Methods for diagnosing and predicting the progression of neurodegenerative diseases or disorders - Patents.com

Abstract

The present invention relates to a method for determining a diagnosis of a neurodegenerative disease or disorder in a subject, a probability of the subject developing a neurodegenerative disease or disorder, and / or a score indicative of disease progression of a neurodegenerative disease or disorder based on comparing the methylation status to a reference pattern or comparing the methylation status and a further marker to a reference pattern. The methylation status can be obtained, for example, from cell-free DNA in a plasma sample. The reference pattern can be embodied in a library or storage device and can be obtained, for example, by machine learning techniques, from a reference subject. The present invention further relates to a method for monitoring a neurodegenerative disease or disorder, for example Alzheimer's disease or Parkinson's disease.

Description

[Technical field]

[0001] The present invention relates to a method for determining a diagnosis of a neurodegenerative disease or disorder in a subject, a probability of the subject developing a neurodegenerative disease or disorder, and / or a score indicative of disease progression of a neurodegenerative disease or disorder based on comparing the methylation status to a reference pattern or comparing the methylation status and a further marker to a reference pattern. The methylation status can be obtained, for example, from cell-free DNA in a plasma sample. The reference pattern can be embodied in a library or storage device and can be obtained, for example, by machine learning techniques, from a reference subject. The present invention further relates to a method for monitoring a neurodegenerative disease or disorder, for example Alzheimer's disease or Parkinson's disease. [Background technology]

[0002] Neurodegenerative diseases or disorders are the leading cause of morbidity and mortality in aging societies, for which no cure has yet been established. For example, at least 40 million people worldwide suffer from dementia, and that number could increase to 120 million in the next 30 years (Alzheimer's disease facts and figures, 2020, Alzheimer's & Dementia 16,391-460). Therefore, there is a strong need to better understand the etiology and pathogenesis of neurodegenerative diseases and disorders. Dementia is a multifactorial neurodegenerative disease caused by a combination of genetic and environmental factors. Alzheimer's disease (AD) is the most common type of dementia and the leading cause of death. The pathogenesis leading to dementia may begin decades before the first clinical symptoms appear, for which unbiased, quantitative and unambiguous measurements are lacking (Weller, J. & Budson, A., 2018, F1000Res 7). Clinically, AD is characterized by progressive impairment of memory and cognitive function accompanied by abnormal protein deposition and neuronal loss. Neuropathological changes in AD begin with extracellular deposition of β-amyloid peptides in senile plaques, decades before the first clinical symptoms appear (Makin, S., 2018, Nature 559, S4-S4). Abnormal β-amyloid peptides may promote the accumulation of hyperphosphorylated and fibrillary tau protein in intraneuronal neurofibrillary tangles, suggesting that the two disease hallmarks, senile plaques and neurofibrillary tangles, may cooperate in the progression of AD (Kametani, F. &Hasegawa, M., 2018, Front.Neurosci.12).

[0003] Deposition of neurofibrillary tangles in AD brains follows a stereotypical pattern, beginning in the entorhinal cortex, progressing to the hippocampus, and invading the frontal, temporal, and parietal cortices, consistent with the severity of neuronal loss and cognitive impairment (Schultz, SA et al. , 2018, Neurobiology of Aging 72, 177-185).

[0004] Neurodegenerative diseases and disorders are not normal manifestations of aging, and their causes remain unclear. When AD begins before age 65, it is classified as early-onset AD, and is most often an expression of autosomal dominant genetic comorbidities, such as inherited mutations in genes encoding amyloid precursor protein and presenilin, which cause familial AD (Desikan, RS et al., 2017, PLOS Medicine 14, e1002258; Bertram, L, Lili, CM & Tanzi, RE, 2010, Neuron 68, 270- 281). Late-onset AD is seen in more than 90% of cases, but without clear genetic association (sporadic AD), suggesting that environmental and lifestyle factors may negatively affect the genetic program of cells. This epigenetic regulation includes modifications of histones, other regulatory proteins, non-coding RNA, and DNA.

[0005] The accuracy of clinical diagnosis of AD reaches up to 75% (Hansson O. Nat Med.2021 Jun;27(6):954-963), and therefore often improves to 90-92% when complemented by measuring beta-amyloid peptides (Aβ42, Aβ40, Aβ42 / Aβ40 ratio, phosphorylated tau (pTau181) in the cerebrospinal fluid (El Kadmiri, N., et al., 2018, S. Neuroscience 370, 181 -190; Blennow, K., Hampel, H., Weiner, M. & Zetterberg, H., 2010, Nature Reviews Neurology 6, 131 -144). Amyloid or tau positron emission tomography (PET) is an emerging technique to detect protein deposits in the brain and represents an instrumental solution for the diagnosis of AD (Lemoine, L, et al., 2018, Alzheimer's & Dementia:Diagnosis, Assessment s Disease Monitoring 10, 232-236; 8.Palmqvist, S. et al., 2014, JAMA NeurologyTi, 1282-1289); however, their use before signs of cognitive decline is rare and costly. Changes related to disease-nonspecific neurodegenerative processes are detected, for example, as atrophy of certain brain regions using MRI, or as an increase in total tau or neurofilament light chains released from dying neurons in the cerebrospinal fluid. These IVD (in vitro diagnostic) markers do not show satisfactory specificity in the early stages of dementia, requiring relatively invasive interventions. New IVD blood markers currently under investigation, such as the Aβ42 / Aβ40 ratio, phosphorylated tau (pTau217), neurofilament light chains, and glial fibrillary acidic protein (GFAP), reveal ongoing neuroinflammation. Summary of the Invention [Problem to be solved by the invention]

[0006] Thus, there is a need for improved methods for diagnosing and / or predicting the progression of neurodegenerative diseases or disorders, and / or distinguishing between different diseases or disorders. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] The above technical problems are solved by the embodiments disclosed herein and defined in the claims. Thus, the present invention relates in particular to the following embodiments:

[0008] 1. A method for diagnosing a neurodegenerative disease or disorder in a subject, determining a score indicative of the probability that the subject will develop a neurodegenerative disease or disorder, and / or disease progression of a neurodegenerative disease or disorder, comprising the steps of: a) obtaining from a subject's sample the methylation status of at least one of two or more genes selected from Table 1 and / or two or more regions selected from Table 2; b) comparing the methylation status obtained in (a) with a reference pattern; and c) determining a score based on the comparison obtained in (b) that is indicative of a diagnosis of a neurodegenerative disease or disorder in the subject, a probability that the subject will develop a neurodegenerative disease or disorder, and / or disease progression of the neurodegenerative disease or disorder.

[0009] 2. The method of embodiment 1, wherein the sample is a body fluid sample, preferably a plasma or serum sample, more preferably a plasma sample.

[0010] 3. The method of embodiment 2, wherein the sample is frozen or has been frozen.

[0011] 4. The method of any one of embodiments 1 to 3, wherein the methylation status is obtained from cell-free DNA in the sample.

[0012] 5. The method of any one of embodiments 1 to 4, wherein obtaining at least one methylation state comprises genomic methylation profiling.

[0013] 6. The method according to any one of the preceding embodiments, wherein the at least one methylation status comprises the methylation status of at least 100 regions selected from Table 2, preferably at least 150 regions selected from Table 2, preferably at least 200 regions selected from Table 2, more preferably between 200 and 600 regions selected from Table 2, more preferably between 250 and 500 regions selected from Table 2, more preferably between 250 and 300 regions selected from Table 2.

[0014] 7. The method of embodiment 6, wherein the at least one methylation status comprises the methylation status of at least 80% of the regions selected from Table 3, preferably 90% of the regions selected from Table 3, more preferably all of the regions selected from Table 3.

[0015] 8. a) an average nucleotide width of less than 5000, preferably less than 4000, more preferably less than 3000, even more preferably less than 2000, even more preferably less than 1000; and / or b) a median nucleotide width of less than 5000, preferably less than 4000, more preferably less than 3000, even more preferably less than 2000, more preferably less than 1000 4. The method of any of the previous embodiments, wherein the methylation status is determined in a region having the formula:

[0016] 9. The method according to any one of the preceding embodiments, wherein in a) at least one further marker is obtained compared to (b).

[0017] 10. The method of embodiment 9, wherein the at least one further marker is a marker selected from the group of subject background data, cognitive function markers, autonomic nervous system biomarkers.

[0018] 11. The method according to any one of embodiments 1 to 10, wherein the reference pattern is obtained from the methylation status of two or more genes selected from Table 1 and / or two or more regions selected from Table 2 from samples of at least two reference subjects, at least one of the reference subjects suffering from a neurodegenerative disease or disorder.

[0019] 12. The method according to embodiment 10 or 11, wherein obtaining a reference pattern from the methylation status of two or more genes selected from Table 1 and / or two or more regions selected from Table 2 from samples of at least two reference subjects comprises machine learning techniques.

[0020] 13. A method for monitoring a neurodegenerative disease or disorder, comprising the steps of: i) determining at a first time point a first score indicative of the subject's probability of developing a neurodegenerative disease or disorder and / or disease progression of the neurodegenerative disease or disorder according to a method according to any one of embodiments 1 to 12; ii) determining, at a second time point, a second score indicative of the subject's probability of developing a neurodegenerative disease or disorder and / or disease progression of the neurodegenerative disease or disorder according to a method according to any one of embodiments 1 to 12; iii) comparing the first score from step (i) with the second score from step (ii); and iv) monitoring disease progression of the neurodegenerative disease or disorder in the subject based on the comparison of step (iii).

