Methods of identifying dementia with lewy bodies in subject

Abstract

There is provided a method for identifying dementia with Lewy bodies (DLB) in a subject, the method comprising determining a methylation pattern in a D-loop and / or ND1 gene of mitochondrial DNA in a sample of the subject comprising the mitochondrial DNA; in addition, classification models, oligonucleotides and kits for performing the methods are also provided.

Description

Technical Field

[0001] The present invention relates to the fields of medicine and the diagnosis or identification of a neurodegenerative disease in a subject, and in particular to methods for diagnosing and identifying Lewy body dementia in a subject. Background Art

[0002] Lewy body dementia (DLB), i.e. Lewy body dementia, is the second most common cause of degenerative dementia after Alzheimer's disease (AD), and is characterized by non-motor cognitive changes preceding Parkinsonism. DLB belongs to a heterogeneous group of disorders known as Lewy body diseases (LBD), which are synucleinopathies with heterogeneous clinical manifestations. LBD is characterized by abnormal accumulation and deposition of misfolded and aggregated α-synuclein (α-syn) producing Lewy bodies and Lewy neurites.

[0003] DLB shares features with Alzheimer's disease and Parkinson's disease (PD). In addition to other differences in clinical features, people with DLB have a higher risk of falls, lower quality of life, greater care burden, and higher mortality compared with AD. On the other hand, DLB and PD dementia (PDD) present with overlapping clinical manifestations, including symptoms of attention deficits, visual hallucinations, parkinsonism, fluctuating cognitive impairment, and rapid eye movement (REM) sleep behavior disorder. However, patients with DLB often develop dementia before the first symptoms of parkinsonism appear, while the opposite is often true for patients with PD.

[0004] In those aspects, due to their common features and common clinical manifestations, there are new challenges for the accurate identification and diagnosis of degenerative dementia. At present, a combination of medical and neuropsychological tests (e.g., mini-mental state, 18F-fluorodeoxyglucose PET, etc.) is used to diagnose DLB. However, these diagnostic methods are sometimes not enough to obtain reliable and clear diagnosis, and may lead to misdiagnosis, which is very dangerous for DLB patients. Patients with DLB are highly sensitive to typical neuroleptics for the treatment of delusions and hallucinations. Therefore, when DLB patients are mistakenly diagnosed as suffering from AD and therefore treated with typical neuroleptics, they will suffer significant side effects, including high mental confusion, deterioration of Parkinson's syndrome, extreme drowsiness, and neuroleptic malignant syndrome in most severe cases. Therefore, it is crucial to develop efficient and feasible and non-invasive diagnostic tools to improve DLB diagnosis, and finally make a clear and definite diagnosis of DLB.

[0005] Epigenetic mechanisms such as DNA methylation regulate the brain transcriptome and play a key role in neurodegeneration. Among those, genetic and epigenetic variations in the apolipoprotein E and α-synuclein genes have been observed in DLB, and methylation changes in the promoter and intron 1 of the SNCA gene encoding α-synuclein have also been observed in brain and blood samples from PD and DLB cases (De Boni et al., 2011; Desplats et al., 2011; Funahashi et al., 2017).

[0006] In addition, Urbizu et al. (2020) conducted a detailed review of epigenetic modifications in DLB in human cells, postmortem tissues, and peripheral tissues, highlighting the almost non-existent number of studies targeting DLB. In addition, Chouliaras et al. (2020) summarized different methylation studies in DLB, which identified differentially methylated regions involving both APOE and SNCA genes, along with other new potential targets.

[0007] In an extensive epigenomic study analyzing several tissues and conditions, Fernandez et al. (2012) identified a set of genes that could be used to distinguish DLB samples from controls in cortical samples. In addition, Sanchez-Mutet al. (2016) exploited a cohort of postmortem brain samples using whole-genome bisulfite sequencing to identify a set of genes that were differentially methylated in DLB and other neurodegenerative conditions. They were able to validate these findings using bisulfite pyrosequencing in a larger cohort, which resulted in 1428 differentially methylated regions shared by PD and DLB subjects when compared to controls.

[0008] In contrast, a recent study compared the blood methylomes of patients with DLB and Parkinson's disease dementia (PDD) and found significant differences in blood methylation, suggesting that clinical variation in DLB can be reflected in the blood epigenome. The LASSO method with regularized regression was used for the 26 significantly differentially methylated positions, and discriminant analysis was used to identify the best predictors for distinguishing DLB from PDD cases (Nasamran et al., 2021).

[0009] In summary, we know very little about the role and characteristics of epigenetics in DLB. As mentioned above, all studies published to date have focused on epigenetics in the nuclear genome, and it is noteworthy that these studies have not yielded clear results, as the significance and potential similarities or differences in methylation patterns between DLB and other neurodegenerative diseases are unclear.

[0010] On the other hand, WO2015 / 144964A2 discloses potential mitochondrial methylation patterns for both AD and PD, which come from the analysis of postmortem brain samples from subjects, and the results correspond to a very early stage of research. In addition, the data disclosed in WO2015 / 144964A2 are also discussed in Blanch et al. (2016). Similarly, Stoccoro et al. (2017 & 2022) proposed mitochondrial methylation patterns in late-onset AD patients compared with a control group, and then compared the patterns between subjects diagnosed with mild cognitive impairment, AD patients, and controls.

[0011] Currently, there is no cure or treatment that can slow the progression of DLB, and only a few drugs are available for symptomatic treatment (e.g., cholinesterase inhibitors). Therefore, there is a great need for clinical trials, which require accurate recruitment to be successful and ultimately explore new treatment options for patients with DLB. Overall, there is a great need for an accurate, reliable, non-invasive, and feasible method for diagnosing and identifying DLB to improve the selection of the most appropriate treatment options for patients, as well as to improve the recruitment efficiency of clinical trials and accelerate the development of treatments for DLB. Summary of the invention

[0012] One problem to be solved by the present invention is to provide a method for diagnosing or identifying Dementia with Lewy Bodies (referred to herein as DLB) in a subject.

[0013] The present invention discloses a method, the method can identify samples from subjects suffering from DLB, and thus diagnose DLB in subjects in need thereof. The distinction that makes the diagnosis of DLB ​​feasible is obtained by processing feasible and available samples such as blood samples. In addition, the present invention also discloses a method, the method can calculate or determine a score to identify the presence of DLB ​​in a subject / diagnose DLB in a subject, and thus classify the subject according to the diagnosis. The method disclosed herein can also identify subjects who may not have progressed to DLB, but have a high risk of suffering from DLB. The method includes executing a classification model that can process more than one data set, and the data set includes biomarker screening data (i.e., mitochondrial methylation data) and other relevant clinical data (e.g., MMSE). The biomarker screening data is obtained from a blood sample, thus allowing for a fast, non-invasive and effective methodology for diagnosing / identifying this disease.

[0014] WO2015 / 144964A2 discloses the use of mitochondrial markers to diagnose other neurodegenerative diseases, such as AD and PD. However, no information has been previously described regarding the potential role or significance of mitochondrial markers in DLB, nor their potential usefulness in the diagnosis or identification of this disease.

[0015] As for other epigenetic markers for DLB, there are few studies published in this regard, and all studies focus on the analysis of epigenetics of the nuclear genome. However, no clear conclusions can be drawn from these studies. Although some studies have identified differences in methylation of certain positions / regions in samples from DLB subjects compared with subjects with other neurodegenerative diseases and control subjects, other studies have shown common patterns of nuclear genome methylation in DLB and other neurodegenerative diseases (e.g., PD). In summary, to date, there are no clues to the characteristics or significance of potential patterns of mitochondrial DNA (referred to herein as mtDNA) methylation in DLB.

[0016] Surprisingly, the inventors have found that samples from DLB patients do show a distinct pattern of mtDNA methylation in certain regions of mtDNA, such as the D-loop region, while other regions studied herein do not aid in the identification of DLB ​​patients when compared to controls. As shown herein, differences in mitochondrial methylation of sites contained in the D-loop region and the ND1 gene, including all three possible contexts (i.e., CpG, CHG, and CHH sites), are highly statistically significant for DLB subjects.

[0017] As shown herein, several methylation sites showing highly significant methylation patterns correspond to CHG sites in the D-loop region. Considering the common concern of epigenetic studies on CpG site methylation, these results are significant. Methylation of non-CpG sites is generally considered to be unrelated or less relevant to methylation of CpG sites, which may lead to biased results. In order to avoid the bias, the inventors of the present invention used a set of primers that equally consider the potential methylation of all sites. In addition, the primers used herein are unusually effective in detecting the methylation of mtDNA extracted from blood samples (see Example 1). Finally, examples of the present invention collect information from blood samples, which are examples of non-invasive and feasible samples that do not require expensive processing.

[0018] The working examples herein provide detailed experimental data, demonstrating the effective treatment of blood samples for detecting and calculating mtDNA methylation. Therefore, it is feasible to effectively diagnose DLB in a subject in need thereof. In addition, the information about the mitochondrial methylation is combined with other relevant clinical data and processed together by a classification model. As a result, the method provided herein determines a score corresponding to the presence (i.e., diagnosis) of DLB ​​in a subject.

[0019] Example 1 shows a method for detecting mtDNA methylation, which includes collecting a blood sample, extracting and processing DNA (bisulfite treatment), and preparing an amplicon library to detect, quantify and normalize the methylation of the target mtDNA site. The use of degenerate primers results in an abnormally high sensitivity for detecting mtDNA methylation in all three backgrounds (i.e., CpG, CHG, CHH). In addition, in all different backgrounds of the D-loop region, comparison of methylation levels between DLB subjects and controls resulted in a large number of significant differentially methylated sites.

[0020] Embodiment 2 shows the development of classification model, and it not only considers the data of purpose methylation site, but also considers other relevant clinical data (for example, MMSE).In order to select the most suitable model (that is, random forest), several supervised methods have been applied.Basically, after pre-processing data to process categorical variables, missing values, outliers and normalize continuous variables of appropriate level, data set is split into training set and validation set.Each method can build classification model differently.In the case of random forest, the set of decision tree is constructed using random subsets of training data, wherein each tree uses random feature subsets (that is, clinical and / or methylation variables) at each split.Decision tree grows to its maximum depth, or meets stopping criteria.In order to classify new observations, it will pass through each tree in random forest, and determine final prediction label based on the majority of votes of all trees.The performance of model is evaluated using metrics such as accuracy and κ, and accuracy and κ respectively measure the proportion of correctly classified individuals and the consistency between prediction and true label.After setting up random forest model, based on the highest probability predicted by model, test data is classified by specifying label (that is, DLB or control). Performance statistics such as accuracy, kappa, sensitivity, specificity, precision, recall, and F1 score are calculated to evaluate the classification results. These metrics provide an assessment of the accuracy and consistency of the model, as well as its ability to correctly identify positive and negative examples.

[0021] Example 3 shows the methylation pattern of a larger number of samples (84). In all different backgrounds of the D-loop region and the ND1 gene, the comparison of methylation levels between DLB subjects and controls resulted in a large number of significant differential methylation sites. Example 1 also shows the development of a classification model that takes into account the data of methylation sites provided herein. In this embodiment, during the pre-processing step, in order to reduce the dimension of the variable (i.e., methylation measure), two methods were applied, first, clustered feature selection based on the Spearman rank correlation coefficient and then principal component analysis (PCA). Based on the first 10 components generated by PCA, the model was constructed according to the same method described in Example 2. In other words, in order to select the most suitable model (i.e., random forest), several supervised methods were performed. The resulting training model showed outstanding high performance, as indicated by an overall accuracy score of 0.83 and a κ value of 0.65. Therefore, the classification model can identify Lewy body dementia in a subject by rapidly processing the individual information of the subject with very good performance.

[0022] Furthermore, it is worth noting that this first prototype used only methylation data and the methylation patterns presented here are robust enough to distinguish DLB patients from healthy controls. When a larger number of samples are available and different neuropsychological variables can be introduced into the algorithm, more effective classification models are expected to become available.

[0023] In this regard, it is noted that obtaining samples and clinical information of subjects is a complex process for many reasons: suitable subjects are limited and they have to be monitored for a long time; the availability of samples is limited; the quality of samples may be affected; the subject information includes missing data or is heterogeneous due to, for example, variability between neuropsychological tests used in each country, etc. Overall, available data for developing classification models is not obtained automatically but is the result of a tedious and difficult process.

[0024] Accordingly, a first aspect of the invention relates to a method for identifying Lewy body dementia in a subject, the method comprising:

[0025] (a) determining, in a sample of the subject comprising mitochondrial DNA, a methylation pattern in the D-loop region and / or the ND1 gene of the mitochondrial DNA, wherein the methylation pattern is determined in at least one site selected from the group consisting of:

[0026] (i) CpG sites in the D-loop region shown in Table 1,

[0027] (ii) a CHG site in the D-loop region shown in Table 3,

[0028] (iii) a CHH site in the D-loop region shown in Table 5,

[0029] (iv) the CpG sites of the ND1 gene shown in Table 2,

[0030] (v) the CHG site of the ND1 gene shown in Table 4, and

[0031] (vi) CHH site of ND1 gene shown in Table 6.

[0032] A second aspect relates to a method for identifying Lewy body dementia in a subject, the method comprising:

[0033] (a) determining, in a sample of the subject comprising mitochondrial DNA, a methylation pattern in the D-loop region and / or the ND1 gene of the mitochondrial DNA, wherein the methylation pattern is determined in at least one site selected from the group consisting of:

[0034] (i) CpG sites in the D-loop region shown in Table 1,

[0035] (ii) a CHG site in the D-loop region shown in Table 3,

[0036] (iii) a CHH site in the D-loop region shown in Table 5,

[0037] (iv) the CpG sites of the ND1 gene shown in Table 2,

[0038] (v) the CHG site of the ND1 gene shown in Table 4, and

[0039] (vi) the CHH site of the ND1 gene shown in Table 6; and

[0040] (b) optionally combining the methylation pattern of the one or more sites determined in step (a) with at least one clinical variable of the subject, wherein the combining is performed using a classification model for determining a score associated with identification of Lewy body dementia in the subject.

[0041] Other aspects of the invention relate to methods for diagnosing DLB in a subject; methods for identifying a subject with DLB; methods for identifying a subject suitable for treatment of DLB; methods for selecting a subject for treatment of DLB; methods for selecting an appropriate therapy to treat a subject with DLB; methods for classifying a subject according to the presence or absence of DLB; methods for treating DLB in a subject, the method comprising administering a treatment for DLB if the subject has been identified as having DLB; methods for treating DLB, the method comprising determining the presence or absence of the dementia; and methods for treating DLB in a subject, the method comprising assigning a dementia classification status to the subject using the above methods prior to administration. Other aspects of the invention relate to methods for monitoring the progression of DLB ​​in a subject using the above methods, and methods for monitoring and treating a subject with DLB.

[0042] Other aspects of the invention relate to oligonucleotides and kits comprising the oligonucleotides for determining mitochondrial DNA methylation patterns to identify DLB in a subject, wherein the methylation pattern is determined in at least one methylation site selected from the group consisting of:

[0043] (i) CpG sites in the D-loop region shown in Table 1,

[0044] (ii) a CHG site in the D-loop region shown in Table 3, and

[0045] (iii) a CHH site in the D-loop region shown in Table 5,

[0046] (iv) the CpG sites of the ND1 gene shown in Table 2,

[0047] (v) the CHG site of the ND1 gene shown in Table 4, and

[0048] (vi) CHH site of ND1 gene shown in Table 6.

[0049] Throughout the specification and claims, the word "comprise" and its variants are not intended to exclude other technical features, additives, components or steps. Other objects, advantages and features of the present invention will become apparent to those skilled in the art after reading the specification, or can be understood through the practice of the present invention. In addition, the present invention covers all possible combinations of the specific embodiments and preferred embodiments described herein. The following examples and drawings are provided herein for illustrative purposes, not for limiting the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1Shown are box plots of the percent methylation at each position in the ND1 gene in the context of CpGs for control subjects (n=18) and DLB subjects (n=18).

[0051] Figure 2 Shown are the median and confidence interval (95%) of the methylation percentage in the ND1 gene in the CpG context for each individual including control subjects (n=18) and DLB subjects (n=18).

[0052] Figure 3 Shown are box plots of the percent methylation at each position in the ND1 gene in the context of CHG for control subjects (n=18) and DLB subjects (n=18).

[0053] Figure 4 Shown are the median and confidence interval (95%) of the methylation percentage in the ND1 gene in the context of CHG for each individual including control subjects (n=18) and DLB subjects (n=18).

[0054] Figures 5A-5D Shown are box plots of the percent methylation at each position in the ND1 gene in the CHH background for control subjects (n=18) and DLB subjects (n=18).

[0055] Figure 6 Shown are the median and confidence interval (95%) of the methylation percentage in the ND1 gene in the CHH background for each individual including control subjects (n=18) and DLB subjects (n=18).

[0056] Figure 7 Shown are box plots of the percent methylation at each position in the D-loop region in the context of CpGs for control subjects (n=18) and DLB subjects (n=18).

[0057] Figure 8 Shown are the median and confidence interval (95%) of the methylation percentage in the D-loop region in the context of CpG for each individual including control subjects (n=18) and DLB subjects (n=18).

[0058] Fig. 9 Shown are box plots of the percent methylation at each position in the D-loop region in the context of CHG for control subjects (n=18) and DLB subjects (n=18).

[0059] Fig.10 Shown are the median and confidence interval (95%) of the methylation percentage in the D-loop region in the context of CHG for each individual including control subjects (n=18) and DLB subjects (n=18).

[0060] Fig.11A -B shows box plots of the percent methylation at each position in the D-loop region in the CHH context for control subjects (n=18) and DLB subjects (n=18).

[0061] Fig.12 Shown are the median and confidence interval (95%) representing the percentage of methylation in the D-loop region in the context of CHH for each individual including control subjects (n=18) and DLB subjects (n=18).

[0062] Fig.13 Shown are box plots of the percent methylation at each position in the ND1 gene in the context of CpGs for control subjects (n=42) and DLB subjects (n=42).

[0063] Fig.14 Shown are box plots of the percent methylation at each position in the ND1 gene in the context of CHG for control subjects (n=42) and DLB subjects (n=42).

[0064] Fig.15 Shown are box plots of the percent methylation at each position in the ND1 gene in the CHH background for control subjects (n=42) and DLB subjects (n=42).

[0065] Fig.16 Shown are box plots of the percent methylation at each position in the D-loop region in the context of CpGs for control subjects (n=42) and DLB subjects (n=42).

[0066] Fig.17 Shown are box plots of the percent methylation at each position in the D-loop region in the context of CHG for control subjects (n=42) and DLB subjects (n=42).

[0067] Fig.18 Shown are box plots of the percent methylation at each position in the D-loop region in the CHH context for control subjects (n=42) and DLB subjects (n=42).

[0068] Fig.19 The accuracy and κ metric of the supervised learning model trained in Example 3 are shown.

[0069] Fig. 20 ROC curves are shown showing the performance of the classification model for patients with dementia with Lewy bodies (DLB) versus controls (CTL) at all classification thresholds using the model constructed using the random forest method of Example 3. DETAILED DESCRIPTION

[0070] For the avoidance of doubt, the methods provided herein do not relate to diagnosis practiced on the human or animal body. The methods of the present invention are particularly performed on a sample previously taken from a subject. The kits provided herein may include a tool for extracting a sample from a subject.

[0071] definition

[0072] diagnosis : The term "diagnosis" refers to the process of attempting to determine and / or identify possible diseases in a subject, i.e., a diagnostic procedure, and the opinions obtained through the process, i.e., diagnostic opinions. Therefore, it can also be regarded as an attempt to classify an individual's condition into separate and different categories in order to make medical decisions about treatment and prognosis. As those skilled in the art will appreciate, this diagnosis may not be correct for 100% of the subjects to be diagnosed, although it is preferred. However, the term requires that a statistically significant portion of the subject can be identified as suffering from DLB in the context of the present invention, or a susceptibility to DLB. Those skilled in the art can use the following to determine whether a portion is statistically significant: different well-known statistical evaluation tools, such as by determining a confidence interval, determining a p value, a corrected p value, a student's t test, a Mann-Whitney test, etc. The specific confidence interval is at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 95%. The p value or the corrected p value is specifically 0.1, 0.05, 0.025, 0.001 or lower.

[0073] Lewy body dementia : The term "dementia with Lewy bodies" or "dementia with Lewy bodies" or DLB refers to a degenerative dementia characterized by slow and progressive cognitive decline. Other features of DLB ​​are spontaneous parkinsonism, recurrent visual hallucinations, cognitive fluctuations, rapid eye movement (REM) sleep behavior disorder (RBD), and severe sensitivity to antipsychotic drugs with regard to the development of extrapyramidal symptoms. Like other neurodegenerative diseases, DLB does not include standardized disease stages because it is a highly variable and fluctuating disease in which the progression of the disease can vary greatly between patients. Not only do symptoms vary greatly between patients, but the presentation of symptoms in a patient can also fluctuate greatly over a short period of time.

