A biomarker combination for screening cognitive impairment of cerebral small vessel disease and application thereof

Abstract

The present application belongs to the technical field of biological medicine, and particularly relates to a biomarker combination for screening cognitive impairment of cerebral small vessel disease and application thereof. The present application finds that DSBP ARV and RMSSD have significant differences in patients with different cognitive functions of CSVD, and are significantly related to plasma biomarkers; combined detection of multiple indexes can significantly improve the diagnostic efficiency of cognitive impairment of CSVD. On this basis, the present application provides a layer-by-layer progressive stratified diagnosis system, which firstly performs preliminary risk stratification by double indexes, and then adds one more index for patients screened as cognitive impairment patients to realize three-index joint verification. The system realizes the transformation from standardized detection to adaptive individualized diagnosis, and is helpful for clinicians to develop differentiated treatment and monitoring programs.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to a combination of biomarkers for screening cognitive impairment in cerebral small vessel disease and their application. Background Technology

[0002] Cerebral small vessel disease (CSVD) is a brain tissue disorder caused by damage to deep penetrating arterioles, capillaries, and venules due to various factors. With advancements in imaging technology, an increasing number of brain lesions are being defined as CSVD categories, currently including: white matter hyperintensities (WMH), lacunar infarcts (LI), cerebral microbleeds (CMB), perivascular spaces (PVS), recent subcortical small infarcts, and brain atrophy. The prevalence of CSVD increases with age, with no significant sex difference, and no known differences across racial or geographical groups. Because CSVD has an insidious onset and atypical early symptoms, patients often have significant brain structural damage before the appearance of obvious clinical symptoms, posing a significant challenge to early identification and timely intervention.

[0003] Cognitive impairment is a significant clinical outcome of CSVD, which can cause damage to multiple cognitive domains, including executive function, attention, language, visuospatial function, and memory. The characteristic of cognitive decline in CSVD is that early impairment primarily affects attention, processing speed, and executive function, while memory function remains relatively intact. The pathogenesis of cognitive impairment in CSVD involves multiple intertwined pathological processes, including damage to neural network connectivity due to destruction of white matter fiber tracts, blood-brain barrier permeability, and neuroinflammatory responses. Microbleeds (WMH) can directly lead to dementia and functional disability, while small punctate lesions (Fazekas grade 1), although highly prevalent, have limited clinical significance. WMH mainly affects information processing speed and executive function, depending on the size and rate of progression of the lesion. A prospective study following 85 patients with CSVD and vascular cognitive impairment for one year found that those who progressed to dementia had significantly more microbleeds in the subcortical frontal loop. The presence of microbleeds in the subcortical frontal loop and a total of ≥5 microbleeds were independent risk factors for dementia conversion, suggesting that microbleeds can independently drive the progression of cognitive impairment to dementia by disrupting the subcortical frontal cognitive loop. Therefore, accurate assessment and risk stratification in the early stages of the disease are of great clinical significance for developing individualized intervention strategies and delaying cognitive decline.

[0004] Multiple studies have shown that elevated blood pressure variability (BPV) is associated with the progression of deep and infratentorial microbleeds and cognitive decline, suggesting that BPV has some predictive value for cognitive impairment in CSVD. Disruptions in the diurnal rhythm of blood pressure have also been reported to increase the risk of cognitive impairment in CSVD. Meanwhile, some studies have indicated a certain association between decreased heart rate variability (HRV) and cerebrovascular disease-related cognitive impairment. However, current research lacks uniformity in the measurement methods and indicator selection for BPV and HRV, and studies on their combined value in reflecting autonomic dysfunction for the comprehensive assessment of cognitive impairment in CSVD are still relatively limited. Furthermore, although plasma biomarkers have shown potential value in neurodegenerative diseases, their specificity and early warning threshold in CSVD-related cognitive impairment remain unclear, and their use alone is difficult to overcome interference from individual differences and changes in blood-brain barrier permeability.

[0005] Although previous studies have confirmed the association between BPV, HRV, and plasma biomarkers and CSVD cognitive impairment, there are still shortcomings: most studies focus only on a single indicator, lacking multidimensional joint assessment; the synergistic assessment value of BPV and HRV has not been systematically validated; and there is currently a lack of multimodal assessment integrating cardiovascular autoregulation indicators with peripheral blood biomarkers. Therefore, it is necessary to systematically explore the synergistic assessment value of the above indicators from a multidimensional perspective. Integrating BPV and HRV, which reflect cardiovascular autoregulation, with plasma biomarkers reflecting brain tissue neurological damage, to construct a multi-parameter comprehensive assessment index from both the aspects of autonomic nervous function and neurological damage, is expected to achieve certain results in improving diagnostic efficacy. Summary of the Invention

[0006] This invention discovered significant differences in DSBP ARV and RMSSD among patients with different cognitive functions in CSVD, and these differences were significantly correlated with plasma biomarkers. Combined detection of multiple indicators can significantly improve the diagnostic efficacy of CSVD cognitive impairment. Based on this, this invention provides a progressively layered diagnostic system. This system first performs preliminary risk stratification using two indicators, and then adds an additional indicator for patients screened as having cognitive impairment, achieving combined verification of three indicators. This system realizes the transformation from standardized testing to adaptive, individualized diagnosis, helping clinicians develop differentiated treatment and monitoring plans. Based on this, this invention was completed.

[0007] In a first aspect, the present invention provides the application of a set of products for detecting the expression levels of two or more biomarkers among p-tau217, NfL, DSBP ARV, and RMSSD in the preparation of a system for diagnosing cognitive impairment in patients with cerebral small vessel disease. The system comprises reagents and equipment for detecting the expression levels of p-tau217, NfL, DSBP ARV, and RMSSD. The system calculates a patient risk score (P) by detecting the expression levels of two or more of the biomarkers p-tau217, NfL, DSBP ARV, and RMSSD to determine whether a patient with cerebral small vessel disease has cognitive impairment. When the biomarker combination is DSBP ARV and RMSSD, the risk score is calculated using the following formula: ; Among them, DSBP ARV and RMSSD both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is DSBP ARV and p-tau217, the risk score is calculated using the following formula: ; Among them, DSBP ARV and p-tau217 both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is DSBP ARV and NfL, the risk score is calculated using the following formula: ; Among them, DSBP ARV and NfL both represent the expression level of this indicator in patients; the cut-off value is 0.4473, that is, when the P value of patients with cerebral small vessel disease is greater than or equal to the cut-off value, it indicates that the patients have cognitive impairment; When the biomarker combination is RMSSD and p-tau217, the risk score is calculated using the following formula: ; RMSSD and p-tau217 both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is RMSSD and NfL, the risk score is calculated using the following formula: ; RMSSD and NfL both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is DSBP ARV, RMSSD, and p-tau217, the risk score is calculated using the following formula: ; Among them, DSBP ARV, RMSSD, and p-tau217 all represent the patient's level of this indicator; when the P value of a patient with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patient has cognitive impairment. When the biomarker combination is DSBP ARV+RMSSD+NfL, the risk score is calculated using the following formula: ; Among them, DSBP, ARV, RMSSD, and NfL all represent the patient's level of this indicator; when the P value of a patient with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patient has cognitive impairment.