[0021] 14. A library comprising at least one score indicative of the probability that a subject will develop a neurodegenerative disease or disorder and / or disease progression of a neurodegenerative disease or disorder, determined according to any one of embodiments 1 to 12.

[0022] 15. A storage device containing computer readable program instructions for carrying out the method according to any one of embodiments 1 to 12, preferably further comprising the library according to embodiment 14.

[0023] 16. A server comprising the storage device of embodiment 15, at least one processing device, and a network connection for receiving data indicative of at least one methylation state.

[0024] 17. The method according to any one of embodiments 1 to 13, the library according to embodiment 14, the storage device according to embodiment 15, and the server according to embodiment 16, wherein the neurodegenerative disease or disorder is a disease or disorder characterized by cognitive dysfunction.

[0025] 18. The method according to any one of embodiments 1 to 13, the library according to embodiment 14, the storage device according to embodiment 15, and the server according to embodiment 16, wherein the neurodegenerative disease or disorder is Alzheimer's disease and / or Parkinson's disease.

[0026] 19. The method of embodiment 18, the library of embodiment 18, the storage device of embodiment 18, or the server of embodiment 18, wherein the neurodegenerative disease or disorder is Alzheimer's disease.

[0027] 20. A device comprising a methylation-specific oligonucleotide probe, wherein the probe is specific for determining the methylation status of at least 80% of a region selected from Table 3, preferably all of the regions selected from Table 3.

[0028] 21. The device of claim 20, wherein the device is a microarray.

[0029] 22. Use of a device according to claim 20 or 21 for the following method: a) determining a score indicative of the diagnosis of a neurodegenerative disease or disorder in a subject, the probability that a subject will develop a neurodegenerative disease or disorder, and / or disease progression of a neurodegenerative disease or disorder; and / or b) monitoring neurodegenerative diseases or disorders;

[0030] 23. Use of a device according to claim 20 or 21 for the method according to claims 1 to 13.

[0031] Accordingly, the present invention relates to a method for diagnosing a neurodegenerative disease or disorder in a subject, for determining the probability that a subject will develop a neurodegenerative disease or disorder, and / or for determining a score indicative of disease progression of a neurodegenerative disease or disorder, the method comprising the steps of: a) obtaining from a subject's sample the methylation status of at least one of two or more genes selected from Table 1 and / or two or more regions selected from Table 2; b) comparing the methylation status obtained in (a) with a reference pattern; c) determining a score indicative of a diagnosis of a neurodegenerative disease or disorder in the subject, a probability that the subject will develop a neurodegenerative disease or disorder, and / or disease progression of the neurodegenerative disease or disorder based on the comparison obtained in (b).

[0032] The term "neurodegenerative disease or disorder" as used herein refers to a group of diseases or disorders of the nervous system characterized by damage and / or death of neuronal subtypes. In some embodiments, the neurodegenerative disease or disorder described herein is a disease or disorder having or characterized by cognitive impairment as a symptom. In some embodiments, the neurodegenerative disease or disorder described herein is a disease or disorder having or characterized by dementia as a symptom. In some embodiments, the dementia described herein is caused by Alzheimer's disease, tauopathy, vascular dementia, Lewy body disease, frontotemporal dementia, alcohol-related dementia, Down's syndrome, HIV-associated dementia, chronic traumatic encephalopathy (CTE) dementia, and childhood dementia. In some embodiments, the neurodegenerative disease or disorder described herein is at least one disease or disorder selected from the group of dementia, Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, multiple sclerosis, Huntington's disease, and prion disease.

[0033] The term "reference pattern" as used herein refers to a predetermined pattern that can be used for comparison, preferably obtained from a reference subject. The reference pattern includes at least one data point, such as a data point that can be used as a threshold. In some embodiments, the reference pattern is a (machine learning) model.

[0034] As used herein, the term "score" refers to a value, category, diagnosis and / or classification.

[0035] The term "sample" as used herein refers to a biological sample of a subject that may contain nucleic acids. In some embodiments, the sample is a sample selected from the group of bronchoalveolar lavage, bronchial washings, pharyngeal exudate, tracheal aspirate, blood, serum, plasma, bone, skin soft tissue, intestinal specimen, genital tract specimen, breast milk, lymph, cerebrospinal fluid, pleural fluid, sputum, urine, nasal discharge, tears, bile, peritoneal fluid, pus, synovial fluid, vitreous fluid, vaginal fluid, semen and urethral tissue. In some embodiments, the sample described herein is a sample selected from the group of a blood sample, a serum sample, a plasma sample and a urine sample. The sample can provide any form in which the methylation status of the genes or regions described herein can be determined. In some embodiments, the sample comprises genomic DNA and / or cell-free DNA. In some embodiments, the methylation status of the genes or regions is determined in the cell-free DNA of the sample, or in the cell-free DNA and the genomic DNA. In some embodiments, the genomic DNA is isolated from the sample. Genomic DNA can be isolated by any means standard in the art, including the use of commercially available kits. Briefly, if the DNA of interest is encapsulated in a cell membrane, the biological sample must be disrupted and lysed by enzymatic, chemical, or mechanical means. The DNA solution can then be digested with proteinase K or the like to remove proteins and other contaminants. The genomic DNA is then recovered from the solution.

[0036] This can be done by a variety of methods, including salting out, organic extraction, binding of DNA to a solid support, etc. The choice of method will be influenced by several factors, including time, cost, and the amount of DNA required.

[0037] If the sample DNA is not cellular or membrane-encased (e.g., circulating DNA from a blood sample), methods standard in the art for DNA isolation and / or purification can be employed. Such methods include the use of reagents that denature proteins, such as chaotropic salts, e.g., guanidine hydrochloride or urea; or detergents, e.g., sodium dodecyl sulfate (SDS) or cyanogen bromide. Alternative methods include, but are not limited to, ethanol precipitation, propanol precipitation, and vacuum concentration by centrifugation. Those skilled in the art can also utilize devices such as filter devices, e.g., ultrafiltration, silica surfaces or membranes, magnetic particles, polystyrene particles, polystyrene surfaces, positively charged surfaces, and positively charged membranes, charged membranes, charged surfaces, charged switch membranes, charged switch surfaces, and the like.

[0038] The term "methylation" as used herein refers to the covalent attachment of a methyl group to the C5 position of the nucleotide base cytosine in a CpG dinucleotide of a gene regulatory region. The term "methylation state" or "methylation status" refers to the presence or absence of i) 5-methyl-cytosine ("5-mCyt") or ii) 5-hydroxy-methyl-cytosine (5-hmC), and N6-methyladenine (6-mA) at one or more CpG dinucleotides in a DNA sequence. As used herein, the terms "methylation status" and "methylation state" are used interchangeably. A methylation site is a sequence of contiguous nucleotides that is recognized and methylated by a sequence-specific methylase. A methylase is an enzyme that methylates (i.e., covalently adds a methyl group) one or more nucleotides at a methylation site. The methylation state at one or more CpG methylation sites (each having two CpG dinucleotide sequences) or adenines in a DNA sequence can be "unmethylated", "fully methylated" or "hemimethylated". Various methylation analysis methods are known in the art and can be used to practice the present invention. The methylation state can be obtained by any method known in the art. These assays can determine the methylation state of one or more CpG sites or adenines in a tissue sample. Furthermore, these methods can be used for absolute or relative quantification of methylated nucleic acids. Such methylation assays have two main steps, among others. The first step is a methylation-specific reaction or separation, such as (i) bisulfite treatment, (ii) methylation-specific binding, (iii) methylation-specific restriction enzyme and / or (iv) enzymatic conversion of methylated nucleic acids.The second major step involves (i) amplification and detection, or (ii) direct detection, by various methods such as a) PCR (sequence-specific amplification) such as Taqman®, (b) DNA sequencing of untreated and bisulfite-treated DNA, (c) sequencing by ligation of dye-modified probes (including circular ligation and cleavage), (d) pyrosequencing, (e) single-molecule sequencing, (f) mass spectrometry, or (g) Southern blot analysis.

[0039] Additionally, restriction enzyme digestion of PCR products amplified from bisulfite converted DNA may be used, for example, as described by Sadri and Hornsby (1996, Nucl.Acids Res.24:5058-5059), or COBRA (Combined Bisulfite Restriction Analysis) (Xiong and Laird, 1997, Nucleic Acids Res.25:2532-2534). The COBRA analysis is a quantitative methylation assay useful for determining DNA methylation levels at specific loci from small amounts of genomic DNA. Briefly, restriction enzyme digestion is used to reveal methylation-dependent sequence differences in PCR products of bisulfite-treated DNA. Methylation-dependent sequence differences are first introduced into genomic DNA by standard bisulfite treatment, following the procedure described by Frommer et al. (Frommer et al, 1992, Proc.Nat.Acad.Sci.usa, 89, 1827-1831). This is followed by PCR amplification of the bisulfite converted DNA with primers specific for the CpG sites of interest, followed by restriction enzyme digestion, gel electrophoresis and detection with specific labeled hybridization probes. Methylation levels in the original DNA sample are represented by the relative amounts of digested and undigested PCR products, linearly and quantitatively over a wide range of DNA methylation levels. Moreover, the technique can be reliably applied to DNA obtained from microdissected paraffin-embedded tissue samples. Representative reagents for COBRA analysis (e.g., as included in a typical COBRA-based kit) include, but are not limited to: PCR primers for specific genes (or methylated DNA sequences or CpG islands), restriction enzymes and appropriate buffers, gene hybridization oligos, control hybridization oligos, kinase labeling kits for oligo probes, and radioactive nucleotides.Additionally, bisulfite conversion reagents include: DNA denaturing buffers; sulfonation buffers; DNA recovery reagents or kits (precipitation, ultrafiltration, affinity columns, etc.); desulfonation buffers; and DNA recovery components.

