CN111904382A

CN111904382A - Method for predicting Parkinson's disease cognitive dysfunction based on GDNF

Info

Publication number: CN111904382A
Application number: CN202010793866.8A
Authority: CN
Inventors: 史明语; 高殿帅; 马成成; 吴姣; 秦登利; 陈刚
Original assignee: Xuzhou Medical University
Current assignee: Xuzhou Medical University
Priority date: 2020-08-10
Filing date: 2020-08-10
Publication date: 2020-11-10

Abstract

The invention discloses a method for predicting Parkinson disease cognitive dysfunction based on GDNF, which comprises the steps of including 53 Parkinson disease patients, dividing the Parkinson disease patients into a Parkinson disease accompanied cognitive dysfunction group (27 cases) and a Parkinson disease unconventional cognitive dysfunction group (26 cases) according to cognitive mental function scale scores such as a simple mental state scale, a Montreal cognitive assessment scale, a clinical dementia rating scale and the like, selecting 26 healthy old people as healthy control groups, detecting the levels of GDNF and precursors thereof in serum of each group by using an Elisa method, and analyzing the relationship between the GDNF and the cognitive mental function scores.

Description

Method for predicting Parkinson's disease cognitive dysfunction based on GDNF

Technical Field

The invention relates to the field of Parkinson disease related products, in particular to a method for predicting Parkinson disease cognitive dysfunction based on GDNF.

Background

Parkinson's Disease (PD) is the second leading neurodegenerative disease, with a prevalence of about 2% -3% in people over 65 years of age. Loss of degeneration of Dopaminergic neurons of the substantia nigra (DN) leading to striatal Dopamine (DA) deficiency and intracellular formation of inclusion bodies of the substantia nigra DN characterised by alpha-synuclein are the main neuropathological features of parkinson's disease. Many Non-motor symptoms (NMS) accompany the PD course, which exacerbate the disability level of the patient and cause great physical and psychological trauma. In recent years, NMS of PD has received increasing attention, especially Cognitive Impairment (CI). About 30% of PD patients have Mild Cognitive Impairment (MCI), which is a risk factor for developing dementia, and CI is difficult to block and has poor therapeutic effect, greatly affecting the quality of life of the patients themselves and family members. Therefore, it is important to recognize and improve cognitive impairment (PDCI) of Parkinson's disease as early as possible. However, CI is hidden from disease onset, is not easily perceived, and is relatively difficult to diagnose at an early stage, and the cognitive function scale evaluation has a certain subjectivity, and the evaluation result fluctuates according to the state of the patient, so that an objective and accurate diagnosis marker is urgently needed.

Currently, the possible diagnostic markers of PDCI mainly include Homocysteine (Hcy) in serum, Uric Acid (UA), Insulin-like growth factor (ILGF), triglyceride and the like, Abeta 42[7] and total tau protein in cerebrospinal fluid, iron content in brain images, hippocampal atrophy, expression of APOE allele 4 and the like. Serum from patients with clinical PD has been extensively studied because of its relative ease of acquisition, for example, studies have shown that decreased levels of serum Insulin-like growth factor 1 (IGF-1) are associated with decreased speech memory, executive function, and attention in PD patients. The role of Neurotrophic Factor (NF) in maintaining normal brain function and protecting nerve cells has been largely confirmed, but its role and mechanism studies in CI patients are lacking.

Glial cell line-derived neurotrophic factor (GDNF) is a potent survival factor, a "distant parent" of the transforming growth factor β superfamily, which has been widely studied since its discovery for its potent neurotrophic effect on DN. For example, studies have shown that GDNF plays a crucial role in the survival of nigrostriatal DN; additional studies have shown that peripheral serum GDNF concentrations are reduced in MCI patients and Alzheimer's Disease (AD) patients. However, in PD, there has been little research into the relationship between GDNF and PDCI, and there has been no doubt. GDNF gene will produce two precursor proteins of alpha-pro-GDNF and beta-pro-GDNF during the process of transcription and translation, which can be secreted to the outside of cells to play a role like mature GDNF, but there is no report about the distribution of GDNF precursor in neurodegenerative diseases and the relationship between serum GDNF precursor and PDCI at present, and it is unclear how GDNF precursor regulates the content change of GDNF.

