SNP (Single Nucleotide Polymorphism) and gene analysis-based glycolipid metabolism abnormality risk dynamic evaluation system and application thereof in neuropsychiatric drug therapy

Abstract

The invention provides an abnormal glycolipid metabolism risk dynamic assessment system based on SNP and gene analysis and application of the system in neuropsychiatric drug therapy, and belongs to the technical field of biological medicine. The system disclosed by the invention contains the primer probe group for detecting the metabolic influence of the antipsychotic drug, so that specific amplification detection of a specific SNP site can be realized, clinical guidance is targeted, and abnormal glucose and lipid metabolism and significant change of body weight are avoided. According to the method, glucose and lipid metabolism and effect target gene detection products are adopted for detection, a detection result is evaluated and interpreted through a glucose and lipid metabolism abnormal risk dynamic evaluation algorithm, retrospective investigation and actual case observation are combined, the detection result is good in sensitivity, high in accuracy and high in practicability, sampling is convenient, the evaluation algorithm is professional and reasonable, and the method is suitable for popularization and application. Results are easy to understand and easy to popularize and use.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to a dynamic risk assessment system for abnormal glucose and lipid metabolism based on SNP and gene analysis, and its application in neuropsychiatric drug therapy. Background Art

[0002] Genetic testing refers to the technique of detecting DNA through blood, other bodily fluids, or cells (oral mucosal cells or other tissue cells). It involves taking peripheral venous blood or other tissue cells from the subject, extracting DNA from the cells, amplifying relevant genes, and then using specific instruments to analyze the DNA molecular information in the subject's cells. This method analyzes the types of genes contained within, gene defects, and whether their expression function is normal, allowing individuals to understand their genetic information, identify the causes of diseases, predict the risk of developing certain diseases, or the risk of adverse reactions when using treatment drugs. This enables targeted measures to prevent or mitigate the potential impact of related genes. Through genetic testing, an individual's SNP genotype can be determined, and interpreting these SNP loci allows for the assessment of the individual's genetic characteristics and appropriate treatment drugs.

[0003] The effects of antipsychotic drugs on patients' weight and lipid and glucose metabolism during long-term treatment are closely related to genes. Individuals with different SNP locus genotypes show significant differences in the degree of weight gain and loss. The treatment period for first-episode mental illness patients can be as long as 5 years, during which patients often discontinue medication on their own due to weight gain and abnormal blood lipids. Relapse soon follows, and the duration of secondary drug treatment needs to be extended to 8-10 years. Schizophrenia patients typically develop the disease between the ages of 14 and 16, and the critical period for treatment coincides with the age of marriage and childbearing. Significant changes in weight and abnormal blood lipids at this time become direct causes of medication discontinuation. However, currently, there are no known methods or products for dynamic risk assessment of abnormal glucose and lipid metabolism based on SNP and gene analysis in the treatment of neuropsychiatric drugs. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic risk assessment system for abnormal glucose and lipid metabolism based on SNP and gene analysis, and its application in neuropsychiatric drug treatment, providing an effective means for clinical application.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0006] This invention provides an application of a primer-probe set in the preparation of a detection product for the metabolic effects of antipsychotic drugs, comprising:

[0007] A probe set for the specific detection of nucleic acid fragments at the c.102T>C site of the HTR2A gene, the rs1414334C>G site of the HTR2C gene, the -1131T>C site of the APOA5 gene, and the rs1000940A>G site of the RABEP1 gene;

[0008] Primer set for amplifying the c.102T>C site of the HTR2A gene, the rs1414334C>G site of the HTR2C gene, the -1131T>C site of the APOA5 gene, and the rs1000940A>G site of the RABEP1 gene.

[0009] Preferably, the primer set contains primers with nucleotide sequences as shown in SEQ ID NO:9 to SEQ ID NO:16.

[0010] Preferably, the probe set contains probes with nucleotide sequences as shown in SEQ ID NO:1 to SEQ ID NO:8.

[0011] This invention also provides an application of a reagent kit in the preparation of a product for detecting the metabolic effects of antipsychotic drugs, comprising:

[0012] A probe set for the specific detection of nucleic acid fragments at the c.102T>C site of the HTR2A gene, the rs1414334C>G site of the HTR2C gene, the -1131T>C site of the APOA5 gene, and the rs1000940A>G site of the RABEP1 gene, wherein the probe set contains probes with nucleotide sequences as shown in SEQ ID NO:1 to SEQ ID NO:8, and the probes are immobilized on a solid-phase support;

[0013] Primer sets for amplifying the c.102T>C site of the HTR2A gene, the rs1414334C>G site of the HTR2C gene, the -1131T>C site of the APOA5 gene, and the rs1000940A>G site of the RABEP1 gene, wherein the primer sets contain primers with nucleotide sequences as shown in SEQ ID NO:9 to SEQ ID NO:16.