[0008] Furthermore, when the biomarker combination is DSBP ARV and RMSSD, the risk score cut-off value is 0.5563.

[0009] Furthermore, when the biomarker combination is DSBP ARV and p-tau217, the risk score cut-off value is 0.5747.

[0010] Furthermore, when the biomarker combination is DSBP ARV and NfL, the risk score cut-off value is 0.4473.

[0011] Furthermore, when the biomarker combination is RMSSD and p-tau217, the risk score cut-off value is 0.6306.

[0012] Furthermore, when the biomarker combination is RMSSD and NfL, the risk score cut-off value is 0.5553.

[0013] Furthermore, when the biomarker combination is DSBP ARV, RMSSD, and p-tau217, the risk score cutoff value is 0.5978.

[0014] Furthermore, when the biomarker combination is DSBP ARV+RMSSD+NfL, the risk score cut-off value is 0.6360.

[0015] Secondly, the present invention provides a system for diagnosing cerebral small vessel disease-related cognitive impairment, the system comprising reagents for detecting the expression levels of p-tau217 and NfL, and devices for detecting the levels of DSBP ARV and RMSSD; the system calculates a patient risk score (P) by detecting the expression levels of two or more of the biomarkers p-tau217, NfL, DSBP ARV, and RMSSD, in order to determine whether cognitive impairment exists; When the biomarker combination is DSBP ARV and RMSSD, the risk score is calculated using the following formula: ; Among them, DSBP ARV and RMSSD both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is DSBP ARV and p-tau217, the risk score is calculated using the following formula: ; Among them, DSBP ARV and p-tau217 both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is DSBP ARV and NfL, the risk score is calculated using the following formula: ; Among them, DSBP ARV and NfL both represent the expression level of this indicator in patients; the cut-off value is 0.4473, that is, when the P value of patients with cerebral small vessel disease is greater than or equal to the cut-off value, it indicates that the patients have cognitive impairment; When the biomarker combination is RMSSD and p-tau217, the risk score is calculated using the following formula: ; RMSSD and p-tau217 both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is RMSSD and NfL, the risk score is calculated using the following formula: ; RMSSD and NfL both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is DSBP ARV, RMSSD, and p-tau217, the risk score is calculated using the following formula: ; Among them, DSBP ARV, RMSSD, and p-tau217 all represent the patient's level of this indicator; when the P value of a patient with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patient has cognitive impairment. When the biomarker combination is DSBP ARV+RMSSD+NfL, the risk score is calculated using the following formula: ; Among them, DSBP, ARV, RMSSD, and NfL all represent the patient's level of this indicator; when the P value of a patient with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patient has cognitive impairment.

[0016] Furthermore, when the biomarker combination is DSBP ARV and RMSSD, the risk score cut-off value is 0.5563.

[0017] Furthermore, when the biomarker combination is DSBP ARV and p-tau217, the risk score cut-off value is 0.5747.

[0018] Furthermore, when the biomarker combination is DSBP ARV and NfL, the risk score cut-off value is 0.4473.

[0019] Furthermore, when the biomarker combination is RMSSD and p-tau217, the risk score cut-off value is 0.6306.

[0020] Furthermore, when the biomarker combination is RMSSD and NfL, the risk score cut-off value is 0.5553.

[0021] Furthermore, when the biomarker combination is DSBP ARV, RMSSD, and p-tau217, the risk score cutoff value is 0.5978.

[0022] Furthermore, when the biomarker combination is DSBP ARV+RMSSD+NfL, the risk score cut-off value is 0.6360.

[0023] Thirdly, the present invention provides a stratified diagnostic system for diagnosing cognitive impairment in patients with cerebral small vessel disease, the stratified diagnostic system comprising: Data collection: Data on DSBP ARV, RMSSD, p-tau217 and NfL were collected from patients with cerebral small vessel disease, and corresponding datasets were generated; Preliminary assessment: Select any two diagnostic indicators from DSBP ARV, RMSSD, p-tau217 and NfL, calculate the patient risk score, and classify the patient into cognitively impaired and cognitively normal populations based on the risk score; Advanced assessment: For individuals initially assessed as having cognitive impairment, a different biomarker than the initial assessment is included as a third diagnostic indicator. The risk score of the patient is calculated by combining the three indicators, and the presence of cognitive impairment in patients with cerebral small vessel disease is confirmed based on the risk score.

[0024] Furthermore, during the initial assessment phase, if a patient is assessed as having normal cognitive function, no additional examinations are required, and routine treatment can proceed directly.

[0025] Furthermore, in the initial assessment phase, when the biomarker combination is DSBP ARV and RMSSD, the risk score is calculated using the following formula: ; Among them, DSBP ARV and RMSSD both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is DSBP ARV and p-tau217, the risk score is calculated using the following formula: ; Among them, DSBP ARV and p-tau217 both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is DSBP ARV and NfL, the risk score is calculated using the following formula: ; Among them, DSBP ARV and NfL both represent the expression level of this indicator in patients; the cut-off value is 0.4473, that is, when the P value of patients with cerebral small vessel disease is greater than or equal to the cut-off value, it indicates that the patients have cognitive impairment; When the biomarker combination is RMSSD and p-tau217, the risk score is calculated using the following formula: ; RMSSD and p-tau217 both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is RMSSD and NfL, the risk score is calculated using the following formula: ; RMSSD and NfL both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment.

[0026] Furthermore, when the biomarker combination is DSBP ARV and RMSSD, the risk score cut-off value is 0.5563; when the biomarker combination is DSBP ARV and p-tau217, the risk score cut-off value is 0.5747; when the biomarker combination is DSBP ARV and NfL, the risk score cut-off value is 0.4473; when the biomarker combination is RMSSD and p-tau217, the risk score cut-off value is 0.6306; and when the biomarker combination is RMSSD and NfL, the risk score cut-off value is 0.5553.

