Biomarker combination for predicting Parkinson's disease classification and progression and its use

Abstract

The present invention provides a biomarker combination for predicting the classification and progression of Parkinson's disease and uses thereof, wherein the biomarker combination comprises a single nucleotide polymorphism (SNP) selected from: rs6808178, rs591323 or a combination of the two, and the reference genome used by the SNP is the GRCh37 / hg19 human reference genome. The biomarker combination of the present invention and uses thereof can predict the clinical classification of Parkinson's disease and the progression of motor function and cognitive decline.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and specifically relates to a biomarker combination for predicting Parkinson's disease typing and progression and a use thereof. Background Art

[0002] Parkinson's disease (PD) is a complex neurodegenerative disease with a global prevalence of more than 6 million people. The prevalence of PD is about 1-2‰ in all age groups, and its prevalence is more than 1.7% in people over 60 years old. The typical clinical features of PD are symptoms such as bradykinesia, resting tremor and muscle rigidity caused by the degeneration of dopaminergic neurons in the substantia nigrostriatum system. The above symptoms are collectively referred to as motor symptoms. In recent years, people have realized that in addition to a wide range of motor symptoms, PD can also have non-motor symptoms, including: hyposmia, constipation, cognitive impairment, mental disorders, sleep disorders, etc. Both motor symptoms and non-motor symptoms have an important impact on the quality of life of patients, increasing the economic burden on families and society. There are also significant differences in the clinical manifestations and progression of PD. Some patients progress faster and progress to the loss of self-care ability within 5 years, while some patients progress more slowly and it takes more than 20 years to reach the same state of loss of life ability as patients with rapid progression. The disease progression of each patient varies greatly. Disease manifestations and disease progression rate are the main factors related to prognosis and quality of life. Therefore, determining the factors that affect PD clinical subtypes and the progression of motor symptoms and cognitive function is particularly important for predicting the prognosis of PD patients and discovering new PD treatments.

[0003] The Parkinson's Progression Markers Initiative (PPMI) is an international, observational, multicenter clinical cohort study that aims to study and identify biomarkers that can be used to assess PD and influence PD progression through advanced imaging, biospecimens, and clinical behavioral assessments. In addition, PPMI provides genetic information for each patient. Using the PPMI database to clarify the classification of PD subtypes will help design clinical trials that include PD interventions in the future and avoid potential confounding and misjudgment of subsequent progression. In addition, identifying these subtypes and associated genetic variants will help understand the pathophysiology of the disease, discover biomarkers of disease progression, and develop personalized treatment options.

[0004] The impact of genetic factors on PD has been widely recognized. Genome-wide association studies (GWAS) in 2017 and 2019 identified more than 40 and 90 PD-related single nucleotide polymorphisms (SNPs), respectively. These 90 variants explain 16-36% of the genetic risk of PD. However, the correlation between these newly discovered SNPs and cognitive function and motor symptoms of PD has not yet been confirmed. At the same time, no studies have predicted the clinical classification of PD through SNPs, nor have any studies predicted the progression of motor symptoms and cognitive function, the two most prominent clinical manifestations of PD, through SNPs. Therefore, it is very important and urgent to develop a combination of SNPs as biomarkers to predict the clinical manifestations and progression of PD. Summary of the invention

[0005] The present invention provides a biomarker combination, use and kit for predicting Parkinson's disease classification and progression. The biomarker combination in the subject's nucleic acid sample is detected, and the subject's Parkinson's disease progression prediction value is calculated according to the allele effect value corresponding to the SNP in the biomarker combination, thereby predicting which subtype or rapid motor symptom progression type or rapid cognitive impairment progression type the subject belongs to.

[0006] In one aspect, the present invention provides a biomarker combination for predicting Parkinson's disease typing and progression, wherein the biomarker combination comprises a single nucleotide polymorphism (SNP) selected from: rs6808178, rs591323 or a combination of both, and the reference genome used by the SNP is the GRCh37 / hg19 human reference genome.

[0007] In some embodiments, the biomarker combination comprises at least 6 SNPs selected from Table 1.

