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

Abstract

The invention discloses a method for predicting cognitive dysfunction of Parkinson's disease based on GDNF. By including 53 patients with Parkinson's disease, according to the simple mental state scale, Montreal cognitive assessment scale, clinical dementia assessment scale and other cognitive psychological functions The scale scores were divided into Parkinson's disease with cognitive impairment group (27 cases) and Parkinson's disease without cognitive impairment group (26 cases). The levels of serum GDNF and its precursors in the two groups were analyzed, and the relationship between them and cognitive and psychological function scores was analyzed.

Description

A method for predicting cognitive impairment in Parkinson's disease based on GDNF

technical field

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

Background technique

Parkinson's disease (PD) is the second most common neurodegenerative disease, with a prevalence of approximately 2%-3% in people over the age of 65. Dopaminergic neuron (DN) degeneration and loss in the substantia nigra leading to striatal dopamine (DA) deficiency and the formation of intracellular inclusion bodies characterized by α-synuclein in the substantia nigra DN are the main causes of Parkinson's disease. neuropathological features. The course of PD is accompanied by many non-motor symptoms (NMS), which aggravate the degree of disability of patients and cause huge physical and psychological trauma. In recent years, NMS of PD has received more and more attention, especially cognitive impairment (CI). About 30% of PD patients have mild cognitive impairment (Mild cognitive impairment, MCI), which is a risk factor for developing dementia, and CI is difficult to block and the treatment effect is not good, which greatly affects the patients themselves and their families. quality of life. Therefore, it is particularly important to identify and improve Parkinson's disease with cognitive impairment (PDCI) as early as possible. However, the onset of CI is insidious and difficult to detect, and early diagnosis is relatively difficult, and the evaluation of cognitive function scales is subject to a certain degree, and the evaluation results will fluctuate with the patient's state.

At present, the possible diagnostic markers for PDCI mainly include homocysteine (Hcy), uric acid (Uric acid, UA), insulin-like growth factor (ILGF), triglyceride, etc. in serum. Aβ42[7] and total tau protein in cerebrospinal fluid, iron content in brain imaging, hippocampal atrophy, expression of APOE allele ε4, etc. Serum from clinical PD patients has been widely studied because of its relative ease of acquisition. For example, studies have shown that decreased serum insulin-like growth factor-1 (IGF-1) levels are associated with verbal memory, executive function, and attention in PD patients. related to decline. The role of neurotrophic factor (NF) in maintaining normal brain function and protecting nerve cells has been widely confirmed, but the research on its role and mechanism in patients with CI is relatively lacking.

Glial cell line-derived neurotrophicfactor (GDNF), a potent survival factor and a "distant relative" of the transforming growth factor beta superfamily, has been known to be potent for DN since its discovery. has been extensively studied for its neurotrophic effect. For example, studies have shown that GDNF plays a crucial role in the survival of nigrostriatal DN; another study has shown that peripheral serum GDNF concentrations in MCI patients and Alzheimer's Disease (AD) patients decrease. However, in PD, there are few studies on the relationship between GDNF and PDCI, and it has been inconclusive. In the process of transcription and translation of the GDNF gene, two precursor proteins, α-pro-GDNF and β-pro-GDNF, are produced, which can be secreted to the outside of the cell like mature GDNF to play a role. The distribution of precursors in neurodegenerative diseases and the relationship between serum GDNF precursors and PDCI have been reported, and it is unclear how GDNF precursors regulate GDNF content changes.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a method for predicting cognitive dysfunction in Parkinson's disease based on GDNF, so as to solve the problems raised in the above background art.

