PLAQUE FORMATION

FIELD OF THE INVENTION

The present invention relates to methods for predicting a risk and a rate of amyloid deposition and plaque formation. More particularly, the invention relates to prognostic methods and treatment methods for amyloid deposition and plaque formation in these patients. It relates to a correlation between brain iron load and amyloid deposition and plaque formation.

BACKGROUND

The already extensive burden of Alzheimer's disease (AD) to Australia is projected to increase due to an aging population demographic and no effective treatments. There is an emerging consensus that disease-modifying treatments should be delivered during the pre-clinical phase of the disease, as AD pathology begins to accumulate. Identifying early signs of possible amyloid accumulation is therefore necessary for effectively treating, preventing or managing this disease. There are very few known risk factors that impact on the rate at which pathology accumulates and knowing a patients' propensity to deteriorate to AD and the rate at which this may occur can be particularly helpful in managing its progression.

Αβ and tau form the brain amyloid and tangle proteopathies of AD and have been the subjects of extensive biomarker research. AD brain pathology starts developing approximately two decades prior to the onset of cognitive symptoms. Consequently, anti-AD therapies may have the best chance of success when given in this preclinical period. There is a need to identify risk factors that predict whether someone will accumulate AD pathology in the future, and the rate at which this will occur. High Αβ burden (Αβ+), identified by PET (using PiB, flutemetamol, or florbetapir radioligands), or declining values of Αβ in the CSF (declining because the Αβ in the CSF is redirected to be deposited in amyloid plaque) is a sensitive predictor of Alzheimer's disease. High amyloid is also observed in elderly people without a current diagnosis of AD; in these subjects, high Αβ load (high PET, low CSF Αβ) predicts future cognitive decline until they ultimately acquire a diagnosis of dementia. Attempts have been made to diagnose or differentially diagnose AD by measuring the level of a target such as tau and Αβ in the patient whose level specifically increases or decreases in the cerebrospinal fluid ("CSF") of a dementia patient. However, there are few known risk factors or biomarkers that predict rate of pathology accumulation.

In light of the above, there is a need for an improved method of identifying those with a greater propensity for amyloid deposition and plaque formation leading to neurological disorders such as AD or those displaying cognitive decline, particularly at the onset of the disease, which may assist in delaying disease progression. The ability to detect preclinical or early stage disease progression through reliable measurement of risk factors present in biological samples would also allow treatment and management of the disease to begin earlier if a future rate of progression could be predicted. There is currently no simple test that could be used to predict pathology build up.

A test which can provide assistance to clinicians in reaching an early stage prognosis of a rate of progression prior to the portrayal of detectable clinical indicators in some more vulnerable patients and which would obviate the need for actual confirmatory brain imaging tests would be useful for managing the disease by early intervention. A test that could predict the future rate of decline in a subject with manifest symptoms or AD diagnosis would also be beneficial for the clinician in managing the patient.

With disease modifying therapies for AD undergoing clinical trials, there is a social and economic imperative to identify risk factors that can detect features of the disease in at-risk individuals in the earliest possible stage, so anti-AD therapy can be administered at a time when the disease burden is mild and it may prevent or delay functional and irreversible cognitive loss, particularly in those patients that are more vulnerable to AD. Accordingly, there is a desire to provide a simple and effective measure of a risk for amyloid deposition and plaque formation in patients and a means to predict a rate of deposition of amyloid that can be used to diagnose, prognose or monitor a patient. This early detection may assist in delaying the onset of conditions associated with amyloid deposition and plaque formation such as AD and related conditions if treated early and appropriately or to monitor progression of a patient undergoing drug therapy for these conditions.

SUMMARY OF THE INVENTION

Detecting early prognostic factors predisposing at-risk patients before the onset of conditions associated with amyloid deposition and plaque formation such as AD and related conditions may enable early treatment that would delay disease progression.

Accordingly, in an aspect of the present invention there is provided a method for predicting a risk of amyloid deposition and plaque formation in a patient, said method comprising:

determining a first level of brain iron load in the patient;

comparing the first level of brain iron load to a reference level of brain iron load;

determining a difference between the first level of brain iron load and the reference level; and

deducing a risk for amyloid deposition and plaque formation in the patient from the difference. Applicants have identified brain iron load measurement as a method for predicting amyloid deposition and plaque formation. They have found that brain iron load is an indicator of whether a patient with has a high risk or low risk of β-amyloid deposition and plaque formation in the future. In an embodiment of the present invention, the levels of brain iron load may be determined as a measure of any iron related protein levels such as but not limited to ceruloplasmin, amyloid precursor protein, tau, ferritin, transferrin, and transferrin binding protein. Preferably, the brain iron load is determined by ferritin levels or by MRI using Quantitative Susceptibility Mapping, or by any method available to the skilled addressee. In a preferred embodiment the level of brain iron load is determined as a measure of cerebrospinal fluid (CSF) ferritin.

Using the major iron binding protein ferritin in CSF as an index, high brain-iron load was associated with declining CSF Αβ levels (reporting increasing amyloid deposition and plaque formation) over 5 years in a cohort of cognitively normal, mild cognitive impairment and AD subjects preferably with an established AD risk variable such as APOE ε4 genotype or being amyloid positive. In another aspect of the invention there is provided a method of predicting a rate of amyloid deposition and plaque formation in a patient method comprising:

determining a first level of brain iron load in the patient;

comparing the first level of brain iron load to a reference level of brain iron load;

determining a difference between the first level of brain iron load and the reference level;

deducing a rate of amyloid deposition and plaque formation in the patient from the difference. The rate at which amyloid deposition and plaque formation will can be determined by the brain iron load levels in these patients.

In yet another aspect of the present invention there is provided a method for monitoring a rate of amyloid deposition and plaque formation in a patient, said method comprising:

determining a level of brain iron load in the patient at first time point; determining a level of brain iron load at in the same patient at a second time point which is after the first time point;

optionally comparing the levels of brain iron load from the first and second time points to a reference level;

determining a difference in the levels of brain iron load at each of the first and second time points;

deducing a change in the rate of amyloid deposition and plaque formation from the difference in brain iron load levels from the first and the second time points.

The changes in the levels of brain iron load can additionally be used in assessing for any changes in a rate of amyloid deposition and plaque formation of a patient. Accordingly, in the monitoring of the levels of brain iron load, it is possible to monitor for a change in the rate of amyloid deposition and plaque formation over a period of time, or to track a rate at which amyloid deposition and plaque formation progression occurs in a patient. For instance, a rate of amyloid deposition and plaque formation may increase over time showing a greater rate of progression toward conditions associated with amyloid deposition and plaque formation such as AD and related conditions.

In yet another embodiment, the present method further includes determining a level of a biomarker of amyloid deposition and plaque formation such as but not limited to APOE genotype such as APOE ε4 genotype, Tau or Αβ used singularly or in combination with the method to assess a rate of amyloid deposition and plaque formation. These additional markers may enhance the accuracy of the method for determining a risk of amyloid deposition and plaque formation or a rate at which amyloid deposition and plaque formation occurs.

In another aspect of the invention there is provided a method for diminishing progression rate of amyloid deposition and plaque formation in a patient said method comprising lowering brain iron load levels. In another aspect of the invention there is provided a method for diminishing progression rate of amyloid deposition and plaque formation, said method comprising lowering CSF ferritin levels.

To lower brain iron load or CSF ferritin levels compounds such as iron chelators such as Deferiprone may be used.