[0008] In some preferred embodiments, the above 6 SNPs are rs115185635, rs11724635, rs591323, rs13294100, rs2251776 and rs12456492.

[0009] In some embodiments, the biomarker combination comprises at least 7 SNPs selected from Table 1.

[0010] In some preferred embodiments, the 7 SNPs are rs6808178, rs115185635, rs12497850, rs34311866, rs3793947, rs11060180 and rs9568188.

[0011] In some preferred embodiments, the above biomarker combination further comprises rs10797576, rs4784227, rs34043159 and rs11950533.

[0012] In some embodiments, the above biomarker combination comprises the 44 SNPs shown in Table 1.

[0013] In some embodiments, Parkinson's disease classification includes: subtype 1-severe, subtype 2-benign, subtype 3-intermediate, and / or subtype 4-progressive.

[0014] In some embodiments, Parkinson's disease progression comprises progression of cognitive impairment and / or progression of motor symptoms.

[0015] In another aspect, the present invention provides a reagent for detecting the biomarker combination according to any one of the above aspects.

[0016] In another aspect, the present invention provides use of a reagent for detecting any of the above-mentioned biomarker combinations in preparing a kit for predicting Parkinson's disease typing and progression or in constructing a model for predicting Parkinson's disease typing and progression.

[0017] In another aspect, the present invention provides a method for constructing a model for predicting Parkinson's disease classification and progression, the method comprising the following steps:

[0018] Step 1: Detecting the above biomarker combination for predicting Parkinson's disease typing and progression in the subject's nucleic acid sample and obtaining SNP genotype data corresponding to the above biomarker combination;

[0019] Step 2: Obtain the allele effect value B based on logistic regression. According to the allele effect value B in the SNP genotype data, calculate the subject's Logit (P1) value, Logit (P2) value, Logit (P3) value, Logit (P4) value, Logit (P5) value and / or Logit (P6) value according to the following formula:

[0020] Step 3: According to the Logit(P1) value, Logit(P2) value, Logit(P3) value, Logit(P4) value, Logit(P5) value, and Logit(P6) value, the subtype 1 prediction value P1, subtype 2 prediction value P2, subtype 3 prediction value P3, subtype 4 prediction value P4, rapid motor progression prediction value P5, and / or rapid cognitive progression prediction value P6 of the subject are calculated according to the following formula:

[0021] Among them, parameter m is the number of SNPs included in the biomarker combination, parameter Bi is the allele effect value B of the i-th SNP in the SNP genotype data.

[0022] In some embodiments, the subject's subtype 1 prediction value P1, subtype 2 prediction value P2, subtype 3 prediction value P3, and subtype 4 prediction value P4 are sorted according to size, and the subtype corresponding to the largest prediction value is the predicted subtype of the subject; and if the rapid motor progression prediction value P5>0.5, the subject's Parkinson's disease is predicted to progress to the rapid motor symptom progression type; if the rapid cognitive progression prediction value P6>0.5, the subject's Parkinson's disease is predicted to progress to the rapid cognitive impairment progression type.

[0023] In another aspect, the present invention provides a kit for predicting the classification and progression of Parkinson's disease, wherein the kit comprises reagents for detecting the biomarker combination of any of the above aspects.

[0024] In some embodiments, the kit further comprises a Parkinson's disease assessment scale.

[0025] In some preferred embodiments, the above-mentioned Parkinson's disease assessment scale is selected from one or more of the International Parkinson's Disease and Movement Disorder Association-Unified Parkinson's Disease Rating Scale MDS-UPDRS, Benton Line Direction Judgment Scale BJLO, Montreal Cognitive Assessment Scale MoCA, Semantic Verbal Fluency Test Scale SF, Digit Symbol Transformation Test Scale SDMT, Parkinson's Disease Autonomic Nerve Scale SCOPA-AUT and University of Pennsylvania Smell Identification Test Scale UPSIT.