[0040] In some embodiments, the methylation status of selected CpG sites is determined using MethyLight and Heavy Methyl methods. MethyLight and Heavy Methyl assays are high-throughput quantitative methylation assays that utilize fluorescence-based real-time PCR (Taq Man®) technology and do not require any further manipulation after the PCR step (Eads, CA et al, 2000, Nucleic Acid Res.28, e 32; Cottrell et al, 2007, J. Urology 177, 1753, US Pat.6,331,393 (Laird et al.)). In some embodiments, the methylation status of selected CpG sites is determined using methylation-specific PCR (MSP). MSP can assess the methylation status of virtually any CpG site within a CpG island, independent of the use of methylation-sensitive restriction enzymes (Herman et al., 1996, Proc. Nat. Acad.Sci. USA, 93, 9821 -9826; US Pat. Nos. 5,786,146, 6,017,704, 6,200,756, 6,265,171 (Herman and Baylin) US Pat. Pub.No. 2010 / 0144836 (Van Engeland et al)).

[0041] Additionally, the enzymatic methyl-seq (EM-seq) method can be used. Specifically, this technique selectively deaminates unmethylated cytosines to uracil and determines the sequence of the newly created library based on the input DNA. Whole genome sequencing or specific locus sequencing can be applied (Hoppers, Amanda et al. ,2020, Journal of Biomolecular Techniques:JBT vol. 31, Suppl:S15; Williams, Louise, et al., 2019, “Enzymatic Methyl-seq: the next generation of methylome analysis.” NEB expressions).

[0042] The term "subject" as used herein refers to a mammal, such as a mouse, guinea pig, rat, dog, human, etc. It is understood that the preferred subject is a human. In some embodiments, the subject is a human aged 40 years or older, preferentially 40-75 years. The inventors have found that the means and methods described herein are particularly effective for early detection, scoring, and / or diagnosis of the neurodegenerative diseases or disorders described herein. In some embodiments, the subject is a human aged 40-75 years, preferably 40-65 years. In some embodiments, the subject is a human who does not have or does not have substantial cognitive symptoms, or whose symptoms alone are insufficient to make a diagnosis with a high degree of certainty (e.g., greater than 80%, 90% or 95% certainty) for the respective neurodegenerative disease or disorder. In some embodiments, the subject described herein is a subject at increased risk of developing a neurodegenerative disease or disorder. A subject at increased risk of developing a neurodegenerative disease or disorder is, for example, a subject who has above-average exposure to at least one risk factor or an above-average number of risk factors selected from the group consisting of cardiovascular disease, cerebrovascular disease, smoking, history of head trauma, genetics, diet, lack of sleep, alcohol use, depression, reduced physical fitness, high blood pressure, and uncontrolled diabetes.

[0043] The term "develop" as used herein in the context of a neurodegenerative disease or disorder means to develop at least one symptom of a neurodegenerative disease or disorder. In some embodiments, developing a neurodegenerative disease or disorder as described herein refers to developing sufficient symptoms to qualify for a diagnosis.

[0044] In some embodiments, the present invention relates to a method for diagnosing a subject having a neurodegenerative disease or disorder, the method comprising the steps of: a) obtaining from a sample of the subject a methylation status of at least one of two or more genes selected from Table 1 and / or two or more regions selected from Table 2; b) comparing the methylation status obtained in (a) with a reference pattern; and c) diagnosing the subject as having a neurodegenerative disease or disorder based on the comparison obtained in (b).

[0045] In some embodiments, the present invention relates to a method for diagnosing a subject having a neurodegenerative disease or disorder, the method comprising the steps of: a) obtaining from a subject's sample at least one methylation status of two or more genes selected from Table 1 and / or two or more regions of Table 2; b) comparing the methylation status obtained in (a) with a reference pattern; and c) diagnosing the subject with a neurodegenerative disease or disorder based on the comparison obtained in (b).

[0046] The inventors have discovered that epigenetic marks are specifically altered in neurodegenerative diseases or disorders. Neurodegeneration and / or cell death are reflected in the methylation status of two or more genes selected in Table 1 and / or two or more regions in Table 2. The present invention provides a low-cost and minimally invasive approach using a method that includes using a combination of diagnostic methylation statuses as described herein. Thus, the present invention provides epigenetic fingerprints as biomarkers for neurodegenerative diseases or disorders.

[0047] The present invention is therefore based, at least in part, on the discovery that the combinations of methylation states described herein are particularly useful for the efficient, early and / or non-invasive detection of parameters relevant for the diagnosis, prediction of onset and / or progression of a neurodegenerative disease or disorder.

[0048] In some embodiments, the present invention relates to a method for distinguishing a diagnosis of a subject, the method comprising the steps of: a) obtaining from a sample from a subject at least one methylation status of two or more genes selected from Table 1 and / or two or more regions of Table 2; wherein the subject has been diagnosed with at least two neurodegenerative diseases or disorders and / or symptoms indicative of at least two neurodegenerative diseases or disorders; b) comparing the methylation status obtained in (a) with a reference pattern; and c) Differentiating the diagnosis of interest based on the comparison obtained in (b).

[0049] In some embodiments, the present invention relates to a method for distinguishing between forms of dementia in a subject, the method comprising the steps of: a) obtaining from a sample from a subject at least one methylation status of two or more genes selected from Table 1 and / or two or more regions of Table 2; wherein the subject has been diagnosed with at least two forms of dementia and / or symptoms indicative of at least a form of dementia; b) comparing the methylation status obtained in (a) with a reference pattern; and c) Differentiating between the forms of dementia of interest based on the comparison obtained in (b).

[0050] In some embodiments, the present invention relates to a method for distinguishing between Alzheimer's disease and other forms of dementia in a subject, the method comprising the steps of: a) obtaining from a sample of a subject at least one methylation status of two or more genes selected from Table 1 and / or two or more regions of Table 2; wherein the subject has at least two forms of dementia and / or symptoms indicative of at least two forms of dementia, and at least one form of dementia is diagnosed as Alzheimer's disease; b) comparing the methylation status obtained in (a) with a reference pattern; and c) distinguishing between Alzheimer's disease and other dementia in the subject based on the comparison obtained in (b).

[0051] In a particular embodiment, the present invention relates to a method of the present invention, wherein the sample is a body fluid sample.

[0052] In a particular embodiment, the present invention relates to a method according to the invention, wherein the sample is a sample selected from the group of a blood sample, a serum sample, a plasma sample and a urine sample.

[0053] In a particular embodiment, the present invention relates to a method of the invention, wherein the sample is a plasma sample.

[0054] The inventors have found that plasma samples contain sufficient information regarding the methylation status described herein to be indicative of the onset and / or progression of a neurodegenerative disease or disorder, and plasma samples are quick and easy to obtain, allowing for inexpensive and scalable early screening.

[0055] The present invention is therefore based, at least in part, on the discovery that the method is particularly efficient by using a body fluid sample such as a plasma sample as described herein.

[0056] In a particular embodiment, the present invention relates to a method according to the invention, wherein the sample is frozen or has been frozen.

[0057] The samples may, for example, have a known history of being frozen or may have just been thawed for application of the method of the invention. Analysis of frozen samples allows for long-term stable storage. This storage allows, for example, retrospective diagnosis and analysis of disease progression over time (with two measurement time points). Analysis of past disease progression allows for estimation of future disease progression.

[0058] Thus, the present invention is based, at least in part, on the discovery that the methods of the present invention can be applied to previously frozen samples.

[0059] In a particular embodiment, the present invention relates to a method according to the invention, wherein the methylation status is obtained from cell-free DNA in the sample.

[0060] The terms "circulating DNA", "cell-free DNA", "cfDNA", "circulating cell-free DNA" and "ccfDNA" are used interchangeably herein and refer to free DNA molecules of 25 nucleotides or more that are not contained in intact cells or membranes. In certain embodiments, the cfDNA described herein has a minimum length of at least 50 nucleotides, at least 75 nucleotides, at least 100 nucleotides, or at least 125 nucleotides.

[0061] The free circulation of fragmented DNA in the blood has been reported several decades ago. However, cfDNA and its methylation alone were not expected to be sufficient for early detection of disease. For example, the use of liquid biopsies for the detection of circulating tumor DNA (ctDNA) is based on the identification of somatic mutations that accumulate in cancer cells, and therefore lacks the sensitivity to detect early cancers with a limited range of recurrent mutations (Kustanovich, A., et al., 2019, Cancer Biology & Therapy 20, 1057-1067).