BRIEF DESCRIPTION OF THE FIGURES

Figure 1 shows an impact of CSF ferritin on CSF Αβ _-42 levels over time. (A) Visual display of mixed effects model of change in CSF Αβι _-42 levels over time in subjects with high baseline tau/APi _-42 (>0.39 units) and stratfiied according to baseline CSF ferritin level (low < 6.6 ng/ml < high). Model was adjusted for age, sex, APOE ε4, diagnosis, and CSF ApoE and tau. Figure 2 shows a change in SUVR (a PET measurement of amyloid) over time in people with low and high quantitative susceptibility mapping (QSM) (an MRI measure of iron). DETAILED DESCRIPTION OF THE INVENTION

Measuring a rate of amyloid deposition and plaque formation before the onset of conditions associated with amyloid deposition and plaque formation such as AD and related conditions may enable early treatment intervention to delay disease progression. Anti-AD therapies given in the pre-clinical period will have the best chance of success. However, in some cases dementia or AD may not fully develop, despite the patient possessing risk factors associated with AD. Determining a patients' propensity for amyloid deposition and plaque formation will enable earlier treatment for the more vulnerable patients. Accordingly, in an aspect of the present invention there is provided a method for predicting a risk of amyloid deposition and plaque formation, said method comprising:

determining a first level of brain iron load in the patient;

comparing the first level of brain iron load to a reference level of brain iron load;

determining a difference between the first level of brain iron load and the reference level; and

deducing a risk for amyloid deposition and plaque formation in the patient from the difference. Applicants have identified brain iron load measurement as a method for predicting a rate for amyloid deposition and plaque formation in patients and thereby providing an insight to the risks of amyloid deposition and plaque formation for these patients. Iron accumulates in affected regions during the disease but, until recently, there was debate about its impact on pathogenesis.

The present invention relates to assessing a risk of future amyloid deposition and plaque formation in a patient measured as a degree of decline of CSF Αβ or elevation of signal using PET radioligands specific for amyloid, in patients having high or low CSF ferritin. The present invention has identified that for amyloid deposition and plaque formation, the brain iron load levels are an indication of a risk for amyloid deposition and plaque formation determined by the rate at which amyloid deposition and plaque formation can occur. In particular, Applicants have found that higher iron predicts a greater risk for enhanced amyloid deposition and plaque formation thereby leading to a greater risk of conditions associated with amyloid deposition and plaque formation such as AD and related conditions including cognitive deterioration. Mild cognitive impairment (MCI) is an intermediate stage between the expected cognitive decline of normal aging and the more serious decline of dementia. It can involve problems with memory, language, thinking and judgment that are greater than normal age-related changes. Mild cognitive impairment causes cognitive changes that are serious enough to be noticed by the individuals experiencing them or to other people, but the changes are not severe enough to interfere with daily life or independent function.

Currently, the clinical diagnosis in the areas of dementias, cognitive disorders and/or affective disorders and/or behavioural dysfunction, Alzheimer's Disease and related dementias generally requires an evaluation of medical history and physical examination including neurological, neuropsychological and psychiatric assessment including memory and/or psychological tests, assessment of language impairment and/or other focal cognitive deficits (such as apraxia, acalculia and left-right disorientation), assessment of impaired judgment and general problem-solving difficulties, assessment of personality changes ranging from progressive passivity to marked agitation, as well as various biological, radiological and electrophysiological tests, such as for instance measuring brain volume or activity measurements derived from neuroimaging modalities such as magnetic resonance imaging (MRI) or positron emission tomography (PET) of relevant brain regions.

Applicants have found a correlation between brain iron load measured by MRI (quantitative susceptibility mapping), or ferritin and CSF ferritin and a future rate of amyloid deposition and plaque formation in a patient. This correlation will enable a simple assessment of the risk for conditions associated with amyloid deposition and plaque formation such as AD and related conditions in these patients. The risk can be associated with the rate at which amyloid deposition and plaque formation can occur based on the brain iron load levels. As used herein, reference to cognitive deterioration includes mild cognitive impairment (MCI), MCI conversion to Alzheimer's disease (AD), and AD. However, the invention also relates broadly to the areas of dementias, cognitive disorders and/or affective disorders and/or behavioural dysfunction, Alzheimer's disease and related dementias which are associated with an established AD risk variable. The term "cognitive deterioration" may be used interchangeably with "cognitive decline".

The term "cognitively normal (CN) patient" as used herein means a subject which has no significant cognitive impairment or impaired activities of daily living. Patients that are suspected of, or are at risk of cognitive deterioration may be compared against a CN patient. This includes patients that are cognitively normal but show changed levels of a marker indicative of a neurological disease such as amyloid loading in the brain (preferably determined by PET imaging). The characteristics of a CN patient will assist in providing a reference level or reference value to which deterioration from normal can be determined. In one embodiment, the CN patient does not carry an AD risk variable such as being amyloid positive or carrying the APOE ε4 allele. However, CN patients that do carry an AD risk variable may have a variable selected from APOE genotype, CSF/tau-Ap _-42 , PET/tau-Ap _-42 and ApoE levels. More preferably, the AD risk variable is the presence of Αβ or the carriage of an APOE ε4 allele. Preferably the AD risk variable is an APOE genotype selected from APOE ε4/ε4, APOE ε4/ε3, APOE ε4/ε2.

A risk of amyloid deposition and plaque formation may be assessed relative to the CN patient which will provide a reference level. Patients who are at risk of amyloid deposition and plaque formation include those with family histories, genetic vulnerability and deficiency alleles and hence may carry an AD risk variable. They may be vulnerable and carry genes which predispose them to a more rapid amyloid deposition and plaque formation leading to conditions associated with amyloid deposition and plaque formation such as AD and related conditions including cognitive deterioration and dementia. Patients who can be tested and/or treated according to any of the methods of the present invention include those who present with cognitive dysfunction with a history of treated depression, cognitive dysfunction with a history of depression, cognitive dysfunction with bipolar disease or schizoaffective disorders, cognitive dysfunction with generalized anxiety disorder, cognitive dysfunction with attention deficit, ADHD disorder or both attention deficit and ADHD disorder, dyslexia, developmental delay, school adjustment reaction, Alzheimer's disease, amnesic mild cognitive impairment, non-amnesic mild cognitive impairment, cognitive impairment with white matter disease on neuroimaging or by clinical examination, frontotemporal dementia, cognitive disorders in those under 65 years of age, those with serum homocysteine levels of less than 10 nmol/l, and those with high serum transferrin levels (uppermost population quartile). The patient may also be CN but may or may not carry an AD risk variable.

As used herein, the terms "individual," "subject," and "patient," generally refer to a human subject, unless indicated otherwise, e.g., in the context of a non-human mammal useful in an in vivo model (e.g., for testing drug toxicity), which generally refers to murines, simians, canines, felines, ungulates and the like (e.g., mice, rats, other rodents, rabbits, dogs, cats, swine, cattle, sheep, horses, primates, etc.).

The terms "determining," "measuring," "evaluating," "assessing," and "assaying," as used herein, generally refer to any form of measurement, and include determining if an element is present or not in a biological sample. These terms include both quantitative and/or qualitative determinations, which require sample processing and transformation steps of the biological sample. Assessing may be relative or absolute. The phrase "determining a level of" can include determining the amount of something present, as well as determining whether it is present or absent. A level of brain iron load may be determined from a normal patient, a patient suspected of amyloid deposition and plaque formation, a patient that is CN and may or may not carry an AD risk variable or is the same patient from another time period. Alternatively, a level of brain iron load may be determined from a patient that is known not to have amyloid deposition and plaque formation providing a reference value or reference level or a control level. Preferably this will be from a healthy control or a cognitively normal individual (CN). More preferably, the patient may have with an established AD risk variable selected from APOE genotype, CSF/tau-APi _-42 , PET/tau- Αβ _-42 and ApoE levels. More preferably, the AD risk variable is the carriage of an APOE ε4 allele. Preferably the AD risk variable is an APOE genotype selected from APOE ε4/ε4, APOE ε4/ε3, APOE ε4/ε2.

As used herein, a "reference value" or "reference level" may be used interchangeably and may be selected from the group comprising an absolute value; a relative value; a value that has an upper and/or lower limit; a range of values; an average value; a median value, a mean value, a shrunken centroid value, or a value as compared to a particular control or baseline value. Preferably it is a predetermined reference value obtained from a known sample prepared in parallel with the biological or test sample in question. It is to be understood that other statistical variables may be used in determining the reference value. A reference value can be based on an individual sample value, such as for example, a value obtained from a sample from the individual with a known rate of amyloid deposition and plaque formation, but at an earlier point in time, or a value obtained from a sample from a patient or another patient with the disorder other than the individual being tested, or a "normal" or "healthy" individual, that is an individual not diagnosed with amyloid deposition and plaque formation otherwise a CN individual. The reference value can be based on a large number of reference samples, such as from AD patients or patients with amyloid deposition and plaque formation, cognitive deterioration, normal individuals or based on a pool of samples including or excluding the sample to be tested.

