Application of substance for detecting high signal of dorsal-lateral nigra in preparation of product for detecting alpha-synucleinopathy

Abstract

The invention relates to an application of a substance for detecting high signals of dorsal-lateral nigra in preparation of a product for detecting alpha-synucleinopathies. The Parkinson's disease and rapid eye movement sleep behavior disorder diagnosis system is constructed based on an analysis strategy of magnetic sensitive weighted imaging, the diagnosis system can be effectively applied to auxiliary diagnosis, the diagnosis sensitivity of the Parkinson's disease is 93.8%, the specificity is 96.7%, the diagnosis sensitivity of the rapid eye movement sleep behavior disorder is 66.7%, and the diagnosis sensitivity of the rapid eye movement sleep behavior disorder is 96.7%. The kit has the advantages that the sensitivity is high, the specificity is 98.0%, the rapid eye movement sleep behavior disorder or the Parkinson's disease can be subjected to etiological typing, systematic management and effective prevention of patients are facilitated, the clinical diagnosis uncertainty of the Parkinson's disease is remarkably reduced through the diagnosis mode, early screening diagnosis is promoted, and the clinical diagnosis and treatment level is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of medical diagnosis, and relates to the application of a substance for detecting high signals of dorsolateral substantia nigra in the preparation of a product for detecting alpha-synuclein disease. Background Art

[0002] α-Synuclein (α-syn) is a protein deposited in the central nervous system and peripheral nervous system. Its misfolding and aggregation are associated with the occurrence of various α-synuclein diseases, such as Parkinson's disease (PD), dementia with Lewy bodies (DLB) and multiple system atrophy (MSA). Among them, Rapid Eye Movement Sleep Behavior Disorder (RBD) is considered to be a prodromal symptom of α-synuclein diseases. The diagnosis of α-synuclein disease mainly relies on clinical symptoms, auxiliary examinations and biomarker detection. The diagnosis of α-synuclein disease can be based on the patient's clinical symptoms, such as motor dysfunction, muscle stiffness, tremor and balance disorders. In particular, RBD is considered to be a prodromal symptom of α-synuclein disease. Polysomnography (PSG) is an important tool for diagnosing RBD, which can record skeletal muscle activity during REM sleep. In addition, positron emission tomography (PET) and single photon emission computed tomography (SPECT) can evaluate the loss of dopaminergic neurons and help in the diagnosis of Parkinson's disease. The pathological form of α-synuclein can be detected in cerebrospinal fluid and plasma, and its increased level is related to the occurrence and development of the disease. The latest research shows that the seed amplification technology (SAA) of α-synuclein can quickly and accurately detect the pathological state of patients with Parkinson's disease and other synuclein diseases. The sensitivity of detection for early Parkinson's disease is 87.7%, and the specificity for the healthy control group is 96.3%.

[0003] At present, the gold standard for clinically applied α-synuclein disease (such as Parkinson's disease) detection is dopamine transporter positron emission tomography / computed tomography (DAT-PET / CT) brain imaging and cerebrospinal fluid α-synuclein detection, but PET is expensive and difficult to popularize, and cerebrospinal fluid detection requires puncture, and patient compliance is poor. In addition, China's epidemiological survey shows that the prevalence of people over 65 years old is about 1.7%, but only about 3.75% of patients are aware of their illness at the onset of the disease, and the misdiagnosis rate is about 23.5%. Misdiagnosis of Parkinson's disease will cause patients to miss the best treatment time. Therefore, it is urgent to develop economical, sensitive, specific, simple and easy-to-use diagnostic tools to improve the level of clinical diagnosis and treatment. Summary of the invention

[0004] In view of the deficiencies of the prior art and actual needs, the present invention provides the use of a substance for detecting high signals in the dorsolateral substantia nigra in the preparation of a product for detecting α-synuclein disease, in order to enable etiological typing of rapid eye movement sleep behavior disorder or Parkinson's disease, promote early screening and diagnosis, and improve the level of clinical diagnosis and treatment.

