Screening and genetic verification method of blood and urine biomarkers for effectively predicting onset risk of Parkinson's disease

Abstract

The invention discloses a method for screening and genetically verifying blood and urine biomarkers for effectively predicting the onset risk of Parkinson's disease. The method comprises the following steps: S1, screening a research sample; s2, screening an optimal hematuria biomarker based on an LASSO regularization method; s3, establishing a COX proportional risk regression model under multi-covariable adjustment; s4, carrying out enrichment analysis on genetic correlation and multiple-effect property; s5, analyzing the causal relationship between the fraction of C-reactive protein, insulin growth factor-1 and immature reticulocyte and PD through Mendel randomization, and comprehensively analyzing the relationship between blood and urine biomarkers at various baseline levels and the PD onset risk by utilizing a large longitudinal queue in an British biological sample library. Through genetic analysis and a Mendel randomization method, not only are associated evidences enhanced, but also a potential genetic mechanism is deeply discussed.

Description

Technical Field

[0001] The present application relates to the field of risk prediction for Parkinson's disease patients, and more specifically, to a method for screening and genetically verifying blood and urine biomarkers that effectively predict the risk of developing Parkinson's disease. Background Art

[0002] Parkinson's disease (PD) is a neurodegenerative disease that mainly affects the elderly. Its pathological features include the loss of dopaminergic neurons in the substantia nigra and the formation of Lewy bodies. This disease not only causes typical motor symptoms such as resting tremor, bradykinesia, muscle rigidity, and abnormal posture and gait, but is also accompanied by a variety of non-motor symptoms, such as constipation, olfactory disorders, sleep disorders, autonomic dysfunction, cognitive impairment, and psychiatric symptoms. As the disease progresses, these symptoms will gradually intensify, seriously affecting the patient's quality of life and daily function.

[0003] According to the 2019 Global Burden of Disease, Injury and Risk Factors Study (GBD), more than 8.5 million people worldwide suffer from Parkinson's disease. As the incidence of Parkinson's disease continues to grow, it gradually evolves from the early stage marked by non-motor symptoms (such as rapid eye movement sleep behavior disorder) to the late stage causing severe disability, and known risk factors can only explain part of the difference in disease risk. In-depth research on risk factors and biomarkers for Parkinson's disease is of vital importance to reveal the disease mechanism and develop strategies to prevent, treat or delay the progression of the disease.

[0004] Currently, a variety of biomarkers, including imaging techniques, cerebrospinal fluid analysis, oxidative stress markers, neuroprotective factors, and inflammatory indicators, can help with the early diagnosis of the disease. Although previous studies have mainly focused on the correlation between biomarkers in cerebrospinal fluid, blood, and urine and Parkinson's disease (PD), these methods still have significant shortcomings. Specifically, cerebrospinal fluid studies have focused on α-synuclein, and found that α-synuclein levels are lower in PD patients, indicating that α-synuclein seeding and expansion assays can distinguish PD patients from healthy controls. However, the invasiveness and high cost of collecting cerebrospinal fluid by lumbar puncture limit its widespread application in PD diagnosis. In contrast, blood and urine biomarkers have the potential for wider clinical application due to their non-invasiveness, low cost, and convenience for frequent monitoring. However, these studies are often limited to a few markers and involve small clinical cohorts. In addition, it is unclear whether the levels of these markers before diagnosis can effectively predict future PD risk.

[0005] To address these challenges, the present invention aims to identify biomarkers that can more effectively help clinically prevent, diagnose and treat PD. This study further clarifies the potential association and mechanism between these biomarkers and PD risk, providing a new perspective and scientific basis for the formulation of early diagnosis and treatment strategies. Summary of the invention

[0006] The main purpose of this application is to provide a method for screening and genetic verification of blood and urine biomarkers that effectively predict the risk of Parkinson's disease. Using a large longitudinal cohort in the UK Biobank, a comprehensive analysis of the relationship between multiple baseline levels of blood and urine biomarkers and the risk of PD was conducted. Through genetic analysis and Mendelian randomization methods, not only the evidence of these associations was strengthened, but also the potential genetic mechanisms were deeply explored.

