Blood-based screen for detecting neurological diseases in primary care settings

Abstract

The present invention includes methods and kits for the diagnosing a neurological disease within primary care settings comprising: obtaining a blood test sample from a subject, measuring IL-7 and TNFα biomarkers in the blood sample, comparing the level of the one or a combination of biomarkers and neurocognitive screening tests with the level of a corresponding one or combination of biomarkers in a normal blood sample and neurocognitive screening tests, and predicting that an increase in the level of the blood test sample in relation to that of the normal blood sample indicates that the subject is likely to have a neurological disease.

Claims

1. A method of screening biomarkers in a primary care setting, the method consisting of: obtaining a blood sample from a subject suffering from cognitive impairment, and measuring expression levels of each biomarker in the group consisting of: interleukin-7 (IL7), tumor necrosis factor alpha (TNFα), interleukin-5 (IL5), and interleukin-6 (IL6), wherein the measuring is done with a nucleic acid assay, an immunoassay, chemiluminescence detection, electrochemiluminescence detection, or an enzymatic activity assay.

2. The method of claim 1, wherein the subject is determined to suffer from cognitive impairment for having a lower score in at least one neurocognitive evaluation selected from the group consisting of a clock drawing test, verbal fluency test, trail making test, list learning test, sleep disturbances, visual hallucinations, behavioral disturbances, motor disturbances, and any combinations thereof, as compared to a normal subject.

3. The method of claim 1, wherein the blood sample is a serum or a plasma sample.

4. A method of screening biomarkers in a primary care setting, the method consisting of: obtaining a blood sample from a subject suffering from cognitive impairment, and measuring expression levels of each biomarker in the group consisting of: interleukin-7 (IL7), tumor necrosis factor alpha (TNFα), interleukin-5 (IL5), interleukin-6 (IL6), and C-reactive protein (CRP), wherein the measuring is done with a nucleic acid assay, an immunoassay, chemiluminescence detection, electrochemiluminescence detection, or an enzymatic activity assay.

5. A method of screening biomarkers in a primary care setting, the method consisting of: obtaining a blood sample from a subject suffering from cognitive impairment, and measuring expression levels of each biomarker in the group consisting of: IL-7, and TNFα, wherein the measuring is done with a nucleic acid assay, an immunoassay, chemiluminescence detection, electrochemiluminescence detection, or an enzymatic activity assay.

6. The method of claim 5, wherein the subject is determined to suffer from cognitive impairment for having a lower score in at least one neurocognitive evaluation selected from the group consisting of clock drawing test, verbal fluency test, trail making test, list learning test, sleep disturbances, visual hallucinations, behavioral disturbances, motor disturbances, and any combinations thereof, as compared to a normal subject.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) For a more complete understanding of the features and advantages of the present invention, reference is now made to the detailed description of the invention along with the accompanying figures and in which:

(2) FIG. 1 shows data from the Neurodegenerative Panel 1 that assays THPO, FABP3, PPY, IL18, and I309 on an MSD platform from two control participants in duplicate. As can be seen, the assays are highly reliable;

(3) FIG. 2 is a box Plot of Random Forest Risk Scores for AD vs. normal controls (NC);

(4) FIG. 3 is a receiver operation characteristic (ROC) plot of serum biomarker profile;

(5) FIG. 4 is a Gini Plot from Random Forest Biomarker Model;

(6) FIG. 5 is a receiver operation characteristic (ROC) plot of serum biomarker profile; and

(7) FIG. 6 highlights the importance of the relative profiles in distinguishing between neurodegenerative diseases. The relative profiles across disease states varied.

(8) FIG. 7 shows the selection of the specialist for referral, and hence the course of treatment, based on the results of the screen of the two or more biomarkers measured at the primary care center or point of care.

DESCRIPTION OF THE INVENTION

(9) While the making and using of various embodiments of the present invention are discussed in detail below, it should be appreciated that the present invention provides many applicable inventive concepts that can be embodied in a wide variety of specific contexts. The specific embodiments discussed herein are merely illustrative of specific ways to make and use the invention and do not delimit the scope of the invention.

(10) To facilitate the understanding of this invention, a number of terms are defined below. Terms defined herein have meanings as commonly understood by a person of ordinary skill in the areas relevant to the present invention. Terms such as “a”, “an” and “the” are not intended to refer to only a singular entity, but include the general class of which a specific example may be used for illustration. The terminology herein is used to describe specific embodiments of the invention, but their usage does not delimit the invention, except as outlined in the claims.

(11) As used herein, the phrase “primary care clinic”, “primary care setting”, “primary care provider” are used interchangeably to refer to the principal point of contact/consultation for patients within a health care system and coordinates with specialists that the patient may need.

(12) As used herein, the phrase “specialist” refers to a medical practice or practitioner that specializes in a particular disease, such as neurology, psychiatry or even more specifically movement disorders or memory disorders.

(13) As used herein, the following abbreviations are used and can include mammalian version of these genes but in certain embodiments the genes are human genes: IL7—interleukin-7, TNFα—tumor necrosis factor alpha, IL5—interleukin-5, IL6—interleukin-6, CRP— C-reactive protein, IL10—interleukin-10, TNC—Tenascin C, ICAM1—intracellular adhesion molecule 1, FVII—factor VII, I309—chemokine (C-C motif) ligand 1, TNFR1—tumor necrosis factor receptor 1, A2M—alpha-2-microglobulin, TARC—Chemokine (C-C Motif) Ligand 17, eotaxin3, VCAM1—Vascular Cell Adhesion Molecule 1, TPO—thyroid peroxidase, FABP3—fatty acid binding protein 3, IL18-interleukin-18, B2M—beat-2-microglobulin, SAA—serum amyloid A1 cluster, PPY—pancreatic polypeptide, DJ1—Parkinson Protein 7, α-synuclein.