For diagnostic and prognostic methods, the "reference level" is typically a predetermined reference level, such as an average of levels obtained from a population that may or may not be afflicted with amyloid deposition and plaque formation. In some instances, the predetermined reference level is derived from (e.g., is the mean or median of) levels obtained from an age-matched population. In some examples disclosed herein, the age-matched population comprises individuals with non-AD or neurodegenerative disease individuals and may be CN. For methods providing a prediction of a rate of amyloid deposition and plaque formation or a risk of amyloid deposition and plaque formation, a reference level may also be considered as generally a predetermined level considered "normal" for the particular diagnosis (e.g., an average level for age-matched individuals not diagnosed with amyloid deposition and plaque formation or an average level for age-matched individuals diagnosed with amyloid deposition and plaque formation other than AD and/or healthy age-matched individuals), although reference levels which are determined contemporaneously (e.g., a reference value that is derived from a pool of samples including the sample being tested) are also contemplated.

A reference level may also be a measure of a constant internal control to standardize the measurements of the first level and reference level to decrease the variability between the tests. The internal control may be a sample from a blood bank such as the Red Cross.

Hence in conducting the method of the present invention, a set of samples can be obtained from individuals having amyloid deposition and plaque formation and a set of samples can be obtained from individuals not having amyloid deposition and plaque formation and preferably with an established AD risk variable.

The measured level of brain iron load may be a primary measurement of the level of bound or unbound iron in the brain or it may be a secondary measurement of the iron (a measurement from which the quantity of the iron can be determined but not necessarily deduced (qualitative data)), such as a measure of iron related protein levels such as ferritin. Hence, a sample may be processed to exclude unbound cellular iron if measuring iron related protein levels like ferritin levels.

In an embodiment of the present invention, the levels of brain iron load may be determined as a measure of any iron related protein levels such as but not limited to ceruloplasmin, amyloid precursor protein, tau, ferritin, transferrin, transferrin binding protein etc. Preferably, the brain iron load level is determined by ferritin levels or by Gradient echo based MRI technique such as but not limited to QSM-MRI or T2 ^* mapping or sonography or by any method available to the skilled addressee. Where the invention provides a use of iron related protein levels (e.g. ceruloplasmin, amyloid precursor protein, tau, ferritin, transferrin, transferrin binding protein etc.), preferably in conjunction with information regarding APOE genotype, CSF tau, Αβ and ApoE levels, to predict the rate of amyloid deposition and plaque formation in a CN patient, the patient preferably has an established AD risk variable such as, but not limited to an APOE genotype, CSF/tau-Ap _-42 , PET/tau-Ap _-42 and ApoE levels. Preferably the AD risk variable is an APOE genotype selected from APOE ε4/ε4, APOE ε4/ε3, APOE ε4/ε2. Ferritin is the iron storage protein of the body and is elevated in AD brain tissue. In cultured systems, ferritin expression and secretion by glia is dependent on cellular iron levels. Ferritin levels in CSF likely reflect iron levels in the brain.

Accordingly, in a preferred embodiment the level of brain iron load is determined as a measure of cerebrospinal fluid (CSF) ferritin. Hence the invention provides use of a measurement of CSF ferritin concentration, (in conjunction with information regarding APOE genotype, CSF tau, Αβ and ApoE levels) to predict the risk and rate of amyloid deposition and plaque formation in an individual. Preferably the individual has an established AD risk variable, more preferably the patient is amyloid positive.

Applicants have found that when CSF ferritin is measured as a measure of brain iron load, the rate of amyloid deposition and plaque formation is more accurate in those patients that are amyloid positive. Accordingly in an embodiment there is provided a use of a measurement of CSF ferritin concentration, (preferably in conjunction with information regarding APOE genotype, CSF tau, Αβ and ApoE levels) to predict the rate of amyloid deposition and plaque formation in an individual who exhibits little or no symptoms (normal) of cognitive deterioration but is preferably amyloid positive.

In performing the presently claimed method the level of brain iron load, preferably ferritin or more preferably CSF ferritin is determined. As would be appreciated by one of skill in the art, the level (e.g., concentration, expression and/or activity) of brain iron load, preferably ferritin or more preferably CSF ferritin can be qualified or quantified. Preferably, the level of brain iron load, preferably ferritin or more preferably CSF ferritin is quantified as a level of DNA, RNA, lipid, carbohydrate, protein, metal or protein expression. It will be apparent that numerous qualitative and quantitative techniques can be used to identify the level of brain iron load, preferably ferritin or more preferably CSF ferritin. These techniques may include 2D DGE, mass spectrometry (MS) such as multiple reaction monitoring mass spectrometry (MRM-MS), Real Time (RT)-PCR, nucleic acid array; ELISA, functional assay, by enzyme assay, by various immunological methods, or by biochemical methods such as capillary electrophoresis, high performance liquid chromatography (HPLC), thin layer chromatography (TLC), hyper-diffusion chromatography, two-dimensional liquid phase electrophoresis (2-D- LPE) or by their migration pattern in gel electrophoreses, MRI such as ultra field 7T MRI or clinical 1 .5T or 3T MRI imaging or Quantitative Susceptibility Mapping (QSM).

Preferably the selection of the method for measuring brain iron load is dependent on whether the patient does or does not carry an AD risk variable. The presence of an AD risk variable (preferably patients identified as amyloid positive) is preferable for the measurement of CSF ferritin and CSF Ab. For the measurement of brain iron load using QSM, an AD risk variable is not required and preferably, the patient is amyloid negative.

Three main methods exist to quantify iron in vivo with MRI. 1 ) T2 ^* map: The presence of iron disturbs locally the coherent spins of protons, shortening T2 ^* relaxation time, which can be estimated using multiple gradient echo, (GRE) magnitude images. 2) QSM: Iron presence affects the susceptibility of tissues that can be mapped also using gradient echo phase images. 3) Field-Dependent Relaxation Rate Increase (FDRI): By using T2W-MRI data collected at two different field strengths (1 .5T, 3T & 7T), iron levels may be estimated.

QSM is a measure of magnetic susceptibility. Magnetic susceptibility is a measure of the magnetic properties of a material including tissue. The susceptibility indicates whether a material is attracted into or repelled out of a magnetic field. It can also be a measure the degree of magnetization of a material in response to an applied magnetic field. Hence, it often reflects iron levels in tissue where iron is the most abundant magnetic material in the tissue.

MRI may be used to measure brain iron load content, revealing iron elevation in the ageing brain, and that is exaggerated in AD. In cross sectional studies, an inverse correlation exists between brain iron load concentration and memory functions in subjectively impaired individuals and individuals with AD, however there has not been a longitudinal study on the impact of iron measured by MRI on AD pathologies. Applicants now show that that high brain iron load content translates to an earlier age onset associated with a greater rate of amyloid deposition and plaque formation.

However, it will be apparent to the skilled addressee that the appropriate technique used to identify the level of brain iron load, preferably ferritin or more preferably CSF ferritin will depend on the characteristics of the molecule. For example, if the molecule is iron, MRI or QSM-MRI may be used to quantify the level of the molecule.

