A biomarker for assessing the risk of motor complications in patients with parkinson's disease and a corresponding kit

Abstract

The application provides a biomarker dopamine-3-O-sulfate (DA3S) for evaluating the risk of movement complications of Parkinson's disease patients. The application also provides a cerebrospinal fluid kit for evaluating the risk of movement complications of Parkinson's disease patients. The application is based on the concentration of DA3S in cerebrospinal fluid, and combines with clinical variables such as daily dose of levodopa (LEDD), disease duration, etc. to construct a joint prediction model, so as to realize early and quantitative individualized evaluation of the risk of movement complications of Parkinson's disease patients. The application also provides a plasma kit and a urine kit for evaluating the risk of movement complications of Parkinson's disease patients.

Description

Technical Field

[0001] This invention relates to a biomarker and a corresponding kit for assessing the risk of motor complications in patients with Parkinson's disease. Background Technology

[0002] Parkinson's disease (PD) is a common neurodegenerative disease, and levodopa is its most commonly used treatment. However, long-term use of levodopa can lead to motor complications in approximately 50% of patients after 5-10 years of treatment, mainly including motor fluctuations (such as the "end-of-dose phenomenon") and dyskinesia (such as levodopa-induced dyskinesia, LID). These complications severely impair patients' quality of life and increase the difficulty of treatment. Early identification of these levodopa-related complications and the development of corresponding biomarkers are key goals in PD management. Currently, the technological approaches in this field mainly focus on the following aspects:

[0003] (1) Machine learning algorithms for symptom classification and risk prediction

[0004] Neural and non-neural machine learning algorithms have been used to classify motor symptoms such as tremor, gait abnormalities, bradykinesia, and movement disorders in Parkinson's disease patients. These models typically rely on the analysis of video data or clinical scales to extract key motor features and achieve automatic symptom severity rating. However, this approach analyzes symptoms after they have appeared, has limited prospective predictive ability for complications, and its performance is significantly affected by data quality and labeling consistency.

[0005] (2) Objective quantification technology combining sensors and artificial intelligence

[0006] Inertial sensors (such as smartwatches and wearable devices like MM4PD) are being combined with artificial intelligence to continuously monitor tremors and movement disorders in home environments, thereby objectively quantifying motor symptoms. These systems aim to capture symptom fluctuations to support personalized treatment decisions. However, their application is limited by device cost, patient compliance (requiring long-term wear), and the complexity of data processing algorithms, making widespread adoption in primary healthcare institutions difficult.

[0007] (3) Remote monitoring and digital assessment tools

[0008] By collecting continuous sensor data (such as wrist movement data), digital assessment tools can remotely monitor the severity of movement and capture subtle movement changes that are difficult for the human eye to detect. Although these technologies show potential in predicting disease progression, they also face the limitations mentioned above (2) and primarily reflect the results of movement output rather than the underlying pathophysiological processes that lead to complications.

[0009] In summary, most existing technologies focus on monitoring and analyzing motor symptoms in Parkinson's disease (PD). Biomarkers are primarily used for diagnosing or predicting Parkinson's disease itself or its cognitive and gait symptoms, but do not address the prediction of motor complications. Furthermore, these methods largely rely on specialized, high-end equipment, limiting their widespread clinical application. Currently, there is a lack of prospective molecular biomarkers for predicting the risk of motor complications. To address this gap, research is actively exploring the levels of dopamine metabolites in cerebrospinal fluid and plasma as potential indicators of dopaminergic function. Dopamine-3-O-sulfate is a major phase metabolite of dopamine, but its value in assessing the risk of PD motor complications has not been fully explored. Currently, dopamine-3-O-sulfate is not used as a biomarker for motor complications, and a combined predictive model based on LEDD (radioactive diagnoses) and disease progression has not been constructed, hindering early identification and precise stratified intervention for PD patients at high risk of motor complications. Summary of the Invention

[0010] To address the shortcomings of existing technologies, this invention proposes for the first time the use of dopamine-3-O-sulfate as a biomarker for assessing the risk of motor complications in Parkinson's disease. It also constructs for the first time a combined predictive model based on DA3S+LEDD+disease course, and provides specific coefficients. The main objective of this invention is to provide a biomarker and kit for assessing the risk of motor complications in Parkinson's disease patients. The proposed solution is characterized by its strong prospective predictive ability and ease of clinical application.