Disclosure of Invention

The present invention aims to provide a method for predicting cognitive dysfunction in parkinson's disease based on GDNF, in order to solve the problems set forth in the background art described above.

In order to achieve the purpose, the invention provides the following technical scheme: the method for predicting Parkinson's disease cognitive dysfunction based on GDNF comprises test objects, materials and an evaluation method, wherein the test objects in the test objects and the materials comprise subject screening and specimen collection, the subject screening is to collect the clinic of Xuzhou medical university affiliated hospital and the primary PD patients who are hospitalized between 2018 and 04 and 2019, two experienced neurologists respectively collect and evaluate detailed data such as demographics, disease history course, motion symptoms, treatment conditions and the like of the patients, the diagnosis is established, the correct rate of PD diagnosis can be ensured to be more than 90%, all neural scales are evaluated under the condition that the mental states of the patients are good and the coordination is good, and the grouping standard is as follows: (1) age 55-75 years; (2) all neuropsychological and mental behavior assessment can be completed under the guidance of a doctor, and hearing, speaking, reading and understanding are barrier-free; (3) parkinson's disease diagnosis must be independently diagnosed by two experienced neurologists according to the 2015 society for dyskinesia (MDS) diagnostic criteria and with reference to the british parkinsonian brain pool diagnostic criteria, exclusion criteria: (1) all patients should have other neurological history excluding PD by CT or MRI: moderate or severe craniocerebral injury, stroke or vascular dementia and the like; (2) secondary parkinsonism such as drug-induced, head trauma and vascular; progressive supranuclear palsy, multiple system atrophy and other parkinsonian superposition syndromes; (3) psychological disorders such as major anxiety, depression and schizophrenia; (4) systemic diseases of heart, liver, kidney and other diseases which may affect cognitive function;

the sample collection is that the serum of a patient is collected at 7:00-8:00 in the morning on the next morning of admission (outpatient and healthy control groups are between 7:00 and 8:00 on the current day of treatment), the patient is fasted and water is forbidden 22:00 later in the morning and evening, the sample is taken and then is kept stand for 2 hours at room temperature, the sample is centrifuged for 10 minutes at 1000g at 4 ℃, and in order to ensure that the serum components are not damaged as much as possible, the sample is immediately subpackaged into 500ul EP tubes after the centrifugation is finished and stored at-80 ℃ for further detection;

the evaluation method comprises the following steps:

step one, neuropsychological assessment: overall cognitive function was assessed in all subjects, all patients were assessed in good mental state with good fit using the mini mental state scale MMSE, montreal cognitive scale MoCA and the clinical dementia rating scale CDR: the total score of MMSE is 30, and cognitive dysfunction exists in less than 26; the total score of MoCA is 30 points, cognitive dysfunction exists in less than 26 points, and 1 point is added when the education age is less than or equal to 12 years; the CDR scale was rated at 0min, 3 min and 0.5 min or more, and was considered to have cognitive dysfunction. Because the MMSE and MoCA scales are used independently to increase the false negative rate and the false positive rate respectively, HC and PDN group subjects need to simultaneously meet MMSE more than or equal to 26 points, MOCA more than or equal to 26 points and CDR less than 0.5 point, PDCI group patients need to simultaneously meet MMSE less than 26 points, MoCA less than 26 points and CDR more than or equal to 0.5 point;

step two, statistical analysis: all statistical analyses were performed on SPSS22.0, and normal distribution-satisfying metrology data was averaged ± standard deviation using graphpadprism8.0.2, medcalc19.0.4 as an aid