[0062] The present inventors have surprisingly found that cell-free DNA in a sample (e.g., a bodily fluid sample such as a plasma sample) is useful for determining methylation status, including early detection of neurodegenerative diseases or disorders.

[0063] Therefore, the advantages of determining cfDNA epigenetic markers (e.g. in plasma) are: i) the use of minimally invasive techniques; ii) an integrated signature of multiple modifier molecules; iii) integration of analyses related to neurodegenerative diseases or disorders; and iv) longitudinal analysis of disease progression in the same patient.

[0064] In a particular embodiment, the present invention relates to a method of the invention, wherein obtaining at least one methylation state comprises genomic methylation profiling.

[0065] As used herein, the term "genomic methylation profiling" refers to a set of data representing the methylation state of at least two, at least three, at least four, at least five, or all loci in a DNA molecule. A profile can represent the methylation state of all bases in an individual, or it can have information about a subset of base pairs in the genome, or it can have information about the local methylation density of each locus.

[0066] Thus, the present invention is based, at least in part, on the discovery that the use of genomic methylation profiling enables the methods described herein to be particularly sensitive / specific.

[0067] In a particular embodiment, the present invention relates to a method of the present invention, wherein the at least one methylation status comprises the methylation status of at least 100 regions selected from Table 2, preferably at least 150 regions selected from Table 2, more preferably at least 200 regions selected from Table 2.

[0068] The inventors have discovered that analyzing a certain number of relevant regions provides specific and accurate results.

[0069] In a particular embodiment, the present invention relates to a method of the present invention, wherein at least one methylation status comprises a methylation status of a region selected from Table 2 between 200 and 600, preferably a region between 250 and 500, more preferably a region selected from Table 2 between 250 and 300.

[0070] The inventors have identified that a particular range of region numbers provides a particular beneficial combination of accuracy and efficiency.

[0071] In a particular embodiment, the present invention relates to a method according to the invention, wherein at least one methylation status comprises the methylation status of at least 80% of the regions selected from Table 3, preferably 85% of the regions selected from Table 3, preferably 90% of the regions selected from Table 3, more preferably 95% of the regions selected from Table 3, and even more preferably all of the regions selected from Table 3.

[0072] The inventors have identified a set of regions that enable the means and methods of the present invention to be particularly accurate, and certain regions of this set (e.g., 20%, 15%, 10% or 5%) can be omitted or replaced with other regions (e.g., similar or nearby regions) without substantially affecting the performance of such set of regions.

[0073] In a particular embodiment, the present invention relates to a method of the present invention, wherein the methylation status is determined in a region having a) an average nucleotide width of less than 5000, preferably less than 4000, more preferably less than 3000, even more preferably less than 2000, even more preferably less than 1000; and b) a median nucleotide width of less than 5000, preferably less than 4000, more preferably less than 3000, even more preferably less than 2000, even more preferably less than 1000.

[0074] In a particular embodiment, the present invention relates to a method according to the invention, wherein the methylation status is determined in a region with a median width of less than 5000, preferably less than 4000, more preferably less than 3000, more preferably less than 2000, more preferably less than 1000 nucleotides.

[0075] In a particular embodiment the invention relates to a method according to the invention, wherein the methylation status is determined in a region with an average width of less than 5000, preferably less than 4000, more preferably less than 3000, even more preferably less than 2000, even more preferably less than 1000 nucleotides.

[0076] The inventors have found that the use of small regions improves the accuracy of the means and methods of the present invention.

[0077] In certain embodiments, the present invention relates to a method of the invention, wherein at least one, at least two, or at least three additional marker(s) are obtained in (a) compared to (b).

[0078] In some embodiments, the additional markers comprise at least one clinical parameter and / or at least one pathological feature.

[0079] In a particular embodiment, the present invention relates to a method of the present invention, wherein the at least one further marker is a marker selected from the group of subject background data, cognitive function markers, autonomic nervous system biomarkers.

[0080] As used herein, the term "subject background data" refers to characteristics of a subject that, for technical or time reasons, are not available in the method used to determine methylation status.

[0081] In some embodiments, the subject demographic data described herein is a subject demographic parameter selected from the group of age, sex, family history of neurodegenerative disease.

[0082] The term "cognitive function marker" as used herein refers to a marker of mental function, such as learning, problem solving, remote memory, recent memory, word comprehension, sense of direction, attention span, calculation, abstract thinking, judgment, etc. In some embodiments, the cognitive function marker described herein is a memory function marker. In some embodiments, the cognitive function marker described herein is a manufacturer selected from the group of GPCOG, Mini-Cog, "Eight-item Informant Interview to Differentiate Aging and Dementia", and "Short Informant Questionnaire on Cognitive Decline in the Elderly". In some embodiments, the cognitive function marker described herein comprises MMSE and / or MoCa score.

[0083] The term "autonomic nervous system biomarker" as used herein refers to nervous system-related data obtained from a subject by a sensor or protein measurement without the subject having to actively and consciously input the data. In some embodiments, the autonomic nervous system biomarkers described herein include imaging markers, such as a positron emission tomography (PET) scan or an MRI. In some embodiments related to the present invention in the context of Parkinson's disease (e.g., the methods of the present invention for scoring, diagnosing, or differentiating Parkinson's disease), the autonomic nervous system biomarkers described herein include movement-related data, such as data indicative of gait or tremor. In some embodiments, the autonomic nervous system biomarkers described herein are markers selected from the group consisting of a positron emission tomography (PET) scan, amyloid in CSF measurements, and tau protein in CSF measurements.

[0084] The inventors have found that additional markers, as described above, provide increased resolution in characterizing the disease.

[0085] Thus, the present invention is based, at least in part, on the discovery that the additional markers described herein can increase the sensitivity and / or specificity of the methods described herein.

[0086] In a particular embodiment, the present invention relates to a method of the present invention, wherein a reference pattern is obtained from the methylation status of two or more genes selected from Table 1 and / or at least two regions selected from Table 2 from samples of at least two reference subjects, wherein at least one of the reference subjects suffers from a neurodegenerative disease or disorder.

[0087] The present inventors have found that data from subjects suffering from a neurodegenerative disease or disorder can be used as a reference. Preferably, the reference subject is at least partially composed of subjects having the same neurodegenerative disease or disorder as the subject or subject sample to be assessed, predicted or monitored according to the methods described herein.

[0088] Thus, the present invention is based, at least in part, on the discovery that data from diseased subjects is particularly useful for referencing patterns in the methods described herein.

[0089] In a particular embodiment, the present invention relates to a method of the present invention, wherein a reference pattern is obtained from the methylation status of two or more genes selected from Table 1 and / or two or more regions selected from Table 2 from samples of at least two reference subjects, wherein at least one of the reference subjects is affected by a neurodegenerative disease or disorder and at least one of the reference subjects is not affected by a neurodegenerative disease or disorder, preferably at least one of the reference subjects not affected by a neurodegenerative disease or disorder is a healthy subject.

[0090] In a particular embodiment, the present invention relates to a method according to the invention, wherein the samples of the at least two referent subjects comprise at least one brain tissue sample.

[0091] As used herein, the term "brain tissue sample" refers to a sample of the CNS, such as a cortical brain tissue sample.

[0092] The inventors have found that brain tissue samples provide information about disease, and this information can be used to identify disease-related methylation patterns outside of brain tissue.Therefore, the invasive procedure of collecting brain tissue samples can be limited to reference subjects.The disease-related information contained in brain tissue samples can reduce the number of reference subjects required to achieve a certain sensitivity / specificity.

[0093] Thus, the present invention is based, at least in part, on the discovery that brain tissue samples contain information that improves sensitivity / specificity.

[0094] In a particular embodiment, the present invention relates to a method of the present invention, wherein obtaining a reference pattern from the methylation status of two or more genes selected from Table 1 and / or two or more regions selected from Table 2 from samples of at least two reference subjects comprises machine learning techniques.

[0095] The term "machine learning techniques" as used herein refers to computer-implemented techniques that allow automatic learning and / or improvement from experience (e.g., training data and / or derived data) without the need to explicitly program the learning and / or improved lessons. In some embodiments, the machine learning techniques described herein are artificial intelligence techniques. In some embodiments, the machine learning includes at least one selected from the group of logistic regression, CART, bagging, random forest, gradient boosting, linear discriminant analysis, Gaussian process classifier, Gaussian NB, Linear, Lasso, Ridge, ElasticNet, partial least squares, KNN, DecisionTree, SVR, support vector machine, AdaBoost, GradientBoost, neural net, ExtraTrees, fuzzy neural network, linear regression, decision tree, naive Bayes, K-Means.

[0096] The inventors have discovered that machine learning techniques provide an efficient and / or unbiased method of identifying fingerprints in the context of neurodegenerative diseases or disorders, and these fingerprints can be generic to one or more neurodegenerative diseases or disorders.