[0074] The main symptom of DLB ​​is dementia, usually accompanied by visual and spatial perception impairment (i.e., difficulty in correctly perceiving distance and depth and misidentification of objects), together with other difficulties in cognitive functions such as planning, multitasking, problem solving and reasoning. Dementia can also include changes in mood and behavior, decreased judgment, loss of initiative, confusion in time and place, and difficulties in language and numbers. In addition, DLB symptoms also include cognitive fluctuations and hallucinations, and memory loss may occur in the later stages of the disease. Cognitive fluctuations are common and include unpredictable changes in concentration, attention, alertness and wakefulness, which may vary greatly from one day to another, or even within the same day. Hallucinations can be visual and / or auditory, but hallucinations are more common, affecting up to 80% of DLB ​​patients, and are usually very realistic and detailed. Hallucinations are perceptions with the nature of real perceptions in the absence of external stimulation.

[0075] Regarding motor symptoms, parkinsonism is a common symptom in patients with DLB, and it affects each patient very differently. Parkinsonism in DLB causes slow movements, difficulty walking, stiffness, and postural instability. DLB patients can also exhibit REM sleep behavior disorder, a parasomnia with abnormal dreaming behavior during REM sleep. It can include vivid dreams, sleep talking, and violent movements. In addition, some DLB patients suffer from dysautonomia or autonomic dysfunction caused by impaired function of the autonomic nervous system (ANS).

[0076] Subjects: The terms "subject," "patient," "individual," and variations thereof are used interchangeably herein and refer to any mammalian subject, particularly a human subject. The terms do not denote a particular age or sex.

[0077] Samples including mitochondrial DNA : The expression "a sample comprising mitochondrial DNA" as used herein refers to any sample obtainable from a subject in which genetic material from mitochondria suitable for detecting methylation patterns is present.

[0078] Mitochondrial DNA: As used herein, the term "mitochondrial DNA" or "mtDNA" refers to the genetic material located in the mitochondria of living organisms. It is a closed circular double-stranded molecule. In humans, it consists of 16,569 base pairs, contains a small number of genes, and is distributed between the H and L chains. Mitochondrial DNA encodes 37 genes: two ribosomal RNAs, 22 transfer RNAs, and 13 proteins involved in oxidative phosphorylation.

[0079] Methylation patterns and methylation status: As used herein, the term "methylation pattern" refers to, but is not limited to, the presence or absence of methylation of one or more nucleotides (particularly methylation in cytosine). Thus, the one or more nucleotides are included in a single nucleic acid molecule. The one or more nucleotides can be methylated or not. When only a single nucleotide is considered, the term "methylation state" may also be used. The methylation pattern can be quantified; in this case, it is considered to be more than one nucleic acid molecule.

[0080] D-Ring Area : As used herein, this term refers to a non-coding mtDNA region that serves as a promoter for the heavy and light chains of mtDNA and includes essential transcription and replication elements. The D-loop region includes approximately 1120 base pairs, visible under an electron microscope, and is generated during H-chain replication of a short segment of the synthetic heavy chain 7S DNA. The sequence of the human D-loop region is stored in the GenBank database with accession number NC_012920.1.

[0081] ND1 gene : As used herein, the term "ND1 gene" or "NADH dehydrogenase 1" or "ND1mt" refers to a gene encoding NADH dehydrogenase 1 or ND1 protein located in the mitochondrial genome. The human ND1 gene sequence is stored in the GenBank database with accession number NC_012920.1. The ND1 protein is part of an enzyme complex called complex I, which is active in mitochondria and participates in the oxidative phosphorylation process. In some embodiments, the term "ND1 gene" may refer to the above-mentioned gene further including about 50 additional base pairs at one or both ends of the sequence.

[0082] CpG sites : As used herein, the term distinguishes this single-stranded linear sequence from the CG base pairing of cytosine and guanine of the double-stranded sequence. "CpG" is an abbreviation for "C-phosphate-G", i.e., cytosine and guanine separated by only one phosphate; the phosphate binds any two nucleosides in DNA together. The term "CpG" is used to distinguish the linear sequence of CG base pairing of guanine and cytosine. The cytosine in the CpG dinucleotide can be methylated to form 5-methylcytosine.

[0083] CHG site : As used herein, the term refers to a DNA region, specifically a mitochondrial DNA region, in which a cytosine nucleotide and a guanine nucleotide are separated by a variable nucleotide (H), which can be adenine, cytosine or thymine. The cytosine at the CHG site can be methylated to form 5-methylcytosine.

[0084] CHH locus: As used herein, the term refers to a DNA region, specifically a mitochondrial DNA region, wherein the cytosine nucleotide is followed by a first and a second variable nucleotide (H), which can be adenine, cytosine or thymine. The cytosine of CHH can be methylated to form 5-methylcytosine.

[0085] Determination of methylation patterns in CpG sites : As used herein, the term "determination of the methylation pattern in a CpG site" refers to the determination of the methylation state of a specific CpG site. The determination of the methylation pattern of a CpG site can be performed by a variety of methods known to those skilled in the art.

[0086] Determination of methylation patterns in CHG sites CHG site methylation pattern determination can be performed by a variety of methods known to those skilled in the art.

[0087] Determination of methylation patterns in CHH sites : As used herein, the term refers to determining the methylation status of a specific CHH site. The determination of the methylation pattern of a CHH site can be performed by a variety of methods known to those skilled in the art.

[0088] To determine the methylation pattern in mitochondrial DNA, the sample can be chemically treated so that all cytosine unmethylated bases are modified at the uracil base or at another base that differs from cytosine in base pairing behavior, while the 5-methylcytosine base remains unchanged. As used herein, the term " Modification " refers to the conversion of unmethylated cytosine to another nucleotide, which distinguishes unmethylated cytosine from methylated cytosine. In a sample containing mitochondrial DNA, the conversion of unmethylated cytosine bases but not methylated cytosine bases is performed using a conversion agent. As used herein, the term " Transformation Agent "or" Transformation reagents " refers to an agent capable of converting unmethylated cytosine to uracil or another base that is detectable in a distinguishable manner to cytosine in terms of hybridization properties. The converting agent is specifically a bisulfate, such as bisulfite or hydrogen sulfite. However, other agents that similarly modify unmethylated cytosine rather than methylated cytosine can also be used in the methods of the invention, such as bisulfite. The reaction is carried out according to standard procedures. The conversion can also be performed enzymatically, for example using a cytidine deaminase-specific methylation.

[0089] Reference samples: The term as used herein refers to a sample containing mitochondrial DNA obtained from a subject who does not suffer from DLB. Therefore, the reference DNA sequence is the NCBI reference sequence: NC_012920.1. Specifically, the term refers to a small amount of 5-methylcytosine in one or more CpG sites in the D-loop region shown in Table 1, one or more CpG sites of the ND1 gene shown in Table 2, one or more CHG sites in the D-loop region shown in Table 3, one or more CHG sites of the ND1 gene shown in Table 4, one or more CHH sites in the D-loop region shown in Table 5, and / or one or more CHH sites in the ND1 gene shown in Table 6 in the mitochondrial DNA sequence compared to the relative amount of 5-methylcytosine present in the one or more CpG sites, one or more CHG sites, and / or one or more CHH sites in the subject sample.

[0090] Treatment of Lewy body dementia :The term as used herein refers to the treatment of a disease or any associated symptoms. This treatment may include drug therapy, epigenetic therapy or any cognitive stimulation therapy. Some cognitive stimulation treatments are digital cognitive therapy (i.e., using digital devices). The term may include any treatment known in the art for DLB or future development. The treatment of DLB ​​may include, but is not limited to, drugs for treating dementia symptoms, which may include cholinesterase inhibitors (e.g., rivastigmine), drugs for treating Parkinson's syndrome (e.g., levodopa), or drugs for treating psychiatric symptoms such as hallucinations (which can be treated with atypical antipsychotics) (e.g., quetiapine). The treatment of DLB ​​may include preventive treatment for subjects who have been identified as having a high risk of suffering from DLB but are not yet in the dementia stage. Preventive treatment includes any method known in the field of cognitive stimulation. Preventive treatment may include suitable future development.

[0091] Dementia Stage Classification (DSC) : The term "dementia stage classification" as used herein refers to the classification of subjects based on the diagnosis or identification of dementia. The classification is derived from a score determined by the classification model disclosed herein, which determines the presence or absence of dementia. The classification model according to an example of the present invention classifies subjects with DLB and subjects without the disease. Subjects without DLB are not necessarily healthy subjects, but may suffer from other diseases such as Alzheimer's disease. Therefore, the "dementia stage classification" includes two categories: subjects with Lewy body dementia and subjects without DLB.

[0092] Specific hybridization: As used herein, the expression "specific hybridization" or "capable of hybridizing in a specific form" refers to the ability of an oligonucleotide or polynucleotide to specifically recognize a particular sequence of interest, for example, the D-loop region or the ND1 gene. The target sequence may refer to a reference sequence or a sequence produced by certain modification treatments, such as bisulfite treatment, in which unmethylated cytosine is modified to uracil. As used herein, the term "hybridization" refers to the process of combining two nucleic acid molecules or single-stranded molecules with a high degree of similarity into a simple double-stranded molecule by specific pairing between complementary bases. Typically, hybridization occurs under very stringent conditions or moderately stringent conditions.

[0093] Oligonucleotides : As used herein, the term "oligonucleotide" is used indiscriminately with "primer" and "nucleic acid sequence", and refers to a DNA molecule or a short RNA of up to many bases in length. Oligonucleotide of the present invention is specifically a DNA molecule of at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 15, at least 20, at least 30, at least 40, at least 45 or 50 bases in length.

[0094] score : The term as used herein refers to one or more values, specifically a single value, which can be used as a constituent element in a classification model for diagnosing or identifying a disease in a subject. Such a single value can be calculated or determined (i.e., evaluated) by combining descriptive feature values ​​processed by an explanation function or algorithm. The score can be a score of 0.0-1, with a score of more than 0.5 indicating that the subject has a disease, and a score of 0 indicating that the disease is not present. The score can be divided into groups, i.e., categories, such as none, low, medium, and high.

[0095] Computer-implemented methods : The term "computer-implemented method" refers to a method in which all or some of the steps of a method are performed by computers, other programmable devices or computer networks.

[0096] Supervised Machine Learning Methods : This term refers to the method of developing a model using a training set to generate the desired output. This training data set includes inputs and correct outputs, which allows the model to learn over time. Its accuracy is measured by a loss function, and its model can be adjusted until the generalization error is sufficiently minimized.

[0097] Unsupervised Machine Learning Methods : The term "unsupervised learning methods", also known as "unsupervised learning methods", refers to methods that use machine learning algorithms to analyze unlabeled data sets. These methods identify similarities and differences in information to detect data groups or patterns. These methods are often used for clustering, association, and dimensionality reduction of data sets.

[0098] Deep Learning Methods : This term refers to a subfield of machine learning that uses neural network models with multiple layers to automatically extract and learn features and patterns from raw data. A layer in a neural network is a group of interconnected artificial neurons (also called nodes). These neurons receive input data and apply a specific type of computational task to it. Usually, these tasks involve weighted sums as well as mathematical activation functions. The output of one layer is passed to the next layer, and so on. This process allows the network to gradually learn more complex features, properties, and patterns in the data. There are different types of layers in neural networks. Some examples are fully connected layers, convolutional layers, pooling layers, and recurrent layers.

[0099] Artificial Intelligence Methods : This term refers to methods that use artificial intelligence, which is defined as the ability of a computer or computer-controlled robot to imitate a human's response to certain stimuli.

[0100] Classification Model : The term "classification model" herein refers indiscriminately to a "classifying model". A classification model can be developed using, for example, a type of supervised learning method that can accurately assign test data / new observations to specific categories based on training data. The model is trained to learn from a given training data set, and is therefore able to classify new data sets into a certain score / number or category / group. Examples of classification algorithms are logistic regression, linear discriminant analysis (LDA), classification and regression trees (CART), k-nearest neighbors (kNN), naive Bayes (NB), support vector machines (SVM) with linear kernel, random forests (RF), neural networks (NNET), generalized boosted regression models (GBM), and binomial logistic regression (GLM).

[0101] Conversion: The term as used herein means an explanation function or algorithm for a prediction model for a disease, specifically DLB, that is experienced with one or more descriptive features. In some embodiments, the explanation function may also be generated by a plurality of prediction models. In one embodiment, the prediction model comprises a regression model and a Bayesian classifier or score. In one embodiment, the explanation function comprises one or more terms associated with one or more biomarkers or biomarker groups. In one embodiment, the explanation function comprises one or more terms related to the presence or absence or spatial distribution of a specific cell type disclosed herein. In one embodiment, the explanation function comprises one or more terms related to the presence, absence, quantity, intensity or spatial distribution of morphological features of cells in a cell sample. In one embodiment, the explanation function comprises one or more terms related to the presence, absence, quantity, intensity or spatial distribution of descriptive features of cells in a cell sample.

[0102] Methods for Identification of DLB

[0103] One aspect of the invention relates to a method for identifying DLB in a subject, comprising determining a methylation pattern in the D-loop region and / or the ND1 gene in a sample from the subject comprising mitochondrial DNA, wherein the methylation pattern is determined in at least one site selected from the group consisting of:

[0104] (i) CpG sites in the D-loop region shown in Table 1,

[0105] (ii) a CHG site in the D-loop region shown in Table 3,

[0106] (iii) a CHH site in the D-loop region shown in Table 5,

[0107] (iv) the CpG sites of the ND1 gene shown in Table 2,

[0108] (v) the CHG site of the ND1 gene shown in Table 4, and

[0109] (vi) CHH site of ND1 gene shown in Table 6.

[0110] A computer-implemented method for identifying DLB in a subject may include receiving a methylation pattern of a D-loop region and / or an ND1 gene of mitochondrial DNA from a sample of the subject, and assigning a score to a classification model for the subject.

[0111] In an embodiment, the method comprises:

[0112] (a) determining, in a subject sample comprising mitochondrial DNA, a methylation pattern in the D-loop region of the mitochondrial DNA and / or the ND1 gene;

[0113] wherein the methylation pattern is determined in at least one site selected from the group consisting of:

[0114] (i) CpG sites in the D-loop region shown in Table 1,

[0115] (ii) a CHG site in the D-loop region shown in Table 3,

[0116] (iii) a CHH site in the D-loop region shown in Table 5,

[0117] (iv) the CpG sites of the ND1 gene shown in Table 2, and

[0118] (v) the CHG site of the ND1 gene shown in Table 4,

[0119] And wherein the methylation pattern is determined in at least one of the CHH sites of the ND1 gene shown in (vi) Table 6.

[0120] Alternatively, the methods of the present invention may be formulated as a method for diagnosing DLB in a subject, comprising the steps as described above.Alternatively, the methods of the present invention may be formulated as a method for identifying a subject with DLB, comprising the steps as described above.

[0121] In some embodiments, a method, particularly a computer-implemented method, comprises determining a methylation pattern in the D-loop region, wherein hypomethylation in at least one of the CpG sites in the D-loop region, wherein hypomethylation in at least one of the CHG sites in the D-loop region and / or wherein hypomethylation in at least one of the CHH sites in the D-loop region indicates that the subject suffers from DLB. Alternatively, these methylation patterns indicate that the subject is at increased risk of suffering from DLB.

[0122] In some embodiments, the method further comprises determining a methylation pattern in the ND1 gene, wherein the methylation pattern is determined in at least one site selected from the group consisting of:

[0123] (iv) the CpG sites of the ND1 gene shown in Table 2,

[0124] (v) the CHG site of the ND1 gene shown in Table 4 and,

[0125] (vi) CHH site of ND1 gene shown in Table 6.

[0126] In some embodiments, the method includes determining a methylation pattern in the ND1 gene, wherein no significant difference in methylation levels in at least one of the CpG sites in the ND1 gene, wherein no significant difference in methylation levels in at least one of the CHG sites in the ND1 gene, and / or wherein no significant difference in methylation levels in at least one of the CHH sites in the ND1 gene indicates that the subject suffers from DLB. Alternatively, these methylation patterns indicate that the subject is at increased risk for DLB.

[0127] The statistical significance of the methylation pattern in each methylation site can vary according to the threshold value (i.e., the corrected p-value) of the significance group. In some embodiments, the p-value is corrected using the false discovery rate (FDR) method or the multiple comparison error (FWER) method. In a specific embodiment, the p-value is corrected using the FDR method. The difference in methylation patterns may be considered to be statistically significant or not statistically significant, depending on the significance threshold established, which may be, for example, a corrected p-value ≥ 0.05 or a corrected p-value ≥ 0.1. In some embodiments, if the corrected p-value ≥ 0.05, the difference in methylation patterns is not considered to be statistically significant. In other embodiments, if the corrected p-value ≥ 0.1, the difference in methylation patterns is not considered to be statistically significant. In another embodiment, the method further comprises determining a methylation pattern in the ND1 gene, wherein there is no significant difference in the methylation level of at least one of the CpG sites in the ND1 gene, wherein there is no significant difference in the methylation level of at least one of the CHG sites in the ND1 gene, and / or wherein hypomethylation in at least one of the CHH sites in the ND1 gene indicates that the subject has DLB. Alternatively, these methylation patterns indicate that the subject is at increased risk for DLB.

[0128] In some embodiments, the method comprises determining the methylation pattern in the D-loop region and the ND1 gene, wherein:

[0129] Hypomethylation in at least one of said CpG sites in the D-loop region,

[0130] Hypomethylation in at least one of the CHG sites in the D-loop region, and / or

[0131] Hypomethylation in at least one of the CHH sites in the D-loop region; and

[0132] There is no significant difference in the methylation level of at least one of the CpG sites in the ND1 gene,

[0133] There is no significant difference in the methylation level of at least one of the CHG sites in the ND1 gene, and / or

[0134] There is no significant difference in the methylation level or hypomethylation of at least one of the CHH sites in the ND1 gene,

[0135] The subjects were indicated to have DLB.

[0136] The methods described herein can be used to diagnose DLB in subjects and can be used clinically to make treatment decisions by selecting the most appropriate treatment for any particular patient. The methods can also be used to classify subjects for, for example, entry into clinical trials or for receiving appropriate treatment at an early stage. Therefore, the application of the methods disclosed herein can improve clinical outcomes by matching patients with therapies, and can also improve the accuracy of selection of patients required for clinical trials to successfully evaluate potential DLB treatments.

[0137] Within those aspects, another aspect relates to a method for identifying a subject suitable for treatment of DLB, in particular a computer-implemented method, the method comprising determining, in a sample from said subject comprising mitochondrial DNA, a methylation pattern in the D-loop region and / or the ND1 gene, wherein the methylation pattern is determined in at least one site selected from the group consisting of:

[0138] (i) CpG sites in the D-loop region shown in Table 1,

[0139] (ii) a CHG site in the D-loop region shown in Table 3, and

[0140] (iii) a CHH site in the D-loop region shown in Table 5,

[0141] (iv) the CpG sites of the ND1 gene shown in Table 2,

[0142] (v) the CHG site of the ND1 gene shown in Table 4, and

[0143] (vi) CHH site of ND1 gene shown in Table 6.

[0144] Alternatively, this aspect may be formulated as a method of selecting a subject for treatment of DLB ​​comprising the steps as described above.

[0145] In some embodiments, the method (ie, a method for identifying a subject suitable for treatment of DLB ​​or for selecting a subject for treatment of DLB) further comprises administering the DLB treatment to the subject.

[0146] In some embodiments, a method comprises determining a methylation pattern in the D-loop region, wherein hypomethylation in at least one of the CpG sites in the D-loop region, wherein hypomethylation in at least one of the CHG sites in the D-loop region and / or wherein hypomethylation in at least one of the CHH sites in the D-loop region indicates that the subject has DLB or that the subject has a high risk of developing DLB.

[0147] In some embodiments, the method further comprises determining the methylation pattern in the ND1 gene, as described above.

[0148] The diagnosis or identification of DLB ​​in a subject indicates that the subject can be treated for DLB. If the subject is not diagnosed or identified as having DLB, the subject cannot receive any DLB treatment and may be suspected of having another disease and therefore receive clinical follow-up and / or other diagnostic methods. Alternatively, if necessary, the subject can be treated for other dementias.

[0149] Therefore, another aspect of the present invention relates to a method for selecting an appropriate therapy to treat a subject with DLB, in particular a computer-implemented method, comprising determining a methylation pattern in the D-loop region and / or the ND1 gene in a sample from said subject comprising mitochondrial DNA, wherein the methylation pattern is determined in at least one site selected from the group consisting of:

[0150] (i) CpG sites in the D-loop region shown in Table 1,

[0151] (ii) a CHG site in the D-loop region shown in Table 3,

[0152] (iii) a CHH site in the D-loop region shown in Table 5,

[0153] (iv) the CpG sites of the ND1 gene shown in Table 2,

[0154] (v) the CHG site of the ND1 gene shown in Table 4, and

[0155] (vi) CHH site of ND1 gene shown in Table 6.

[0156] Alternatively, this aspect of the invention may be formulated as a method of classifying a subject according to the presence or absence of DLB, comprising the steps as described above.

[0157] In some embodiments, the method (ie, for selecting an appropriate therapy to treat a subject having DLB and for classifying a subject according to the presence or absence of DLB) further comprises administering a DLB treatment to the subject when the subject has been classified as having DLB.

[0158] In some embodiments, the method further comprises determining the methylation pattern in the ND1 gene, as described above.