Data of a non-normal distribution is expressed by a median (interquartile range) [ M (Q) ]_R)]In two comparison sets, two sets satisfying the parameter checking conditionIndependent sample t test, Mann-Whitney U test for nonparametric test, multiple groups of samples meeting the parametric test condition, and single-factor analysis of variance (One-Way ANOVA), and LSD method or Dunnett's T3 method according to the homogeneity of variance when further comparing two by two; and (3) performing Kruskal-Wallis test on the samples which do not meet the parameter test conditions, further performing pairwise comparison to correct the P value by adopting a Bonferroni method, performing intergroup comparison on the counting data by using a Chi-square test, performing correlation analysis between variables, performing Pearson or Spearman correlation analysis according to the normal distribution condition of the variables, and setting the significance level to be P less than 0.05. Wherein, the non-parameter test needs to be corrected by a Bonferroni method in pairwise comparison after the test to control the total occurrence probability of class I errors, and the difference with P less than 0.0167 on both sides has statistical significance.

Preferably, the laboratory measurements in all cases and controls in the specimen collection include levels of GDNF, alpha-pro-GDNF, beta-pro-GDNF, and were performed using an enzyme linked immunosorbent kit (GDNF: R & D USA; GDNF precursor: Shanghai enzyme-linked Chinese) following strictly the protocol of the test.

Compared with the prior art, the invention has the beneficial effects that:

the method for predicting Parkinson's Disease Cognitive dysfunction based on GDNF comprises the steps of taking 53 Parkinson's Disease (PD) patients, dividing the patients into a Parkinson's Disease With Cognitive dysfunction (PDCI) group (27 cases) and a Parkinson's Disease without Cognitive dysfunction (PDN) group (26 cases) according to Cognitive Mental dysfunction scores such as a simple Mental State scale (MMSE), a Montreal Cognitive assessment scale (MoCA) and a Clinical Dementia assessment scale (CDR) and selecting 26 healthy old people as a Health Mental Control group (Health Control, HC), and detecting and analyzing the relationship between serum GDNF and precursor level thereof and the Cognitive dysfunction scores of the groups by using the isa method.

Drawings

FIG. 1 is a table comparing demographic data and clinical characteristics of three groups of people;

FIG. 2 is a table of the concentrations of GDNF and its precursors for three groups of people;

FIG. 3 is a table comparing indices of GDNF and its precursors in three groups of HC, PDN, PDCI;

FIG. 4 is a table of overall cognitive function scale scores for three groups of people;

FIG. 5 is a table of correlations between serum GDNF and its precursors and cognitive metrics;

FIG. 6 is a graph of the correlation of serum GDNF and its precursors to cognitive scales;

FIG. 7 is a table of a binary Logistic regression analysis (retroLR method) of cognitive impairment in PD patients;

FIG. 8 is a stepwise linear regression table of MMSE score, MoCA score, CDR score for PD patients;

FIG. 9 is a ROC curve for predicting PDCI by GDNF and its complexes.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.

Referring to fig. 1-9, an embodiment of the present invention is shown: the method for predicting Parkinson's disease cognitive dysfunction based on GDNF comprises test objects, materials and an evaluation method, wherein the test objects in the test objects and the materials comprise subject screening and specimen collection, the subject screening is to collect the clinic of Xuzhou medical university affiliated hospital and inpatient primary PD patients between 2018 and 2019 and 08, two experienced neurologists respectively collect and evaluate detailed data such as demographics, disease history and course, motion symptoms, treatment conditions and the like of the patients, the diagnosis is established, the accuracy of PD diagnosis can be ensured to be more than 90%, and all neural scales are evaluated under the condition that the mental states of the patients are good and the coordination is good, and the grouping standard is as follows: (1) age 55-75 years; (2) all neuropsychological and mental behavior assessment can be completed under the guidance of a doctor, and hearing, speaking, reading and understanding are barrier-free; (3) parkinson's disease diagnosis must be independently diagnosed by two experienced neurologists according to the 2015 society for dyskinesia (MDS) diagnostic criteria and with reference to the british parkinsonian brain pool diagnostic criteria, exclusion criteria: (1) all patients should have other neurological history excluding PD by CT or MRI: moderate or severe craniocerebral injury, stroke or vascular dementia and the like; (2) secondary parkinsonism such as drug-induced, head trauma and vascular; progressive supranuclear palsy, multiple system atrophy and other parkinsonian superposition syndromes; (3) psychological disorders such as major anxiety, depression and schizophrenia; (4) systemic diseases of heart, liver, kidney and other diseases which may affect cognitive function;