In order to achieve the above object, the present invention provides the following technical solutions: a method for predicting cognitive dysfunction in Parkinson's disease based on GDNF, including test objects, materials and evaluation methods, and test objects in the test objects and materials include subject screening and specimens. Collection, the subjects were selected to collect outpatient and inpatient primary PD patients in the Affiliated Hospital of Xuzhou Medical University from April 2018 to August 2019, and two experienced neurologists conducted detailed demographic data on the patients respectively. The collection and evaluation of statistics, history and course of disease, motor symptoms, treatment conditions, etc., to establish the diagnosis, can ensure that the correct rate of PD diagnosis is more than 90%. Evaluation, inclusion criteria: (1) aged 55-75 years old; (2) able to complete all neuropsychological and psychobehavioral assessments under the guidance of a physician, with no barriers to listening, speaking, reading, and comprehension; (3) Parkinson's disease The diagnosis of the disease must be independently diagnosed by two experienced neurologists according to the 2015 Movement Disorders Society (MDS) diagnostic criteria and the British Parkinson's disease brain bank diagnostic criteria. Exclusion criteria: (1) All patients should be excluded by CT or MRI. Other neurological history other than PD: moderate to severe brain injury, stroke or vascular dementia, etc.; (2) secondary Parkinson's syndrome such as drug-induced, head trauma and vascular; progressive supranuclear palsy, Multiple system atrophy and other Parkinsonian syndromes; (3) mental diseases such as severe anxiety, depression and schizophrenia; (4) systemic diseases such as heart, liver, kidney and other diseases that may affect cognitive function;

The specimens were collected as the patient serum was collected at 7:00-8:00 in the morning of the second day of admission (outpatients and healthy control groups were collected between 7:00 and 8:00 on the day of consultation), and the serum was not allowed after 22:00 the night before. Food and water were forbidden. After taking the specimen, let it stand at room temperature for 2 hours, and centrifuge at 1000g for 10 minutes at 4°C. In order to ensure that the serum components are not destroyed as much as possible, immediately after the centrifugation, it was divided into 500ul EP tubes and stored at -80°C for further testing;

The evaluation method includes the following steps:

Step 1, neuropsychological assessment: conduct a comprehensive assessment of the overall cognitive function of all subjects. The assessment of all patients is completed under the condition of good mental state and good cooperation. The overall cognitive function uses the Simple Mental State Scale. MMSE, Montreal Cognitive Scale MoCA and Clinical Dementia Rating Scale CDR: MMSE total score 30 points, <26 points, cognitive dysfunction; MoCA total score 30 points, <26 points, cognitive dysfunction, years of education ≤ At 12 years, 1 point is added; the lowest score of the CDR scale is 0, the highest score is 3, and ≥0.5 points are considered as cognitive impairment. Because using the MMSE and MoCA scales alone will increase the false-negative rate and false-positive rate, respectively, the subjects in the HC and PDN groups should meet the requirements of MMSE≥26 points, MOCA≥26 points and CDR<0.5 points, and patients in the PDCI group. MMSE < 26 points and MoCA < 26 points and CDR ≥ 0.5 points at the same time;

表示，非正态分布的数据以中位数(四分位数间距)[M(Q_R)]，在两组比较中，满足参数检验条件的用两独立样本t检验，Mann-Whitney U检验则用于非参数检验，多组比较满足参数检验条件的用单因素方差分析(One-Way ANOVA)，进一步两两比较时根据方差齐性与否采用LSD法或Dunnett’s T3法；不满足参数检验条件的用Kruskal-Wallis检验，进一步的两两比较采用Bonferroni法校正P值，计数资料用卡方检验进行组间比较，变量间的相关分析，依据变量正态分布情况，采用Pearson或者Spearman相关分析，显著性水平设定为P＜0.05。其中，非参数检验事后两两比较需用Bonferroni法校正，以控制Ⅰ类错误总的发生概率，以双侧P＜0.0167为差异有统计学意义。Step 2: Statistical analysis: All statistical analyses were performed on SPSS 22.0, and assisted by GraphPad Prism 8.0.2 and MedCalc 19.0.4. Measurement data that met the normal distribution were calculated as mean ± standard deviation.

Indicates that the non-normally distributed data is expressed as the median (interquartile range) [M(Q _R )]. In the comparison of two groups, two independent samples t test and Mann-Whitney U test are used if the parametric test conditions are met. It is used for non-parametric test. One-way analysis of variance (One-Way ANOVA) is used for multi-group comparisons that meet the parametric test conditions. For further pairwise comparisons, LSD method or Dunnett's T3 method is used according to whether the variance is homogenous or not; it does not meet the parametric test. Conditional Kruskal-Wallis test was used, and the Bonferroni method was used to correct the P value for further pairwise comparisons. The count data were compared between groups using the chi-square test. The correlation analysis between variables was based on the normal distribution of variables. Pearson or Spearman correlation analysis was used. , and the significance level was set at P<0.05. Among them, the non-parametric test post-hoc comparison needs to be corrected by the Bonferroni method to control the total occurrence probability of type I error, and the difference is statistically significant with a two-sided P<0.0167.