[0159] Another aspect of the invention relates to a method, particularly a computer-implemented method, for treating DLB in a subject, the method comprising administering a DLB treatment if the subject has been identified as having DLB by determining the methylation pattern of the D-loop region and / or the ND1 gene in a sample from the subject comprising mitochondrial DNA, wherein the methylation pattern is determined in at least one site selected from the group consisting of:

[0160] (i) CpG sites in the D-loop region shown in Table 1,

[0161] (ii) a CHG site in the D-loop region shown in Table 3,

[0162] (iii) a CHH site in the D-loop region shown in Table 5,

[0163] (iv) the CpG sites of the ND1 gene shown in Table 2,

[0164] (v) the CHG site of the ND1 gene shown in Table 4, and

[0165] (vi) CHH site of ND1 gene shown in Table 6.

[0166] Alternatively, this aspect of the invention may be formulated as a method for treating DLB comprising determining the presence or absence of said dementia by determining the methylation pattern in the D-loop region and / or the ND1 gene in a sample from said subject comprising mitochondrial DNA, wherein the methylation pattern is determined in at least one site selected from the group consisting of:

[0167] (i) CpG sites in the D-loop region shown in Table 1,

[0168] (ii) a CHG site in the D-loop region shown in Table 3,

[0169] (iii) a CHH site in the D-loop region shown in Table 5,

[0170] (iv) the CpG sites of the ND1 gene shown in Table 2,

[0171] (v) the CHG site of the ND1 gene shown in Table 4, and

[0172] (vi) the CHH locus of the ND1 gene shown in Table 6, wherein if the subject has been identified as having DLB, a DLB treatment is administered.

[0173] In some embodiments, a method comprises determining a methylation pattern in the D-loop region, wherein hypomethylation in at least one of the CpG sites in the D-loop region, wherein hypomethylation in at least one of the CHG sites in the D-loop region and / or wherein hypomethylation in at least one of the CHH sites in the D-loop region indicates that the subject has DLB.

[0174] In some embodiments, the method further comprises determining the methylation pattern in the ND1 gene, comprising the steps as described above.

[0175] In addition, other aspects relate to a method for treating a subject, particularly a subject diagnosed with DLB, by administering a DLB treatment, wherein, before administration, a dementia state classification (DSC) is assigned to the subject, which is determined by applying a classification model to the D-loop region of the mitochondrial DNA from the subject sample and / or the methylation pattern of the ND1 gene, wherein the DSC is selected from a subject with DLB or a subject without DLB. In an embodiment, when the subject is designated as a DSC with DLB, the subject is suitable for administering DLB treatment. In another embodiment, if the subject is designated as not suffering from DLB, the subject cannot receive any DLB treatment, but can undergo clinical follow-up or test alternative diseases such as AD. Alternatively, if necessary, the subject can be treated with other dementias.

[0176] This aspect of the invention may alternatively be tailored as a method, particularly a computer-implemented method, for treating a subject, the method comprising:

[0177] (i) prior to administration, assigning a DCS to the subject by applying a classification model to the methylation pattern of the D-loop region and / or the ND1 gene of mitochondrial DNA from a sample from the subject, wherein the DSC is selected from a subject with DLB and a subject without DLB; and

[0178] (ii) if the DSC is a subject with DLB, administering DLB treatment to the subject, or if the DSC is a subject without DLB, administering no DLB treatment to the subject or clinical follow-up or administering therapy for other dementias.

[0179] In some embodiments, the methylation pattern in the D-loop region of mitochondrial DNA is determined in at least one site selected from the group consisting of:

[0180] (i) CpG sites in the D-loop region shown in Table 1,

[0181] (ii) a CHG site in the D-loop region shown in Table 3, and

[0182] (iii) CHH sites in the D-loop region shown in Table 5.

[0183] In other embodiments, the method further comprises determining the methylation pattern of at least one site in the ND1 gene of the mitochondrial DNA selected from the group consisting of:

[0184] (iv) the CpG sites of the ND1 gene shown in Table 2,

[0185] (v) the CHG site in the ND1 gene shown in Table 4, and

[0186] (vi) The CHH site in the ND1 gene shown in Table 6.

[0187] In some embodiments, the methods described herein comprise:

[0188] (a) determining, in a subject sample comprising mitochondrial DNA, a methylation pattern in the D-loop region of the mitochondrial DNA and / or the ND1 gene;

[0189] Wherein, the methylation pattern is determined in at least one of the CHH sites of the ND1 gene shown in Table 6, and is determined in at least one site selected from the group consisting of:

[0190] (i) CpG sites in the D-loop region shown in Table 1,

[0191] (ii) a CHG site in the D-loop region shown in Table 3,

[0192] (iii) a CHH site in the D-loop region shown in Table 5,

[0193] (iv) the CpG sites of the ND1 gene shown in Table 2, and

[0194] (v) The CHG site of the ND1 gene shown in Table 4.

[0195] Table 1. List of CHG sites (ie, positions) in the D-loop region from 16491 to 202.

[0196]

[0197] Table 2. List of CpG sites from 3284 to 3657 in the ND1 gene.

[0198]

[0199] Table 3. List of CHG sites from 16491 to 202 in the D-loop region.

[0200]

[0201] Table 4. List of CHG sites from 3284 to 3657 in the ND1 gene.

[0202]

[0203] Table 5. List of CHH sites from 16491 to 202 in the D-loop region.

[0204]

[0205] Table 6. List of CHH sites from 3284 to 3657 in the ND1 gene.

[0206]

[0207] Another aspect of the present invention relates to a method, in particular a computer-implemented method, for monitoring the progression of DLB ​​in a subject, the method comprising:

[0208] (a) determining a methylation pattern as described herein in a sample from said subject comprising mitochondrial DNA; and

[0209] (b) comparing the methylation pattern determined in step (a) with the methylation pattern obtained at an earlier stage of the disease. A risk score that is higher than a previous risk score indicates progression of DLB ​​and, therefore, a poor prognosis. The risk score may be monitored, for example, annually.

[0210] In some embodiments, increased hypomethylation in at least one of the CpG sites in the D-loop region, increased hypomethylation in at least one of the CHG sites in the D-loop region, and / or increased hypomethylation in at least one of the CHH sites in the D-loop region over time indicates progression of DLB.

[0211] Alternatively, this aspect may be formulated as a method, particularly a computer-implemented method, for monitoring and treating a subject suffering from DLB, the method comprising:

[0212] (a) administering a DLB treatment to a subject; and

[0213] (b) if the treatment is effective, continuing to administer the treatment to the subject, or

[0214] (c) if the treatment is ineffective, stopping administering the treatment to the subject,

[0215] in,

[0216] (i) relative to the methylation levels before administration of the treatment, there is no increase in hypomethylation in at least one of the CpG sites in the D-loop region, no increase in hypomethylation in at least one of the CHG sites in the D-loop region, and / or no increase in hypomethylation in at least one of the CHH sites in the D-loop region indicating that the treatment is effective; and

[0217] (ii) an increase in hypomethylation in at least one of the CpG sites in the D-loop region, an increase in hypomethylation in at least one of the CHG sites in the D-loop region, and / or an increase in hypomethylation in at least one of the CHH sites in the D-loop region relative to the methylation level before administration of the treatment indicates that the treatment is ineffective.

[0218] Another aspect of the invention relates to a method for monitoring the progression of DLB ​​in a subject, the method comprising:

[0219] (a) determining a methylation pattern in the D-loop region in a sample from the subject comprising mitochondrial DNA, wherein the methylation pattern is determined in at least one site selected from the group consisting of:

[0220] (i) CpG sites in the D-loop region shown in Table 1,

[0221] (ii) a CHG site in the D-loop region shown in Table 3, and

[0222] (iii) a CHH site in the D-loop region shown in Table 5;

[0223] (b) combining the methylation pattern of the one or more sites determined in step (a) with at least one clinical variable of the subject,

[0224] wherein the combining is performed using a classification model for determining a score associated with the presence of DLB ​​in the subject; and

[0225] (c) comparing the scoring pattern determined in step (b) with scores obtained at an earlier stage of the disease.

[0226] In embodiments, the change in score over time is associated with the progression of the disease. In some embodiments, a higher score indicates the progression of the disease over time. In another embodiment, a lower score or no difference in the score over time indicates that the disease is not progressing.

[0227] Alternatively, this aspect of the invention relates to a method for monitoring and treating a subject suffering from DLB, the method comprising:

[0228] (a) administering a DLB treatment to a subject; and

[0229] (b) if the treatment is effective, continuing to administer the treatment to the subject, or

[0230] (c) if the treatment is ineffective, stopping administering the treatment to the subject,

[0231] in,

[0232] (i) lower or equal scores indicate that the treatment is effective; and

[0233] (ii) Higher scores indicate that the treatment is ineffective.

[0234] Subjects

[0235] In some embodiments, the subject is an animal, particularly a mammal. In a specific embodiment, the subject is selected from the group consisting of rats, mice, cats, dogs, chimpanzees, and humans. More particularly, the subject is a human.

[0236] In some embodiments, the subject is suspected of having dementia. Specifically, the subject is suspected of suffering from DLB. In another embodiment, the subject is at risk of suffering from dementia or has been diagnosed with dementia. In some embodiments, the subject has at least one symptom of mild dementia. Specifically, the subject has at least one symptom selected from the group consisting of mild memory loss, mild loss of attention ability, difficulty in reasoning, planning or problem solving, language difficulties and decreased visual depth perception. In other embodiments, the subject has at least one symptom selected from the group consisting of Parkinson's syndrome, visual and / or auditory hallucinations and REM sleep behavior disorder.

[0237] In some embodiments, the subject is diagnosed with a dementia other than DLB. Specifically, the subject is mistakenly diagnosed with a dementia other than DLB. In some embodiments, the subject is suspected of being mistakenly diagnosed. In some embodiments, the subject has a Clinical Dementia Rating Scale (CDR) score of 0.5. Specifically, the subject is diagnosed with mild cognitive impairment. In another embodiment, the subject has a Clinical Dementia Rating Scale score greater than 0.5. Specifically, the subject's CDR is 1. In another embodiment, the subject's CDR is greater than 1. Specifically, the subject's CDR is 2 or 3.

[0238] The "Clinical Dementia Rating Scale" is a global summary obtained through a semi-structured interview with the patient and informant, in which the cognitive state of the subject is graded in six functional domains, including memory, orientation, judgment and problem solving, community affairs, home and hobbies, and personal care. Each domain is assigned a score between 0 and 3, and the results are calculated via an algorithm to obtain a CDR global score. The CDR global score ranges from 0 to 3 and allows subjects to be grouped according to the severity of their dementia, where CDR=0 corresponds to the absence of cognitive impairment, CDR=0.5 is suspicious or very mild dementia, CDR=1 is mild dementia, CDR=2 is moderate dementia, and CDR=3 is severe dementia.

[0239] Subjects with a CDR of 0.5 are diagnosed with mild cognitive impairment (MCI), an early stage of memory loss or other cognitive abilities (such as language or visual / spatial perception) loss in subjects who retain the ability to independently perform most daily life activities. Subjects with a CDR of 1 or greater are considered to have progressed to dementia.

[0240] Methylation patterns and their measurement

[0241] Methylation pattern

[0242] The methylation pattern of the mitochondrial DNA loci described herein can be determined using any method known in the art or future methods developed in the art. For example, mtDNA methylation can be determined by treating the sample with bisulfite and sequencing the treated sample.

[0243] In some embodiments, determining a methylation pattern comprises determining a methylation pattern in at least one site in a D-loop region selected from the group consisting of:

[0244] (i) CpG sites in the D-loop region shown in Table 1,

[0245] (ii) a CHG site in the D-loop region shown in Table 3,

[0246] (iii) CHH sites in the D-loop region shown in Table 5.

[0247] In some embodiments, determining a methylation pattern comprises determining a methylation pattern in at least one site in the ND1 region selected from the group consisting of:

[0248] (iv) the CpG sites of the ND1 gene shown in Table 2,

[0249] (v) the CHG site in the ND1 gene shown in Table 4, and

[0250] (vi) The CHH site in the ND1 gene shown in Table 6.

[0251] In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least one of the CpG sites in the D-loop region in Table 1. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least five CpG sites in the D-loop region in Table 1. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least ten CpG sites in the D-loop region in Table 1. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of all CpG sites in the D-loop region in Table 1.

[0252] In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least one of the CHG sites in the D-loop region in Table 3. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least five CHG sites in the D-loop region in Table 3. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least ten CHG sites in the D-loop region in Table 3. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of all CHG sites in the D-loop region in Table 3.

[0253] In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least one of the CHH sites in the D-loop region in Table 5. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least five CHH sites in the D-loop region in Table 5. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least ten CHH sites in the D-loop region in Table 5. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least twenty-five CHH sites in the D-loop region in Table 5. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least fifty CHH sites in the D-loop region in Table 5. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of all CHH sites in the D-loop region in Table 5.

[0254] In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least one of the CHG sites in the D-loop region in Table 3. Specifically, determining the methylation pattern comprises determining the methylation pattern of all CHG sites in the D-loop region in Table 3. In a specific embodiment, hypomethylation in at least one of the CHG sites in the D-loop region indicates that the subject suffers from DLB. In some embodiments, determining the methylation pattern further comprises determining the methylation pattern of at least one of the CHH sites in the D-loop region in Table 5 and / or at least one of the CpG sites in the D-loop region in Table 1. Specifically, determining the methylation pattern further comprises determining the methylation pattern of all CHH sites in the D-loop region in Table 5 and / or all CpG sites in the D-loop region in Table 1. In a specific embodiment, hypomethylation in at least one of the CHH sites in the D-loop region and hypomethylation in at least one of the CpG sites in the D-loop region indicate that the subject suffers from DLB.

[0255] In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least one of the CpG sites of the ND1 gene in Table 2. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least five CpG sites of the ND1 gene in Table 2. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least ten CpG sites of the ND1 gene in Table 2. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of all CpG sites of the ND1 gene in Table 2.

[0256] In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least one of the CHG sites of the ND1 gene in Table 4. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least five CHG sites of the ND1 gene in Table 4. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of all CHG sites of the ND1 gene in Table 4.

[0257] In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least one of the CHH sites of the ND1 gene in Table 6. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least five CHH sites of the ND1 gene in Table 6. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least ten CHH sites of the ND1 gene in Table 6. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least twenty-five CHH sites of the ND1 gene in Table 6. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least fifty CHH sites of the ND1 gene in Table 6. In some embodiments, determining the methylation pattern comprises determining the methylation pattern of all CHH sites of the ND1 gene shown in Table 6.

[0258] In some embodiments, determining the methylation pattern comprises determining the methylation pattern of at least one of the CHH sites of the ND1 gene in Table 6. Specifically, determining the methylation pattern comprises determining the methylation pattern of all CHH sites of the ND1 gene in Table 6. In a specific embodiment, the methylation level in at least one of the CHH sites in the ND1 gene is not significantly different, indicating that the subject suffers from DLB. Alternatively, low methylation in at least one of the CHH sites in the ND1 gene indicates that the subject suffers from DLB. In some embodiments, determining the methylation pattern further comprises determining the methylation pattern of at least one of the CpG sites of the ND1 gene in Table 2 and / or at least one of the CHG sites of the ND1 gene in Table 4. Specifically, determining the methylation pattern further comprises determining the methylation pattern of all CpG sites of the ND1 gene in Table 2 and / or all CHG sites of the ND1 gene in Table 4. In a specific embodiment, the methylation level in at least one of the CpG sites in the ND1 gene is not significantly different, and the methylation level in at least one of the CHG sites in the ND1 gene is not significantly different, indicating that the subject suffers from DLB.

[0259] In some embodiments, determining the methylation pattern comprises determining the methylation of all CpG, CHG, and CHH sites in the D-loop region shown in Tables 1, 3, and 5.

[0260] In some embodiments, determining the methylation pattern comprises determining the methylation of all CpG, CHG, and CHH sites of the ND1 gene shown in Tables 2, 4, and 6.

[0261] In some embodiments, determining the methylation pattern comprises determining the methylation pattern of all CpG, CHG, and CHH sites of the D-loop region and the ND1 gene.

[0262] The methylation pattern can be determined by any method known in the art. In some embodiments, the methylation pattern is determined by a technique selected from the group consisting of bisulfite treatment-based techniques, bioassay-based techniques, and bisulfite-free and enzyme-free techniques.

[0263] In some embodiments, techniques based on bisulfite treatment include, but are not limited to, sequence-based analysis, analysis based on melting temperature, and analysis based on interaction. In some embodiments, sequence-based analysis includes, but is not limited to, bisulfite sequencing, methylation-specific PCR (MS-PCR), methylation-sensitive single nucleotide primer extension (Ms-SnuPE), and simplified representational bisulfite sequencing (RRBS). In some embodiments, analysis based on melting temperature includes, but is not limited to, methylation-specific denaturing gradient gel electrophoresis (MS-DGGE), methylation-specific melting curve analysis (MS-MCA), and methylation-specific high-resolution melting (MS-HRM). In some embodiments, interaction-based analysis includes, but is not limited to, combined bisulfite restriction analysis (COBRA) and Methylight assay.

[0264] In some embodiments, bioassay-based techniques include, but are not limited to, enzymatic digestion-based methods and bio-dependent reactions. In some embodiments, enzymatic digestion-based methods include, but are not limited to, restriction tag genomic scanning (RLGS), online monitoring, and methylation-sensitive restriction enzyme-PCR (MS-RE-PCR / Southern). In a specific embodiment, the bio-dependent reaction is methylation capture using a methylation-CpG binding domain (MBD) protein.

[0265] In some embodiments, bisulfite-free and enzyme-free techniques include, but are not limited to, direct oxidation-based assays and oxidation-based chemical decomposition assays. In a specific embodiment, the direct oxidation-based assay is choline chloride monolayer-supported multi-walled carbon nanotubes (MWCNTs / Ch / GCE). In a specific embodiment, the oxidation-based chemical decomposition assay is Na1O4 / LiBr.

[0266] In a specific embodiment, the methylation pattern is determined by a technique based on bisulfite treatment. Specifically, the methylation pattern is determined by sequence-based analysis. More specifically, the methylation pattern is determined by bisulfite sequencing.

[0267] In some embodiments, the methylation pattern is determined by a sequencing technology selected from the group consisting of: methylation-specific PCR (MS-PCR), quantitative methylation-specific polymerase chain reaction (qMSP), bisulfite sequencing, pyrosequencing, nanopore sequencing, MassArray, methylation-sensitive single nucleotide primer amplification (Ms-SnuPE), degenerate representation bisulfite sequencing (RRBS), methylation-specific denaturing gradient gel electrophoresis (MS-DGGE), methylation-specific melting curve analysis (MS-MCA), methylation-specific high-resolution melting (MS-HRM), combined bisulfite restriction analysis (COBRA) and Methylight assay, methylation-specific restriction endonuclease assay (MSRE), methylation-sensitive restriction enzyme sequencing (MRE-seq), restriction-tagged genomic scanning (RLGS), methylation-DNA immunoprecipitation MeDIP or MeDIP-seq, methylation capture using methylation-CpG binding domain (MBD) protein, ChIP assay, methylation chip (methylation array), multi-walled carbon nanotubes supported by choline chloride monolayer (MWCNTs / Ch / CGE), and analysis based on oxidation chemical decomposition (NaIO4 / LiBr).

[0268] In a specific embodiment, the methylation pattern is determined by bisulfite sequencing. In some embodiments, bisulfite sequencing includes a step of treating the sample with bisulfite and another step of sequencing the bisulfite treated sample by PCR. In a specific embodiment, the bisulfite treated sample is sequenced using a kit, which can be but is not limited to a kit produced by Illumina. More specifically, the bisulfite treated sample is sequenced using a kit selected from the group consisting of: MiSeq reagent kit v3-600-cycle number (#MS-102-3003, Illumina), MiSeq reagent kit v2-500-cycle number (#MS-102-2003, Illumina) and MiSeq reagent Nano kit v2-500 cycle number (#MS-103-1003, Illumina).

[0269] The samples can be sequenced using any method known in the art. The sequencing platforms include, but are not limited to, Roche, Illumina, Life Technologies, Polonator, Helicos Bioscience, Pacific Biosciences, HTG Molecular Diagnostic, Singular Genomics, Element Biosciences, Oxford Nanopore, and Nanostring Technology.

[0270] In some embodiments, determining the methylation pattern includes a step of library quantification. In some embodiments, library quantification is performed using a fluorescence quantification method. Alternatively, quantification of the methylation pattern is determined using fluorescence. In a specific embodiment, the fluorescence quantification method is characterized by the use of a kit including a dsDNA binding dye. In a specific embodiment, library quantification is performed using a Qubit 3.0 Fluorometer produced by Thermo Fisher Scientific and other kits produced by Thermo Fisher Scientific. More specifically, library quantification is performed using a Qubit TM dsDNA assay kit. Specifically, Qubit TM The dsDNA assay kit is selected from the group consisting of: Qubit TM dsDNA HS Assay Kit #Q32854 and Qubit TM dsDNA BR Assay Kit #Q32850.

[0271] It should be understood that library quantification using fluorescence quantification methods in the present invention can be performed using fluorometers and kits including dsDNA binding dyes from brands other than Thermofisher Scientific. Another example of a suitable fluorometer is, but is not limited to, the QFX fluorometer produced by DeNovix and the Quantus fluorometer produced by Promega. TM Other suitable dsDNA fluorescence kits include, but are not limited to, the fluorometer produced by Promega. Dye systems and In addition, the QFX Fluorometer from DeNovix can be used with its own DeNovix dsDNA Fluorescence Quantitation Kit and other common commercially available assays.

