Composition for predicting risk of dementia onset
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2026-02-10
- Publication Date
- 2026-08-13
Smart Images
Figure KR2026002485_13082026_PF_FP_ABST
Abstract
Description
Composition for predicting the risk of developing dementia
[0001] The present invention relates to a composition for predicting the risk of developing dementia.
[0002] Advancements in modern medicine have led to a rapid increase in average life expectancy, resulting in a surge in the elderly population. However, healthy life expectancy—that is, the period of health without illness—is not increasing at the same pace as average life expectancy, and various age-related diseases are appearing in the majority of the elderly population. Among these various age-related diseases, degenerative brain diseases such as dementia are on the rise, and based on this trend, this is causing serious health problems not only in Korea but also globally, including in developed countries.
[0003] In particular, dementia is a social disease that incurs high national healthcare costs, as it not only has mental and economic impacts on the patient and their family members but also incurs high management costs per patient, especially for terminal dementia patients who require 24-hour care. Therefore, it is expected that failure to detect and control it early could pose a significant threat to the national health insurance system.
[0004] A common challenge in treating the majority of dementia patients is that by the time symptoms appear, it is often too late for treatment. In fact, the accumulation of amyloid beta protein or the modification of tau protein begins as early as 10 years before cognitive impairment occurs. Since the brain cell death that occurs as dementia progresses is irreversible damage and currently approved treatments only manage to slow the progression, the best approach at present is to diagnose individuals at risk of dementia early and slow its progression.
[0005] In particular, since predicting and identifying high-risk groups in advance during the preclinical stage of dementia and delaying the onset of the disease through drug administration or lifestyle interventions can significantly reduce management costs for patients with terminal dementia, research to discover biomarkers capable of screening high-risk groups in advance is expected to be important.
[0006] The present invention aims to provide a biomarker composition capable of early diagnosis of the risk of developing dementia and a method for providing information using the same.
[0007] The present invention relates to a biomarker composition for diagnosing the risk of developing dementia and a method for providing information using the same.
[0008] The biomarker composition of the present invention comprises one or more strains selected from the group consisting of Parvimonas micra, Prevotella baroniae, Lachnoanaerobaculum saburreum, Campylobacter rectus, Prevotella nigrescens, Streptococcus pneumoniae, Filifactor alocis, and Abiotrophia defectiva, or a substance for detecting substances derived from such strains.
[0009] The biomarker composition of the present invention may further include one or more strains selected from the group consisting of Treponema denticola and Porphyromonas gingivalis as additional biomarkers.
[0010] In the biomarker composition of the present invention, the strain may have a correlation with clinical dementia diagnostic stages consisting of a normal control group (HC), an amyloid-positive normal group (CN), mild cognitive impairment (MCI), and Alzheimer's dementia (AD).
[0011] In the biomarker composition of the present invention, the strain may have a correlation with the state of amyloid beta accumulation in the brain.
[0012] In the biomarker composition of the present invention, the strain may have a correlation with the rate of decrease in hippocampal volume or the rate of decrease in entorhinal cortex thickness measured through time-series tracking of brain MRI (Magnetic Resonance Imaging) images.
[0013] In the biomarker composition of the present invention, when the relative abundance of the strain in the oral cavity increases compared to a normal control group, the rate of volume reduction of the hippocampus or the rate of thickness reduction of the non-endocortex may be accelerated.
[0014] In the biomarker composition of the present invention, the substance for detecting the strain may be a strain-specific primer, probe, antibody, or selective medium.
[0015] The present invention relates to a method for providing information necessary for diagnosing the risk of developing dementia, comprising the steps of: measuring the presence or abundance of the strains in an oral sample taken from a subject; comparing the measured abundance with a control sample to determine whether there is an increase or decrease; and providing information to predict that the risk of developing dementia in the subject is relatively higher than that of the control group based on the confirmed increase or decrease result.
[0016] The present invention can diagnose the risk of developing dementia early by measuring the abundance of oral microbial strains.
[0017] The present invention can easily predict the risk of developing dementia through non-invasive oral sample collection.