collecting the sample, namely collecting the serum of the patient 7:00-8:00 in the morning on the next day of admission (7: 00-8:00 for outpatient and healthy control groups on the current day of treatment), fasting and water-depriving 22:00 in the evening before, taking the sample, standing for 2 hours at room temperature, centrifuging for 10 minutes at 1000g at 4 ℃, immediately packaging into 500ul EP tubes after centrifugation is finished and storing at-80 ℃ for further detection in order to ensure that the serum components are not destroyed as much as possible;

the evaluation method comprises the following steps:

step one, neuropsychological assessment: the overall cognitive function of all subjects was evaluated in a comprehensive manner, and all patients were evaluated in a mental well-matched condition. Overall cognitive function was measured using the mini-mental state scale MMSE, montreal cognitive scale MoCA and the clinical dementia rating scale CDR: the total score of MMSE is 30, and cognitive dysfunction exists in less than 26; the total score of MoCA is 30 points, cognitive dysfunction exists in less than 26 points, and 1 point is added when the education age is less than or equal to 12 years; CDR score 0 at the lowest and 3 at the highest, and score 0.5 or more is considered to have cognitive dysfunction. Because the MMSE and MoCA scales are used independently to increase the false negative rate and the false positive rate respectively, HC and PDN group subjects need to simultaneously meet MMSE more than or equal to 26 points, MoCA more than or equal to 26 points and CDR less than 0.5 point, PDCI group patients need to simultaneously meet MMSE less than 26 points, MoCA less than 26 points and CDR more than or equal to 0.5 point;

Data of a non-normal distribution is expressed by a median (interquartile range) [ M (Q) ]_R)]In two groups of comparison, two independent samples are used for t test when the parameter test condition is met, Mann-Whitney U test is used for non-parameter test, a plurality of groups of comparison are used for single-factor analysis of variance (One-Way ANOVA) when the parameter test condition is met, and an LSD method or a Dunnett's T3 method is adopted when the comparison is further carried out pairwise according to the equality of the variance; and (3) performing Kruskal-Wallis test on the samples which do not meet the parameter test conditions, further performing pairwise comparison to correct the P value by adopting a Bonferroni method, performing intergroup comparison on the counting data by using a Chi-square test, performing correlation analysis between variables, performing Pearson or Spearman correlation analysis according to the normal distribution condition of the variables, and setting the significance level to be P less than 0.05. Wherein, the non-parameter test needs to be corrected by a Bonferroni method in pairwise comparison after the test to control the total occurrence probability of class I errors, and the difference with P less than 0.0167 on both sides has statistical significance.

Furthermore, laboratory measurements in all cases and controls in the specimen collections included levels of GDNF, α -pro-GDNF, and β -pro-GDNF, and were performed using an enzyme-linked immunosorbent kit (GDNF: R & D USA; GDNF precursor: Shanghai enzyme-linked Chinese) strictly in accordance with the experimental instructions.

Referring to fig. 1, a total of 26 PDNs, 27 PDCIs, and 26 HCs were included in the study, and demographic information, clinical characteristics, and disease status are shown in fig. 1. The three groups of people have no significant difference (P is more than 0.05) in data such as gender, age, smoking, drinking, hypertension, high school and the above academic proportions, the education degree time (year) and the proportion of diabetes of the three groups of people and the H-Y stage and course of PDN and PDCI of the two groups of people have significant difference (P is less than 0.05); HC in fig. 1, normal control group; PDN, parkinson's disease without cognitive dysfunction group; PDCI, parkinson's disease with cognitive dysfunction. A: checking a chi square; b: one-way ANOVA; c: non-parametric test, Kruskal-Wallis test; d: non-parameter testing: Mann-Whitney U test. Course (month), education (high school and above), updrs (iii) between groups using non-parametric tests, where course (month), updrs (iii) two groups were compared using the Mann-Whitney U test and education (year) three groups were compared using the Kruskal-Wallis test;