[0272] In an example of the present invention, the DSS (discrete shrinkage of sequencing data) Bioconductor package is used to perform an analysis for comparing the methylation level of each methylation site. In other embodiments, any other appropriate method known in the art can be used. In some embodiments, the analysis is performed using a model based on β-binomial. In a specific embodiment, the model is a β-binomial generalized linear model with a logit link function. In another specific embodiment, the model is locally smoothed in each sample, and then a β regression with a probit link function is applied. In some embodiments, an alternative method using an empirical Bayesian method, standard maximum likelihood or other methods known in the art is used to perform analysis. In some embodiments, analysis includes performing inference using standard techniques known in the art or other suitable techniques developed in the future. In a specific embodiment, inference is performed using a standard technique selected from a group consisting of a Wald test, a likelihood ratio test, a permutation test and an ANOVA.

[0273] In examples of the present invention, the threshold-corrected p-value for establishing differential methylation is 0.05. In other embodiments, the threshold-corrected p-value is established at 0.25. In other embodiments, the threshold-corrected p-value is established at 0.2. In other embodiments, the threshold-corrected p-value is established at 0.15. In other embodiments, the threshold-corrected p-value is established at 0.1. In other embodiments, the threshold-corrected p-value is established at 0.09. In other embodiments, the threshold-corrected p-value is established at 0.08. In other embodiments, the threshold-corrected p-value is established at 0.07. In other embodiments, the threshold-corrected p-value is established at 0.06. In other embodiments, the threshold-corrected p-value is established at 0.05. In other embodiments, the threshold-corrected p-value is established at 0.04. In other embodiments, the threshold-corrected p-value is established at 0.03. In other embodiments, the threshold-corrected p-value is established at 0.02. In other embodiments, the threshold-corrected p-value is established at 0.01.

[0274] Samples and sample handling

[0275] As used herein, the term "biological sample" or "sample" refers to biological material isolated from a subject. A biological sample can include any biological material suitable for determining methylation patterns, for example, by processing and sequencing nucleic acids.

[0276] In some embodiments, the sample is selected from a biopsy of a biological fluid or a solid tissue. Specifically, the sample is selected from the group consisting of blood, plasma, saliva, cerebrospinal fluid, brain samples, skin samples and urine. Specifically, the sample is blood, particularly peripheral blood.

[0277] The source of the sample can be solid tissue, for example, from fresh, frozen and / or preserved organs, tissue samples, biopsies or extracts. In some embodiments, the sample is a cell-free sample, for example, including cell-free nucleic acids (e.g., DNA or RNA). In some embodiments, the sample can include compounds that do not naturally mix with tissues in nature, such as preservatives, anticoagulants, buffers, fixatives, nutrients, antibiotics, etc.

[0278] In some embodiments, the method includes obtaining a sample. In a specific embodiment, the sample is blood or plasma, and the sample is extracted using a needle. In another embodiment, the sample is saliva, and the sample is obtained using a method selected from the group consisting of drainage, spitting, aspiration, and swab methods. In some embodiments, the sample can be obtained, for example, from surgical material or biopsy. In some embodiments, the biopsy can be an archival tissue from a previous line of therapy. In some embodiments, the biopsy can be from an initial tissue of the treatment.

[0279] In some embodiments, the sample is frozen or stored. In some embodiments, the sample is stored as a frozen sample or a formalin-fixed, formaldehyde-fixed or paraformaldehyde-fixed paraffin-embedded (FFPE) tissue specimen. For example, the sample can be embedded in a matrix, such as an FFPE block, or a frozen sample. In some embodiments, the sample can include bone marrow; an aspirate; a scraping; a bone marrow specimen; a tissue biopsy specimen; a surgical specimen; etc. In some embodiments, the sample is or includes the following: a cell obtained from an individual, for example, a cell obtained from an individual from whom the sample was obtained.

[0280] In some embodiments, the sample is a fresh sample (or non-archived sample) or an archived sample. As used herein, the terms "fresh sample", "non-archived sample" and grammatical variants thereof refer to samples that have been processed before a predetermined time (e.g., one week) after extraction from a subject. In some embodiments, the fresh sample is not frozen. In some embodiments, the fresh sample is not fixed. In some embodiments, the fresh sample has been stored less than about two weeks, less than about one week, or less than six days, five days, four days, three days, or two days before processing. As used herein, the terms "archived sample" and grammatical variants thereof refer to samples that have been processed after a predetermined time (e.g., one week) after extraction from a subject. In some embodiments, the archived sample is not frozen. In some embodiments, the archived sample is not fixed. In some embodiments, an archived sample with a known diagnosis history and / or treatment history. In some embodiments, the archived sample has been stored for at least one week, at least one month, at least six months, or at least one year before processing.

[0281] Oligonucleotides and kits

[0282] In some embodiments, in any of the methods described herein, the methylation pattern is determined using at least one oligonucleotide that is capable of specifically hybridizing to mitochondrial DNA including the D-loop region or the ND1 gene. In particular, the oligonucleotide is capable of specifically hybridizing under high stringency conditions.

[0283] The target sequence can refer to a reference sequence or a sequence produced by certain modification treatments, such as bisulfite treatment, in which unmethylated cytosine is modified to uracil. In some embodiments, the oligonucleotide hybridizes to a reference mitochondrial DNA sequence comprising the D-loop region or the ND1 gene. In a specific embodiment, the oligonucleotide hybridizes to a modified mitochondrial DNA sequence comprising the D-loop region. Alternatively, the oligonucleotide hybridizes to a modified mitochondrial DNA sequence comprising the ND1 gene. In a specific embodiment, the modified mitochondrial DNA sequence has been modified by bisulfite treatment. In a more specific embodiment, the modified mitochondrial DNA sequence has been modified by bisulfite treatment, in which unmethylated cytosine has been modified to uracil.

[0284] In some embodiments, the methylation pattern is determined using at least one oligonucleotide capable of specifically hybridizing to a mitochondrial DNA sequence comprising at least one methylation site selected from the group consisting of:

[0285] (i) CpG sites in the D-loop region shown in Table 1,

[0286] (ii) a CHG site in the D-loop region shown in Table 3,

[0287] (iii) CHH sites in the D-loop region shown in Table 5, (iv) CpG sites of the ND1 gene shown in Table 2,

[0288] (v) the CHG site in the ND1 gene shown in Table 4, and

[0289] (vi) The CHH site in the ND1 gene shown in Table 6.

[0290] Alternatively, oligonucleotide of the present invention is referred to as nucleic acid sequence. In some embodiments, oligonucleotide is a DNA sequence. In some embodiments, the length of oligonucleotide is between 15 and 100 nucleotides.

[0291] In an embodiment, the methylation pattern is determined using oligonucleotides capable of specifically hybridizing and amplifying a mitochondrial DNA sequence comprising nucleotides 16,465 to 230 of the NCBI reference sequence: NC_012920.1 (corresponding to the D-loop region) and / or a mitochondrial DNA sequence comprising nucleotides 3,257 to 3,682 of the NCBI reference sequence: NC_012920.1 (corresponding to the ND1 gene).

[0292] The inventors herein designed primers (also referred to herein as oligonucleotides or nucleic acid sequences) that include a minimum number of cytosines. In some embodiments, due to the uncertain C / U conversion of a small number of cytosine residues included in the sequence, the primers are degenerate to cover all possible methylation and non-methylation situations. These primers are mixtures of oligonucleotide sequences that contain several possible nucleotide bases at specific positions. Therefore, the probability of detecting mitochondrial methylation is higher. The forward degenerate primer includes a Y at any position, which refers to C or T (Y=C / T), where the reference sequence is C. In addition, as known in the art, the reverse primer does not correspond to the reference sequence, but to the reverse complementary sequence of the reference sequence. Therefore, the reverse primer does not include the C site of the reference sequence, but includes their complementary G site. Therefore, the reverse degenerate primer includes R at any position, which refers to G or A (R=A / G), where the reference sequence is C, or where the complementary sequence includes G.

[0293] Thus, in some embodiments, the oligonucleotide is a degenerate oligonucleotide.A degenerate oligonucleotide / primer is a mixture of oligonucleotide sequences in which some positions contain many possible bases, resulting in a population of oligonucleotides with similar sequences covering all possible nucleotide combinations.

[0294] In a specific embodiment, the methylation pattern is determined using at least one oligonucleotide between 15 and 100 nucleotides in length, comprising a nucleic acid sequence selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, and SEQ ID NO: 4. The oligonucleotide may include additional nucleotides sufficient for, e.g., sequencing (i.e., sequencing adapters) at its termini.

[0295] In a specific embodiment, the methylation pattern is determined using at least one oligonucleotide selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, and SEQ ID NO: 4. In a more specific embodiment, the methylation pattern is determined using oligonucleotides SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, and SEQ ID NO: 4. In another embodiment, the methylation pattern is determined using at least one oligonucleotide selected from SEQ ID NO: 1 and SEQ ID NO: 2. In some embodiments, the methylation pattern is determined using oligonucleotides SEQ ID NO: 1 and SEQ ID NO: 2.

[0296] Another aspect of the present invention relates to an oligonucleotide having a length of between 15 and 100 nucleotides, comprising a nucleic acid sequence selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3 and SEQ ID NO: 4. As described above, in an embodiment, the oligonucleotide comprises a sequencing adapter located at the end of SEQ ID NO: 1, 2, 3 and / or 4. Specifically, the oligonucleotide is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3 and SEQ ID NO: 4. In a specific embodiment, the nucleic acid sequence is SEQ ID NO: 1. In another specific embodiment, the oligonucleotide is SEQ ID NO: 2. In another specific embodiment, the oligonucleotide is SEQ ID NO: 3. In another specific embodiment, the oligonucleotide is SEQ ID NO: 4.

[0297] Another aspect of the present invention relates to the use of an oligonucleotide having a length between 15 and 100 nucleotides for determining the methylation pattern of mitochondrial DNA, the oligonucleotide comprising a nucleic acid sequence selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3 and SEQ ID NO: 4. In particular, the oligonucleotide is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3 and SEQ ID NO: 4. In a specific embodiment, the present invention relates to the use of an oligonucleotide having SEQ ID NO: 1 for determining the methylation pattern of a mitochondrial DNA sequence comprising the D-loop region. In another embodiment, the present invention relates to the use of an oligonucleotide having SEQ ID NO: 2 for determining the methylation pattern of a mitochondrial DNA sequence comprising the D-loop region. In another embodiment, the present invention relates to the use of an oligonucleotide having SEQ ID NO: 3 for determining the methylation pattern of a mitochondrial DNA sequence comprising the ND1 gene. In another embodiment, the present invention relates to the use of an oligonucleotide having SEQ ID NO: 4 for determining the methylation pattern of a mitochondrial DNA sequence comprising the ND1 gene.

[0298] In some embodiments, the present invention relates to the use of an oligonucleotide selected from SEQ ID NO: 1 and SEQ ID NO: 2 for determining the methylation pattern of a mitochondrial DNA sequence including the D-loop region. In another embodiment, the present invention relates to the use of an oligonucleotide selected from SEQ ID NO: 1 and SEQ ID NO: 2 for determining the methylation pattern to diagnose or identify DLB in a subject.

[0299] In other embodiments, the present invention relates to an oligonucleotide having a length of between 15 and 100 nucleotides, comprising a nucleic acid sequence selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3 and SEQ ID NO: 4 (and specifically having SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3 and SEQ ID NO: 4), for use in determining the methylation pattern of a mitochondrial DNA sequence comprising the D-loop region and / or the ND1 gene. In another embodiment, the present invention relates to the use of said oligonucleotide for determining the methylation pattern to diagnose or identify DLB in a subject.

[0300] In some embodiments, the oligonucleotides defined herein are included in a kit. Within those aspects, another aspect of the invention relates to the use of the kit for determining the methylation pattern of mitochondrial DNA. In another aspect, the invention relates to the use of the kit as defined above for determining the methylation pattern of mitochondrial DNA to diagnose or identify DLS in a subject. In another aspect, the invention relates to the use of the kit as defined above according to the method described herein.

[0301] Such a kit may include containers, each container having one or more of the various reagents (e.g., in concentrated form) used in the method, including, for example, one or more oligonucleotides (e.g., oligonucleotides with SEQ ID NOs: 1-4 as provided herein, specifically oligonucleotides with SEQ ID NOs: 1-2 as provided herein). The kit may also provide reagents, buffers and / or instruments to support the practice of the methods provided herein.

[0302] The kit provided according to the present invention may also include a brochure or instructions describing the methods disclosed herein or their practical application to diagnose or identify DLS in a subject. The instructions included in the kit may be affixed to the packaging material, or may be included as a packaging appendix. Although the instructions are typically written or printed materials, they are not limited to this. Any medium capable of storing such instructions and delivering them to an end user is contemplated. Such media include, but are not limited to, electronic storage media (e.g., disks, tapes, cassettes, chips), optical media (e.g., CD ROM), etc. As used herein, the term "instructions" may include the address of an Internet website that provides the instructions.

[0303] In some embodiments, the kit is an Illumina sequencing kit. More particularly, the kit is selected from the group consisting of MiSeq reagent kit v3-600-cycles (#MS-102-3003, Illumina), MiSeqreagent kit v2-500-cycles (#MS-102-2003, Illumina), and MiSeq reagent Nano kit v2-500 cycles (#MS-103-1003, Illumina).

[0304] Clinical variables

[0305] The methods described herein can diagnose or identify DLB in a subject. The information generated by the clinical variables of the subject can be used in the methods described herein to improve or supplement diagnosis. In addition, the clinical variables can be used as input data in the classification model described herein. Moreover, the classification model can be trained with a training data set including the clinical variables associated with multiple subjects.

[0306] Therefore, in some embodiments, any method described herein also includes considering at least one clinical variable of the subject. In other embodiments, any method described herein also includes combining the methylation pattern described herein with at least one clinical variable of the subject. Specifically, any method of the present invention also includes considering at least one clinical variable of the subject to identify or diagnose DLB in the subject, or identify a subject with DLB.

[0307] Clinical variables can include any clinical variables known in the art. In some embodiments, the method includes considering at least one clinical variable of the subject or combining the methylation pattern described herein with at least one clinical variable of the subject. In some embodiments, the clinical variable is selected from the group consisting of: demographic variables, neuropsychological variables, clinical observation variables, and clinical test variables.

[0308] Demographic variables

[0309] "Gender" is a categorical variable with two possible categories: female or male.

[0310] "Age" is a numerical variable corresponding to the age of the subject.

[0311] "Race" is a categorical variable that includes two or more possible categories: White or Caucasian, Black, American Indian, Latino or Hispanic, Asian, etc. "Race" is a category of humans that share certain unique physical characteristics.

[0312] “Learning” is a numerical variable corresponding to the number of years of education starting from the age of 6.

[0313] "Family history" is a categorical variable that includes two possible categories: familiar precursor to dementia or unfamiliar precursor to dementia.

[0314] In an embodiment, the demographic variable is selected from the group consisting of gender, age, race, education, and family history.

[0315] Neuropsychological variables

[0316] Neuropsychological testing can help stratify the level of cognitive impairment and in situations where clinicians suspect the diagnosis of DLB ​​or attempt to differentiate DLB from other types of dementia. Given the cognitive features of DLB, neuropsychological assessment should focus on attention, executive function, visual perceptual abilities, language function, and memory (immediate and delayed recall). Neuropsychological tests may vary in each center / country, but they assess the same cognitive functions.

[0317] Some of the neuropsychological tests are Clinical Dementia Rating Scale (CDR), Clinical Dementia Rating Scale Sum of Boxes (CDR-SOB), Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Global Decline Scale (GDS), Functional Activities Questionnaire (FAQ), Geriatric Depression Scale Yesavage (GDS-Yesavage), Neuropsychiatric Inventory score (NPI), Delayed Verbal Memory score, Verbal Learning Curve score, Verbal Recognition score, Semantic Verbal Fluency score, Structural Application score, Clock Drawing Test-Luria.

[0318] "MMSE" (Mini-Mental State Examination) is a numerical variable that evaluates five items: orientation, immediate memory (fixation), attention and calculation, memory and language, and construction, with an output score between 1 and 30.

[0319] In cases where the subject is diagnosed with MCI and the diagnosis is unclear, the following neuropsychological variables can be considered: CDR refers to the numerical variable of the "Clinical Dementia Rating Scale", which is a global summary obtained through a semi-structured interview with the patient and the informant, and the cognitive state of the subject is graded in six functional domains, including memory, orientation, judgment and problem solving, community affairs, family and hobbies, and personal care. Each domain is assigned a score between 0 and 3, and the result is calculated via an algorithm to obtain a CDR global score. The CDR global score ranges from 0 to 3 and allows the subjects to be grouped according to the severity of their dementia, where CDR = 0 corresponds to the absence of cognitive impairment, CDR = 0.5 is suspected or very mild dementia, CDR = 1 is mild dementia, CDR = 2 is moderate dementia, and CDR = 3 is severe dementia.

[0320] Subjects with a CDR of 0.5 are diagnosed with MCI, an early stage of loss of memory or other cognitive abilities (such as language or visual / spatial perception) in subjects who retain the ability to perform most activities of daily living independently.

[0321] "SOB" (Clinical Dementia Rating Summary) is a numerical variable which refers to the "sum of box scores" ranging from 0 to 18, which is obtained by summing each of the domain box scores mentioned above for calculating the CDR global score.

[0322] In an embodiment, the neuropsychological variable is selected from the group consisting of MMSE, CDR and CDR-SOB. More specifically, the neuropsychological variable is MMSE.

[0323] In an embodiment, the neuropsychological variable is the praxis test, the Luria test, the CDR, the GDS, the MMSE, or the CDR-SOB.

[0324] Clinical observation variables

[0325] "Neuronotide intolerance" is a categorical variable that includes two possible categories: neuroleptic intolerance or non-neuroleptic intolerance. This variable corresponds to the specific sensitivity of DLB ​​patients to neuroleptic drugs in relation to extrapyramidal symptoms.

[0326] "REM sleep behavior disorder" is a categorical variable that includes two possible categories: presence of REM sleep behavior disorder or absence of REM sleep behavior disorder. REM sleep behavior disorder is a parasomnia in which abnormal dreaming behavior occurs during rapid eye movement sleep.

[0327] "Dysautonomia" is a categorical variable that includes two possible categories: dysautonomia present or dysautonomia absent. Dysautonomia or autonomic dysfunction is a condition in which the function of the autonomic nervous system (ANS) is impaired.

[0328] "Parkinsonism" is a categorical variable that includes two possible categories: Parkinsonism present or Parkinsonism absent. Parkinsonism is a clinical syndrome characterized by tremor, bradykinesia (slowed movements), rigidity, and postural instability.

[0329] "Pseudopsis" is a categorical variable that includes two possible categories: presence of psipossibilities or absence of psipossibilities. A psipossibilities is a visual perception that has the properties of a real visual perception in the absence of external visual stimulation.

[0330] "Cognitive Fluctuations" is a categorical variable that includes two possible categories: cognitive fluctuations present or absent. Cognitive fluctuations are spontaneous changes in concentration, attention, alertness, and wakefulness that vary widely from day to day, or even within the same day.

[0331] Clinical variables defined herein as categorical variables with only two possible categories may be considered as categorical variables with more than two options where the degree, grade, or rank of such a variable is determined, rather than simply the presence or absence of such a variable. Similarly, clinical variables defined herein as categorical variables with two or more possible categories may also be considered as numerical variables.

[0332] In an embodiment, clinical observation variables are neuroleptic intolerance, REM sleep behavior disorder, autonomic dysfunction, parkinsonism, visual hallucinations, and cognitive fluctuations.

[0333] Clinical test variables

[0334] In some embodiments, clinical variables may include clinical test variables, such as tests obtained by neuroimaging techniques or medical imaging techniques. In some embodiments, clinical variables are selected from the group consisting of: MRI, DaTSCAN, and 18F-FDG-PET.

[0335] "MRI" (magnetic resonance imaging) is a categorical variable that includes two possible categories: positive MRI and negative MRI. MRI is a scan that uses a strong magnetic field and radio waves to produce detailed images of the inside of the body.

[0336] "DaTSCAN" (dopamine transporter scan) is a categorical variable comprising two possible categories: positive DaTSCAN and negative DaTSCAN and is a diagnostic method used to determine the loss of dopaminergic neurons in the striatum.

[0337] Positron emission tomography (PET) is a nuclear medicine procedure that measures the metabolic activity of cells of body tissues. PET can be performed with different types of tracers, each used for a specific purpose, study, or detection. For example, PET can measure glucose levels, beta-amyloid plaques, or tau protein. FDG-PET refers to a type of PET designed to detect glucose and therefore analyze the metabolic activity of a tissue or body part. In another example, PET is used to determine the presence of beta-amyloid plaques in the brain. PET can also be used to measure glucose, beta-amyloid substances, and / or tau.

[0338] "18F-FDG-PET" (18F-fluorodeoxyglucose positron emission tomography) is a categorical variable that includes two possible categories: positive 18F-FDG-PET and negative 18F-FDG-PET. "18F-FDG-PET" is an imaging metabolic technique that uses a radioactive tracer, which refers to PET designed to detect glucose and thus analyze the metabolic activity of a tissue or body part.