[0005] To achieve this object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides an application of a substance for detecting high signals in the dorsolateral substantia nigra in the preparation of a product for detecting α-synuclein diseases, wherein the α-synuclein diseases include Parkinson's disease and rapid eye movement sleep behavior disorder; the substance for detecting high signals in the dorsolateral substantia nigra includes a magnetic resonance imaging device.

[0007] In the present invention, the detection of dorsolateral substantia nigra high signal loss is combined with rapid eye movement sleep behavior disorder, which can be used to etiologically classify rapid eye movement sleep behavior disorder (RBD) or Parkinson's disease, thereby helping to evaluate and identify whether early motor dysfunction and sleep disorders are caused by PD or other neurodegenerative Parkinson's diseases, predict the risk of PD in RBD patients, promote early screening and diagnosis, and improve clinical diagnosis and treatment.

[0008] In a second aspect, the present invention provides a method for constructing a diagnostic system for α-synuclein disease, the method comprising:

[0009] Perform MRI on the brains of healthy people, people with REM sleep behavior disorder, and people with Parkinson's disease to obtain images; and examine information on REM sleep behavior disorder symptoms;

[0010] Susceptibility-weighted imaging (SWI) was used to analyze the acquired images and obtain the dorsolateral substantia nigra high signal information of the substantia nigra corpuscles. The acquired dorsolateral substantia nigra high signal information of the substantia nigra corpuscles and the rapid eye movement sleep behavior disorder symptom information were randomly divided into a training set and a test set.

[0011] Based on the machine learning algorithm, the training set was used to train and construct the diagnostic system for α-synuclein disease, and the test set was used to verify the constructed diagnostic system for α-synuclein disease.

[0012] In the present invention, the information on the loss of high signals in the dorsolateral substantia nigra is combined with the information on the symptoms of rapid eye movement sleep behavior disorder for the first time to construct a diagnostic system, which can be effectively used in auxiliary diagnosis. The diagnostic sensitivity for Parkinson's disease is 93.8%, and the specificity is 96.7%. The diagnostic sensitivity for rapid eye movement sleep behavior disorder is 66.7%, and the specificity is 98.0%. It can help evaluate and identify whether RBD is caused by PD or other neurodegenerative Parkinson's diseases, identify which RBD patients may develop into PD, promote early screening and diagnosis, and improve the level of clinical diagnosis and treatment.

[0013] Preferably, the nuclear magnetic resonance imaging includes 3.0 Tesla (T) nuclear magnetic resonance imaging and / or 7.0 Tesla nuclear magnetic resonance imaging.

[0014] Preferably, the dorsolateral substantia nigra high signal information includes whether the dorsolateral substantia nigra high signal is absent.

[0015] Preferably, the machine learning algorithm includes any one of linear regression, logistic regression, random forest or support vector machine.

[0016] In a third aspect, the present invention provides a diagnostic system for α-synuclein diseases, wherein the α-synuclein diseases include Parkinson's disease and rapid eye movement sleep behavior disorder, and the input data of the diagnostic system are dorsolateral substantia nigra high signal information of substantia nigra corpuscles and rapid eye movement sleep behavior disorder symptom information;

[0017] The criteria are:

[0018] If there are symptoms of rapid eye movement sleep behavior disorder and the high signal of the dorsolateral substantia nigra is absent, it is diagnosed as Parkinson's disease-induced rapid eye movement sleep behavior disorder;

[0019] If there are symptoms of REM sleep behavior disorder and the high signal of the dorsolateral substantia nigra is not lost, it is judged as non-Parkinson's disease-induced REM sleep behavior disorder;

[0020] If there are no symptoms of rapid eye movement sleep behavior disorder and the dorsolateral substantia nigra high signal is absent, it is judged as preclinical Parkinson's disease;

[0021] If there are no symptoms of rapid eye movement sleep behavior disorder and the dorsolateral substantia nigra high signal is not lost, it is judged as negative.

[0022] Preferably, the diagnostic system is constructed by the method for constructing a diagnostic system for α-synuclein disease described in the second aspect.