[0097] In certain embodiments, the present invention relates to a method for monitoring a neurodegenerative disease or disorder, the method comprising the steps of: i) determining, at a first time point, a first score indicative of the subject's probability of developing a neurodegenerative disease or disorder and / or disease progression of a neurodegenerative disease or disorder according to the method of the invention; ii) determining, at a second time point, a second score indicative of the subject's probability of developing a neurodegenerative disease or disorder and / or disease progression of the neurodegenerative disease or disorder according to the methods of the invention; iii) comparing the first score from step (i) with the second score from step (ii); and iv) monitoring disease progression of the neurodegenerative disease or disorder in the subject based on the comparison of step (iii).

[0098] Thus, the present invention is based, at least in part, on the discovery that the methods described herein can be used to provide temporal information regarding a neurodegenerative disease or disorder.

[0099] In certain embodiments, the present invention relates to a library comprising at least one score indicative of the probability of a subject developing a neurodegenerative disease or disorder and / or the disease progression of a neurodegenerative disease or disorder, determined according to the present invention.

[0100] In some embodiments, the libraries described herein are machine learning models trained based on at least one score indicative of the probability of a subject developing a neurodegenerative disease or disorder and / or disease progression of a neurodegenerative disease or disorder, as determined according to the present invention.

[0101] The present invention may be a system, a method, and / or a computer program product.

[0102] A computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to execute an embodiment of the invention, such as a computer-implemented method for classification in accordance with the invention and / or a computer-implemented method for acquisition in accordance with the invention.

[0103] In a particular embodiment, the present invention relates to a storage device including computer readable program instructions for carrying out the methods according to the present invention, preferably further including the library of the present invention.

[0104] The computer readable program instructions described herein may be downloaded into the respective computing / processing device from a computer readable storage medium or from an external computer or storage device over a network.

[0105] Computer readable program instructions for carrying out the operations of the present invention may be either assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages, such as the "C" programming language.

[0106] The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on a remote computer or server.

[0107] As used herein, the term "storage device" refers to a tangible device capable of holding and storing instructions for use by an instruction execution device.

[0108] In some embodiments, the storage devices described herein are at least one selected from the group of electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, and any suitable combination thereof.

[0109] A non-exhaustive list of more specific examples of storage devices include: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory sticks, floppy disks, mechanically encoded devices such as punch cards or raised structures in grooves having instructions recorded thereon, and any suitable combination of the foregoing. Storage devices, as used herein, are not to be construed as being themselves ephemeral signals, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through wave guides or other transmission media (e.g., light pulses passing through fiber optic cables), or electrical signals transmitted over electrical wires.

[0110] In certain embodiments, the present invention relates to a server comprising a storage device of the present invention, at least one processing device, and a network connection for receiving data indicative of at least one methylation status.

[0111] As used herein, the term "data indicative of at least one methylation state" refers to any raw or processed data describing a methylation state and / or a characteristic of a methylation state.

[0112] The term "network connection" as used herein refers to a communication channel of a data network. The communication channel allows at least two computing systems to communicate data with each other. In some embodiments, the data network is selected from the group of the Internet, a local area network, a wide area network, and a wireless network. The network may be comprised of copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, edge servers, and the like. A network adapter card or network interface of each computing / processing device receives computer-readable program instructions from the network and transfers the computer-readable program instructions for storage in a computer-readable storage medium within each computing / processing device.

[0113] The server described herein is capable of receiving data indicative of at least one methylation state, processing it according to the methods of the present invention, and providing results. Transmitting the data indicative of at least one methylation state to the server reduces processing power requirements on the device acquiring the data indicative of at least one methylation state, allowing efficient processing of large data sets. In an embodiment, where the present invention relates to a server, the data indicative of at least one methylation state may be acquired by any device with a network connection. The server may be connected to the device for obtaining data indicative of at least one methylation state via any type of network, including a local area network (LAN) or wide area network (WAN), or the connection may be to an external computer (e.g., via the Internet using an Internet Service Provider). In some embodiments, an electronic circuit, including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may utilize the state information of the computer readable program instructions to execute the computer readable program instructions and personalize the electronic circuit to carry out embodiments of the present invention.

[0114] Thus, the server described herein allows for an efficient application of the method of the present invention.

[0115] In certain embodiments, the present invention relates to the method of the present invention, the library of the present invention, the storage device of the present invention, and the server of the present invention, wherein the neurodegenerative disease or disorder is Alzheimer's disease and / or Parkinson's disease.

[0116] The term "Alzheimer's disease" or "AD," as used herein, refers to mental decline associated with certain degenerative brain disorders characterized by neuritic plaques, neuronal tangles, and progressive neuronal loss and manifested clinically as progressive memory loss, confusion, behavioral problems, inability to care, and / or gradual physical decline. In some embodiments, subjects suffering from Alzheimer's disease are identified using the NINCDS-ADRDA (National Institute of Neurological and Communicative Disorders and the Alzheimer's Disease and Related Disorders Association) criteria:

[0117] 1) Clinical Dementia Rating (CDR)=1, Mini-Mental State Examination (MMSE) score ≥16 but <24, and medial temporal lobe atrophy (determined by magnetic resonance imaging MRI) Scheltens scale score ≥3. In some embodiments, the term Alzheimer's disease includes all stages of the disease, including the following stages defined by the 1984 NINCDS-ADRDA Alzheimer's Disease Diagnostic Criteria:

[0118] 2) Definite Alzheimer's Disease: Patients who meet criteria for possible Alzheimer's disease and have histopathological evidence of AD by autopsy or biopsy. Possible or prodromal Alzheimer's disease: Dementia is established by clinical and neuropsychological examinations, the cognitive impairment must be progressive and present in two or more domains of cognition, the onset of the impairment must be between the ages of 40 and 90, and there must be no other illnesses that may cause the cognitive syndrome.

[0119] 3) Possible Alzheimer's Disease: There is a dementia syndrome with atypical onset, symptoms, and no clear etiology, but it is believed that there is no comorbid condition that may cause dementia. In some embodiments, the term Alzheimer's Disease refers to one stage of Alzheimer's Disease. In some embodiments, the term Alzheimer's Disease refers to two stages of Alzheimer's Disease. In some embodiments, the term "Alzheimer's Disease" refers to symptoms of Alzheimer's Disease, including, but not limited to, memory loss, confusion, thinking difficulties, language changes, behavioral changes, and / or personality changes.

[0120] The term "Parkinson's disease" as used herein refers to a neurological syndrome characterized by dopamine deficiency resulting from degenerative, vascular, or inflammatory changes in the basal ganglia. Symptoms of Parkinson's disease include, but are not limited to, resting tremor, cogwheel rigidity, bradycardia, impaired postural reflexes, good response to 1-dopa treatment, absence of significant oculomotor palsy, cerebellar or pyramidal signs, muscle atrophy, apraxia, and / or agnosia. In specific embodiments, the present invention is utilized to treat dopaminergic dysfunction-related syndromes. In some embodiments, Parkinson's disease includes any stage of Parkinson's disease. In some embodiments, the term Parkinson's disease refers broadly to the early stages of Parkinson's disease, where a person suffering from Parkinson's disease exhibits mild, non-disabling symptoms that affect only one side of the body, such as episodic tremor of a single limb (e.g., hand).

[0121] In some embodiments, the term Parkinson's disease includes advanced stages of Parkinson's disease, which refers to a more progressive stage of Parkinson's disease, and people with Parkinson's disease typically exhibit symptoms that are severe and may lead to some disability (e.g., tremors that involve both sides of the body, balance problems, etc.). Symptoms associated with advanced Parkinson's disease vary greatly from person to person, and may take years to appear after the onset of the disease.

[0122] In some embodiments, the term "Parkinson's Disease" refers to symptoms of Parkinson's Disease, including, but not limited to, tremor (e.g., tremor most noticeable at rest), tremor (e.g., tremor of the hands, arms, legs, jaw and face), muscle rigidity, lack of postural reflexes, slowing of voluntary movements, backing, mask-like facial expression, stooped posture, poor balance, poor coordination, bradycardia, postural instability, and / or gait abnormalities.

[0123] In certain embodiments, the present invention relates to the method of the present invention, the library of the present invention, the storage device of the present invention, and the server of the present invention, wherein the neurodegenerative disease or disorder is Alzheimer's disease.

[0124] In a particular embodiment, the present invention relates to a device comprising a methylation-specific oligonucleotide probe, wherein the probe is specific for determining the methylation status of at least 80% of a region selected from Table 3, preferably all of the regions selected from Table 3.

[0125] In a particular embodiment, the invention relates to a device according to the invention, wherein the device is a microarray.

[0126] In a particular embodiment, the present invention relates to the use of a device of the invention for a method according to the invention for a) diagnosing a neurodegenerative disease or disorder in a subject, determining the probability that a subject will develop a neurodegenerative disease or disorder, and / or determining a score indicative of disease progression of a neurodegenerative disease or disorder.

[0127] In a particular embodiment, the present invention relates to the use of the device of the present invention in a method for monitoring a neurodegenerative disease or disorder.

[0128] In a particular embodiment, the present invention relates to the use of a device of the invention for a method according to the invention for a) diagnosing a neurodegenerative disease or disorder in a subject, determining the probability that a subject will develop a neurodegenerative disease or disorder and / or a score indicative of disease progression of a neurodegenerative disease or disorder, and b) monitoring a neurodegenerative disease or disorder.

[0129] In a particular embodiment, the present invention relates to the use of the device of the present invention for the method according to the present invention.

