METHOD OF DIAGNOSING AND TREATING ALZHEIMER DISEASE USING PLASMA TAU LEVEL IN CONJUNT ION WITH BETA- AMYLOID LEVEL AS

DIAGNOSTIC INDEX

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. provisional patent application No. 62/716,168, filed August 08, 2018, the contents of which are incorporated herein by reference.

FIELD OF THE INVENTION

The present invention is related to a diagnostic method and kit for Alzheimer disease using plasma tau with b-amyloid levels and a method of treating patients diagnosed by the same.

BACKGROUND OF THE INVENTION

Alzheimer’s disease (AD) is the most common neurodegenerative disease in the elderly population. Cerebral accumulation of the b-amyloid (Ab) peptide and neurofibrillary tangles (NFTs) of tau are the main pathological hallmarks of AD and are closely related to the neurodegenerative mechanism that lead to the toxicity and disruption of neurons and synapses during its pathogenesis. Tau in particular is known to play a critical role in AD pathogenesis through its association with Ab, and several lines of evidence have suggested tau-dependent Ab toxicity and a feedback loop connecting tau and Ab during the pathogenesis of AD.

Tau is encoded by the MAPT (microtubule associated-protein tau) gene and exists in the human brain as six isoforms that differ in their amino-terminal inserts and microtubule-binding- domain repeats. Tau acts through its microtubule-binding-domain repeats to promote tubulin assembly and stabilize microtubule structure and function. Tau contains a number of phosphorylation sites, and its phosphorylation status influences its effects on microtubule assembly. Whether tau is the critical contributor to AD pathogenesis— along the epicenter of AD research— is a question that has been undergoing a recent reassessment because of disappointing results of clinical trials of Ab-targeting therapeutic strategies based on the amyloid hypothesis. Positron emission tomography (PET) imaging has been a valuable technique for monitoring brain tau pathology, and a variety of recently developed tau radiotracers for identification of neurofibrillary pathology through PET imaging provide great AD diagnostic and prognostic potential. However, although PET imaging of tau provides a wealth of information and reflects AD pathology fairly well, PET instrumentation is not available in many clinical settings, and PET imaging is associated with high costs and concerns about radiation hazards. Therefore, there is a desperate unmet need for a convenient and accessible method for detecting and monitoring brain tau deposition.

SUMMARY OF THE INVENTION

The present invention concerns correlation between plasma t-tau, p-tau and Abi _-42 levels and AD-associated tau pathology. The diagnostic kit of the present invention can be useful predicting and determining brain tau accumulation level in subjects of interest.

One aspect of the invention is a method for determining a level of brain tau accumulation using plasma t-tau, p-tau and Abi _-42 levels, comprising obtaining a plasma sample from a subject; measuring plasma total tau, p-tau and plasma Ab under presence of a mixture of protease inhibitors and phosphatase inhibitors; and quantifying ratio of plasma tau/Ab _1-42 wherein the plasma tau/Ab _1-42 ratio is an indicative parameter for the level of brain tau accumulation.

Another aspect of the invention is a diagnostic kit for determining a tau positive patient comprising protease inhibitors and phosphatase inhibitors (MPP) and further comprising a blood coagulating inhibitor.

In still another aspect of the invention is a method for treating or ameliorating the symptoms of a tau positive patient determined or predicted using the method described herein.

In still another aspect of the invention is a method for treating or ameliorating the symptoms of a tau positive patient determined or diagnosed or predicted using the diagnostic kit described herein.

Still another aspect of the invention is a method of detecting a sign or a symptom of Alzheimer disease in a subject, the method comprising: detecting an amount of Abi _-42 and t-tau or p-tau in a blood sample of the subject and in a blood sample of a control; quantifying a ratio of t-tau/Ab _1-42 or p-tau/ Abi _-42 in the blood sample of the subject and in the blood sample of the control; and comparing the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject and in the blood sample of the control, wherein the higher ratio of t-tau/ Abi _-42 or p-tau/ Abi _-42 in the blood sample of the subject compared with the ratio of t-tau/Ab _1-42 or p-tau/ Abi _-42 in the blood sample of the control indicates a presence of the sign or the symptom of Alzheimer disease in the subject. In embodiments, the sign or the symptom of Alzheimer disease is a tau accumulation in the brain of the subject and/or an Ab deposition in the brain of the subject and/or a neuronal dysfunction in the brain of the subject and/or dysfunctional brain glucose metabolism in the subject and/or a cognitive functional impairment and/or neurodegeneration of the subject. In embodiments, the cognitive functional impairment is measured by an MMSE (Mini-Mental State Examination) score. In embodiments, the amount of Abi _-42 and t-tau or p-tau is detected in plasma of the blood sample or in serum of the blood sample. In embodiments, the amount of Abi-42 is detected in a presence of a mixture of protease inhibitors and phosphatase inhibitors.

Still another aspect of the invention is a method of diagnosing a tau positive subject, the method comprising: detecting an amount of Abi _-42 and t-tau or p-tau in a blood sample of the subject and in a blood sample of a control; quantifying a ratio of t-tau/ Abi _-42 or p-tau/ Ab ₄ i ₂ n the blood sample of the subject and in the blood sample of the control; comparing the ratio of t- tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject and in the blood sample of the control; and evaluating a brain tau accumulation level of the subject, wherein the higher ratio of t-tau/Ab _1-42 or p-tau/ Abi _-42 in the blood sample of the subject compared with the ratio of t- tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the control indicates a presence of brain tau deposition in the subject. In embodiments, the amount of Abi _-42 and t-tau or p-tau is detected in plasma of the blood sample or in serum of the blood sample. In embodiments, the amount of Abi- ₄₂ is detected in a presence of a mixture of protease inhibitors and phosphatase inhibitors.

In embodiments, the tau positive subject has Alzheimer disease.

Still another aspect of the invention is a method of diagnosing a tau positive subject, the method comprising: genotyping ApoE of the subject; detecting an amount of Abi _-42 and t-tau or p-tau in a blood sample of the subject and in a blood sample of a control; quantifying a ratio of t- tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject and in the blood sample of the control; and evaluating a brain tau accumulation level of the subject, wherein a presence of a ApoE genotype for risk of AD and the higher amount of t-tau or p-tau or the higher ratio of t- tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject compared with the ratio of t-tau/Abi- 42 or p-tau/Ab _1-42 in the blood sample of the control indicates a presence of tau deposition in the brain of the subject. In embodiments, the ApoE genotype for risk of AD is ApoE4 genotype.

In embodiments, the amount of Abi _-42 and t-tau or p-tau is detected in plasma of the blood sample or in serum of the blood sample. In embodiments, the amount of Abi _-42 is detected in a presence of a mixture of protease inhibitors and phosphatase inhibitors. In embodiments, the tau positive subject has Alzheimer disease.

Still another aspect of the invention is a diagnostic kit for detecting a sign or a symptom of Alzheimer disease in a subject or diagnosing a tau positive subject, comprising a protease inhibitor and a phosphatase inhibitor (MPP).

Still another aspect of the invention is a method of treating a subject having a sign or a symptom of Alzheimer disease, the method comprising: detecting the sign or the symptom of Alzheimer disease in the subject according to the aforementioned method; and administering a therapeutically effective amount of a medication to the subject in need thereof, thereby alleviating or reducing the sign or the symptom of Alzheimer disease or delaying development of Alzheimer disease. In embodiments, the medication is a pharmacologic treatment, a

nonpharmacologic treatment, or a combination thereof. In embodiments, the pharmacologic treatment is cholinesterase-inhibitors, N-methyl-d-aspartate blockers, anti-amyloid disease modifying therapies, anti-Tau disease-modifying therapies, other mechanisms of action and symptomatic agents, or a combination thereof.

Still another aspect of the invention is a method of treating a tau positive subject, the method comprising: evaluating a brain tau accumulation level of the subject according to the aforementioned methods; and administering a therapeutically effective amount of a medication to the subject in need thereof, thereby alleviating or reducing a sign or a symptom of the tau positive subject or delaying development of a sign or a symptom of tau positive subject. In embodiments, the medication is a pharmacologic treatment, a nonpharmacologic treatment, or a combination thereof. In embodiments, the pharmacologic treatment is cholinesterase-inhibitors, N-methyl-d-aspartate blockers, anti-amyloid disease-modifying therapies, anti-Tau disease modifying therapies, other mechanisms of action and symptomatic agents, or a combination thereof.

Still another aspect of the invention is a method of determining a brain tau accumulation level in a brain region of a subject comprising: detecting an amount of Abi _-4 2 and t-tau or p-tau in a blood sample of the subject and in a blood sample of a control; quantifying a ratio of t-tau/Abi- 42 or p-tau/Ab _1-42 in the blood sample of the subject and in the blood sample of the control;

comparing the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject and in the blood sample of the control; and evaluating a brain tau accumulation level of the subject, wherein the higher ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject compared with the ratio of t-tau/Ab _1-42 or p-tau/ Ab ₄₂ in the blood sample of the control indicates a presence of tau deposition in the brain of the subject.

Still another aspect of the invention is a method of assessing a progression of a sign or a symptom of Alzheimer disease in a subject comprising: (i) detecting an amount of Abi-42 and t- tau or p-tau in a blood sample of the subject at a first time point; (ii) quantifying a ratio of t- tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject at the first time point; (iii) detecting the amount of Abi-42 and t-tau or p-tau in a blood sample of the subject at a second time point; (iv) quantifying the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject at the second time point; and (v) comparing the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject at the first time point and the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject at the second time point, wherein the higher ratio of t-tau/Ab _1-42 or p- tau/Ab _1-42 in the blood sample of the subject at the second time point compared with the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject at the first time point indicates the progression of the sign or the symptom of Alzheimer disease in the subject. In embodiments, the step (i) of the method further comprising detecting an amount of Abi-42 and t-tau or p-tau in a blood sample of a control at a first time point; the step (ii) of the method further comprising quantifying a ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the control at the first time point; the step (iii) of the method further comprising detecting the amount of Abi-42 and t- tau or p-tau in a blood sample of the control at a second time point; the step (iv) of the method further comprising quantifying the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the control at the second time point; and the step (v) of the method further comprising comparing the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject at the first time point and the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the control at the first time point, the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject at the second time point and the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the control at the second time point, and the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the control at the first time point and the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the control at the second time point, wherein a bigger difference between the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject at the second time point and the ratio of t-tau/Ab _1-42 or p-tau/Abi- 42 in the blood sample of the subject at the first time point compared with a difference between the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the control at the second time point and the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the control at the first time point further indicates the progression of the sign or the symptom of Alzheimer disease in the subject. In embodiments, the first time point and the second time point are separated by 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 12 months, 13 months, 14 months, 15 months, 16 months, 17 months, 18 months, 19 months, 20 months, 21 months, 22 months, 23 months, 24 months, 25 months, 26 months, 27 months, 28 months, 29 months, 30 months, 31 months, 32 months, 33 months, 34 months, 35 months, 36 months, 37 months, 38 months, 39 months, 40 months, 41 months, 42 months, 43 months, 44 months, 45 months, 46 months, 47 months, 48 months, 49 months, 50 months, 51 months, 52 months, 53 months, 54 months, 55 months, 56 months, 57 months, 58 months, 59 months, or 60 months. In embodiments, the sign or the symptom of Alzheimer disease is a tau accumulation in the brain of the subject. In embodiments, the sign or the symptom of Alzheimer disease is a tau accumulation in the brain of the subject and/or an Ab deposition in the brain of the subject and/or a neuronal dysfunction in the brain of the subject and/or dysfunctional brain glucose metabolism in the subject and/or a cognitive functional impairment and/or neurodegeneration of the subject. In embodiments, the cognitive functional impairment is measured by an MMSE (Mini-Mental State Examination) score. In

embodiments, the amount of Abi _-4 2 and t-tau or p-tau is detected in plasma of the blood sample or in serum of the blood sample. In embodiments, the amount of Abi _-4 2 is detected in a presence of a mixture of protease inhibitors and phosphatase inhibitors. In embodiments, the method further comprising repeating at multiple time points after the second time points, detecting the amount of Abi _-4 2 and t-tau or p-tau in a blood sample of the subject; quantifying the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject; and comparing the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject with the ratio of t-Xsaxl RfΐLΐ or p- t-tau/Ab _1-42 in the blood sample of the subject at the first time point and/or the ratio of t-Xsaxl Rfi-h orp-tau/ Ab ₄₂ in the blood sample of the subject at the second time point. In embodiments, the multiple points are separated by 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 12 months, 13 months, 14 months, 15 months, 16 months, 17 months, 18 months, 19 months, 20 months, 21 months, 22 months, 23 months, 24 months, 25 months, 26 months, 27 months, 28 months, 29 months, 30 months, 31 months, 32 months, 33 months, 34 months, 35 months, 36 months, 37 months, 38 months, 39 months, 40 months, 41 months, 42 months, 43 months, 44 months, 45 months, 46 months, 47 months, 48 months, 49 months, 50 months, 51 months, 52 months, 53 months, 54 months, 55 months, 56 months, 57 months, 58 months, 59 months, or 60 months.

Still another aspect of the invention is a method of treating a subject having a progression of a sign or a symptom of Alzheimer disease comprising detecting the progression of the sign or the symptom of Alzheimer disease in the subject according to the aforementioned methods; and administering a therapeutically effective amount of a medication to the subject in need thereof, thereby preventing or delaying the progression of Alzheimer disease. In embodiments, the medication is a pharmacologic treatment, a nonpharmacologic treatment, or a combination thereof. In embodiments, the pharmacologic treatment is cholinesterase-inhibitors, N-methyl-d- aspartate blockers, anti-amyloid disease-modifying therapies, anti-Tau disease-modifying therapies, other mechanisms of action and symptomatic agents, or a combination thereof.

Still another aspect of the invention is a method of determining brain glucose metabolism in a subject comprising: detecting an amount of Abi _-4 2 and t-tau or p-tau in a blood sample of the subject and in a blood sample of a control; quantifying a ratio of t-tau/ Abi _-4 2 or p-tau/ Ab ₄₂ in the blood sample of the subject and in the blood sample of the control; and comparing the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject and in the blood sample of the control, wherein the higher ratio of t-tau/Ab _1-42 or p-tau/ Ab ₄₂ in the blood sample of the subject compared with the ratio of t-tau/Ab _1-42 or p-tau/ Ab ₄₂ in the blood sample of the control indicates dysfunctional brain glucose metabolism in the subject. In embodiments, the amount of Abi - ₄ 2 and t-tau or p-tau is detected in plasma of the blood sample or in serum of the blood sample. In embodiments, the amount of Abi _-4 2 is detected in a presence of a mixture of protease inhibitors and phosphatase inhibitors. In embodiments, the subject has Alzheimer disease.