In another example in determining the presence of ferritin or more preferably CSF ferritin, the level of the ferritin or more preferably CSF ferritin could be determined through ELISA techniques utilising a secondary detection reagent such as a tagged antibody specific for ferritin. To enhance the accuracy, the CSF sample taken from the patient may be pre-processed prior to detecting iron levels to remove other non- iron binding molecules, or other iron-binding molecules except ferritin. Hence the sample may be treated prior to assessment. In a non-limiting example where the iron binding molecule is protein, the level of protein can also be detected by an immunoassay. An immunoassay would be regarded by one skilled in the art as an assay that uses an antibody to specifically bind to the antigen (i.e. the protein). The immunoassay is thus characterised by detection of specific binding of the proteins to antibodies. Immunoassays for detecting proteins may be either competitive or non-competitive. Non-competitive immunoassays are assays in which the amount of captured analyte (i.e. the protein) is directly measured. In competitive assays, the amount of analyte (i.e. the protein) present in the sample is measured indirectly by measuring the amount of an added (exogenous) analyte displaced (or competed away) from a capture agent (i.e. the antibody) by the analyte (i.e. the protein) present in the sample.

In one example of a competition assay, a known amount of the (exogenous) protein is added to the sample and the sample is then contacted with the antibody. The amount of added (exogenous) protein bound to the antibody is inversely proportional to the concentration of the protein in the sample before the exogenous protein is added. In another assay, for example, the antibodies can be bound directly to a solid substrate where they are immobilized. These immobilised antibodies then capture the protein of interest present in the test sample. Other immunological methods include but are not limited to fluid or gel precipitation reactions, immunodiffusion (single or double), agglutination assays, Immunoelectrophoresis, radioimmunoassays (RIA), enzyme- linked immunosorbent assays (ELISA), Western blots, liposome immunoassays, complement-fixation assays, immunoradiometric assays, fluorescent immunoassays, protein A immunoassays or immunoPCR.

Ferritin can be measured conveniently by means of an enzyme-linked immunosorbent assay (ELISA) or any method available to the skilled addressee. Hence the brain iron load levels that are capable of providing an indication or prediction of an individual's likelihood of amyloid deposition and plaque formation or a rate at which amyloid deposition and plaque formation occurs potentially leading to conditions associated with amyloid deposition and plaque formation such as AD and related conditions, can be measured by any methods available to the skilled addressee preferably by measuring ferritin, most preferably CSF ferritin.

CSF ferritin is measured in CSF samples obtained from cerebral spinal fluid usually by lumbar puncture (spinal tap). As an example, CSF can be collected into polypropylene tubes or syringes and then be transferred into polypropylene transfer tubes without any centrifugation step followed by freezing on dry ice within 1 hour after collection. They may be analysed immediately, or frozen at -80°C. CSF ferritin protein levels were determined using Myriad Rules Based Medicine platform (Human Discovery MAP, v1 ) Accordingly the brain iron load levels may be measured using any available measurement technology capable of specifically determining the levels of the brain iron load from a subject or individual to be tested. The measurement may be either quantitative or qualitative, as long as the measurement is capable of indicating whether the level of brain iron load is above or below a reference value from a reference sample.

In another preferred embodiment, the level of brain iron load is determined by MRI, optionally ultra field 7T MRI or clinical 1 .5T or 3T MRI imaging or QSM.

Based on the finding that high brain iron load content relative to a reference level, as preferably measured via CSF ferritin, correlates with amyloid deposition and plaque formation, it is considered in the present invention that an increase in brain iron load and CSF ferritin would translate to a difference between the patient and the reference level. This difference assists in deducing a risk for amyloid deposition and plaque formation or a rate of future amyloid deposition and plaque formation for a patient.

A difference in brain iron load level which is an elevation between the patient and the reference level would indicate an increased risk or rate of amyloid deposition and plaque formation. The degree of elevation will provide an indication of whether there is a diagnosis or an assessment of risk or rate of amyloid deposition and plaque formation. A small elevation may indicate a small risk or small increase in a rate of amyloid deposition and plaque formation whereas a high elevation is likely to indicate a higher risk or rate of amyloid deposition and plaque formation. An increasing elevation between the patient and the reference level will indicate an increasing risk or rate of amyloid deposition and plaque formation.

For the purpose of brevity, some of the description contained herein will be made in the context of AD. It is considered however that the skilled addressee would be capable of understanding that the present invention may also be used as a prognostic or diagnostic or in aiding in the diagnosis/prognosis and/or monitoring of the progression of other conditions associated with amyloid deposition and plaque formation such as AD and related conditions such as but not limited to multiple sclerosis, cerebral palsy, Parkinson's disease, neuropathy (conditions affecting the peripheral nerves), dementia, dementia with Lewy bodies (DLB), multi-infarct dementia (MID), vascular dementia (VD), schizophrenia and/or depression, cognitive impairment and frontal temporal dementia which may be associated with amyloid deposition and plaque formation.

In another aspect of the invention there is provided a method of predicting a rate of amyloid deposition and plaque formation in a patient said method comprising:

determining a first level of brain iron load in the patient;

comparing the first level of brain iron load to a reference level of brain iron load;

determining a difference between the first level of brain iron load and the reference level;

deducing a rate of amyloid deposition and plaque formation in the patient from the difference.

The finding by the applicants that high brain iron load load is associated with an increased rate of amyloid deposition and plaque formation in patients can be used to predict a rate of progression toward conditions associated with amyloid deposition and plaque formation such as AD and related conditions. A difference in brain iron load level which is an elevation between the patient level and the reference level would indicate a greater chance of conditions associated with amyloid deposition and plaque formation such as AD and related conditions. The degree of elevation will provide an indication of the severity of the deterioration. A small elevation may indicate a risk whereas a high elevation is likely to indicate a diagnosis of eventual cognitive deterioration or even AD. An increasing elevation between the patient and the reference level will indicate an increasing amyloid deposition and plaque formation signalling a propensity for a faster rate of deterioration over time.

Moreover, a positive diagnosis of a determination of a propensity for amyloid deposition and plaque formation in a patient can be validated or confirmed if warranted, such as determining the amyloid load or amyloid level to confirm the presence of high neocortical amyloid. The terms "amyloid load" or "amyloid level", often used interchangeably, or "presence of amyloid and amyloid fragments", refers to the concentration or level of cerebral amyloid beta (Αβ or amyloid-β) deposited in the brain; amyloid-beta peptide being the major constituent of (senile) plaques.

A patient can also be confirmed as being positive for amyloid deposition and plaque formation using imaging techniques including, PET and MRI, or with the assistance of diagnostic tools such as PiB when used with PET (otherwise referred to as PiB-PET). Preferably, the patient that is positive for amyloid deposition and plaque formation is PiB positive. More preferably, the patient has a standard uptake value ratio (SUVR) which corresponds with high neocortical amyloid load (PiB positive). For instance, current practice regards a SUVR can reflect 1 .4 - 1 .5 as a high level in the brain and below 1 .4 - 1 .5 may reflect low levels of neocortical amyloid load in the brain. A skilled person would be able to determine what is considered a high or low level of neocortical amyloid load. As would be appreciated by one of skill in the art, a patient can also be confirmed as being positive for a neurological disease by measuring amyloid beta and tau from the CSF.

Furthermore, in characterising the diagnostic capability of brain iron load, preferably ferritin or more preferably CSF ferritin to predict a risk or a rate of amyloid deposition and plaque formation one of skill in the art may calculate a diagnostic cut-off for brain iron load, preferably ferritin or more preferably CSF ferritin. This cut-off may be a value, level or range. The diagnostic cut-off should provide a value level or range that assists in the process of attempting to determine or predict a rate of amyloid deposition and plaque formation. For example, the level of brain iron load, preferably ferritin or more preferably CSF ferritin may be diagnostic for a risk or a rate of amyloid deposition and plaque formation in a patient if the level is above the diagnostic cut-off. Alternatively, as would be appreciated by one of skill in the art, the level of brain iron load, preferably ferritin or more preferably CSF ferritin may be diagnostic for a risk or a rate of amyloid deposition and plaque formation in a patient if the level is below the diagnostic cut-off.