[0130] The words "a," "an," and "the" are used herein to refer to one or to more than one (i.e., to at least one or more) of the grammatical object of the article.

[0131] "Or" should be understood to mean either one, both, or any combination of the alternatives.

[0132] "And / or" should be understood to mean either or both.

[0133] Throughout this specification, unless the context indicates otherwise, the word "comprising" is understood to mean the inclusion of a stated step or element or group of steps or elements, but not to the exclusion of other steps or elements or group of steps or elements.

[0134] The terms "comprising" and "comprising" are used interchangeably, "preferably" means one option out of a set of options that does not exclude other options, and "for example" means one example that is not limited to the examples mentioned. "Consisting of" means including and limited to what follows the phrase "consisting of."

[0135] As used herein, the term "about" or "approximately" refers to "within 20%," more preferably "within 10%, and even more preferably "within 5%" of a given value or range.

[0136] Throughout this specification, references to "one embodiment," "one embodiment," "particular embodiment," "related embodiment," "an embodiment," "additional embodiments," "some embodiments," "particular embodiments," or "further embodiments," or combinations thereof, mean that the particular features, structures, or characteristics described in connection with the embodiment are included in at least one embodiment of the invention. Thus, the appearance of such phrases in various places throughout this specification do not necessarily all refer to the same embodiment. Moreover, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should also be understood that the positive recitation of a feature in an embodiment serves as a basis for excluding the feature in the particular embodiment.

[0137] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs. Although methods and materials similar or equivalent to those described herein can be used to carry out or test the present invention, suitable methods and materials are described below. In case of conflict, the present specification, including definitions, shall prevail. Furthermore, the materials, methods, and examples are illustrative only and are not intended to be limiting.

[0138] The general methods and techniques described herein can be carried out according to conventional methods well known in the art, unless otherwise indicated, as described in various general and more specific references cited and discussed throughout this specification.

[0139] See, e.g., Sambrook et al., Molecular Cloning: A Laboratory Manual, 2d ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY (1989), and Ausubel et al., Current Protocols in Molecular Biology, Greene Publishing Associates (1992), and Harlow and Lane Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY (1990).

[0140] While embodiments of the present invention have been illustrated and described in detail in the drawings and foregoing description, such illustration and description are exemplary or illustrative and not restrictive. It will be understood that changes and modifications may be made by those skilled in the art within the scope and spirit of the following claims. In particular, the present invention is directed to further embodiments having any combination of features from the different embodiments described above and below. The present invention further relates to the following items:

[0141] 1. A method for diagnosing a neurodegenerative disease or disorder in a subject, determining a score indicative of the probability that the subject will develop a neurodegenerative disease or disorder, and / or disease progression of a neurodegenerative disease or disorder, comprising the steps of: a) obtaining from a subject's sample the methylation status of at least one of two or more genes selected from Table 1 and / or two or more genes selected from Table 2; b) comparing the methylation status obtained in (a) with a reference pattern; and c) determining a score based on the comparison obtained in (b) that is indicative of a diagnosis of a neurodegenerative disease or disorder in the subject, a probability that the subject will develop a neurodegenerative disease or disorder, and / or disease progression of the neurodegenerative disease or disorder.

[0142] 2. The method according to item 1, wherein the sample is a plasma sample.

[0143] 3. The method according to item 1 or 2, wherein the methylation status is obtained from cell-free DNA in the sample.

[0144] 4. The method according to any one of items 1 to 3, wherein obtaining at least one methylation state comprises genomic methylation profiling.

[0145] 5. The method according to any one of items 1 to 4, wherein in a) at least one further marker is obtained compared to (b).

[0146] 6. The method of item 5, wherein the at least one additional marker is a marker selected from the group consisting of subject background data, cognitive function markers, and autonomic nervous system biomarkers.

[0147] 7. The method according to any one of items 1 to 6, wherein the reference pattern is obtained from the methylation status of two or more genes selected from Table 1 and / or two or more regions selected from Table 2 from samples of at least two reference subjects, wherein at least one of the reference subjects is affected by a neurodegenerative disease or disorder.

[0148] 8. The method of item 7, wherein the samples of the at least two reference subjects include at least one brain tissue sample.

[0149] 9. The method according to item 7 or 8, wherein obtaining a reference pattern from the methylation status of two or more genes selected from Table 1 and / or two or more regions selected from Table 2 from samples of at least two reference subjects comprises machine learning techniques.

[0150] 10. A method for monitoring a neurodegenerative disease or disorder, comprising the steps of: i) determining at a first time point a first score indicative of the subject's probability of developing a neurodegenerative disease or disorder and / or disease progression of a neurodegenerative disease or disorder according to the method of any one of items 1 to 9; ii) determining, at a second time point, a second score indicative of the subject's probability of developing a neurodegenerative disease or disorder and / or disease progression of a neurodegenerative disease or disorder according to the method of any one of items 1 to 9; iii) comparing the first score from step (i) with the second score from step (ii); and iv) monitoring disease progression of the neurodegenerative disease or disorder in the subject based on the comparison of step (iii).

[0151] 11. A library comprising at least one score indicative of the probability that a subject will develop a neurodegenerative disease or disorder and / or disease progression of a neurodegenerative disease or disorder, determined according to any one of items 1 to 9.

[0152] 12. A storage device containing computer readable program instructions for carrying out the method according to any one of items 1 to 9, preferably further comprising the library according to item 11.

[0153] 13. A server comprising the storage device of item 12, at least one processing device, and a network connection for receiving data indicative of at least one methylation state.

[0154] 14. The method according to any one of items 1 to 10, the library according to item 11, the storage device according to item 12, and the server according to item 13, wherein the neurodegenerative disease or disorder is Alzheimer's disease and / or Parkinson's disease.

[0155] 15. The method of item 14, the library of item 14, the storage device of item 14, or the server of item 14, wherein the neurodegenerative disease or disorder is Alzheimer's disease. [Brief description of the drawings]

[0156] [Figure 1] Cross-validation AUC distribution (median = 0.99) [Diagram 2] Number of features included (median = 274) [Diagram 3] Distribution of AUC validation set (median=0.83) EXAMPLES

[0157] The aspects of the present invention are further described by the following illustrative, non-limiting examples for a better understanding of the embodiments of the present invention and its many advantages. The following examples are included to illustrate preferred embodiments of the present invention. Those skilled in the art should understand that the techniques disclosed in the following examples represent techniques used by the present invention to function well in the practice of the present invention and therefore can be considered to constitute preferred modes for its practice. However, those skilled in the art should understand in light of this disclosure that many changes can be made to the specific embodiments disclosed and still obtain the same or similar results without departing from the spirit and scope of the present invention.

[0158] Example 1 Project objectives and design

[0159] Hypothesis and main objective Our working hypothesis is that ongoing neurodegenerative processes result in detectable brain-derived cfDNA circulating in plasma whose epigenetic signature accurately reflects not only the mechanisms underlying and resulting pathogenic mechanisms of AD, but also its origins. The primary objective of this observational pilot study is to reach proof-of-concept for in vitro diagnosis of AD based on analysis of cfDNA.

[0160] Primary endpoint The primary endpoint was the difference in the total amount of cfDNA in plasma and its epigenetic signature between AD patients and healthy controls. The total amount of cfDNA present in plasma and its epigenetic signature are expected to differ significantly between age- and sex-matched healthy controls and clinically diagnosed AD patients at a stage of disease showing significant neuronal and synaptic loss, providing an epigenetic signature of AD that can be used for in vitro diagnostic (IVD) diagnosis.

[0161] Project Design This observational pilot study is based on a single blood sample collection from two cohorts: subjects diagnosed with AD and age- and sex-matched healthy subjects, which will be used to purify cfDNA from plasma and analyze epigenetic status at the whole genome level. This research project will recruit up to 50 participants: 30 AD patients and 20 age- and sex-matched controls.

[0162] Project population and research procedures Project population, inclusion and exclusion criteria Project Group: Patients with clinically diagnosed AD Inclusion criteria - Age at clinical diagnosis of AD: 40 years or older, dementia with probable AD and evidence of pathophysiological process of AD according to international guidelines (McKhann, GM et al., 2011, Alzheimers Dement 7, 263-269) or mild cognitive impairment (MCI) due to AD according to international guidelines (Albert, MS et al., 2011 , Alzheimers Dement 7, 270-279). Age- and sex-matched healthy subjects Inclusion criteria Healthy subjects, age- and sex-matched to the AD patient population, with no known disease and cognitively preserved abilities. Exclusion criteria for both populations: Other neurodegenerative disorders or causes of cognitive decline Recruitment, screening and informed consent procedures The participants of the study will be enrolled in the "Illnita disturbi cognitivi e logopedia Neurocentro" at the Italian Institute of Spinal Cord Diseases (Istituto di Neuroscienze Cliniche della Svizzera Italiana, Ente Ospedaliero Cantonale). Healthy volunteers will be recruited from the patients' relatives or from staff and their families. Participants who meet the inclusion criteria for the two cohorts (AD, healthy controls) will be contacted and invited to participate in the research project.