[0026] The biomarker combination, use and kit for predicting Parkinson's disease typing and progression provided by the present invention detect the biomarker combination in the subject's nucleic acid sample and calculate the subject's Parkinson's disease progression prediction value according to the allele effect value corresponding to the SNP in the biomarker combination, so as to predict which subtype or rapid motor symptom progression type or rapid cognitive impairment progression type the subject belongs to, and provide a fast, convenient and accurate reference for professionals to conduct research and judgment in clinical applications, avoiding potential confusion and misjudgment of subsequent progression. In addition, identifying these subtypes and related gene variations helps to understand the pathophysiology of the disease and discover biomarkers of disease progression. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the specification and, together with the description, serve to explain the principles of the specification.

[0028] Figure 1The longitudinal score changes of various PD cognitive assessment scales during the 5-year follow-up are shown: (A) JLO score; (B) MoCA score; (C) SDMT score; (D) LNS score; (E) SF score; (F) Comparison of the five cognitive assessment scales, among which only the SDMT score decreased year by year, while the other four scales showed a fluctuating decrease during the 5-year follow-up.

[0029] Figure 2 The figure shows the predictive value of susceptibility genes and corresponding SNPs for different PD phenotypes. Among them, (A) is subtype 1-severe type, (B) is subtype 2-benign type, (C) is subtype 3-intermediate type, and (D) is subtype 4-progressive type. The AUCs of all 44 SNPs for the four subtypes are 0.838, 0.790, 0.840, and 0.778, respectively.

[0030] Figure 3 The results show the predictive value of susceptibility genes and corresponding SNPs for the progression of PD motor symptoms. The AUC of rs6808178, which is significantly associated with the progression of motor symptoms, is 0.614, and the AUC of 7 related SNPs is 0.728. All 44 SNPs can achieve the best predictive value, with an AUC of 0.813.

[0031] Figure 4 The results show the predictive value of susceptibility genes and corresponding SNPs for the progression of cognitive impairment in PD. The AUC of rs591323, which is significantly associated with the progression of cognitive impairment, is 0.604, and the AUC of the six related SNPs is 0.723. All 44 SNPs can achieve the best predictive value, with an AUC of 0.831. DETAILED DESCRIPTION

[0032] I. Definition

[0033] In the present invention, unless otherwise specified, the scientific and technical terms used herein have the meanings commonly understood by those skilled in the art. In addition, the protein and nucleic acid chemistry, molecular biology, cell and tissue culture, microbiology, immunology and laboratory operation procedures used herein are terms and routine procedures widely used in the corresponding fields. At the same time, in order to better understand the present invention, the definitions and explanations of the relevant terms are provided below.

[0034] Provided herein is a biomarker combination, use, and kit for predicting Parkinson's disease classification and progression. The biomarker combination in the subject's nucleic acid sample is detected, and the subject's Parkinson's disease classification and progression prediction value is calculated based on the allele effect value corresponding to the SNP in the biomarker combination, thereby predicting the subject as a specific subtype or a rapid motor symptom progression type or a rapid cognitive impairment progression type.

[0035] As used herein, the term "International Parkinson's Disease and Movement Disorder Society-Unified Parkinson's Disease Rating Scale MDS-UPDRS" consists of four subscales, including UPDRS-I, UPDRS-II, UPDRS-III, and UPDRS-IV. The scores of the four subscales are added together to calculate the total score. The higher the score, the more severe the patient's symptoms.

[0036] As used in this article, the term "Parkinson's Progression Markers Initiative (PPMI)" refers to an international observational multicenter clinical cohort study that mainly collects clinical data and biological specimens from patients at various medical sites in the United States and Europe for related research.

[0037] As used in this article, the term "Benton Judgement of Line Orientation (BJLO)" refers to a cognitive scale that mainly evaluates patients' visual space and orientation. There are 30 items in total, and the score is the total number of correct answers, which needs to be adjusted according to gender and age.

[0038] As used in this article, the term “Montreal Cognitive Assessment” (MoCA) is the most widely used cognitive function assessment scale with a full score of 30 points, which needs to be adjusted for educational length.

[0039] As used herein, the term “Semantic Fluency test (SF) is a cognitive assessment scale that examines language function.

[0040] As used herein, the term "Symbol Digit Modalities Test (SDMT)" refers to a test in which a subject makes as many symbol-digit associations as possible within 90 seconds, with a maximum score of 110.