Still another aspect of the invention is a method of determining a neurodegeneration level in a subject comprising: detecting an amount of Abi _-4 2 and t-tau or p-tau in a blood sample of the subject and in a blood sample of a control; quantifying a ratio of t-tau/ Abi _-4 2 or p-tau/ Ab ₄₂ in the blood sample of the subject and in the blood sample of the control; and comparing the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject and in the blood sample of the control, wherein the higher ratio of t-tau/Ab _1-42 or t-tau/Ab _1-42 in the blood sample of the subject compared with the ratio oft-tau/Ab _1-42 or p-tau/ Ab ₄₂ in the blood sample of the control indicates the higher neurodegeneration level in the subject. In embodiments, the amount of Abi - ₄ 2 and t-tau or p-tau is detected in plasma of the blood sample or in serum of the blood sample. In embodiments, the amount of Abi _-4 2 is detected in a presence of a mixture of protease inhibitors and phosphatase inhibitors. In embodiments, the subject has Alzheimer disease.

Still another aspect of the invention is a method of determining cognitive function in a subject, the method comprising: detecting an amount of Abi _-4 2 and t-tau or p-tau in a blood sample of the subject and in a blood sample of a control; quantifying a ratio of t-tau/ Abΐ42 or p- tau/Ab _1-42 in the blood sample of the subject and in the blood sample of the control; and comparing the ratio of t-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the subject and in the blood sample of the control, wherein the higher ratio of t-tau/ Abΐ42 or p-tau/Ab _1-42 in the blood sample of the subject compared with the ratio oft-tau/Ab _1-42 or p-tau/Ab _1-42 in the blood sample of the control indicates cognitive functional impairment in the subject. In embodiments, the amount of Abi _-4 2 and t-tau or p-tau is detected in plasma of the blood sample or in serum of the blood sample. In embodiments, the amount of Abi _-4 2 is detected in a presence of a mixture of protease inhibitors and phosphatase inhibitors. In embodiments, the subject has Alzheimer disease.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 A and FIG. 1B are graphs showing that plasma tau-related biomarkers are significantly correlated with brain tauopathy. FIG. 1 A shows plasma biomarker values according to tau Braak stage. FIG. 1B shows partial correlation plots of plasma biomarker values versus a priori ROIs of AD-signature regions. Braak stage: disease staging in

Alzheimer's disease. Braak stages I and II are used when neurofibrillary tangle involvement is confined mainly to the transentorhinal region of the brain, stages III and IV when there is also involvement of limbic regions such as the hippocampus, and V and VI when there is extensive neocortical involvement. This should not be confused with the degree of senile plaque involvement, which progresses differently

FIG. 2A and FIG. 2B are graphs showing the relationship between plasma tau related biomarkers and cognitive function states. FIG. 3A-FIG. 3D are images showing the correlations between plasma tau-related biomarkers and Tau-PET. Results represents voxel-wise associations between each of the plasma tau biomarkers and regional brain tau burden. FIG. 3 A and FIG. 3B show that higher plasma p-tau and t-tau values were associated with higher brain tau deposition only in the medial temporal regions. FIG. 3C and FIG. 3D show that plasma p-tau/ Ab ₄₂ and t-tau/Ab _1-4 r ₂ atios are having positive correlation with tau deposition in diffuse brain regions including the cingulate, lateral temporal, frontal, and parietal cortices as well as the medial temporal regions.

In particular, the brain regions where plasma t-tau/Ab _1-42 ratio correlated with brain tau were very similar to the typical deposition sites of neurofibrillary tangles in AD.

FIG. 4A-FIG. 4D are graphs showing the correlations between plasma p-tau (FIG. 4A), plasma t-tau (FIG. 4B), plasma p-tau/Ab _1-42 ratio (FIG. 4C) and t-tau/ Abΐ42 ratio (FIG. 4D) and neurodegeneration markers such as hippocampal volume, cortical thickness, and FDG-PET ( ¹⁸ F- labeled fluoro-2-deoxyglucose ( ¹⁸ F-FDG)-positron emission tomography).

FIG. 5A-FIG. 5E are an illustration and graphs showing performance of plasma biomarkers in discriminating Tau-PET ⁺ from Tau-PET- subjects. FIG. 5A shows Tau-PET positivity criteria for ROCs. FIG. 5B shows differences in plasma biomarker levels between Tau-PET- and Tau-PET ⁺ subjects. FIG. 5C and FIG. 5D show comparison of ROC analyses among plasma p-tau, t-tau, p-tau/ Ab ₄₂ , and t-tau/amyloid- FIG.5E _. shows Relative risk (RR) analysis of Tau-PET positivity using plasma biomarker quartiles. Relative risk (RR) of brain Tau-PET positivity (fraction of PET ⁺ subjects in each quartile) was significantly increased in all plasma biomarker quartile 4 groups compared with quartile 1 groups.

FIG. 6A and FIG. 6B are an illustration and graphs showing the relationship between plasma Abi _-4 2 and cerebral Ab deposition.

FIG. 7 is Table 5 showing demographic characteristics of participants. Total, n = 76. Data are presented as mean ± SEM or n. AD = Alzheimer’s disease; CDR = clinical dementia rating; CN = cognitively normal; MCI, mild cognitive impairment; MMSE zscore = mini-mental state examination with the correction for age, sex, and education level. Subjects were grouped as their 2-year cognitive states and used to perform cross-sectional studies (FIGs. 8 - 10). ^a P- values from chi-square test. ^b P-values from ANOVA with Tukey’s post hoc test.

FIG. 8A and FIG. 8B are graphs showing that plasma tau-related biomarkers are significantly correlated with brain tauopathy. FIG. 8A shows the plasma biomarker values according to tau Braak stage (P-values by ANOVA followed by Tukey’s multiple comparison test). FIG. 8B shows the partial correlation plots of plasma biomarker values versus a priori regions of interest of Alzheimer’s disease signature regions. Covariates (age and sex) were adjusted and SUVR values for Alzheimer’s disease signature regions (ROI) were natural log transformed to normalize variance n = 51 for plasma p-tau, n = 75 for plasma t-tau, « = 51 for plasma p tau/amyloid-b _1-42; n = 75 for plasma t-tau/amyloid-b _1-42 . ^# P < 0.10, ^* P < 0.05, ^** P < 0.01 , ^*** P < 0.001 , and ^**** p < 0.0001. The statistical values for the group effects (ANOVA F- test) are marked above the graphs.

FIG. 9A-FIG. 9D are images showing the correlations between plasma tau-related biomarkers and tau-PET. Results are shown using a threshold combining Puncorrected < 0.005 at the voxel level and PFWE-corrected < 0.05 at the cluster level; T- values were converted to r values for illustration purposes. Voxel-wise associations were assessed between partial volume error-corrected tau-PET SUVR and plasma p-tau (FIG. 9A) and t-tau (FIG. 9B), and p- tau/amyloid-Pi-42 (FIG. 9C) levels and t-tau/amyloid-b _1-42 . (FIG. 9D) values. Correlation coefficients were positive for all plasma tau biomarkers. All voxel-wise associations were adjusted for age and sex.

FIG. 10A-FIG. 10E are an illustration and graphs showing performance of plasma biomarkers in discriminating Tau-PET ⁺ from Tau-PET subjects. FIG. 10A shows the Tau-PET positivity criteria for the ROCs. FIG. 10B shows the differences in plasma biomarker levels between Tau-PET and Tau-PET ⁺ subjects (P-values by unpaired /-test; n = 51 for plasma p-tau, n = 75 for plasma t-tau, n = 51 for plasma p-tau/amyloid-Pi-42; n = 75 for plasma t-tau/amyloid-bi- 42). FIG. 10C and FIG. 10D show the comparison of ROC analyses among plasma p-tau, t-tau, p-tau/amyloid-bi -42, and t-tau/amyloid^i-42 (« = 35 Tau-PET , n = 15 Tau-PET ⁺ ). Each cut-off value was determined using Youden’s index for ROC analysis (details are described in Table 6). FIG. 10E shows the Relative risk (RR) analysis of Tau-PET positivity using plasma biomarker bisection (group 1, n = 25; group 2, n = 25). Relative risk of brain Tau-PET positivity (fraction of PET ⁺ subjects among all participants in the group) was significantly increased in group 2 compared with group 1 (P-values from relative risk analysis) in all plasma biomarkers. See Table 7 for details. ^* P < 0.05, ^** P < 0.01, ^*** P < 0.001, and ^**** P < 0.0001.

FIG. 11 is Table 6 showing details of ROC curve analyses (Tau-PET versus Tau-PET ⁺ ). AUC = area under curve; 95% Cl = 95% confidence interval; cut-off criterion was determined by Youden index. The number of subjects were matched equally in all four models for comparing ROC curve analysis (n = 35 for Tau-PET , « = 15 for Tau-PET ⁺ ). The cut-off values were logit values derived from logistic regression models with the controlling for age and sex.

FIG. 12 is Table 7 showing details of relative risk analyses. Significance by relative risk analyses. The number of subjects were matched equally in all four models ( n = 35 for Tau- PET , n = 15 for Tau-PET ⁺ ). ^* P < 0.05, ^** P < 0.01, ^*** P < 0.001, significance by relative risk analysis and chi-square test. RR = relative risk.

FIG. 13A-FIG. 13G are an illustration and graphs showing that baseline plasma t-tau/ amyloid-Pi-42 predicts the longitudinal changes of neurodegeneration. FIG. 13A shows the timeline of the longitudinal study. Tau-PET scans were only performed in the 2nd year cohort. FIG. 13B-FIG. 13E show the baseline plasma t-tau/amyloid-Pi-42 levels correlated with the longitudinal changes of hippocampal volume, cerebral amyloid deposition, and glucose metabolism. FIG. 13F shows the timeline of the longitudinal study using delta (A) plasma t-tau and t-tau/amyloid-Pi-42 levels with 2-year tau-PET results. FIG. 13G show that Aplasma t-tau and A(t-tau/amyloid-Pi -42) correlate with 2-year Alzheimer’s disease signature regional tau accumulation. A, difference between the second and first measurement values /’-values by partial correlation analysis with the controlling for age and sex, ^# P < 0.1, ^* P < 0.05, ^** P < 0.01.

FIG. 14 is Table 8 showing demographic characteristics of participants for longitudinal changes ( n = 76). Data were presented as mean ± SEM or N (%); CN, cognitively normal;

MCI, mild cognitive impairment; AD, Alzheimer’s disease; MMSE z-score, mini-mental state examination with the correction for age, sex, and education level; CDR, clinical dementia rating; ApoE, Apolipoprotein e4. P-value, p-values between baseline and 2nd year; ^a p, p-values from paired /-test; ^b p, p-values from unpaired /-test; ^c p, p-values from chi-square test.

FIG. 15 is Table 9 showing demographic data of the participants (related to FIGs. 8A and 9B, and FIGs. 9A-9D, 2nd year samples). Data were presented as mean ± SEM or N (%); ^a P, significance by chi-squared test; ^b P, significance by ANOVA with Tukey’s post hoc test; CN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer’s disease; MMSE z-score, mini-mental state examination with the correction for age, sex, and education level; CDR, clinical dementia rating; ApoE, Apolipoprotein e4; A beta, Ab, amyloid beta, b-amyloid;

FIG. 16 is Table 10 showing the correlation between plasma markers and brain regional tauopathy (related to FIG. 8B). P, significance by partial correlation analyses (correction for age and sex differences).

FIG. l7A and FIG. 17B are graphs showing the relationships between plasma tau-related biomarkers and cognitive function. FIG. 17A shows the plasma biomarker levels in relation to subjects’ cognitive function states (by ANOVA followed by Tukey’s multiple comparison test). FIG. 17B shows the correlation between CERAD-K delayed verbal memory and plasma biomarkers. Variables were adjusted with statistical control for the effect of covariates (age, sex) by partial correlation analyses. ^# P < 0.10, ^* P < 0.05, and ^** P < 0.01. CERAD-K refers to a Korean version of the consortium to establish a Registry for Alzheimer's Disease Assessment Packet.

FIG. 18 A-FIG. 18D are graphs showing the correlations between plasma tau-related biomarkers and neurodegeneration markers. FIG. 18A-FIG. 18D show that plasma p-tau, plasma t-tau, p-tau/APi _-4 2, and t-tau/Ab _1-42 were significantly correlated with neurodegeneration markers (hippocampal volume, cortical thickness, and FDG-PET). Variables were adjusted with statistical control for the effect of covariates (age, sex). ^# P < 0.10, ^* P < 0.05, ^** P < 0.01, and ^*** P < 0.001 ; partial correlation analyses.

FIG. 19A-19C are an illustration and graphs showing the relationship between plasma Abi - ₄ 2 and brain Ab deposition. FIG. 19A shows criterion for PiB (Pittsburgh compound B) - PET vs PiB-PET ⁺ . FIG. 19B shows the plasma Abi _-4 2 levels between PiB-PET and PiB-PET ⁺ (by unpaired /-test) and the correlation between brain Ab deposition and plasma Abi _-4 2 levels. Covariates (age and sex) were adjusted and SUVR values for global Ab deposition (ROI) were natural log transformed to normalize variance (by partial correlation analyses). FIG. 19C shows the plasma Abi _-4 2 levels between Tau-PET and Tau-PET ⁺ (by unpaired /-test) and the correlation between brain tau deposition and plasma Abi _-4 2 levels. Covariates (age and sex) were adjusted and SUVR values for AD signature ROI were natural log transformed to normalize variance (by partial correlation analyses). ^* P < 0.05, ^* P < 0.01, and ^*** P < 0.001. PIB:

Pittsburgh compound B (PiB), a radioactive analog of thioflavin T, which can be used in positron emission tomography scans to image beta-amyloid plaques in neuronal tissue.

FIG. 20 is a graph and a table showing the logistic regression followed by ROC curve analysis using plasma Abi _-4 2. Plasma Abi _-4 2 (black dotted line) only had also high AUC value (0.800 with 66.67% sensitivity and 82.86% specificity). ROC curves except for plasma Abi _-4 2 were equal graphs displayed in FIG. 10. FIG. 21 A- FIG. 21D are graphs and a table showing that there are no significant correlations between baseline plasma t-tau/Ab _1-42 and baseline neurodegeneration. FIG. 21A shows that there are no significant correlations between plasma t-tau/Ab _1-42 and FDG-PET or hippocampal volumes. Only PiB-PET SUVR had a trend toward significant tendency with plasmat-tau/Ab _1-42 (P = 0.1, r = 0.2; partial correlation with the controlling for age and sex).