[0007] In order to achieve the above objectives, in the first aspect of the present application, a method for screening and genetically verifying blood and urine biomarkers that effectively predict the risk of Parkinson's disease is proposed, comprising the following steps:

[0008] S1. Sample screening for inclusion in the study, using large longitudinal population cohort data from the UK Biobank, excluding non-British whites and participants with neurological diseases;

[0009] S2. The optimal blood and urine biomarkers were screened based on the LASSO regularization method. 67 biomarkers in blood and urine were included. By using LASSO regression and adjusting the covariates of sex, age and PD, 46 key biomarkers closely related to PD survival outcomes were screened.

[0010] S3. COX proportional hazard regression model under multiple covariate adjustment. Through multivariate COX proportional hazard regression analysis, all covariates and significant biomarkers were included to screen out 13 biomarkers that were highly independently associated with PD. The covariates included gender, age, smoking, drinking, physical activity level, education, diabetes, hypertension and stroke.

[0011] S4. Genetic correlation and pleiotropic enrichment analysis. The genetic correlation between 13 biomarkers and PD was estimated using linkage disequilibrium score regression and genetic covariance analysis. C-reactive protein and insulin growth factor-1 showed significant genetic correlation in both analysis methods. Pleiotropic enrichment was evaluated by stratified QQ plots and folded enrichment plots, and significant enrichment effects were found between C-reactive protein, insulin growth factor-1 and immature reticulocyte fraction and PD. Conditional false discovery rate analysis was used to identify pleiotropic sites associated with PD risk in three biomarkers, including MAPT, HLA-DRB1, HLA-DQA1 and SETD1A.

[0012] S5. The causal relationship between C-reactive protein, insulin growth factor-1, immature reticulocyte fraction and PD was analyzed by Mendelian randomization. It was found that C-reactive protein was negatively correlated with PD risk, insulin growth factor-1 was positively correlated, and immature reticulocyte fraction did not show a significant association.

[0013] Optionally, step S1 also includes data interpolation for missing data in the research sample.

[0014] Optionally, the data interpolation uses a chain equation method to perform multiple interpolation.

[0015] Optionally, the study sample included 392,280 participants, including 3,084 patients with Parkinson's disease.

[0016] Optionally, in step S3, a multivariate COX proportional hazards regression analysis is performed using stepwise adjustment of covariates from basic demographic and lifestyle factors to the impact of cardiovascular disease.

[0017] Optionally, in step S4, the stratified QQ plot analyzes the statistical association between the biomarkers PD, and stratifies according to p<0.1, p<0.01, p<0.001. If there is a significant difference in the degree of deviation between different layers, it indicates that there is a strong pleiotropic enrichment phenomenon between the two phenotypes.

[0018] Optionally, in step S4, the fold enrichment graph shows the fold enrichment of the -log10p value of the biomarker under PD conditions.

[0019] The technical solution provided by the embodiments of the present application may include the following beneficial effects: Previous clinical and epidemiological surveys provide evidence that there is a potential link between blood and urine biomarkers and the risk of Parkinson's disease. However, due to the types of biomarkers included and the limitations of the sample size of clinical studies, the results are often inconsistent. In addition, the participation of epidemic cases cannot determine whether the pre-diagnosis level of the biomarker will lead to the risk of future Parkinson's disease. In view of these challenges, we conducted a series of rigorous and comprehensive analyses based on a large prospective cohort of the UK Biobank, which currently has the largest sample size, to examine the association between the level of baseline blood and urine biomarkers and the subsequent risk of PD, and used genetics to deeply verify the associations found, using the large-scale data set of the UK Biobank, which significantly revealed the understanding of the pathogenesis of PD. We identified 13 biomarkers associated with PD, confirmed new biomarkers associated with PD such as the ratio of aspartate aminotransferase (AST) to alanine aminotransferase (ALT), IRF and phosphate, and verified markers such as IGF-1 and CRP whose previous results were inconsistent. Through a series of complex genetic analyses, the results showed that PD was significantly associated with multiple biomarkers, with a particular emphasis on the role of IGF-1 and CRP. We also identified four shared genetic loci associated with PD and these biomarkers, including well-known loci such as HLA-DRB1, HLA-DQA1, MAPT, and the less common SETD1A locus. Finally, the causal relationship between IGF-1 and CRP and PD was further confirmed using Mendelian randomization analysis, indicating that higher IGF-1 levels increase the risk of PD, while higher CRP levels reduce the risk. These findings provide us with a better understanding of the role of blood and urine biomarkers in the risk of PD and suggest the significance of targeting CRP and IGF-1 as potential treatment options in future clinical trials. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The schematic embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0021] Figure 1 is a flow chart of the present invention;

[0022] Figure 2 It is a layered QQ graph;

[0023] Figure 3 This is a fold enrichment plot.