Preferably, the laboratory measurements of all cases and control groups in the specimen collection include the levels of GDNF, α-pro-GDNF, and β-pro-GDNF, using enzyme-linked immunosorbent assay kits (GDNF: R&D America; GDNF precursors) : Shanghai Enzyme Link China) in strict accordance with the test instructions.

Compared with the prior art, the beneficial effects of the present invention are:

The method based on GDNF to predict cognitive impairment in Parkinson's disease, by including 53 patients with Parkinson's Disease (PD), according to Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment Scale Cognitive psychological function scales such as Montreal cognitive assessment (MoCA) and Clinical Dementia Rating (CDR) were divided into Parkinson's Disease With Cognitive Impairment (PDCI) group (27 cases). ) and Parkinson's Disease With Normal Cognitive Function (PDN) group (26 cases), another 26 healthy elderly were selected as healthy control group (Health Control, HC), and Elisa method was used to detect and analyze The relationship between serum GDNF and its precursor levels and cognitive and psychological function scores in each group.

Description of drawings

Figure 1 is a comparison table of demographic data and clinical characteristics of the three groups of people;

Fig. 2 is the concentration table of GDNF and its precursors of three groups of people;

Figure 3 is a comparison table of GDNF and its precursors among the three groups of HC, PDN and PDCI;

Figure 4 is the overall cognitive function scale score table of the three groups of people;

Fig. 5 is the correlation analysis table of serum GDNF and its precursor and cognitive scale;

Figure 6 is a graph showing the correlation between serum GDNF and its precursors and cognitive scale;

Figure 7 is a binary Logistic regression analysis (backward LR method) table of cognitive impairment in PD patients;

Figure 8 is a stepwise linear regression table of the MMSE score, MoCA score, and CDR score of PD patients;

Figure 9 is the ROC curve of GDNF and its complexes to predict PDCI.

Detailed ways

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

Please refer to Figures 1-9, an embodiment provided by the present invention: a method for predicting cognitive dysfunction in Parkinson's disease based on GDNF, including test objects and materials and evaluation methods, test objects and materials in test objects including subject screening With specimen collection, subjects were screened as outpatient and inpatient primary PD patients were collected from the Affiliated Hospital of Xuzhou Medical University between April 2018 and August 2019, and two experienced neurologists conducted detailed demographic data on the patients respectively. The collection and evaluation of statistics, history and course of disease, motor symptoms, treatment conditions, etc., to establish the diagnosis, can ensure that the correct rate of PD diagnosis is more than 90%. Evaluation, inclusion criteria: (1) aged 55-75 years old; (2) able to complete all neuropsychological and psychobehavioral assessments under the guidance of a physician, with no barriers to listening, speaking, reading, and comprehension; (3) Parkinson's disease The diagnosis of the disease must be independently diagnosed by two experienced neurologists according to the 2015 Movement Disorders Society (MDS) diagnostic criteria and the British Parkinson's disease brain bank diagnostic criteria. Exclusion criteria: (1) All patients should be excluded by CT or MRI. Other neurological history other than PD: moderate to severe brain injury, stroke or vascular dementia, etc.; (2) secondary Parkinson's syndrome such as drug-induced, head trauma and vascular; progressive supranuclear palsy, Multiple system atrophy and other Parkinsonian syndromes; (3) mental diseases such as severe anxiety, depression and schizophrenia; (4) systemic diseases such as heart, liver, kidney and other diseases that may affect cognitive function;

Specimens were collected from patients' serum at 7:00-8:00 in the morning of the second day of admission (outpatients and healthy control groups between 7:00 and 8:00 on the day of treatment), and fasting after 22:00 the night before. Water, after taking the specimen, let it stand at room temperature for 2 hours, and centrifuge it at 1000g for 10 minutes at 4°C. In order to ensure that the serum components are not destroyed as much as possible, immediately after the centrifugation, it is divided into 500ul EP tubes and stored at -80°C for further testing;