[0011] To achieve the aforementioned main objectives, in one respect, the present invention provides a biomarker for assessing the risk of motor complications in patients with Parkinson's disease, the biomarker being dopamine-3-O-sulfate.

[0012] On the other hand, the present invention provides a cerebrospinal fluid kit for assessing the risk of motor complications in patients with Parkinson's disease, the cerebrospinal fluid kit including a detection reagent for measuring the concentration of dopamine-3-O-sulfate in the cerebrospinal fluid.

[0013] Furthermore, the cerebrospinal fluid reagent kit performs the following steps:

[0014] a) Measure the concentration of dopamine-3-O-sulfate in cerebrospinal fluid;

[0015] b) Obtain the daily levodopa dose and disease course in Parkinson's disease patients through external input;

[0016] c) Establish the following risk assessment model

[0017] logit(p)=β0+β1·DA3S+β2·LEDD+β3·Duration

[0018] Where p is the probability of developing sports complications.

[0019]

[0020] Intercept β0 = -1.4525, DA3S coefficient β1 = 1.4374, LEDD coefficient β2 = 0.0014, Duration coefficient β3 = 0.2239;

[0021] DA3S is the logarithm of the concentration of dopamine-3-O-sulfate in cerebrospinal fluid. 10 Conversion value, LEDD is the levodopa equivalent daily dose for Parkinson's disease patients, and Duration is the duration of Parkinson's disease (in years);

[0022] d) Output the risk probability value p.

[0023] In this invention, the presence of exercise complications is defined by an internationally recognized MDS-UPDRS Part IV score greater than 0.

[0024] Based on in-depth mining and analysis of PPMI (Programme for Progression of Parkinson's Disease) cohort data, this invention has achieved the following key parameters and effects:

[0025] 1. Independent predictive value: DA3S is an independent predictor of exercise complications; for every 1 log unit increase in its concentration, the risk of exercise complications increases 4.21-fold (OR = 4.21, 95% CI: ).

[0026] 1.994-9.733).

[0027] 2. High-performance predictive model: The three-indicator combination model consisting of DA3S, LEDD and disease course has a predictive efficacy AUC of 0.807, a specificity of 85.6%, a positive predictive value of 72.4%, and a negative predictive value of 79.6%, showing good discriminative ability.

[0028] 3. Effective Risk Stratification: The introduction of decision tree methods for stratified modeling enables more refined risk differentiation within specific clinical subgroups. For example, in patients with LEDD ≥ 360 mg and disease duration < 3.14 years, using DA3S concentration of 0.209 as a threshold, high-risk individuals (complication rate 54.9%) can be effectively distinguished from low-risk individuals (complication rate 13.2%).

[0029] In another aspect, the present invention provides a plasma kit for assessing the risk of motor complications in patients with Parkinson's disease, the kit comprising a detection reagent for measuring the concentration of dopamine-3-O-sulfate in plasma.

[0030] In another aspect, the present invention provides a urine kit for assessing the risk of motor complications in patients with Parkinson's disease, the kit comprising a detection reagent for measuring the concentration of dopamine-3-O-sulfate in urine.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. Prospective and Mechanistic Relevance: DA3S originates directly from the dopamine metabolic pathway and can more directly reflect the metabolic status of dopamine in the brain, providing a molecular basis for early risk warning of motor complications, rather than monitoring only after symptoms appear.