[0023] The diagnostic system of the present invention has a diagnostic sensitivity of 93.8% and a specificity of 96.7% for PD, and a diagnostic sensitivity of 66.7% and a specificity of 98.0% for RBD. It can help evaluate and identify whether RBD is caused by PD or other neurodegenerative Parkinson's diseases, identify which RBD patients are likely to develop into PD, promote early screening and diagnosis, and improve clinical diagnosis and treatment levels.

[0024] In the present invention, according to the criteria for RBD in the 3rd edition of the International Classification of Sleep Disorders (ICSD) in 2014, A to D criteria must be met simultaneously to be confirmed as having RBD symptoms: A. Recurrent sleep-related vocalizations and / or complex movements; B. Abnormal behavior confirmed by polysomnography (PSG) occurring during REM sleep, or based on the history of dream interpretation, it is speculated that abnormal behavior occurs during REM sleep; C. PSG indicates the presence of RSWA in REM sleep; D. It cannot be explained by another sleep disorder, mental illness, medication or substance use.

[0025] In a fourth aspect, the present invention provides a device for assisting in the diagnosis of α-synuclein disease, the device comprising a detection unit, a processing unit and a determination unit;

[0026] The detection unit is used to perform the following steps:

[0027] Perform MRI on the brains of healthy people, patients with REM sleep behavior disorder, and patients with Parkinson's disease to obtain images; and examine the symptoms of REM sleep behavior disorder;

[0028] The processing unit is used to perform the following steps:

[0029] The acquired images were analyzed using the susceptibility-weighted imaging algorithm to obtain the dorsolateral substantia nigra high signal information in the substantia nigra corpuscle area;

[0030] The determination unit is used to perform the following steps:

[0031] Auxiliary diagnosis is performed according to the system for auxiliary diagnosis of subcortical ischemic vascular dementia described in the third aspect.

[0032] Preferably, the nuclear magnetic resonance imaging includes 3.0 Tesla nuclear magnetic resonance imaging and / or 7.0 Tesla nuclear magnetic resonance imaging.

[0033] In a fifth aspect, the present invention provides a computer-readable storage medium having a computer program or instruction stored thereon, which, when executed by a processor, implements the functions of the device for assisting in diagnosing α-synuclein disease described in the fourth aspect.

[0034] In a sixth aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program or instructions, and when the computer program or instructions are executed by the processor, the functions of the device for assisting in diagnosing α-synuclein disease described in the fourth aspect are realized.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] In response to the current problem of lack of specific prospective diagnostic tools to judge the long-term transformation and prognosis of RBD behavior, the present invention designs a diagnostic system based on an analysis strategy based on susceptibility weighted imaging, which can classify the etiology of RBD patients or patients with Parkinson's disease, thereby facilitating the systematic management and effective prevention of patients. For example, if an RBD patient suffers from Lewy body dementia, psychotropic drugs should be used with caution, because improper use may lead to worsening of the disease or life-threatening conditions. If there is a trend or possibility of transformation into Parkinson's disease, standardized intervention can be carried out to avoid adverse prognosis and possible drug side effects caused by improper use of drugs, and early intervention can be carried out to prevent recurrence. This diagnostic model will significantly reduce the clinical uncertainty in the diagnosis of Parkinson's disease, emphasize the benefits of early identification and screening for diagnosis and early intervention, reduce the twists and turns of patient diagnosis and treatment, and improve diagnostic efficiency. DETAILED DESCRIPTION

[0037] The technical solution of the present invention is further described below by specific implementation methods. However, the following examples are only simplified examples of the present invention and do not represent or limit the scope of protection of the present invention. The scope of protection of the present invention shall be subject to the claims.

[0038] If no specific techniques or conditions are specified in the examples, the techniques or conditions described in the literature in the field or the product instructions are used. If no manufacturer is specified for the reagents or instruments used, they are all conventional products that can be purchased through regular channels.