[0027] Furthermore, in the advanced assessment phase, when the biomarker combination is DSBP ARV, RMSSD, and p-tau217, the risk score is calculated using the following formula: ; Among them, DSBP ARV, RMSSD, and p-tau217 all represent the patient's level of this indicator; when the P value of a patient with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patient has cognitive impairment. When the biomarker combination is DSBP ARV+RMSSD+NfL, the risk score is calculated using the following formula: ; Among them, DSBP, ARV, RMSSD, and NfL all represent the patient's level of this indicator; when the P value of a patient with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patient has cognitive impairment.

[0028] Furthermore, when the biomarker combination is DSBP ARV, RMSSD, and p-tau217, the risk score cut-off value is 0.5978; when the biomarker combination is DSBP ARV+RMSSD+NfL, the risk score cut-off value is 0.6360.

[0029] Beneficial effects This invention discovers a combination of biomarkers for diagnosing cognitive impairment in cerebral small vessel disease (CSVD). Among them, the combination of DSBPARV and RMSSD demonstrates significantly better diagnostic efficacy in distinguishing between cognitively normal and cognitively impaired individuals than a single indicator. Further analysis revealed that plasma NfL is not only closely related to overall cognitive function and specific cognitive domain impairment in CSVD, but also significantly correlated with blood pressure variability and heart rate variability. The combination of multiple indicators can significantly improve the diagnostic efficacy of cognitive impairment in CSVD.

[0030] Building upon the above, this invention also provides a system for diagnosing cognitive impairment in cerebral small vessel disease based on the aforementioned combination of biomarkers. This system employs a step-by-step examination approach. Specifically, the system first detects any two indicators (especially the combination of DSBP, ARV, and RMSSD) to preliminarily differentiate patients' cognitive impairment. If the patient is assessed as having normal cognition, no further testing is required, and conventional treatment is directly implemented. If the patient is assessed as having cognitive impairment, another biomarker (such as a plasma biomarker) is additionally tested for further confirmation through the combined use of three indicators. This step-by-step, segmented examination system has the following significant advantages: 1. Achieve individualized and tiered precise triage: Compared with the traditional fixed testing model, the solution of this invention can significantly reduce the waste of medical resources.

[0031] 2. Reduce unnecessary testing and financial burden for patients with normal cognitive function: For patients with truly mild symptoms, risk assessment can be completed by testing only two indicators, avoiding the time, cost and sample consumption caused by additional testing.

[0032] 3. Effectively avoids the risk of misdiagnosis near the threshold: A single indicator or a combination of two indicators often exists in a "grey area" near the threshold, which can easily lead to false positives or false negatives. This invention, by initiating a third biomarker for verification when the risk of cognitive impairment is indicated in the initial assessment stage, forms a multi-indicator cross-confirmation, which significantly improves the diagnostic accuracy for patients near the threshold and reduces the rate of misdiagnosis and missed diagnosis.

[0033] 4. Supports dynamic and progressive clinical decision-making pathways: The present invention is not limited to fixed test indicators, but allows for flexible selection of test indicator combinations (such as DSBP ARV and RMSSD, DSBP ARV and p-tau217, DSBP ARV and NfL, etc.) according to the patient's actual situation, truly realizing the transformation from "standardized testing" to "adaptive individualized diagnosis and treatment", which helps clinicians develop differentiated treatment and monitoring plans. Attached Figure Description

[0034] Figure 1 shows vascular-derived high signal intensity in the white matter of the brain (Fazekas: 1-3 points).

[0035] Figure 2 shows lacunar infarction, perivascular spaces, and microbleeding lesions.

[0036] Figure 3 To screen the diagnostic value of cohort blood pressure variability for cognitive impairment in CSVD.

[0037] Figure 4 To screen the diagnostic value of cohort heart rate variability in cognitive impairment with CSVD.

[0038] Figure 5 To screen cohort plasma biomarkers for the diagnostic value of cognitive impairment in CSVD.

[0039] Figure 6 To screen the diagnostic value of dual-indicator joint analysis for cognitive impairment in CSVD.

[0040] Figure 7 To screen the diagnostic value of cohort multimodal index joint analysis for cognitive impairment in CSVD.

[0041] Figure 8 To verify the diagnostic value of cohort blood pressure variability in cognitive impairment with CSVD.

[0042] Figure 9 To verify the diagnostic value of cohort heart rate variability in cognitive impairment caused by CSVD.

[0043] Figure 10 To verify the diagnostic value of cohort plasma biomarkers for cognitive impairment in CSVD.

[0044] Figure 11 To verify the diagnostic value of cohort dual-indicator joint analysis for cognitive impairment in CSVD.

[0045] Figure 12 To verify the diagnostic value of cohort multimodal index joint analysis for cognitive impairment in CSVD. Detailed Implementation

[0046] The specific embodiments of the present invention will be further described below. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the embodiments described below can be combined with each other as long as they do not conflict with each other.

[0047] Unless otherwise specified, the experimental methods used in the following embodiments are conventional methods, and the experimental materials used in the following embodiments are all available through conventional commercial channels.

[0048] The biomarker combination of this invention is a layered diagnostic system. Specifically, when a patient is admitted to the hospital, only DSBP, ARV, and RMSSD indicators need to be tested, which can be tested on an outpatient basis without hospitalization. If the patient is diagnosed with cognitive impairment based on DSBP, ARV, and RMSSD indicators, another plasma biomarker indicator needs to be tested for further confirmation to avoid misdiagnosis due to dual indicators. If the diagnosis of cognitive impairment is also confirmed by all three indicators, the patient needs further comprehensive treatment. If the patient's cognition is normal according to dual indicators, no further testing is required, and conventional treatment can be used directly.

[0049] Example 1: Correlation Study of Cognitive Impairment in Cerebral Small Vessel Disease (CSVD) (a) Research Subjects This application included 110 CSVD patients hospitalized in the Department of Neurology. After screening, 75 CSVD patients who completed 24-hour ambulatory blood pressure monitoring, ambulatory electrocardiogram monitoring and were able to cooperate with the complete plasma biomarker testing were selected for the study. The eligible patients were randomly divided into a screening cohort and a validation cohort, with 30 patients in the screening cohort and 45 patients in the validation cohort.