[0033] 2. Precise Quantification and Personalization: By establishing a mathematical model that integrates biomarkers and clinical variables, it outputs individualized risk probabilities, achieving a leap from qualitative judgment to quantitative assessment and supporting clinical decision-making.

[0034] 3. Flexible sample selection and non-invasive / minimally invasive options: In addition to cerebrospinal fluid, this invention is also applicable to plasma and urine samples. Obtaining plasma and urine samples is more convenient and non-invasive, greatly improving accessibility to testing and patient acceptance, facilitating large-scale screening and long-term dynamic monitoring.

[0035] 4. Strong clinical applicability: The reagent kit is simple in design, and the model calculation can be completed automatically by software. It is easy to integrate into the existing medical system and is suitable for promotion and application in hospitals at all levels. It overcomes the dependence of wearable devices and other technologies on hardware and algorithms.

[0036] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0037] Figure 1 This is the ROC curve of the univariate model using DA3S, LEDD, disease duration, and age in Example 1;

[0038] Figure 2 This is the ROC curve of the multivariate model combining DA3S, LEDD, and disease course in Example 1. Detailed Implementation

[0039] Example 1 (Cerebrospinal Fluid Reagent Kit)

[0040] This embodiment provides a cerebrospinal fluid (CSF) kit for assessing the risk of motor complications in patients with Parkinson's disease. The CSF kit includes a detection reagent for measuring the concentration of dopamine-3-O-sulfate in the CSF.

[0041] The cerebrospinal fluid reagent kit is prepared using the following steps:

[0042] a) Collect cerebrospinal fluid samples from Parkinson's disease patients, centrifuge, collect the supernatant, and store at -80°C for later analysis. Utilize liquid chromatography-tandem mass spectrometry (LC-MS / MS) to target and detect the concentration of dopamine-3-O-sulfate in the cerebrospinal fluid. Specific detection methods can be found in standard metabolite-targeting detection protocols used in this field.

[0043] b) Obtain and input the patient's daily levodopa dose (LEDD) (mg / day) and disease duration (years) through external medical records or inquiries.

[0044] c) Establish the following risk assessment model

[0045] logit(p)=β0+β1·DA3S+β2·LEDD+β3·Duration

[0046] Where p is the probability of developing sports complications.

[0047]

[0048] Intercept β0 = -1.4525, DA3S coefficient β1 = 1.4374, LEDD coefficient β2 = 0.0014, Duration coefficient β3 = 0.2239;

[0049] DA3S is the logarithm of the concentration of dopamine-3-O-sulfate in cerebrospinal fluid. 10 The conversion value, LEDD is the levodopa equivalent daily dose (mg / day) for Parkinson's disease patients, and Duration is the duration of Parkinson's disease (years);

[0050] d) Output the probability risk value p.

[0051] A total of 597 participants were included in the PPMI cohort, including PD patients (n=279), individuals in the prodromal phase (n=226), and healthy controls (n=92). The analysis was performed using the methods described in this embodiment, and the key results are as follows:

[0052] (1) DA3S was significantly elevated in PD patients receiving levodopa treatment.

[0053] (2) DA3S concentration was significantly positively correlated with LEDD (correlation coefficient r = 0.7091) and disease duration (r = 0.436).

[0054] (3) The results of single-factor and multi-factor logistic regression analysis are shown in the table below, confirming that DA3S is an independent predictor.

[0055] (4) The area under the ROC curve (AUC) of the multivariate model (DA3S+LEDD+disease duration) reached 0.806, demonstrating excellent predictive accuracy (see [link to relevant documentation]). Figure 2 ).

[0056] Table 1. Univariate and multivariate logistic regression analyses of predictors of exercise complications

[0057]

[0058] Note: Odds ratios, their 95% confidence intervals, and p-values ​​represent the associations between DA3S, equivalent daily dose of levodopa, disease duration, and age with the presence of exercise complications after log10 transformation. The multivariate model included DA3S, LEDD, and disease duration as independent predictors.