[0014] Preferably, the solid support is selected from nylon membrane, nitrocellulose membrane, quartz glass sheet or polystyrene.

[0015] Preferably, the kit further includes a hybridization solution and an elution solution, wherein the hybridization solution contains 0.3–0.8 mol / L formamide and 0.001–0.003 mol / L sodium dodecyl sulfate.

[0016] This invention also provides a metabolic risk assessment system for antipsychotic drugs, comprising the following modules:

[0017] (1) Gene detection module, wherein the gene detection module is configured to input user gene samples and output genotype data of four SNP sites in the sample: HTR2A gene c.102T>C, HTR2C gene rs1414334C>G, APOA5 gene -1131T>C and RABEP1 gene rs1000940A>G;

[0018] (2) Dynamic database module, which receives the genotype data and patient clinical monitoring data, and outputs real-time optimized SNP risk contribution value and indicator threshold parameters;

[0019] (3) Risk calculation module, which receives the genotype data output by the gene detection module, retrieves the risk contribution value of each SNP site in the dynamic database module, and calculates the risk contribution value based on the proportion of abnormal biochemical indicators and clinical indicators. The risk contribution values ​​of the four SNP sites are integrated by equal weighting and output an initial risk score ranging from 0 to 12.

[0020] (4) Risk assessment and output module, which receives the genotype data and initial risk score, and outputs a glucose and lipid metabolism risk level report and targeted medication recommendations.

[0021] 8. The system according to claim 7, wherein the risk assessment and output module includes components that perform the following functions:

[0022] The initial risk score is mapped to a four-level risk level: low risk (0-3 points), medium risk (4-6 points), medium-high risk (7-9 points), and high risk (10-12 points).

[0023] When the genotype meets the following conditions simultaneously, "high risk of abnormal glucose and lipid metabolism" is directly output:

[0024] HTR2Ac.102T>C is of type TT or CT, HTR2C rs1414334C>G is of type GG, APOA5-1131T>C is of type CC, and RABEP1 rs1000940A>G is of type AG.

[0025] Preferably, the risk assessment and output module includes components that perform the following functions:

[0026] The targeted medication recommendations include:

[0027] a) When HTR2Ac.102T>C is detected as having the CC genotype, output "Olanzapine is recommended to improve drug response rate";

[0028] b) When HTR2Ac.102T>C is detected as the TT genotype, output "Recommended treatment period ≥28 days to improve drug response rate";

[0029] c) When HTR2C rs1414334C>G is detected as having the GG genotype, output "Avoid using olanzapine or risperidone to prevent weight gain >7% and metabolic syndrome";

[0030] d) When APOA5-1131T>C is detected as having the CC genotype, output "Avoid using olanzapine, quetiapine, or risperidone to prevent lipid metabolism abnormalities";

[0031] e) When RABEP1 rs1000940A>G is detected as having the AA genotype, output "Aripiprazole is recommended to reduce the risk of abnormal blood sugar, and blood sugar levels should be monitored and sugar intake controlled".

[0032] Preferably, the dynamic database module is configured with a SNP weight database, which includes the association mapping relationship between each genotype and abnormal glucose and lipid metabolism phenotype, and a database of abnormal indicator thresholds. The risk weight parameters are dynamically updated based on newly added patient genotype and clinical indicator data. The dynamic update iterates the risk contribution value through a statistical method of equal weight summation.

[0033] The technical effects and advantages of this invention are as follows:

[0034] This invention provides a primer and probe set for detecting the metabolic effects of antipsychotic drugs. It can specifically amplify and detect the c.102T>C site of the HTR2A gene, the rs1414334C>G site of the HTR2C gene, the -1131T>C site of the APOA5 gene, and the rs1000940A>G site of the RABEP1 gene. This allows for targeted clinical guidance, avoiding abnormal glucose and lipid metabolism and significant weight changes. The detection protocol provided by this invention can specifically detect the genetic information related to a patient's glucose and lipid metabolism and weight changes. This allows doctors to understand the patient's individual genetic characteristics in advance, enabling targeted selection of antipsychotic drugs, better avoiding detectable adverse reactions, preventing self-discontinuation of medication due to significant changes in glucose and lipid metabolism and weight, avoiding relapse of mental disorders, and achieving individualized drug treatment for mental disorders.