[0050] This study was reviewed and approved by the Biomedical Research Committee of the Second Affiliated Hospital of Naval Medical University. Written informed consent forms were obtained from all participants and their families after fully informing them of the research objectives, procedures, and potential risks and impacts.

[0051] 1. Inclusion criteria 1) Age: 50-85 years old; 2) Education level: ≥6 years; 3) A cranial MRI showing any of the following: white matter hyperintensity Fazekas score ≥2, microbleeds ≥1, perivascular spaces in the basal ganglia region ≥10, lacunar infarcts ≥1; 4) Improved Rankin score ≤ 3 points; 5) Understand and sign the informed consent form.

[0052] 2. Exclusion criteria 1) The presence of cortical or subcortical infarcts in the non-acute phase on cranial MRI, with a diameter exceeding 1.5 cm, or a possible cardiogenic stroke indicated by the TOAST classification; 2) Cranial MRI showed high signal intensity in white matter due to other diseases, such as multiple sclerosis, Fabry disease, and lysosomal storage diseases; 3) Vascular imaging shows intracranial and extracranial vascular stenosis >50%; 4) Atrial fibrillation or other cardiac structural abnormalities, severe liver and kidney dysfunction, and metabolic diseases; 5) Severe mental illness; 6) History of cognitive impairment with other identifiable causes; 7) Those whose clinical records are missing or incomplete, those who do not cooperate or refuse to undergo imaging, laboratory tests and cognitive scale assessments, or those deemed unsuitable to participate by other researchers.

[0053] (II) Research Methods 1. General Clinical Data Collection After admission, the patient's age, BMI, history of hypertension, diabetes, cerebrovascular disease, hyperlipidemia, coronary heart disease, smoking history, and alcohol consumption history were collected. Fasting venous blood was collected at 7:00 AM on the second day after admission to perform complete blood count, liver and kidney function tests, blood lipid tests, and thyroid function tests.

[0054] 2. Imaging assessment All subjects underwent cranial MRI with SWI and MRA and carotid ultrasound upon admission. Two associate chief physicians from our hospital's Department of Radiology interpreted the images to determine the type of cerebral small vessel disease, and the mentors conducted the final review of the interpretations. Cerebral small vessel disease lesions were classified according to the STRIVE criteria (2013 international consensus), with the specific interpretation criteria as follows: (1) High signal in white matter: The periventricular white matter region and the deep white matter region are high signal on T2-weighted sequences, and may appear as isointense or low signal on T1-weighted sequences. The high signal in white matter was further graded according to the Fazekas scale: Fazekas 0: No abnormality in high signal in periventricular and deep white matter; Fazekas 1: Cap-shaped or pencil-like thin-layer lesions in periventricular white matter, and punctate lesions ≤9mm in deep white matter; Fazekas 2: Periventricular lesions are smooth halos, and deep white matter lesions begin to merge; Fazekas 3: Periventricular lesions are irregular and extend into deep white matter, and deep white matter lesions merge over a large area.

[0055] (2) Lacunar infarction: a round or oval fluid cavity with a diameter of 3-15 mm, low signal on T1, high signal on T2 / FLAIR, with no or only slight glial proliferation around it, located in the basal ganglia, internal capsule, brainstem or white matter.

[0056] (3) Perivascular spaces: linear or punctate structures with a diameter of <3 mm running along the perforating vessels, with T1 / T2 signals consistent with cerebrospinal fluid. Common sites: basal ganglia and centrum semiovale. The number of perivascular spaces is graded according to visual scoring based on quantity: Grade 1: 1-10; Grade 2: 11-20; Grade 3: 21-40; Grade 4: >40; (4) Microbleeds: 2-10 mm in diameter, round low-signal lesions visible on SWI / T2* sequences, mainly distributed in deep and lobular regions. See Figure 1 , Figure 2 .

[0057] 3. 24-hour ambulatory blood pressure monitoring Patients should undergo ambulatory blood pressure monitoring within 24-72 hours of admission. The following precautions should be taken during monitoring: (1) Instruct the patient to avoid adjusting the antihypertensive drug treatment plan and to take the antihypertensive drug orally at the regular time; (2) Avoid strenuous exercise and drastic emotional fluctuations during the monitoring period, and avoid drinking stimulating beverages such as coffee and tea; (3) Dynamic blood pressure monitoring equipment shall not be removed at will.

[0058] The 24-hour ambulatory blood pressure monitoring device was set to record blood pressure values ​​at 30-minute intervals, for a total of 48 measurements. Blood pressure was defined as measured between 06:00 and 22:00 in the morning, and as measured between 22:00 and 06:00 in the morning.

[0059] The following indicators were calculated based on the monitored blood pressure values: 24-hour mean systolic blood pressure (24h SBP), 24-hour standard deviation of systolic blood pressure (24h SBP SD), 24-hour coefficient of variation of systolic blood pressure (24h SBP CV), 24-hour average real variability of systolic blood pressure (24h SBP ARV), daytime mean systolic blood pressure (DSBP), daytime standard deviation of systolic blood pressure (DSBP SD), daytime coefficient of variation of systolic blood pressure (DSBP CV), daytime average real variability of systolic blood pressure (DSBP ARV), and nighttime mean systolic blood pressure (Nighttime mean systolic blood pressure). Pressure (NSBP), nighttime standard deviation of systolic blood pressure (NSBP SD), nighttime coefficient of variation of systolic blood pressure (NSBP CV), nighttime average real variability of systolic blood pressure (NSBP ARV), 24-hour mean diastolic blood pressure (NSBP).24-hour DBP, 24-hour standard deviation of diastolic blood pressure (SD), 24-hour coefficient of variation of diastolic blood pressure (DBPCV), 24-hour average real variability of diastolic blood pressure (ARV), daytime mean diastolic blood pressure (DDBP), daytime standard deviation of diastolic blood pressure (DDBP SD), daytime coefficient of variation of diastolic blood pressure (DDBP CV), daytime average real variability of diastolic blood pressure (DDBP ARV), nighttime mean diastolic blood pressure (NDBP), nighttime standard deviation of diastolic blood pressure (NDBP). The metrics include: NDBP (SD), nighttime coefficient of variation of diastolic blood pressure (NDBP CV), and nighttime average real variability of diastolic blood pressure (NDBPPARV). The coefficient of variation is calculated as: (Standard deviation / Mean) * 100. The average real variability is calculated as: i = 1 ∑N-1|BPi+1-BPi| / N.

