CN115312187A

CN115312187A - System for predicting Parkinson's disease cognitive dysfunction based on GDNF

Info

Publication number: CN115312187A
Application number: CN202210948420.7A
Authority: CN
Inventors: 史明语; 高殿帅; 刘依放; 徐伟; 陈雾; 严亚龙
Original assignee: Xuzhou Medical University
Current assignee: Xuzhou Medical University
Priority date: 2022-08-09
Filing date: 2022-08-09
Publication date: 2022-11-08

Abstract

The invention discloses a system for predicting Parkinson's disease cognitive dysfunction based on GDNF, which comprises a data acquisition and input module and a data statistics module connected with the data acquisition and input module, wherein the output end of the data statistics module is connected with a central processing unit, the output end of the central processing unit is respectively connected with a neuropsychological evaluation test statistics module and a data analysis and screening module, and the output end of the data analysis and screening module is connected with a prediction result output module. The method is divided into a Parkinson disease accompanied cognitive dysfunction PDCI group and a Parkinson disease unconjugated cognitive dysfunction PDN group according to a simple mental state quantity MMSE, a Montreal cognitive assessment scale MoCA and a clinical dementia assessment scale CDR cognitive mental function scale score, and the relationship between serum GDNF and precursor levels thereof and the cognitive mental function scores of all groups of people is detected and analyzed by an Elisa method, so that whether the patient meets the cognitive dysfunction condition of the Parkinson disease is judged.

Description

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

Technical Field

The invention relates to the technical field of Parkinson's disease cognitive dysfunction prediction, in particular to a system for predicting Parkinson's disease cognitive dysfunction based on GDNF.

Background

Parkinson's disease PD is the second largest neurodegenerative disease, with a prevalence of about 2% -3% in people over 65 years of age. The loss of DN degeneration in dopaminergic neurons of the substantia nigra leads to DA deficiency of dopamine in the striatum and intracellular formation of inclusion bodies characterized by alpha-synuclein, which are the main neuropathological features of Parkinson's disease. The PD course is accompanied by a number of non-motor symptoms NMS that exacerbate the disability level of the patient, causing significant physical and psychological trauma. In recent years NMS of PD has received increasing attention, especially cognitive dysfunction CI. About 30% of PD patients have mild cognitive dysfunction MCI, which is a risk factor for developing dementia, and CI is difficult to block, the treatment effect is poor, and the quality of life of the patients themselves and family members is greatly affected. Therefore, it is important to recognize and improve cognitive dysfunction PDCI of parkinson's disease as early as possible. However, CI is hidden, is not easy to be perceived, early diagnosis is relatively difficult, the evaluation of the cognitive function scale has certain subjectivity, and the evaluation result fluctuates along with the state of a patient.

Disclosure of Invention

The invention aims to provide a system for predicting Parkinson's disease cognitive dysfunction based on GDNF, which solves the existing problems.

In order to achieve the purpose, the invention provides the following technical scheme: the system for predicting Parkinson's disease cognitive dysfunction based on GDNF comprises a data acquisition and entry module and a data statistics module connected with the data acquisition and entry module, wherein the output end of the data statistics module is connected with a central processing unit, the output end of the central processing unit is respectively connected with a neuropsychological evaluation test statistics module and a data analysis and screening module, and the output end of the data analysis and screening module is connected with a prediction result output module;

the data acquisition and input module is used for acquiring the medical history, motion symptoms and treatment conditions of the Parkinson's disease and inputting information into the system;

the data statistics module is used for establishing a database of the Parkinson cognitive dysfunction according to the age group and the cognitive dysfunction degree of the acquired data;

the central processing unit is used for receiving the data fed back by the data statistical module, screening the data meeting the judgment standard of the Parkinson cognitive dysfunction by adopting data analysis software, and summarizing and establishing a Parkinson cognitive dysfunction prediction model;

the neuropsychological evaluation test statistic module is used for counting test data of the Parkinson disease patient to be tested according with the cognitive dysfunction judgment standard by using the simple mental state scale MMSE, the Montreal cognitive scale MoCA and the clinical dementia rating scale CDR, and eliminating abnormal test data;

the data analysis and screening module is used for screening the Parkinson disease patients meeting the conditions through a simple mental state scale MMSE, a Montreal cognition scale MoCA and a clinical dementia rating scale CDR and meeting the range of GDNF serum level;

and the prediction result output module is used for outputting the prediction result of the cognitive dysfunction of the Parkinson disease.