The diagnostic cut-off for brain iron load, preferably ferritin or more preferably CSF ferritin can be derived using a number of statistical analysis software programs known to those skilled in the art. As an example common techniques of determining the diagnostic cut-off include determining the mean of normal individuals and using, for example, +/- 2 SD and/or ROC analysis with a stipulated sensitivity and specificity value. Typically a sensitivity and specificity greater than 80% is acceptable but this depends on each disease situation. The definition of the diagnostic cut-off may need to be rederived if used in a clinical setting different to that in which the test was developed. To achieve this control individuals are measured to determine the mean +/- SD. As one of skill in the art would appreciate, using +/- 2 SD outside or away from the measurement obtained from control individuals can be used to identify individuals outside of the normal range. Individuals outside of the normal range can be considered positive for disease. The values obtained in a new clinical setting would then be compared to the historic values to determine if the old diagnostic criteria are still applicable as judged by a statistical test. Individuals known to have the disease condition would also be included in the analysis. In situations where both the disease and control state samples are available ROC analysis method with a chosen sensitivity and specificity may be chosen, typically 80%, to determine the diagnostic value that indicates a rate of amyloid deposition and plaque formation. The determination of the diagnostic cut-off can also be determined using statistical models that are known to those skilled in the art. The diagnostic cut-off to determine a high or low rate of amyloid deposition and plaque formation will be based on an averaged level of brain iron load, ferritin or CSF ferritin from a mean of individuals having the same conditions as that determined for the samples to be tested. Applicants have found that a suitable diagnostic cut off for brain iron load, ferritin or CSF ferritin is at 6.6ng/ml. Above this level the patient is considered to be high iron and below is considered as low iron. This demarcation point provides an indication of whether the patient will deposit amyloid and plaque at a faster or slower rate. The higher the iron level, the faster the deposition of amyloid and plaque formation occurs. Additionally, this level provides an indication as to whether the patient will or will not deposit amyloid and plaque in the foreseeable future.

A diagnostic cut off can also act as a reference value from which a change in brain iron load, preferably ferritin or more preferably CSF ferritin can be compared. It would be contemplated that the use of brain iron load, preferably ferritin or more preferably CSF ferritin in the methods of the present invention could also be used in combination with other methods of clinical assessment of a neurological disease known in the art in providing a prognostic evaluation of the presence of a neurological disease.

The definitive diagnosis can be validated or confirmed if warranted, such as through imaging techniques including, PET, QSM and MRI, or for instance with the assistance of diagnostic tools such as PiB when used with PET (otherwise referred to as PiB- PET).

In applying the methods of the present invention, it is considered that a clinical or near clinical determination regarding a rate of amyloid deposition and plaque formation in a patient can be made and which may or may not be conclusive with respect to the definitive diagnosis.

Similarly, the methods of the present invention can be used in providing assistance in the prediction of a rate of amyloid deposition and plaque formation and would be considered to assist in making an assessment of a pre-clinical determination regarding a propensity for amyloid deposition and plaque formation. This would be considered to refer to making a finding that a mammal has a significantly enhanced probability of developing AD. It would be understood by one skilled in the art that clinical determinations for the presence of amyloid deposition and plaque formation in combination with the assessment of the levels of brain iron load, preferably ferritin or more preferably CSF ferritin (preferably in conjunction with information regarding APOE genotype, CSF tau, Αβ and ApoE levels) would be considered in conjunction with assessments that include, but are not necessarily limited to, memory and/or psychological tests, assessment of language impairment and/or other focal cognitive deficits (such as apraxia, acalculia and left-right disorientation), assessment of impaired judgment and general problem-solving difficulties, assessment of personality changes ranging from progressive passivity to marked agitation. It would be contemplated that the methods of the present invention could also be used in combination with other methods of clinical assessment of a neurological disease known in the art in providing a prognostic evaluation of the presence of conditions associated with amyloid deposition and plaque formation such as AD and related conditions.

The definitive diagnosis of a risk or propensity for an increased rate of amyloid deposition and plaque formation of a patient suspected of cognitive deterioration can be validated or confirmed if warranted, such as through imaging techniques including, PET and MRI, or for instance with the assistance of diagnostic tools such as PiB when used with PET (otherwise referred to as PiB-PET). Accordingly, the methods of the present invention can be used in a pre-screening or prognostic manner to assess a patient for their risk of amyloid deposition and plaque formation and a potential rate of which amyloid deposition and plaque formation may occur, and if warranted, a further definitive diagnosis can be conducted with, for example, PiB-PET to clarify the patients' possible rate of deterioration over time.

In yet another aspect of the present invention there is provided a method for monitoring a rate of amyloid deposition and plaque formation in a patient, said method comprising:

determining a level of brain iron load in the patient at first time point; determining a level of brain iron load at in the same patient at a second time point which is after the first time point;

optionally comparing the levels of brain iron load from the first and second time points to a reference level;

determining a difference in the levels of brain iron load at each of the first and second time points;

deducing a rate of amyloid deposition and plaque formation from the difference in brain iron load levels from the first and the second time points. The changes in the levels of brain iron load can additionally be used in assessing for any changes in a rate at which amyloid deposition and plaque formation in a patient may occur. Accordingly, in the monitoring of the levels of brain iron load, it is possible to monitor a rate of amyloid deposition and plaque formation over a period of time, or to track deposition in a patient and whether the rate will increase or decrease over time.

Accordingly, changes in the level of brain iron load from a patient can be used to assess a rate for amyloid deposition and plaque formation, to diagnose or aid in the prognosis or diagnosis of amyloid deposition and plaque formation and/or to monitor progression toward conditions associated with amyloid deposition and plaque formation such as AD and related conditions in a patient (e.g., tracking progression in a patient and/or tracking the effect of medical or surgical therapy in the patient).

It may be contemplated to also relate to an altered level relative to a sample previously taken for the same mammal. Hence, there may not be a requirement to compare against a reference level such as from a CN sample. In this regard, a reference level may be the level of brain iron load at an earlier time point.

It is contemplated that levels for brain iron load can also be obtained from a patient at more than one time point. Such serial sampling would be considered feasible through the methods of the present invention related to monitoring a rate of amyloid deposition and plaque formation in a patient. Serial sampling can be performed on any desired timeline, such as monthly, quarterly (i.e. , every three months), semiannually, annually, biennially, or less frequently. The comparison between the measured levels of brain iron load and predetermined levels may be carried out each time a new sample is measured, or the data relating to levels may be held for less frequent analysis.

In another embodiment, the difference in brain iron load level is an elevation between the first and second time points such that the iron levels in the second time point are higher than the first time point relative to the reference level thereby indicating an increased a rate of amyloid deposition and plaque formation in a patient. Applicants have shown that patients with comparatively low ferritin (<6.6 ng/ml) will not deposit amyloid and plaque in the foreseeable future. This may potentially explain why 30% of ε4+νβ subjects do not develop AD. Conversely, each unit increase of ferritin above this threshold predicted more rapid deterioration. Hence changes in brain iron load or ferritin can signal greater deterioration rates over time. The methods of the invention can additionally be used for monitoring the effect of therapy administered to a mammal, also called therapeutic monitoring, and patient management. Changes in the level of brain iron load, preferably ferritin or more preferably CSF ferritin can be used to evaluate the response of a patient to drug treatment. In this way, new treatment regimens can also be developed by examining the levels of brain iron load, preferably ferritin or more preferably CSF ferritin in a patient following commencement of treatment for the patients' response to amyloid deposition and plaque formation.

A CSF sample may be pre-processed prior to assessment for ferritin levels to remove unbound iron.

The method of the present invention can thus assist in monitoring a clinical study, for example, for evaluation of a certain therapy for a neurological disease. For example, a chemical compound can be tested for its ability to normalise the level of brain iron load, preferably ferritin or more preferably CSF ferritin in a patient that has a propensity to deposit amyloid and plaque to levels found in controls or CN patients. In a treated patient, a chemical compound can be tested for its ability to maintain the levels of brain iron load, preferably ferritin or more preferably CSF ferritin at a level at or near the level seen in controls or CN patients.