[0060] 4. HRV indicator 24-hour Holter monitoring was performed, and HRV (Heart Rate Variation) parameters were collected based on the final report. Frequency domain parameters included: LF (Left Flow), HF (Heart Rate), and VLF (Volume Flow); the time domain parameters of heart rate variability included: SDNN (Slow Narrowing), SDNNI (Slow Narrowing Intensity), and RMSSD (Responsive Minor Flow). Precautions: Patients were instructed to avoid emotional excitement and strenuous activity during Holter monitoring, and to avoid consuming stimulants such as coffee, alcohol, and strong tea.

[0061] 5. Detection of peripheral plasma biomarkers Fasting blood samples were collected from all subjects on the second day of admission. After centrifugation, the supernatant was extracted from the blood samples and stored for later use. Peripheral blood biomarkers, including plasma Aβ40, Aβ42, p-tau181, p-tau217, NfL, and GFAP, were detected using the Novizan Elf S240 fully automated chemiluminescence immunoassay platform (Vazyme, Nanjing, China).

[0062] 6. Neuropsychological cognitive scale assessment All participants underwent cognitive function assessments using professional neuropsychological cognitive scales within 24 hours of admission. The assessments included: (1) Overall cognitive function: The assessment was conducted using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). MMSE: Total score 0-30, normal cognitive function range >17 (illiterate), >20 (primary school), >24 (junior high school and above). MoCA: Total score 0-30, with 26 as the cutoff. ≥26 indicates normal cognition, <26 indicates cognitive impairment. For subjects with ≤12 years of education, 1 point was added to the original total score obtained from the test.

[0063] (2) Assessment of cognitive functions in a single domain: Language function was assessed using the Boston Naming Test (BNT) and Verbal Fluency Test (VFT); Visuospatial function was assessed using the Rey Complex Figure Test (RCFT); Attention and executive function was assessed using the Digit Span Test (DS Forward / Reverse); Memory and learning function was assessed using the Auditory Verbal Learning Test (AVLT).

[0064] Single cognitive function test (cutoff value is 1.5 SD of the norm): (1) AVLT 3 min: 50~60 years old: 5; 61~70 years old: 4; 71~85 years old: 3; AVLT 20 min: 50~60 years old: 4; 61~70 years old: 3; 71~85 years old: 2.

[0065] (2) Rey complex graphic copying: Education years ≤ 12 years: 26 points; Education years > 12 years: 29 points.

[0066] (3) Boston Naming Test: 18 for junior high school; 20 for senior high school; 21 for university.

[0067] (4) Language fluency test: 9 for junior high school students; 10 for senior high school students; 11 for university students.

[0068] (III) Statistical Methods All research results in this invention were statistically analyzed and plotted using SPSS 27.0 and GraphPad Prism 10 software. Normally distributed quantitative data were analyzed using... ±s represents the mean; quantitative data that do not conform to a normal distribution are represented by M(P). 25 P 75 Data for counts are expressed as n (%). Independent samples t-tests were used to compare normally distributed continuous data, while Mann-Whitney U tests were used to compare non-normally distributed continuous data. Chi-square tests were used to compare count data. Binary logistic regression analysis was used to analyze the impact of blood pressure variability on various cognitive functions in CSVD. ROC curve analysis was used to analyze the diagnostic efficacy of blood pressure variability on overall cognitive function.

[0069] (iv) Results 1. Comparison of general clinical data among different cognitive function groups Patients included in the screening cohort were divided into two groups based on their MoCA scores, with a cutoff of 26 points: a cognitively normal group (MoCA ≥ 26 points) and a cognitive impairment group (MoCA < 26 points). Twelve patients were in the cognitively normal group, and eighteen patients were in the cognitive impairment group. General clinical data and laboratory blood test results were compared between the two groups. The results showed that the age of patients in the cognitive impairment group was significantly older than that in the cognitively normal group (P < 0.05). See Table 1 for details.

[0070] Table 1 Comparison of general clinical data of patients in different cognitive function groups

[0071] Note: Gender; Age; BMI; Edu; HBP; DM; HLP; Smoke; Drink; CHD; CVD; FPG; LDL; TC; TG; Hcy; ALT; AST; Cr; eGFR; Folic acid.

[0072] 2. Correlation between blood pressure variability and cognitive impairment in CSVD 2.1 Comparison of blood pressure variability among different cognitive function groups Comparison of blood pressure variability between the two groups revealed that the levels of 24-hour SBP SD, 24-hour SBP ARV, DSBP SD, DSBP ARV, NSBP, NSBP SD, NSBP CV, and DDBP ARV in the cognitive impairment group were significantly higher than those in the cognitively normal group, with statistically significant differences (P<0.05). See Table 2 for details.

[0073] Table 2 Comparison of blood pressure variability among different cognitive function groups

[0074] 2.2 Diagnostic value of blood pressure variability in cognitive impairment of CSVD To verify the diagnostic efficacy of blood pressure variability (BP) indicators in differentiating between cognitively normal and cognitively impaired individuals, a ROC curve was constructed using DSBP ARV, an indicator with a highly significant impact, and the AUC value was calculated to assess diagnostic efficacy. The results showed that the ROC curve plotted using DSBP ARV had an AUC of 0.745, a specificity of 77.78%, and a sensitivity of 66.67%, demonstrating good diagnostic efficacy. See details... Figure 3 .

[0075] 3. Correlation between heart rate variability and cognitive impairment in CSVD 3.1 Comparison of Heart Rate Variability Indicators among Different Cognitive Function Groups Comparing HRV indices between patients with cognitive impairment and those with normal cognitive function, the results showed that the RMSSD level of heart rate variability was significantly lower in the cognitive impairment group than in the cognitively normal group (P<0.05). See Table 3 for details.

[0076] Table 3 Comparison of heart rate variability among different cognitive function groups

[0077] 3.2 Diagnostic value of heart rate variability in cognitive impairment of CSVD To verify the diagnostic efficacy of HRV in differentiating between cognitively normal and cognitively impaired individuals, a ROC curve was constructed using RMSSD, which has a highly significant effect, and the AUC value was calculated to assess diagnostic efficacy. The results showed that the ROC curve plotted using RMSSD had an AUC of 0.738, a specificity of 66.67%, and a sensitivity of 72.22%. See details... Figure 4 .

[0078] 4. Correlation between plasma biomarkers and cognitive impairment in CSVD 4.1 Comparison of plasma biomarkers among different cognitive function groups The differences in plasma biomarkers between the cognitively normal group and the cognitive impairment group were compared. The results showed that the plasma Aβ42 / 40, p-tau181, p-tau217 and NfL levels in the cognitive impairment group were significantly higher than those in the cognitively normal group, and the differences were statistically significant (all P<0.05). See Table 4 for details.