[0018] The present invention is useful for predicting the prognosis of dementia by providing a biomarker associated with the rate of progression of brain atrophy.
[0019] Fig. 1. Results of analyzing the patterns of volume change of the hippocampus and thickness change of the entorhinal cortex over time based on long-term follow-up of brain MRI images.
[0020] Figure 2. Results of a comparative analysis of the degree of brain atrophy calculated based on the measurement of the rate of brain volume reduction according to MMRI time series observation and the abundance of the oral microbial strain Treponema denticola.
[0021] Figure 3. An association analysis between the abundance of oral microbial strains and the degree of brain atrophy in the hippocampus and entorhinal cortex was confirmed based on Microbiome Multivariable Association with Linear Models 2 (MaAsLin2) statistical analysis, and the effect size (coefficients) were determined (15% prevalence, coefficient greater than 0.15, q-value < 0.05).
[0022] The present invention will be described in detail below.
[0023] The present invention relates to a biomarker composition for diagnosing the risk of developing dementia and a method for providing information using the same.
[0024] The biomarker composition of the present invention comprises one or more strains selected from the group consisting of Parvimonas micra, Prevotella baroniae, Lachnoanaerobaculum saburreum, Campylobacter rectus, Prevotella nigrescens, Streptococcus pneumoniae, Filifactor alocis, and Abiotrophia defectiva, or a substance for detecting substances derived from such strains.
[0025] The aforementioned strains are oral microbial strains that exhibit a characteristic of increased relative abundance compared to the control group in groups with a high risk of developing dementia.
[0026] Fabimonas microcra is a Gram-positive anaerobic coccus associated with periodontal disease in the oral cavity.
[0027] Prevotella baronia is a Gram-negative anaerobic bacillus and is part of the normal bacterial flora in the oral cavity.
[0028] Lachnoaerobiculum saburéum is a Gram-positive anaerobic bacillus that is a microorganism inhabiting the oral cavity.
[0029] Campylobacter rectus is a Gram-negative anaerobic spiral bacterium associated with periodontal disease.
[0030] Prevotella nigrecens is a Gram-negative anaerobic bacillus that inhabits the oral cavity and gastrointestinal tract.
[0031] Streptococcus pneumoniae is a Gram-positive facultative anaerobic diplococcus and is a major causative agent of respiratory infections.
[0032] Filipactor alocysis is a Gram-positive anaerobic bacillus and is a microorganism associated with periodontitis.
[0033] Aviotropia defectiva is a Gram-positive facultative anaerobic coccus and is a type of nutrient-deficient streptococcus.
[0034] The biomarker composition of the present invention may further include one or more strains selected from the group consisting of Treponema denticola and Porphyromonas gingivalis as additional biomarkers.
[0035] Treponema denticola is a Gram-negative spiral anaerobic bacterium and is one of the major causative agents of periodontal disease.
[0036] Porphyromonas gingivalis is a Gram-negative anaerobic bacillus and is a major pathogen of chronic periodontitis.
[0037] The above strains may be correlated with the clinical dementia diagnostic stage.
[0038] Clinical dementia diagnostic stages can be classified, for example, into Healthy Control (HC), Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD).
[0039] The normal control group is a group with cognitive function within the normal range and negative amyloid beta accumulation in the brain.
[0040] The amyloid-positive normal group consists of individuals with normal cognitive function but confirmed positive for amyloid beta accumulation in the brain, corresponding to the preclinical stage of Alzheimer's disease.
[0041] Mild cognitive impairment is a stage where cognitive decline is more pronounced than in normal aging, but the ability to perform daily activities is maintained; it is a group at high risk of progression to dementia.
[0042] Alzheimer's dementia is a stage where the decline in cognitive function intensifies, causing difficulties in performing daily activities.
[0043] The relative abundance of the aforementioned strains in the oral cavity may show a tendency to increase stepwise as the condition progresses from a normal control group to an amyloid-positive normal group, mild cognitive impairment, and Alzheimer's dementia. This implies that changes in the abundance of oral microbial strains may reflect the stage of dementia progression.
[0044] The above strains may be correlated with the state of amyloid beta accumulation in the brain.