FIG. 21B shows the correlation tendency between baseline plasma t-tau/Ab _1-42 and 2nd year AD signature tau accumulation in the brain ( ^# P < 0.10, r = 0.21; partial correlation with the controlling for age and sex). FIG. 21 C show the correlation between neurodegeneration markers (FDG-PET, hippocampal volumes) and PiB-PET SUVR both baseline and 2nd year time-points. FIG. 21D shows that conjectured subject distribution range of both baseline samples and 2nd year samples were marked on a hypothetical AD progression-abnormality plot.

FIG. 22A-FIG. 22D are pairwise plots showing longitudinal age (X axis) and the neurodegeneration markers (Y axis). Dotted circles (baseline timepoint) and solid circles (follow-up 2nd year timepoint) indicate ranges of the values from ADD patients (red).

FIG. 23A-FIG. 23D are graphs and tables showing the comparison of ROC curves with ApoE genotype.

FIG. 24 is a graph and a table showing the correlation between plasma t-tau/Ap42 and MMSE score.

FIG. 25 is an illustration showing the longitudinal study design for plasma t-tau/Ap42.

FIG. 26 is Table 11 showing the correlation between FDG-PET standardized uptake ratio value (SUVR) and plasma t-tau/Ap42.

FIG. 27 is Table 12 showing the correlation between PiB-PET standardized uptake ratio value (SUVR) and plasma t-tau/Ap42.

FIG. 28 is a graph and a table showing that adding blood t-tau/Ap42 as a variable increases AUC values compared with ApoE alone (0.803 to 0.911).

FIG. 29A and 29B are Table 13 and Table 14, respectively, showing that blood t-tau/Ap42 reflects brain glucose metabolism both longitudinally and cross-sectionally.

FIG. 30 is Table 15 showing that blood t-tau/Ap42 reflects cognitive functions of patients.

FIG. 31 is Table 16 showing that blood t-tau/Ap42 reflects brain Ab deposition (another pathological hallmark of AD). FIG. 32 is an illustration showing the summary of Ab-independent roles for ApoE in the pathogenesis of AD (reproduced from Yu et al., 2014, Annu. Rev. Neurosci. 37:79-100). The isoform-dependent effects of ApoE are indicated. Abbreviations: ApoE, apolipoprotein E; BBB, blood-brain barrier; NFT, neurofibrillary tangle.

DETAILED DESCRIPTION OF THE INVENTION

In an embodiment of the present invention, the invention relates the method for determining a level of brain tau accumulation using plasma t-tau, p-tau and Abi _-4 2 levels, comprising obtaining a plasma sample from a subject; measuring plasma total tau, p-tau and plasma Ab under presence of a mixture of protease inhibitors and phosphatase inhibitors; and quantifying ratio of plasma t-tau/Ab _1-4 ; ₂ wherein the plasma tau/Ab _1-42 ratio is an indicative parameter for the level of brain tau accumulation.

Another embodiment of the present invention, the invention provides the diagnostic kit for determining a tau positive patient comprising protease inhibitors and phosphatase inhibitors (MPP) and further comprising a blood coagulating inhibitor.

In still another embodiment of the present invention, the invention provides the method for treating or ameliorating the symptoms of a tau positive patient determined or predicted using the method described herein.

In still another embodiment of the present invention, the invention provides the method for treating or ameliorating the symptoms of a tau positive patient determined or diagnosed or predicted using the diagnostic kit described herein.

Alzheimer’s Disease

The term“Alzheimer’s Disease” as used herein, refers to a neurodegenerative disorder and encompasses familial and sporadic AD. Symptoms indicative of AD in human subjects typically include, but are not limited to, mild to severe cognitive impairment dementia, progressive impairment of memory (ranging from mild forgetfulness to disorientation and severe memory loss), poor visio-spatial skills, personality changes, poor impulse control, poor judgment, distrust of others, increased stubbornness, restlessness, poor planning ability, poor decision making, and social withdrawal. Hallmark pathologies within brain tissues include extracellular neuritic b-amyloid plaques, neurofibrillary tangles, neurofibrillary degeneration, granulovascular neuronal degeneration, synaptic loss, and extensive neuronal cell death.

The term“AD patient” as used herein, refers to an AD patient that is identified as having or likely to have AD based on known AD pathologies or symptoms. For example, AD patient is individuals with abovementioned hallmark pathologies and can be in cognitively normal, mild cognitive impairment, or dementia state.

Braak Stage

The term“Braak Stages” or“Braaking Stages” as used herein, refers to

neuroanatomically approximate pathologic stages of tangle deposition delineated by Braak and Braak (1995, Neurobiol. Aging, 16: 271-8; discussion 8-4). Specifically, three composite regions-of-interest (ROIs) of weighted mean SUVR that correspond to anatomical definitions of Braak stages I/II (transentorhinal), III/IV (limbic), and V/VI (neocortical) are utilized.

Braak stages I and II are used when neurofibrillary tangle involvement is confined mainly to the transentorhinal region of the brain, stages III and IV when there is also involvement of limbic regions such as the hippocampus, and V and VI when there is extensive neocortical involvement. Braak stages 0-II are determined as Tau-PET negative and Braak stages III -VI are determined as Tau-PET positive.

Method of measuring Plasma tau and Ab

The present invention provides a method of measuring plasma tau and Ab. The contents of US Patent Application 15/570,186 filed April 30, 2018 is incorporated herein by reference.

Overnight fasting blood samples are collected in a blood collection tube including a blood coagulation inhibitor such as EDTA such as, for example, but not limited to K2 EDTA tube (BD Vacutainer Systems, Plymouth, UK). Collected blood samples are stabilized and centrifuged to obtain plasma and serum supernatants, and huffy coat. To obtain samples with high purity, the plasma and serum supernatants are further centrifuged under the same conditions, and the collected pure plasma and serum supernatants are aliquoted and immediately stored at -80°C. The levels of plasma total tau and p-tau (Thr 181) are measured using analytic assays including but not limited to immunoassay and mass spectrometry. A mixture of protease inhibitors and phosphatase inhibitor (MPP) are pretreated to stabilize plasma Ab and plasma Ab levels are quantified using analytic assays including but not limited to immunoassay and mass spectrometry.

Diagnostic Kit

The present invention provides a diagnostic kit for determining a tau positive patient, wherein the kit comprising a protease inhibitor and a phosphatase inhibitor (MPP). The kit further comprises a blood coagulating inhibitor.

The diagnostic kit comprises components and/or compositions for determining a quantitative ratio of t-tau, p-tau and Abi _-4 2 in a plasma sample of a subject and/or patient, wherein the components and/or compositions are selected such that they enable the quantitative determination of t-tau, p-tau and Abi-42 for the purposes of determining the quantitative ratio of t- tauM^i _-4 2 or p-tau/ Abi _-4 2. The components or compositions of the inventive kit may especially be compositions or components for purification, concentration, separation or the like of the tau and Ab to be examined.

Treatment of AD

Cholinesterase inhibitors such as donepezil (Aricept®), galantamine (Razadyne®) and rivastigmine (Exelon®) delays the loss of mental abilities in patients with mild to moderate Alzheimer' s disease. These drugs work by boosting levels of a cell-to-cell communication by providing a neurotransmitter (acetylcholine) that is depleted in the brain by Alzheimer's disease. Cholinesterase inhibitors can improve neuropsychiatric symptoms, such as agitation or depression, as well.

Memantine (Namenda®), an N-methyl D-aspartate antagonist, is used to treat moderate to severe Alzheimer' s Disease. This medication works in another brain cell communication network by regulating glutamate and slows the progression of symptoms with moderate to severe Alzheimer's disease. Memantine can be used in combination with a cholinesterase inhibitor.

Other medications such as antidepressants and anti-anxiety medications can be used to help with behavioral symptoms of Alzheimer' s Disease.

For example, Food and Drug Administration (FDA) approved AD medications, cholinesterase-inhibitors (ChEIs), and the N-methyl-d-aspartate (NMD A) antagonist memantine, when utilized as part of a comprehensive care plan, while generally considered symptomatic medications can provide modest“disease course-modifying” effects by enhancing cognition, and reducing loss of independence. When combined, pharmacologic and nonpharmacologic treatments can meaningfully mitigate symptoms and reduce clinical progression and care burden. AD pharmacotherapy first involves identification and elimination of potentially harmful medications and supplements. First line treatment for neuropsychiatric symptoms and problem behaviors is nonpharmacological and involves psychoeducation, trigger identification, and implementation, iterative evaluation, and adjustment of behavioral and environmental interventions. Ongoing research studies for primary and secondary prevention of AD and clinical trials evaluating symptomatic and disease-modifying treatments in symptomatic AD are directed at diverse therapeutic targets including neurochemicals, amyloid and tau pathological processes, mitochondria, inflammatory pathways, neuroglia, and multimodal lifestyle interventions. The key elements of effective multifactorial management of AD, for example, include patient-caregiver dyad-centered evaluation, diagnosis and disclosure, and care planning processes, nonpharmacological management, such as interventions and behavioral approaches and strategies, pharmacological management, and pragmatic modifications to sustain alliance, adherence and well-being of patient-caregiver dyad. Regarding pharmacological management, AD medications include, for example, cholinesterase-inhibitors (ChEI), such as donepezil, rivastigmine, rivastigmine transdermal patch, galantamine, and galantamine extended-release, and AD dementia voltage-dependent, low affinity, open-channel NMDA (N-methyl-d-aspartate) blockers (NMDA antagonist), such as memantine, AVP-786, AXS-05, and Riluzole. In addition, other disease-modifying therapies (DMTs), such as anti-amyloid DMTs focused on reduction of Ab42 production (e.g., secretase inhibitors: gamma-secretase inhibitors (for example, Semagacestat, Avagacestat, EVP-0962) and b-secretase inhibitors (for example, BACE inhibitor BI 1181181, RG7129, LY2811376, LY2886721, E2609, AZD3293, CNP520, JNJ- 54861911, AZD3293 (LY3314814), CAD106 & CNP520, Crenezumab, and Verubecestat), reduction of Ab-plaque burden via aggregation inhibitors (for example, GV-971 (Sodium Oligo- mannurarate)), and promotion of Ab clearance via active or passive immunotherapy (for example, Abclearance AN-1792, Bapineuzumab, AAB-003, GSK933776, Solanezumab, Crenezumab, Gantenerumab, BAN2401, and Aducanumab), anti-Tau DMTs (for example, Tau stabilization Epothilone D, Tau aggregation Inhibitor Rember TM and TRx0237, and p-Tau clearance AADvac-l and ACI-35), and other mechanisms of actions and symptomatic agents (for example, microglial activation inhibitor Alzhemed™, Azeliragon, Ibuprofen, and Flurizan™, serotonin reuptake inhibition Escitalopram, neuroprotective Icosapent ethyl (IPE), metabolic Insulin intranasal (Humulin), 5-HT2A antagonist & dopamine receptor modulator ITI-007, dopamine reuptake inhibitor Methylphenidate, dual or exin receptor antagonist MK-4305 (suvorexant), cannabinoid (receptor agent) Nabilone, acetylcholinesterase inhibitor

Octohydroaminoacridine succinate, RAGE antagonist TTP488 (azeliragon), and positive allosteric modulator of GAB A-A receptors Zolpidem) are also contemplated (see, e.g., Atri, Semin Neurol 2019;39:227-240, Graham et al. Annu. Rev. Med. 2017. 68:413-30, Cummings et al., Alzheimer’s & Dementia: Translational Research & Clinical Interventions 2018, 4: 195-214, and Pinheiro and Faustino, Current Alzheimer Research, 2019, 16, 418-452, the contents of which are incorporated herein by reference).

The AD treatment also includes nonpharmacologic treatments. For example, cognitive impairment, dementia, and AD are multifactorial and complex conditions with several potentially modifiable risk factors including vascular and lifestyle factors, such as hypertension,

dyslipidaemia and obesity at midlife, diabetes mellitus, smoking, physical inactivity, depression and low levels of education. Owing to the multifactorial aetiology of dementia and AD, multidomain interventions that target several risk factors and mechanisms simultaneously, such as dietary interventions, physical activity interventions, and cognitive stimulation interventions, might be needed for effective prevention. The results from the first large randomized controlled trials of multidomain lifestyle interventions to prevent cognitive impairment suggest that targeting interventions to individuals at risk of dementia is an effective strategy. Hence, a life- course approach is needed to facilitate optimal lifestyle intervention strategies for different age groups and for individuals with different risk profiles (see, e.g., Kivipelto et al. Nat. Rev. Neurol. 2018, l4(l l):653-666, the contents of which are incorporated herein by reference.)

Advantageous Effects

The inventive concept is meaningful since it is based on the analysis of defined neurochemical biomarkers in a defined sample, these being directly correlated to Alzheimer' s disease. In addition, the method and use of the invention as described herein, can be performed in a much specific, sensitive, simplified and inexpensive manner compare to the methods used in the prior art, such as PET imaging methods or the like. ApoE genotype for risk of Alzheimer disease

ApoE has been recognized as the strong late onset (LOAD)- Alzheimer’s disease (AD) risk factor (Yu et al, 2014, Annu Rev Neurosci. 2014;37:79-100). In 1993, there was a report that ApoE4 genotype is the major risk allele (Corder et al, 1993, Science. 26l(5l23):92l-3) and ApoE2 was identified as being protective against AD (Chartier-Harlin et al, 1994, Hum Mol Genet. 3(4): 569-74). Difference of ApoE genotypes contributes to AD pathogenesis through various pathways such as beta-amyloid clearance, mitochondrial dysfunctions, and

neuroinflammation (Yu et al, 2014, Annu Rev Neurosci. 37:79-100) (see, e.g., FIG. 32).

Furthermore, Previous studies have reported the riskiness of ApoE4 genotypes than ApoE3; e.g, acceleration of seeding of amyloid pathology (Liu et al, 2017, Neuron 96(5): l024-l032.e3), neurotoxic effect and stimulation of tau phosphorylation (Harris et al, 2003, Proc. Natl. Acad. Sci. U.S.A. 100(19): 10966-71), fewer and shorter dendritic spines in cortical neurons (Dumanis et al, 2009, J. Neurosci. 29(48): 15317-22).