[0079] Table 4 Comparison of plasma biomarkers in different cognitive function groups

[0080] The correlation between plasma biomarkers Aβ42 / 40, p-tau181, p-tau217, and NfL levels and various cognitive functions was validated using Spearman correlation analysis. The results showed that plasma Aβ42 / 40 was positively correlated with overall cognitive function (P<0.05); plasma p-tau181 and p-tau217 were both negatively correlated with overall cognitive function and learning / memory function (P<0.05); plasma NfL was negatively correlated with attention and executive function (P<0.05); and plasma p-tau217 was negatively correlated with BNT (P<0.05). See Table 5 for details.

[0081] Table 5. Correlation between plasma biomarkers and overall and individual cognitive function performance.

[0082] Note: * indicates P<0.05, ** indicates P<0.01, and *** indicates P<0.001.

[0083] 4.2 Diagnostic value of plasma biomarkers for cognitive impairment in CSVD Plasma biomarkers showing significant differences between cognitively normal and cognitively impaired groups were screened, and the discriminative power of single plasma biomarkers in distinguishing between cognitively normal and cognitively impaired individuals was evaluated. Results indicated that p-tau181 had the highest AUC of 0.773. See details. Figure 5 .

[0084] Example 2: Relationship between plasma biomarkers and autonomic nervous system function indicators and their combined diagnostic efficacy 1. Relationship between plasma biomarkers and autonomic nervous system function indicators 1.1 Correlation analysis between blood pressure variability and plasma biomarkers In Example 1, several 24-hour and diurnal systolic blood pressure variability indicators were found to be closely related to cognitive decline in CSVD. Therefore, further analysis was conducted to examine the correlation between these indicators and plasma biomarkers. Spearman correlation analysis revealed that plasma Aβ42 / 40 was negatively correlated with 24hSBP SD, 24hSBP ARV, DSBP SD, DSBP ARV, NDBP SD, and DDBP ARV (P<0.05). Plasma p-tau181 was positively correlated with 24hSBP SD, 24hSBPARV, DSBP ARV, and DDBP ARV. See Table 6 for details.

[0085] Table 6. Correlation analysis between blood pressure variability and plasma biomarkers

[0086] Note: * indicates P<0.05, ** indicates P<0.01.

[0087] 1.2 Correlation analysis between heart rate variability indicators and plasma biomarkers The correlation between heart rate variability (NfL) indicators and plasma biomarkers was also analyzed. Spearman correlation analysis revealed that NfL was negatively correlated with SDNN, SDNNI, RMSSD, HF, LF, and VLF (P<0.05). No significant correlations were found between the remaining indicators and plasma biomarkers. See Table 7 for details.

[0088] Table 7. Correlation analysis between heart rate variability and plasma biomarkers

[0089] Note: * indicates P<0.05, ** indicates P<0.01.

[0090] 2. Diagnostic value of dual-indicator combined analysis for cognitive impairment in CSVD Based on the study in Example 1, it was found that the discriminative power of a single plasma biomarker is relatively limited. This invention further introduces a dual-indicator combined diagnostic approach. To evaluate the relationship between autonomic nervous system function indicators combined with plasma biomarkers and CSVD cognitive impairment, a multivariate logistic regression model was constructed with CSVD cognitive function as the dependent variable and DSBP ARV, RMSSD, p-tau181, p-tau217, and NfL as independent variables. The cognitive function of patients with cerebral small vessel disease was assessed by calculating a risk score (P). The formula for calculating the risk score (P) is shown in Table 8.

[0091] Table 8. Risk Score (P) Formula for Assessing Cognitive Function in CSVD with Two or Three Indicators

[0092] When two indicators are selected for assessment, whether the patient with cerebral small vessel disease has cognitive impairment is determined by calculating a risk score (P). When the P value of the patient with cerebral small vessel disease is higher than or equal to the cut-off value, it indicates that the patient has cognitive impairment.

[0093] After incorporating DSBP ARV, RMSSD, and plasma biomarkers into the constructed combined models (Table 14), the differential diagnostic efficacy of each combined model was improved to some extent. Among them, RMSSD+p-tau181 showed the highest diagnostic efficacy, with an AUC of 0.870. See Table 9 for details. Figure 6 .

[0094] Table 9. Diagnostic efficacy of combined dual indicators

[0095] 3. Diagnostic value of combined multimodal indicators for cognitive impairment in CSVD The combined predicted probability values ​​were calculated based on a multivariate regression model (Table 9), and ROC curves were plotted to evaluate the discriminative power of the combined model for cognitive function in CSVD. The results showed that all three models had high discriminative power. Among them, the DSBP ARV+RMSSD+p-tau 217 combination had the highest AUC and sensitivity, indicating that this combination has the strongest overall discriminative power and an advantage in identifying true positive patients. See Table 10 for details. Figure 7 .

[0096] Table 10 Differential efficacy of different combined assessment models for cognitive function

[0097] Example 3: Validation of the diagnostic efficacy of plasma biomarkers and autonomic nervous system function indicators for CSVD cognitive impairment Forty-five patients were selected for the validation cohort according to the inclusion and exclusion criteria of Example 1, and the method was the same as in Examples 1 and 2.

[0098] 1. Comparison of general clinical data among different cognitive function groups Forty-five patients were selected for the validation cohort in this invention. The included patients were divided into two groups based on their MoCA scores, with a cutoff of 26 points: a cognitively normal group (MoCA ≥ 26 points) and a cognitive impairment group (MoCA < 26 points). There were 17 patients in the cognitively normal group and 28 patients in the cognitive impairment group. The general clinical data and laboratory blood test results of the two groups are detailed in Table 11.

[0099] Table 11 Comparison of general clinical data of patients in different cognitive function groups

[0100] Note: Edu (Years of Education); HBP (Hypertension); DM (Diabetes Mellitus); HLP (Hyperlipidemia); Smoke (Smoking History); Drink (Alcohol Consumption History); CHD (Coronary Heart Disease); CVD (Cerebrovascular Disease); FPG (Fasting Fasting Blood Glucose); HbA1c (Hydrated Hemoglobin); LDL (Low-Density Lipoprotein); HDL (High-Density Lipoprotein); TC (Total Cholesterol); TG (Triglycerides); Hcy (Homocysteine); ALT (Alanine Aminotransferase); AST (Aspartate Aminotransferase); Cr (Cretinol); eGFR (Estimated Glomerular Filtration Rate); FT3 (Free Triiodothyronine); FT4 (Free Thyroxine); TSH (Thyroid Stimulating Hormone).