[0039] Example 1

[0040] A total of 40 healthy subjects without PD (10 of whom had RBD) and 10 PD patients were selected, including 36 male patients with an age range of 64-78 years and 14 female patients with an age range of 61-76 years. Among the PD patients, there were 7 males and 3 females. PD determination: Step 1: Diagnosis of Parkinson's syndrome: bradykinesia and at least one of the following symptoms: muscle rigidity, resting tremor (4-6Hz), postural instability (not caused by primary visual, cerebellar, vestibular and proprioceptive dysfunction); Step 2. Exclusion criteria for Parkinson's disease: history of recurrent stroke, step-by-step progression, history of repeated brain injury, history of taking antipsychotics or dopamine-depleting drugs, clear history of encephalitis or non-drug-induced oculomotor crisis, more than one relative with Parkinson's disease, continuous remission of symptoms, no response to high-dose levodopa treatment (excluding malabsorption), 3 years after onset, still strictly unilateral involvement, other neurological symptoms: supranuclear gaze palsy, cerebellar signs, severe autonomic nerve involvement at an early stage , Babinski sign is positive, severe dementia with language, memory and executive dysfunction in the early stage, known history of exposure to neurotoxins, neuroimaging examination shows intracranial tumors or communicating hydrocephalus; Step 3. Supportive criteria for Parkinson's disease: The diagnosis of Parkinson's disease must meet 3 or more supporting criteria, unilateral onset, good response to levodopa treatment, presence of resting tremor, severe dyskinesia caused by levodopa, gradual progression of the disease, response to levodopa treatment for more than 5 years, continuous asymmetric involvement after onset, clinical course of more than 10 years, Note: Patients who meet the diagnostic criteria of Parkinson's syndrome in the first step, if they do not meet any of the items in the second step, and meet three or more of the items in the third step, can be clinically diagnosed with Parkinson's disease. The criteria for positive rapid eye movement sleep behavior disorder are as follows: According to the diagnostic criteria for RBD in the 2014 3rd edition of the International Classification of Disorders (ICSD), criteria A to D must be met simultaneously to be diagnosed as RBD: A. Recurrent sleep-related vocalizations and / or complex movements; B. Abnormal behaviors confirmed by polysomnography (PSG) to occur during REM sleep, or based on a history of dream interpretation, it is speculated that abnormal behaviors occur during REM sleep; C. PSG indicates the presence of RSWA during REM sleep; D. It cannot be explained by another sleep disorder, mental illness, medication, or substance use.

[0041] Perform 3T and 7T magnetic resonance imaging: Volunteers will undergo magnetic resonance imaging of at least 3.0T, including SWI, T1, T2, and T2flair sequence detection. Polysomnography will be performed on patients with abnormal sleep behavior.

[0042] The results showed that all 10 PD patients showed loss of high signal in the dorsolateral substantia nigra on 3.0T MRI SWI. In the normal population, 9 of the 10 patients with RBD had specific SWI manifestations: the high signal in the dorsolateral substantia nigra was weakened or disappeared, which was similar to the manifestations of PD cases, with only a difference in degree. One case had no SWI abnormality, but his father had similar symptoms for more than 30 years, but did not progress to PD and had no obvious Parkinson's symptoms. It was not ruled out that it was caused by hereditary familial diseases, but because the patient and his family refused to further complete the whole exome sequencing, the final diagnosis could not be made. The current evidence ruled out the possibility of PD. Among the 30 normal healthy people enrolled, 4 had SWI abnormalities, but not in the dorsolateral area. The patients had hypertension. Combined with other MR sequence conditions, it was considered to be microbleeds of cerebral small vessel disease.

[0043] Example 2

[0044] This embodiment constructs a PD and RBD diagnosis system.

[0045] The diagnostic system for α-synuclein disease was constructed by training using the analysis data of the embodiment, and the diagnostic efficacy was further analyzed. A total of 150 healthy control groups, 16 PD groups, and 9 RBD groups (patients were diagnosed with PD after 18 months of follow-up) were collected for the study. The prediction results of Parkinson's disease are shown in Table 1 and the prediction results of RBD are shown in Table 2.