[0041] As used herein, the term "Scales for Outcomes in Parkinson's disease - Autonomic (SCOPA-AUT)" is a scale that assesses six major autonomic nervous functions including gastrointestinal, urinary, cardiovascular, thermoregulatory, pupillary movement and sexual function.

[0042] As used herein, the term "the University of Pennsylvania Smell Identification test (UPSIT)" refers to a test for diagnosing olfactory dysfunction.

[0043] As used herein, the term “NeuroX” is an Illumina Infinium iSelect HD custom genotype matrix containing 274,000 functional exons and approximately 12,000 custom SNPs for PD research.

[0044] As used herein, the "development" or "progression" of a disease refers to the initial manifestation of the disease and / or the subsequent progression. The development of a disease can be detected and assessed using standard clinical techniques well known in the art. However, development also refers to progression that may not be detected. For purposes of the present invention, development or progression refers to the biological course of the symptom. "Development" includes occurrence, recurrence, and onset.

[0045] As used herein, the term "Genome wide association study (GWAS)" is to detect genetic variation (marker) polymorphisms of multiple individuals across the genome to obtain genotypes, and then conduct statistical analysis of genotypes and observable traits, i.e., phenotypes, at the population level, screen out the genetic variation (marker) that is most likely to affect the trait based on statistics or significant p-values, and mine genes related to trait variation. The term "allele effect value (B value)" used in this article, or the Beta value of SNP, is the regression coefficient corresponding to each SNP in the Logistic regression analysis.

[0046] As used herein, the term "SNP", i.e., single nucleotide polymorphism, refers to a nucleic acid sequence polymorphism caused by a single nucleotide change. Generally speaking, a SNP site has only two alleles, so it is also called a double allele. The frequency of SNP in the human genome is relatively high, with an average of about one polymorphic site per 1,000 bases.

[0047] As used herein, the term "human reference genome" generally refers to a human genome that can serve as a reference in gene sequencing. Information on the human reference genome can be referenced to UCSC. The human reference genome can have different versions, for example, hg19, hg38, GRCh37, GRCh38, GCA_000001405, GCF_000001405, or Ensembl 75.

[0048] As used herein, the term "subtype" refers to the clinical classification of Parkinson's disease. The present invention divides Parkinson's disease into 4 subtypes through data-driven cluster analysis: subtype 1-severe, subtype 2-benign, subtype 3-intermediate, and subtype 4-progressive. Subtype 1-severe has the most severe motor and non-motor symptoms at baseline, and the fastest cognitive decline, but the progression of motor symptoms is the most benign. Subtype 2-benign has the youngest age of onset at baseline, and the most benign motor and non-motor symptoms. Subtype 3-intermediate is an intermediate subtype with intermediate motor and non-motor symptom manifestations. Subtype 4-progressive patients have the oldest onset age, moderate motor and non-motor symptoms at baseline, but the fastest progression of motor symptoms.

[0049] II. Detailed description of specific implementation plan

[0050] In one aspect, the present invention provides a biomarker combination for predicting Parkinson's disease typing and progression, wherein the biomarker combination comprises a single nucleotide polymorphism (SNP) selected from: rs6808178, rs591323 or a combination of both, and the reference genome used by the SNP is the GRCh37 / hg19 human reference genome.

[0051] In some embodiments, the biomarker combination comprises at least 6 SNPs selected from Table 1.

[0052] In some preferred embodiments, the above 6 SNPs are rs115185635, rs11724635, rs591323, rs13294100, rs2251776 and rs12456492.

[0053] In some embodiments, the biomarker combination comprises at least 7 SNPs selected from Table 1.

[0054] In some preferred embodiments, the 7 SNPs are rs6808178, rs115185635, rs12497850, rs34311866, rs3793947, rs11060180 and rs9568188.

[0055] In some preferred embodiments, the above biomarker combination further comprises rs10797576, rs4784227, rs34043159 and rs11950533.

[0056] In some preferred embodiments, the above biomarker combination comprises the 44 SNPs shown in Table 1.

[0057] In some preferred embodiments, Parkinson's disease classification includes: subtype 1-severe type, subtype 2-benign type, subtype 3-intermediate type and / or subtype 4-progressive type.