In yet another embodiment, the present method further includes determining a level of a biomarker of amyloid deposition and plaque formation such as but not limited to amyloid β peptides, tau, phospho-tau, synuclein, Rab3a, Αβ and neural thread protein. These additional biomarkers may be used singularly or in combination with the method to assess amyloid deposition and plaque formation. Hence the methods of the present invention need not be limited to assessing only brain iron load, preferably ferritin or more preferably CSF ferritin for determining amyloid deposition and plaque formation. These additional markers may enhance the accuracy of the method for determining a risk and rate of amyloid deposition and plaque formation in a patient and reduce false positives in the assessment. Moreover, these additional measures will assist in the techniques used for determining brain iron load. For instance brain iron load levels in patients that are amyloid positive (Ab+ve) are best determined by CSF ferritin levels whereas patients that are amyloid negative (Ab-ve) are best determined by QSM.

In another aspect of the invention there is provided a method for diminishing progression rate of amyloid deposition and plaque formation in a patient, said method comprising lowering brain iron load levels.

This method is based on the finding that CN individuals have enhanced rates of amyloid deposition and plaque formation when they have higher CSF ferritin levels. By measuring the CSF ferritin levels, applicants have correlated the measurements to brain iron load and a measure of a future rate of amyloid deposition and plaque formation. Without being limited by theory, lowering brain iron load, may lower the CSF ferritin levels associated with amyloid deposition and plaque formation such that the future rate of amyloid deposition and plaque formation is reduced and hence the amyloid deposition and plaque formation is reduced.

In another aspect of the invention there is provided a method for diminishing progression rate of amyloid deposition and plaque formation in patients, said method comprising lowering CSF ferritin levels.

To lower brain iron load or CSF ferritin levels compounds such as iron chelators such as Deferiprone may be used. However other compounds that would similarly lower brain iron load or CSF ferritin are also included in the scope of the present invention. The administration of an iron chelator to a patient may reduce the levels of iron in the brain or the CSF in the form of CSF ferritin. This will be particularly effective for patients that already show signs of amyloid deposition and plaque formation. Since high CSF ferritin levels correlate to high brain iron load, patients that are already amyloid positive may also benefit from this treatment.

Administration of an iron chelator or an iron lowering drug may be made via any suitable route such as intravenously, subcutaneously, parenterally, orally or topically providing the drug is able to access the area to be treated to lower the iron levels. Improvements may be determined by methods to assess amyloid deposition and plaque formation as herein described.

In a further aspect, the present invention provides a kit that can be used for the diagnosis and/or prognosis in a patient of a future rate of amyloid deposition and plaque formation or for identifying a patient at risk of amyloid deposition and plaque formation.

Accordingly, the present invention provides a kit that can be used in accordance with the methods of the present invention for diagnosis and/or prognosis in a patient to determine a rate of amyloid deposition and plaque formation or for identifying a patient at risk of amyloid deposition and plaque formation, or for monitoring the effect of therapy administered to a patient for amyloid deposition and plaque formation. The kit may comprise a panel of reagents, that can include, but are not necessarily limited to, polypeptides, proteins, and/or oligonucleotides that are specific for determining levels of brain iron load, preferably ferritin or more preferably CSF ferritin. Accordingly, the reagents of the kit may be used to determine the level of brain iron load, preferably ferritin or more preferably CSF ferritin to indicate that a subject has a propensity for amyloid deposition and plaque formation and to predict a future rate of amyloid deposition and plaque formation. The reagents will be capable of use in any of the methods that will detect brain iron load, preferably ferritin or more preferably CSF ferritin such as but not limited to 2D DGE, mass spectrometry (MS) such as multiple reaction monitoring mass spectrometry (MRM-MS), Real Time (RT)-PCR, nucleic acid array; ELISA, functional assay, by enzyme assay, by various immunological methods, or by biochemical methods such as capillary electrophoresis, high performance liquid chromatography (HPLC), thin layer chromatography (TLC), hyper-diffusion chromatography, two-dimensional liquid phase electrophoresis (2-D- LPE) or by their migration pattern in gel electrophoreses. For instance, it is envisioned that any antibody that recognises brain iron load, preferably ferritin or more preferably CSF ferritin can be used in the kit.

In a preferred embodiment, the present invention provides a kit of reagents for use in the methods for the screening, diagnosis or prognosis in a patient for amyloid deposition and plaque formation, wherein the kit provides a panel of reagents to quantify the level of at least brain iron load, preferably ferritin or more preferably CSF ferritin in a sample from a mammal. In an even further embodiment, the kit further provides means to determine other AD risk variables such as but not limited to ΑΡΟΕ-ε4, CSF tau/Ap _-42 and ApoE for use in combining with the panel of reagents to quantify the level of brain iron load, preferably ferritin or more preferably CSF ferritin in a sample from a mammal. The AD risk variables may be determined by quantifying amyloid β peptides, tau, phospho-tau, synuclein, Rab3a, Αβ or neural thread protein. Hence reagents suitable to determine these risk variables may be included in the kit.

A person skilled in the art could use any suitable reagents to determine and quantify the presence of the AD risk variables, APOE-zA, CSF tau/Ap _-42 and ApoE and more preferably the amyloid β peptides, tau, phospho-tau, synuclein, Rab3a, Αβ and neural thread proteins.

Other aspects of the present invention will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments of the invention.

Where the terms "comprise", "comprises", "comprised" or "comprising" are used in this specification (including the claims) they are to be interpreted as specifying the presence of the stated features, integers, steps or components, but not precluding the presence of one or more other features, integers, steps or components, or group thereof.

The present invention will now be more fully described by reference to the following non-limiting Examples. EXAMPLES

Example 1 : Ferritin levels in the cerebrospinal fluid predict rate of Αβ deposition in biomarker determined AD

Ferritin is the major iron storage protein of the body; by using cerebrospinal fluid (CSF) levels of ferritin as an index, brain iron load status impact on longitudinal outcomes was studied in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.

This example shows that CSF ferritin, a reporter of brain iron load load, was associated with changes in the CSF biomarkers, β-amyloid and tau, over 5 years in 296 participants along the Alzheimer's disease spectrum. In subjects with biomarker- confirmed Alzheimer's pathology, high CSF ferritin (>6.6 ng/ml) was associated with accelerated depreciation of CSF Αβι _-42 levels (reporting increased Αβ-plaque) over 5- years (P=0.007). CSF ferritin was neither associated with changes in CSF tau in the same subjects, nor changes in CSF tau or Αβ _-42 levels in subjects with low baseline pathology. Elevated brain iron load levels in Alzheimer's might therefore facilitate Αβ deposition and accelerate disease progression.

(i) Methods

ADNI description

Data were downloaded on 15 July 2014 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI study protocols and patient inclusion criteria have been reported previously ^{1 2} . The collection and storage procedures for CSF have been previously described ¹ . Baseline CSF levels of ApoE, and ferritin were measured with the RBM multiplex platform ³ , and yearly levels of CSF Αβ _-42 and tau were measured with the multiplex xMAP Luminex platform, as previously described ¹ . Linear mixed-models were used to assess the relationship between baseline CSF ferritin and CSF tau or Αβι _-42 levels collected annually for up to 5 years. Data from subjects who left prematurely were included to the point of leaving. Models were performed in R (version 3.2.4) and tested for normal distribution of the residuals and absence of multicolinearity. Minimal models were obtained using Bayesian information criterion. Hypothesis tests were 2- sided. Significance was inferred when P<0.05. 296 participants had baseline CSF measurements of CSF ferritin and repeated measurement of CSF tau and Αβ _-42 annually for up to 5 years (Table 1). The cohort was stratified into subjects with either the absence or presence of AD pathology by using a threshold in the tau/APi _-42 ratio (0.39 units) that has previously been determined for this cohort ¹ . Subjects were classified into those with high or low ferritin based on a threshold (6.6 ng/ml) that were previously showed as highly predictive of cognitive decline ⁴ . Table 1. Subject characteristics

CSF biomarker collection and analysis.

CSF was collected once in a subset of ADNI participants at baseline. Abi_ ₂ and tau levels in CSF were measured using the Luminex platform. ApoE and ferritin protein levels were determined using a Myriad Rules Based Medicine platform (Human Discovery MAP, v1 .0; see ADNI materials and methods). CSF Factor H (FH) levels were measured using a multiplex human neurodegenerative kit (HNDG1 -36K; Millipore, Billerica, MA) according to the manufacturer's overnight protocol with minor modifications.