[0045] Amyloid beta is a protein that makes up amyloid plaques, a major pathological feature of Alzheimer's disease, and begins to accumulate in the brain about 10 years before cognitive impairment appears.
[0046] The state of amyloid beta accumulation can be evaluated, for example, through amyloid PET (Positron Emission Tomography) imaging, cerebrospinal fluid examination, or blood tests.
[0047] Amyloid PET imaging is, for example, 18 F-florbetaben, 18 F-florbetapir, 18 It is a method to visualize and quantify amyloid beta accumulation in the brain using radioactive tracers such as F-flutemetamol.
[0048] Cerebrospinal fluid examination is a method of collecting cerebrospinal fluid through a lumbar puncture and measuring the concentrations of Aβ42 and Aβ40, as well as the Aβ42 / Aβ40 ratio.
[0049] Blood tests are a method to indirectly assess the state of amyloid beta accumulation in the brain by measuring the Aβ42 / Aβ40 ratio in plasma.
[0050] The relative abundance of the aforementioned strains in the oral cavity may show an increasing tendency as the degree of amyloid beta accumulation increases. This suggests that changes in oral microbial strains are associated with amyloid beta pathology in the brain.
[0051] The above strains may be correlated with the rate of decrease in hippocampus volume or the rate of decrease in entorhinal cortex thickness measured through time-series tracking of brain MRI (Magnetic Resonance Imaging) images.
[0052] The hippocampus is a brain region that plays a crucial role in memory formation and learning, and it is the area where early atrophy is observed in dementia patients. The hippocampus is divided into the left and right hippocampuses, and the volume of each can be measured.
[0053] The non-endocortex is a brain region involved in memory and spatial cognition, and it is a key area where neurodegeneration begins in the early stages of Alzheimer's disease. The non-endocortex is divided into the left and right non-endocortex, and the thickness of each can be measured.
[0054] When the relative abundance of the aforementioned strains in the oral cavity increases compared to normal control groups, the rate of decrease in hippocampal volume or non-endocortical thickness may be accelerated. This implies that an increase in specific oral microorganisms is associated with the progression of brain atrophy.
[0055] The substance for detecting the strain may be, for example, the strain-specific primer, probe, antibody, or selective medium.
[0056] The primer may be an oligonucleotide that specifically binds to the 16S rRNA gene or species-specific gene sequence of the strain. The primer may consist of, for example, 15 to 30 bases.
[0057] The probe may be a labeled oligonucleotide that specifically binds to a specific gene sequence of the strain. The label may be, for example, a fluorescent substance, a radioisotope, an enzyme, etc.
[0058] The antibody may specifically bind to the cell wall or cell membrane proteins of the above strain. The antibody may be a monoclonal antibody or a polyclonal antibody.
[0059] The selective medium may be a culture medium containing a component that selectively promotes the growth of the strain or inhibits the growth of other microorganisms.
[0060] The strain-derived material may be, for example, the DNA, RNA, protein, metabolite, cell wall components, etc. of the strain.
[0061] DNA can be, for example, genomic DNA or 16S rRNA gene sequence.
[0062] RNA can be, for example, mRNA, rRNA, tRNA, etc.
[0063] Proteins can be, for example, cell wall proteins, cell membrane proteins, metabolic enzymes, toxic factors, etc.
[0064] Metabolites may be, for example, short-chain fatty acids, amines, sulfur compounds, etc.
[0065] Cell wall components may be, for example, peptidoglycan, lipopolysaccharide (LPS), teichoic acid, etc.
[0066]
[0067] The present invention provides a method comprising the steps of: measuring the presence or abundance of the strains in an oral sample taken from a subject; confirming whether the measured abundance has increased or decreased by comparing it with a control sample; and providing information to predict that the risk of developing dementia in the subject is relatively higher than that of the control group based on the confirmed increase or decrease result.
[0068] Oral samples may be, for example, saliva, gingival crevicular fluid, plaque, tongue surface swabs, etc.
[0069] Saliva is saliva secreted in the oral cavity and is a representative sample of the microbial community. Saliva can be, for example, non-stimulating saliva that is secreted naturally without stimulation, or stimulating saliva whose secretion is promoted by chewing gum, etc.