[0058] In some preferred embodiments, Parkinson's disease progression includes progression of cognitive impairment and / or progression of motor symptoms.

[0059] In some embodiments, the Parkinson's disease progression corresponding to the above 6 SNPs is the progression of cognitive impairment.

[0060] In some embodiments, the Parkinson's disease progression corresponding to the above 7 SNPs is motor symptom progression.

[0061] In another aspect, the present invention provides a reagent for detecting the combination of biomarkers according to any one of the above aspects.

[0062] In another aspect, the present invention provides use of a reagent for detecting any of the above-mentioned biomarker combinations in preparing a kit for predicting Parkinson's disease typing and progression or in constructing a model for predicting Parkinson's disease typing and progression.

[0063] In another aspect, the present invention provides a method for constructing a model for predicting Parkinson's disease classification and progression, the method comprising the following steps:

[0064] Step 1: Detecting a biomarker combination for predicting Parkinson's disease typing and progression in any of the above aspects in a nucleic acid sample of a subject and obtaining SNP genotype data corresponding to the biomarker combination;

[0065] Step 2: Obtain the allele effect value B based on logistic regression. According to the allele effect value B in the SNP genotype data, calculate the subject's Logit (P1) value, Logit (P2) value, Logit (P3) value, Logit (P4) value, Logit (P5) value and / or Logit (P6) value according to the following formula:

[0066] Step 3: Based on the Logit(P1) value and / or the Logit(P2) value, the Logit(P3) value, the Logit(P4) value, the Logit(P5) value and / or the Logit(P6) value, calculate the subject's subtype 1 prediction value P1, subtype 2 prediction value P2, subtype 3 prediction value P3, subtype 4 prediction value P4, rapid motor progression prediction value P5 and / or rapid cognitive progression prediction value P6 according to the following formula:

[0067] Among them, the parameter m is the number of SNPs included in the biomarker combination, and the parameter Bi is the allele effect value (B value) of the i-th SNP in the SNP genotype data.

[0068] In some embodiments, the subject's subtype 1 prediction value P1, subtype 2 prediction value P2, subtype 3 prediction value P3, and subtype 4 prediction value P4 are sorted according to size, and the subtype corresponding to the largest prediction value is the predicted subtype of the subject; and if the rapid motor progression prediction value P5>0.5, the subject's Parkinson's disease is predicted to progress to the rapid motor symptom progression type; if the rapid cognitive progression prediction value P6>0.5, the subject's Parkinson's disease is predicted to progress to the rapid cognitive impairment progression type.

[0069] In another aspect, the present invention provides a kit for predicting the classification and progression of Parkinson's disease, wherein the kit comprises reagents for detecting the biomarker combination of any of the above aspects.

[0070] In some embodiments, the kit further comprises a Parkinson's disease assessment scale.

[0071] In some preferred embodiments, the Parkinson's disease assessment scale is selected from one or more of the International Parkinson's Disease and Movement Disorders Association-Unified Parkinson's Disease Rating Scale (MDS-UPDRS), Benton Line Direction Judgment Scale (BJLO), Montreal Cognitive Assessment Scale (MoCA), Semantic Verbal Fluency Test Scale (SF), Digit Symbol Transformation Test Scale (SDMT), Parkinson's Disease Autonomic Nerve Scale (SCOPA-AUT) and University of Pennsylvania Smell Identification Test Scale (UPSIT).

[0072] For purposes of clarity and concise description, features are described herein as part of the same or separate embodiments, however, it will be understood that the scope of the invention may include embodiments having a combination of all or some of the described features.

[0073] Example

[0074] 1. Research subjects

[0075] The data used in this study were obtained from the Parkinson's progression markers initiative (PPMI) database on January 10, 2022, and the use of the data complied with the PPMI data use agreement. The local institutional review boards of all participating sites approved the PPMI protocol before recruitment. Written informed consent was obtained from all study participants. The collected data were then de-identified and collated in the PPMI database, and all individuals who withdrew after data collection were excluded. This invention has been approved by the Michael J. Fox Foundation (MJFF) for the use of PPMI data. The PPMI data included in this study were taken from January 2011 to December 2021.