[0035] Therefore, the detection scheme provided by this invention can utilize glucose and lipid metabolism and effector target gene detection products for detection, and evaluate and interpret the detection results through a dynamic risk assessment algorithm for abnormal glucose and lipid metabolism. Combined with retrospective surveys and actual case observations, the detection results have good sensitivity, high accuracy, and strong practicality. Moreover, sampling is convenient, the evaluation algorithm is professional and reasonable, and the result interpretation is easy to understand and easy to promote and use. DETAILED DESCRIPTION

[0036] The technical solutions provided by the present invention will be described in detail below with reference to the embodiments, but they should not be construed as limiting the scope of protection of the present invention.

[0037] Example 1

[0038] This embodiment provides a gene detection kit for detecting changes in glucose and lipid metabolism and weight caused by the use of antipsychotic drugs. The kit includes a gene chip for glucose and lipid metabolism and effector targets, wherein the gene chip for glucose and lipid metabolism and effector targets includes probe set A and multiplex PCR primer set 1.

[0039] The probe set is used to specifically amplify nucleic acid fragments containing the c.102T>C site of the HTR2A gene, the rs1414334C>G site of the HTR2C gene, the -1131T>C site of the APOA5 gene, and the rs1000940A>G site of the RABEP1 gene.

[0040] The multiplex PCR primer set was used to specifically detect the c.102T>C site of the HTR2A gene, the rs1414334C>G site of the HTR2C gene, the -1131T>C site of the APOA5 gene, and the rs1000940A>G genotype of the RABEP1 gene.

[0041] The multiplex PCR primer set includes primers 1, 2, 3, 4, 5, 6, 7, and 8 (as shown in SEQ.ID.NO.9 to SEQ.ID.NO.16 in the nucleotide sequence listing), and the sequences of the above primers are as follows:

[0042] (1) Primer 1: 5'-CTCAACTACGAACTCCCTAATGCAA-3', as shown in SEQ.ID.NO.9;

[0043] (2) Primer 2: 5'-CGACTGTCCAGTTAAATGCATCAGA-3', as shown in SEQ.ID.NO.10;

[0044] (3) Primer 3: 5'-GATAAAATTGGCCTCATCTACC-3', as shown in SEQ.ID.NO.11;

[0045] (4) Primer 4: 5'-CACTTGTCTAAGGGATTCTTGAG-3', as shown in SEQ.ID.NO.12;

[0046] (5) Primer 5: 5'-CCCTGCGAGTGGAGTTCA-3', as shown in SEQ.ID.NO.13;

[0047] (6) Primer 6: 5'-CTCTGAGCCCCAGGAACTG-3', as shown in SEQ.ID.NO.14;

[0048] (7) Primer 7: 5'-TGTTGGCTCCCAACGTGCTGTGCTG-3', as shown in SEQ.ID.NO.15;

[0049] (8) Primer 8: 5'-TACAGGGCAAAGACAAGGGTGAC-3', as shown in SEQ.ID.NO.16;

[0050] The probe set includes probe 1, probe 2, probe 3, probe 4, probe 5, probe 6, probe 7, and probe 8 (as shown in SEQ.ID.NO.1 to SEQ.ID.NO.8 in the nucleotide sequence listing). The sequences of the above probes are as follows:

[0051] (1) The probe 1: 5'-CTTCTCCGGAGTTAAA-3', as shown in SEQ.ID.NO.1;

[0052] (2) The probe 2: 5'-CTTCTCCAGAGTTAAA-3', as shown in SEQ.ID.NO.2;

[0053] (3) The probe 3: 5'-GCTACCCTGTCTTGCTG-3', as shown in SEQ.ID.NO.3;

[0054] (4) The probe 4: 5'-GCTACCCTCTCTTGCTG-3', as shown in SEQ.ID.NO.4;

[0055] (5) The probe 5: 5'-AGCGAAAGTGAGATTT-3', as shown in SEQ.ID.NO.5;

[0056] (6) The probe 6: 5'-AGCGAAAGTAAGATTT-3', as shown in SEQ.ID.NO.6;

[0057] (7) The probe 7: 5'-GTGTTTTTAGTGCGTGC-3', as shown in SEQ.ID.NO.7;

[0058] (8) The probe 8: 5'-GTGTTTTTGGTGCGTGC-3', as shown in SEQ.ID.NO.8;

[0059] The probe assembly includes multiple probes, each of which is fixed on a solid support. The solid support can be one of nylon membrane, nitrocellulose membrane, quartz glass sheet, or polystyrene.