referring to fig. 2 and 3, the differences between the PDN group serum GDNF levels (679.43 ± 175.58) pg/ml and the HC group (494.80 ± 188.92) pg/ml and the PDCI group (444.15 ± 96.11) pg/ml were statistically significant (F ═ 16.101, P < 0.001); further, two-by-two comparison shows that the GDNF level of the PDN group is higher than that of the HC group, the difference is statistically significant (P is less than 0.001), the GDNF level of the PDN group is higher than that of the PDCI group, the difference is statistically significant (P is less than 0.001), and the HC group is higher than that of the PDCI group, but the difference is not statistically significant (P is more than 0.05). Meanwhile, we compared the differences between the concentrations of α -pro-GDNF and β -pro-GDNF in three groups and the differences between the ratios of GDNF/α -pro-GDNF, GDNF/β -pro-GDNF and α -pro-GDNF in three groups. GDNF/α -pro-GDNF has statistical significance in comparison with the PDN group (0.34 ± 0.11), HC group (0.31 ± 0.11), PDCI group (0.27 ± 0.09) (F ═ 3.297, P ═ 0.042); further comparing every two, the GDNF/alpha-pro-GDNF level in the PDN group is higher than that in the PDCI group, the difference has statistical significance (P is 0.012), the PDN group has no statistical significance (P is 0.232) compared with that in the HC group, and the HC group has no statistical significance (P is 0.181) compared with that in the PDCI group, and the differences among the three groups of the alpha-pro-GDNF, the beta-pro-GDNF, the GDNF/beta-pro-GDNF and the alpha-pro-GDNF/beta-pro-GDNF are not found; HC in fig. 2, normal control group; PDN, parkinson's disease without cognitive dysfunction group; PDCI, parkinson's disease with cognitive dysfunction; GDNF: glial cell line-derived neurotrophic factor. And comparing the mean values of the three groups of data by using one-way ANOVA, and according to the result of the homogeneity test of the variances, the variance is 1: dunnett's T3 method, variance taken at 2: LSD method, significance level was set at P < 0.05. *: comparative differences among groups are statistically significant, #: the comparison difference between groups has no statistical significance; a: the differences were statistically significant compared to the PDN group; HC in fig. 3, normal control; PDN, parkinson's disease without cognitive dysfunction group; PDCI, parkinson's disease with cognitive dysfunction group. A: the distribution of GDNF in HC, PDN and PDCI is compared pairwise, the difference between the PDN group and the HC group has statistical significance, P is less than 0.001, the difference between the PDN group and the PDCI group has statistical significance, P is less than 0.001, and the difference between the HC group and the PDCI group has no statistical significance; b: the distribution of the alpha-pro-GDNF in HC, PDN and PDCI, and the comparative difference of the alpha-pro-GDNF among groups has no statistical significance; c: the distribution of beta-pro-GDNF in HC, PDN and PDCI, and the comparative difference of beta-pro-GDNF among groups has no statistical significance; d: the distribution of GDNF/alpha-pro-GDNF in HC, PDN and PDCI is compared pairwise, the difference between the PDN group and the PDCI group has statistical significance, P is 0.012, and the difference between the PDN group and the HC group and between the HC group and the PDCI group has no statistical significance; e: GDNF/beta-pro-GDNF did not statistically differ in comparison between groups; f: the distribution of alpha-pro-GDNF/beta-pro-GDNF in HC, PDN and PDCI, and the comparative difference of alpha-pro-GDNF/beta-pro-GDNF among groups has no statistical significance. Significance levels were set at P < 0.05, P < 0.01, P < 0.001.