General Definitions

The term“subject” as used herein is intended to include a living organism in which alleviation of symptoms or inhibition of a neurological disorder is sought. Preferred subjects are mammals. Examples of subjects include but are not limited to, humans, monkeys, dogs, cats, mice, rats, cows, horses, pigs, goats and sheep.

The terms“neurodegenerative disorder” or a“neurological disorder” as used herein refers to a disorder which causes morphological and/or functional abnormality of a neural cell or a population of neural cells. The neurodegenerative disorder can result in an impairment or absence of a normal neurological function or presence of an abnormal neurological function in a subject. For example, neurodegenerative disorders can be the result of disease, injury, and/or aging. Non-limiting examples of morphological and functional abnormalities include physical deterioration and/or death of neural cells, abnormal growth patterns of neural cells, abnormalities in the physical connection between neural cells, under- or over production of a substance or substances, e.g, a neurotransmitter, by neural cells, failure of neural cells to produce a substance or substances which it normally produces, production of substances, e.g., neurotransmitters, and/or transmission of electrical impulses in abnormal patterns or at abnormal times.

Neurodegeneration can occur in any area of the brain of a subject and is seen with many disorders including, for example, epilepsy, head trauma, stroke, ALS, multiple sclerosis,

Huntington's disease, Parkinson's disease, and Alzheimer's disease.

By“amyloid protein” is meant a protein or peptide that is associated with an AD neuritic senile plaque. Preferably, the amyloid protein is amyloid precursor protein (APP) or a naturally-occurring proteolytic cleavage product. APP cleavage products include, but are not limited to, Abi _-4 o, Ab _2-4 o, Abi _-4 2, as well as oxidized or crosslinked Ab. AD is characterized by pathologic accumulation of insoluble protein in vulnerable brain regions and followed neuronal toxicity and dysfunction. The toxic amyloid Ab peptides and tau are generally considered to be major pathogenic participants in AD. These various peptides are generated by cleavage of a larger protein called the b-amyloid precursor protein (APP). Proteins called presenilins (PS1, PS2) may mediate cleavage. Other neuritic plaque-associated proteins include b-amyloid secretase enzymes I and II (BASE I and II) which associate with amyloid proteins. Some of the resulting Ab peptides are more toxic than others. Elevation of specific Ab peptides in the brain is believed to be causally associated with all known forms of AD. This generally accepted“Ab hypothesis” states that Ab generation, deposition and/or accumulation in the brain is an important final common pathway which underlies the disease process in this devastating neurological disorder.

The terms“Braak stage” as used herein refer to a disease staging in Alzheimer's disease. Braak stages I and II are used when neurofibrillary tangle involvement is confined mainly to the transentorhinal region of the brain, stages III and IV when there is also involvement of limbic regions such as the hippocampus, and V and VI when there is extensive neocortical involvement. This should not be confused with the degree of senile plaque involvement, which progresses differently.

The terms“MMSE (Mini-Mental State Examination)” as used herein refer to a type of neurocognitive function test. It is a 30-point questionnaire that is used extensively in clinical and research settings to measure cognitive impairment. Equally, any score greater than or equal to 24 points (out of 30) indicates a normal cognition. Below this, scores can indicate severe (<9 points), moderate (10-18 points) or mild (19-23 points) cognitive impairment.

The terms“CDR (Clinical Dementia Rating)” as used herein refer to another type of neurocognitive function test. The terms“CDR-K” as used herein refer to a Korean version of CDR. The terms“FDG PET” as used herein refer to ¹⁸ F-labeled fluoro-2-deoxyglucose ( ¹⁸ F- FDG)-positron emission tomography. ¹⁸ F-labeled fluoro-2-deoxyglucose ( ¹⁸ F-FDG) is glucose analog. The uptake of ¹⁸ F-FDG by tissues is a marker for the tissue uptake of glucose, which in turn is closely correlated with certain types of tissue metabolism. After ¹⁸ F-FDG is injected into a patient, a PET scanner can form two-dimensional or three-dimensional images of the distribution of 18F-FDG within the body, which indicates brain glucose metabolism.

The terms“cross sectional study” as used herein refer to a comparative analysis of samples taken at the same time. For example, as shown in, e.g., FIG. 13 A, first, the blood samples from patients and the normal control group were collected as the baseline, and second, after two years, the blood samples were collected again from the same patients and the same normal group. A cross sectional study as used herein refers to a cross-sectional analysis of comparing the blood samples from patients with those from the normal control group collected at the baseline, or comparing the blood samples from patients with those from the normal control group collected at the 2 ^nd year.

The terms“longitudinal study” as used herein refer to a comparative analysis of samples collected from the same group at different times. For example, it means an analysis of comparing the blood samples from patients collected at the baseline with those collected at the 2 ^nd year (see, e.g., FIG. 13 A).

The term“treatment” or“treating” refers to a decrease in the symptoms associated with the disorder or an amelioration of the recurrence of the symptoms of the disorder, prophylaxis, or reversal of a disease or disorder, or at least one discernible symptom thereof. The term “treatment” or“treating” refers to inhibiting or slowing the progression of a disease or disorder, e.g., epilepsy, physically, e.g., stabilization of a discernible symptom, such as seizures. The term “treatment” or“treating” refers to delaying the onset of a disease or disorder. The term “prevention” or“preventing” refers to delaying the onset of the symptoms of the disorder.

The terms“control,”“control sample,”“normal,” or“normal sample” as used herein, refer to a subject or sample that serves as a reference, usually a known reference, for comparison to a test subject or a test sample. In embodiments, the control or the normal may be two types, a disease control (or disease-associated control) and a normal (non-disease control). In embodiments, a control, in particular a disease control (or reference) is a control subject, sample or value taken from a patient who was previously diagnosed with a neurological disease of interest or any population thereof. In embodiments, a disease control is a control subject, sample, image or value taken from a subject who was previously known to have symptoms that are indicative of or associated ( i.e . known to be present) with a neurological disease of interest or any population thereof. In the cases concerning a certain neurological disease, the symptoms present in the neurological disease control can include any morphological feature(s) of cribriform plate that are indicative of or associated with the concerned neurological disease. In embodiments, a normal (non-disease) control refers to a subject, sample, image or value taken from a health subject who is known not to have or suspected of not having a neurological disease. In embodiments, a control or reference can also represent an average value gathered from a population of similar individuals, e.g., neurological and/or psychiatric patients or healthy individuals with a similar medical background, same age, weight, etc. In embodiments, a control or reference sample, value or image can also be obtained from the same individual, e.g, from an earlier-obtained sample, prior to disease, or prior to treatment, especially when a plurality of samples, values or images from the same individual are monitored over a course of time.

The term“therapeutic intervention,” as used herein, refers to, for example, medicine (e.g. therapeutic compositions) and/or therapy (e.g. chemical and/or surgical procedures) used to reduce or cure disease or pain by the involvement and intercession of therapeutic practice.

Therapeutic intervention can vary in methods, for example, depending on a condition or disease of a patient who is in need of such a therapeutic intervention. In the context of a method treating a neurological disease in a subject, a therapeutic intervention can be performed to a subject identified for the treatment.

The terms“administration,”“administering” and the like refer both to direct

administration, which may be administration to cells in vitro, administration to cells in vivo, administration to a subject by a medical professional or by self-administration by the subject and/or to indirect administration, which may be the act of prescribing a composition. Typically, an effective amount is administered, which amount can be determined by one of skill in the art. Any method of administration may be used. Compounds (e.g., drugs) can be administered to a subject. Administration to a subject can be achieved by, for example, oral delivery, parenteral delivery, intravascular injection, direct intracranial delivery, intranasal delivery, and the like.

The appropriate dose of the pharmaceutical composition is that amount effective to prevent occurrence of the symptoms of the disorder or to treat some symptoms of the disorder from which the patient suffers. By“effective amount”,“therapeutic amount” or“effective dose” is meant that amount sufficient to elicit the desired pharmacological or therapeutic effects, thus resulting in effective prevention or treatment of the disorder. The effective dose varies, depending upon factors such as the condition of the patient, the severity of the symptoms of the disorder, and the manner in which the pharmaceutical composition is administered. The effective dose of the composition differs from patient to patient but in general includes amounts starting where desired therapeutic effects occur, but below that amount where significant undesirable side effects are observed. Thus, when treating a symptom of neurological diseases, an effective amount of composition is an amount sufficient to pass across the blood-brain barrier of the subject and to interact with relevant sites in the brain of the subject, thus resulting in effective prevention or treatment of the disorder.

Pharmaceutical preparations suitable for use with the present invention include compositions wherein the purified compounds or functional analogs are present in effective amounts, i.e., in amounts effective to achieve the intended purpose, for example, treating, preventing, or reducing a symptom of neurological diseases. Of course, the actual amounts of the compounds effective for a particular application depends upon a variety of factors including, inter alia, the type of disorder being treated, and the age and weight of the subject. When administered to treat or prevent a symptom of neurological diseases, such compositions contain amounts of compound effective to achieve these results. Determination of effective amounts is well within the capabilities of those skilled in the art. The compounds can be administered in any manner that achieves the requisite therapeutic or prophylactic effect. Therapeutically or prophylactically effective doses of the compounds of the invention can be determined from animal or human data for analogous compounds that are known to exhibit similar

pharmacological activities. The applied doses are adjusted based on the relative bioavailability, potency and in vivo half-life of the administered compounds as compared with these other agents. Adjusting the dose to achieve maximal efficacy in humans based on the methods described above and other methods that are well-known is well within the capabilities of the ordinarily skilled artisan. The ratio between toxicity and therapeutic effect for a particular compound is its therapeutic index and is expressed as the ratio between LD 50 (the amount of compound lethal in 50% of the population) and ED 50 (the amount of compound effective in 50% of the population). Therapeutic index data is obtained from animal studies and used in formulating a range of dosages for use in humans. The dosage of such compounds preferably lies within a range of plasma concentrations that include the ED 50 with little or no toxicity.

The dosage varies within this range depending upon the dosage form employed and the route of administration utilized.

The phrase“pharmaceutically acceptable salt(s)” as used herein includes, but is not limited to, salts of acidic or basic groups that may be present in the natural product compounds, and hydrates thereof. Natural product compounds, and hydrates thereof that are basic in nature are capable of forming a wide variety of salts with various inorganic and organic acids. The acids that may be used to prepare pharmaceutically acceptable salts of such basic compounds are those that form salts comprising pharmacologically acceptable anions including, but not limited to, acetate, benzenesulfonate, benzoate, bicarbonate, bitartrate, edetate, camsylate, carbonate, bromide, chloride, iodide, citrate, dihydrochloride, edisylate, estolate, esylate, fumarate, gluceptate, gluconate, glutamate, glycollylarsanilate, hexylresorcinate, hydrabamine, hydroxynaphthoate, isethionate, lactate, lactobionate, malate, maleate, mandelate, mesylate, methylsulfate, muscate, napsylate, nitrate, pantothenate, phosphate/diphosphate,

polygalacturonate, salicylate, stearate, succinate, sulfate, tannate, tartrate, teoclate, triethiodide, and pamoate (i.e., l,r-methylene-bis-(2-hydroxy-3-naphthoate)). Natural product compounds, and hydrates thereof that include an amino moiety can also form pharmaceutically acceptable salts with various amino acids, in addition to the acids mentioned above. Natural product compounds, and hydrates thereof that are acidic in nature are capable of forming base salts with various pharmacologically acceptable cations. Examples of such salts include alkali metal or alkaline earth metal salts and, particularly, calcium, magnesium, sodium, lithium, zinc, potassium, and iron salts.

The term“Amyloid-beta A4 protein (Amyloid precursor protein (APP)” as provided herein includes any of Amyloid-beta A4 protein naturally occurring forms, homologs or variants that maintain the protein activity ( e.g ., within at least 50%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or 100% activity compared to the native protein). In embodiments, variants or homologs have at least 90%, 95%, 96%, 97%, 98%, 99% or 100% amino acid sequence identity across the whole sequence or a portion of the sequence (e.g., a 50, 100, 150 or 200 continuous amino acid portion) compared to a naturally occurring form. In embodiments, Amyloid-beta A4 protein is the protein as identified by UniProt P05067 or a functional fragment thereof. In embodiments, Amyloid-beta A4 protein includes the sequence of SEQ ID NO: 1. In embodiments, Amyloid- beta protein 42 (Abeta42) corresponds to the fragment of amino acid residues 672-713 of Amyloid-beta A4 protein. In embodiments, Amyloid-beta protein 42 includes the sequence of SEQ ID NO: 2. In embodiments, Amyloid-beta protein 40 (Abeta40) corresponds to the fragment of amino acid residues 672-711 of Amyloid-beta A4 protein. In embodiments, Amyloid-beta protein 42 includes the sequence of SEQ ID NO: 3.

The term“Tau protein” as provided herein includes any of Tau protein naturally occurring forms, homologs or variants that maintain the protein activity ( e.g ., within at least 50%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or 100% activity compared to the native protein).

In embodiments, variants or homologs have at least 90%, 95%, 96%, 97%, 98%, 99% or 100% amino acid sequence identity across the whole sequence or a portion of the sequence (e.g., a 50, 100, 150 or 200 continuous amino acid portion) compared to a naturally occurring form. In embodiments, Tau protein is the protein as identified by UniProt P 10636 or a functional fragment thereof. In embodiments, Tau protein includes the sequence of SEQ ID NO: 4.

A“Apolipoprotein E (ApoE) gene” as referred to herein includes any of the recombinant or naturally-occurring forms of the gene encoding Apolipoprotein E (ApoE), homologs or variants thereof that maintain ApoE protein activity (e.g., within at least 50%, 60%, 70%, 80%, 90%,

95%, 96%, 97%, 98%, 99% or 100% activity compared to ApoE). In some aspects, variants have at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99% or 100% amino acid or nucleic acid sequence identity across the whole sequence or a portion of the sequence (e.g., a 50, 100, 150 or 200 continuous amino acid or nucleic acid portion) compared to a naturally occurring ApoE polypeptide or ApoE nucleotide. For example, three types of the APOE gene (alleles), ApoE2, E3 and E4 (or ApoE epsilon4 or ApoE e4), have been identified. In embodiments, the ApoE gene is substantially identical to the nucleic acid identified by the NCBI reference number Gene ID: 348 (ApoE4) or a variant having substantial identity thereto. EXAMPLES

Example 1. Demographics

Participants characteristics are described in Table 1. They were classified into four groups according to their brain tau Braak staging (25 Braak stage 0, 28 stage I-II, 15 stage III-IV, and 8 stage V-VI; Table 2). There were no differences in gender or education level among the four groups (gender, /? = 0.628, chi-squared test; education level , p = 0.788, ANOVA with Tukey’s post hoc test; Table 2), but age was significantly different among groups (**p = 0.002, ANOVA with Tukey’s post hoc test; Table 2). CN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer’s disease; MMSE z-score, mini-mental state examination with the correction for age, sex, and education level; CDR, clinical dementia rating; ApoE,

Apolipoprotein e4.