[0024] Figure 4 To analyze the effects of C-reactive protein and IGF-1 on the risk of Parkinson's disease using Mendelian randomization;

[0025] Figure 5 It is the basic information of demographics and related covariates before and after imputation;

[0026] Figure 6 The results of the stepwise adjusted Cox proportional hazards regression model for Parkinson's disease risk prediction - from basic demographic and lifestyle factors to the effects of cardiovascular disease;

[0027] Figure 7 It is a pleiotropic locus shared between Parkinson's disease and biomarkers. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] In the present application, the terms "upper", "lower", "left", "right", "front", "back", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings. These terms are mainly used to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to have a specific orientation, or to be constructed and operated in a specific orientation.

[0031] In addition, some of the above terms may be used to express other meanings in addition to indicating orientation or positional relationship. For example, the term "on" may also be used to express a certain dependency or connection relationship in some cases. For those of ordinary skill in the art, the specific meanings of these terms in this application can be understood according to specific circumstances.

[0032] In addition, the terms "installed", "set", "provided with", "connected", "connected", and "socketed" should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be an internal connection between two devices, elements, or components. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0033] Parkinson's disease is a chronic progressive neurodegenerative disease. Early diagnosis helps patients receive relevant treatment, which is of great significance for delaying disease progression and improving patient prognosis.

[0034] like Figure 1 As shown, a method for screening and genetically validating blood and urine biomarkers that effectively predict the risk of Parkinson's disease includes:

[0035] 1. Screening of hematuria biomarkers

[0036] 1. Sample screening for inclusion in the study

[0037] This study used data from a large longitudinal population cohort in the UK Biobank for analysis. The UK Biobank is one of the largest population-based biobanks in the world. Since 2006, it has collected data from more than 500,000 participants aged 37 to 74 years and has conducted continuous follow-up. To reduce the interference of ethnicity and related diseases, the study excluded non-British whites and participants with neurological diseases, and ultimately included 392,280 participants, including 3,084 patients with Parkinson's disease (PD). The average follow-up time was 9.5 years. For missing data, we performed data interpolation and generated ten complete interpolated data sets for subsequent analysis ( Figure 5 ).

[0038] 2. Screening of optimal hematuria biomarkers based on LASSO regularization method

[0039] A total of 67 biomarkers in blood and urine were initially included. By combining LASSO regression with COX proportional hazard regression and adjusting for covariates highly correlated with PD, such as gender and age, 46 key biomarkers closely related to PD survival outcomes were screened out from high-dimensional data.

[0040] 3. COX proportional hazards regression model with multiple covariates adjusted

[0041] Our model gradually incorporated more stringent covariates to accurately estimate the relationship between specific biomarkers and the onset of PD and control potential confounding variables. The preliminary model took into account basic factors such as gender, age, smoking, drinking, physical activity level, and education. After adjustment, 22 significant biomarkers were identified from 46 biomarkers. After further adding covariates such as diabetes, hypertension, and stroke, the correlation of most biomarkers did not change significantly, but the association between total protein and PD was no longer significant. In the final model, all covariates and significant biomarkers were included through multivariate COX proportional hazard regression analysis, and 13 biomarkers that were highly independently associated with PD were screened out. These markers may become potential targets for the prevention and treatment of PD in the future ( Figure 6 Results were not significantly different in sensitivity analyses performed by sex and age and after excluding individuals with disease onset within three years of baseline measurement.

[0042] 2. Genetic Validation of Hematuria Biomarkers

[0043] 1. Genetic correlation and pleiotropic enrichment analysis

[0044] The genetic associations between 13 biomarkers and PD were estimated using linkage disequilibrium score regression (LDSC) and genetic analysis of covariance (GNOVA). Four biomarkers were significant in only one method, while C-reactive protein (CRP) and insulin growth factor-1 (IGF-1) showed significant genetic associations in both analysis methods. Pleiotropic enrichment was assessed by stratified QQ plots and fold enrichment plots, and the results showed significant enrichment effects between CRP, IGF-1, and immature reticulocyte fraction (IRF) and PD (e.g., Figure 2 , Figure 3 By conditional false discovery rate (conjFDR) analysis, we also identified pleiotropic loci associated with PD risk among these three biomarkers, including MAPT, HLA-DRB1, HLA-DQA1, and SETD1A (as shown in Figure 7 shown).