[0076] Patients recruited must have at least two or more PD motor symptoms to be included, namely: resting tremor, bradykinesia, and myotonia (one of which must have resting tremor or bradykinesia). They must have been diagnosed with PD within 2 years of the screening visit. Patients must have dopamine transport defects confirmed by imaging, be over 30 years old, and not take anti-PD medications within 6 months of the baseline assessment visit. Patients were excluded from the study if they had taken PD medications including levodopa, dopamine agonists, monoamine oxidase-B inhibitors, etc. for more than 60 days before the baseline visit.

[0077] 2. Baseline and Clinical Assessment

[0078] The PPMI database records the assessment results of the motor, cognitive and non-motor symptoms of the study participants. The present invention specifically uses the following data:

[0079] (1) Demographics: age of onset, gender, disease duration, education level, etc.

[0080] (2) PD-specific clinical assessment: International Parkinson's Disease and Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Parts I, II, and III.

[0081] (3) Cognitive function: Benton Judgement of Line Orientation (BJLO), Montreal Cognitive Assessment (MoCA), Semantic Fluency Test (SF), Symbol Digit Modalities Test (SDMT).

[0082] (4) Other non-motor manifestations: Scales for Outcomes in Parkinson's disease – Autonomic (SCOPA-AUT), the University of Pennsylvania Smell Identification test (UPSIT).

[0083] In PPMI, the clinical manifestations of patients are evaluated every year, so as to conduct a comprehensive clinical evaluation of the progression of the disease. Currently, all Parkinson's disease drugs are symptom-relieving drugs and cannot change the development process of Parkinson's disease. The implementation of clinical symptom evaluation indicators is evaluated when the influence of drugs is negligible. Therefore, the deterioration of clinical evaluation indicators reflects the progression of the disease. The present invention uses the baseline and annual data during the 5-year follow-up period to evaluate the patient's disease progression.

[0084] Clinical division of rapid motor disorder and rapid cognitive impairment progression: Based on the division experience of previous related studies1,2 and the overall distribution of SDMT z score and UPDRS-III z score, the top 15% of the population with an increase in UPDRS-III score and a decrease in SDMT score during the 5-year follow-up were defined as rapid motor symptom progression type (in the PPMI population, the 5-year UPDRS-III score increased by at least 22 points) and rapid cognitive impairment progression type (in the PPMI population, the 5-year SDMT score increased by at least 6 points).

[0085] 3. Genetic Data Collection

[0086] The 44 PD risk SNPs collected according to PPMI were selected as target SNPs ( Table 1 ).

[0087] Table 1 Target SNPs included in the study

[0088]

[0089] 4. Subtype classification method

[0090] K-means cluster analysis was used for classification. The variables used for classification included: (1) age of onset; (2) gender; (3) MDS-UPDRS-I score; (4) MDS-UPDRS-III score. Individuals were assigned to clusters with the greatest similarity using Euclidean distance. The optimal number of clusters was determined using the elbow method. The selection criteria for classification were: (1) there was no significant difference in disease duration between different subtypes; and (2) cluster convergence could be achieved within the minimum number of iterations.

[0091] 5. Statistical methods

[0092] The present invention uses SPSS 26, MedCalc, and CGTA for statistical analysis. Principal component analysis (PCA) is used to exclude outliers. The chi-square test is used for inter-group comparison. Repeated measures analysis of variance is used to evaluate the cognitive scale that best expresses PD cognitive progress. In order to evaluate the genetic association between rapid movement and cognitive progress, the Logistic regression model is used to calculate the odds ratio (OR). The course of disease, gender, family history, and age of onset are covariates. Receiver operating curve (ROC) analysis is used to evaluate the predictive value of 44 SNPs for movement and cognitive progress, and the area under the curve (AUC) is used to evaluate the prediction model. The Youden index is used to find the optimal cutoff point and its sensitivity and specificity. The Bonferroni method is used for multiple correction (p<0.0011).