Example 2. Quantification of plasma tau and Ab

Overnight fasting blood samples (70 mL, n = 76) were collected in the morning by venipuncture and collected in K2 EDTA tubes. They were stabilized and centrifuged (700g, 5 min, room temperature) to obtain plasma and serum supernatants, and buffy coat. To obtain samples with high purity, the plasma and serum supernatants were further centrifuged under the same conditions, and the collected pure plasma and serum supernatants were aliquoted and immediately stored at -80°C. The levels of plasma total tau (n = 75) and p-tau (Thr 181, n =

51) were measured using the Simoa Human tau immunoassay (Total Tau 2.0 or Phospho-Tau Thr 181 immunoassay) kits on the Simoa HD-l Analyzer (Quanterix, Lexington, MA, USA) at Seoulin Bioscience (Seongnam, Gyeonggi-do, South Korea). Plasma Ab levels were determined by xMAP technology (Bioplex 200 System; Bio-Rad, Hercules, CA, USA) using an INNO-BIA plasma Ab forms kit (Fujirebio Diagnostics, Ghent, Belgium) and a modified, stable quantification method employing MPP (mixture of protease inhibitors and phosphatase inhibitors).

Example 3. Relationship between plasma tau-related biomarkers and brain tauopathy

The levels of all four plasma tau-related biomarkers (p-tau, t-tau, p-tau/Ab _1-42 , and t- tauM^i-42) significantly increased as brain tauopathy progressed (Braak stage 0 to VI) (FIG. 1 A; *p < 0.05, **p < 0.01, and ***p < 0.001; ANOVA with Tukey’s post hoc test; ^& p < 0.05 and ^&& p

< 0.01; unpaired /-test). To evaluate the link between plasma tau-related biomarkers and brain tau burden in an AD-signature region-of-interest (ROI), the partial correlation analyses after correcting for covariates (age and sex) was conducted (FIG. 1B). All markers showed a highly significant correlation with tau burden in brain AD-signature ROIs (*p < 0.05, **p < 0.01, * * *p

< 0.001, and ****/? < 0.0001; partial correlation plot).

Example 4. High performance of plasma tau-related biomarkers for discriminating tau PET- negative and PET-positive subjects - Receiver Operating Curve

To evaluate the performance of plasma tau-related biomarkers in predicting brain tau burden, a logistic regression followed by receiver operating curve (ROC) was conducted. For these analyses, subjects were divided into two groups: a tau-PET-negative (Tau-PET-)/Braak 0- II staging group, and a tau-PET-positive (Tau-PET+)/Braak III- VI staging group. Subjects were grouped accordingly because levels of plasma tau-related biomarkers (especially t-tau/Abi- 42) were dramatically higher in both Braak III/IV and V/VI groups compared with Braak 0 and I/II groups, and there were no significance differences in biomarkers between Braak 0 and I/II or Braak III/IV and V/VI (FIG. 1 A). In addition, the levels of plasma tau-related biomarkers were significantly higher in Tau-PET+ subjects compared with Tau-PET- subjects (*p < 0.05, **p < 0.01, and ****p < 0.0001, unpaired /-test; FIG. 5B). In particular, levels of the t-tau/Ab I -42 showed a relatively clear dichotomy between Tau-PET- and Tau-PET+ subjects (FIG. 5B).

Four logistic regression models using plasma tau-related biomarkers were generated, corrected for covariates (age and sex), and used predicted probabilities as independent values for ROC models. All four ROC models— p-tau, t-tau, p-tau/Ab _1-42 , and t-tau/Ab _1-42 — showed high performance (**p = 0.0029 for p-tau, = 0.0003 for p-tau/ Abi-42, and ****/? < 0.0001 for t- tau and t-tau/Ab _1-42 ) for discriminating Tau-PET- and Tau-PET+ subjects (FIG. 5C and FIG. 5D; Table 3). Furthermore, a comparison of ROC models showed that AUC value for t-tau/Ab _1-42 (AUC, 0.890; sensitivity, 80.0%; specificity, 91.4%) was dramatically higher than those for t-tau (AFTC, 0.802; sensitivity, 93.3%; specificity, 62.9%) only (*p < 0.05, comparison of ROC analyses). Details are presented in Table 3.

Example 5. High performance of plasma tau-related biomarkers for discriminating tau PET- negative and PET-positive subjects - Relative Risk

To evaluate the performance of plasma tau-related biomarkers in predicting brain tau burden, a logistic regression followed by relative risk (RR) analyses was conducted. For these analyses, subjects were divided into two groups: a tau-PET-negative (Tau-PET-)/Braak 0-II staging group, and a tau-PET-positive (Tau-PET+)/Braak III- VI staging group. Subjects were grouped accordingly because levels of plasma tau-related biomarkers (especially t-tau/Ab! -42) were dramatically higher in both Braak III/IV and V/VI groups compared with Braak 0 and I/II groups, and there were no significance differences in biomarkers between Braak 0 and I/II or Braak III/IV and V/VI (FIG. 1A). In addition, the levels of plasma tau-related biomarkers were significantly higher in Tau-PET+ subjects compared with Tau-PET- subjects (*p < 0.05, **p < 0.01, and ****p < 0.0001, unpaired /-test; FIG. 3B). In particular, levels of the t-tau/Ab! -42 showed a relatively clear dichotomy between Tau-PET- and Tau-PET+ subjects (FIG. 5B).

Relative risk (RR) analyses were conducted using predicted probabilities generated by logistic regression models (FIG. 5E; Table 4). Subgroups were categorized into quartiles (Q) in order of their plasma tau- related biomarker levels (Ql < Q2 < Q3 < Q4). The fraction of Tau- PET+ subjects in Q4 was dramatically higher than that in Ql (p-tau, t-tau, and p-tau/Ab _1-42 : *p = 0.0410, RR = 7.4; t-tau/Ab _1-42 : *p = 0.0140, RR = 10.8). These results indicate that subjects with low levels of plasma tau-related biomarkers show a higher risk of Tau-PET positivity.

Example 6. Plasma tau/amyloid^i-42 ratio predicts brain tau deposition and

neurodegeneration in Alzheimer’s disease

One of the hallmarks of Alzheimer’s disease is abnormal deposition of tau proteins in the brain. Although plasma tau has been proposed as a potential biomarker for Alzheimer’s disease, a direct link to brain deposition of tau is limited. Here, the amount of in vivo tau deposition in the brain by PET imaging was estimated, and plasma levels of total tau (t-tau), phosphorylated tau (p-tau, T181) and amyloid-Pi-42 was measured. Significant correlations of plasma p-tau, t-tau, p-tau/amyloid-bi -42, and t-tau/amyloid-Pi-42 with brain tau deposition in cross-sectional and longitudinal manners were found. In particular, t-tau/amyloid-Pi-42 in plasma was highly predictive of brain tau deposition, exhibiting 80% sensitivity and 91% specificity. Interestingly, the brain regions where plasma t-tau/amyloid-Pi-42 correlated with brain tau were similar to the typical deposition sites of neurofibrillary tangles in Alzheimer’s disease. Furthermore, the longitudinal changes in cerebral amyloid deposition, brain glucose metabolism, and hippocampal volume change were also highly associated with plasma t- tau/amyloid-Pi-42. These results indicate that combination of plasma tau and amyloid-Pi-42 levels might be potential biomarkers for predicting brain tau pathology and neurodegeneration.

Alzheimer’s disease is the most common neurodegenerative disease in the elderly population. Cerebral accumulation of the amyloid-b peptide and neurofibrillary tangles (NFTs) of tau are the main pathological hallmarks of Alzheimer’s disease, and are closely related to the neurodegenerative mechanism through interacting with binding partners that lead to the toxicity and disruption of neurons and synapses during its pathogenesis (Ballatore et al, 2007, Nat. Rev. Neurosci., 8: 663-72; Han et al., 2016, Prog. Neurobiol., 137: 17-38). Tau in particular is known to play a critical role in Alzheimer’s disease pathogenesis through its association with amyloid-b, and several lines of evidence have suggested tau-dependent amyloid-b toxicity and a feedback loop connecting tau and amyloid- b during the pathogenesis of Alzheimer’s disease (Rapoport et al, 2002, Proc. Natl. Acad. Sci. USA, 99: 6364-9).

Tau is encoded by the MAPT (microtubule associated protein tau) gene and exists in the human brain as six isoforms that differ in their amino terminal inserts and microtubule binding domain repeats (Goedert et al., 1989, EMBO I, 8: 393-9). Tau acts through its microtubule binding domain repeats that promote tubulin assembly and stabilize microtubule structure as well as function (Weingarten et al., 1975, Proc. Natl. Acad. Sci. USA, 72: 1858-62). Tau contains a number of phosphorylation sites, and its phosphorylation status influences its effects on microtubule assembly (Lindwall and Cole, 1984, J. Biol. Chem., 259: 5301-5). Whether tau is the critical contributor to Alzheimer’s disease pathogenesis is a question that has been undergoing a recent reassessment because of the disappointing results of the clinical trials of amyloid-b-targeting therapeutic strategies based on the amyloid hypothesis (Kametani and Hasegawa, 2018, Front. Neurosci., 12: 25). PET imaging is a valuable technique for monitoring brain tau pathology, and a variety of recently developed tau radiotracers for identification of neurofibrillary pathology through PET imaging provide great Alzheimer’s disease diagnostic and prognostic potential (Saint- Aubert et al., 2017, Mol. Neurodegener., 12: 19). Although PET imaging of tau offers a wealth of information and reflects Alzheimer’s disease pathology fairly well, PET instrumentation is unavailable in many clinical settings and PET imaging is associated with high costs and concerns of radiation hazards.

Therefore, there is an unmet need for a more convenient and accessible method for detecting and monitoring brain tau deposition.

The association between tau levels in the CSF of Alzheimer’s disease pathogenesis is consistently reported in the literature. Increased levels of tau in CSF are detected in patients with Alzheimer’s disease dementia (Vandermeeren et al., 1993, J. Neurochem., 61 : 1828-34).

In addition, the concentration of total tau (t-tau) and hyperphosphorylated tau (p-tau, T181), together with amyloid-Pi-42, in CSF predicts incipient Alzheimer’s disease dementia among patients with mild cognitive impairment (MCI) with high sensitivity and specificity (Hansson et al., 2006, Lancet Neurol., 5: 228-34).

The aim of the current study, therefore, was threefold. First, the levels of plasma t-tau, p-tau, and amyloid-Pi-42 was quantified and the relationship between the degree of brain tau deposition observed on tau-PET with these plasma markers was examined. Also, which form of tau level in plasma (t-tau or p-tau) has stronger correlation with Alzheimer’s disease- associated tauopathy in brain was tested. Second, the association between Alzheimer’s disease- associated tauopathy seen on tau-PET and the composite biomarkers of plasma tau and amyloid- bΐ -42, such as p-tau/amyloid-Pi-42 and t-tau/amyloid-Pi-42 was examined. Also, the ability of the composite plasma biomarkers in discriminating tau-PET positivity was examined. The idea that considering plasma amyloid-Pi-42 level together with plasma t-tau or p-tau level would result in better association with Alzheimer’s disease-associated tauopathy and increase specificity in discriminating tau-PET positivity was tested. Lastly, the effectiveness of the composite plasma biomarkers for predicting neuropathological changes was examined over 2 years.

Materials and methods used in Example 6. Details of the methods are also described in Park et al, 2017. Alz. Res. Ther, 9:20, doi: lO. H86/sl 3195-017-0248-8; and Park et al, 2019. Brain. 2019, 142, 3, 771-786,

https://doi.org/l0. l093/brain/awy347, the contents of which are incorporated herein by reference.

Subjects: seventy-six subjects (52 cognitively normal, nine MCI, 15 Alzheimer’s disease dementia) participated in this study. Details of inclusion and exclusion criteria are described previously (Byun et al., 2017, Psychiatry Invest., 14: 851-63). Briefly, all cognitively normal individuals had Clinical Dementia Rating (CDR) global score of 0 and no diagnosis of MCI or dementia. MCI individuals had CDR of 0.5 and met the inclusion criteria based on core clinical criteria for diagnosis of MCI according to the recommendations of the NIA-AA guidelines (Albert et al., 2011, Alzheimers Dement., 7: 270-9). Participants with Alzheimer’s disease dementia had CDR score 5 0.5 and met the inclusion criteria for dementia in accordance with the Diagnostic and Statistical Manual of Mental Disorders 4th edition (DSM-IV-TR) (American Psychiatric Association, 2000) and the criteria for probable Alzheimer’s disease dementia in accordance with the NIA-AA guidelines (McKhann et al., 2011, Alzheimers Dement., 7: 263-9). Individuals with major psychiatric illness or significant neurological or medical conditions or comorbidities that could affect mental functioning were not included in the study. All participants or their legal representatives provided written informed consent to participate in this study after receiving a complete description of the study, which is approved by Seoul National University Hospital Institutional Review Board. All assessments for the subjects except tau-PET scan were taken twice (at baseline and 2-year follow-up). Tau- PET scans were performed only at the 2-year follow-up.

Clinical and neuropsychological assessments: all participants were administered standardized clinical assessments by trained board-certified psychiatrists based on the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s disease (KBASE) clinical assessment protocol which incorporated the CERAD-K clinical assessment (Lee et al., 2002, J. Gerontol. B Psychol. Sci. Soc. Sci., 57: 47-53). All subjects were also given a comprehensive neuropsychological assessment battery, administered by a clinical

neuropsychologist or trained psychometrists according to a standardized protocol incorporating the CERAD-K neuropsychological battery (Lee et al., 2004, J. Int. Neuropsychol. Soc., 10: 72- 81). Details on full assessment battery are described previously (Byun et al., 2017, Psychiatry Invest., 14: 851-63.). Genomic DNA was extracted from whole blood and apolipoprotein (APOE) genotyping was performed (Wenham et al., 1991, Lancet, 337: 1158-9); subjects with at least one e4 allele were identified as ApoE4 carriers.