(14) As used herein, the phrase “neurological disease” refers to a disease or disorder of the central nervous system and many include, e.g., neurodegenerative disorders such as AD, Parkinson's disease, mild cognitive impairment (MCI) and dementia and neurological diseases include multiple sclerosis, neuropathies. The present invention will find particular use in detecting AD and for distinguishing the same, as an initial or complete screen, from other neurodegenerative disorders such as Parkinson's Disease, Frontotemporal dementia, Dementia with Lewy Bodies, and

(15) Down's syndrome. As used herein, the terms “Alzheimer's patient”, “AD patient”, and “individual diagnosed with AD” all refer to an individual who has been diagnosed with AD or has been given a probable diagnosis of Alzheimer's Disease (AD).

(16) As used herein, the terms “Parkinson's disease patient”, and “individual diagnosed with Parkinson's disease” all refer to an individual who has been diagnosed with PD or has been given a diagnosis of Parkinson's disease.

(17) As used herein, the terms “Frontotemporal dementia”, and “individual diagnosed with frontotemporal dementia” all refer to an individual who has been diagnosed with FTD or has been given a diagnosis of FTD.

(18) As used herein, the term “Dementia with Lewy bodies” (DLB), and “individual diagnosed with DLB” all refer to an individual who has been diagnosed with DLB or has been given a diagnosis of DLB.

(19) As used herein, the term “Down's syndrome” (DS), and “individual diagnosed with Down's syndrome” all refer to an individual who has been diagnosed with DS or has been given a diagnosis of DS.

(20) As used herein, the phrase “neurological disease biomarker” refers to a biomarker that is a neurological disease diagnosis biomarker.

(21) As used herein, the term “neurological disease biomarker protein”, refers to any of: a protein biomarkers or substances that are functionally at the level of a protein biomarker.

(22) As used herein, methods for “aiding diagnosis” refer to methods that assist in making a clinical determination regarding the presence, or nature, of the neurological disease (e.g., AD, PD, DLB, FTD, DS or MCI), and may or may not be conclusive with respect to the definitive diagnosis. Accordingly, for example, a method of aiding diagnosis of neurological disease can comprise measuring the amount of one or more neurological disease biomarkers in a blood sample from an individual.

(23) As used herein, the term “stratifying” refers to sorting individuals into different classes or strata based on the features of a neurological disease. For example, stratifying a population of individuals with Alzheimer's disease involves assigning the individuals on the basis of the severity of the disease (e.g., mild, moderate, advanced, etc.).

(24) As used herein, the term “predicting” refers to making a finding that an individual has a significantly enhanced probability of developing a certain neurological disease.

(25) As used herein, “biological fluid sample” refers to a wide variety of fluid sample types obtained from an individual and can be used in a diagnostic or monitoring assay. Biological fluid sample include, e.g., blood, cerebral spinal fluid (CSF), urine and other liquid samples of biological origin. Commonly, the samples are treatment with stabilizing reagents, solubilization, or enrichment for certain components, such as proteins or polynucleotides, so long as they do not interfere with the analysis of the markers in the sample.

(26) As used herein, a “blood sample” refers to a biological sample derived from blood, preferably peripheral (or circulating) blood. A blood sample may be, e.g., whole blood, serum or plasma. In certain embodiments, serum is preferred as the source for the biomarkers as the samples are readily available and often obtained for other sampling, is stable, and requires less processing, thus making it ideal for locations with little to refrigeration or electricity, is easily transportable, and is commonly handled by medical support staff.

(27) As used herein, a “normal” individual or a sample from a “normal” individual refers to quantitative data, qualitative data, or both from an individual who has or would be assessed by a physician as not having a disease, e.g., a neurological disease. Often, a “normal” individual is also age-matched within a range of 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 years with the sample of the individual to be assessed.

(28) As used herein, the term “treatment” refers to the alleviation, amelioration, and/or stabilization of symptoms, as well as delay in progression of symptoms of a particular disorder. For example, “treatment” of AD includes any one or more of: (1) elimination of one or more symptoms of AD, (2) reduction of one or more symptoms of AD, (3) stabilization of the symptoms of AD (e.g., failure to progress to more advanced stages of AD), and (4) delay in onset of one or more symptoms of AD delay in progression (i.e., worsening) of one or more symptoms of AD; and (5) delay in progression (i.e., worsening) of one or more symptoms of AD.

(29) As used herein, the term “fold difference” refers to a numerical representation of the magnitude difference between a measured value and a reference value, e.g., an AD biomarker, a Parkinson's biomarker, a dementia biomarker, or values that allow for the differentiation of one or more of the neurological diseases. Typically, fold difference is calculated mathematically by division of the numeric measured value with the numeric reference value. For example, if a measured value for an AD biomarker is 20 nanograms/milliliter (ng/ml), and the reference value is 10 ng/ml, the fold difference is 2 (20/10=2). Alternatively, if a measured value for an AD biomarker is 10 nanograms/milliliter (ng/ml), and the reference value is 20 ng/ml, the fold difference is 10/20 or −0.50 or −50%).

(30) As used herein, a “reference value” can be an absolute value, a relative value, a value that has an upper and/or lower limit, a range of values; an average value, a median value, a mean value, or a value as compared to a particular control or baseline value. Generally, a reference value is based on an individual sample value, such as for example, a value obtained from a sample from the individual with e.g., a neurological disease such as AD, Parkinson's Disease, or dementia, preferably at an earlier point in time, or a value obtained from a sample from an neurological disease patient other than the individual being tested, or a “normal” individual, that is an individual not diagnosed with AD, Parkinson's Disease, or dementia. The reference value can be based on a large number of samples, such as from AD patients, Parkinson's Disease patients, dementia patients, or normal individuals or based on a pool of samples including or excluding the sample to be tested.

(31) As used herein, the phrase “a predetermined amount of time” is used to describe the length of time between measurements that would yield a statistically significant result, which in the case of disease progression for neurological disease can be 7 days, 2 weeks, one month, 3 months, 6 months, 9 months, 1 year, 1 year 3 months, 1 year 6 months, 1 year 9 months, 2 years, 2 years 3 months, 2 years 6 months, 2 years 9 months, 3, 4, 5, 6, 7, 8, 9 or even 10 years and combinations thereof.