Referring to fig. 4, in the comparison between groups of the three groups of global cognitive function tests, MMSE scores were statistically significant (P < 0.001) in the difference between the three groups, and further two-by-two comparisons were performed, with statistical significance (P < 0.001) in the difference between HC group and PDCI group, statistical significance (P < 0.001) in the difference between PDN group and PDCI group, statistical significance (P > 0.05) in the difference between HC group and PDN group, unadjusted P values were < 0.001, < 0.951, respectively, and P values after Bonferroni adjustment were < 0.001, < 1.000, respectively; comparison of MoCA and CDR scores among groups was similar to MMSE score; HC in fig. 4, normal control; PDN, parkinson's disease without cognitive dysfunction group; PDCI, parkinson's disease with cognitive dysfunction group; MMSE, simple intelligent state table; MoCA, montreal cognitive assessment scale, CDR, clinical dementia rating scale. MMSE, MoCA and CDR scale scores do not meet normal distribution, and a non-parametric test Kruskal-Wallis test is used for comparing three groups, so that Bonferroni is corrected, and the significance level is set to be P less than 0.0167.

Referring to FIGS. 5 and 6, to verify the utility of GDNF and its precursors in clinical practice, we performed a correlation analysis of serum GDNF and its precursors with cognitive scale scores, with positive correlation between GDNF levels and MMSE scores, MoCA scores (r 0.610, P < 0.001; r 0.579, P < 0.001) and negative correlation between GDNF levels and CDR scores (r-0.573, P < 0.001). We also analyzed the correlations between a-pro-GDNF (pg/ml), β -pro-GDNF (pg/ml), etc. and cognitive scale scores; MMSE, simple intelligent state table in fig. 5; MoCA, montreal cognitive assessment scale; CDR, clinical dementia rating scale; GDNF: glial cell line-derived neurotrophic factor. Significance levels were set at P < 0.05, P < 0.01, P < 0.001 using Spearman correlation analysis; MMSE, simple intelligent state table in fig. 6; MoCA, montreal cognitive assessment scale; CDR, clinical dementia rating scale; GDNF: glial cell line-derived neurotrophic factor. (A-C) is the sperman correlation coefficient for MMSE and GDNF, r is 0.610, P < 0.001 (A); with GDNF/α -pro-GDNF, r ═ 0.467, P < 0.001 (B); and GDNF/β -pro-GDNF, r ═ 0.455, P < 0.001 (C). (D-F) is the spearman correlation coefficient of MoCA with GDNF, r is 0.579, P < 0.001 (D); and GDNF/α -pro-GDNF, r ═ 0.323, P ═ 0.018 (E); and GDNF/β -pro-GDNF, r ═ 0.362, P ═ 0.008 (F). (G-I) is the spearman correlation coefficient of CDR to GDNF, r ═ 0.573, P < 0.001 (G); and GDNF/α -pro-GDNF, r ═ 0.379, P ═ 0.005 (H); and GDNF/β -pro-GDNF, r ═ 0.390, P ═ 0.004 (I). As shown in the above graph, the hatched portion is a 95% confidence interval, and n is 53.

