variates and p values reported for Alzheimer's disease v control
  • Alzheimer' s disease 0.0054 Thrombospon Alzheimer's disease 0.0491 ⁇ ng ml) 0.53 + 0.25 din- 1 (ng.ml) 293.2 + 11228.5 ⁇ 0.0001*
  • a logistic regression model was utilized to compare performance of a baseline model (age, gender and ApoE allele status) to a model including only the 24 top plasma analytes and a model including all 24 plasma analytes plus the baseline model. All models were benchmarked at 80% sensitivity to allow comparisons across the models. In general, the baseline demographic model using only age, gender and ApoE4 allele showed positive predictive value of 52%. A model including all 24 analytes showed a positive predictive value of 64% and a model including all 24 analytes and baseline demographics showed a reasonable positive predictive value of 75% (see Table 4). ROC curves for each of the models are depicted in Figure 3.
  • the final logistic regression model including age, gender, ApoE allele and 24 plasma analytes yielded a 80% sensitivity and 80% specificity in differentiating Alzheimer's disease from controls. Model showed very good performance in differentiating Alzheimer's disease from other forms of dementia as well.
  • a model was attempted using 12 of the 18 analytes described by Ray et al. (2007 Nat Med. 13(1 1): 1359-1362) (Angiopoietin-2, EGF, G-CSF, ICAM- 1, Interleukin 3, Interleukin 8/CXCL8, M-CSF, PDGF-BB, PARC/CCL18,
  • FIG. 4A Correlation matrix analysis in the Alzheimer's disease samples showed a high degree of correlation amongst the 24 analytes (Figure 4A).
  • Figure 4B is a chart also demonstrating high correlation of the analytes with each other and that there is high correlation with other analytes on the panel.
  • any of the individual 24 analyte can be switched out with any analyte that showed significant correlation with another analyte on the panel (p ⁇ 0.05).
  • NT-proBNP showed high correlation with IGFBP2 and beta2 microglobulin suggesting these analytes may be linked in an Alzheimer's disease specific pathophysiological pathway.
  • Many of the analytes in the 190 panel showed high correlation and it was possible to interchange the top 24 analytes with other highly correlated analytes and obtain similar differentiation performance (data not shown).
  • Figure 5 compares apolipoprotein E levels in individuals as a function of their ApoE genotype.
  • individuals with either an e3/e4 or e4/e4 genotype showed lower apolipoprotein levels compared to individuals with e2/e2, e3/e2 or e3/e3 genotypes.
  • Individuals with an e2/e4 genotype exhibited levels more closely matched to e3 and e2 groups. Although the numbers are very small, individuals homozygous for E2 exhibited much higher protein levels.
  • Alzheimer's disease individuals were present in each of the ApoE genetic groups with the exception of e2 homozygotes.
  • Alzheimer's disease individuals prior to the onset of dementia will be critical for successful development of Alzheimer's disease disease modifying drug.
  • CSF and imaging research tools currently available to identify individuals at risk of progressing to dementia, these tools are difficult to implement as screening tools for individual enrollment due to the cost and invasive nature of CSF testing.
  • a simple blood test would aid in identifying those eligible for more confirmatory biomarker and cognitive testing and would help raise awareness around dementia prevention.
  • the top lists of obtained from multivariate approaches were compared to ANOVA lists and lists obtained from examining expression of analytes in CSF from autopsy confirmed individuals.
  • a final model containing 24 analytes was selected for further analysis. Inclusion of the 24 multiplex analytes improved performance over baseline demographic models in identifying individuals with Alzheimer's disease. Logistic regression estimates of model performance showed 80% sensitivity and 80% specificity in differentiating Alzheimer's disease from controls. However, caution should be exercised as these estimates are likely to degrade in a population setting and when tested in independent datasets.
  • Correlation analysis utilized in the current study to identify analyte redundancy in the panel was actually quite revealing as many of the analytes showed strong correlations. The finding may be indicative of a biological signaling cascade activated during pathological sequeale associated with dementia. Further pathway analysis may shed insight into the underlying pathophysiological and specific endophenotypes associated with Alzheimer's disease and other forms dementia.