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

the method is divided into a Parkinson disease accompanied cognitive dysfunction PDCI group and a Parkinson disease unconjugated cognitive dysfunction PDN group according to a simple mental state quantity MMSE, a Montreal cognitive assessment scale MoCA and a clinical dementia assessment scale CDR cognitive mental function scale score, and the relationship between serum GDNF and precursor levels thereof and the cognitive mental function scores of all groups of people is detected and analyzed by an Elisa method, so that whether the patient meets the cognitive dysfunction condition of the Parkinson disease is judged.

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 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 the correlation analysis of serum GDNF and its precursors to 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 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 with GDNF and its complexes;

FIG. 10 is a system diagram of the present invention.

In the figure: 1. a data acquisition and input module; 2. a data statistics module; 3. a central processing unit; 4. a neuropsychological evaluation test statistic module; 5. a data analysis screening module; 6. and a prediction result output module.

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.

Embodiment 1

As shown in FIG. 10, the system for predicting Parkinson's disease cognitive dysfunction based on GDNF comprises a data acquisition and entry module 1 and a data statistics module 2 connected with the data acquisition and entry module 1, and is characterized in that: the output end of the data statistical module 2 is connected with a central processing unit 3, the output end of the central processing unit 3 is respectively connected with a neuropsychological evaluation test statistical module 4 and a data analysis screening module 5, and the output end of the data analysis screening module 5 is connected with a prediction result output module 6;

the data acquisition and recording module 1 is used for acquiring the medical history, the motion symptoms and the treatment conditions of the Parkinson disease patient and recording information into a system;

the data statistics module 2 is used for establishing a database of the Parkinson cognitive dysfunction according to the age group and the cognitive dysfunction degree of the acquired data;

the central processing unit 3 is used for receiving the data fed back by the data statistical module 2, screening the data meeting the judgment standard of the Parkinson cognitive dysfunction by adopting data analysis software, and summarizing and establishing a Parkinson cognitive dysfunction prediction model;

the neuropsychological evaluation test statistic module 4 is used for counting test data of the Parkinson disease patients tested by the simple mental state scale MMSE, the Montreal cognition scale MoCA and the clinical dementia rating scale CDR according with the cognitive dysfunction judgment standard, and eliminating abnormal test data;

the data analysis and screening module 5 is used for screening the Parkinson's disease patients meeting the conditions through a simple mental state scale MMSE, a Montreal cognition scale MoCA and a clinical dementia rating scale CDR and meeting the range of GDNF serum level;

and the prediction result output module 6 is used for outputting the prediction result of the cognitive dysfunction of the Parkinson disease.

Example II

1. Criteria for the test Parkinson's disease

Grouping standard: (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) The diagnosis of Parkinson's disease must be independently diagnosed by two experienced neurologists according to 2015 society for dyskinesia (MDS) diagnosis standards and referring to British Parkinson brain library diagnosis standards;

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, etc. and other diseases that may affect cognitive function.

2. Collecting serum from Parkinson's disease patient

Collecting samples, namely collecting the serum of a patient on the second morning of admission, wherein the serum is collected at 7-00;

laboratory measurements in all cases and controls in specimen collections included 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) strictly in accordance with the experimental instructions.