The assessment method includes the following steps:

Step 1, neuropsychological assessment: conduct a comprehensive assessment of the overall cognitive function of all subjects. The assessment of all patients is completed under the condition of good mental state and good cooperation. Overall cognitive function was assessed using the Mini-Mental State Scale MMSE, Montreal Cognitive Scale MoCA and Clinical Dementia Rating Scale CDR: MMSE total score of 30 points, <26 points with cognitive impairment; MoCA total score of 30 points, <26 points Cognitive dysfunction is present, and the number of years of education is less than or equal to 12 years, plus 1 point; the lowest CDR score is 0, the highest score is 3, and ≥0.5 points are considered to have cognitive dysfunction. Because the use of MMSE and MoCA scales alone will increase the false negative rate and false positive rate, respectively, the subjects in the HC and PDN groups should meet the requirements of MMSE≥26 points, MoCA≥26 points and CDR<0.5 points, and patients in the PDCI group. MMSE < 26 points and MoCA < 26 points and CDR ≥ 0.5 points at the same time;

表示，非正态分布的数据以中位数(四分位数间距)[M(Q_R)]，在两组比较中，满足参数检验条件的用两独立样本t检验，Mann-Whitney U检验则用于非参数检验，多组比较满足参数检验条件的用单因素方差分析(One-Way ANOVA)，进一步两两比较时根据方差齐性与否采用LSD法或Dunnett’s T3法；不满足参数检验条件的用Kruskal-Wallis检验，进一步的两两比较采用Bonferroni法校正P值，计数资料用卡方检验进行组间比较，变量间的相关分析，依据变量正态分布情况，采用Pearson或者Spearman相关分析，显著性水平设定为P＜0.05。其中，非参数检验事后两两比较需用Bonferroni法校正，以控制Ⅰ类错误总的发生概率，以双侧P＜0.0167为差异有统计学意义。Step 2: Statistical analysis: All statistical analyses were performed on SPSS 22.0, and assisted by GraphPad Prism 8.0.2 and MedCalc 19.0.4. Measurement data that met the normal distribution were calculated as mean ± standard deviation.

Indicates that the non-normally distributed data is expressed as the median (interquartile range) [M(Q _R )]. In the comparison of two groups, two independent samples t test and Mann-Whitney U test are used if the parametric test conditions are met. It is used for non-parametric test. One-way analysis of variance (One-Way ANOVA) is used for multi-group comparisons that meet the parametric test conditions. For further pairwise comparisons, LSD method or Dunnett's T3 method is used according to whether the variance is homogenous or not; it does not meet the parametric test. Conditional Kruskal-Wallis test was used, and the Bonferroni method was used to correct the P value for further pairwise comparisons. The count data were compared between groups using the chi-square test. The correlation analysis between variables was based on the normal distribution of variables. Pearson or Spearman correlation analysis was used. , and the significance level was set at P<0.05. Among them, the non-parametric test post-hoc comparison needs to be corrected by the Bonferroni method to control the total occurrence probability of type I error, and the difference is statistically significant with a two-sided P<0.0167.

Further, laboratory measurements of all cases and controls in specimen collection included GDNF, α-pro-GDNF, β-pro-GDNF levels, using enzyme-linked immunosorbent assay kits (GDNF: R&D America; GDNF precursor: Shanghai Enzyme) United China) in strict accordance with the test instructions.

Referring to Figure 1, a total of 26 PDNs, 27 PDCIs, and 26 HCs were included in the study, and demographic information, clinical characteristics, and disease conditions are shown in Figure 1. There were no significant differences among the three groups in terms of gender, age, smoking, drinking, hypertension, and the proportion of high school education or above (P>0.05). There were significant differences in H-Y staging and disease course between the two groups of PDCI (P<0.05); in Figure 1, HC, normal control group; PDN, Parkinson's disease without cognitive dysfunction group; PDCI, Parkinson's disease with cognitive dysfunction . A: Chi-square test; B: One-way ANOVA; C: Nonparametric test: Kruskal-Wallis test; D: Nonparametric test: Mann-Whitney U test. Disease duration (months), education level (high school and above), and UPDRS (III) were compared between groups using nonparametric tests, among which disease duration (months), UPDRS (III) were compared between two groups using Mann-Whitney U test, education Extent (years) were compared between three groups using the Kruskal-Wallis test;