Neuroimaging data: participants underwent PET magnetic resonance scanning sessions to obtain multimodal imaging including l8F-fluorodeoxyglucose (FDG)-PET, MRI using 3.0 T Biograph mMR (PET-MR) scanner (Siemens). They also underwent simultaneous 3D 11C- Pittsburgh compound B (PiB)-PET and 3D Tl -weighted MRI using the 3.0 T PET-MR scanner, and 18FAV-1451 PET scans (Siemens) according to the manufacturer’s approved guidelines.

MRI acquisition and processing: Tl -weighted images were acquired in the sagittal orientation and the parameters were as follows: repetition time = 1670 ms, echo time = 1.89 ms, field of view 250 mm, and 256 X 256 matrix with 1.0 mm slice thickness. All MRI were automatically segmented using FreeSurfer version 6.0 (http://surfer.nmr.mgh.harvard.edu/) with manual correction of minor segmentation errors. Based on the Desikan-Killiany atlas (Desikan et al, 2006, Neuroimage, 31 : 968-80), mean cortical thickness values were obtained from Alzheimer’s disease signature regions, including the entorhinal, inferior temporal, middle temporal, and fusiform gyrus according to a previous study (Jack et al, 2014, Lancet Neurol.,

13: 997-1005). Adjusted hippocampal volume was calculated as the residuals from linear regression of hippocampal volume against total intracranial volume of young normal controls as the reference group ( n = 73, age range = 20-55) (Jack et al, 2014, Lancet Neurol., 13: 997- 1005); it is interpreted as the deviation in cubic millimetres in a participant’s hippocampal volume from what is expected based on their head size.

FDG-PET acquisition and processing: the participants fasted for at least 6 h and rested in a waiting room for 40 min prior to the scans after intravenous administration of 0.1 mCi/kg of 18F-FDG radioligands. The PET data collected in list mode (5 min X four frames) were processed for routine corrections such as uniformity, ultrashort echo time (UTE)-based attenuation, and decay corrections. After inspecting the data for any significant head movements, they were reconstructed into a 20-min summed image using iterative methods (six iterations with 21 subsets). The following image processing steps were performed using SPM12 (http://www.fil.ion.ucl.ac.uk/spm) implemented in MATLAB 20l4a (Mathworks, Natick, MA, USA). First, static FDG-PET images were coregistered to individual Tl structural images that were simultaneously taken, and transformation parameters for the spatial normalization of individual Tl images to a standard MNI template were calculated and used to spatially normalize the PET images to the MNI template. After smoothing the spatially normalized FDG-PET images with a l2-mm Gaussian filter, intensity normalization was performed using the pons as the reference region. ETsing the automated anatomical labelling (AAL) atlas (Tzourio-Mazoyer et al., 2002, Neuroimage, 15: 273-89), standard uptake value ratio (SETVR) values were extracted from angular gyrus, posterior cingulate cortex, precuneus, and inferior temporal gyrus to derive a voxel-weighted mean region of interest (i.e. FDG 4roi).

Tau-PET acquisition and processing: 18F-AV-1451 PET scans were performed for 10 min on a Biograph True point 40 PET/CT scanner (Siemens) as dynamic scans using LIST-mode 80 min after an injection of 370 MBq of 18F-AV-1451. Low-dose CT scans for attenuation correction were performed in the same patient position immediately prior to the PET scans. Iterative (OSEM3D+PSF) True X algorithm was used for PET data reconstruction with 24 subsets, six iterations with 3 mm Gaussian filter. Average interval between blood samples and 18F-AV-1451 PET imaging was 135 days (range 7-277 days). To measure tau protein, 18F-AV- 1451 PET SETVR images were created based on the mean uptake over 80 to 100 min post injection, normalized by the mean inferior cerebellar grey matter uptake. The SETVR images were coregistered and resliced into structural MRIs. To account for partial volume effects due to atrophy and signal spillover, the Geometric Transfer Matrix approach was used for partial volume correction based on FreeSurfer-derived regions of interest from the Tl taken at the follow-up visit, including corrections for extracerebral tissue as described previously (Baker et al., 2017, Data Brief, 15: 648-57; Jack et al., 2017, Alzheimers Dement., 13: 205-16).

Voxelwise analyses used SETVR images (with partial volume correction) that were transformed into MNI- 152 space. For the region of interest analyses, the data were extracted from native space, according to the method published by Baker et al. (2017, Data Brief, 15: 648-57). 18F-

AV-1451 PET uptake was quantified by grouping together regions of interest that corresponded to the pathological stages of tau protein tangle deposition in Alzheimer’s disease described by Braak and Braak (1995, Neurobiol. Aging, 16: 271-8; discussion 8-4). The regions grouped in each Braak stage region of interest have been published previously (Baker et al., 2017, Data Brief, 15: 648-57; Maass et al., 2017, Neuroimage, 157: 448-63). Weighted mean SETVR in native space (after partial volume correction) was calculated to form three composite regions of interest that roughly correspond to anatomical definitions of Alzheimer’s disease Braak stages 0, stage I/II, stage III/IV, and stage V/VT Participants were then categorized into Alzheimer’s disease Braak stages according to thresholds that were based on Braak region of interest-specific tracer uptake (Maass et al, 2017, Neuroimage, 157: 448-63). A priori region of interest of ‘Alzheimer’s disease signature’ regions of tau accumulation was also included in these analyses, which is a size-weighted average of partial volume corrected uptake in entorhinal, amygdala, parahippocampal, fusiform, inferior temporal and middle temporal regions of interest, based on previous report (Jack et al., 2017, Alzheimers Dement., 13: 205-16).

PiB-PET acquisition and processing: participants underwent simultaneous 3D HC-PiB- PET and 3D Tl -weighted MRI using the 3.0 T PET-MR scanner. After intravenous administration of 555 MBq of HC-PiB (range, 450-610 MBq), a 30-min emission scan was obtained 40 min after injection. The PiB-PET data collected in list mode were processed for routine corrections such as uniformity, UTE-based attenuation, and decay corrections, and were reconstructed into a 256 X 256 image matrix using iterative methods (six iterations with 21 subsets). For each participant, inverse transformation parameter was obtained from SPM12 D ARTEL segmentation procedure using individual Tl that was taken at the same day as the PiB- PET and MNI template. Obtained inverse transformation parameters were applied to AAL atlas to acquire AAL atlas in native space for each participant, which were then used to extract PiB retention levels. To improve intensity normalization process, retention in cerebellum was separately extracted using a spatially unbiased atlas template of the cerebellum and brainstem (SUIT) (Diedrichsen et al., 2011, Neuroimage, 54: 1786-94); retention in cerebellar grey matter was used for intensity normalization. PiB retention index as SUVR for each region of interest was calculated by dividing regional mean value by the individual mean cerebellar uptake values. The AAL algorithm (Rolls et al., 2015, Neuroimage, 122: 1-5) and a region combining method (Reiman et al, 2009, Proc. Natl. Acad. Sci. USA, 106: 6820-5) were applied to set the regions of interest to characterize PiB retention level in frontal, lateral parietal, posterior cingulate- precuneus, and lateral temporal regions. Each participant was classified as amyloid-P-positive if the SUVR value was 41.4 in at least one of the four regions of interest or as amyloid-b- negative if the SUVR values of all four regions of interest was 41.4 (Villeneuve et al, 2015, Brain, 138 (Pt 7): 2020-33). Also, global weighted region of interest was calculated to represent in vivo global PiB retention level of the four abovementioned regional regions of interest. Longitudinal imaging data processing: the same processing methods were applied to the follow-up imaging data (i.e. MRI, FDG-PET, and PiB-PET) as the baseline.

Plasma tau and amyloid-b measurements: overnight fasting blood samples (70 ml, n =

76) were collected in the morning by venipuncture and were collected in K2 EDTA tubes. They were stabilized and centrifuged (700g, 5 min, room temperature) to obtain plasma and serum supernatants, and huffy coat. To obtain samples with high purity, the plasma and serum supernatants were further centrifuged under the same conditions, and the collected pure plasma and serum supernatants were aliquoted and immediately stored at -80°C. The levels of plasma t-tau and p-tau (Thrl8l) were measured using the Simoa Human tau immunoassay (Total Tau 2.0 or Phospho-Tau Thr 181 immunoassay) kits on the Simoa HD-l Analyzer (Quanterix) at Seoulin Bioscience. Plasma p-tau was quantified in only 51/76 individuals and plasma t-tau in 75/76 individuals. The results associated with plasma t-tau (t-tau or t-tau/amyloid-b _1-42 ) remained essentially similar when restricted to the 50 individuals having all the measurements including plasma amyloid-b _1-42 . Plasma amyloid-b _1-42 levels (n = 76) were determined by xMAP technology (Bio-Plex ^® 200 System; Bio-Rad) using an INNO-BIA plasma amyloid-b forms kit (Fujirebio Diagnostics) and a modified, stable quantification method using MPP (mixture of protease inhibitors and phosphatase inhibitors), as described previously (Park et al., 2017, Alzheimers Res. Ther. 9: 20). The measurement of plasma Ab ₄ 2 as described herein was performed by treating with the mixture of protease inhibitors and phosphatase inhibitors (MPP), which allows stabilization of plasma Ab, followed by measuring (see, e.g., Park et al, 2017. Alz. Res. Ther., 9:20, doi: 10.1186/S13195-017-0248-8 and Korean patent KR 10-1786859, the contents of which are incorporated herein by reference.

Statistical analysis: data analyses were performed using Medcalc 17.2 (Medcalc

Software, Ostend, Belgium) and GraphPad Prism 7 (GraphPad Software, San Diego, CA, USA). For demographic tables, categorical data (sex, CDR score, APOE4 positivity, cognitive function states) were analysed using the chi-square (c ² ) test, and numerical data (age, education, MMSE z-score, CERAD-K scores, and plasma biomarker levels) were analysed using ANOVA with Tukey’s post hoc test. To mirror the association analyses conducted on numerical variables, voxel wise linear regression analyses were run between partial volume corrected 18F-AV-1451 PET SUVR images and plasma tau biomarkers using the Voxelstats toolbox (Mathotaarachchi et al, 2016, Front. Neuroinform., 10: 20), displaying the results using a Puncorrected < 0.005 at the voxel level combined with a PFWE-corrected < 0.05 at the cluster level. To simplify the interpretation of the voxel-wise results, T-maps were transformed to correlation coefficient maps (for PET and plasma tau associations). The relationship between plasma biomarkers and brain tau was determined by partial correlation analyses, with a correction for the effects of covariates (age and sex). Optionally, variables were log-transformed to control for the skewness and to normalize the variance (Han et al, 2018, Neurobiol. Aging, 73: 21-9). Logistic regression analyses followed by receiver operating curve (ROC) and relative risk analyses were also performed (restricted to the 50 individuals having all the measurements). Specifically, plasma biomarkers (p-tau, t-tau, p-tau/amyloid-b _1-42 , and t-tau/amyloid-b _1-42 ) and covariates (age and sex) were appropriately integrated and the predicted probabilities from each regression model were generated. Each was then used as an independent variable for the ROC models to predict tau PET positivity (Tau-PET ⁺ , Braak stage III to VI subjects; Tau-PET , Braak stage 0 to II subjects) or for the relative risk analyses. Of note, Tau-PET ⁺ versus Tau-PET was set to be between Braak stages 0-II and III- VI based on previous reports that showed significant early tau aggregation associated with Alzheimer’s disease dementia in the stage III (Mattsson et al, 2017, EMBO Mol. Med., 9: 1212-23), which is the first stage where the Alzheimer’s disease-related tau pathology extends into the hippocampal formation (Rub et al, 2017, 1. Alzheimers Dis., 57: 683-96). For the relative risk analyses, participants were categorized into two groups: (i) subjects were sorted in ascending order of their levels of each predicted probabilities using plasma tau-related biomarkers; (ii) plasma marker low group (Subjects 1-25) was named‘group 1’ and high group (Subjects 26-50) was named‘group 2’). All experimental procedures and statistical analyses were carried out in a blind manner.

Demographics

Characteristics of the participants are described in Table 1 (n = 76; 52 cognitively, nine MCI, 15 Alzheimer’s disease dementia). As previously mentioned, tau-PET scans were performed only at the follow-up, whereas all the other assessments were taken twice (at baseline and 2-year follow-up), thus, Table 5 presents the demographic data based on the 2 year follow up. FIG. 14, Table 8 includes the data of longitudinal changes. Furthermore, subjects were classified into four groups according to brain tau Braak staging (25 Braak stage 0, 28 stage I— II,

15 stage III-IV, and eight stage V-VI; FIG. 15, Table 9) (Braak and Braak, 1995, Neurobiol. Aging, 16: 271-8; discussion 8-4). There were no significant gender or education differences among the four groups, but age was significantly different among the groups (FIG. 15, Table 9).

Relationships between plasma tau-related biomarkers and brain tauopathy

The levels of all four plasma tau-related biomarkers (p-tau, t-tau, p-tau/amyloid-b _1-42 , and t-tau/amyloid-b _1-42 ) significantly increased as brain tauopathy progressed (Braak stages 0 to VI) (FIG. 8A). To evaluate the link between plasma tau-related biomarkers and brain tau burden in an Alzheimer’s disease signature region of interest, partial correlation analyses were conducted after correcting for covariates (age and sex) (FIG. 8B). All markers showed highly significant correlations with tau burden in the Alzheimer’s disease signature region of interest in the brain (FIG. 8B). FIG. 16, Table 10 shows all possible correlations between markers and tau burden in each individual brain region. FIGS. 9A-9D represent voxel- wise associations between each of the plasma tau biomarkers and regional brain tau burden. While higher plasma p-tau and t-tau values were associated with higher brain tau deposition only in the medial temporal regions (FIG. 9A and FIG. 9B), plasma p-tau/amyloid-b _1-42 and t-tau/amyloid-b _1-42 ratios showed positive correlations with tau deposition in diffuse brain regions including the cingulate, lateral temporal, frontal, and parietal cortices as well as the medial temporal regions (FIG. 9C and FIG. 9D). In particular, the brain regions where plasma t-tau/amyloid-b _1-42 ratio correlated with brain tau were very similar to the typical deposition sites of neurofibrillary tangles in Alzheimer’s disease (FIG. 9D).