CSF was collected into polypropylene tubes or syringes provided to each site, and then was transferred into polypropylene transfer tubes without any centrifugation step followed by freezing on dry ice within 1 h after collection for subsequent shipment overnight to the ADNI Biomarker Core laboratory at the University of Pennsylvania Medical Center on dry ice. Aliquots (0.5 ml) were prepared from these samples after thawing (1 h) at room temperature and gentle mixing. The aliquots were stored in bar code-labelled polypropylene vials at -80°C. Fresh, never before thawed, 0.5ml aliquots for each subject's set of longitudinal time points were analysed on the same 96-well plate in the same analytical run for this study to minimize run to run and reagent kit lot sources of variation. Within run coefficient of variation (%CV) for duplicate samples ranged from 2.5 to 5.9% for Ab-i_ ₄₂ , 2.2-6.3% for tau and the inter- run %CV for CSF pool samples ranged from 5.1 to 14% for Ab1-42, 2.7-1 1 .2% for tau.

MRI

Subjects underwent 3D T1 -weighted MPRAGE, 3D T2 ^* -weighted gradient echo, and 3D fluid-attenuated inversion recovery (FLAIR), on a 3T Siemens TRIO scanner (12- channel head coil). The T1 -weighted sequence was acquired with 1 x1 mm in-plane resolution and 1 .2 mm slice thickness, TR/TE/TI = 2300/2.98/900 ms, flip angle 9°, field of view 256 χ 256, and 160 slices. 3D MR images used for QSM were acquired with 0.93x0.93/77/7? in-plane resolution and 1 .75/77/77 slice thickness, TR/TE = 27/20 msec, flip angle 20° and field-of-view 240 χ 256, for 80 slices. The magnitude and phase images were retrospectively reconstructed for each head coil channel from the k-space data. The FLAIR sequence was acquired with 0.98 χ 0.98 mm in plane resolution and 0.9 mm slice thickness, TR/TE/TI = 6000/421/2100 ms, flip angle 120°, field of view 256 χ 240, and 176 slices. Structural MRI Processing

The low frequency intensity non-uniformity present in T1 -weighted and FLAIR images was corrected using the N4 bias field correction ⁵ . The MRI data were rigidly aligned to MNI space using the open source Mirorr tool ⁶ . T1 -weighted data were then parcellated into 45 grey-matter and 34 white-matter regions by segmentation propagation of an atlas database that was previously parcellated using Automated Anatomical Labeling ⁷ and FreeSurfer (FS) white matter parcellations ⁸ , respectively. White-matter lesions (WML) were segmented and masked out from the parcellated regions using the LST-LGA v2.0.15. The combination of T1 -weighted and FLAIR images with a kappa=0.1 were used ⁹ QSM-MRI reconstruction and processing

A brain mask was generated from the bias-field corrected combined magnitude image (after combining the coil data) using FSL's BET with the robust parameter set. A Laplacian-based method was used to unwrap each coil phase image followed by background field elimination using vSHARP ¹⁰ . The corrected phase images were then combined by weighting the magnitude of the corresponding channel. STI Suite software (v2.2) ¹¹ was used for QSM reconstruction by performing dipole inversion using an iLSQR technique.

The middle-frontal white matter region was chosen as a reference region for normalizing QSM values. The cortical contribution of vascular iron (in blood vessels and large microbleeds) was removed by performing an automated series of image processing operations: maximum intensity projection on QSM image along superior- anterior direction, thresholding, morphological filtering, and finding spherical- and cylinder-like structures based on the property of the connected components.

PET

The carbon-1 1 -labelled Pittsburgh compound B (1 1 C-PiB) PET scans were performed using a Phillips Allegro (Phillips Medical Systems, Eindhoven, The Netherlands) camera. Each subject received -370 MBq ¹¹ C-PiB IV over 1 minute. A 30-minute acquisition in 3D mode consisting of 6 frames each of 5 minutes, starting 40 minutes after PiB infusion. A transmission scan was performed for attenuation correction. PET images were reconstructed using a 3D Ramla algorithm.

PET Processing

¹¹ C-PiB scans were processed using the CapAIBL method ¹² . In brief, an adaptive atlas was automatically fitted to each PET image to match its PET retention pattern. Each PET image was then spatially normalized to the best fitting atlas, and rescaled using the standardized uptake value (SUVR). Neocortical retention was estimated using a composite region of frontal, parietal, lateral temporal, lateral occipital lobe, and anterior and posterior cingulate. A neocortical SUVR >1 .5 was considered Αβ+ve ¹³ , while a neocortical SUVR <1 .5 was considered Αβ-ve. Statistical analysis.

All statistical work was conducted with R (version 3.1 .0). The conditions necessary to apply the regression models, normal distribution of the residuals and the absence of multicollinearity were tested. All models satisfied these conditions. Minimal models were obtained via step down regression using Akaike information criterion (AlC), and Bayesian information criterion (BIC), ensuring that the central hypotheses were maintained. Subjects were excluded from analysis if they had one or more covariates missing. Where subjects prematurely left the study, their data were included in modelling to the point at which they left. The following variables were natural log- transformed to ensure normality: CSF ferritin, Factor H, tau and haemoglobin.

(i) Results

In a mixed effects model of CSF levels of Αβ _-42 controlling for age, sex, APOE ε4, CSF ApoE, CSF tau, and diagnosis, high baseline CSF ferritin was not associated with change in Αβ _-42 in subjects with low baseline tau/Ap _-42 (P=0.549). However, in subjects with elevated baseline CSF tau/Ap _-42 ratio (those with AD pathology), high baseline CSF ferritin strongly predicted decreasing levels of CSF Αβι _-42 (reporting Αβ deposition) over the 5-year period (β [S.E.] = -1 .9 [0.7], P = 0.007; Figure 1 ). In mixed-effects models of CSF tau, high baseline CSF ferritin was not significantly associated with annual change in tau levels over the study period, both in subjects with low baseline CSF tau/Ap _1-42 (P=0.916), or high baseline CSF tau/Ap _1-42 (P=0.088).

CSF biomarker Low tau/ 1-42 High tau/ApL 42

p ^* (S.E.) P P* (S.E.) P

Αβ _1-42 0.463 (1 .6 9) 0.785 -1 .907 (0.70) 0.007 tau -0.069 (0.6 35) 0.916 -2.338 (1 .36) 0.088

Table 2. Association between change in CSF biomarkers and baseline CSF ferritin. Separate mixed effects linier models of annual measurements of CSF tau, Αβ _-42 . Models were adjusted for age, sex, APOE ε4, diagnosis, and CSF ApoE. Models were additionally controlled for CSF tau or Αβι _-42 in the models where these variables were not the outcome variable (i.e. tau was included as a controlling variable in mixed effects models of Αβι _-42 ). ^* beta coifficent is annual change in biomarker associated with having high (>6.6 ng/ml) baseline CSF ferritin.

Monitoring of Αβ load was performed using PET in a separate cohort of 98 subjects across the AD spectrum (57 cognitively normal, 17 MCI and 24 AD). Patients had baseline QSM scan, and PET scans to detect amyloid (reported by SUVR level) every 1 .5 years for up to 7.5 years. In a mixed effects model of SUVR levels (controlling for APOE ε4, age, sex, diagnosis), high neocortical QSM was associated more rapid accumulation of SUVR in individuals with baseline levels of SUVR below 1 .6 (P= 0.002). In individuals with baseline levels of SUVR above 1 .6, high QSM was associated with an elevation in SUVR (p=0.012) but not rate of change in SUVR. (P>0.05)

This example provides evidence that iron elevation, as reported by CSF ferritin or QSM-MRI, may accelerate amyloid deposition in people with biomarker-determined AD pathology. These findings accord with a recent cross-sectional study showing the association between iron elevation (measured with MRI) and amyloid deposition (measured with PET) in vivo . Iron oxide magnetite particles have also recently been observed in the core of Αβ plaque, suggesting that iron might be involved in plaque genesis ¹⁵ . Numerous other papers have reported that iron is enriched in Αβ plaque ^{16, 17} , iron increases the aggregation of Αβ in w ^' iro ^18"20 and deposition of Αβ in mouse models ^{21 , 22} , and iron elevation increases the translation of the amyloid precursor protein via a iron responsive element in the 5' untranslated mRNA ²³ , which are possible biochemical mechanisms that underlie our clinical observations.