(32) As used herein, the phrases “neurocognitive screening tests”, or “cognitive test” are used to describe one or more tests known to the skilled artisan for measuring cognitive status or impairment and can include but is not limited to: a 4-point clock drawing test, an verbal fluency test, trail making test, list learning test, and the like. The skilled artisan will recognize and know how these tests can be modified, how new tests that measure similar cognitive function can be developed and implemented for use with the present invention.

(33) The differential diagnosis of neurodegenerative diseases is difficult, yet of critical importance for clinical treatment and management as well as for designing therapeutic and prevention trials.sup.1-4. In order for patients to be referred to specialty clinics for advanced assessments and treatment implementation, an appropriate referral is normally required from primary care providers. However, prior work demonstrates that the assessment and management of neurodegenerative diseases is poor in primary care settings.sup.5-8 with inappropriate medications frequently administered.sup.9. Given that the average physician visit duration in an ambulatory setting for those age 65+ is approximately 18 minutes.sup.10, primary care providers are in desperate need for a rapid and cost-effective method for screening neurological illness within their geriatric patients so appropriate referrals to a specialist can be made as warranted.

(34) The availability of blood-based screening tools that can be implemented within primary care clinic settings has significant implications. From a clinical standpoint, while fewer than half of physicians surveyed believed screenings for neurodegenerative disease was important, the vast majority of the general public and caregivers believed such screenings were vitally important.sup.11. Additionally, the average physician visit is less than 20 minutes for elderly patients in an ambulatory setting.sup.10, severely limiting the time available for even brief neurological and cognitive assessments. Therefore, primary care providers are in desperate need of a method for determining which patients should be referred to a specialist for advanced clinical evaluation of possible neurodegenerative disease. While a tremendous amount of work has been completed demonstrating the utility of advanced neuroimaging techniques (MRI, fMRI, DTI, PET) in diagnosing neurodegenerative diseases, they are cost prohibitive as the first step in a multi-stage diagnostic process. Due to cost and access, it has been proposed that blood-based biomarkers “will most likely be the prerequisite to future sensitive screening of large populations” at risk for neurodegenerative disease and the baseline in a diagnostic flow approach.sup.12. For example, PET amyloid-beta (Aβ) scans were recently FDA approved for use in the diagnostic process of Alzheimer's disease. If PET Aβ imaging were made available at even $1,000 per exam (less than a third to one tenth of the actual cost) and only 1 million elders were screened annually within primary care settings (there are 40 million Americans age 65+), the cost would be $1 billion (U.S. dollars) annually for neurodegenerative screening. If a blood-based screener were made available at $100/person, the cost would be $100 million annually. If 15% tested positive and went on to PET Aβ imaging ($150 million), the cost savings of this screen—follow-up procedure would be $750 million dollars annually screening less than one fortieth of those who actually need annual screening.

(35) A blood-based tool can easily fit the role as the first step in the multi-stage diagnostic process for neurodegenerative diseases with screen positives being referred to specialist for confirmatory diagnosis and treatment initiation. In fact, this is the process already utilized for the medical fields of cancer, cardiology, infectious disease and many others.

(36) While application of specialty clinic-based screens to primary care settings seems straight forward, this is not the case and no prior procedures will work within primary care settings as demonstrated below. The ability to implement blood-based screenings as the first step in a multi-stage diagnostic process is critical, yet very complicated due to substantially lower base rates of disease presence as compared to specialty clinics.sup.13 and this lower base rate has a tremendous impact on the predictive accuracy of test results.

(37) Another substantial advancement comes from the current procedure. Specifically, the procedure can also be utilized for screening patients prior to entry into a clinical trial. A major impediment to therapeutic trials aimed at preventing, slowing progression, and/or treating AD is the lack of biomarkers available for detecting the disease.sup.14,15. The validation of a blood-based screening tool for AD could significantly reduce the costs of such trials by refining the study entry process. If imaging diagnostics (e.g., Aβ neuroimaging) are required for study entry, only positive screens on the blood test would be referred for the second phase of screening (i.e., PET scan), which would drastically reduce the cost for identification and screening of patients. The new methods for screening of the present invention facilitate recruitment, screening, and/or selection of patients from a broader range of populations and/or clinic settings, thereby offering underserved patient populations the opportunity to engage in clinical trials, which has been a major limitation to the majority of previously conducted trials.sup.16.

(38) The present inventors provide for the first time, data that demonstrates the following: a novel procedure can detect and discriminate between neurodegenerative diseases with high accuracy. The current novel procedure which can be utilized for implementation as the first line screen within primary care settings that leads to specific referrals to specialist providers for disease confirmation and initiation of treatment.

(39) Methods. Neurodegenerative disease patients. AD and Control Patients. Non-fasting serum samples from the 300 TARCC participants (150 AD cases, 150 controls) were analyzed. Additionally, 200 plasma samples (100 AD cases and 100 controls), from the same subject group were analyzed. The methodology of the TARCC protocol has been described elsewhere.sup.21,22. Briefly, each participant undergoes an annual standardized assessment at one of the five participating TARCC sites that includes a medical evaluation, neuropsychological testing, and a blood draw. Diagnosis of AD is based on NINCDS-ADRDA criteria.sup.23 and controls performed within normal limits on psychometric testing. Institutional Review Board approval was obtained at each site and written informed consent is obtained for all participants.

(40) Non-AD Patients. Down's Samples. Serum samples were obtained from 11 male patients diagnosed with Down's syndrome (DS) from the Alzheimer's Disease Cooperative Studies core at the University of California San Diego (UCSD). Parkinson's disease Samples. Serum samples from 49 patients (28 males and 21 females) diagnosed with Parkinson's disease (PD) came from the University of Texas Southwestern Medical Center (UTSW) Movement Disorders Clinic. Dementia with Lewy Bodies (DLB) and Frontotemporal dementia (FTD) Samples. Serum samples from 11 DLB and 19 FTD samples were obtained from the UTSW Alzheimer's Disease Coordinating Center (ADCC).