[0101] 2. Correlation between blood pressure variability and cognitive impairment in CSVD 2.1 Comparison of blood pressure variability among different cognitive function groups Comparison of blood pressure variability between the two groups revealed that the DSBP ARV level in the cognitive impairment group was significantly higher than that in the cognitively normal group, with a statistically significant difference (P<0.05). See Table 12 for details.

[0102] Table 12 Comparison of blood pressure variability among different cognitive function groups

[0103] 2.2 Diagnostic value of blood pressure variability in cognitive impairment of CSVD To verify the diagnostic efficacy of DSBP ARV in differentiating between cognitively normal and cognitively impaired individuals, an ROC curve was constructed, and the AUC value was calculated to assess diagnostic efficacy. The results showed that the ROC curve plotted for DSBP ARV had an AUC of 0.730, a specificity of 57.14%, and a sensitivity of 82.35%, demonstrating good diagnostic efficacy. See details... Figure 8 .

[0104] 3. Correlation between heart rate variability and cognitive impairment in CSVD 3.1 Comparison of Heart Rate Variability Indicators among Different Cognitive Function Groups Comparing HRV indicators between patients with cognitive impairment and those with normal cognitive function, the results showed that the RMSSD level of heart rate variability was significantly lower in the cognitive impairment group than in the cognitively normal group (P<0.05). See Table 13 for details.

[0105] Table 13 Comparison of Heart Rate Variability Indicators among Different Cognitive Function Groups

[0106] 3.2 Diagnostic value of heart rate variability in cognitive impairment of CSVD To verify the diagnostic efficacy of RMSSD in differentiating between cognitively normal and cognitively impaired individuals, an ROC curve was constructed, and the AUC value was calculated to assess diagnostic efficacy. The results showed that the ROC curve plotted by RMSSD had an AUC of 0.701, a specificity of 76.47%, and a sensitivity of 57.14%. See details... Figure 9 .

[0107] 4. Correlation between plasma biomarkers and cognitive impairment in CSVD 4.1 Comparison of plasma biomarkers among different cognitive function groups The differences in plasma biomarkers between the cognitively normal group and the cognitive impairment group were compared. The results showed that the plasma p-tau217 and NfL levels in the cognitive impairment group were significantly higher than those in the cognitively normal group (both P<0.05). See Table 14 for details.

[0108] Table 14 Comparison of plasma biomarkers in different cognitive function groups

[0109] 4.2 Diagnostic value of plasma biomarkers for cognitive impairment in CSVD The study evaluated the differential diagnostic efficacy of single plasma biomarkers between cognitively normal and cognitively impaired individuals. Results indicated that NfL exhibited the best overall diagnostic efficacy, with the highest AUC (AUC of 0.681). See details... Figure 10 .

[0110] Example 4: Validation of the combined diagnostic efficacy of plasma biomarkers and autonomic nervous system function indicators for CSVD cognitive impairment The validation experiment found that combining plasma biomarkers with autonomic nerve function indicators and using the following formula to analyze their diagnostic value for CSVD cognitive function (Table 15).

[0111] Table 15 Model formulas for assessing cognitive function in CSVD using two or three indicators.

[0112] When two or three indicators are selected for assessment, the presence of cognitive impairment in patients with cerebral small vessel disease is determined by calculating a risk score (P). When the P value of a patient with cerebral small vessel disease is higher than or equal to the cut-off value, it indicates that the patient is a CSVD cognitive impairment individual.

[0113] 1. Diagnostic value of dual-indicator combined analysis for cognitive impairment in CSVD Validation experiments revealed that combining plasma biomarkers with autonomic nervous system function indicators, and analyzing their diagnostic value for the presence of cognitive impairment in CSVD using the formula in Table 28, showed that each combination improved the differential efficacy for cognitive impairment. Among them, RMSSD+NfL exhibited the highest diagnostic efficacy, with an AUC of 0.765. See Table 16 for details. Figure 11 .

[0114] Table 16 Diagnostic efficacy of dual indicators

[0115] 2. Diagnostic value of combined multimodal indicators for cognitive impairment in CSVD In the three-indicator combination model, DSBP ARV+RMSSD+p-tau217 had the highest AUC value of 0.813, indicating that this combination has the strongest overall discriminative power. DSBP ARV+RMSSD+NfL had the highest sensitivity of 0.821, suggesting its advantage in identifying true positive patients. See Table 17 for details. Figure 12 .

[0116] Table 17 Differential efficacy of different combined assessment models for cognitive function

Claims

1. The application of a group of products for detecting the expression levels of two or more biomarkers among p-tau217, NfL, DSBP ARV, and RMSSD in the preparation of a system for diagnosing cognitive impairment in patients with cerebral small vessel disease, characterized in that, The system contains reagents and equipment for detecting the expression levels of p-tau217, NfL, DSBP ARV, and RMSSD; the system calculates a patient risk score (P) by detecting the expression levels of two or more of the biomarkers p-tau217, NfL, DSBP ARV, and RMSSD, in order to determine whether patients with cerebral small vessel disease have cognitive impairment; When the biomarker combination is DSBP ARV and RMSSD, the risk score is calculated using the following formula: ； Among them, DSBP ARV and RMSSD both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is DSBP ARV and p-tau217, the risk score is calculated using the following formula: ； Among them, DSBP ARV and p-tau217 both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is DSBP ARV and NfL, the risk score is calculated using the following formula: ； Among them, DSBP ARV and NfL both represent the expression level of this indicator in patients; the cut-off value is 0.4473, that is, when the P value of patients with cerebral small vessel disease is greater than or equal to the cut-off value, it indicates that the patients have cognitive impairment; When the biomarker combination is RMSSD and p-tau217, the risk score is calculated using the following formula: ； RMSSD and p-tau217 both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is RMSSD and NfL, the risk score is calculated using the following formula: ； RMSSD and NfL both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is DSBP ARV, RMSSD, and p-tau217, the risk score is calculated using the following formula: ； Among them, DSBP ARV, RMSSD, and p-tau217 all represent the patient's level of this indicator; when the P value of a patient with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patient has cognitive impairment. When the biomarker combination is DSBP ARV+RMSSD+NfL, the risk score is calculated using the following formula: ； Among them, DSBP, ARV, RMSSD, and NfL all represent the patient's level of this indicator; when the P value of a patient with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patient has cognitive impairment.