Referring to FIGS. 7 and 8, in order to find the risk factors for cognitive dysfunction in PD, we performed regression analysis, and included variables such as gender, age, education (year), H-Y stage, course (month), GDNF (pg/ml), a-pro-GDNF (pg/ml), β -pro-GDNF (pg/ml), GDNF/a-pro-GDNF, GDNF/β -pro-GDNF, a-pro-GDNF/β -pro-GDNF, and the like in a binary Logistic regression analysis model of cognitive dysfunction in PD patients (FIG. 7), and analyzed using Likelihood Ratio Test (LRT). Hosimer-Leimei (H-L) test P > 0.05 suggested that the regression model could fit the experimental data well, with the results showing that the variables that had significant impact on cognition were GDNF (pg/ml) and H-Y staging. We then performed stepwise linear regression analysis (FIG. 8) and showed that the variables affecting MMSE score were GDNF (pg/ml), H-Y staging, a-pro-GDNF, and R adjusted²Is 0.561; the variables affecting the MoCA score were GDNF (pg/ml), H-Y staging, education (years), adjusted R²Is 0.521; the variables that have significant effects on CDR scores include GDNF (pg/ml), H-Y staging, adjusted R²Is 0.465. Wherein, GDNF (pg/m)l) has significant influence on scores of MMSE, MoCA and CDR, and the education degree (year) only has influence on the score of the MoCA scale; HC in fig. 7, normal control; PDN, parkinson's disease without cognitive dysfunction group; PDCI, parkinson's disease with cognitive dysfunction. The binary Logistic regression analysis of PDCI, Likelihood Ratio Test (LRT) is used for evaluating the influence factors of PDCI, such as sex, age, education degree (year), H-Y stage, disease course, UPDRS (III), LED, GDNF (pg/ml), a-pro-GDNF (pg/ml), beta-pro-GDNF (pg/ml), GDNF/a-pro-GDNF, GDNF/beta-pro-GDNF, a-pro-GDNF/beta-pro-GDNF and the like are included in the equation, and the result of the binary Logistic regression analysis shows that the H-isty stage and the GDNF (pg/ml) have obvious influence on the cognitive function of PD patients; GDNF in fig. 8: glial cell line-derived neurotrophic factor. The influence factors of MMSE, MoCA and CDR scores of PD patients are evaluated by stepwise linear regression. Factors such as sex, age, education (year), H-Y staging, course of disease, UPDRS (III), LED, GDNF (pg/ml), a-pro-GDNF (pg/ml), β -pro-GDNF (pg/ml), GDNF/a-pro-GDNF, GDNF/β -pro-GDNF, a-pro-GDNF/β -pro-GDNF, and the like are included in the equations. FIG. 8 is a stepwise linear regression analysis of the influencing factors of MMSE score, MoCA score and CDR score of PD patients, and the results show that GDNF (pg/ml), H-Y stage and a-pro-GDNF have a significant influence on MMSE score of PD patients; GDNF (pg/ml), H-Y staging, education level (year) have significant impact on MoCA in PD patients; GDNF (pg/ml), H-Y staging, CDR scores for PD patients had significant impact.

Referring to FIG. 9, the efficacy of predicting PDCI by indices such as GDNF and its precursor levels was analyzed by establishing a ROC curve to evaluate its clinical value for diagnosing PDCI. In the prediagnosis of PDCI, two groups of patients are included, namely a PDN group and a PDCI group, and the differentiation of PDN and PDCI is judged according to the comprehensive scores of MMSE, MoCA and CDR. GDNF serum levels predict the AUROC curve (AUC ═ 0.859, P < 0.001, 95% CI: 0.736-0.939) for PDCI. The optimal cut-off value of serum GDNF for PDCI diagnosis is 508.99pg/ml, and the sensitivity and specificity values are 85.19% and 84.62% respectively, namely, in PD patients, the accuracy of the GDNF concentration of 508.991pg/ml or more is PDN, and the accuracy of 508.991pg/ml is PDCI is about 0.859. We further discuss whether GDNF, GDNF/alpha-pro-GDNF and GDNF/beta-pro-GDNF have better diagnostic value on PDCI as a composite biomarker, the evaluation is carried out by Logistic regression analysis, and the comparison is carried out by ROC analysis, the results of ROC curve of the PDCI are predicted by the composite (AUC is 0.862, P is less than 0.001), the sensitivity is 92.11 percent, and the specificity is 72.22 percent), which shows that the combined diagnostic effect of the composite is not obviously superior to that of GDNF. The level (679.43 +/-175.58) pg/ml of GDNF in the PDN group is obviously higher than that of pg/ml in the HC group (494.80 +/-188.92) and that of pg/ml in the PDCI group (444.15 +/-96.11), the difference is statistically significant (P is less than 0.001 and P is less than 0.001), but the difference between the HC group and the PDCI group is not statistically different (P is more than 0.05), and the concentration difference between the alpha-pro-GDNF and the beta-pro-GDNF in each group is not statistically different. GDNF concentration is increased firstly and then decreased in HC, PDN and PDCI groups, GDNF level has more than moderate correlation with MMSE, MoCA and CDR (r is 0.610, P is less than 0.001; r is 0.579, P is less than 0.001; r is-0.573, P is less than 0.001), GDNF/alpha-pro-GDNF and GDNF/beta-pro-GDNF have higher correlation with cognition psychofunction tables. Establishing a Receiver Operating Characteristic Curve (ROC) for predicting the cognitive function state of the Parkinson disease by using the GDNF, wherein the Area Under the Curve (AUC) is 0.859, and P is less than 0.001, so the serum GDNF can be used as a diagnostic marker of PDCI, and the serum GDNF can effectively predict the PDCI; in fig. 9, the GDNF line represents the ROC curve for GDNF predicting PDCI, AUC 0.859, 95% CI: 0.736-0.939, sensitivity 85.19%, specificity 84.62%; the Composite line is the ROC curve of the compound prediction PDCI, and the compound is (GDNF vs GDNF/a-pro-GDNF vs GDNF/beta-pro-GDNF), AUC is 0.862, and P is less than 0.001. The cutoff value for GDNF diagnostic PDCI was 508.99 pg/ml.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.