[0070] Gingival sulcus is a fluid secreted from the gingival sulcus between the teeth and gums, reflecting the inflammatory state of the periodontal tissues.
[0071] Dental plaque is an aggregate of microorganisms that forms on the surface of teeth and contains various oral microorganisms.
[0072] A tongue surface swab is a sample taken by rubbing the dorsal surface of the tongue with a cotton swab and represents the microorganisms inhabiting the tongue.
[0073] Oral samples can be collected using, for example, sterile cotton swabs, collection kits, or collection containers.
[0074] The collected oral samples can be stored frozen at, for example, -20°C to -80°C, or stored at room temperature or in a refrigerator in a DNA preservation solution.
[0075] The presence or abundance of strains can be measured through methods such as DNA sequencing, quantitative polymerase chain reaction (qPCR), fluorescence in situ hybridization (FISH), culture methods, and mass spectrometry.
[0076] DNA sequencing can be, for example, 16S rRNA gene amplicon sequencing or shotgun metagenome sequencing.
[0077] 16S rRNA gene amplicon sequencing is a method for analyzing the structure of microbial communities by amplifying and sequencing the 16S rRNA genes of bacteria. The 16S rRNA gene is a widely used gene for bacterial classification, and because it includes conserved and variable regions, classification at the genus or species level is possible.
[0078] Shotgun metagenome sequencing is a method that sequences the entire genome of all microorganisms within a sample, enabling species-level classification and functional gene analysis. Shotgun metagenome sequencing provides higher taxonomic resolution compared to 16S rRNA gene sequencing.
[0079] Quantitative polymerase chain reaction is a method for quantifying the absolute or relative amount of a strain using primers that target the genes of a specific strain. qPCR enables quantitative analysis by monitoring the amplification of PCR products in real time.
[0080] Fluorescence in situ hybridization is a method of directly observing and quantifying specific strains under a microscope using a fluorescently labeled probe.
[0081] The culture method is a technique for confirming the presence of a specific strain and counting the number of colonies by culturing the strain in a selective medium.
[0082] Mass spectrometry is a method for identifying bacterial strains by analyzing their proteins or metabolites using a mass spectrometer. For example, MALDI-TOF MS (Matrix-Assisted Laser Desorption / Ionization Time-Of-Flight Mass Spectrometry) can be used.
[0083] Abundance can be expressed, for example, as relative abundance or absolute abundance.
[0084] Relative abundance is the proportion of a specific strain within the entire microbial community, which can be expressed, for example, as a percentage (%). Relative abundance can be calculated by dividing the number of reads of a specific strain in metagenomic sequencing data by the total number of reads.
[0085] Absolute abundance is the number of cells or the amount of DNA of a specific strain per unit volume or weight of a sample, and can be expressed, for example, as CFU / mL (Colony Forming Units per milliliter) or gene copies / mL. Absolute abundance can be calculated by quantifying the number of gene copies of a specific strain through qPCR.
[0086] Control group samples may be oral samples taken from healthy individuals with normal cognitive function and no risk factors for dementia. The control group may be a group matched to the subject group in terms of, for example, age, gender, oral health status, and general health status.
[0087] The normal control group may be, for example, individuals who score within the normal range on cognitive function tests, have no amyloid beta accumulation, and have no family history of dementia.
[0088] Whether there is an increase or decrease can be determined, for example, through statistical tests. Statistical tests may include, for example, the t-test, Mann-Whitney U test, Wilcoxon signed-rank test, ANOVA, etc.
[0089] The t-test is a method for testing the difference in mean values between two groups and is used when data follows a normal distribution.
[0090] The Mann-Whitney U test is a non-parametric test method used to test the difference in medians between two groups when the data does not follow a normal distribution.
[0091] The Wilcoxon signed-rank test is a nonparametric method for testing the difference between paired samples.
[0092] ANOVA is a method for testing the difference in mean values among three or more groups.
[0093] Cases where the risk of developing dementia is predicted to be high may, for example, when the abundance of the above strains increases statistically significantly compared to the control group.