2. The application as described in claim 1, characterized in that, The risk score cutoff value was 0.5563 when the biomarker combination was DSBP ARV and RMSSD; 0.5747 when the biomarker combination was DSBP ARV and p-tau217; 0.4473 when the biomarker combination was DSBP ARV and NfL; 0.6306 when the biomarker combination was RMSSD and p-tau217; 0.5553 when the biomarker combination was RMSSD and NfL; 0.5978 when the biomarker combination was DSBP ARV, RMSSD, and p-tau217; and 0.6360 when the biomarker combination was DSBP ARV+RMSSD+NfL.

3. A system for diagnosing cerebral small vessel disease-related cognitive impairment, characterized in that, The system contains reagents for detecting the expression levels of p-tau217 and NfL, and devices for detecting the levels of DSBP ARV and RMSSD; the system calculates a patient risk score (P) by detecting the expression levels of two or more of the biomarkers p-tau217, NfL, DSBP ARV and RMSSD, in order to determine whether cognitive impairment exists; When the biomarker combination is DSBP ARV and RMSSD, the risk score is calculated using the following formula: ； Among them, DSBP ARV and RMSSD both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is DSBP ARV and p-tau217, the risk score is calculated using the following formula: ； Among them, DSBP ARV and p-tau217 both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is DSBP ARV and NfL, the risk score is calculated using the following formula: ； Among them, DSBP ARV and NfL both represent the expression level of this indicator in patients; the cut-off value is 0.4473, that is, when the P value of patients with cerebral small vessel disease is greater than or equal to the cut-off value, it indicates that the patients have cognitive impairment; When the biomarker combination is RMSSD and p-tau217, the risk score is calculated using the following formula: ； RMSSD and p-tau217 both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is RMSSD and NfL, the risk score is calculated using the following formula: ； RMSSD and NfL both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is DSBP ARV, RMSSD, and p-tau217, the risk score is calculated using the following formula: ； Among them, DSBP ARV, RMSSD, and p-tau217 all represent the patient's level of this indicator; when the P value of a patient with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patient has cognitive impairment. When the biomarker combination is DSBP ARV+RMSSD+NfL, the risk score is calculated using the following formula: ； Among them, DSBP, ARV, RMSSD, and NfL all represent the patient's level of this indicator; when the P value of a patient with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patient has cognitive impairment.

4. The system as described in claim 3, characterized in that, The risk score cutoff value was 0.5563 when the biomarker combination was DSBP ARV and RMSSD; 0.5747 when the biomarker combination was DSBP ARV and p-tau217; 0.4473 when the biomarker combination was DSBP ARV and NfL; 0.6306 when the biomarker combination was RMSSD and p-tau217; 0.5553 when the biomarker combination was RMSSD and NfL; 0.5978 when the biomarker combination was DSBP ARV, RMSSD, and p-tau217; and 0.6360 when the biomarker combination was DSBP ARV+RMSSD+NfL.

5. A stratified diagnostic system for diagnosing cognitive impairment in patients with cerebral small vessel disease, the stratified diagnostic system comprising: Data collection: Data on DSBP ARV, RMSSD, p-tau217 and NfL were collected from patients with cerebral small vessel disease, and corresponding datasets were generated; Preliminary assessment: Select any two diagnostic indicators from DSBP ARV, RMSSD, p-tau217 and NfL, calculate the patient risk score, and classify the patient into cognitively impaired and cognitively normal populations based on the risk score; Advanced assessment: For individuals initially assessed as having cognitive impairment, a different biomarker than the initial assessment is included as a third diagnostic indicator. The risk score of the patient is calculated by combining the three indicators, and the presence of cognitive impairment in patients with cerebral small vessel disease is confirmed based on the risk score.

6. The hierarchical diagnostic system as described in claim 5, characterized in that, In the initial assessment phase, if the patient is assessed as having normal cognitive function, no additional examination is required, and routine treatment can proceed directly.

7. The hierarchical diagnostic system as described in claim 5, characterized in that, During the initial assessment phase, when the biomarker combination is DSBP ARV and RMSSD, the risk score is calculated using the following formula: ； Among them, DSBP ARV and RMSSD both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is DSBP ARV and p-tau217, the risk score is calculated using the following formula: ； Among them, DSBP ARV and p-tau217 both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is DSBP ARV and NfL, the risk score is calculated using the following formula: ； Among them, DSBP ARV and NfL both represent the expression level of this indicator in patients; the cut-off value is 0.4473, that is, when the P value of patients with cerebral small vessel disease is greater than or equal to the cut-off value, it indicates that the patients have cognitive impairment; When the biomarker combination is RMSSD and p-tau217, the risk score is calculated using the following formula: ； RMSSD and p-tau217 both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment. When the biomarker combination is RMSSD and NfL, the risk score is calculated using the following formula: ； RMSSD and NfL both represent the expression level of this indicator in patients; when the P value of patients with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patients have cognitive impairment.

8. The hierarchical diagnostic system as described in claim 5, characterized in that, The risk score cut-off value was 0.5563 when the biomarker combination was DSBP ARV and RMSSD; 0.5747 when the biomarker combination was DSBP ARV and p-tau217; 0.4473 when the biomarker combination was DSBP ARV and NfL; 0.6306 when the biomarker combination was RMSSD and p-tau217; and 0.5553 when the biomarker combination was RMSSD and NfL.

9. The hierarchical diagnostic system as described in claim 5, characterized in that, In the advanced assessment phase, when the biomarker combination is DSBP ARV, RMSSD, and p-tau217, the risk score is calculated using the following formula: ； Among them, DSBP ARV, RMSSD, and p-tau217 all represent the patient's level of this indicator; when the P value of a patient with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patient has cognitive impairment. When the biomarker combination is DSBP ARV+RMSSD+NfL, the risk score is calculated using the following formula: ； Among them, DSBP, ARV, RMSSD, and NfL all represent the patient's level of this indicator; when the P value of a patient with cerebral small vessel disease is greater than or equal to the Cut-off value, it indicates that the patient has cognitive impairment.

10. The hierarchical diagnostic system as described in claim 5, characterized in that, When the biomarker combination is DSBP ARV, RMSSD, and p-tau217, the risk score cut-off value is 0.5978; when the biomarker combination is DSBPARV+RMSSD+NfL, the risk score cut-off value is 0.6360.

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