[0163] Test procedure One visit to the Italian National Hospital Neuroscience Clinic “llnita disturbi cognitivi e logopedia Neurocentro” for blood sampling was made. Blood samples are collected in 5x10ml_(50ml) PAXgene Blood ccfDNA Tubes, Qiagen. cfDNA is then purified from the plasma using the QIAamp Circulating Nucleic Acid Kit and its quantity is measured using a fluorescence-based commercial kit. cfDNA is sequenced according to standard protocols for methylated DNA (e.g., chemistry-busted methylation sequencing or enzyme-based methylation sequencing) (Illumina). Healthy subjects also undergo a diagnostic test consisting of a questionnaire or an interview with a neurologist to rule out the presence or absence of signs and symptoms of cognitive decline.

[0164] Protocol #1 1. Collect 10mL to 20mL of blood from the subject. 2. Separate the plasma using a conventional centrifugation step 3. Purify cfDNA (10ng-1ug) from plasma samples using molecular biology techniques such as column-based nucleic acid purification. 4. cfDNA Quality Assessment Using a Dye-based Fluorescent Assay 5. Bisulfite conversion of cfDNA (e.g., using Zymo EZ DNA Methylation Lightning Kit) 6. Purification of bisulfite-treated DNA using spin columns 7. Preparation of sequencing libraries (e.g., EpiGnome from Epicentre) TM (Use the kit) 8. Sequence the libraries (e.g., using a HiSeq 2500 system) 9. Creating a sequence file (e.g. FASTQ file) 10. Annotation of sequenced methylated DNA to the human reference genome (e.g., bisulfite converted LICSC HG19 reference genome) 11. Calculation of differential methylation by statistical analysis 12. Comparison of methylation signatures between AD and healthy subjects using computational methods or machine learning algorithms (e.g., Random Forest) 13. Identification of methylation signatures: a list of genes with altered methylation status in AD subjects.

[0165] Protocol #2 1. Collect 20mL of blood from the subject 2. Separate the plasma using a conventional centrifugation step 3. Purify cfDNA (10ng-1ug) from plasma samples using molecular biology techniques such as column-based nucleic acid purification. 4. cfDNA quality assessment using a dye-based fluorescent assay 5. Bisulfite conversion of cfDNA (e.g., using Zymo EZ DNA Methylation Lightning Kit) 6. Purification of bisulfite-treated DNA using spin columns 7. Preparation of sequencing libraries (e.g., EpiGnome from Epicentre) TM (Use the kit) 8. Sequence the libraries (e.g., using a HiSeq 2500 system) 9. Creating sequence files (e.g., FASTQ files) 10. Read mapping on the human reference genome (e.g., bisulfite-converted LICSC GRCh38 reference genome) 11. Identifying and quantitating the methylation status of annotated genomic regions (e.g., promoter regions) at base resolution 12. Differences in methylation levels between cases and controls were determined by statistical analysis. 13. Developing class prediction models using machine learning algorithms (e.g., random forests) to discriminate between AD and healthy subjects, which involves selecting the optimal set of features in a cross-validation setting and subsequent evaluation of performance. 14. The selected features constitute the methylation signature (a list of genomic regions with altered methylation status in AD subjects).

[0166] Protocol #3 1. Collect 20mL of blood from the subject 2. Separate the plasma using a conventional centrifugation step 3. Purify cfDNA (10ng-1ug) from plasma samples using molecular biology techniques such as column-based nucleic acid purification. 4. cfDNA Quality Assessment Using a Dye-based Fluorescent Assay 5. Bisulfite conversion of cfDNA (e.g., using Zymo EZ DNA Methylation Lightning Kit) 6. Purification of bisulfite-treated DNA using spin columns 7. Preparation of sequencing libraries (e.g., EpiGnome from Epicentre) TM (Use the kit) 8. Sequence the libraries (e.g., on a HiSeq 2500 system) to determine the methylation status of the genes in Table 1. 9. Creating sequence files (e.g., FASTQ files) 10. Read mapping on the human reference genome (e.g., bisulfite-converted LICSC GRCh38 reference genome) 11. Identifying and quantitating the methylation status of annotated genomic regions (e.g., promoter regions) at base resolution 12. Differences in methylation levels between cases and controls were determined by statistical analysis. 13. Development of class prediction models using machine learning algorithms (e.g. Random Forest) was used to discriminate between AD and healthy subjects. This involves the selection of an optimal set of features in a cross-validation setting and subsequent evaluation of performance. Both methylation signals and clinical parameters and pathological features are considered as candidate features. 14. The selected features constitute the methylation signature (a list of genomic regions with altered methylation status in AD subjects).

[0167] Analysis of markers in Table 1: Based on the subject sample data from the above studies, candidate combinations of biomarkers are identified from Table 1. [Table 1] JPEG2024537106000002.jpg119170

[0168] Table 2 (All locations of regions / features are described herein with reference to the numbering of the human reference genome GRCh38, which has GenBank assembly accession code GCA_000001405.15) [Table 2] JPEG2024537106000004.jpg226170JPEG2024537106000005.jpg227170JPEG2024537106000006.jpg226170JPEG2024537106000007.jpg227170JPEG2024537106000008.jpg227170JPEG2024537106000009.jpg227170JPEG2024537106000010.jpg227170JPEG2024537106000011.jpg227170JPEG2024537106000012.jpg226170JPEG2024537106000013.jpg227170JPEG2024537106000014.jpg226170JPEG2024537106000015.jpg226170JPEG2024537106000016.jpg227170JPEG2024537106000017.jpg227170JPEG2024537106000018.jpg226170JPEG2024537106000019.jpg227170JPEG2024537106000020.jpg229170JPEG2024537106000021.jpg227170JPEG2024537106000022.jpg227170JPEG2024537106000023.jpg227170JPEG2024537106000024.jpg227170JPEG2024537106000025.jpg226170JPEG2024537106000026.jpg227170JPEG2024537106000027.jpg226170JPEG2024537106000028.jpg227170JPEG2024537106000029.jpg226170JPEG2024537106000030.jpg227170JPEG2024537106000031.jpg226170JPEG2024537106000032.jpg227170JPEG2024537106000033.jpg226170JPEG2024537106000034.jpg227170JPEG2024537106000035.jpg228170JPEG2024537106000036.jpg228170JPEG2024537106000037.jpg226170JPEG2024537106000038.jpg226170JPEG2024537 106000039.jpg227170JPEG2024537106000040.jpg228170JPEG2024537106000041.jpg227170J PEG2024537106000042.jpg227170JPEG2024537106000043.jpg228170JPEG2024537106000044. jpg227170JPEG2024537106000045.jpg226170JPEG2024537106000046.jpg226170JPEG2024537 106000047.jpg226170JPEG2024537106000048.jpg227170JPEG2024537106000049.jpg226170J PEG2024537106000050.jpg226170JPEG2024537106000051.jpg227170JPEG2024537106000052. jpg225170JPEG2024537106000053.jpg226170JPEG2024537106000054.jpg227170JPEG2024537 106000055.jpg227170JPEG2024537106000056.jpg226170JPEG2024537106000057.jpg174170.

[0169] Table 3 (All locations of regions / features are described herein with reference to the numbering of the human reference genome GRCh38, which has GenBank assembly accession code GCA_000001405.15) [Table 3] JPEG2024537106000059.jpg226170JPEG2024537106000060.jpg226170JPEG2024537106000061.jpg42170

[0170] Example 2: Use of methylation signatures as IVDs in clinical settings Protocol #1 1. Collect 20mL of blood from the subject 2. Separate the plasma using a conventional centrifugation step 3. Purify cfDNA (10ng-1ug) from plasma samples using molecular biology techniques such as column-based nucleic acid purification. 4. cfDNA Quality Assessment Using a Dye-based Fluorescent Assay 5. Bisulfite conversion of cfDNA (e.g., using Zymo EZ DNA Methylation Lightning Kit) 6. Purification of bisulfite-treated DNA using spin columns 7. Preparation of sequencing libraries (e.g., EpiGnome from Epicentre) TM (Use the kit) 8. Sequencing the library to determine the methylation status of genes identified in Example 1 (e.g., using a HiSeq 2500 system) 9. Creating a sequence file (e.g. FASTQ file) 10. Annotation of sequenced methylated DNA to the human reference genome (e.g., bisulfite converted LICSC HG19 reference genome) 11. Calculating differential methylation by statistical analysis 12. Using computational and / or machine learning techniques to determine a diagnosis of AD pathology

[0171] Example 3 Project population, inclusion and exclusion criteria Patients with clinically diagnosed AD Inclusion criteria - Clinical diagnosis of AD - 40 years or older - Possible AD dementia and evidence of AD pathophysiological process according to international guidelines (McKhann et al., 201116) or Mild Cognitive Impairment (MCI) due to AD according to international guidelines (Albert 201117).

[0172] Age- and sex-matched healthy subjects Inclusion criteria - Healthy subjects - Age- and sex-matched AD patient population - No known medical conditions - Maintaining cognitive function Exclusion criteria for both populations: - Other neurodegenerative diseases or causes of cognitive decline - Subjects at risk of blood sampling

[0173] Sample collection Blood samples are collected in 5x10mL (50ml) PAXgene Blood ccfDNA Tubes, Qiagen. cfDNA is then purified from the plasma using the QIAamp Circulating Nucleic Acid Kit and its quantity is measured using a fluorescence-based commercial kit. Analysis of ccfDNA Quality control of ccfDNA - Amount of cfDNA extracted from plasma: 10ng to 50ng of ctDNA - Microelectrophoretic profile on the Fragment AnalyzerTM Go / NoGo Quality control should show a typical nucleosome peak at 166 bp.