(41) Serum sample collection. TARCC and UTSW ADC serum samples were collected as follows: (1) non-fasting serum samples was collected in 10 ml tiger-top tubes, (2) allowed to clot for 30 minutes at room temperature in a vertical position, (3) centrifuged for 10 minutes at 1300×g within one hour of collection, (4) 1.0 ml aliquots of serum were transferred into cryovial tubes, (5) Freezerworks™ barcode labels were firmly affixed to each aliquot, and (6) samples placed into −80° C. freezer for storage until use in an assay. Down's syndrome serum samples were centrifuged at 3000 rpm for 10 minutes prior to aliquoting and storage in a −80° C. freezer.

(42) Plasma: (1) non-fasting blood was collected into 10 ml lavender-top tubes and gently invert 10-12 times, (2) centrifuge tubes at 1300×g for 10 minutes within one hour of collection, (3) transfer 1 ml aliquots to cryovial tubes, (4) affix Freezerworks™ barcode labels, and (5) placed in −80° C. freezer for storage.

(43) Human serum assays. All samples were assayed in duplicate via a multi-plex biomarker assay platform using electrochemiluminescence (ECL) on the SECTOR Imager 2400A from Meso Scale Discovery (MSD; www.mesoscale.com). The MSD platform has been used extensively to assay biomarkers associated with a range of human disease including AD.sup.24-28. ECL technology uses labels that emit light when electrochemically stimulated, which improves sensitivity of detection of many analytes at very low concentrations. ECL measures have well-established properties of being more sensitive and requiring less volume than conventional ELISAs.sup.26, the gold standard for most assays. The markers assayed were from a previously generated and cross-validated AD algorithm.sup.17,19,29 and included: fatty acid binding protein (FABP3), beta 2 microglobulin, pancreatic polypeptide (PPY), sTNFR1, CRP, VCAM1, thrombopoeitin (THPO), α2 macroglobulin (A2M), exotaxin 3, tumor necrosis factor α, tenascin C, IL-5, IL6, IL7, IL10, IL18, I309, Factor VII, TARC, SAA, and ICAM1. FIG. 1 illustrates the reliability of the MSD assay of the present invention.

(44) Statistical Analyses. Analyses were performed using R (V 2.10) statistical software.sup.30 and IBM SPSS19. Chi square and t-tests were used to compare case versus controls for categorical variables (APOE ε4 allele frequency, gender, race, ethnicity, presence of cardiovascular risk factors) and continuous variables (age, education, Mini Mental State Exam [MMSE] and clinical dementia rating sum of boxes scores [CDR-SB]), respectively. The biomarker data was transformed using the Box-Cox transformation. The random forest (RF) prediction model was performed using R package randomForest (V 4.5).sup.31, with all software default settings. The ROC (receiver operation characteristic) curves were analyzed using R package AUC (area under the curve) was calculated using R package DiagnosisMed (V 0.2.2.2). The sample was randomly divided into training and test samples separately for serum and plasma markers. The RF model was generated in the training set and then applied to the test sample. Logistic regression was used to combine demographic data (i.e. age, gender, education, and APOE4 presence [yes/no]) with the RF risk score as was done in the present inventors' prior work.sup.17,19,29,32. Clinical variables were added to create a more robust diagnostic algorithm given the prior work documenting a link between such variables and cognitive dysfunction in AD.sup.33-36. In order to further refine the algorithm, the biomarker risk score was limited to the smallest set of markers that retained optimal diagnostic accuracy as a follow-up analysis. For the second aim of these studies, support vector machines (SVM) analysis was utilized for multi-classification of all diagnostic groups. A random sample of data from 100 AD cases and controls utilized in the first set of analyses (AD n=51; NC n=49) was selected and combined with serum data from 11 DS, 49 PD, 19 FTD and 11 DLB cases along with 12 additional normal controls (NC) (62 total NCs). The SVM analyses were run on the total combined sample with five-fold cross-validation. SVM is based on the concept of decision planes that define decision boundaries and is primarily a method that performs classification tasks by constructing hyperplanes in a multidimensional space that separates cases of different class labels. An SVM-based method was used with five-fold cross-validation to develop the classifier for the combined samples, and then applied the classifier to predict the combined samples.

(45) Results. As with prior work from the present inventors, the AD patients were significantly older (p<0.001), achieved fewer years of education (p<0.001), scored lower on the MMSE (p<0.001) and higher on the CDR-SB (p<0.001) (see Table 1). There was no significant difference between groups in terms of gender or presence of dyslipidemia, diabetes, or hypertension. The AD group had significantly more APOE4 carriers while the NC group had significantly more individuals who were classified as obese (BMI>=30).

(46) TABLE-US-00001 TABLE 1 Demographic Characteristics of Cohort AD (N = 150) Control (N = 150) P-value Gender (male) 35% 31% 0.46 Age (years) 78.0 (8.2) 70.6 (8.9) <0.001 57-94 52-90 Education (years) 14.0 (3.4) 15.6( 2.7) <0.001 0-22 10-23 APOE4 presence (yes/no) 61% 26% <0.001 Hispanic Ethnicity  5%  5% 0.61 Race (non-Hispanic white) 95% 97% 0.49 MMSE 19.2(6.1) 29.4 (0.9) <0.001 1-30 26-30 CDR-SB  7.8 (4.4)  0.0 (0.04) <0.001 1-18 0-1 Hypertension (% yes) 56% 59% 0.73 Dyslipidemia (% yes) 53% 56% 0.49 Diabetes (% yes) 12% 13% 0.60 Obese (% yes) 13% 24% 0.04

(47) When the serum-based RF biomarker profile from the ECL assays was applied to the test sample, the obtained sensitivity (SN) was 0.90, specificity (SP) was 0.90 and area under the ROC curve (AUC) was 0.96 (See FIGS. 2 and 3, and Table 2).