[0045] 2. Two-Sample Mendelian Randomization Analysis

[0046] The causal relationship between IRF, IGF-1, and CRP and PD was explored by Mendelian randomization analysis. CRP was negatively correlated with PD risk, IGF-1 was positively correlated, and IRF did not show a significant association (e.g. Figure 4 These findings highlight the key role of inflammation in PD and provide a basis for the development of more precise treatment strategies.

[0047] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for screening and genetically verifying blood and urine biomarkers that effectively predict the risk of Parkinson's disease, characterized in that: The following steps are involved: S1. Sample screening for inclusion in the study, using large longitudinal population cohort data from the UK Biobank, excluding non-British whites and participants with neurological diseases; S2. The optimal blood and urine biomarkers were screened based on the LASSO regularization method. 67 biomarkers in blood and urine were included. By using LASSO regression and adjusting the covariates of sex, age and PD, 46 key biomarkers closely related to PD survival outcomes were screened. S3. COX proportional hazard regression model under multiple covariate adjustment. Through multivariate COX proportional hazard regression analysis, all covariates and significant biomarkers were included to screen out 13 biomarkers that were highly independently associated with PD. The covariates included gender, age, smoking, drinking, physical activity level, education, diabetes, hypertension and stroke. S4. Genetic correlation and pleiotropic enrichment analysis. The genetic correlation between 13 biomarkers and PD was estimated using linkage disequilibrium score regression and genetic covariance analysis. C-reactive protein and insulin growth factor-1 showed significant genetic correlation in both analysis methods. Pleiotropic enrichment was evaluated by stratified QQ plots and folded enrichment plots, and significant enrichment effects were found between C-reactive protein, insulin growth factor-1 and immature reticulocyte fraction and PD. Conditional false discovery rate analysis was used to identify pleiotropic sites associated with PD risk in three biomarkers, including MAPT, HLA-DRB1, HLA-DQA1 and SETD1A. S5. The causal relationship between C-reactive protein, insulin growth factor-1, immature reticulocyte fraction and PD was analyzed by Mendelian randomization. It was found that C-reactive protein was negatively correlated with PD risk, insulin growth factor-1 was positively correlated, and immature reticulocyte fraction did not show a significant association.

2. The method for screening and genetically verifying blood and urine biomarkers that effectively predict the risk of Parkinson's disease according to claim 1, characterized in that: Step S1 also includes data interpolation for missing data in the research sample.

3. The method for screening and genetically verifying blood and urine biomarkers that effectively predict the risk of Parkinson's disease according to claim 2, characterized in that: The data interpolation uses the chain equation method to perform multiple interpolation.

4. The method for screening and genetically verifying blood and urine biomarkers that effectively predict the risk of Parkinson's disease according to claim 1, characterized in that: The study sample included 392,280 participants, including 3,084 people with Parkinson's disease.

5. The method for screening and genetically verifying blood and urine biomarkers that effectively predict the risk of Parkinson's disease according to claim 1, characterized in that: In step S3, multivariate COX proportional hazards regression analysis was performed using stepwise adjustment of covariates from basic demographic and lifestyle factors to the impact of cardiovascular disease.

6. The method for screening and genetically verifying blood and urine biomarkers that effectively predict the risk of Parkinson's disease according to claim 1, characterized in that: In step S4, the stratified QQ plot analyzes the statistical association between the biomarkers PD, and stratifies according to p<0.1, p<0.01, and p<0.

001. If there is a significant difference in the degree of deviation between different layers, it indicates that there is a strong pleiotropic enrichment phenomenon between the two phenotypes.

7. The method for screening and genetically verifying blood and urine biomarkers that effectively predict the risk of Parkinson's disease according to claim 1, characterized in that: In step S4, the fold enrichment plot shows the fold enrichment of the -log10p value of the biomarker under PD conditions.

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