[0174] Preparation of ccfDNA library Library preparation using New England Biolabs® Ultra II kit - Specific enzymatic conversion using NEBNext® Enzymatic Methyl-seq (includes a provided spike for calculation of methylation conversion rates) - Unique double indexing (UDI) of libraries PCR amplification monitoring

[0175] quality control Dosage by qPCR and Microelectrophoresis Profiles on Fragment AnalyzerTM High-throughput human genome sequencing Libraries were sequenced on an Illumina® NovaSeqTM6000 full S4 flow cell (corresponding to 3x flow cells for 50 samples) using 2x100 base paired-end mode to obtain paired-end 150 bp reads aiming for 30x coverage over the whole genome.

[0176] Bioinformatics Extensive sequence quality control, including sample demultiplexing and trimming, generated FASTQ sequence and FASTQC files for subsequent analysis.

[0177] Differential methylation analysis The raw sequence data in the FastQ file format were aligned to the human genome (GRCh38 version) using Bismark software (version 0.23.1). The software was run using default parameters, except for the following: -L 28 -D 3 -R 0. The deduplicate_bismark command (with default parameters) was used to remove alignments to the same position in the genome from the Bismark mapping output, which may result from PCR amplification. In a next step, the methylation calls of all analyzed Cs were extracted using bismark_methylation_extractor. The following arguments were set: --no_overlap --comprehensive --merge_non_CpG.

[0178] Differences in methylation status between cases and controls were assessed using the dmrseq package (version 1.16.0) in the R / Bioconductor (version 4.1) environment. CpG coverage and methylation status data were saved in "*bismark.cov.gz", one file per patient, and imported and merged using the read.bismarkO function. CpGs were filtered by selecting 19715509 loci with >8-fold coverage in all 50 samples.

[0179] From the study cohort, a training set (80%, 24 cases and 16 controls) and a validation set (20%, 6 cases and 4 controls) were identified. A differential analysis was performed on the training set using the dmrseq() function. The default parameters were: cutoff=0.02, minNumRegion=20, maxPerms=50. 9149 regions were identified genome-wide, with 14 regions having a p-value<0.001. These regions served as input for training a classifier that could accurately discriminate between cases and controls using a subset of the most relevant CpG regions. A regularized logistic regression model was used as implemented in the glmnet R / Bioconductor package (version 4.1 -4). All 9149 previously identified regions in the 40 samples were used as candidate features. The predictive performance (measured as the area under the ROC curve or AUC) in a 4-fold cross-validation setting was optimized by varying the penalty parameter lambda between 10-15 and 1 to select the optimal subset of required features. The cv.glmnet() function was used with alpha=0.5, keep=T, and grouped=FALSE. The fully trained model was applied to 10 samples from an independent validation set that were not used to develop the classifier. To quantify the variability due to the sampling procedure, the entire cross-validation procedure was repeated 100 times. For each iteration, the optimal number of features (median=274), the cross-validation AUC (median=0.99), and the AUC on the independent validation set (median=0.83) were collected.

[0180] Example 4 The microarray comprises probes for detecting genes / regions selected from Table 1, Table 2, and / or Table 3, as well as additional genomic regions (e.g., as negative controls an equal number of those in Table 1, Table 2, and Table 3). The microarray can be a customized array or a commercially available one, such as the Illumina Infinium MethylationEPIC BeadChip array for methylation profiling (catalog no. WG-317-1003). The purified cfDNA is subjected to methylation (bisulfite or enzymatic) conversion. After methylation conversion, methylation profiling is obtained by incubating the converted cfDNA with a microarray and imaging it with specific instruments such as the Illumina iScan System (Illumina, USA). The resulting microarray results are compared to a machine learning model (trained on pre-labeled data as described herein) and classified based on this comparison, resulting in a score for AD diagnosis.

[0181] Example 5 Subjects exhibit symptoms common to various types of dementia-related diseases. The methylation status of cell-free DNA obtained from fresh or frozen plasma is analyzed, and the results are compared to a machine learning model (trained on pre-labeled data as described herein) and classified to identify AD or non-AD dementia based on this comparison.

Claims

1. 1. A method for diagnosing a neurodegenerative disease or disorder in a subject, determining a probability that a subject will develop a neurodegenerative disease or disorder, and / or determining a score indicative of disease progression of a neurodegenerative disease or disorder, the method comprising the steps of: a) obtaining the methylation status of at least one of two or more genes selected from Table 1 and / or two or more regions selected from Table 2 from a sample of a subject; b) comparing the methylation status obtained in (a) with a reference pattern; and c) determining a score indicative of the subject's diagnosis of a neurodegenerative disease or disorder, the subject's probability of developing a neurodegenerative disease or disorder, and / or disease progression of the neurodegenerative disease or disorder based on the comparison obtained in (b).

2. The method of claim 1, wherein the sample is a body fluid sample, preferably a plasma or serum sample, more preferably a plasma sample.

3. The method of claim 2, wherein the sample was frozen.

4. 3. The method of claim 1 or 2, wherein the methylation status is obtained from cell-free DNA in the sample.

5. 3. The method of claim 1 or 2, wherein obtaining at least one methylation state comprises genomic methylation profiling.

6. 3. The method of claim 1 or 2, wherein the at least one methylation status comprises the methylation status of at least 100 regions selected from Table 2, preferably at least 150 regions selected from Table 2, preferably at least 200 regions selected from Table 2, more preferably between 200 and 600 regions selected from Table 2, more preferably between 250 and 500 regions selected from Table 2, more preferably between 250 and 300 regions selected from Table 2.

7. 7. The method of claim 6, wherein the at least one methylation status comprises the methylation status of at least 80% of the regions selected from Table 3, preferably 90% of the regions selected from Table 3, more preferably all of the regions selected from Table 3.

8. a) an average nucleotide width of less than 5000, preferably less than 4000, more preferably less than 3000, even more preferably less than 2000, even more preferably less than 1000; and / or b) a median nucleotide width of less than 5,000, preferably less than 4,000, more preferably less than 3,000, even more preferably less than 2,000, more preferably less than 1,000 3. The method of claim 1 or 2, wherein the methylation status is determined in a region having the formula:

9. The method of claim 1 or 2, wherein at least one additional marker is obtained in (a) compared to (b).

10. The method of claim 9 , wherein the at least one additional marker is a marker selected from the group consisting of subject background data, cognitive function markers, and autonomic nervous system biomarkers.

11. 3. The method of claim 1 or 2, wherein the reference patterns are obtained from the methylation status of two or more genes selected from Table 1 and / or two or more regions selected from Table 2 from samples of at least two reference subjects, wherein at least one of the reference subjects is suffering from a neurodegenerative disease or disorder.

12. 11. The method of claim 10, wherein obtaining reference patterns from the methylation status of two or more genes selected from Table 1 and / or two or more regions selected from Table 2 from samples of at least two reference subjects comprises machine learning techniques.

13. 1. A method for monitoring a neurodegenerative disease or disorder, comprising the steps of: i) determining, at a first time point, a first score indicative of the subject's probability of developing a neurodegenerative disease or disorder and / or disease progression of a neurodegenerative disease or disorder according to the method of claim 1 or 2; ii) determining, at a second time point, a second score indicative of the probability that the subject will develop a neurodegenerative disease or disorder and / or disease progression of the neurodegenerative disease or disorder according to the method of claim 1 or 2; iii) comparing the first score of step (i) with the second score of step (ii); and iv) monitoring the disease progression of the neurodegenerative disease or disorder in the subject based on the comparison of step (iii).

14. A library comprising at least one score indicative of the probability that a subject will develop a neurodegenerative disease or disorder and / or the disease progression of a neurodegenerative disease or disorder, determined according to claim 1 or 2.

15. A storage device containing computer readable program instructions for carrying out the method of claim 1 or 2, preferably further comprising a library according to claim 14.

16. 16. A server comprising the storage device of claim 15, at least one processing device, and a network connection for receiving data indicative of at least one methylation status.

17. 3. The method of claim 1 or 2, wherein the neurodegenerative disease or disorder is a disease or disorder characterized by cognitive impairment.

18. 3. The method of claim 1 or 2, wherein the neurodegenerative disease or disorder is Alzheimer's disease and / or Parkinson's disease.

19. 19. The method of claim 18, wherein the neurodegenerative disease or disorder is Alzheimer's disease.

20. 1. A device comprising methylation-specific oligonucleotide probes, wherein the probes are specific for determining the methylation status of at least 80% of the regions selected from Table 3, preferably all of the regions selected from Table 3.

21. 21. The device of claim 20, wherein the device is a microarray.

22. 22. The device according to claim 20 or 21, a) to diagnose a neurodegenerative disease or disorder in a subject, to determine a score indicative of the probability that a subject will develop a neurodegenerative disease or disorder, and / or the disease progression of a neurodegenerative disease or disorder; and / or b) Use for a method for monitoring a neurodegenerative disease or disorder.

23. Use of the device according to claim 20 or 21 for the method according to claim 1 or 2.