(48) TABLE-US-00002 TABLE 2 Statistical results for AD biomarker sensitivity and specificity and area under the receiver operating characteristic curve (AUC). Sensitivity Specificity AUC (95% CI) (95% CI) Serum Biomarker alone 0.96 0.90 (0.81, 0.95) 0.90 (0.82, 0.95) Clinical variables alone 0.85 0.77 (0.66, 0.85) 0.82 (0.72, 0.89) Biomarkers + Clinical 0.98 0.95 (0.87, 0.98) 0.90 (0.81, 0.95) variables Abbreviated Biomarker 0.95 0.88 (0.79, 0.94) 0.92 (0.83, 0.96) Profile (8 proteins) Abbreviated Biomarker 0.98 0.92 (0.84, 0.96) 0.94 (0.87, 0.98) Profile (8 proteins) + Clinical Variables Plasma Biomaker alone 0.76 0.65 (0.46, 0.74) 0.790.69, 0.95)

(49) FIG. 3 shows a ROC plot for a serum biomarker profile using 21 serum biomarkers. The plasma-based algorithm yielded much lower accuracy estimates of SN, SP, and AUC of 0.65, 0.79, and 0.76, respectively. Therefore, the remaining analyses focused solely on serum. Inclusion of age, gender, education and APOE4 into the algorithm with the RF biomarker profile increased SN, SP, and AUC to 0.95, 0.90, and 0.98, respectively (Table 2). Next the RF was re-run to determine the optimized algorithm with the smallest number of serum biomarkers. Using only the top 8 markers from the biomarker profile (see FIG. 4) yielded a SN, SP, and AUC of 0.88, 0.92 and 0.95, respectively (see FIG. 5 and Table 2). The addition of age, gender, education and APOE4 genotype increased SN, SP, and AUC to 0.92, 0.94, and 0.98, respectively.

(50) FIG. 4 shows a Gini Plot from Random Forest Biomarker Model demonstrating variable importance and differential expression. FIG. 5 shows a ROC plot using only the top 8 biomarkers for the AD algorithm.

(51) For the SVM multi-classifier analyses to determine if the AD blood-based biomarker profiles could be utilized to discriminate AD from other neurological diseases, analyses were conducted on protein assays from 203 participants (AD n=51, PD n=49, DS n=11, FTD n=19, DLB n=11, NC n=62). Demographic characteristics of this sample are provided in Table 3.

(52) TABLE-US-00003 TABLE 3 Demographic characteristics of a second cohort for multivariate classification AD PD DS FTD DLB NC N = 51 N = 49 N = 11 N = 19 N = 11 N = 61 Age 78.0 68 52 65.8 75.6 70 (9.0) (9.6) (2.0) (8.8) (4.5) (9.0) Education 15.0 — — 14.8 14.8 16.2 (3.0) (3.2) (2.8) (2.7) Gender 22 M; 28 M; 52 M 14 M; 8 M; 23 M; 29 F 21 F 5 F 3 F 38 F Note: information not available regarding education for PD and DS cases. Abbreviations: AD, Alzheimer's disease. PD, Parkinson's disease. DS, Down's syndrome. FTD, Frontotemporal dementia. DLB, Lewy Body dementia. NC, normal controls.

(53) FIG. 6 highlights the importance of the relative profiles in distinguishing between neurodegenerative diseases. The relative profiles across disease states varied. For example, A2M and FVII are disproportionately elevated in DLB and FTD whereas TNFα is disproportionately elevated in AD and lowest in PD and DLB whereas PPY is lowest in PD and highest in DLB. Using the SVM-based algorithm, biomarker profiles combining all proteins were created to simultaneously classify all participants. Surprisingly, the overall accuracy of the SVM was 100% (SN=1.0, SP=1.0) with all of the individuals being correctly classified within their respective categorizations.

(54) Implementing the blood screen in a community-based setting. The 1998 Consensus Report of the Working Group on: “Molecular and Biochemical Markers of Alzheimer's Disease”.sup.37 provided guidelines regarding the minimal acceptable performance standards of putative biomarkers for AD. It was stated that sensitivity (SN) and specificity (SP) should be no less than 0.80 with positive predictive value (PPV) of 80% or more, with PPV approaching 90% being best. The report also states that a “high negative predictive value [NPV] would be extremely useful.” The PI and bioinformatics team on this grant have extensive experience calculating diagnostic accuracy statistics, including PPV and NPV.sup.17-20,38-43. The important difference between SN/SP and PPV/NPV is that the latter are prediction accuracy statistics (i.e. how correct is a clinician when diagnosing a patient based on the test). PPV/NPV are dependent on base rates of disease presence.sup.44. With regards to AD, it is estimated that the base rate of disease presence in the community is 11% of those age 65 and above.sup.13 as compared to 50% or more in specialty clinic settings. PPV and NPV are based on Bayesian statistics and calculated as outlined here:

(55) $\begin{array}{c}\mathrm{PPV}=\frac{\left(\mathrm{SN}\times \mathrm{BR}\right)}{\left(\mathrm{SN}\times \mathrm{BR}\right)+\left[\left(1-\mathrm{SP}\right)\times \mathrm{RC}\right]}\\ \mathrm{NPV}=\frac{\left(\mathrm{SP}\times \mathrm{RC}\right)}{\left(\mathrm{SP}\times \mathrm{RC}\right)+\left[\left(1-\mathrm{SN}\right)\times \mathrm{BR}\right]}\end{array}$

(56) PPV=positive predictive value, SN=sensitivity, BR=base rate, RC=remaining cases, NPV=negative predictive value, SP=specificity. In an 8-protein screen or algorithm, when SP was held at 0.98, SN fell to 0.86. Applying PPV and NPV calculations with an estimated base rate of AD of 11% within the community.sup.13, the screen and/or algorithm of the present invention is very accurate and can be used within a community-based setting, that is, at the primary point-of-care. This is in comparison to the minimal requirements to be acceptable based on the 1998 Consensus Report where PPV was less than 35% (see Table 4).