[0059] 1. Results showed that DA3S was significantly elevated in PD patients receiving Levodopa treatment, while there was no statistically significant difference between untreated PD patients, prodromal patients, and healthy individuals.

[0060] 2. DA3S concentration was significantly correlated with LEDD (r = 0.7091) and disease duration (r = 0.436);

[0061] 3. The multivariate logistic regression model suggests that DA3S is an independent predictor;

[0062] 4. ROC analysis showed that AUC = 0.806, indicating that the decision tree model supports the risk stratification effect of DA3S in the key subgroups.

[0063] Example 2 (Plasma Reagent Kit)

[0064] This embodiment provides a plasma kit for assessing the risk of motor complications in patients with Parkinson's disease.

[0065] Venous blood was collected from patients, and plasma was obtained after anticoagulation and centrifugation. The concentration of DA3S in the plasma was detected using a targeted metabolomics method similar to that in Example 1. Based on the established predictive model (model coefficients may require calibration based on plasma concentrations or the establishment of an independent conversion system), and in conjunction with the patient's LEDD and disease duration, the probability of exercise-related complications was calculated. Plasma samples are more readily available and suitable for routine physical examinations and large-scale outpatient screenings.

[0066] Example 3 (Urine Test Kit)

[0067] This embodiment provides a urine test kit for assessing the risk of motor complications in patients with Parkinson's disease.

[0068] Random urine or morning urine samples are collected from patients, appropriately diluted and pretreated, and the concentration of DA3S in the urine is detected using LC-MS / MS (usually creatinine concentration needs to be measured simultaneously for correction). Risk is assessed using a predictive model validated by urine data, taking into account the patient's LEDD and disease duration. Urine sample acquisition is completely non-invasive, resulting in the highest patient compliance, making it ideal for long-term, frequent dynamic monitoring.

[0069] While the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the scope of the invention. Any person skilled in the art can make modifications without departing from the scope of the invention; all equivalent modifications made in accordance with the invention should be covered by the scope of the invention.

Claims

1. A biomarker for assessing the risk of motor complications in patients with Parkinson's disease, characterized in that, The biomarker is dopamine-3-O-sulfate.

2. A cerebrospinal fluid reagent kit for assessing the risk of motor complications in patients with Parkinson's disease, characterized in that, The cerebrospinal fluid kit includes a detection reagent for determining the concentration of dopamine-3-O-sulfate in cerebrospinal fluid.

3. The cerebrospinal fluid reagent kit as described in claim 2, characterized in that, The cerebrospinal fluid reagent kit is prepared using the following steps: a) Measure the concentration of dopamine-3-O-sulfate in cerebrospinal fluid; b) Obtain the daily levodopa dose and disease course in Parkinson's disease patients through external input; c) Establish the following risk assessment model: logit(p)=β0+β1·DA3S+β2·LEDD+β3·Duration Where p is the probability of developing exercise complications. β0 is the intercept, with a value of -1.4525; β1 is the coefficient of DA3S, with a value of 1.4374; β2 is the coefficient of LEDD, with a value of 0.0014; β3 is the coefficient of Duration, with a value of 0.2239; DA3S is the concentration log of dopamine-3-O-sulfate in cerebrospinal fluid measured in step a). 10 Conversion value; LEDD is the daily dose of levodopa for patients with Parkinson's disease; Duration refers to the course of Parkinson's disease in patients, expressed in years. d) Calculate and output the risk probability value p.

4. A plasma reagent kit for assessing the risk of motor complications in patients with Parkinson's disease, characterized in that, The plasma kit includes a detection reagent for determining the concentration of dopamine-3-O-sulfate in plasma.

5. A urine test kit for assessing the risk of motor complications in patients with Parkinson's disease, characterized in that, The urine test kit includes a detection reagent for determining the concentration of dopamine-3-O-sulfate in urine.

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