[0339] Clinical variables defined herein as categorical variables with only two possible categories may be considered as categorical variables with more than two options where the degree, grade, or rank of such a variable is determined, rather than simply the presence or absence of such a variable. Similarly, clinical variables defined herein as categorical variables with two or more possible categories may also be considered as numerical variables.

[0340] In cases where a subject has been diagnosed with MCI, but the diagnosis is uncertain, the following clinical testing variables may be considered:

[0341] "Amyloid PET" is a categorical variable that includes two possible categories: positive amyloid PET and negative amyloid PET, which refer to the presence or absence of beta-amyloid, respectively. Amyloid PET is used to determine the presence of beta-amyloid plaques in the brain.

[0342] Clinical variables may include biomarkers known in the art, as well as other neuropsychological tests and other radiological imaging techniques known in the art. In some embodiments, clinical variables include the presence, absence or level of biomarkers (e.g., APOE) known in the art. In particular, clinical variables include the presence or absence of the biomarkers. In some embodiments, biomarkers may be measured in any sample or fluid from a subject, specifically in a cerebrospinal fluid sample. In other embodiments, biomarkers are measured in a blood sample. In other embodiments, biomarkers may be detected using other radiological imaging techniques, for example, positron emission tomography (PET). In some embodiments, clinical variables include APOE, aIE4, β-42, tau-T and / or tau-P.

[0343] "APOE" is a categorical variable including multiple categories corresponding to different genotypes of apolipoprotein E: E2.E2, E2.E3, E2.E4, E3.E3, E3.E4, and E4.E4.

[0344] "alE4" is a categorical variable that corresponds to the reclassification of the APOE genotype into the following categories: 0 (including E2.E2, E2.E3, and E3.E3), 1 (including E2.E4 and E3.E4), and 2 (including E4.E4).

[0345] "β-42" is a categorical variable referring to the presence or absence of β-42 peptide in cerebrospinal fluid samples.

[0346] "tau-T" is a categorical variable that refers to the presence or absence of the protein Tau in a cerebrospinal fluid sample.

[0347] "Tau-P" is a categorical variable that refers to the presence or absence of phosphorylated tau protein in cerebrospinal fluid samples.

[0348] In some embodiments, the clinical test variables are magnetic resonance imaging (MRI), dopamine transporter scan (DaTSCAN), 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET), amyloid PET, apolipoprotein E (APOE) genotype, aIE4, beta-42, tau-T, and tau-P.

[0349] In some embodiments, clinical variables can include variables related to a subject's current treatment or medication (ie, concomitant medications), particularly a subject with dementia or dementia-related symptoms.

[0350] In some embodiments, the clinical variables are selected from the group consisting of: sex, age, race, education, family history, MMSE, neuroleptic intolerance, REM sleep behavior disorder, autonomic dysfunction, Parkinson's syndrome, visual hallucinations, cognitive fluctuations, MRI, DaTSCAN, and 18F-FDG-PET.

[0351] In another embodiment, the clinical variable is selected from the group consisting of: CDR, SOB, amyloid PET, APOE, aIE4, β-42, tau-T and tau-P.

[0352] In some embodiments, the clinical variables are selected from the group consisting of: sex, age, race, education, family history, praxis test, Luria test, CDR, GDS, MMSE, CDR-SOB, neuroleptic intolerance, REM sleep behavior disorder, autonomic dysfunction, Parkinson's syndrome, visual hallucinations, cognitive fluctuations, magnetic resonance imaging (MRI), dopamine transporter scan (DaTSCAN), 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET), amyloid PET, apolipoprotein E (APOE) genotype, aIE4, β-42, tau-T and tau-P.

[0353] In a specific embodiment, the clinical variable is selected from the group consisting of: age, CDR, GDS, REM sleep behavior disorder, autonomic dysfunction, Parkinsonism, visual hallucinations, cognitive fluctuations, PET-FDG, and DatTSCAN.

[0354] Classification Model

[0355] On the other hand, the present invention provides a classification model that can classify subjects into two categories: subjects with DLB and subjects without DLB. These classes are related to subjects with DLB and subjects without DLB. Subjects not classified as suffering from DLB can be healthy subjects, subjects in the early stages of dementia, or subjects with other types of dementia or other diseases.

[0356] Therefore, the present invention provides a classification model for identifying DLB in a subject, wherein the classification model uses data on methylation patterns obtained from samples from the subject and / or data on clinical variables of the subject to identify the subject as belonging to a category in the group consisting of a subject with DLB and a subject without DLB, and wherein the subject identified as having DLB indicates that the subject suffers from DLB. In some embodiments, the classification model uses data on methylation patterns obtained from subject samples to identify the subject as belonging to a category in the group consisting of a subject with DLB and a subject without DLB, and wherein the subject identified as having DLB indicates that the subject suffers from DLB. In other embodiments, the classification model uses data on clinical variables of the subject to identify the subject as belonging to a category in the group consisting of a subject with DLB and a subject without DLB, and wherein the subject identified as having DLB indicates that the subject suffers from DLB.

[0357] Accordingly, the present invention provides a method for identifying Lewy body dementia in a subject, the method comprising:

[0358] (a) determining a methylation pattern in the D-loop region of mitochondrial DNA and / or the ND1 gene in a sample of the subject comprising mitochondrial DNA, wherein the methylation pattern is determined in at least one site selected from the group consisting of:

[0359] (i) CpG sites in the D-loop region shown in Table 1,

[0360] (ii) a CHG site in the D-loop region shown in Table 3,

[0361] (iii) a CHH site in the D-loop region shown in Table 5,

[0362] (iv) the CpG sites of the ND1 gene shown in Table 2,

[0363] (v) the CHG site of the ND1 gene shown in Table 4, and

[0364] (vi) the CHH site of the ND1 gene shown in Table 6; and

[0365] (b) optionally combining the methylation pattern of the one or more sites determined in step (a) with at least one clinical variable of the subject, wherein the combining is performed using a classification model for determining a score associated with identification of Lewy body dementia in the subject.

[0366] In some embodiments, the classification model is obtained by an artificial intelligence method. In a specific embodiment, the classification model is obtained by a machine learning method. In a more specific embodiment, the classification model is obtained by a supervised machine learning method or related software. In a more specific embodiment, the supervised machine learning method or related software is selected from the group consisting of: linear discriminant analysis (LDA), classification and regression tree (CART), k-nearest neighbor (kNN), naive Bayes (NB), support vector machine (SVM) with linear kernel, random forest (RF), neural network (NNET), generalized boosted regression model (GBM), binomial logistic regression (GLM), artificial neural network (ANN), GBoost (XGB; implementation of gradient boosted decision tree designed for speed and performance), GLMNET (generalized linear model based on penalized maximum likelihood, a package for fitting generalized linear model based on penalized maximum likelihood, for example, implementation of logistic regression)), cforest (implementation of random forest and bagging ensemble algorithm using conditional inference tree as basic learner), Treebag (bagging, i.e., bootstrap aggregation algorithm (bootstrap aggregating), an algorithm for improving the accuracy of models in regression and classification problems, which constructs multiple models from separate subsets of training data and constructs a final aggregated model), AdaBoost (AdaBoost classification tree), PMR (penalized polynomial regression) or a combination thereof. In another embodiment, the supervised machine learning method is any supervised machine learning method known in the art or any other suitable method developed in the future.

[0367] In some embodiments, the classification model is obtained by a supervised machine learning method. Specifically, the supervised machine learning method is selected from the group consisting of: LDA, CART, kNN, NB, SVM with linear kernel, RF, NNET, GBM, GLM, GLMNET, AdaBoost and PMR. In a specific embodiment, the supervised machine learning method is selected from the group consisting of: LDA, CART, kNN, NB, SVM, RF, NNET, GLMNET, AdaBoost and PMR. Specifically, the method is RF, GLMNET or AdaBoost, and more specifically, RF.

[0368] In another embodiment, the classification model is obtained by an unsupervised machine learning method. In a specific embodiment, the unsupervised machine learning method is selected from the group consisting of: K-means, K-medoids, fuzzy C-means, agglomerative hierarchical clustering, Gaussian mixture model (GMM), neural network, hidden Markov model (HMM), mean shift, DBSCAN clustering, apriori algorithm, principal component analysis (PCA), independent component analysis (ICA), linear discriminant analysis (LDA), singular value decomposition (SVD), linear semantic analysis (LSA, Linear Semantic Analysis), t-distributed stochastic neighbor embedding (t-SNE, t-Distributed Stochastic Neighbor Embedding), nonlinear multidimensional scaling (Nonlinear Multidimensional Scalling), principal curve (Principal Curve), k-nearest neighbor (kNN), local Kinear embedding and autoencoder (Locally Kinear Embedding and Autoencoder).

[0369] Alternatively, the classification model is obtained by using a deep learning algorithm as a set of machine learning methods. In a specific embodiment, the deep learning method is selected from the group consisting of: convolutional neural network (CNN), long short-term memory network (LSTM), recurrent neural network (RNN), generative adversarial network (GAN), radial basis function network (RBFN), multilayer perceptron (MLP), self-organizing map (SOM), deep belief network (DBN), restricted Boltzmann machine (RBM) and autoencoder.

[0370] In some embodiments, a classification model is trained or has been trained with a training set comprising mitochondrial methylation patterns of methylation sites as defined herein in a plurality of samples associated with a plurality of subjects. In a specific embodiment, the training set also comprises clinical variables associated with a plurality of subjects. More specifically, each subject of the training set is assigned a dementia stage classification. In a specific embodiment, the dementia stage classification is selected from a group consisting of a control and a subject with DLB.

[0371] In another embodiment, the classification model is trained or has been trained with a training set that includes clinical variables associated with multiple subjects. In a specific embodiment, the training set also includes mitochondrial methylation patterns for each methylation site in multiple samples associated with multiple subjects. Specifically, each subject in the training set is assigned a dementia stage classification. More specifically, the dementia stage classification is selected from a group consisting of a control and a subject with DLB.

[0372] In some embodiments, subjects classified as controls are characterized by having a CDR score of 0 and a clinical follow-up greater than 10 years (i.e., clinical follow-up greater than 10 years). In some embodiments, subjects classified as subjects having DLB have been clinically diagnosed as DLB.

[0373] In some embodiments, a training data set includes correct outputs corresponding to a dementia stage category assigned to each subject, wherein the dementia stage categories are control, and subject with DLB.

[0374] In some embodiments, the data is preprocessed or has been preprocessed before training the classification model. This step is performed to ensure and enhance the performance of the model training process. Data preprocessing includes:

[0375] (1) Create dummy variables,

[0376] (2) Remove variables with zero variance or close to zero variance,

[0377] (3) Split the data into training and testing data sets,

[0378] (4) Centralization and calibration,

[0379] (5) dimensionality reduction (i.e., identification and removal of correlated variables), and

[0380] (6) Inspect and visualize the training dataset.

[0381] (1) The process of creating dummy variables is performed to handle categorical data. Basically, each categorical variable is converted to a numerical variable by creating dummy variables using the so-called "one-hot encoding" method (i.e., each new variable is forced to have a value of 0 or 1, indicating the presence or absence of the attribute). This process is performed to ensure that the variables are encoded consistently. That is, it is encoded to ensure that there are no linear dependencies between the new attributes and thus avoid the dummy variable trap. In addition, the data is reviewed to ensure that all categorical 1-encoded variables do not show any unusual linear combinations, and if so, redundant variables are removed until the linear combinations are eliminated.

[0382] (2) The procedure of removing variables with zero and near-zero variance is performed to remove variables that show a single unique value and variables with a few highly unbalanced unique values. Otherwise, these predicted values ​​may cause instability problems or model crashes during fitting.

[0383] (3) The data splitting process is performed to randomly divide into two main subsets: one subset is used to perform model training (80% of the samples), and the other subset is used to test the classification model (20% of the samples). A random sampling process is performed in each class to maintain the overall class distribution of the data. To ensure reproducibility, a random seed is considered.

[0384] (4) The centering and scaling process is applied to the continuous features (variables) of the training dataset in order to estimate the centering and scaling factors that must be applied to both datasets to generate normalized datasets for performing the training and testing process of the classification model.

[0385] (5) The dimensionality reduction process is performed to reduce the number of variables (i.e., features or attributes) in a dataset while retaining as much relevant information as possible. In other words, the goal is to remove redundant or irrelevant features in order to improve the efficiency and effectiveness of the learning methods applied to build classification models. In this context, there are two main approaches: feature selection and feature extraction.

[0386] (i) Feature selection techniques: The basic idea of ​​these methods is to select a subset of variables based on some criteria, for example, by identifying and removing correlated variables. The purpose of performing this process is to reduce highly correlated variables. To perform this step, a correlation matrix is ​​calculated. Usually, the correlation measure applied is the Pearson correlation coefficient. Then, in order to detect highly correlated variables, based on the absolute value of the pairwise correlation coefficients, the average absolute correlation coefficient of each variable is looked at and the variable with the largest average absolute correlation coefficient is removed if two variables are highly correlated. In this context, the pairwise absolute correlation coefficient cutoff value can usually be set by studying, for example, a linear regression between each pair of variables. However, this approach may not reveal additional feature correlations. For this reason, on the one hand, other monotonic methods can also be considered to identify correlations between variables. For example, the non-parametric methods Spearman's Rank Correlation and Kendall's Tau Correlation measure the association between each pair of variables based on the ranks of the observed values ​​rather than their actual values. They are usually applied when there is a reasonable suspicion of nonlinear relationships, skewness or ordinal measures between attributes (variables). Another type of method can be the distance correlation coefficient (also called dCor). It measures the dependence between each pair of variables in a way that is sensitive to nonlinear relationships. Most of these methods are considered to study the relationship between continuous and / or ordinal variables, but there are also methods to study the strength of the relationship between dichotomous variables and continuous variables (e.g., point biserial correlation) or when both variables are dichotomous variables (Φ coefficient). On the other hand, in order to determine the appropriate threshold for detecting highly correlated variables, it is recommended to use a target method such as cross-validation. That is, to determine the required cutoff value, first, the data must be split into a training data set and a test data set using a random seed to ensure repeatability. Second, the model is trained using the training data set and its performance is evaluated using the test set. Third, the threshold for considering the variables as highly correlated is changed, and then the second step is repeated for each threshold. Fourth, the threshold that generates the best performance on the test set is selected, such as the threshold with the highest accuracy (or lowest error rate). Fifth, after the cutoff value is selected, it must be applied to the entire data set and the model is retrained using all the data.

[0387] Other feature selection methods that can be applied are Genetic Algorithm (GA), L1 regularized logistic regression, Lasso regression or hybrid methods.

[0388] (ii) Feature extraction techniques: These methods generate new variables by combining or applying transformations on the original features into a reduced dimensionality space. Some examples are principal component analysis (PCA), multifactor analysis (MFA), t-SNE or alternatively UMAP and / or multidimensional scaling (MDS) methods, partial least squares-discriminant analysis (PLS-DA) or autoencoders.

[0389] (6) Checking and visualizing the training dataset is a process that is performed after the previous preprocessing tasks have been performed. This is the second exploratory data analysis (EDA) performed on the training dataset and is directed to review and check that there is no bias with the original data. Classical statistical descriptive methods (i.e., univariate, bivariate, and multivariate descriptive methods) are applied.

[0390] In an embodiment, data preprocessing includes:

[0391] (1) Data splitting,

[0392] (2) Centralization and calibration,

[0393] (3) identification and removal of correlated variables, and

[0394] (4) Inspect and visualize the training dataset.

[0395] (1) In data splitting, the original corrected data is randomly split into major subsets; one subset is used to perform model training (e.g., 80% of the samples), and the other subset is used to test the classification model (e.g., 20% of the samples). A random sampling process is performed in each class to maintain the overall class distribution of the data.

[0396] (2) In centering and scaling, continuous variables from the training dataset are used to estimate the centering and scaling factors, which are applied to the two datasets to generate normalized datasets for performing the training and testing process of the classification model.

[0397] (3) Identification and removal of correlated variables are performed to reduce the level of correlation between variables. Pairwise correlation analysis based on, for example, the Spearman rank correlation coefficient is performed. For those pairs showing high levels of absolute correlation values ​​(e.g., >0.65), the variables with the largest average absolute correlation are removed from the data set. In addition, principal component analysis (PCA) can be applied to reduce the dimensionality of the features, for example, to 10 variables.

[0398] (4) Checking and visualizing the training dataset includes applying exploratory data analysis (EDA) to the training dataset to review and check that the original data is free of bias.

[0399] The classification model disclosed herein can be trained with data corresponding to a set of samples for which methylation data corresponding to a set of methylation sites has been obtained. For example, the training set includes methylation pattern data from the methylation sites shown in Tables 1-6, or any combination thereof. Specifically, the training set includes methylation pattern data from the methylation sites shown in Tables 1-6.In some embodiments, methylation pattern data comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75 4, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 84, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 1 38, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195 In some embodiments, methylation pattern data comprises data for more than 50 methylation sites. In some embodiments, methylation pattern data comprises data for more than 100 methylation sites.In some embodiments, the methylation pattern data comprises data for more than 200 methylation sites. In some embodiments, the methylation pattern data comprises data for more than 200 methylation sites. In some embodiments, the methylation pattern data comprises about 10 to about 20, about 20 to about 30, about 30 to about 40, about 40 to about 50, about 50 to about 60, about 60 to about 70, about 70 to about 80, about 80 to about 90, about 90 to about 100, about 100 to about 110, about 110 to about 120, about 120 to about 130, about 130 to about 140, about 140 to about 150, about 150 to about 160, about 160 to about 170, about 170 to about 180, about 180 to about 190, about 190 to about 200, about 200 to about 210, about 210 to about 220, about 220 to about 230, about 230 to about 240, about 240 to about 250 methylation sites selected from Tables 1-6.

[0400] In some embodiments, the training data set includes clinical variables for each subject, such as subject classification according to a classification model disclosed herein. In other embodiments, the training data includes data about the subject, such as weight, presence or absence of biomarkers, medication, etc.

[0401] In some embodiments, the training set includes at least about 5, 10, 20, 30, 40, 0, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 250, 300, 350, 400, 450, 500, 600, 700, 800, 900 or at least about 1000 subjects. In other embodiments, the training set includes more than 1000 subjects.

[0402] In some embodiments, the classification model comprises determining a (relative) weight for each methylation pattern and each clinical variable taken into account. In other embodiments, the classification model comprises determining a (relative) weight for each methylation pattern and / or each clinical variable. In other embodiments, the classification model comprises determining a weight for each methylation pattern, each clinical variable, and / or any additional variables.

[0403] In some embodiments, the classification model uses data indicating the (relative) weight of each methylation pattern and each clinical variable for identifying DLB in a subject. In other embodiments, the classification model uses data indicating the (relative) weight of each methylation pattern and / or each clinical variable for identifying DLB in a subject.

[0404] In some embodiments, determining a score comprises associating each of at least one determined methylation pattern and each of at least one clinical variable with its determined weight. In some embodiments, determining a score comprises associating each of at least one determined methylation pattern and / or each of at least one clinical variable with its determined weight.

[0405] With respect to the variables regarding the mitochondrial methylation pattern at different sites, those variables that contribute most to the identification of DLB ​​in a subject may be different. This is because the contribution of each variable depends on several aspects, the number of variables considered in the model (e.g., which clinical variables are included), the number of subjects used in the training of the classification model (to avoid overfitting / underfitting problems), the customization process of the parameters during the training of the model, etc.

[0406] In those aspects, the variables included in each classification model will depend on the information available for each subject, and therefore the importance / contribution of each selected variable will vary from classification model to classification model. Therefore, it is difficult to define a precise set of variables or a specific number of variables to be considered when building a classification model. Instead, it is necessary to develop classification models that can adapt to the available information, and classification models can be built using variables selected according to the importance / contribution of the variables in each specific case. In other words, the variables included in each classification model will be defined according to their contribution during the training process, rather than arbitrarily predefining a set of variables or a minimum number of variables that may not contribute much in other cases. In either case, the main goal is to obtain the highest possible accuracy value.

[0407] Classification model described herein can include different sets and combinations of methylation patterns and / or clinical variables. The classification model selects methylation patterns and / or clinical variables according to the contribution or importance associated with each methylation pattern and / or clinical variable (i.e., the weight determined). In other words, the classification model is configured to combine the methylation pattern of the site associated with the determined weight (e.g., 0.25, 0.5, 1, 2, 2.5, 5, etc.) and / or the clinical variable associated with the determined weight of at least 1 (e.g., 0.25, 0.5, 1, 2, 2.5, 5, etc.).

[0408] In some embodiments, the range of determined weights associated with a methylation pattern or a range of clinical variables or a combination of measured variables (e.g., principal components) is 0 to 100. Alternatively, the determined weights associated with a methylation pattern or a clinical variable are selected from 0 to 100. The value "0" corresponds to the lowest variable importance or the lowest determined weight, and the value "100" corresponds to the highest variable importance or the highest determined weight. In a specific embodiment, the determined weights are selected from the group consisting of: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85 8, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 and 100.