[0046] Table 1

[0047] Predict positive Predict negative Actual positive 15 1 Actual negative 5 145

[0048] Table 2

[0049] Predict positive Predict negative Actual positive 6 3 Actual negative 3 147

[0050] Sensitivity = TP / (TP+FN), specificity = TN / (TN+FP);

[0051] TP: number of positive patients identified by SWI test;

[0052] FN: number of positive patients not identified by SWI test;

[0053] FP: the number of healthy people falsely identified as positive patients by SWI test;

[0054] TN: The number of healthy subjects correctly identified as negative by SWI test.

[0055] Compared with the healthy control group, PD can be determined by the loss of DNH on SWI. The sensitivity and specificity of DNH loss on SWI in patients are very high, which are 93.8% and 96.7% respectively. SWI shows good diagnostic effect in identifying the healthy control group and RBD, with a sensitivity of 66.7% and a specificity of 98%. Then, the information of the loss of high signal in the dorsolateral substantia nigra and the information of RBD are integrated to classify the REM sleep behavior disorder or Parkinson's disease etiology. The judgment criteria are: if there are symptoms of REM sleep behavior disorder and the high signal in the dorsolateral substantia nigra is lost, it is determined to be Parkinson's disease-induced REM sleep behavior disorder; if there are symptoms of REM sleep behavior disorder and the high signal in the dorsolateral substantia nigra is not lost, it is determined to be non-Parkinson's disease-induced REM sleep behavior disorder; if there are no symptoms of REM sleep behavior disorder and the high signal in the dorsolateral substantia nigra is lost, it is determined to be preclinical Parkinson's disease; if there are no symptoms of REM sleep behavior disorder and the high signal in the dorsolateral substantia nigra is not lost, it is determined to be negative for the related disease. This will help evaluate and identify whether RBD is caused by PD or other neurodegenerative parkinsons, predict the risk of PD in RBD patients, promote early screening and diagnosis, and improve clinical diagnosis and treatment.

[0056] In summary, the present invention aims to address the problem of the current lack of specific prospective diagnostic tools to judge the long-term transformation and prognosis of RBD behavior. Based on the analysis strategy based on susceptibility weighted imaging, a diagnostic system is designed to classify the etiology of RBD patients or patients with Parkinson's disease, which is helpful for the systematic management and effective prevention of patients, promotes the realization of early PD screening and diagnosis, and improves the level of clinical diagnosis and treatment.

[0057] The applicant declares that the above is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention shall fall within the protection scope and disclosure scope of the present invention.

Claims

1. Use of a substance for detecting high signals in the dorsolateral substantia nigra in the preparation of a product for detecting α-synuclein disease, characterized in that: The alpha-synuclein diseases include Parkinson's disease and rapid eye movement sleep behavior disorder; The material for detecting high signals in the dorsolateral substantia nigra includes a magnetic resonance imaging device.

2. A method for constructing a diagnostic system for α-synuclein disease, characterized in that: The method comprises: Perform MRI on the brains of healthy people, people with REM sleep behavior disorder, and people with Parkinson's disease to obtain images; and examine information on REM sleep behavior disorder symptoms; The acquired images were analyzed using the magnetic susceptibility weighted imaging algorithm to obtain the dorsolateral substantia nigra high signal information of the substantia nigra corpuscles area, and the acquired dorsolateral substantia nigra high signal information of the substantia nigra corpuscles area and the rapid eye movement sleep behavior disorder symptom information were randomly divided into a training set and a test set; Based on the machine learning algorithm, the training set was used to train and construct the diagnostic system for α-synuclein disease, and the test set was used to verify the constructed diagnostic system for α-synuclein disease.

3. The method for constructing a diagnostic system for α-synuclein disease according to claim 2, characterized in that: The nuclear magnetic resonance imaging includes 3.0 Tesla nuclear magnetic resonance imaging and / or 7.0 Tesla nuclear magnetic resonance imaging; Preferably, the dorsolateral substantia nigra high signal information includes whether the dorsolateral substantia nigra high signal is absent.