  • Apolipoprotein E is a glycoprotein involved in lipid transport and a ligand for receptor mediated endocytosis. It is one of the major high density lipid (HDL) components in brain and believed to be critical for cholesterol transport involved in brain synaptic turnover.
  • ApoE is the only gene consistently shown to increase the risk of late onset Alzheimer's disease and E4 is believed to confer greater risk while E2 is believed to be protective for dementia.
  • a novel genetic risk factor has been identified in another apolipoprotein, ApoJ also known as clusterin. The current study did not identify any changes in ApoJ levels in either cohort and additional studies in CSF are needed to further understand relevance.
  • the current findings support that a plasma based signature for Alzheimer's disease can be detected in plasma and that signature can differentiate Alzheimer's disease from healthy controls and other forms of dementia.
  • the signature can improve diagnostic accuracy over age and ApoE4 allele status alone, but does not rise to the level of a standalone diagnostic tool.
  • the panel may, when combined with a simple memory test, have utility as a screening tool for individuals with a cognitive complaint who would then be eligible for more confirmatory CSF and imaging testing.
  • the data set consisted of 165 characteristics (i.e. variables) collected on
  • Random Forests A sequence of 3 values for the number of retained variables were used to tune the model (ranging from 2 to 2). Breiman. Random forests. Machine learning (2001) vol. 45 (l) pp. 5-32.
  • Boosted Tree candidate models had interaction depths raining from 2 to 10, while the number of boosting iterations ranged from 100 to 2000. Friedman. Greedy function approximation: a gradient boosting machine. Annals of Statistics (2001) pp. 1 189-1232.
  • ⁇ Naive Bayes models were computed using a Gaussian kernel or a non-parametric density estimate.
  • Nearest Shrunken Centroids candidate models used threshold parameters ranging from 0.092 to 2.583.
  • Nearest Shrunken Centroids Tibshirani et al. Class prediction by nearest shrunken centroids, with applications to DNA microarrays. Statistical Science (2003) pp. 104-117.
  • Support Vector Machines a radial basis function was used with cost parameter values ranging from 0.1 to 100.
  • the tuning parameter used 50 iterations of the bootstrap to compute resampled estimates of the sensitivity and specificity. Previous modeling for these data showed that high specificity can be easily attained, so the final model was chosen by maximizing the sensitivity value across the candidate models.
  • Sensitivity and specificity were used to evaluate the data on the training and test sets. On the training set, 50 iterations of the bootstrap were used to get reasonable estimates of performance and to differentiate models. Sensitivity, specificity and the area under the ROC curve were also calculated on the 43 samples in the test set. Table 5 shows the results for the training data across all the models. Table 6 also contains the test set results. Figure 6 shows these results in graphical terms. Table 5: Training set resampling results across various models
  • Naivsj B& is 0.611 I i t
  • Figure 1 shows the ROC curves for the test set with each model.
  • the solid black point shows the sensitivity/specificity combination that corresponds to using a cutoff of 0.50 on the probability estimate of being Alzheimer's disease.
  • the solid square indicates an alternate choice based on getting the sensitivity as close as possible.
  • the test in each panel shows the alternative cutoff and the corresponding sensitivity/specificity estimates.
  • Figure 7 also shows the sensitivity and specificity profiles across various probability cutoffs. This plot can demonstrate how adaptable each model is to alternative cutoffs. It was observed from the models that were run on these data, the partial least squares (PLS) model appeared to have the best combination of sensitivity and specificity, as well as a high test set ROC AUC value. The PLS model used all the predictors in the data set.
  • PLS partial least squares
  • Table 7 has a list of the top 15 predictors that contribute to the model. The overall distribution of the scores is shown in Figure 8. In Figure 8, there is not clear cluster of variables with "high” and “low” importance. The rankings of the genotype variables were not in the top 15.
  • Table 7 The top 15 most important predictors for the PLS model (from most important to least)
  • Figure 9 shows the correlation patterns between the variables and Figure 8 shows the distribution of the training set data for each predictor.