[0409] In some embodiments, the determined weight is at least 0.25. In particular, the determined weight is selected from the group consisting of 0.25, 0.3, 0.35, 0.40, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95 and 1. In another embodiment, the determined weight is at least 1. In particular, the determined weight is selected from the group consisting of: 1, 1.25, 1.5, 1.75, 2, 2.25, 2.5, 2.75, 3, 3.25, 3.5, 3.75, 4, 4.25, 4.5, 4.75, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 and 100. In another embodiment, the determined weight is at least 2. In particular, the determined weight is selected from the group consisting of: 2, 2.25, 2.5, 2.75, 3, 3.25, 3.5, 3.75, 4, 4.25, 4.5, 4.75, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 and 100. In another embodiment, the determined weight is selected from the group consisting of: at least 1, at least 2, at least 2.5, at least 3, at least 3.5, at least 4, at least 4.5, at least 5, at least 7.5, at least 10, at least 15, at least 20, at least 25, at least 50 and at least 75.

[0410] In some embodiments, determining DLB in a subject comprises considering methylation patterns of sites associated with a determined weight of at least 0.5. In another embodiment, determining DLB in a subject comprises combining methylation patterns of sites associated with a determined weight of at least 0.5.

[0411] In another embodiment, determining DLB in a subject comprises considering clinical variables associated with a determined weight of at least 0.5. In another embodiment, determining DLB in a subject comprises combining clinical variables associated with a determined weight of at least 0.5.

[0412] In another embodiment, determining DLB in a subject comprises combining a methylation pattern of a site associated with a determined weight of at least 0.5 and a clinical variable associated with a determined weight of at least 0.5. Alternatively, determining DLB in a subject comprises combining or taking into account: a methylation pattern and / or a clinical variable associated with a determined weight of at least 0.5.

[0413] In some embodiments, determining DLB in a subject comprises considering methylation patterns of sites associated with a determined weight of at least 1. In another embodiment, determining DLB in a subject comprises combining methylation patterns of sites associated with a determined weight of at least 1.

[0414] In another embodiment, determining DLB in a subject comprises considering clinical variables associated with a determined weight of at least 1. In another embodiment, determining DLB in a subject comprises combining clinical variables associated with a determined weight of at least 1.

[0415] In another embodiment, determining DLB in a subject comprises combining a methylation pattern of a site associated with a determined weight of at least 1 and a clinical variable associated with a determined weight of at least 1. Alternatively, determining DLB in a subject comprises combining or taking into account: a methylation pattern and / or a clinical variable associated with a determined weight of at least 1.

[0416] In some embodiments, determining DLB in a subject comprises considering methylation patterns of sites associated with at least 2 determined weights. In another embodiment, determining DLB in a subject comprises combining methylation patterns of sites associated with at least 2 determined weights.

[0417] In another embodiment, determining DLB in a subject comprises considering clinical variables associated with at least 2 of the determined weights. In another embodiment, determining DLB in a subject comprises combining clinical variables associated with at least 2 of the determined weights.

[0418] In another embodiment, determining DLB in a subject comprises combining methylation patterns of sites associated with at least 2 of the determined weights and clinical variables associated with at least 2 of the determined weights. Alternatively, determining DLB in a subject comprises combining or taking into account: methylation patterns and / or clinical variables associated with at least 2 of the determined weights.

[0419] In some embodiments, determining DLB in a subject comprises considering methylation patterns of sites associated with a determined weight of at least 2.5. In another embodiment, determining DLB in a subject comprises combining methylation patterns of sites associated with a determined weight of at least 2.5.

[0420] In another embodiment, determining DLB in a subject comprises considering clinical variables associated with a determined weight of at least 2.5. In another embodiment, determining DLB in a subject comprises combining clinical variables associated with a determined weight of at least 2.5.

[0421] In another embodiment, determining DLB in a subject comprises combining a methylation pattern of a site associated with a determined weight of at least 2.5 and a clinical variable associated with a determined weight of at least 2.5. Alternatively, determining DLB in a subject comprises combining or taking into account: a methylation pattern and / or a clinical variable associated with a determined weight of at least 2.5.

[0422] In some embodiments, determining DLB in a subject comprises considering methylation patterns of sites associated with a determined weight of at least 5. In another embodiment, determining DLB in a subject comprises combining methylation patterns of sites associated with a determined weight of at least 5.

[0423] In another embodiment, determining DLB in a subject comprises considering clinical variables associated with at least 5 determined weights. In another embodiment, determining DLB in a subject comprises combining clinical variables associated with at least 5 determined weights.

[0424] In another embodiment, determining DLB in a subject comprises combining methylation patterns of sites associated with a determined weight of at least 5 and clinical variables associated with a determined weight of at least 5. Alternatively, determining DLB in a subject comprises combining or taking into account: methylation patterns and / or clinical variables associated with a determined weight of at least 5.

[0425] Alternatively, the methylation pattern and / or clinical variables to be combined or considered by the classification model for determining the DLB of the subject are selected according to the weights determined by them. In particular, the methylation pattern and / or clinical variables combined by the classification model are selected according to the weights determined by them, and the weight determined is at least 1. In another specific embodiment, the methylation pattern and / or clinical variables combined by the classification model are selected according to the weights determined by them, and the weight determined is at least 2. In some embodiments, the methylation pattern and / or clinical variables combined by the classification model for determining the DLB in the subject are related to the weight determined by the group consisting of: at least 0.25, at least 0.5, at least 1, at least 2, at least 2.5, at least 3, at least 3.5, at least 4, at least 4.5, at least 5, at least 7.5, at least 10, at least 15, at least 20, at least 25, at least 50 and at least 75.

[0426] In some embodiments, the classification model is capable of classifying a subject into two categories: CTL or DLB. In some embodiments, the classification model calculates a risk score for each category, which corresponds to the probability of the subject being assigned to each category. In some embodiments, the probability of each category adds up to at most 1.

[0427] In some embodiments, the classification model is capable of classifying the subject into two categories: subjects with DLB and control subjects. In some embodiments, the classification model calculates a score for each category, which corresponds to the probability that the subject is assigned to each category. In some embodiments, the probability of each category adds up to a maximum of 1.

[0428] In the above, the weights associated with methylation patterns or clinical variables range from 0 to 100, or are selected from 0 to 100, i.e., a scale from 0 to 100 is used. For this scale, a specific minimum weight value is defined herein. However, it should be clear that any other suitable scale may be used, such as a scale from 0 to 1 or a scale from 0 to 1000 or a scale from 0 to 20. With such different scales, the minimum values ​​of the weights mentioned above may be changed proportionally.

[0429] By determining the ability of the model (i.e., classifier) ​​to correctly classify each test subject, the classification model generated by the machine learning method disclosed herein or any other supervised classification method known in the art (e.g., LDA, CART, kNN, NB, SVM with linear kernel, RF, NNET, GBM and GLM) can be evaluated subsequently. In some embodiments, the subject of the training population for deriving the model is different from the subject of the test population for testing the model. As will be appreciated by those skilled in the art, this allows those skilled in the art to predict the ability of the data set for training the classifier to correctly classify the output classification (e.g., dementia stage classification, i.e., subject with DLB and control subject) unknown subject.

[0430] In some embodiments, the ability of the classification model to correctly classify each subject in the training population is evaluated using methods known to those skilled in the art. For example, one skilled in the art can use cross validation (CV), omission of cross validation (LOOCV), k-fold cross validation, or jackknife analysis using standard statistical methods to evaluate the classification model. In other embodiments, the ability of each classifier to correctly characterize those subjects in the training population that were not used to generate the classifier is evaluated.

[0431] In some embodiments, the metric for evaluating the ability of a classification model to correctly classify each subject in a training population is selected from the group consisting of: classification accuracy (ACC), kappa statistics, error rate (i.e., misclassification rate), sensitivity (true positive fraction or ratio, TPF or TPR), specificity (true negative fraction or ratio, TNF or TNR), positive predictive value (PPV), negative predictive value (NPV) or any combination thereof. In some embodiments, the metric for evaluating the ability of a classification model to correctly classify each subject in a training population also includes recall, precision, F-score (F-measure or F1 score), confusion matrix (or contingency table) and / or the area under the receiver operating characteristic curve (AUC ROC).

[0432] In a specific embodiment, the metrics used to evaluate the ability of the classification model to correctly classify each subject in the training population include evaluating the sensitivity (TPF, true positive fraction) and 1-specificity (FPF, false positive fraction) of the model. In one embodiment, the method used to test the classifier is the receiver operating characteristic ("ROC"), which provides several parameters to evaluate the sensitivity and specificity of the results of the generated classification model.

[0433] Another aspect of the invention relates to a computer-implemented method for identifying DLB in a subject, comprising:

[0434] (a) providing or receiving data related to a methylation pattern in the D-loop region of mitochondrial DNA and / or the ND1 gene of a subject, wherein the methylation pattern is determined in at least one site selected from the group consisting of:

[0435] (i) CpG sites in the D-loop region shown in Table 1,

[0436] (ii) a CHG site in the D-loop region shown in Table 3,

[0437] (iii) a CHH site in the D-loop region shown in Table 5,

[0438] (iv) the CpG sites of the ND1 gene shown in Table 2,

[0439] (v) the CHG site of the ND1 gene shown in Table 4, and

[0440] (vi) the CHH site of the ND1 gene shown in Table 6,

[0441] and optionally, at least one clinical variable of the subject, and

[0442] (b) determining a risk score associated with DLB in the identified subject, wherein the risk score is calculated or determined using a classification model configured to combine the methylation pattern of the one or more sites in step (a) with, optionally, at least one clinical variable of the subject.

[0443] Another aspect of the present invention relates to a computer-implemented method for obtaining a score associated with identifying DLB in a subject, the method comprising the steps of:

[0444] (a) provide or receive as input data:

[0445] (1) a methylation pattern in the D-loop region and / or the ND1 gene of the mitochondrial DNA of the subject, wherein the methylation pattern is determined in at least one site selected from the group consisting of:

[0446] (i) CpG sites in the D-loop region shown in Table 1,

[0447] (ii) a CHG site in the D-loop region shown in Table 3,

[0448] (iii) a CHH site in the D-loop region shown in Table 5,

[0449] (iv) the CpG sites of the ND1 gene shown in Table 2,

[0450] (v) the CHG site of the ND1 gene shown in Table 4, and

[0451] (vi) the CHH site of the ND1 gene shown in Table 6; and optionally

[0452] (b) The methylation pattern is weighted using a classification model to obtain a score.

[0453] Another aspect of the present invention relates to a computer-implemented method for obtaining a score associated with identifying DLB in a subject, the method comprising the steps of:

[0454] (a) provide or receive as input data:

[0455] (1) at least one clinical variable of the subject, and

[0456] (b) Weighting the clinical variables using a classification model to obtain a score.

[0457] In some examples, input data may be received from which a computer or other data processing system may derive a methylation pattern in the D-loop region of the mitochondrial DNA of a subject and / or a methylation pattern in the ND1 gene. And the computer-implemented method may then additionally include receiving at least one clinical variable of the subject as described herein, and combining and weighting one or more methylation patterns and one or more clinical variables using a classification model to obtain a risk score.

[0458] score

[0459] In some embodiments, the score is a score of 0-1, wherein 0 indicates that the subject has the lowest probability of suffering from DLB, and 1 indicates that the subject has the highest probability of suffering from DLB. In a specific embodiment, a score of 0.5 or higher indicates that the subject may suffer from ADD. Alternatively, a score of 0.5 or higher indicates that the subject has a high probability of suffering from DLB. In a specific embodiment, a score of 0.75 or higher indicates that the subject has a very high probability of suffering from DLB. In some embodiments, a score below 0.5 indicates that the subject has a low probability of suffering from DLB. In a specific embodiment, a risk score of 0.25 or lower indicates that the subject has a very low probability of suffering from DLB.

[0460] In some embodiments, the score is a score of 0-100, where 0 represents the lowest probability that the subject suffers from DLB, and 100 represents the highest probability that the subject suffers from DLB. In some embodiments, the score can be, but is not limited to, 1-2, 1-5, 1-10, 1-100, 0-10, and 0-100. It should be understood that the score in the present invention can be any range of values ​​used to correlate with the probability of a subject suffering from DLB.

[0461] Companion diagnostic system

[0462] The methods disclosed herein can be provided as a companion diagnostic (e.g., available via a web server) to inform a clinician or patient of potential treatment options or for selection of patients for clinical trials. The methods disclosed herein can include collecting or otherwise obtaining a biological sample and performing an analytical method disclosed herein to diagnose DLB in a subject.

[0463] In one aspect of the invention, there is provided a computing system comprising suitable means for performing any of the computer-implemented methods described herein.

[0464] Due to the complexity of the calculations involved, at least some embodiments of the methods described herein may be implemented using a computer. In some embodiments, a computer system includes hardware elements electrically coupled via a bus, the hardware elements including a processor, an input device, an output device, a storage device, a computer-readable storage medium reader, a communication system, a processing accelerator (e.g., a DSP or a dedicated processor), and a memory. A computer-readable storage medium reader may additionally be coupled to a computer-readable storage medium, the combination comprehensively representing remote, local, fixed, and / or removable storage devices plus storage media, memory, etc. for temporarily and / or more permanently containing computer-readable information, which may include storage devices, memory, and / or any other such accessible system resources.

[0465] A single architecture can be utilized to implement one or more servers, which can be further configured according to currently required protocols, protocol variants, extensions, etc. However, it will be apparent to those skilled in the art that implementations can be utilized according to more specific application requirements. Customized hardware and / or specific elements can also be implemented with hardware, software, firmware, or a combination thereof. In addition, although connections with other computing devices such as network input / output devices (not shown) can be employed, it should be understood that wired, wireless, modem, and / or other connections with other computing devices or other multiple connections may also be utilized.

[0466] In one embodiment, the system further comprises one or more devices for providing input data to the one or more processors. The system further comprises a memory for storing a data set of hierarchical data elements. In another embodiment, the device for providing input data comprises a detector for detecting features of the data elements, such as, for example, a fluorescent plate reader, a mass spectrometer, or a gene chip reader.

[0467] The system may additionally include a database management system. User requests or queries may be formatted into an appropriate language understood by the database management system, thereby processing the query to extract relevant information from the database of the training set. The system may be connected to a network server and a network to which one or more clients are connected. As known in the art, the network may be a local area network (LAN) or a wide area network (WAN). Specifically, the server includes hardware necessary for running a computer program product (e.g., software) to access database data to process user requests. The system may communicate with an input device for providing data (e.g., methylation patterns) about data elements to the system.

[0468] In other aspects, the present invention relates to a computer program product comprising instructions which, when executed by a computer, cause the computer to perform any of the computer-implemented methods described herein.

[0469] Some embodiments described herein can be implemented to include computer program products.Computer program products can include computer-readable media, and the media has a computer-readable program code containing the media for making the application program execute on a computer with a database.As used herein, "computer program product" refers to an organized set of instructions in the form of natural or programming language statements, which is included in any property (e.g., written, electronic, magnetic, optical or other) physical media, and can be used together with a computer or other automatic data processing system.When executed by a computer or data processing system, this programming language statement causes the computer or data processing system to operate according to the specific content of the statement.

[0470] Computer program products include, but are not limited to, source code and object code embedded in a computer-readable medium and / or a program in a test or database. In addition, a computer program product that enables a computer system or data processing instrument to operate in a preselected manner can be provided in a variety of forms, including, but not limited to, original source code, assembly code, object code, machine language, encrypted or compressed versions of the foregoing, and any and all equivalents. In one aspect, a computer program product is provided to implement the treatment, diagnosis, and method disclosed herein, for example, to determine whether to administer a therapy based on the score obtained.

[0471] The computer program product comprises a computer readable medium embodying program code executable by a processor of a computing device or system, the program code comprising:

[0472] (a) retrieving code for data believed to be from a biological sample of a subject, wherein the data comprises a methylation pattern corresponding to at least one of the methylation sites of Tables 1-6 in the biological sample, or wherein the methylation pattern can be derived from the data. Combining these values ​​with values ​​corresponding to clinical variables; and,

[0473] (b) Code for executing a classification method that indicates, for example, whether to administer a therapeutic agent to a patient in need thereof based on the obtained score.

[0474] Although various embodiments have been described as methods or apparatus, it should be understood that embodiments may be implemented by code coupled to a computer, e.g., code resident on a computer or accessible by a computer. For example, many of the methods discussed above may be implemented using software and databases. Thus, in addition to embodiments implemented by hardware, it should be noted that embodiments may be implemented using an article of manufacture consisting of a computer-usable medium having computer-readable program code embodied therein, which enables the functions disclosed in this specification to be implemented.

[0475] Furthermore, some embodiments may be code stored in almost any kind of computer-readable memory, including but not limited to RAM, ROM, magnetic media, optical media, or magneto-optical media. More generally, some embodiments may be implemented in software, hardware, or any combination thereof, including but not limited to software running on a general-purpose processor, microcode, PLA, or ASIC.

[0476] It is also contemplated that some embodiments may be implemented as computer signals embodied in carrier waves, as well as signals (e.g., electrical and optical signals) propagating through transmission media. Thus, the various types of information discussed above may be formatted into structures such as data structures and transmitted as electrical signals through transmission media or stored on computer-readable media.

[0477] Method Execution

[0478] In some embodiments, the sample can be requested, for example, by a healthcare provider (e.g., a doctor) or a healthcare benefit provider, obtained and / or processed by the same or different healthcare provider (e.g., a nurse, a hospital) or a clinical laboratory, and after processing, the results can be forwarded to the original healthcare provider or another healthcare provider, healthcare benefit provider, or patient. Similarly, determining the methylation patterns disclosed herein; obtaining clinical variables from a subject; combining the methylation patterns with clinical information; identifying / diagnosing DLB in a subject; application of a classification model; determination of a score; diagnostic / prognostic decisions; treatment decisions; clinical trial inclusion decisions; or a combination thereof can be performed by one or more healthcare providers, healthcare benefit providers, and / or clinical laboratories.

[0479] As used herein, the term "health care provider" refers to an individual or institution that directly interacts with a living subject (e.g., a human patient) and manages the patient. Non-limiting examples of health care providers include doctors, nurses, technicians, therapists, pharmacists, consultants, alternative medicine physicians, medical institutions, doctors' offices, hospitals, emergency rooms, clinics, emergency centers, alternative medicine clinics / institutions, and any other entity that provides general and / or specialized treatment, diagnosis, evaluation, maintenance, therapy, medication, and / or suggestions related to all or any part of a patient's health status, including but not limited to general medicine, specialized medicine, surgery, and / or any other type of treatment, diagnosis, evaluation, maintenance, therapy, medication, and / or suggestions. Health care providers herein also refer to drug companies or their suppliers / intermediaries (e.g., CROs) involved in clinical trial development.

[0480] As used herein, the term "clinical laboratory" refers to a facility for examining or processing materials from a subject. These examinations may also include procedures for collecting or otherwise obtaining samples, preparing, determining, measuring or otherwise describing the presence or absence of various substances in a subject or samples obtained from a subject (e.g., methylation patterns of mtDNA or biomarkers used herein as clinical variables). Examinations may also include procedures such as medical imaging procedures (e.g., PET, MRI) used to obtain clinical variable data.

[0481] As used herein, the term "health care benefit provider" encompasses a group of individuals, organizations, or entities that provide, present, grant, pay in whole or in part, or are otherwise associated with giving patients access to one or more health care benefits, benefit plans, health insurance, and / or health expense account plans.

[0482] A healthcare provider may perform or instruct another healthcare provider or patient to perform the following activities: for example, obtaining a sample / clinical variable, processing a sample / clinical variable, submitting a sample / clinical variable, receiving a sample / clinical variable, transferring a sample / clinical variable, analyzing or measuring a sample / clinical variable (e.g., obtaining a methylation pattern), quantifying a sample / clinical variable, providing a result obtained after analyzing / measuring / quantifying a sample / clinical variable, receiving a result obtained after analyzing / measuring / quantifying a sample / clinical variable, applying a classification model, obtaining a result after analyzing / measuring / quantifying one or more samples / clinical variables, providing a result, diagnosing / identifying DLB in a subject, identifying a subject suitable for treatment of DLB, selecting a subject for DLB Treating, selecting an appropriate therapy to treat a subject with DLB, classifying a subject according to the presence or absence of DLB; treating DLB in a subject, including if the subject has been identified as having DLB, administering a therapy for DLB, administering a therapy, initiating administration of a therapy, discontinuing administration of a therapy, continuing administration of a therapy, temporarily interrupting administration of a therapy, increasing the amount of a therapeutic agent administered, decreasing the amount of a therapeutic agent administered, continuing administration of an amount of a therapeutic agent, increasing the frequency of administration of a therapeutic agent, decreasing the frequency of administration of a therapeutic agent, maintaining the same dosing frequency of a therapeutic agent, replacing a therapy or therapeutic agent with at least another therapy or therapeutic agent, combining a therapy or therapeutic agent with at least another therapy or additional therapeutic agent, and / or monitoring the progression of DLB.

[0483] In some embodiments, a healthcare benefits provider can authorize or deny any of the above activities, e.g., collection of samples / clinical variables, processing of samples / clinical variables, submission of samples / clinical variables, receipt of samples / clinical variables, transfer of samples / clinical variables, analysis or measurement of samples / clinical variables (e.g., to obtain methylation patterns), quantification of samples / clinical variables, application of classification models, provision of results obtained after analysis / measurement / quantification of samples / clinical variables, transfer of results obtained after analysis / measurement / quantification of samples / clinical variables, and / or Scoring, transfer of results from one or more samples / clinical variables, administration of a therapy or therapeutic agent, initiation of administration of a therapy or therapeutic agent, cessation of administration of a therapy or therapeutic agent, continuation of administration of a therapy or therapeutic agent, temporary interruption of administration of a therapy or therapeutic agent, increase in the amount of a therapeutic agent administered, decrease in the amount of a therapeutic agent administered, continuation of administration of a certain amount of a therapeutic agent, increase in the frequency of administration of a therapeutic agent, decrease in the frequency of administration of a therapeutic agent, maintenance of the same dosing frequency of a therapeutic agent, replacement of a therapy or therapeutic agent by at least another therapy or therapeutic agent, combination of a therapy or therapeutic agent with at least another therapy or additional therapeutic agent. In addition, a healthcare benefit provider may, for example, authorize or deny prescription of a therapy, authorize or deny coverage of a therapy, authorize or deny reimbursement of the cost of a therapy, determine or deny eligibility for a therapy, etc.