Referring to Figure 2 and Figure 3, the serum GDNF level in the PDN group (679.43±175.58) pg/ml was compared with the HC group (494.80±188.92) pg/ml and the PDCI group (444.15±96.11) pg/ml, the difference was statistically significant ( F=16.101, P<0.001); further pairwise comparison, the level of GDNF in the PDN group was higher than that in the HC group, and the difference was statistically significant (P<0.001). The level of GDNF in the PDN group was higher than that in the PDCI group, and the difference was statistically significant Significant (P<0.001), HC group was higher than PDCI group, but the difference was not statistically significant (P>0.05). At the same time, we compared the differences in the concentrations of α-pro-GDNF and β-pro-GDNF among the three groups as well as GDNF/α-pro-GDNF, GDNF/β-pro-GDNF, α-pro-GDNF/β-pro- Differences among the ratios of the three groups of GDNF. GDNF/α-pro-GDNF in PDN group (0.34±0.11), HC group (0.31±0.11), PDCI group (0.27±0.09), the difference was statistically significant (F=3.297, P=0.042); Compared with the two groups, the level of GDNF/α-pro-GDNF in the PDN group was higher than that in the PDCI group, and the difference was statistically significant (P=0.012), but there was no significant difference between the PDN group and the HC group (P=0.232). There was no significant difference between the groups (P=0.181), and there was no significant difference in α-pro-GDNF, β-pro-GDNF, GDNF/β-pro-GDNF and α-pro-GDNF/β-pro-GDNF among the three groups. In Figure 2, HC, normal control group; PDN, Parkinson's disease without cognitive impairment group; PDCI, Parkinson's disease with cognitive impairment; GDNF: glial cell line-derived neurotrophic factor. One-way ANOVA was used to compare the means of the three groups of data. According to the results of the homogeneity of variance test, 1: Dunnett's T3 method was used for unequal variance, and 2: LSD method was used for homogeneity of variance, and the significance level was set at P < 0.05. *: The difference between the groups was statistically significant, #: The difference between the groups was not statistically significant; A: Compared with the PDN group, the difference was statistically significant; HC in Figure 3, normal control group; PDN, Parkinson's disease not With cognitive impairment group; PDCI, Parkinson's disease with cognitive impairment group. A: The distribution of GDNF in HC, PDN, and PDCI. Pairwise comparison, the difference between the PDN group and the HC group was statistically significant, P<0.001, and the difference between the PDN group and the PDCI group was statistically significant, P<0.001. 0.001, there was no significant difference between the HC group and the PDCI group; B: the distribution of α-pro-GDNF in HC, PDN, and PDCI, and there was no significant difference in α-pro-GDNF between the groups; C: The distribution of β-pro-GDNF in HC, PDN and PDCI, there was no significant difference in β-pro-GDNF between groups; D: The distribution of GDNF/α-pro-GDNF in HC, PDN and PDCI , Pairwise comparison, the difference between PDN group and PDCI group was statistically significant, P=0.012, there was no significant difference between PDN group and HC group, HC group and PDCI group; E: GDNF/β-pro-GDNF There was no significant difference between groups; F: Distribution of α-pro-GDNF/β-pro-GDNF in HC, PDN, PDCI, α-pro-GDNF/β-pro-GDNF was different between groups Not statistically significant. Significance levels were set at P<0.05, *P<0.05, **P<0.01, ***P<0.001.

Referring to Figure 4, in the comparison between the three groups of the overall cognitive function test, the MMSE score was significantly different among the three groups (P<0.001). Further pairwise comparison, the difference between the HC group and the PDCI group was statistically significant There was significant difference (P<0.001), PDN group and PDCI group had statistical significance (P<0.001), HC group and PDN group had no significant difference (P>0.05), the unadjusted P values were <0.001, < 0.001, =0.951, P values adjusted by Bonferroni method were <0.001, <0.001, =1.000; the comparison of MoCA and CDR scores among the three groups was similar to the MMSE score; in Figure 4, HC, normal control group; PDN, Parkinson's disease without cognitive impairment group; PDCI, Parkinson's disease with cognitive impairment group; MMSE, Mini-Mental State Scale; MoCA, Montreal Cognitive Assessment Scale, CDR, Clinical Dementia Rating Scale. The scores of the MMSE, MoCA and CDR scales did not meet the normal distribution, and the nonparametric Kruskal-Wallis test was used to compare the three groups.