3. Prediction of cognitive dysfunction in parkinson's disease

31 ), neuropsychological evaluation criteria

Selecting Parkinson's disease patients with relatively stable current mental states, and screening the Parkinson's disease patients meeting the cognitive dysfunction standard by using a simple mental state scale MMSE, a Montreal cognitive scale MoCA and a clinical dementia rating scale CDR;

wherein, the MMSE is divided into 30 points and less than 26 points, and cognitive dysfunction exists; 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 is divided into 0 point at the lowest point and 3 points at the highest point, and the cognitive dysfunction is considered to exist in more than or equal to 0.5 point;

because the false negative rate and the false positive rate are respectively increased by independently using MMSE and MoCA scales, HC and PDN group subjects need to simultaneously meet MMSE which is more than or equal to 26 minutes, moCA which is more than or equal to 26 minutes and CDR which is less than 0.5 minute, PDCI group patients need to simultaneously meet MMSE which is less than 26 minutes, moCA which is less than 26 minutes and CDR which is more than or equal to 0.5 minute, and then the judgment is made that the cognitive dysfunction is not accompanied;

32 Analysis of Parkinson's disease data)

Inputting the collected Parkinson disease information into an SPSS22.0 analysis system, and using GraphPadPrism8.0.2 and MedCac19.0.4 for assistance, and averaging the metering data meeting normal distribution (the average value of)

) Standard deviation of the data for non-normal distributions in median (interquartile range) [ M: (

)]In two groups of comparison, two independent samples are used for t test when the parameter test condition is met, the Mann-WhitneyU test is used for non-parameter test, a plurality of groups of comparisons are used for single-factor analysis of variance (One-WayANOVA) when the parameter test condition is met, and an LSD method or a Dunnett' sT3 method is adopted when the comparison is further carried out pairwise according to the homogeneity of the variance; the method comprises the following steps of (1) performing Kruskal-Wallis test on the samples which do not meet parameter test conditions, further performing pairwise comparison to correct a P value by adopting a Bonferroni method, performing intergroup comparison on 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; after nonparametric inspection, pairwise comparison is corrected by a Bonferroni method to control the total occurrence probability of I-type errors, and the difference with P & lt 0.0167 on two sides has statistical significance.

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. 2; 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 analysis of variance (one-way ANOVA); c: non-parametric test, kruskal-Wallis test; d: non-parameter testing: mann-WhitneyU test; disease duration (month), education (high school and above), UPDRS (III) were compared between groups using a non-parametric test, where disease duration (month), UPDRS (III) were compared between two groups using the Mann-whitney u test and education (year) was compared between three groups using the Kruskal-Wallis test.

Referring to FIGS. 2 and 3, the PDN group serum GDNF levels (679.43 + -175.58) pg/ml were statistically different (F =16.101, P < 0.001) compared to HC (494.80 + -188.92) pg/ml and PDCI (444.15 + -96.11) pg/ml;

further comparing every two, the GDNF level of PDN group is higher than that of HC group, the difference has statistical significance (P is less than 0.001), the GDNF level of PDN group is higher than that of PDCI group, the difference has statistical significance (P is less than 0.001), HC group is higher than PDCI group, but the difference has no statistical significance (P is more than 0.05); meanwhile, we compared the differences between the concentrations of three groups of alpha-pro-GDNF and beta-pro-GDNF and the differences between the ratios of three groups of GDNF/alpha-pro-GDNF, GDNF/beta-pro-GDNF and alpha-pro-GDNF; GDNF/α -pro-GDNF the differences in PDN group (0.34 ± 0.11) compared to HC group (0.31 ± 0.11) and PDCI group (0.27 ± 0.09) were statistically significant (F =3.297, p = 0.042);