[0484] In some embodiments, a clinical laboratory can, for example, collect or obtain samples / clinical variables, process samples / clinical variables, submit samples / clinical variables, receive samples / clinical variables, transfer samples / clinical variables, analyze or measure samples / clinical variables (e.g., to obtain methylation patterns), quantify samples / clinical variables, apply a classification model, provide results obtained after analyzing / measuring / quantifying samples / clinical variables, receive results obtained after analyzing / measuring / quantifying samples / clinical variables, obtain results after analyzing / measuring / quantifying one or more samples / clinical variables, provide results, diagnose / identify DLB in a subject, identify a subject suitable for treatment of DLB, select a subject for treatment of DLB, classify a subject according to the presence or absence of DLB, monitor the progression of DLB, or other related activities.

[0485] In some embodiments, sample / clinical variables can be obtained by the health care professional of treating or diagnosing the patient, by health care provider or by clinical laboratory.The measurement of sample (for example, by using specific determination as described herein) and the acquisition of clinical variables (for example, by medical imaging technology) can be performed by health care provider or clinical laboratory, which is the same or different from the health care provider or clinical laboratory that obtains sample / clinical variables.Classification model can be applied by health care provider or different health care providers or by clinical laboratory.The result obtained is finally sent to the first health care professional or health care provider of treating or diagnosing the patient.Therefore, in some embodiments, health care provider or clinical laboratory can be for health care professional / provider suggestion about diagnosis / prognosis or about whether patient can benefit from treatment.In some embodiments, health care provider is one of pharmaceutical company or its provider / middleman (for example, CRO) involved in clinical trial development.All steps described herein can be performed by one of pharmaceutical company and / or its supplier / middleman, or partly by, for example, clinical laboratory or different health care providers perform.

[0486] Example

[0487] Example 1: Detection of mtDNA methylation in blood samples

[0488] 1.1 Materials and methods

[0489] 1) Blood sample collection

[0490] Human blood samples were collected in EDTA tubes to prevent blood clotting. After obtaining the samples in the laboratory, the blood was directly processed for DNA extraction or aliquoted and stored at -80°C until processing.

[0491] Samples were extracted from a total of 36 subjects, who were recruited from three different cohorts. Controls were recruited from two different cohorts: Cohort A corresponds to the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL), and Cohort B corresponds to the Fundación CITA Foundation (Centro de Investigación y Terapias Avanzadas) in San Sebastian, Spain. In addition, Cohort C corresponds to DLB patients recruited at the Bellvitge Hospital in Barcelona between 2015 and 2019. Overall, subjects were classified into two groups: controls (50%, N=18) and DLB subjects (50%, N=18).

[0492] 2) Total DNA extraction

[0493] Whole blood samples were processed for total DNA extraction and co-purification of genomic and mitochondrial DNA using the manufacturer's instructions. DNA was isolated from human whole blood samples using the GENOMIC DNA Purification Kit (#A1620, Promega). Samples were processed using the RSC instrument, which provides a simple method for efficient, automated purification of DNA from samples. DNA sample capture, washing, and purification were performed using paramagnetic beads. The instrument was used according to the manufacturer's instructions. RSC Blood DNA Kit (#AS1400, Promega).The quality and quantity of purified DNA were determined using a NanoDrop™ One spectrophotometer from Thermofisher Scientific.

[0494] 3) Bisulfite treatment

[0495] Bisulfite conversion involves deamination of unmodified cytosine to uracil, thereby retaining the intact modified base 5-mC, i.e., methylated cytosine. A sample of total DNA (300 ng) was treated with a bisulfite reagent using the EZ DNA Methylation Kit (#D5001, ZymoResearch) according to the manufacturer's protocol. In order to obtain better bisulfite conversion, the incubation conditions included in step 2 of the protocol, which included incubation at 37°C for 15 min, were replaced with incubation at 42°C for 30 min, as indicated in Appendix 1.A of the manufacturer's protocol. These last conditions are recommended to minimize incomplete C to T conversions. The treated DNA was finally resuspended in 30 μL of nuclease-free water.

[0496] 4) Preparation of amplicon library

[0497] The workflow for amplicon library construction is based on the Illumina "16S Metagenomic Sequencing Library Preparation Protocol", which can be used to sequence regions of the 16S rRNA gene and other target amplicon sequences. The preparation of amplicon libraries allows obtaining the target mtDNA amplicons and their preparation for processing on the Illumina MiSeq system.

[0498] 4.1) First PCR: Amplicon PCR

[0499] The target mtDNA region was amplified by PCR using specific degenerate primers corresponding to the sequences SEQ ID NO: 1-4 and containing overhanging Illumina adapters. When designing primers for the target region, as shown in the Illumina protocol, overhanging adapter sequences must be added to the locus-specific primers for the region to be targeted.

[0500] to be added to the locus-specific sequence The overhang adapter sequences are (SEQ ID NO: 5, 6):

[0501] Positive overhang:

[0502] 5'TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG-[locus-specific sequence]

[0503] Reverse overhang:

[0504] 5'GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-[locus-specific sequence]

[0505] Using FastStart TM Amplification of bisulfite-converted DNA was performed using the High Fidelity PCR System (#3553400001, Roche). The final PCR mixture (25 μL) contained: 5 μL of bisulfite-converted DNA, 1x FastStart Buffer #2; 0.05 U FastStart HiFi polymerase; 0.8 mM total dNTPs (0.2 mM each dNTP); and 0.4 μM each of the forward and reverse primers. The reaction of the ND1 amplicon also included 5% DMSO. The final volume was adjusted with nuclease-free water.

[0506] Amplification was performed in a SimpliAmp™ thermal cycler from Applied Biosystems.

[0507] To evaluate the results of the first PCR, the TM 3 μL of each PCR product was electrophoretically analyzed on a 1.5% agarose gel stained with DNA gel stain (#S33102, Thermofisher Scientific) to verify the presence of the band derived from the amplicon.

[0508] 4.2) Cleanup of the first PCR

[0509] AMPure XP beads (#A63881, Beckman Coulter) were used to purify the amplicons and separate them from free primers and primer dimer species. The steps were performed according to the Illumina "16S Metagenomic Sequencing Library Preparation Protocol". PCR amplicon products were purified using 0.8x ratio AMpure beads. Beads were eluted in 14 μL of buffer EB (#19086, Qiagen) and 12 μL was recovered from the beads.

[0510] 4.3) Second PCR: Tag PCR

[0511] Tag PCR was performed to connect the unique dual indice (UDI) from Illumina and the sequencing adapter. The PCR tag reaction was performed in a 50 μL reaction containing: 5 μL of DNA purified in the first PCR cleanup, 25 μL of KAPA HiFi HotStart Ready Mix (2x) (#7958935001, Roche), 10 μL of the unique dual-end tag from Illumina, and 10 μL of nuclease-free water. The SimpliAmpTM thermal cycler from Applied BiosystemsTM was used.

[0512] 4.4) Second PCR cleanup

[0513] Perform a second PCR cleanup using AMPure XP beads to purify the final library before quantification. Purify 50 μL of the second PCR reaction following the steps described in Illumina's "16S Metagenomic Sequencing Library Preparation Protocol". Use AMPure XP beads at a ratio of 1.12x and perform a final elution in 27.5 μL of buffer EB and recover 25 μL from the beads.

[0514] 4.5) Library quantification, normalization and pooling

[0515] Use the Libraries were quantified using a fluorescent quantification method using a dsDNA binding dye using a 3.0 fluorometer. Quantification was performed using the QubitTM dsDNA HS Assay Kit according to the manufacturer's instructions. After obtaining the Qubit quantification value in ng / μL, the DNA concentration in nM was calculated based on the size of the DNA amplicon determined by the Agilent Technologies 2100 Bioanalyzer trace using the following formula:

[0516] (concentration in ng / μL) / (660 g / mol × average library size) × 10 6 = concentration in nM

[0517] For normalization, the final library was diluted to 10 nM using Buffer EB, and a final 4 nM amplicon library pool was prepared in a 20 μL final volume of Buffer EB.

[0518] In parallel with the 10 nM PhiX library (#FC-110-3001, Illumina), a 4 nM PhiX library dilution was prepared in a final volume of 5 μL of Buffer EB (a minimum of 5% PhiX must be included in each run to serve as an internal control for these low diversity libraries).

[0519] To prepare for cluster generation and sequencing, the pooled amplicon library was denatured with NaOH and diluted with HT1 buffer as follows: 5 μL of 4 nM amplicon library and 5 μL of 0.2 N NaOH (freshly prepared) were introduced into a microcentrifuge tube, mixed briefly using a vortex, and centrifuged at 280 x g for 1 minute at 20°C. Incubate at room temperature for 5 minutes to denature the DNA, and add 990 μL of pre-cooled HT1 buffer to 10 μL of the denatured DNA. Finally, the HT1 result was added to 20 pM of the denatured amplicon library in 1 mM NaOH. The denatured DNA was placed on ice until the final dilution was performed.

[0520] The same steps were repeated for 5 μL of 4 nM PhiX library to denature and dilute PhiX, resulting in 20 pM of PhiX-denatured library.

[0521] 210 μL of 20 pM denatured amplicon library was mixed with 390 μL of pre-cooled HT1 buffer to prepare 7 pM denatured amplicon library in a final volume of 600 μL. Afterwards, 300 μL of 20 pM denatured PhiX library was mixed with 300 μL of pre-cooled HT1 buffer to prepare 10 pM denatured PhiX library in a final volume of 600 μL.

[0522] Finally, the 25% denatured PhiX library and the 75% denatured Amplicon library were combined into a final volume of 600 μL (150 μL discarded from the 7 pM denatured Amplicon library and replaced with 150 μL of the 10 pM denatured PhiX library).

[0523] Place the combined amplicon library and PhiX control on ice until ready for heat denaturation. Perform the heat denaturation step immediately before loading the library into the MiSeq reagent cartridge to ensure efficient loading of template on the MiSeq flow cell.

[0524] Using a heat block, incubate the combined library and PhiX control tubes at 96 °C for 2 minutes. After incubation, invert the tubes 1-2 times to mix and immediately place on ice. Keep the tubes on ice for 5 minutes.

[0525] 5) Template loading and run setup on the MiSeq instrument

[0526] Sequencing on a MiSeq instrument using paired 300-bp reads was prepared along with the MiSeq reagent kit v3 (#MS-102-3003, Illumina).

[0527] When the Illumina v3 reagent cartridge was completely thawed and ready to use, the prepared libraries were loaded onto the cartridge and the run was set up on the MiSeq instrument following the manufacturer's instructions.

[0528] 6) Calculation of mitochondrial methylation percentage

[0529] The percentage (%) of methylation at each cytosine site was calculated by the beta value (β). The beta value is the ratio of the methylated reads at each site to the sum of the methylated and unmethylated reads at each site; that is:

[0530] β i =M / (M+U)

[0531] Where M is the number of methylated reads in site (i), and U is the number of unmethylated reads in the same site (i).

[0532] The β value ranges between 0 and 1, with 0 indicating complete unmethylation and 1 indicating complete methylation. The methylation percentage (%) of each site is calculated by multiplying the β value by * 100 is obtained; that is:

[0533] % i =β i *100

[0534] 7) Differential methylation analysis

[0535] To identify differentially methylated sites (DMS, also referred to as DMLs of differentially methylated loci), a standard pipeline consisting of the following five main steps was performed:

[0536] 1. Quality control of raw data: Quality assessment of reads generated by Illumina equipment during the sequencing process.

[0537] 2. Raw data preprocessing: Prepare the data for the alignment process by filtering, cutting and trimming the raw reads according to different criteria.

[0538] 3. Alignment process: Map the preprocessed reads to the reference genome.

[0539] 4. Methylation calling (quantitative): Identification of the cytosine positions in each context (ie, CpG, CHG, and CHH) and the number of methylated and unmethylated reads per site, region, and sample.

[0540] 5. Pile-up methylation profiles: Compile and build the methylation, unmethylation, and percentage matrices to be analyzed (where rows are samples and columns are identified positions), and perform quality control on the reported metrics.

[0541] Based on the mitochondrial methylation percentage matrix (referred to as methylation measure), two data analyses were performed: (1) exploratory data analysis, and (2) identification of DMS.

[0542] 1) Exploratory data analysis (EDA) aims to describe the distribution of measurements at the sample and position levels for each phenotypic group, background and zone. To this end, two different figures are reported. The box plot of the data (also called box plot, box plot or box and whisker plot) graphically describes the distribution of the methylation percentage of different samples at each position in its quartiles in the comparison of DLB ​​and CTL samples. CTL samples are represented in black, while DLB samples are represented in gray. In the box plot, the middle box is the interquartile range and represents the data of the middle 50%. In each box, the median (middle quartile) of the middle point of the marked data is represented and the median is represented by a line that divides the box into two parts. The lower whisker represents quartile 25, and the upper whisker represents quartile 75, and the whisker represents the maximum and minimum values ​​at the end. The global median is also represented by a horizontal gray line and is the global median of all existing values ​​in each group. In addition, the global mean in the CTL group and the DLB group is also represented by a horizontal gray dotted line, which shows the overall mean of the methylation percentage. The second type of graph, the mean and confidence interval (95%) graph, represents the mean and confidence interval (95%) of the methylation percentage for all positions of each subject in CTL and DLB. The mean is represented by a black circle in CTL and by a gray circle in DLB, and the confidence interval is represented by a vertical line. The global median in the CTL group and the DLB group is also represented by a horizontal gray line, which shows the global median of all existing values ​​in each group of samples. The global mean in the CTL group and the DLB group is also represented by a horizontal gray dashed line, which shows the overall mean of the methylation percentage.

[0543] 2) Identification of differentially methylated sites (DMS): The DSS (Dispersion Shrinkage of Sequencing Data) Bioconductor software package was used to perform an analysis to compare the methylation levels between each group (i.e., subjects with DLB and controls) at each methylation site. This software package is designed to identify differentially methylated loci / sites (DML / DMS) on bisulfite sequencing (BS-seq) data. The core of DSS is a procedure based on a Bayesian hierarchical model to estimate and shrink cytosine site-specific background dispersion, and then perform a Wald test on the β-binomial distribution to detect differential methylation. In addition, for conventional experimental designs, DSS is based on a β-binomial regression model taking into account the inverse sine link function, and the model is fitted to the transformed data using generalized least squares.

[0544] The multiple testing problem was solved by correcting the Benjamini-Hochberg false discovery rate (FDR), that is, the p-value was corrected using the FDR method. Other methods can be used to correct the p-value, such as the multiple comparison fallacy (Family Wise Error Rate, FWER). Cytosine site-specific backgrounds with a corrected p-value (i.e., FDR) less than 0.05 were identified as differentially methylated. The model sets a single main effect. In addition, in the case of technical bias due to batch effects or some technical variability, these unnecessary effects will also be taken into account in the model.

[0545] 1.2 Results

[0546] 1.2.1 Optimization of mtDNA methylation detection

[0547] The primers used herein (mentioned in Section 4 - Amplicon Library Preparation) are designed for optimized detection of mtDNA methylation. In order to avoid the bias caused by the general assumption in the art that most non-CpG cytosines are unmethylated, the inventors used primers that include the minimum number of cytosines. In addition, the primers are degenerate to cover all possible methylated and unmethylated situations, thereby solving the problem caused by the uncertainty of C / U conversion, which may affect the small amount of cytosine residues included in the sequence. The degeneracy includes a mixture containing oligonucleotide sequences, which generally contains several possible nucleotide bases at each specific position, thereby significantly increasing the possibility of detecting mitochondrial methylation.

[0548] In summary, the forward degenerate primer includes a Y at any position, which refers to C or T (Y=C / T), where the reference sequence is C. In addition, as is known in the art, the reverse primer does not correspond to the reference sequence, but to the reverse complement of the reference sequence. Therefore, the reverse primer does not include the C site of the reference sequence, but includes their complementary G site. Consistently, the reverse degenerate primer includes an R at any position, which refers to G or A (R=A / G), where the reference sequence is C, or where the complementary sequence includes G. As a result, the four degenerate primers are:

[0549] D-Ring Area:

[0550] Forward primer: YAYTTGGGGGTAGYTAAAGTGAAYTG (SEQ ID NO: 1)

[0551] Reverse primer: TCCTACAARCATTAATTAATTAACACAC (SEQ ID NO: 2)

[0552] ND1 gene:

[0553] Forward primer: ATAAAAYTTAAAAYTTTAYAGTYAGAG (SEQ ID NO: 3)

[0554] Reverse primer: TTRARTTTRATRCTCACCCTRATCA (SEQ ID NO: 4)

[0555] 1.2.2 Mitochondrial DNA methylation patterns

[0556] The methylation levels of two different regions (ie, ND1 gene and D-loop region) were compared between DLB subjects (N=18) and control subjects (N=18) in three different contexts: CpG, CHG, and CHH.

[0557] Regarding the ND1 gene, the results of all three background CpGs, CHG, and CHH in this paper are presented in box plots, corresponding to Figure 1 , Figure 3 and Figures 5A-5D As shown in Table 7, no significant differences in the distribution of methylation profiles were found in any of the three backgrounds of the ND1 gene when comparing control samples and samples from DLB subjects (corrected p-value < 0.05). Therefore, mean values ​​and confidence intervals (95%) plots, corresponding to the representation of the mean methylation percentage for each patient, are shown in the table below. Figure 2 , Figure 4 and Figure 6 There were no significant differences between DLB and CTL subjects in any of the three background CpGs shown, CHG and CHH (corrected p-value < 0.05). However, it is noteworthy that when the statistical significance threshold was set at an adjusted p-value < 0.1, significant differences were found in all CHH positions of the ND1 gene. Therefore, the methylation pattern of the CHH sites in the ND1 region is indeed different between patients with DLB and controls.

[0558] On the other hand, when the methylation levels in the D-loop region were compared across the three contexts, highly significant differences were observed. Surprisingly, in all three contexts (i.e., CpG, CHG, and CHH), DLB subjects showed highly significant hypomethylation (corrected p-value < 0.05) compared to control subjects, as shown in Table 1. Figure 7 , Fig. 9 , Fig.11A -B and Table 8. In addition, the mean and confidence interval (95%) plots also showed that there were strong and significant statistical differences in the mean percentage of methylation between DLB and control subjects in all three contexts (i.e., CpG, CHG, CHH) ( Figure 8 , Fig.10 and Fig.12). Notably, as shown in Table 8, the corrected p-values ​​for these differences were significantly below the threshold for statistical significance set at a corrected p-value < 0.05, with the highest corrected p-value being only 1.04E-11. In summary, these results clearly support the presence of a specific mitochondrial epigenetic signature in the D-loop of the mitochondrial regulatory region that characterizes DLB subjects and can therefore be considered a promising biomarker for DLB.

[0559] Table 7. Corrected p-values ​​for CpG, CHG and CHH methylation sites in the ND1 gene.

[0560]

[0561]

[0562] Table 8. Corrected p-values ​​for CpG, CHG and CHH methylation sites in the D-loop region.

[0563]

[0564] Example 2: Development of a classification model

[0565] The classification model of the present invention is built based on two main types of information sources: (1) clinical variables and (2) in-house generated methylation measures, as described in Example 1.

[0566] The construction procedure is carried out using supervised learning methods. The procedure includes four main steps: (1) exploratory data analysis (EDA), (2) preprocessing data, (3) model training process and (4) performance evaluation of the model, as described below:

[0567] 1) Exploratory Data Analysis (EDA)

[0568] EDA aims to describe all variables examined in the analysis. For categorical variables, frequency tables and bar graphs are generated, and for continuous variables, centrality measures, dispersion measures, and symmetry measures are estimated. In addition, for each continuous variable, histograms and violin plots are provided.

[0569] 2) Preprocessing Data

[0570] This step is performed to ensure and enhance the performance of the model training process. Preprocessing data consists of the following six steps:

[0571] 1. Use the one-hot encoding method to create dummy variables to ensure that there is no linear correlation between the new attributes, thereby avoiding the dummy variable trap.

[0572] 2. Remove zero variance and near-zero variance variables to avoid instability issues or model crashes during the fitting process.

[0573] 3. Split the data into two separate datasets: training dataset and test dataset. The original rectified data is randomly split into two main subsets: one subset is used to perform model training (80% of samples), and the other subset is used to validate the classification model (20% of samples). A random sampling process is performed in each class to maintain the overall class distribution of the data. The percentage depends on the results of EDA.

[0574] 4. Center and scale both datasets. The continuous features from the training dataset are used to estimate the centering and scaling factors, which are then applied to both datasets to generate normalized datasets for performing the training and testing process of the classification model.