(57) TABLE-US-00004 TABLE 4 Diagnostic Accuracy of Blood-Based Screen for Alzheimer's disease in Primary Care Settings Base Rate = 11% SN SP PPV NPV Current Novel Procedure 0.86 0.98 0.84 0.98 1998 Consensus Report minimal 0.80 0.80 0.33 0.97 guidelines.sup.37 Our Prior work.sup.17 0.94 0.84 .42 .99 Our Prior work.sup.18 0.89 0.85 0.42 0.98 Our Prior work.sup.19 0.75 0.91 0.50 0.97 AIBL study.sup.45 0.85 0.85 0.41 0.98 Peptoid approach.sup.46 0.94 0.94 0.66 0.99 Laske and colleagues.sup.47 0.94 0.80 0.37 0.99 BR = base rate, SN = sensitivity, SP = specificity, PPV = positive predictive value, NPV = negative predictive value

(58) The findings from the present inventors' prior work as well as that from other research groups have also been included for comparison. As is clearly illustrated from above, the current novel procedure is the only procedure that can possibly be utilized in primary care settings in order to have an acceptable accuracy in referrals to specialty clinics. With the exception of the peptoid approach, no other efforts would be better than chance (i.e., 50%) when indicating to a primary care provider that a specialty referral would be needed.

(59) TABLE-US-00005 TABLE 5 Diagnostic Accuracy of Blood-Based Screen for Neurodegenerative Diseases in Primary Care Settings Base Rate = 11% SN P PPV NPV Current Novel Procedure 1.0 1.0 1.0 1.0 1998 Consensus Report minimal 0.80 0.80 0.33 0.97 guidelines37 BR = base rate, SN = sensitivity, SP = specificity, PPV = positive predictive value, NPV = negative predictive value

(60) The current approach is 100% at identifying neurodegenerative diseases via the use of overall profiles. Given the very low prevalence of these diseases in the general population, the high accuracy is needed for appropriate referrals to specialist to be made by the primary care practitioners.

(61) Combining specific biomarkers with select cognitive testing. In our recent work, we demonstrated that molecular profiles could be generated for neuropsychological test performance, and that these profiles accounted for upwards of 50% of the variance in test scores.sup.48. It was further demonstrated that specific serum-based biomarkers and select cognitive testing can be combined to refine the assessment process and increase diagnostic accuracy. In one example, only the top 2 markers were selected from the serum-algorithm (TNFα and IL7), in conjunction with a single, easy-to-administer cognitive test (in this example a 4-point clock drawing test, but other short and easy tests can be used, e.g., verbal fluency, trail making, list learning, and the like). When these 3 items were combined into a single logistic regression, 92% accuracy was found (SN=0.94, SP=0.90) in distinguishing all AD (n=150) from NC (n=150). When the sample was restricted only to mild AD (CDR global score<=1.0), an overall accuracy of 94% (SN=0.94, SP=0.83) was found. Lastly, and importantly, the sample was restricted only to very early AD (CDR global score=0.5), which resulted in an overall accuracy of 91% (SN=0.97, SP=0.72). These findings clearly demonstrate the possibility of combining specific biomarkers with select cognitive testing to refine the overall algorithm.

(62) In summary, the current approach: (1) is highly accurate at detecting Alzheimer's disease; (2) is highly accurate at detecting and discriminating between neurodegenerative diseases; (3) can be implemented within primary care settings as the first step in a multi-stage diagnostic process; and (4) the combination of specific serum biomarkers and select neurocognitive screening assessments can refine the screening process with excellent accuracy.

(63) FIG. 7 shows the selection of the specialist for referral, and hence the course of treatment, based on the results of the screen of the two or more biomarkers measured at the primary care center or point of care.

(64) It is contemplated that any embodiment discussed in this specification can be implemented with respect to any method, kit, reagent, or composition of the invention, and vice versa. Furthermore, compositions of the invention can be used to achieve methods of the invention.

(65) It will be understood that particular embodiments described herein are shown by way of illustration and not as limitations of the invention. The principal features of this invention can be employed in various embodiments without departing from the scope of the invention. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, numerous equivalents to the specific procedures described herein. Such equivalents are considered to be within the scope of this invention and are covered by the claims.

(66) All publications and patent applications mentioned in the specification are indicative of the level of skill of those skilled in the art to which this invention pertains. All publications and patent applications are herein incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference.

(67) The use of the word “a” or “an” when used in conjunction with the term “comprising” in the claims and/or the specification may mean “one,” but it is also consistent with the meaning of “one or more,” “at least one,” and “one or more than one.” The use of the term “or” in the claims is used to mean “and/or” unless explicitly indicated to refer to alternatives only or the alternatives are mutually exclusive, although the disclosure supports a definition that refers to only alternatives and “and/or.” Throughout this application, the term “about” is used to indicate that a value includes the inherent variation of error for the device, the method being employed to determine the value, or the variation that exists among the study subjects.

(68) As used in this specification and claim(s), the words “comprising” (and any form of comprising, such as “comprise” and “comprises”), “having” (and any form of having, such as “have” and “has”), “including” (and any form of including, such as “includes” and “include”) or “containing” (and any form of containing, such as “contains” and “contain”) are inclusive or open-ended and do not exclude additional, unrecited elements or method steps.

(69) The term “or combinations thereof” as used herein refers to all permutations and combinations of the listed items preceding the term. For example, “A, B, C, or combinations thereof” is intended to include at least one of: A, B, C, AB, AC, BC, or ABC, and if order is important in a particular context, also BA, CA, CB, CBA, BCA, ACB, BAC, or CAB. Continuing with this example, expressly included are combinations that contain repeats of one or more item or term, such as BB, AAA, AB, BBC, AAABCCCC, CBBAAA, CABABB, and so forth. The skilled artisan will understand that typically there is no limit on the number of items or terms in any combination, unless otherwise apparent from the context. In certain embodiments, the present invention may also include methods and compositions in which the transition phrase “consisting essentially of” or “consisting of” may also be used.

(70) As used herein, words of approximation such as, without limitation, “about”, “substantial” or “substantially” refer to a condition that when so modified is understood to not necessarily be absolute or perfect but would be considered close enough to those of ordinary skill in the art to warrant designating the condition as being present. The extent to which the description may vary will depend on how great a change can be instituted and still have one of ordinary skilled in the art recognize the modified feature as still having the required characteristics and capabilities of the unmodified feature. In general, but subject to the preceding discussion, a numerical value herein that is modified by a word of approximation such as “about” may vary from the stated value by at least ±1, 2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12 or 15%.