[0094] A significant increase can be identified, for example, at the level of p-value < 0.05, p-value < 0.01, or q-value < 0.05.
[0095] The q-value is the corrected significance probability after performing multiple comparison correction, and is used to control the False Discovery Rate (FDR).
[0096] Information may be provided, for example, in the form of a report containing measurement results, a risk score, or a visualized graph.
[0097] The report may include, for example, the types of detected strains, the abundance of each strain, comparison results with a control group, statistical significance, risk assessment, etc.
[0098] The risk score may be calculated, for example, through a multivariate statistical model. The multivariate statistical model may be a machine learning model such as logistic regression, random forest, support vector machine, or neural network.
[0099] Logistic regression is a probabilistic model that predicts the risk of dementia using the abundance of various strains as independent variables.
[0100] Random Forest is a machine learning method that performs classification or regression by ensembling multiple decision trees.
[0101] Support Vector Machines are a method that performs classification by finding the optimal decision boundary in a high-dimensional feature space.
[0102] Visualized graphs can be represented, for example, as bar graphs, heatmaps, box plots, scatter plots, etc.
[0103] The method of the present invention may be performed in combination with brain MRI image analysis results. Brain MRI image analysis may include, for example, hippocampal volume measurement, non-endocortical thickness measurement, total brain atrophy measurement, white matter lesion evaluation, etc.
[0104] Hippocampus volume measurement can be performed automatically using image analysis software such as FreeSurfer, FSL, and SPM, for example.
[0105] Non-endocortical thickness measurement can be performed, for example, by segmenting the non-endocortical region in T1-weighted MRI images and calculating the thickness.
[0106] The rate of brain atrophy can be calculated by measuring changes in brain structure over time through MRI scans at two or more points in time.
[0107] The method of the present invention can be performed in combination with the results of a cognitive function test. The cognitive function test may be, for example, the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Clinical Dementia Rating (CDR), and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog).
[0108] The MMSE is a simple cognitive function screening test that evaluates orientation, memory, attention, calculation ability, and language ability.
[0109] MoCA is a test that is more sensitive than MMSE in detecting mild cognitive impairment and additionally evaluates executive function, visuospatial ability, etc.
[0110] CDR is a tool for evaluating the severity of dementia, and it assesses six areas including memory, orientation, judgment, social activities, home life, and personal hygiene.
[0111] The method of the present invention can be performed in combination with the results of blood biomarker analysis. Blood biomarkers may be, for example, amyloid beta, tau protein, neurofilament light chain (NfL), GFAP (Glial Fibrillary Acidic Protein), etc.
[0112] Amyloid beta is a protein that constitutes amyloid plaques, a major pathological feature of Alzheimer's disease, and the Aβ42 / Aβ40 ratio in the blood is associated with the risk of dementia.
[0113] Tau protein is a protein that forms neurofibrillary tangles, and levels of phosphorylated tau (p-tau) in the blood are associated with the progression of dementia.
[0114] Neurofilament light chains are markers of nerve damage, and blood NfL levels correlate with brain atrophy and cognitive decline.
[0115] The method of the present invention can be performed in combination with an assessment of dementia risk factors. Dementia risk factors may be, for example, age, education level, APOE genotype, history of cardiovascular disease, diabetes, hypertension, smoking, alcohol consumption, physical activity level, etc.
[0116] The APOE ε4 allele is a major genetic risk factor for Alzheimer's disease, and individuals carrying APOE ε4 have an increased risk of developing dementia.
[0117] The method of the present invention can be utilized in the development of customized intervention programs for the prevention and management of dementia. Based on information regarding the abundance of oral microbial strains, intervention measures such as oral hygiene management, probiotic intake, and antibiotic treatment can be suggested.
[0118]
[0119] The present invention will be explained in more detail with reference to the following examples.
[0120]
[0121] Example 1. Profiling of microbial strain abundance in subjects at low and high risk of dementia
[0122] To determine the risk of developing dementia, DNA was extracted from saliva samples of 450 subjects and shotgun metagenomic sequencing was performed. The raw sequencing data was preprocessed to remove human-derived DNA information, and taxonomic profiling was performed based on the DNA sequencing reads of the remaining microbial strains. The abundance of oral microbial strains was calculated for 145 normal individuals, 154 amyloid-positive, cognitively unimpaired subjects, 112 subjects with mild cognitive impairment (MCI), and 39 Alzheimer's patients.