[0575] 5. Identify and remove related variables, together with related factors. In addition, perform paired correlation analysis based on Pearson's correlation coefficient to help reduce the correlation level between variables. For those pairs showing high-level absolute correlation values, the variable with the maximum average absolute correlation is removed from the data set. On the other hand, a generalized linear model (GLM) is applicable to each variable to determine and control potential confounding factors. In addition, an unsupervised multivariate approach is performed to study the relationship between individuals, which is described by a set of quantitative and qualitative variables and structured in clinical and molecular data sets. Therefore, the variables included in the model are identified.

[0576] 6. Inspect and visualize the training dataset to double-check and check any potential bias from the original data.

[0577] 3) Model training

[0578] Nine kinds of supervised learning methods are used to set up classification model.These methods are linear discriminant analysis (LDA), classification and regression tree (CART), k-nearest neighbor (kNN), naive Bayes (NB), support vector machine (SVM) with linear kernel, random forest (RF), neural network (NNET), generalized boosted regression model (GBM) and binomial logistic regression (GLM).These methods are selected as supervised classification methods because they can process both continuous data and categorical data.In addition, the importance measure of considering these methods helps to identify the key variables that can be modified and used for further analysis.Alternative algorithm methods can be considered to develop the model training process.All selected methods use k-fold cross validation to run, and the total number of parameter combinations evaluated, the iteration time of the back propagation algorithm and other types of hyperparameters are mediated and modified in the tuning process.

[0579] 4) Performance evaluation of classification models

[0580] To measure the performance of the trained model, the following metrics were estimated:

[0581] -Accuracy : The overall agreement rate averaged over the cross-validation iterations.

[0582] - κ : Cohen's unweighted Kappastatistic averaged over the resampled results.

[0583] For each of these statistics, the mean, median, minimum, maximum, and first and third quartiles were evaluated.

[0584] In order to measure the performance of the classification prediction of the trained model, a confusion matrix was established to display the cross-tabulation of the observed and predicted categories. The table is accompanied by accuracy and kappa statistics, as well as common metrics for evaluating the model. Sensitivity, specificity, positive predictive value, negative predictive value, precision, prevalence, F1 score, detection rate, and detection prevalence. In addition, ROC curves were generated to measure the performance of the model for classifying subjects with DLB and CTL subjects.

[0585] Example 3: mtDNA methylation patterns with additional samples and development of classification model 2

[0586] 3.1 Mitochondrial DNA methylation patterns

[0587] A total of 84 samples were analyzed according to the method described in Example 1.

[0588] Methylation levels of two different regions (ie, ND1 gene and D-loop region) were compared between DLB subjects (N=42) and control subjects (N=42) in three different contexts: CpG, CHG, and CHH.

[0589] Figure 13-18 The results are shown in Tables 9-10. About the ND1 gene, the results of all three background CpGs, CHG and CHH are represented in this article with box plots. When comparing control samples and samples from DLB subjects, significant differences in the distribution of methylation spectra were found under all three backgrounds of the ND1 gene (corrected p-values ​​< 0.05). Therefore, mean and confidence interval (95%) graphs, corresponding to the representation of the average methylation percentage of each patient, are shown in all three background CpGs, CHG and CHH, and there are significant differences between DLB and CTL subjects (corrected p-values ​​< 0.05).

[0590] On the other hand, when comparing the methylation levels in the D-loop region under three backgrounds, very significant differences were observed. Compared with control subjects, DLB subjects showed very significant differences in methylation under all three backgrounds (i.e., CpG, CHG and CHH) (corrected p value < 0.05). In addition, mean and confidence interval (95%) graphs also show that there are strong and significant statistical differences in the average percentage of methylation between DLB and control subjects under all three backgrounds (i.e., CpG, CHG, CHH). It is worth noting that the corrected p values ​​of these differences are significantly below the statistical significance threshold set when the corrected p value < 0.05. In short, these results clearly support the presence of specific mitochondrial epigenetic features in the mitochondrial regulatory region D-loop and ND1 gene, which characterize DLB subjects and can therefore be considered as promising biomarkers for DLB.

[0591] Table 9. Corrected p-values ​​for CpG, CHG and CHH methylation sites in the ND1 gene.

[0592]

[0593]

[0594] Table 10. Corrected p-values ​​for CpG, CHG and CHH methylation sites in the D-loop region.

[0595]

[0596]

[0597] 3.2. Development of classification model.

[0598] A prototype model for classifying subjects diagnosed with DLB was developed.

[0599] Materials and Methods

[0600] 1) Target data

[0601] The raw data were checked to ensure the performance of the modeling process. Therefore, subjects with missing values ​​were excluded from the analysis. Thus, a total of 84 subjects were recruited from:

[0602] ADmit cohort (15 subjects): Hospital Universitari deBellvitge, Barcelona-Spain;

[0603] AIBL cohort (39 subjects): Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing, Australia;

[0604] CITA cohort (3 subjects): Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Spain;

[0605] Hospital Clínic cohort (3 subjects): Hospital Clínic de Barcelona, ​​Spain; and

[0606] MAP-AD cohort (24 subjects): from Hospital Universitari de Bellvitge, Hospital Clínic de Barcelona, ​​Hospital General de l'Hospitalet, and Hospital de Sant Joan DespíMoisès Broggi, Barcelona, ​​Spain.

[0607] In this regard, two groups of individuals were considered:

[0608] - Control (CTL): 42 (50.0%) subjects, of which:

[0609] o Clinical Dementia Rating (CDR) score of 0 and

[0610] o Clinical follow-up over 10 years

[0611] -DLB: 42 (50.0%) subjects, of whom:

[0612] oDiagnosis of DLB.

[0613] Control subjects were only from the AIBL and CITA cohorts, and patients were from the ADmit, Clinical Hospital, and MAP-AD cohorts. Two main types of information sources were considered for the analysis; (1) clinical variables and (2) internally generated methylation measures. Clinical variables were used to describe the data considered for the analysis, that is, only methylation measures were involved in the construction of the classification model.

[0614] 2) Clinical variables

[0615] In the first prototype, 3 clinical variables were examined:

[0616] • Stage (response variable): Consists of the three levels of Control and DLB as above.

[0617] Gender: Indicates two gender levels: female and male.

[0618] Age: The patient's age when they first visited the clinic.

[0619] 3) Methylation measurement

[0620] 224 variables, as described in Example 1, each variable aggregates the methylation percentage of a single specific cytosine site for each gene (ie, D-loop and ND1) in one of three contexts (ie, CpG, CHG, and CHH).

[0621] 4) Methods

[0622] A workflow divided into four main steps was implemented, namely:

[0623] - Exploratory data analysis,

[0624] - Data preprocessing,

[0625] -Simulation training process and

[0626] - Evaluate performance.

[0627] 5) Exploratory Data Analysis

[0628] Exploratory data analysis (EDA) was designed to describe each individual variable examined in the analysis. For categorical variables, frequency tables and bar graphs were generated. For continuous variables, centrality measures (mean, median, and mode, if applicable), dispersion measures (standard deviation, standard error, median absolute deviation, and range), and symmetry measures (skewness and kurtosi) were estimated. In addition, for each continuous variable, histograms and violin plots were provided.

[0629] 6) Data preprocessing

[0630] This step is performed to ensure and enhance the performance of the model training process. It consists of splitting the data into training and testing datasets, centering and scaling both datasets, identifying appropriate feature variables, and inspecting and visualizing the training dataset.

[0631] 1. Data Splitting: The original rectified data was randomly split into major subsets: one subset was used to perform model training (80% of the samples), and another subset was used to test the classification model (20% of the samples). A random sampling process was performed in each class to maintain the overall class distribution of the data.

[0632] 2. Centering and Scaling: The continuous features from the training dataset are used to estimate the centering and scaling factors which are applied to both datasets to generate a normalized dataset for performing the training and testing process of the classification model.

[0633] 3. Identification and removal of correlated variables: In order to benefit from reducing the level of correlation between variables, pairwise correlation analysis based on Spearman rank correlation coefficient was performed. For those pairs showing high levels of absolute correlation values ​​(>0.65), the variables with the largest average absolute correlation were removed from the dataset. In addition, principal component analysis (PCA) was applied to reduce the dimensionality of the features. In this case, the first 10 variables were considered.

[0634] 4. Check and visualize the training dataset: After applying the previous pre-processing tasks, a second EDA is applied on the training dataset in order to double-check and check that there is no bias from the original data.

[0635] 7) Model training

[0636] Ten supervised learning methods were selected to build the prototype. These methods are:

[0637] GLMNET: Generalized Linear Models (Logistic Regression) based on Penalized Maximum Likelihood

[0638] LDA: Linear Discriminant Analysis

[0639] PMR: Penalized Polynomial Regression

[0640] AdaBoost: AdaBoost classification tree

[0641] CART: Classification and Regression Trees

[0642] kNN: k-Nearest Neighbor

[0643] NB: Naive Bayes

[0644] SVM: Support vector machine with a linear kernel.

[0645] RF: Random Forest

[0646] NNET: Neural Network

[0647] They were chosen for three main reasons:

[0648] 1. These methods are supervised classification methods

[0649] 2. They are able to handle both continuous and categorical data

[0650] 3. Importance measures may help identify key variables that can be modified and used in additional analyses.

[0651] All methods were run using 3 repeated 10-fold cross validation. The total number of parameter combinations evaluated in SVM, RF and NNET was 5. NNET was evaluated using the back propagation method with 1000 iterations.

[0652] 8) Performance evaluation of classification models

[0653] To measure the performance of the trained models, the following metrics were evaluated:

[0654] -Accuracy : The overall agreement rate averaged over the cross-validation iterations.

[0655] - κ : Cohen's unweighted kappa statistic averaged over the resampled results.

[0656] For each of these statistics: mean, median, minimum, maximum, and first and third quartiles were evaluated. To measure the performance of the classification prediction, a confusion matrix was constructed to display the cross-tabulation of the observed and predicted categories. The table is accompanied by the accuracy and kappa statistics, as well as the common metrics used to evaluate the model, sensitivity, specificity, positive predictive value, negative predictive value, accuracy, prevalence, F1 score, detection rate, and -detection prevalence. In addition, to measure the performance of the model in classifying DLB patients from control patients, a ROC curve was constructed.

[0657] Table 11. Summary description of clinical variables

[0658] Stage / Gender F N CTL 17(40.48％) 25(59.52％) DLB 18(42.86％) 24(57.14％) total 24(57.14％) 49(58.33％)

[0659]

[0660] Count: number of individuals; 1sr Qu: first quartile; 3rd Qu: third quartile; IQR: interquartile range; St Dev: standard deviation

[0661] result

[0662] Exploratory Data Analysis

[0663] In brief, according to the EDA results, gender showed a balanced number of subjects, 35 (41.67%) females, and 49 (58.33%) males. The gender percentage of each stage group remained balanced, with 17 (40.48%) females and 25 (59.52%) males in the control group and 18 (42.86%) females and 24 (57.14%) males in the DLB group. In addition, the age of the individuals recruited was 72.01 ± 5.56 years old. The age of the subjects in each stage was 70.57 ± 5.4 years old in the control group and 73.45 ± 5.4 years old in the DLB group.

[0664] Preprocessing Data

[0665] The data split generated a first subset of 69 subjects for the training model process and a second subset of 16 individuals for testing the classification model. More than 2900 pairs of variables were identified as having an absolute correlation higher than 0.65. This fact led to the removal of more than 40% of the quantitative variables from the data, leaving 132 potential predictors. After applying the correlation method, PCA was applied to reduce the dimensionality of the variables.

[0666] Model Training

[0667] The results of the training process show that the best performance seems to be the RF model with an average accuracy of 0.83 and a κ value of 0.65. These values ​​indicate that this is a good model for the classification of DLB ​​patients. Tables 12 and 13 show the accuracy and κ metric of the trained models that have been achieved. Fig.19 A summary of these tables is shown.

[0668] Table 12. Accuracy metrics of trained supervised learning models. NA's: not available.

[0669]

[0670] Table 13. κ measures for trained supervised learning models. NA's: not available.

[0671]

[0672]

[0673] Performance evaluation of the model

[0674] The accuracy of classification of the test data for the RF model was 0.81, with a 95% confidence interval of 0.54 to 0.96, and a κ value of 0.63. The sensitivity and specificity of the model for classifying subjects as DLB were 0.75 and 0.875, respectively. The precision of the model was 0.86, and the F1 score was 0.8. Fig. 20ROC curves showing the performance of the classification model for DLB patients at all classification thresholds are presented.

[0675] In conclusion, the current prototype of the classification model appears to perform well in identifying patients with DLB. However, it requires a re-examination with a larger sample size to improve the tuning process during model training. This fact may lead to slight modifications in the estimates provided in this report and the related metrics used to evaluate classification performance.

[0676] References

[0677] Non-patent literature:

[0678] De Boni,L.,Tierling,S.,Roeber,S.,Walter,J.,Giese,A.,&Kretzschmar,HA(2011).Next-generation sequencing reveals regional differences of theα-synuclein methylation state independent of Lewy body disease.NeuroMolecularMedicine,13(4),310-320.

[0679] Desplats,P.,Spencer,B.,Coffee,E.,Patel,P.,Michael,S.,Patrick,C.,Adame,A.,Rockenstein,E.,&Masliah,E.(2011).α-synuclein sequesters Dnmt1 from the nucleus:A novel mechanism for epigenetic alterations in Lewy bodydiseases.Journal of Biological Chemistry,286(11),9031-9037.

[0680] Funahashi,Y.,Yoshino,Y.,Yamazaki,K.,Mori,Y.,Mori,T.,Ozaki,Y.,Sao,T.,Ochi,S.,Iga,J.I.,&Ueno,S.I.(2017).DNA methylation changes at SNCA intron 1inpatients with dementia with Lewy bodies.Psychiatry and ClinicalNeurosciences,71(1),28-35.

[0681] Urbizu,A.,&Beyer,K.(2020).Epigenetics in lewy body diseases:Impact ongene expression,utility as a biomarker,and possibilities fortherapy.International Journal of Molecular Sciences,21(13),1-31.

[0682] Chouliaras,L.,Kumar,G.S.,Thomas,A.J.,Lunnon,K.,Chinnery,P.F.,&O’Brien,J.T.(2020).Epigenetic regulation in the pathophysiology of Lewy bodydementia.Progress in Neurobiology,192.

[0683] Fernández,AF,Assenov,Y.,Martin-Subero,JI,Balint,B.,Siebert,R.,Taniguchi,H.,Yamamoto,H.,Hidalgo,M.,Tan,AC,Galm,O.,Ferrer,I .,Sanchez-Cespedes,M.,Villanueva,A.,Jarmona,J.,Sanchez-Muth,JV,Berdasco,M.,Moreno,V.,Capella,G.,Monk,D.,Esteller,M.(2012).A. DNA methylation fingerprint of1628human samples.Genome Research,22(2),407-419.

[0684] Sanchez-Muth,JV,Hein,H.,Vidal,E.,Moran,S.,Sayols,S.,Delgado-Morales,R.,Schultz,MD,Ansoleaga,B.,Garcia-Esparcia,P.,Pons-Espinal,M.,De Lagran,MM,Dopazo,J.,Rabano,A.,Avila,J.,Dierssen,M.,Lott,I.,Ferrer,I.,Ecker,JR,&Esteller,M.(2016).Human DNA methylomes of neurodegenerative diseases showcommon epigenomic patterns.Translational Psychiatry,Psychiatry,8(18–18).

[0685] Nasamran,C.A.,Sachan,A.N.S.,Mott,J.,Kuras,Y.I.,Scherzer,C.R.,Ricciardelli,E.,Jepsen,K.,Edland,S.D.,Fisch,K.M.,&Desplats,P.(2020).Differential blood DNA methylation across lewy body dementias.Alzheimer’sandDementia:Diagnosis,Assessment and Disease Monitoring,13(1),1-12.

[0686] Blanch,M.,Mosquera,JL.,Ansoleaga,B.,Ferrer,I.,Barrachina,M.(2016).Altered Mitochondrial DNA methylation pattern in Alzheimer Disease-relatedpathology and in Parkinson disease,The American Journal of Pathology,186(2):385-97.

[0687] Stoccoro,A.,Siciliano,G.,Migliore,L.,Coppedè,F.(2017).Decreasedmethylation of the mitocondrial D-Loop region in late-onset Alzheimer'sdisease,Journal of Alzheimer's Disease,59(2):559-564.

[0688] Stoccoro,A.,Baldacci,F.,Coppedè,F.,Migliore,L.(2022)Mitochondrial DNAmethylation levels are altered in individuals with mild cognitive impairment,Abstract OO016 / #1584On-demand symposium:AD diagnosis&clinical trials&advancesin drug development 1.

[0689] Patent Literature:

[0690] WO2015 / 144964A2。

Claims

1. A method for identifying Lewy body dementia in a subject, the method comprising: (a) in a sample of the subject comprising mitochondrial DNA, determining the methylation pattern in the D-loop region and / or the ND1 gene of the mitochondrial DNA; wherein the methylation pattern is determined in at least one site selected from the group consisting of: (i) CpG sites in the D-loop region shown in Table 1, (ii) a CHG site in the D-loop region shown in Table 3, (iii) a CHH site in the D-loop region shown in Table 5, (iv) the CpG sites of the ND1 gene shown in Table 2, and (v) the CHG site of the ND1 gene shown in Table 4, And wherein the methylation pattern is determined in at least one of the CHH sites of the ND1 gene shown in (vi) Table 6.

2. The method according to claim 1, wherein: The methylation pattern was determined in all CHH sites of the ND1 gene shown in Table 6.

3. The method according to any one of claims 1 to 2, wherein: The methylation patterns were determined at all CHG sites in the D-loop region genes shown in Table 3.

4. The method according to any one of claims 1 to 3, wherein: Hypomethylation in at least one of the CpG sites in the D-loop region, wherein hypomethylation in at least one of the CHG sites in the D-loop region and / or wherein hypomethylation in at least one of the CHH sites in the D-loop region indicates that the subject suffers from Lewy body dementia.

5. The method according to any one of claims 1 to 4, wherein: The methylation patterns were determined in all CpG, CHG and CHH sites in the D-loop region shown in Tables 1, 3 and 5.

6. The method according to any one of claims 1 to 5, wherein: The methylation pattern is determined using at least one oligonucleotide capable of specifically hybridizing to a mitochondrial DNA sequence comprising at least one methylation site selected from the group consisting of (i)-(vi), wherein the oligonucleotide has a length of 15 to 100 nucleotides and is capable of specifically hybridizing to: a mitochondrial DNA sequence comprising nucleotides 16,465 to 230 of the NCBI reference sequence: NC_012920.1 corresponding to the D-loop region and / or a mitochondrial DNA sequence comprising nucleotides 3,257 to 3,682 of the NCBI reference sequence: NC_012920.1 corresponding to the ND1 gene.

7. The method according to claim 6, wherein: The oligonucleotide is a degenerate oligonucleotide.

8. The method according to claim 7, wherein: The at least one oligonucleotide is selected from the group consisting of SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3 and SEQ ID NO:

4.

9. The method according to any one of claims 1 to 8, wherein: The methylation pattern was determined by bisulfite sequencing.

10. The method according to any one of claims 1 to 9, wherein: The method further comprises: (b) combining the methylation pattern of the one or more sites determined in step (a), optionally with at least one clinical variable of the subject selected from the group consisting of: a demographic variable, a neuropsychological variable, a clinical observation variable, and a clinical test variable, wherein said combining is performed using a classification model for determining a score associated with identifying Lewy body dementia in said subject.

11. The method according to claim 10, wherein: The at least one clinical variable of the subject is selected from the group consisting of: sex, age, race, education, family history, praxis test, Luria test, Clinical Dementia Rating Scale, Global Decline Scale, Mini-Mental State Examination, Clinical Dementia Rating Total Scale, neuroleptic intolerance, REM sleep behavior disorder, autonomic dysfunction, Parkinson's syndrome, visual hallucinations, cognitive fluctuations, magnetic resonance imaging, dopamine transporter scanning, 18F-fluorodeoxyglucose positron emission tomography, amyloid positron emission tomography, apolipoprotein E genotype, aIE4, beta-42, tau-T and tau-P.

12. The method according to claim 10, wherein: Changes in the score over time correlate with disease progression.

13. The method according to any one of claims 10 to 12, wherein: The classification model was developed using supervised machine learning methods.

14. The method according to any one of claims 1 to 13, wherein: The sample is a biological fluid selected from the group consisting of blood, plasma, saliva, cerebrospinal fluid, brain sample, skin sample and urine.

15. A computer-implemented method for identifying DLB in a subject, the method comprising: (a) receiving data related to a methylation pattern in the D-loop region and / or the ND1 gene of the mitochondrial DNA of the subject, wherein the methylation pattern is determined in at least one site selected from the group consisting of: (i) CpG sites in the D-loop region shown in Table 1, (ii) a CHG site in the D-loop region shown in Table 3, (iii) a CHH site in the D-loop region shown in Table 5, (iv) the CpG sites of the ND1 gene shown in Table 2, and (v) the CHG site of the ND1 gene shown in Table 4, and wherein the methylation pattern is determined in at least one of the CHH sites of the ND1 gene shown in (vi) Table 6, and optionally, at least one clinical variable of the subject, and (b) determining a risk score associated with DLB in the identified subject, wherein the risk score is calculated using a classification model configured to combine the methylation pattern of the one or more sites in step (a) and optionally at least one clinical variable of the subject.

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