[0093] A total of 365 patients with early PD were included in this study, including 241 males (66%) and 124 females (34%), with a median onset age of 61 years. In the fifth year, the scores of all five cognitive scales (JLO score, MoCA score, SDMT score, LNS score, and SF score) of PD patients declined. Among them, SDMT, LNS, and JLO declined significantly within 5 years ( p <0.001). Only the SDMT score decreased year by year, while the other four scales showed a fluctuating decrease during the 5-year follow-up ( Figure 1). Therefore, the SDMT scale was selected for longitudinal cognitive function evaluation (i.e., the same scale of the same patient was followed up over time to collect annual score changes). The results were divided into four subtypes: subtype 1-severe (18.5%), subtype 2-benign (23.1%), subtype 3-intermediate (20.7%), and subtype 4-progressive (37.7%). There were significant differences in motor and non-motor clinical symptoms among the subtypes at baseline and longitudinal follow-up. The age of onset of subtypes 1 and 4 was the oldest (66.2, 67.2), the age of onset of subtype 2 was the youngest (48.6), and the age of onset of subtype 3 was intermediate (54.0). Subtype 1 had the most severe motor and non-motor symptoms at baseline and the fastest cognitive decline. However, subtype 1 had the most benign motor symptoms. Subtype 2 had the youngest age of onset at baseline and the most benign motor and non-motor symptoms. Subtype 3 was an intermediate subtype with intermediate motor and non-motor symptoms. Patients with subtype 4 had the oldest age of onset and moderate motor and non-motor symptoms at baseline, but the most rapid motor progression.

[0094] As shown in Table 2 , logistic regression analysis showed that 2 SNPs (rs10797576 and rs4784227) were associated with subtype 1, 1 SNP (rs34043159) was associated with subtype 2, 1 SNP (rs11950533) was associated with subtype 3, and no SNP was associated with subtype 4. However, these 4 SNPs (rs10797576, rs4784227, rs34043159, rs11950533) did not pass the multiple correction (p < 0.0011). Logistic regression analysis showed that 7 SNPs (rs6808178, rs115185635, rs12497850, rs34311866, rs3793947, rs11060180 and rs9568188) were associated with faster motor symptom progression (p<0.05), and only LINC00693 rs6808178 passed multiple correction (p<0.0011). The Cox regression model also showed that LINC00693rs6808178 was associated with faster motor symptom progression (p=0.0004, <0.0011). Logistic regression analysis showed that six SNPs (rs115185635, rs11724635, rs591323, rs13294100, rs2251776, and rs12456492) were associated with faster progression of cognitive impairment (p<0.05), and only FGF20 rs591323 passed multiple correction (p<0.0011).

[0095] Table 2 SNPs significantly associated with PD progression and their corresponding gene loci

[0096]

[0097] The predictive value of genetic information based on 44 SNPs for the progression of PD motor symptoms and cognitive function was calculated according to the following process:

[0098] Step 1: According to the following formula, as shown in Table 3, the applied Logit (P) is obtained according to the Beta value (B value) corresponding to each genotype:

[0099]

[0100] Step 2: Calculate the predicted value P1 of subtype 1, the predicted value P2 of subtype 2, the predicted value P3 of subtype 3, the predicted value P4 of subtype 4, the predicted value P5 of rapid motor progression, and the predicted value P6 of rapid cognitive progression for each patient according to the following formula:

[0101] The parameter m is the number of SNPs included in the biomarker combination. In this embodiment, the value of m is 44. i is the allele effect value (B value) of the i-th SNP in the SNP genotype data.

[0102] Among them, the subtype corresponding to the maximum values ​​of P1, P2, P3, and P4 is the predicted subtype corresponding to the patient; if the rapid motor progression prediction value P1>0.5, the patient's Parkinson's disease is predicted to progress to the rapid motor symptom progression type; if the rapid cognitive progression prediction value P2>0.5, the patient's Parkinson's disease is predicted to progress to the rapid cognitive impairment progression type.

[0103] A total of 44 SNPs together constitute the maximum prediction value of the area under the curve (AUC), and the AUCs for predicting the four subtypes are 0.838, 0.790, 0.840, and 0.778, respectively. Figure 2 ), the AUCs for predicting rapid motor symptom progression and rapid cognitive impairment progression phenotypes were 0.813 and 0.831, respectively ( Figure 3 , Figure 4 ).