Referring to Figure 5 and Figure 6, in order to verify the application value of GDNF and its precursors in clinical practice, we conducted a correlation analysis of serum GDNF and its precursors with cognitive scale scores, GDNF levels and MMSE scores, MoCA scores. Positive correlation (r=0.610, P<0.001; r=0.579, P<0.001), GDNF level was negatively correlated with CDR score (r=-0.573, P<0.001). We also analyzed the correlation between a-pro-GDNF (pg/ml), β-pro-GDNF (pg/ml), etc. and cognitive scale scores; MMSE in Figure 5, Mini-Mental State Scale; MoCA , Montreal Cognitive Rating Scale; CDR, Clinical Dementia Rating Scale; GDNF: Glial Cell Line-Derived Neurotrophic Factor. Spearman correlation analysis was used, and the significance level was set at P<0.05, **P<0.01, ***P<0.001; in Figure 6, MMSE, Mini-Mental State Scale; MoCA, Montreal Cognitive Assessment Scale; CDR, Clinical Dementia Rating Scale; GDNF: glial cell line-derived neurotrophic factor. (A-C) is the spearman correlation coefficient between MMSE and GDNF, r=0.610, P<0.001 (A); with GDNF/α-pro-GDNF, r=0.467, P<0.001 (B); with GDNF/β-pro- GDNF, r=0.455, P<0.001 (C). (D-F) is the spearman correlation coefficient between MoCA and GDNF, r=0.579, P<0.001 (D); with GDNF/α-pro-GDNF, r=0.323, P=0.018 (E); with GDNF/β-pro- GDNF, r=0.362, P=0.008 (F). (G-I) is the spearman correlation coefficient between CDR and GDNF, r=-0.573, P<0.001 (G); with GDNF/α-pro-GDNF, r=-0.379, P=0.005 (H); with GDNF/β- pro-GDNF, r=-0.390, P=0.004 (I). The correlation coefficient and P value are shown in the figure above, and the shaded area is the 95% confidence interval, n=53.

Referring to Figure 7 and Figure 8, in order to find risk factors for cognitive impairment in PD, we conducted regression analysis. In the binary Logistic regression analysis model of cognitive impairment in PD patients (Figure 7), we included gender, age, Education level (years), HY stage, disease duration (months), 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 other variables were analyzed by likelihood ratio test (LRT). Hosmer-Lameshaw (HL) test P>0.05 indicated that the regression model could fit the experimental data well. The results showed that the variables that had a significant impact on cognition were GDNF (pg/ml) and HY stage. We then performed a stepwise linear regression analysis (Figure 8), and the results showed that the variables that had an impact on the MMSE score were GDNF (pg/ml), HY stage, a-pro-GDNF, and the adjusted R2 was ^0.561 ; The influencing variables were GDNF (pg/ml), HY stage, education level (years), and the adjusted R ² was 0.521; the variables that had a significant impact on the CDR score were GDNF (pg/ml), HY stage, and the adjusted R ² is 0.465. Among them, GDNF (pg/ml) had a significant impact on the scores of MMSE, MoCA, and CDR, and educational level (years) only had an impact on the scores of the MoCA scale; HC, normal control group in Figure 7; PDN, Parkinson's Disease without cognitive impairment group; PDCI, Parkinson's disease with cognitive impairment. Binary Logistic regression analysis of PDCI, likelihood ratio test (LRT) was used to evaluate the influencing factors of PDCI, gender, age, education level (years), HY stage, disease course, 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 other factors were included in the equation, and the binary logistic regression analysis showed that HY stage and GDNF (pg/ml) had a significant impact on the cognitive function of PD patients; GDNF in Figure 8: glial cell line-derived neural nutritional factors. Stepwise linear regression was used to evaluate the influencing factors of MMSE, MoCA and CDR scores in PD patients. Gender, age, education level (years), HY stage, disease course, 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 other factors were incorporated into the equation. Figure 8 shows the stepwise linear regression analysis of the influencing factors of MMSE score, MoCA score and CDR score in PD patients. The results show that GDNF (pg/ml), HY stage, and a-pro-GDNF have a significant impact on the MMSE score of PD patients; GDNF (pg/ml), HY stage, education level (years) had a significant impact on MoCA in PD patients; GDNF (pg/ml), HY stage, had a significant impact on CDR score in PD patients.