High performance of plasma tau-related biomarkers for discriminating tau PET-negative and PET-positive subjects

To evaluate the performance of plasma tau-related biomarkers in predicting brain tau burden, a logistic regression analysis was conducted followed by ROC and relative risk analyses (FIG. 10A and FIG. 10B). For these analyses, subjects were divided into two groups: Tau-PET and Tau-PET ⁺ (FIG. 10A). There were no significance differences in biomarkers between the groups within Tau-PET or Tau-PET ⁺ (i.e. between Braak stages 0 and I/O, Braak III/IV and V/VI, respectively) (FIG. 8A). In addition, the levels of plasma tau-related biomarkers were significantly higher in Tau-PET ⁺ subjects compared with Tau-PET subjects (FIG. 10B). In particular, the t-tau/amyloid-b _1-42 levels showed a relatively clear dichotomy between Tau-PET and Tau-PET ⁺ subjects (FIG. 10B). Four logistic regression models were generated using plasma tau-related biomarkers, corrected for covariates (age and sex), and predicted probabilities were used as the independent values for the ROC models. All four ROC models - p-tau, t-tau, p-tau/amyloid-b _1-42 , and t-tau/amyloid-b _1-42 - showed significantly high performance for discriminating Tau-PET and Tau-PET ⁺ subjects (FIG. 10C, FIG. 10D and Table 6).

Furthermore, the comparison of ROC models showed that AFiC value for t-tau/amyloid-b _1-42 (AUC, 0.890; sensitivity, 80.0%; specificity, 91.4%) was dramatically higher than those for t-tau (AUC, 0.802; sensitivity, 93.3%; specificity, 62.9%) (Table 6).

Finally, relative risk analyses were conducted using predicted probabilities generated by the logistic regression models (FIG. 10E and Table 7). Two groups were categorized by their plasma tau-related biomarker levels (group 1 < group 2; refer to‘Materials and methods’ section). The fraction of Tau-PET ⁺ subjects (i.e. relative risk value) in group 2 was

significantly higher than that in group 1 (t-tau, relative risk = 6.5, P < 0.01 ; p-tau/amyloid-b _1-42 , relative risk = 2.8, P < 0.05; t-tau/amyloid-b _1-42 , relative risk = 14.0, P < 0.01) except for p-tau (relative risk = 2.0, p = 0.1). These results indicate that subjects with high levels of plasma tau- related biomarkers show a higher risk for Tau-PET positivity. The relative risk values increased in the following order, p-tau, p-tau/amyloid-b _1-42 , t-tau, t-tau/amyloid-b _1-42 ; and, this pattern was in accordance with the AFiC values from the ROC curve analysis (p-tau < p-tau/amyloid-b _1-42 < t-tau < t-tau/amyloid-b _1-42 ) as shown in FIG. 10C and FIG. 10D. Furthermore, an additional analysis (c ² test) were carried out in order to check for the group composition differences (group 1 versus group 2) (Table 7). It showed increased c ² (with decreased P-value) when amyloid-b was added to both p-tau and t-tau (c ² , p-tau < p-tau/amyloid-b _1-42 < t-tau < t-tau/amyloid-b _1-42 ). This pattern, moreover, exactly matched the results of the ROC curve analysis (AFiC, p-tau < p- tau/amyloid-b _1-42 < t-tau < t-tau/amyloid-b _1-42 ) as shown in FIG. 10C and FIG. 10D.

The level of plasma t-tau/amyloid-b _1-42 predicts the neuropathological changes in the brain over 2 years

To evaluate the effectiveness of plasma t-tau/amyloid-b _1-42 ratio as a biomarker for predicting the longitudinal changes in the brain, the plasma levels of t-tau/amyloid-b _1-42 at baseline were compared with the neuropathological changes for two years. The changes were represented as delta (A) (Fig. 13 A). Similar to the cross-sectional study at 2-year follow-up time point (FIG. 18D), the plasma t-tau/amyloid-b _1-42 ratio (at baseline time point) had strong correlation with brain neuropathological changes such as hippocampal volume (Ahippocampal volume, P < 0.05, r =-0.25; FIG. 13B), cerebral amyloid deposition (DRίBRET, P < 0.01, r =

0.37; Fig. 13C), and cerebral glucose metabolism (AFDG-PET, posterior cingulate cortex, P < 0.05, r =-0.30; 4roi, P < 0.01, r =-0.33; FIG. 13D and FIG. 13E). The results suggest that the composite biomarker t-tau/ amyloid-b _1-42 is a useful marker for predicting progressive neurodegeneration as well as reflecting cross-sectional brain states.

The changes in plasma t-tau/amyloid-Pi-42 for 2 years reflect tau-PET results at the 2-year follow-up time point

The association between the 2-year difference of plasma biomarkers [plasma delta (D)] and 2-year brain tau accumulation was examined (FIG. 13F and FIG. 13G). Interestingly, plasma delta tau had a correlation with Alzheimer’s disease signature region of interest tau deposition (P < 0.10, r =0.21; a trend toward significance; FIG. 13G, left). The plasma delta t- tau/amyloid-b _1-42 also significantly correlated with Alzheimer’s disease signature region of interest tau deposition (P < 0.01, r =0.37; Fig. 13G, right). Taken together, the results suggest that the changes in the levels of plasma t-tau and t-tau/ amyloid-b _1-42 over 2 years may predict the accumulation of brain tau at the 2-year time-point. Its statistical significance was relatively lower than the correlation between 2-year brain tau accumulation measured at the 2-year follow up and 2-year plasma t-tau as well as t-tau/amyloid-b _1-42 (Fig. 8B; P < 0.01, r = 0.32 for t-tau; P < 0.0001, r = 0.52 for t-tau/amyloid-b _1-42 ). Nevertheless, this observation offers meaningful information as there were no previous reports showing the association between longitudinal plasma tau changes and brain tau accumulation.

Plasma t-tau/amyloid-Pi-42 did not reflect the neuropathologies in the brain at baseline time point

Given that the plasma t-tau/amyloid-b _1-42 marker reflects neurodegeneration, brain amyloid, and brain tau accumulation both cross-sectionally (2-year time point) and longitudinally (over 2 years), it was investigated whether this composite biomarker (at baseline time point) also correlates with various baseline brain images such as FDG-PET, hippocampal volumes, and PiB- PET; tau-PET scans were not performed at baseline. Partial correlation analyses between baseline plasma t-tau/amyloid-Pi- ₄ 2 and baseline imaging markers (FIG. 21A) were performed. There were no correlations between plasma t-tau/amyloid-b _1-42 and these imaging markers (FIG. 21 A), except for a trend toward significance of the association between PiB-PET SUVR and plasma t-tau/amyloid-b _1-42 (P = 0.1, r = 0.2; FIG. 21 A, bottom right).

Plasma amyloid-Pi- ₄₂ also correlates with brain tau accumulation

Although plasma amyloid-b _1-42 has been implicated as one of the biomarkers for Alzheimer’s disease diagnosis, there are many concerns about instability of amyloid-b in the blood. Hence, an improved quantification method for plasma amyloid-b _1-42 was used in this study ( i.e . treatment of mixture of protease inhibitors and phosphatase inhibitors, MPP) (Park el al., 2017, Alzheimers Res. Ther. 9: 20). Plasma amyloid-b _1-42 also correlates with not only cerebral amyloid-b deposition (FIG. 19B, P < 0.001) but also brain tau accumulation (FIG. 19C, P < 0.01). Furthermore, a logistic regression analysis followed by ROC curve analysis for plasma amyloid^i-42 only was performed (covariates: age and sex) (FIG. 20); plasma amyloid- bi— 42 only also had high AUC value (0.800 with 66.67% sensitivity and 82.86% specificity), which is even higher than that of p-tau/amyloid^i-42 (0.766 with 93.33% sensitivity and 51.43% specificity). However, there was a large difference between the AUC value of plasma amyloid- bi— 42 only (0.800) and that of plasma t-tau/amyloid^i-42 (0.890 with 80.00% sensitivity and 91.43% specificity), suggesting that the combination of t-tau and amyloid^i-42 is the most powerful biomarker compared to the others.

In the current study, plasma t-tau, p-tau, and amyloid^i-42 levels were quantified in cognitively normal, MCI, and Alzheimer’s disease dementia individuals who underwent 11C- PiB-PET and 18F-AV-1451 tau PET imaging. Plasma t-tau and p-tau were strongly and positively associated with the degree of brain tau deposition observed on tau-PET, whereas plasma amyloid^i-42 was negatively correlated with brain tau accumulation. The composite biomarker of plasma tau and amyloid^i-42, including p-tau/amyloid^i-42 and t-tau/amyloid-bi- 42, represented more significant correlations with tau-PET and Alzheimer’s disease-associated tau pathology compared to a single biomarker of plasma tau. Plasma t-tau/ amyloid^i-42 showed the strongest association with brain tau accumulation, exhibiting an increased AUC and relative risk. In addition, the brain regions where plasma t-tau/amyloid^i-42 ratio correlated strongly with brain tau were very similar to the typical deposition sites of neurofibrillary tangles in Alzheimer’s disease. Furthermore, it was found that baseline plasma t-tau/amyloid-Pi- ₄ 2 was highly associated with the longitudinal changes of amyloid-b accumulation in the brain, cerebral glucose metabolism, and hippocampal volume over 2 years. Collectively, these findings indicate that plasma t-tau/amyloid-b _1-42 level is a potential biomarker for predicting Alzheimer’s disease-associated tau pathology in the brain. This study is the first to report the relationship among plasma tau level, plasma amyloid-b level and brain tau accumulation on in vivo PET images. Moreover, longitudinal examinations of plasma tau levels and plasma amyloid-b levels have not been done, especially in relation to various Alzheimer’s disease-associated

neurodegeneration markers before this study.

In this study, there was no significant correlations between composite marker of plasma t- tau/amyloid^i-42 (at baseline time point) and various baseline brain image markers such as FDG-PET, hippocampal volumes, and PiB-PET (at baseline time point) in partial correlation analyses. Lack of significant correlations are conceivable given that the baseline KBASE cohort samples used (n = 16) are mostly composed of individuals who are at very early stage of the disease (i.e. CDR 0 or 0.5) and even the Alzheimer’s disease subjects have relatively low CDR score (0.5 or 1) (at baseline time point). Since the accumulation of amyloid-b and tau in the brain precedes the neurodegenerative changes in the brain (FIG. 21D), the baseline plasma t- tau/amyloid^i-42 might not be able to fully reflect neurodegenerative changes shown on FDG- PET or hippocampal volumes yet. There are several indirect evidences based on the data shown herein that neurodegeneration has not progressed very far yet (compared to the accumulation of brain amyloid or tau) at baseline time point than that of 2- year time point: (i) baseline FDG-PET 4roi and posterior cingulate cortex were not significantly correlated with baseline PiB-PET SUVR, which is the early Alzheimer’s disease phenotype ( P = 0.4, r =-0.1 for 4roi; P = 0.1, r =-0.2 for posterior cingulate cortex). However, the 2-year FDG-PET 4roi and posterior cingulate cortex were strongly correlated with 2-year PiB-PET SUVR ( P < 0.01, r =- 0.36 for 4roi; P < 0.001, r =-0.39 for posterior cingulate cortex) (FIG. 21C); (ii) the correlation between baseline plasma t-tau/amyloid^i-42 and baseline PiB-PET SUVR (P = 0.1, FIG. 21 A) or between baseline plasma t-tau/amyloid^i-42 and 2-year tau-PET SUVR (P < 0.10, FIG. 21B) showed a trend toward significance; and (iii) while Alzheimer’s disease patients (categorized at the time of the 2-year time point) showed broad range of the degree of neurodegeneration at the baseline time point (FIG. 22A-FIG. 22C, dotted circle), they showed a cluster distribution of neurodegeneration at the 2-year time point (FIG. 22A-FIG. 22C, solid circle). PiB-PET SUVR, however, did not show the similar pattern, possibly due to saturation of brain amyloid deposition (FIG. 22D). Based on the 2-year longitudinal imaging data shown herein combined with blood composite biomarker, conjectured subject distribution range of both baseline samples and 2-year samples were marked on a hypothetical Alzheimer’s disease progression abnormality plot (FIG. 21D).

Several previous studies have attempted to investigate the association between plasma tau levels, Alzheimer’s disease dementia and associated cognitive decline using various quantitative methods. Their results, however, have not been consistent. Plasma t-tau levels were found to be increased in Alzheimer’s disease dementia, but not in MCI, compared to controls (Zetterberg et al., 2013, Alzheimers Res. Ther, 5: 9; Mattsson et al., 2016, Neurology, 87: 1827-35), whereas other study found elevated plasma tau levels in both MCI and early Alzheimer’s disease (Chiu et al, 2014, Hum. Brain Mapp., 35: 3132-42). In contrast, significant decrease of plasma tau levels in Alzheimer’s disease has also been reported (Sparks et al, 2012, Am. J.

Neurodegener. Dis., 1 : 99-106; Krishnan and Rani, 2014, Biol. Trace Elem. Res., 158: 158-65) and positive correlations were shown between plasma tau levels and cognitive performance in all diagnosis groups including Alzheimer’s disease patients (Sparks et al, 2012, Am. J.

Neurodegener. Dis., 1 : 99-106). No changes in plasma tau level were suggested between Alzheimer’s disease patients and controls (Wang et al, 2014, Int. J. Geriatr. Psychiatry, 29: 713- 9). Different quantification methods, such as digital array technology, immunomagnetic reduction assay and ELISA, and the lack of verification for brain amyloid or tau accumulation for the Alzheimer’s disease diagnosis in some studies may have contributed to these

discrepancies and inconsistent results for blood tau levels in Alzheimer’s disease.

Numerous studies reported the associations between plasma tau levels and various proxies of neurodegeneration. Plasma tau levels are associated with neurodegeneration and cognitive functions in Alzheimer’s disease, such that higher plasma tau levels were associated with memory decline, abnormal cortical thickness and anatomical volume of various brain regions (Chiu et al, 2014, Hum. Brain Mapp., 35: 3132-42; Mattsson et al, 2016, Neurology,

87: 1827-35). More specifically, high levels of plasma tau were associated with lower grey matter density in regions that are both Alzheimer’ s disease-specific as well as more generally, and this negative association between plasma tau level and grey matter density was shown in amyloid-P-positive patients in the medial temporal lobe, precuneus and frontal cortex (Deters et al, 2017, J. Alzheimers Dis., 58: 1245-54). Plasma amyloid-b is also associated with hippocampal and cortical tau pathology and influences neurodegeneration (Johnson et al, 2016, Ann. Neurol., 79: 110-9; Wang et al., 2016, JAMA Neurol., 73: 1070-7). It was recently shown that plasma amyloid-b _1-42 is strongly associated with brain amyloid-b accumulation in

Alzheimer’s disease (Park et al, 2017, Alzheimers Res. Ther., 9: 20); utilization of plasma tau level and plasma amyloid-b _1-42 level together, therefore, may be synergistic in predicting brain tau accumulation. Thus, considering pathological linkage between cerebral amyloid-b deposition and brain tau pathology (Ittner and Gotz, 2011, Nat. Rev. Neurosci., 12: 65-72), both plasma tau and plasma amyloid-b level could be dual indicators for Alzheimer’s disease-related tau pathology (Mielke et al, 2018, Alzheimers Dement., 14: 989-97).