  • Figure 10 shows no single predictor that significantly differentiates Alzheimer's disease from control. This also supports the idea that the models require a large number of predictors to achieve adequate performance.
  • ANCOVA Alzheimer's disease vs Control only
  • DIAG 1 1 0.09757082 4 .9286 0.0275
  • DIAG 1 1 0.05191313 0.6177 0.4328
  • DIAG 1 1 0.00742880 0.2975 0.5860
  • DIAG 1 1 0.00001020 0.0008
  • Gender 1 1 0.63626015 20.1505 ⁇ .0001
  • DIAG 1 1 0.0021 4 321 0. 44 80 0.5040
  • DIAG 1 1 0.00470212 0.2133 0.6446
  • DIAG 1 1 0.00075280 0.0078
  • DIAG 1 1 0.00196054 0.0675 0.7953
  • DIAG 1 1 0.39391010 4 .0260
  • DIAG 1 1 0.20300 4 86 0.7516
  • Gender 1 1 0.00208025 0.0504 0.8226
  • DIAG 1 1 0.3139331 0.6930 0.4061
  • DIAG 1 1 0.02039153 0.2132 0.6447
  • DIAG 1 1 0.00477385 0.1678 0.6825
  • DIAG 1 1 0.0070432 0.1998 0.6554
  • DIAG 1 1 0.05169529 0.8901 0.3 4 65
  • Table 9 UPenn Plasma Alzheimer's disease vs Other Univariate. Age and Gender
  • DIAG 1 1 0.00309601 0.1352 0.7137

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Abstract

La présente invention a pour objet de nouveaux marqueurs utiles pour le diagnostic de la maladie d'Alzheimer chez un individu. Les marqueurs sont également utiles pour qualifier le statut de la maladie d'Alzheimer chez un individu. En particulier, les marqueurs peuvent être détectés dans un échantillon de plasma d'un individu pour classifier l'échantillon comme diagnostiquant la maladie d'Alzheimer ou ne diagnostiquant pas la maladie d'Alzheimer.
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WO2015193427A1 (fr) * 2014-06-19 2015-12-23 Olink Ab Détermination et analyse de biomarqueurs dans des échantillons cliniques.
WO2016081100A1 (fr) * 2014-11-20 2016-05-26 Ge Healthcare Uk Limited Détection de biomarqueurs associés à la démence et à la maladie d'alzheimer et stabilisés sur des matériaux de support solides
EP3067699A1 (fr) 2015-03-11 2016-09-14 Neuron Bio, S.A. Procédé de diagnostic de la maladie d'alzheimer
EP3376230A1 (fr) 2017-03-13 2018-09-19 Biocross, S.L. Identification de signatures pour des diagnostics de maladies neurodégénératives
RU2687272C2 (ru) * 2014-01-28 2019-05-13 Предемтек Аг Биомаркер и способы для ранней диагностики болезни альцгеймера
WO2019215085A1 (fr) 2018-05-07 2019-11-14 Biotx.Ai Gmbh Procédé de prédiction du risque de maladie d'alzheimer d'apparition tardive
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WO2022120373A1 (fr) * 2020-12-03 2022-06-09 Amgen Inc. Biomarqueurs agonistes de trem2 et leurs méthodes d'utilisation
CN116052767A (zh) * 2023-02-10 2023-05-02 复旦大学 基于微生物与宿主互作的阿尔兹海默症标志物识别方法

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WO2015193427A1 (fr) * 2014-06-19 2015-12-23 Olink Ab Détermination et analyse de biomarqueurs dans des échantillons cliniques.
US10670611B2 (en) 2014-09-26 2020-06-02 Somalogic, Inc. Cardiovascular risk event prediction and uses thereof
WO2016081100A1 (fr) * 2014-11-20 2016-05-26 Ge Healthcare Uk Limited Détection de biomarqueurs associés à la démence et à la maladie d'alzheimer et stabilisés sur des matériaux de support solides
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