[0104] Table 3 B values ​​corresponding to each genotype calculated for clinical classification, rapid motor and cognitive progress prediction

[0105] .

Claims

1. A combination of biomarkers for predicting Parkinson's disease classification and progression, including: The biomarker combination consists of the following: rs6808178, rs591323, rs115185635, rs11724635, rs13294100, rs2251776, rs12497850, rs34311866, rs3793947, rs11060180, rs823118, rs10797576, rs46 53767, rs6430538, rs34043159, rs353116, rs12456492, rs9568188, rs12637471, rs407 3221, rs6812193, rs356182, rs78738012, rs11950533, rs2694528, rs12528068, rs9973 The single nucleotide polymorphism SNPs of rs68, rs9468199, rs199347, rs2280104, rs10748818, rs10906923, rs329648, rs11610045, rs76904798, rs4771268, rs12147950, rs11158026, rs8005172, rs2414739, rs14235, rs11343, rs4784227, rs17649553, and rs2248244 are used, and the reference genome used by the SNPs is the GRCh37 / hg19 human reference genome; the Parkinson's disease classification includes: subtype 1-severe type, subtype 2-benign type, subtype 3-intermediate type and subtype 4-progressive type; the progression includes progression of cognitive impairment and progression of motor symptoms.

2. A reagent for detecting the biomarker combination according to claim 1.

3. Use of the reagent according to claim 2 in preparing a kit for predicting the classification and progression of Parkinson's disease or in constructing a model for predicting the classification and progression of Parkinson's disease.

4. Method for constructing a model to predict Parkinson's disease classification and progression, including: The construction method comprises the following steps: Step 1: Detecting the biomarker combination as claimed in claim 1 in the nucleic acid sample of the subject and obtaining SNP genotype data corresponding to the biomarker combination; Step 2: Obtain the allele effect value B based on logistic regression, and calculate the Logit (P1) value, Logit (P2) value, Logit (P3) value, Logit (P4) value, Logit (P5) value and Logit (P6) value of the subject according to the following formula according to the allele effect value B in the SNP genotype data: Step 3: According to the Logit (P1) value, the Logit (P2) value, the Logit (P3) value, the Logit (P4) value, the Logit (P5) value and the Logit (P6) value, the subtype 1 prediction value P1, the subtype 2 prediction value P2, the subtype 3 prediction value P3, the subtype 4 prediction value P4, the rapid motor progression prediction value P5 and the rapid cognitive progression prediction value P6 of the subject are calculated according to the following formula: Among them, parameter m is the number of SNPs contained in the biomarker combination, parameter B i is the allele effect value B of the ith SNP in the SNP genotype data.

5. The construction method according to claim 4, wherein: The predicted value P1 of subtype 1, the predicted value P2 of subtype 2, the predicted value P3 of subtype 3, and the predicted value P4 of subtype 4 of the subject are sorted according to size, and the subtype corresponding to the largest predicted value is the predicted subtype of the subject; and, If the rapid motor progression prediction value P5>0.5, it is predicted that the subject's Parkinson's disease will progress to the rapid motor symptom progression type; if the rapid cognitive progression prediction value P6>0.5, it is predicted that the subject's Parkinson's disease will progress to the rapid cognitive impairment progression type.

6. A kit for predicting the classification and progression of Parkinson's disease, wherein: The kit comprises the reagents for detecting the biomarker combination of claim 1 as described in claim 2.

7. The kit according to claim 6, wherein The kit also includes a Parkinson's disease assessment scale.

8. The kit according to claim 7, wherein The Parkinson's disease assessment scale is selected from one or more of the International Parkinson's Disease and Movement Disorder Association-Unified Parkinson's Disease Rating Scale MDS-UPDRS, Benton Line Direction Judgment Scale BJLO, Montreal Cognitive Assessment Scale MoCA, Semantic Verbal Fluency Test Scale SF, Digit Symbol Transformation Test Scale SDMT, Parkinson's Disease Autonomic Nerve Scale SCOPA-AUT and University of Pennsylvania Smell Identification Test Scale UPSIT.

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