Referring to Figure 9, the efficacy of GDNF and its precursor levels in predicting PDCI was analyzed by establishing ROC curve to evaluate its clinical value in diagnosing PDCI. Two groups of patients were included in the prediction and diagnosis of PDCI, namely the PDN group and the PDCI group. The distinction between PDN and PDCI was based on the comprehensive scores of MMSE, MoCA and CDR. AUROC curve of GDNF serum levels predicting PDCI (AUC=0.859, P<0.001, 95%CI: 0.736-0.939). The optimal cut-off value of serum GDNF for the diagnosis of PDCI was 508.99pg/ml, and the sensitivity and specificity values were 85.19% and 84.62%, respectively, that is, in PD patients, GDNF concentration ≥508.991pg/ml was PDN, and <508.991pg The correct rate of /ml for PDCI is about 0.859. We further explored whether GDNF, GDNF/α-pro-GDNF, GDNF/β-pro-GDNF as composite biomarkers have better diagnostic value for PDCI, we evaluated by Logistic regression analysis, and compared by ROC analysis , the results of the ROC curve of the complex to predict PDCI (AUC=0.862, P<0.001), sensitivity 92.11%, specificity 72.22%) showed that the combined diagnostic effect of the complex was not significantly better than GDNF. The level of GDNF in the PDN group (679.43±175.58) pg/ml was significantly higher than that in the HC group (494.80±188.92) pg/ml and the PDCI group (444.15±96.11) pg/ml, and the difference was statistically significant (P<0.001, P<0.001). ), but there was no significant difference between the HC group and the PDCI group (P>0.05). The GDNF concentration in the HC, PDN and PDCI groups increased first and then decreased, and the GDNF level was more than moderately correlated with MMSE, MoCA and CDR (r=0.610, P<0.001; r=0.579, P<0.001; r=0.610, P<0.001; r=0.579, P<0.001; =-0.573, P<0.001), GDNF/α-pro-GDNF, GDNF/β-pro-GDNF and cognitive and psychological function scales have a high correlation. The Receiver Operating Characteristic Curve (ROC) of GDNF to predict the cognitive function status of Parkinson's disease was established. The area under the curve (AUC) was 0.859, P<0.001, so serum GDNF can be used as the diagnosis of PDCI. The marker, serum GDNF can effectively predict PDCI; the GDNF line in Figure 9 represents the ROC curve of GDNF for predicting PDCI, AUC=0.859, 95%CI: 0.736-0.939, sensitivity 85.19%, specificity 84.62%; Composite line is a complex ROC curve for predicting PDCI, complex=(GDNF vs GDNF/a-pro-GDNF vs GDNF/β-pro-GDNF), AUC=0.862, P<0.001. The cutoff value of GDNF for diagnosing PDCI was 508.99 pg/ml.

It will be apparent to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, but that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments are to be regarded in all respects as illustrative and not restrictive, and the scope of the invention is to be defined by the appended claims rather than the foregoing description, which are therefore intended to fall within the scope of the claims. All changes within the meaning and scope of the equivalents of , are included in the present invention. Any reference signs in the claims shall not be construed as limiting the involved claim.