(71) All of the compositions and/or methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the compositions and methods of this invention have been described in terms of preferred embodiments, it will be apparent to those of skill in the art that variations may be applied to the compositions and/or methods and in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit and scope of the invention. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined by the appended claims.

REFERENCES

(72) 1. Villemagne V L, Rowe C C. Long night's journey into the day: Amyloid-β imaging in Alzheimer's disease. Journal of Alzheimer's Disease. 2013; 33(SUPPL. 1): S349-S359. 2. Pyykkö O T, Helisalmi S, Koivisto A M, et al. APOE4 predicts amyloid-β in cortical brain biopsy but not idiopathic normal pressure hydrocephalus. Journal of Neurology, Neurosurgery and Psychiatry. 2012; 83(11):1119-1124. 3. Benadiba M, Luurtsema G, Wichert-Ana L, Buchpigel C A, Filho G B. New molecular targets for PET and SPECT imaging in neurodegenerative diseases. Revista Brasileira de Psiquiatria 2012; 34(SUPPL2): S125-S148. 4. McKeith I G, Fairbairn A F, Bothwell R A, et al. An evaluation of the predictive validity and inter-rater reliability of clinical diagnostic criteria for senile dementia of Lewy body type. Neurology. 1994; 44(5):872-877. 5. Van Den Dungen P, Van Marwijk H W M, Van Der Horst H E, et al. The accuracy of family physicians' dementia diagnoses at different stages of dementia: A systematic review. International Journal of Geriatric Psychiatry. 2012; 27(4):342-354. 6. Löppönen M, Räihä I, Isoaho R, Vahlberg T, Kivelä S-L. Diagnosing cognitive impairment and dementia in primary health care—a more active approach is needed. Age and Ageing. Nov. 1, 2003 2003; 32(6):606-612. 7. Belmin J, Min L, Roth C, Reuben D, Wenger N. Assessment and management of patients with cognitive impairment and dementia in primary care. Journal of Nutrition, Health and Aging. 2012; 16(5):462-467. 8. Maeck L, Haak S, Knoblauch A, Stoppe G. Dementia diagnostics in primary care: a representative 8-year follow-up study in lower saxony, Germany. Dementia & Geriatric Cognitive Disorders. 2008; 25(2):127-134. 9. Fiss T, Thyrian J R, Fendrich K, Van Den Berg N, Hoffmann W. Cognitive impairment in primary ambulatory health care: Pharmacotherapy and the use of potentially inappropriate medicine. International Journal of Geriatric Psychiatry. 2013; 28(2):173-181. 10. Lo A, Ryder K, Shorr R I. Relationship between patient age and duration of physician visit in ambulatory setting: Does one size fit all? Journal of the American Geriatrics Society. 2005; 53(7):1162-1167. 11. Bond J, Graham N, Padovani A, MacKell J, Knox S, Atkinson J. Screening for cognitive impairment, Alzheimer's disease and other dementias: Opinions of European caregivers, payors, physicians and the general public. Journal of Nutrition, Health and Aging. 2010; 14(7):558-562. 12. Schneider P, Hampel H, Buerger K. Biological marker candidates of alzheimer's disease in blood, plasma, and serum. CNS Neuroscience and Therapeutics. 2009; 15(4):358-374. 13. Alzheimer's Association. 2013 Alzheimer's Disease Facts and Figures. Alzheimer's & Dementia. 2013; 9(2):1-72. 14. Shaw L M, Korecka M, Clark C M, Lee V M, Trojanowski J Q. Biomarkers of neurodegeneration for diagnosis and monitoring therapeutics. Nature Reviews. Drug Discovery. 2007; 6(4):295-303. 15. Thal L J, Kantarci K, Reiman E M, et al. The role of biomarkers in clinical trials for Alzheimer disease. Alzheimer Disease & Associated Disorders. 2006; 20(1):6-15. 16. Martin M A, Swider S M, Olinger T, et al. Recruitment of Mexican American adults for an intensive diabetes intervention trial. Ethnicity and Disease. 2011; 21(1):7-12. 17. O'Bryant S E, Xiao G, Barber R, et al. A serum protein-based algorithm for the detection of Alzheimer disease. Archives of Neurology. 2010; 67(9):1077-1081. 18. O'Bryant S, Xiao, G, Barber, R, Reisch, J, Hall, J, Cullum, C M, Doody, R, Fairchild, T, Adams, P, Wilhelmsen, K, & Diaz-Arrastia, R. A blood based algorithm for the detection of Alzheimer's disease. Dementia and Geriatric Cognitive Disorders. 2011; 32:55-62. 19. O'Bryant S E, Xiao G, Barber R, et al. A Blood-Based Screening Tool for Alzheimer's Disease That Spans Serum and Plasma: Findings from TARC and ADNI. PLoS ONE. 2011; 6(12): e28092. 20. O'Bryant S E, Xiao G, Edwards M, et al. Biomarkers of Alzheimer's disease among Mexican Americans. Journal of Alzheimer's Disease. 2013; 34(4):841-849. 21. Waring S, O'Bryant, S E, Reisch, J S, Diaz-Arrastia, R, Knebl, J, Doody, R, for the Texas Alzheimer's Research Consortium. The Texas Alzheimer's Research Consortium longitudinal research cohort: Study design and baseline characteristics. Texas Public Health Journal. 2008; 60(3):9-13. 22. O'Bryant S E, Hobson V, Hall J R, et al. Brain-derived neurotrophic factor levels in alzheimer's disease. Journal of Alzheimer's Disease. 2009; 17(2):337-341. 23. McKhann D, Drockman, D., Folstein, M. et al. Clinical diagnosis of Alzheimer's disease: Report of the NINCDS-ADRDA Work Group. Neurology. 1984; 34:939-944. 24. Bjerke M, Portelius E, Minthon L, et al. Confounding factors influencing amyloid beta concentration in cerebrospinal fluid. International Journal of Alzheimer's Disease. 2010. 