Regarding consideration for utilizing a combination of plasma tau and plasma amyloid-b level as mentioned above, the tau/amyloid-b _1-42 ratio was used because it has been commonly used as a CSF biomarker for dementia and Alzheimer’s disease (Gomez-Tortosa et al, 2003, Arch. Neurol., 60: 1218-22; Fagan et al, 2007, Arch. Neurol., 64: 343-9; Pan et al, 2015, J. Alzheimers Dis., 45: 709-19; Ritchie et al, 2017, Cochrane Database Syst. Rev., 3: CD010803). It has long been discussed that CSF t-tau and p-tau levels increased during Alzheimer’s disease pathogenesis (Arai et al, 1995, Ann. Neurol., 38: 649-52; Andreasen et al, 2001, Arch. Neurol., 58: 373-9; Pereira et al, 2017, Neurobiol. Aging, 58: 14-29) and that the tau/amyloid^i-42 ratio in CSF is known to have good ability in distinguishing Alzheimer’s disease patients from normal subjects or all other patients (Gomez-Tortosa et al, 2003, Arch. Neurol., 60: 1218-22; Smach et al, 2009, Eur. Neurol., 62: 349-55). In this context, it was hypothesized that the plasma tau/amyloid^i-42 ratio would also improve the efficiency of predicting Alzheimer’s disease- related neuropathological changes of tau. Indeed, in the current study, it was found that plasma p-tau and t-tau levels showed significant correlations to the Braak stages in the brain (FIG. 8A and FIG. 8B). When the ratio of p-tau/amyloid^i-42 and t-tau/amyloid^i-42 was assessed, t- tau/amyloid^i-42 ratio showed the best performance in predicting brain tau accumulation among the four markers (AUC 0.890, FIG. 10B-FIG. 10D). The current results also showed that plasma amyloid^i-42 level yielded highly significant correlations with not only cerebral amyloid^i-42 deposition but also brain tau accumulation (FIG. 19A-FIG. 19C). The plasma t-tau/amyloid-bi- 42 ratio, in particular, showed higher correlation with Alzheimer’s disease-associated brain tau pathology, various Alzheimer’s disease-associated neurodegeneration markers including hippocampal volume, cortical thickness, cerebral glucose metabolism and episodic memory impairment, compared to p-tau/amyloid-b _1-42 (FIG. 8A-FIG. 8B, FIG. 9A-FIG. 9D, FIG. 10A- FIG. 10E, FIG. 17A, FIG. 17B, FIG 18A-FIG. 18D, Table 6, Table 7, FIG. 15, Table 9, and FIG. 16, Table 10). Furthermore, t-tau/amyloid-b _1-42 showed the best performance in discriminating Tau-PET and Tau-PET ⁺ subjects among four markers of p-tau, t-tau, p-tau/amyloid-bl-42 and t- tau/amyloid-b _1-42 (FIG. 10B-FIG. 10E, Table 6, and Table 7). Longitudinal follow-up study for period of 2 years supported these significant correlations of plasma t-tau/amyloid-b _1-42 ratio with the changes of Alzheimer’s disease-associated neurodegeneration markers (FIG. 13). Taken together, the results further support the possibility of plasma t-tau/amyloid-b _1-42 ratio as a potential prognostic marker for disease progression. A recent study that reported an increase of plasma tau levels in Alzheimer’s disease dementia patients compared with cognitively normal individuals supports the current result (Mielke et al., 2018, Alzheimers Dement., 14: 989-97). Particularly, they showed different patterns of correlations between plasma p-tau T181 level and brain tau deposition depending on the presence of brain amyloid-b accumulation, which suggest the potential influence of brain amyloid-b _1-42 on correlation between plasma tau and brain tau. Interestingly, the results shown here indicate positive correlations between plasma tau and brain tau accumulation while plasma amyloid-b represented negative correlations with brain tau levels (FIG. 8 and FIG. 19A-FIG. 19C). Mielke el al. (2018, Alzheimers Dement., 14: 989-97) reported the association between plasma p-tau T181, amyloid-b and tau PET, and posited that plasma p-tau Tl 81 is a more efficient predictor for cerebral amyloid-b than t-tau. The current results, however, show that t-tau or t-tau/amyloid^i-42 predicts brain tau pathology better than p- tau or p-tau/amyloid^i-42. Discussion on relative strengths of the associations of plasma p-tau versus t-tau with brain tau can be elaborated from previous findings using CSF data. Some previous studies using CSF tau levels reported that t-tau was superior to p-tau in showing association with Alzheimer’s disease-related neuropathology because p-tau represented tau phosphorylation status and tangle formation while t-tau reflected dynamically both acute and chronic neuronal degeneration (Samgard et al, 2010, Int. J. Geriatr. Psychiatry, 25: 403-10; Blennow et al, 2015, Alzheimers Dement., 11 : 58-69). CSF tau has been proven to be associated with altered microstructure of brain on diffusion tensor imaging such that CSF t-tau and t-tau/amyloid-b _1-42 showed a widespread association with alteration in brain microstructure whereas p-tau and p-tau/amyloid-b _1-42 was only related to specific recognition performance and failed to show a widespread relationship (Bendlin et al, 2012, PLoS One, 7: e37720). Another study demonstrated that CSF t-tau had regional correlation with impairment of glucose metabolism whereas CSF p-tau showed no significant regional correlations (Haense et al, 2008, Eur. J. Neurol., 15: 1155-62). Also, high CSF t-tau was related to increased conversion rate from MCI or mild to moderate dementia, whereas CSF p-tau failed to show this association (Degerman Gunnarsson et al, 2016, Alzheimers Res. Ther, 8: 22). In the current study, therefore, it is likely that plasma t-tau represents the stronger correlation with brain tau pathology than p-tau. Notably, voxel-wise analyses demonstrated that associations of plasma tau markers alone with brain tau on PET imaging were relatively circumscribed in the medial temporal regions whereas associations of the combined plasma tau and amyloid-b _1-42 markers (i.e. plasma p-tau/amyloid-b _1-42 and t-tau/amyloid-b _1-42 ratios) with brain tau extended into limbic and neocortical regions. The regional pattern of tau accumulation associated with plasma t- tau/amyloid-b _1-42 ratio was highly consistent with well-established neurofibrillary tangle distribution reported by neuropathology studies (Braak et al., 2006, Acta Neuropathol, 112: 389- 404). It may suggest that amyloid-b, reflected by plasma amyloid-b _1-42 in this study, plays a role in the formation and spreading of tau aggregates in human. While amyloid-b _1-42 plaques and tau aggregates show up at different times in different regions of the brain (Iaccarino et al., 2018, Neuroimage Clin., 17: 452-64), evidence from animal model shows that two pathologies interact such that presence of amyloid-b _1-42 enhances tau aggregation (Bennett et al, 2017, Am.

J. Pathol., 187: 1601-12). In addition, a recent study reported that neuritic plaques trigger the formation of a specific type of tau aggregates (i.e.‘tau aggregates in dystrophic neurites surrounding amyloid-b plaques’), which appear to fuel the formation and spreading of other tau conglomerates, including neurofibrillary tangles (He et al, 2018, Nat. Med., 24: 29-38).

Direct comparisons of concentrations between CSF tau and plasma tau in this study were not possible because CSF collection was not included in the KBASE study design. However, since the various imaging data directly show the status of subjects’ brain, the plasma biomarkers were directly compared to the imaging data without going through CSF data— a proxy to brain markers. Moreover, a recent finding by La Joie et al. (2018, Neurology, 90: e282-e90) that showed a modest correlation between AV1451 SETVR and CSF tau measures but not with CSF amyloid-b _1-42 among amyloid-P-positive patients with clinical Alzheimer’s disease does not negate the current finding that supports uniquely informative nature of the inclusion of plasma amyloid-b _1-42 in the biomarker as a form of ratio. Given that the absence of significant correlation between AV1451 SUVR and CSF amyloid-P42 among amyloid-P-positive patients with clinical Alzheimer’s disease is likely due to saturated levels of amyloid at the given disease state, the current findings elaborate on how consideration of amyloid saturation levels is important in the relationship between plasma tau and brain tauopathy.

A methodological limitation is regarding the measurement of plasma tau since the level of tau in plasma is very low picogram per millilitre range and the sensitivity of tau measuring tools is limited so far. In this study, possible experimental variability was minimized for quantification of tau and amyloid-p. CSF t-tau, p-tau and amyloid-P levels were typically studied using either ELISA or xMAP technology in multicentre population and the experimental variations were corrected by reanalysis and conversion of xMAP technology values to ELISA values (Mattsson et al., 2012, Neurology, 78: 468-76). In the present study, one method for each biomarker, xMAP technology for plasma amyloid-b _1-42 level and Simoa for the plasma tau quantification, was unified. Measurement of plasma amyloid-b _1-42 level was known to be hard to quantify accurately. Such concerns were addressed by using an improved quantification method that was previously invented by Applicant, which enables stable measurement of amyloid-b _1-42 in plasma using mixture of protease inhibitors and phosphatase inhibitors (Park et al., 2017, Alzheimers Res. Ther, 9: 20). Additionally, plasma tau has been fairly fastidious to quantify. Simoa human tau immunoassay was used for plasma t-tau and p-tau levels because several studies using Alzheimer’s Disease Neuroimaging Initiative (ADNI) and BioFINDER data applied Simoa to measure plasma levels of t-tau or p-tau (Mattsson et al, 2016, Neurology, 87: 1827-35; Deters et al., 2017, J. Alzheimers Dis., 58: 1245-54; Zhou et al., 2017, Neurosci. Lett., 650: 60-4). Quantification of plasma tau by the Simoa technique has recently been studied and was shown to be reliable to measure plasma t-tau and p-tau level (Deters et al., 2017, J.

Alzheimers Dis., 58; Tatebe et al., 2017, Mol. Neurodegener, 12: 63). Of note, however, some of the samples in this study showed outlier values for p-tau that were below the detection limit level, which resulted in having different subject numbers for t-tau and p-tau in these analyses.

In addition, quantification of p-tau is limited by the sensitivity of Simoa technique and it is only possible to measure p-tau (T181) and no other forms of p-tau. Quantification of diverse forms of p-tau levels in plasma would give better understanding of their association with brain tau pathology and their roles as biomarker in Alzheimer’s disease.

Further validation studies on a large, diverse population using biomarkers of plasma tau and amyloid- bi-42 levels are planned, and a large-scale longitudinal follow-up study will also be needed to follow the present findings and trace the changes of plasma tau and amyloid-b _1-42 over longer period of Alzheimer’s disease progression. Other forms of phosphorylated tau, such as p-tau 231, would be valuable to be quantified in blood because p-tau 231 was reported to reflect brain neurofibrillary tangles better than p-tau T181 (Buerger et al., 2007, Brain, 130 (Pt 10): e82). In the future, a largescale correlation study of amyloid-PET, tau-PET, plasma t-tau, diverse forms of plasma p-tau and plasma amyloid level would provide more answers for using blood biomarkers to aid in Alzheimer’s disease diagnosis.

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405 410 415 Pro Gly Ser Ser Asp Pro Leu Ile Gln Pro Ser Ser Pro Ala Val Cys

420 425 430

Pro Glu Pro Pro Ser Ser Pro Lys His Val Ser Ser Val Thr Ser Arg

435 440 445

Thr Gly Ser Ser Gly Ala Lys Glu Met Lys Leu Lys Gly Ala Asp Gly

450 455 460

Lys Thr Lys Ile Ala Thr Pro Arg Gly Ala Ala Pro Pro Gly Gln Lys

465 470 475 480

Gly Gln Ala Asn Ala Thr Arg Ile Pro Ala Lys Thr Pro Pro Ala Pro

485 490 495

Lys Thr Pro Pro Ser Ser Ala Thr Lys Gln Val Gln Arg Arg Pro Pro

500 505 510

Pro Ala Gly Pro Arg Ser Glu Arg Gly Glu Pro Pro Lys Ser Gly Asp

515 520 525

Arg Ser Gly Tyr Ser Ser Pro Gly Ser Pro Gly Thr Pro Gly Ser Arg

530 535 540 Ser Arg Thr Pro Ser Leu Pro Thr Pro Pro Thr Arg Glu Pro Lys Lys

545 550 555 560

Val Ala Val Val Arg Thr Pro Pro Lys Ser Pro Ser Ser Ala Lys Ser

565 570 575

Arg Leu Gln Thr Ala Pro Val Pro Met Pro Asp Leu Lys Asn Val Lys

580 585 590

Ser Lys Ile Gly Ser Thr Glu Asn Leu Lys His Gln Pro Gly Gly Gly

595 600 605

Lys Val Gln Ile Ile Asn Lys Lys Leu Asp Leu Ser Asn Val Gln Ser

610 615 620

Lys Cys Gly Ser Lys Asp Asn Ile Lys His Val Pro Gly Gly Gly Ser

625 630 635 640

Val Gln Ile Val Tyr Lys Pro Val Asp Leu Ser Lys Val Thr Ser Lys

645 650 655

Cys Gly Ser Leu Gly Asn Ile His His Lys Pro Gly Gly Gly Gln Val

660 665 670 Glu Val Lys Ser Glu Lys Leu Asp Phe Lys Asp Arg Val Gln Ser Lys

675 680 685

Ile Gly Ser Leu Asp Asn Ile Thr His Val Pro Gly Gly Gly Asn Lys

690 695 700

Lys Ile Glu Thr His Lys Leu Thr Phe Arg Glu Asn Ala Lys Ala Lys

705 710 715 720

Thr Asp His Gly Ala Glu Ile Val Tyr Lys Ser Pro Val Val Ser Gly

725 730 735

Asp Thr Ser Pro Arg His Leu Ser Asn Val Ser Ser Thr Gly Ser Ile

740 745 750

Asp Met Val Asp Ser Pro Gln Leu Ala Thr Leu Ala Asp Glu Val Ser

755 760 765

Ala Ser Leu Ala Lys Gln Gly Leu

770 775