Claims (2)

1. the method for predicting Parkinson's disease cognitive dysfunction based on GDNF, including test object and material and evaluation method, it is characterized in that: in described test object and material, test object includes subject screening and specimen collection, described subject The screening was to collect outpatient and inpatient primary PD patients in the Affiliated Hospital of Xuzhou Medical University from April 2018 to August 2019. Two experienced neurologists conducted detailed demographic, medical history, disease course, The collection and evaluation of data such as motor symptoms and treatment conditions to establish a diagnosis can ensure that the correct rate of PD diagnosis is more than 90%. All neurological scales are evaluated under the condition that the patient is in good mental state and cooperates well. Inclusion criteria : (1) Age 55-75 years old; (2) Be able to complete all neuropsychological and psychobehavioral assessments under the guidance of a physician, with no barriers to listening, speaking, reading, and comprehension; (3) Diagnosis of Parkinson's disease must be made by two Experienced neurologists made independent diagnosis according to the 2015 Movement Disorders Society (MDS) diagnostic criteria and the British Parkinson's disease brain bank diagnostic criteria. Exclusion criteria: (1) All patients should be CT or MRI to exclude other neurological diseases other than PD. Medical history: moderate to severe craniocerebral injury, stroke or vascular dementia, etc.; (2) secondary Parkinson's syndrome such as drug-induced, head trauma and vascular disease; Parkinson's syndrome such as progressive supranuclear palsy and multiple system atrophy Superposition syndrome; (3) mental diseases such as severe anxiety, depression and schizophrenia; (4) systemic diseases such as heart, liver, kidney and other diseases that may affect cognitive function; The specimens were collected as the patient serum was collected at 7:00-8:00 in the morning of the second day of admission (outpatients and healthy control groups were collected between 7:00 and 8:00 on the day of consultation), and the serum was not allowed after 22:00 the night before. Food and water were forbidden. After taking the specimen, let it stand at room temperature for 2 hours, and centrifuge at 1000g for 10 minutes at 4°C. In order to ensure that the serum components are not destroyed as much as possible, immediately after the centrifugation, it was divided into 500ul EP tubes and stored at -80°C for further testing; The evaluation method includes the following steps: Step 1, neuropsychological assessment: conduct a comprehensive assessment of the overall cognitive function of all subjects. The assessment of all patients is completed under the condition of good mental state and good cooperation. Overall cognitive function was assessed using the Mini-Mental State Scale MMSE, Montreal Cognitive Scale MoCA and Clinical Dementia Rating Scale CDR: MMSE total score of 30 points, <26 points with cognitive impairment; MoCA total score of 30 points, <26 points Cognitive dysfunction is present, and the number of years of education is less than or equal to 12 years, plus 1 point; the lowest score of the CDR scale is 0, the highest score is 3, and ≥0.5 points are considered to have cognitive dysfunction. Because using the MMSE and MoCA scales alone will increase the false-negative rate and false-positive rate, respectively, the subjects in the HC and PDN groups should meet the requirements of MMSE≥26 points, MOCA≥26 points and CDR<0.5 points, and patients in the PDCI group. MMSE < 26 points and MoCA < 26 points and CDR ≥ 0.5 points at the same time; Step 2: Statistical analysis: All statistical analyses were performed on SPSS 22.0, and assisted by GraphPad Prism 8.0.2 and MedCalc 19.0.4. Measurement data that met the normal distribution were calculated as mean ± standard deviation.

Indicates that the non-normally distributed data is expressed as the median (interquartile range) [M(Q _R )]. In the comparison of two groups, two independent samples t test and Mann-Whitney U test are used if the parametric test conditions are met. It is used for non-parametric test. One-way analysis of variance (One-Way ANOVA) is used for multi-group comparisons that meet the parametric test conditions. For further pairwise comparisons, LSD method or Dunnett's T3 method is used according to whether the variance is homogenous or not; it does not meet the parametric test. Conditional Kruskal-Wallis test was used, and the Bonferroni method was used to correct the P value for further pairwise comparisons. The count data were compared between groups using the chi-square test. The correlation analysis between variables was based on the normal distribution of variables. Pearson or Spearman correlation analysis was used. , and the significance level was set at P<0.05. Among them, the non-parametric test post-hoc comparison needs to be corrected by the Bonferroni method to control the total occurrence probability of type I error, and the difference is statistically significant with a two-sided P<0.0167. 2. the method for predicting Parkinson's disease cognitive dysfunction based on GDNF according to claim 1, is characterized in that: the laboratory measurement of all cases and control group in described specimen collection all comprises GDNF, α-pro-GDNF, The level of β-pro-GDNF was measured using an enzyme-linked immunosorbent assay kit (GDNF: R&D USA; GDNF precursor: Shanghai Enzyme Link China) in strict accordance with the test instructions.

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