[0123]
[0124] Example 2. Classification of high-risk and low-risk dementia groups based on brain atrophy following brain MRI observation
[0125] To compare high-risk and low-risk groups for dementia, changes in the brain MRI of the subjects are measured at least twice as shown in Figure 1, and the degree of brain atrophy is calculated by determining the rate of volume reduction in the left and right hippocampus regions or the rate of thickness change in the left and right entorhinal cortex. Subsequently, the high-risk and low-risk groups for dementia are classified according to the degree of brain atrophy.
[0126]
[0127] Example 3. Confirmation of changes in the abundance of target microbial strains (e.g., Treponema denticola) in a high-risk group for dementia based on brain atrophy.
[0128] As shown in Figure 2, brain atrophy was identified based on the rate of decrease in brain volume in the left hippocampus region of the subjects, and the association with the abundance of Treponema denticola strains in the oral samples of the subjects was analyzed. It was confirmed that the abundance of Treponema denticola strains increased as the brain atrophy value (the more negative the value, the more severe the atrophy) increased.
[0129]
[0130] Example 4. Confirmation of statistical significance between the abundance of target oral microbial strains and the brain atrophy score based on the rate of volume or thickness reduction of dementia-associated regions in brain MRI.
[0131] In Figure 3, an association analysis was performed between the abundance of oral microbial strains and the degree of brain atrophy in the hippocampus and entorhinal cortex regions based on Microbiome Multivariable Association with Linear Models2 (MaAsLin2) statistical analysis, and the effect sizes (coefficients) were determined (15% prevalence, coefficient greater than 0.15, q-value < 0.05). As a result, at least one brain region associated with dementia showed a significant association with the abundance of one of the target microbial strains (q-value < 0.05).
Claims
1. A biomarker composition for diagnosing the risk of developing dementia, comprising one or more strains selected from the group consisting of Parvimonas micra, Prevotella baroniae, Lachnoanaerobaculum saburreum, Campylobacter rectus, Prevotella nigrescens, Streptococcus pneumoniae, Filifactor alocis, and Abiotrophia defectiva, or a substance for detecting such strains.
2. A biomarker composition for diagnosing the risk of developing dementia according to Claim 1, further comprising one or more strains selected from the group consisting of Treponema denticola and Porphyromonas gingivalis as additional biomarkers.
3. The biomarker composition for diagnosing the risk of developing dementia according to claim 1, wherein the strain has a correlation with clinical dementia diagnostic stages consisting of a normal control group (HC), an amyloid-positive normal group (CN), mild cognitive impairment (MCI), and Alzheimer's dementia (AD).
4. The biomarker composition for diagnosing the risk of developing dementia, wherein the strain is correlated with the state of amyloid beta accumulation in the brain.
5. A biomarker composition for diagnosing the risk of developing dementia, wherein the strain is correlated with the rate of volume reduction of the hippocampus or the rate of thickness reduction of the entorhinal cortex measured through time-series tracking of brain MRI (Magnetic Resonance Imaging) images.
6. A biomarker composition for diagnosing the risk of developing dementia, wherein, in claim 5, the rate of decrease in volume of the hippocampus or the rate of decrease in thickness of the non-endocortex is accelerated when the relative abundance of the strain in the oral cavity increases compared to a normal control group.
7. A biomarker composition for diagnosing the risk of developing dementia, wherein the substance detecting the strain is a strain-specific primer, probe, antibody, or selective medium according to Claim 1.
8. A method for providing information necessary for diagnosing the risk of developing dementia, comprising: a step of measuring the presence or abundance of the strains described in claim 1 in an oral sample taken from a subject; a step of confirming whether the measured abundance has increased or decreased by comparing it with a control sample; and a step of providing information for predicting that the risk of developing dementia in the subject is relatively higher than that of the control group based on the confirmed increase or decrease result.