further comparing two by 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 = 0.012), the difference between the PDN group and the HC group has no statistical significance (P = 0.232), the difference between the HC group and the PDCI group has no statistical significance (P = 0.181), and the differences among the three groups of alpha-pro-GDNF, beta-pro-GDNF, GDNF/beta-pro-GDNF and 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 (3) comparing the mean values of the three groups of data by adopting one-wayaanova, and according to the result of homogeneity test of the variance, the variance is 1: dunnett's T3 method, variance, taken at 2: LSD method, significance level is set as P < 0.05; * : comparative differences among groups are statistically significant, #: the comparison difference between groups has no statistical significance; a: the differences are 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 condition of beta-pro-GDNF in HC, PDN and PDCI, and the comparative difference of the beta-pro-GDNF among groups has no statistical significance; d: the distribution conditions of GDNF/alpha-pro-GDNF in HC, PDN and PDCI are compared pairwise, the difference between the PDN group and the PDCI group has statistical significance, P =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 has no statistical significance in comparison of differences among groups; f: distribution of alpha-pro-GDNF/beta-pro-GDNF in HC, PDN and PDCI, compared difference between alpha-pro-GDNF/beta-pro-GDNF has no statistical significance, and significance level is set as P < 0.05, P < 0.01, P < 0.001

Referring to fig. 4, in the comparison between groups of the three groups of overall cognitive function tests, the MMSE score has statistical significance (P < 0.001) in the difference between three groups, and further, two-by-two comparison is performed, the HC group has statistical significance (P < 0.001) in the difference between the PDCI group, the PDN group has statistical significance (P < 0.001) in the difference between the PDCI group, the HC group has no statistical significance (P > 0.05) in the difference between the PDN group, the unadjusted P values are < 0.001, =0.951, and the P values after Bonferroni adjustment are < 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, a non-parametric test Kruskal-Wallis test is used for comparing the three groups, the results were corrected by Bonferroni and significance levels were set at P < 0.0167.

Referring to FIGS. 5 and 6, to verify the utility of GDNF and its precursors in clinical practice, a correlation analysis of serum GDNF and its precursors with cognitive scale scores was performed, with GDNF levels positively correlated with MMSE score, moCA score (r =0.610, P < 0.001, r =0.579, P < 0.001), GDNF levels negatively correlated with CDR score (r = -0.573, P < 0.001); analyzing the correlation between a-pro-GDNF (pg/ml), beta-pro-GDNF (pg/ml) and the like and the cognitive scale score;

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 spaerman correlation coefficient of MMSE with GDNF, r =0.610, P < 0.001 (A); with GDNF/α -pro-GDNF, r =0.467, P < 0.001 (B); with GDNF/. Beta. -pro-GDNF, r =0.455, P < 0.001 (C); (D-F) is the spaerman correlation coefficient of MoCA with 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 of CDR and GDNF, r = -0.573, P < 0.001 (G); with GDNF/α -pro-GDNF, r = -0.379, p = -0.005 (H); with GDNF/. Beta. -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 =53.

Referring to fig. 7 and 8, in order to find the risk factors for cognitive dysfunction in PD, regression analysis was performed, and in a binary Logistic regression analysis model of cognitive dysfunction in PD patients (fig. 7), we included variables such as sex, age, education degree (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 Likelihood Ratio Test (LRT) was used to analyze;

the Hosimer-Leimex (H-L) test with P > 0.05 suggested that the regression model could fit the experimental data well, with the results showing that the variables that significantly affected 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, adjusted to 0.561R 2; the variables affecting MoCA score were GDNF (pg/ml), H-Y staging, education (years), adjusted to 0.521 for R2; the variables which have obvious influence on the CDR score include GDNF (pg/ml) and H-Y stage, and the adjusted R2 is 0.465; GDNF (pg/ml) has obvious influence on MMSE, moCA and CDR scores, and the education degree (year) only has influence on the MoCA scale score;

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 binary Logistic regression analysis result 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; evaluating the influencing factors of MMSE, moCA and CDR scores of PD patients by stepwise linear regression; factors such as sex, age, education level (year), H-Y staging, 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, etc. are included in the equation;

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 produced significant effects.