25. Kounnas M Z, Danks A M, Cheng S, et al. Modulation of γ-Secretase Reduces β-Amyloid Deposition in a Transgenic Mouse Model of Alzheimer's Disease. Neuron. 2010; 67(5):769-780. 26. Kuhle J, Regeniter A, Leppert D, et al. A highly sensitive electrochemiluminescence immunoassay for the neurofilament heavy chain protein. Journal of Neuroimmunology. 2010; 220(1-2):114-119. 27. Oh E S, Mielke M M, Rosenberg P B, et al. Comparison of conventional ELISA with electrochemiluminescence technology for detection of amyloid-β in plasma. Journal of Alzheimer's Disease. 2010; 21(3):769-773. 28. Alves G, Brønnick K, Aarsland D, et al. CSF amyloid-β and tau proteins, and cognitive performance, in early and untreated Parkinson's Disease: The Norwegian ParkWest study. Journal of Neurology, Neurosurgery and Psychiatry. 2010; 81(10):1080-1086. 29. O'Bryant S E, Xiao G, Barber R, et al. A blood-based algorithm for the detection of Alzheimer's disease. Dementia and Geriatric Cognitive Disorders. 2011; 32(1):55-62. 30. R_Development_Core_Team. R: A language and environment for statistical computing. 2009; www.R-project.org. 31. Breiman L. Random forests. Machine Learning. 2001; 45(1):5-32. 32. O'Bryant S E X G, Edwards M, Devous M D, Gupta V, Martins R, Zhang F, Barber R C for the Texas Alzheimer's Research Consortium. Biomarkers of Alzheimer's Disease Among Mexican Americans. Journal of Alzheimer's Disease. 2013, in press. 33. Dickstein D L, Walsh J, Brautigam H, Stockton Jr S D, Gandy S, Hof P R. Role of vascular risk factors and vascular dysfunction in Alzheimer's disease. Mount Sinai Journal of Medicine. 2010; 77(1):82-102. 34. Piazza F, Galimberti G, Conti E, et al. Increased tissue factor pathway inhibitor and homocysteine in Alzheimer's disease. Neurobiology of Aging. 2010. 35. Okereke O I, Selkoe D J, Pollak M N, et al. A profile of impaired insulin degradation in relation to late-life cognitive decline: A preliminary investigation. International Journal of Geriatric Psychiatry. 2009; 24(2):177-182. 36. van Oijen M, Hofman A, Soares H D, Koudstaal P J, Breteler M M. Plasma Abeta(1-40) and Abeta(1-42) and the risk of dementia: a prospective case-cohort study. [see comment]. Lancet Neurology. 2006; 5(8): 655-660. 37. Anonymous. Consensus report of the Working Group on: “Molecular and Biochemical Markers of Alzheimer's Disease”. The Ronald and Nancy Reagan Research Institute of the Alzheimer's Association and the National Institute on Aging Working Group. [see comment][erratum appears in Neurobiol Aging 1998 May-June; 19(3):285]. Neurobiology of Aging. 1998; 19(2):109-116. 38. O'Bryant S E, Lucas J A. Estimating the predictive value of the Test of Memory Malingering: An illustrative example for clinicians. Clinical Neuropsychologist. 2006; 20(3):533-540. 39. Bauer L, O'Bryant S E, Lynch J K, McCaffrey R J, Fisher J M. Examining the test of memory malingering trial 1 and word memory test immediate recognition as screening tools for insufficient effort. Assessment. 2007; 14(3):215-222. 40. Clark J H, Hobson V L, O'Bryant S E. Diagnostic accuracy of % retention scores on RBANS verbal memory subtests for the diagnosis of Alzheimer's disease and mild cognitive impairment. Archives of Clinical Neuropsychology. 2010; 25(4):318-326. 41. Duff K, Humphreys Clark J D, O'Bryant S E, Mold J W, Schiffer R B, Sutker P B. Utility of the RBANS in detecting cognitive impairment associated with Alzheimer's disease: Sensitivity, specificity, and positive and negative predictive powers. Archives of Clinical Neuropsychology. 2008; 23(5):603-612. 42. Duff K, Hobson V L, Beglinger L J, O'Bryant S E. Diagnostic accuracy of the RBANS in mild cognitive impairment: Limitations on assessing milder impairments. Archives of Clinical Neuropsychology. 2010; 25(5): 429-441. 43. O'Bryant S E, Humphreys J D, Smith G E, et al. Detecting dementia with the mini-mental state examination in highly educated individuals. Archives of Neurology. 2008; 65(7):963-967. 44. O'Bryant S E, Lucas J A, Willis F B, Smith G E, Graff-Radford N R, Ivnik R J. Discrepancies between self-reported years of education and estimated reading level among elderly community-dwelling African-Americans: Analysis of the MOAANS data. Archives of Clinical Neuropsychology. 2007; 22(3):327-332. 45. Doecke J, Laws, S M, Faux, N G, Wilson, W, Burnham, S C, Lam, C P, Mondal, A, Bedo, J, Busy, A I, Brown, B, De Ruyck, K, Ellis, K A, Fowler, C, Gupta, V B, Head, R, Macaulay, L, Fertile, K, Rowe, C C, Rembach, A, Rodrigues, M, Rumble, R, Szoeke, C, Taddei, K, Taddei, T, Trounson, B, Aimes, D, Masters, C L, Martins, R N. Blood-based protein biomarkers for the diagnosis of Alzheimer's disease. Arch Neurol. 2012; published online. 46. Reddy M M, Wilson R, Wilson J, et al. Identification of candidate IgG biomarkers for alzheimer's disease via combinatorial library screening. Cell. 2011; 144(1):132-142. 47. Laske C L T, Stransky E, Hoffmann N, Fallgatter A J, Dietzsch J. Identification of a blood-based biomarker panel for classification of Alzheimer's disease. International Journal of Neuropsychopharmacology. 2011; 14(9):1147-1155. 48. O'Bryant S E G X, Barber R C, Cullum C M, Weiner M, Hall J, Edwards M, Grammas P, Wilhelmsen K, Doody R, Diaz-Arrastia R. Molecular neuropsychology: creation of test-specific blood biomarker algorithms. Dementia and Geriatric Cognitive Disorders. 2013, in press.