Claims

1. A method for predicting Parkinson's disease cognitive dysfunction based on GDNF comprises a test object, a material and an evaluation method, and is characterized in that: the test objects and the test objects in the materials comprise test object screening and specimen collection, the test object screening comprises the steps of collecting the outpatient service and the primary PD patients in hospital at Xuzhou medical university affiliated hospital between 2018 and 2019 and 08, two experienced neurologists respectively collect and evaluate detailed data such as demographics, medical history course, motion symptoms, treatment conditions and the like of the patients, the diagnosis is established, the accuracy of the PD diagnosis can be ensured to be more than 90%, all neural scales are evaluated under the condition that the mental states of the patients are good and the cooperation is good, and the classification standard is as follows: (1) age 55-75 years; (2) all neuropsychological and mental behavior assessment can be completed under the guidance of a doctor, and hearing, speaking, reading and understanding are barrier-free; (3) parkinson's disease diagnosis must be independently diagnosed by two experienced neurologists according to the 2015 society for dyskinesia (MDS) diagnostic criteria and with reference to the british parkinsonian brain pool diagnostic criteria, exclusion criteria: (1) all patients should have other neurological history excluding PD by CT or MRI: moderate or severe craniocerebral injury, stroke or vascular dementia and the like; (2) secondary parkinsonism such as drug-induced, head trauma and vascular; progressive supranuclear palsy, multiple system atrophy and other parkinsonian superposition syndromes; (3) psychological disorders such as major anxiety, depression and schizophrenia; (4) systemic diseases of heart, liver, kidney and other diseases which may affect cognitive function;

the evaluation method comprises the following steps:

step one, neuropsychological assessment: the overall cognitive function of all subjects was evaluated in a comprehensive manner, and all patients were evaluated in a mental well-matched condition. Overall cognitive function was measured using the mini-mental state scale MMSE, montreal cognitive scale MoCA and the clinical dementia rating scale CDR: the total score of MMSE is 30, and cognitive dysfunction exists in less than 26; the total score of MoCA is 30 points, cognitive dysfunction exists in less than 26 points, and 1 point is added when the education age is less than or equal to 12 years; the CDR scale was rated at 0min, 3 min and 0.5 min or more, and was considered to have cognitive dysfunction. Because the MMSE and MoCA scales are used independently to increase the false negative rate and the false positive rate respectively, HC and PDN group subjects need to simultaneously meet MMSE more than or equal to 26 points, MOCA more than or equal to 26 points and CDR less than 0.5 point, PDCI group patients need to simultaneously meet MMSE less than 26 points, MoCA less than 26 points and CDR more than or equal to 0.5 point;

2. The method for predicting cognitive dysfunction in parkinson's disease based on GDNF of claim 1, wherein: laboratory measurements in all cases and controls in the specimen collections included levels of GDNF, alpha-pro-GDNF, and beta-pro-GDNF, and were performed using an enzyme-linked immunosorbent kit (GDNF: R & D USA; GDNF precursor: Shanghai enzyme-linked Chinese) strictly in accordance with the experimental instructions.