Referring to fig. 9, by establishing ROC curves, the efficacy of predicting PDCI by indices such as GDNF and its precursor levels was analyzed to evaluate its clinical value for diagnosing PDCI; the prognostic diagnosis of PDCI comprises two groups of patients, namely a PDN group and a PDCI group, wherein the PDN and the PDCI are distinguished according to the comprehensive scores of MMSE, moCA and CDR; AUROC curve for predicting PDCI for GDNF serum levels (AUC =0.859, P < 0.001, 95% CI; the optimal cut-off value of serum GDNF for diagnosing the PDCI is 508.99pg/ml, the sensitivity and specificity values are 85.19 percent and 84.62 percent respectively, namely in PD patients, the accuracy rate of the PDCI with the GDNF concentration of more than or equal to 508.991pg/ml as PDN and less than 508.991pg/ml as PDCI is about 0.859;

further discussing whether GDNF, GDNF/alpha-pro-GDNF and GDNF/beta-pro-GDNF as composite biomarkers have better diagnostic value on PDCI, evaluating through Logistic regression analysis, and comparing through ROC analysis, the results of ROC curves (AUC =0.862, P < 0.001) of the PDCI predicted by the composite are 92.11% of sensitivity and 72.22% of specificity), which shows that the combined diagnostic effect of the composite is not obviously superior to 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 alpha-pro-GDNF and beta-pro-GDNF in each group is not statistically different; GDNF concentration shows a trend of increasing first and then decreasing in HC, PDN and PDCI groups, GDNF level has more than moderate correlation with MMSE, moCA and CDR (r =0.610, P < 0.001, r =0.579, P < 0.001, r = -0.573, P < 0.001), GDNF/alpha-pro-GDNF, GDNF/beta-pro-GDNF have higher correlation with cognitive mental function tables; establishing a receiver working characteristic curve ROC for predicting the Parkinson disease cognitive function state by GDNF, wherein the area AUC under the curve is 0.859, and P is less than 0.001, so that the serum GDNF can be used as a diagnostic marker of the 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, the sensitivity is 85.19 percent, and the specificity is 84.62 percent; the Composite line predicts the ROC curve of PDCI for the compound, = (GDNFvsGDNF/a-pro-GDNFvsGDNF/beta-pro-GDNF), AUC =0.862, P < 0.001; the cutoff value for GDNF diagnostic PDCI was 508.99pg/ml.

Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims

1. GDNF-based system for predicting Parkinson's disease cognitive dysfunction comprises a data acquisition entry module (1) and a data statistics module (2) connected with the data acquisition entry module (1), and is characterized in that: the output end of the data statistics module (2) is connected with a central processing unit (3), the output end of the central processing unit (3) is respectively connected with a neuropsychological evaluation test statistics module (4) and a data analysis screening module (5), and the output end of the data analysis screening module (5) is connected with a prediction result output module (6);

the data acquisition and recording module (1) is used for acquiring the medical history, motion symptoms and treatment conditions of the Parkinson's disease and recording information into the system;

the data statistics module (2) is used for establishing a database of the Parkinson cognitive dysfunction according to the acquired data and the age group and the cognitive dysfunction degree;

the central processing unit (3) is used for receiving the data fed back by the data statistical module (2), screening the data meeting the judgment standard of the Parkinson cognitive dysfunction by adopting data analysis software, and summarizing and establishing a Parkinson cognitive dysfunction prediction model;

the neuropsychological evaluation test statistic module (4) is used for counting test data of the Parkinson disease patients tested by the simple mental state scale MMSE, the Montreal cognition scale MoCA and the clinical dementia rating scale CDR according with the cognitive dysfunction judgment standard, and eliminating abnormal test data;

the data analysis and screening module (5) is used for screening Parkinson disease patients meeting the conditions through a simple mental state scale MMSE, a Montreal cognition scale MoCA and a clinical dementia assessment scale CDR and meeting the range of GDNF serum level;

and the prediction result output module (6) is used for outputting the prediction result of the cognitive dysfunction of the Parkinson disease.