4. The method for constructing a diagnostic system for α-synuclein disease according to claim 2 or 3, characterized in that: The machine learning algorithm includes any one of linear regression, logistic regression, random forest or support vector machine.

5. A diagnostic system for α-synuclein disease, characterized in that: The diagnostic system is constructed by the method for constructing a diagnostic system for α-synuclein diseases according to any one of claims 2 to 4, wherein the α-synuclein diseases include Parkinson's disease and rapid eye movement sleep behavior disorder, and the input data of the diagnostic system are dorsolateral substantia nigra high signal information of the substantia nigra corpuscle area and rapid eye movement sleep behavior disorder symptom information; The criteria are: If there are symptoms of rapid eye movement sleep behavior disorder and the high signal of the dorsolateral substantia nigra is absent, it is diagnosed as Parkinson's disease-induced rapid eye movement sleep behavior disorder; If there are symptoms of REM sleep behavior disorder and the high signal of the dorsolateral substantia nigra is not lost, it is judged as non-Parkinson's disease-induced REM sleep behavior disorder; If there are no symptoms of rapid eye movement sleep behavior disorder and the dorsolateral substantia nigra high signal is absent, it is judged as preclinical Parkinson's disease; If there are no symptoms of rapid eye movement sleep behavior disorder and the dorsolateral substantia nigra high signal is not lost, it is judged as negative.

6. A device for assisting in the diagnosis of α-synuclein disease, characterized in that: The device comprises a detection unit, a processing unit and a determination unit; The detection unit is used to perform the following steps: Perform MRI on the brains of healthy people, patients with REM sleep behavior disorder, and Parkinson's disease to obtain images; and examine the symptoms of REM sleep behavior disorder; The processing unit is used to perform the following steps: The acquired images were analyzed using the susceptibility-weighted imaging algorithm to obtain the dorsolateral substantia nigra high signal information in the substantia nigra corpuscle area; The determination unit is used to perform the following steps: Auxiliary diagnosis is performed according to the system for auxiliary diagnosis of subcortical ischemic vascular dementia according to claim 5.

7. The device for assisting in the diagnosis of α-synuclein disease according to claim 6, characterized in that: The nuclear magnetic resonance imaging includes 3.0 Tesla nuclear magnetic resonance imaging and / or 7.0 Tesla nuclear magnetic resonance imaging.

8. The device for assisting in the diagnosis of α-synuclein disease according to claim 6 or 7, characterized in that: The device comprises a detection unit, a processing unit and a determination unit; The detection unit is used to perform the following steps: The brains of healthy subjects, REM sleep behavior disorder patients, and Parkinson's disease patients were imaged using 3.0 Tesla magnetic resonance imaging and / or 7.0 Tesla magnetic resonance imaging; The processing unit is used to perform the following steps: The acquired images were analyzed using the susceptibility-weighted imaging algorithm to obtain the dorsolateral substantia nigra high signal information in the substantia nigra corpuscle area; The determination unit is used to perform the following steps: The judgment is made based on the high signal information of the dorsolateral substantia nigra, and the judgment criteria are: If there are symptoms of rapid eye movement sleep behavior disorder and the high signal of the dorsolateral substantia nigra is absent, it is diagnosed as Parkinson's disease-induced rapid eye movement sleep behavior disorder; If there are symptoms of REM sleep behavior disorder and the high signal of the dorsolateral substantia nigra is not lost, it is judged as non-Parkinson's disease-induced REM sleep behavior disorder; If there are no symptoms of rapid eye movement sleep behavior disorder and the dorsolateral substantia nigra high signal is absent, it is judged as preclinical Parkinson's disease; If there are no symptoms of rapid eye movement sleep behavior disorder and the dorsolateral substantia nigra high signal is not lost, it is judged as negative.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the function of the device for assisting in diagnosing α-synuclein disease according to any one of claims 6 to 8 is realized.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program or instruction, wherein: When the computer program or instruction is executed by a processor, the function of the device for assisting in diagnosing α-synuclein disease according to any one of claims 6 to 8 is realized.