In conclusion, it is now shown that brain iron load levels in AD might facilitate the deposition of Αβ and accelerate the disease processs.

Example 2: Assessing a risk of amyloid deposition and plaque formation in a patient

In conducting the methods of the present invention, it is contemplated that a patient will be assessed for an established AD risk variable and a baseline level of Αβ. This level will set a base for determining whether they will over time deposit amyloid and plaque. A CSF sample may be obtained and the CSF ferritin level determined by methods such as immunoassay. This sample may then be compared to a predetermined sample from a CN patient processed in the same manner.

A difference in the CSF ferritin levels of the patient and that of the CN patient will be determined. Depending on the degree of difference, the degree of amyloid deposition and plaque formation can be determined. If the difference is large and the CSF ferritin level of the patient is high relative to the CN patient level, the patient presenting for assessment may show a higher risk of amyloid deposition and plaque formation. If the difference is small relative to the CN patient level, the patient presenting for assessment may show a lower risk of amyloid deposition and plaque formation. This test may be conducted in parallel to determining the genotype of the patient. If the patient carries the Apo ε4 allele, the risk of amyloid deposition and plaque formation will be higher and a rate of amyloid deposition and plaque formation will be higher. Example 3: Monitoring amyloid deposition and plaque formation in a patient

A patient is tested according to Example 2 at a first time point. A second test is conducted at another time point after the first time point. The difference between the patient CSF ferritin and a reference level from a CN patient is assessed. This difference may then be compared to the difference from the first time point.

If the difference is greater, the deposition of amyloid and plaque will have advanced.

The patient may be diagnosed as having a greater propensity for depositing amyloid and plaque based in the increasing CSF ferritin levels.

Example 4: Diminishing progression rate of amyloid deposition and plaque formation in a patient A patient is assessed as in Example 2 for the level of amyloid deposition and plaque formation based on their CSF ferritin levels. Deferiprone is administered to the patient for a time and a dose calculated by the size, age and weight of the patient. The patient is reassessed for amyloid deposition and plaque formation after a time to assess whether amyloid deposition and plaque formation has been diminished.

While the foregoing written description of the invention enables one of ordinary skill to make and use what is considered presently to be the best mode thereof, those of ordinary skill will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiment, method, and examples herein. The invention should therefore not be limited by the above described embodiment, method, and examples, but by all embodiments and methods within the scope and spirit of the invention as broadly described herein.

REFERENCES

I . Shaw LM, Vanderstichele H, Knapik-Czajka M, et al. Cerebrospinal Fluid Biomarker Signature in Alzheimer's Disease Neuroimaging Initiative Subjects. Annals of neurology. 2009 Apr;65(4):403-13.

2. Weiner MW, Veitch DP, Aisen PS, et al. The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2012 Feb;8(1 Suppl):S1 -68.

3. Ayton S, Faux NG, Bush Al, Alzheimer's Disease Neuroimaging I. Ferritin levels in the cerebrospinal fluid predict Alzheimer's disease outcomes and are regulated by APOE. Nature communications. 2015;6:6760.

4. Ayton S, Faux NG, Bush A. Assocaiton of cerebrospinal fluid ferritin level with preclinical cognitive decline in ΑΡΟΕ-ε4 carriers. JAMA neurology. 2016;doi: 10.1001 /jamaneurol.2016.4406.

5. Tustison NJ, Avants BB, Cook PA, et al. N4ITK: Improved N3 Bias Correction. IEEE transactions on medical imaging. 2010 Jun;29(6): 1310-20.

6. Rivest-Henault D, Dowson N, Jason PB, Fripp J, Dowling JA. Robust inverse- consistent affine CT-MR registration in MRI-assisted and MRI-alone prostate radiation therapy. Medical image analysis. 2015 Jul;23(1 ):56-69.

7. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neurolmage. 2002 Jan; 15(1 ):273-89.

8. Salat DH, Greve DN, Pacheco JL, et al. Regional white matter volume differences in nondemented aging and Alzheimer's disease. Neurolmage. 2009 Feb 15;44(4): 1247-58.

9. Schmidt P, Gaser C, Arsic M, et al. An automated tool for detection of FLAIR- hyperintense white-matter lesions in Multiple Sclerosis. Neurolmage. 2012 Feb 15;59(4):3774-83.

10. Wu B, Li W, Guidon A, Liu C. Whole brain susceptibility mapping using compressed sensing. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine. 2012 Jan;67(1 ): 137-47.

I I . Li W, Avram AV, Wu B, Xiao X, Liu C. Integrated Laplacian-based phase unwrapping and background phase removal for quantitative susceptibility mapping. NMR in biomedicine. 2014 Feb;27(2):219-27.

12. Bourgeat P, Villemagne VL, Dore V, et al. Comparison of MR-less PiB SUVR quantification methods. Neurobiology of aging. 2015 Jan;36:S159-S66.

13. Rowe CC, Ellis KA, Rimajova M, et al. Amyloid imaging results from the

Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Neurobiology of aging. 2010 Aug;31 (8): 1275-83.

14. van Bergen JM, Li X, Hua J, et al. Colocalization of cerebral iron with Amyloid beta in Mild Cognitive Impairment. Sci Rep. 2016 Oct 17;6:35514.

15. Plascencia-Villa G, Ponce A, Collingwood JF, et al. High-resolution analytical imaging and electron holography of magnetite particles in amyloid cores of

Alzheimer's disease. Sci Rep. 2016;6:24873.

16. Meadowcroft MD, Peters DG, Dewal RP, Connor JR, Yang QX. The effect of iron in MRI and transverse relaxation of amyloid-beta plaques in Alzheimer's disease.

NMR in biomedicine. 2014 Dec 22.

17. Goodman L. Alzheimer's disease; a clinico-pathologic analysis of twenty-three cases with a theory on pathogenesis. The Journal of nervous and mental disease. 1953 Aug; 1 18(2):97-130. 18. Mantyh PW, Ghilardi JR, Rogers S, et al. Aluminum, iron, and zinc ions promote aggregation of physiological concentrations of beta-amyloid peptide. J Neurochem. 1993 Sep;61 (3): 1 171 -4.

19. Mantyh PW, Ghilardi JR, Rogers S, et al. Aluminum, iron, and zinc ions promote aggregation of physiological concentrations of beta-amyloid peptide. J

Neurochem. 1993 Sep;61 (3): 1 171 -4.

20. Huang X, Atwood CS, Moir RD, Hartshorn MA, Tanzi RE, Bush Al. Trace metal contamination initiates the apparent auto-aggregation, amyloidosis, and oligomerization of Alzheimer's Abeta peptides. J Biol Inorg Chem. 2004 Dec 1 ;9(8):954-60.

21 . Sripetchwandee J, Wongjaikam S, Krintratun W, Chattipakorn N, Chattipakorn SC. A combination of an iron chelator with an antioxidant effectively diminishes the dendritic loss, tau-hyperphosphorylation, amyloids-beta accumulation and brain mitochondrial dynamic disruption in rats with chronic iron-overload. Neuroscience. 2016 Sep 22;332: 191 -202.

22. Becerril-Ortega J, Bordji K, Freret T, Rush T, Buisson A. Iron overload accelerates neuronal amyloid-beta production and cognitive impairment in transgenic mice model of Alzheimer's disease. Neurobiology of aging. 2014 May 1 .

23. Rogers JT, Randall JD, Cahill CM, et al. An iron-responsive element type II in the 5'-untranslated region of the Alzheimer's amyloid precursor protein transcript. The

Journal of biological chemistry. 2002 Nov 22;277(47):45518-28.