[0060] The kit also includes hybridization solution and elution solution (anhydrous ethanol). The hybridization solution contains formamide (0.5 mol / L) and sodium dodecyl sulfate (0.002 mol / L).

[0061] The test results obtained using the above gene testing kit were used to conduct a risk assessment according to the following dynamic assessment method for abnormal glucose and lipid metabolism based on SNP and gene analysis. This method is based on a comprehensive analysis of 114 patients who underwent testing at all four loci in Example 5, and includes the following steps:

[0062] Step S1: Obtain genomic data from 114 users, including: SNP genotypes related to glucose and lipid metabolism: HTR2C (CC, GG, CG), RABEP1 (GG, AA, AG), APOA5 (CC, TT, TC), HTR2A (CC, TT, TC), number of cases, abnormal risk assessment indicators (hypertension, weight change exceeding (±7%), tachycardia (>100 bpm), adverse reactions described as tachycardia, efficacy evaluation (effectiveness rate, score reduction greater than 25%), prolactin out-of-window percentage, extrapyramidal reactions, T wave changes, sinus bradycardia, sleep problems) and their percentage of cases, and abnormal biochemical indicators (out-of-window percentage) (ALT, AST, TG, ApoAI, ApoAB, HDL-C, LDL-C, CHOL), where genotypes with 1 user case were excluded;

[0063] Step S2: Based on the preset SNP weight database, the proportion of cases in the abnormal risk assessment indicators and abnormal biochemical indicators (non-window proportion) are divided equally into risk ranges according to the difference between high and low values, and the risk contribution value (0-3) of each SNP locus is calculated. The database contains the association between SNP loci and glucose and lipid metabolism phenotypes.

[0064] Step S3: Integrate the risk contribution values ​​of multiple SNPs using the equal-weighted summation method to generate initial abnormal risk and abnormal biochemical indicator assessment scores;

[0065] Step S4: Output the comprehensive risk level (low risk, medium risk, medium-high risk, high risk) of abnormal glucose and lipid metabolism, detailed assessment results and interpretation.

[0066] Step S5: Regularly expand the database based on the increase in the number of patients, and dynamically adjust and iterate the risk assessment method for abnormal glucose and lipid metabolism.

[0067] Efficacy evaluation was conducted on 422 subjects using antipsychotic drugs such as amisulpride, aripiprazole, ziprasidone, olanzapine, quetiapine, and risperidone. A PANSS score reduction rate ≥30% was defined as effective, and the efficacy evaluation criteria for other scales were also based on this standard. As shown in Table 1, the overall response rate among the 422 patients with various genotypes at the c.102T>C locus of the HTR2A gene was 77.3%. Patients with the CC genotype at the c.102T>C locus of the HTR2A gene had the highest response rate, reaching 87.5% in patients treated with olanzapine for more than 28 days.

[0068] Table 1. Drug effects regardless of treatment cycle.

[0069]

[0070] Considering the potential time lag in drug response during treatment, the response rates for HTR2A patients were determined according to the timeframes listed in Table 2. As shown in Table 1, when the treatment period was less than 28 days, the response rates for the CC and CT genotypes were higher. However, when the treatment period increased to ≥28 days, the response rate for the TT genotype increased to 80.9%, indicating that patients with the TT genotype at the c.102T>C locus of the HTR2A gene require a longer duration of antipsychotic medication to achieve a significant therapeutic effect.

[0071] Table 2. Drug effects at different treatment cycles

[0072]

[0073] Weight monitoring was conducted on 119 participants. Among patients with the CT genotype (c.102T>C locus) of the HTR2A gene, olanzapine had the greatest impact on weight when using antipsychotic drugs such as amisulpride, aripiprazole, ziprasidone, olanzapine, quetiapine, and risperidone. 16.3% of CT genotype patients experienced a weight gain >7%, with 3 of these cases occurring after more than 28 days of treatment. Aripiprazole users experienced a weight loss >7% in 13.6% of CT genotype patients, and quetiapine users experienced a weight loss >7% in 9.5% of CT genotype patients. Among the TT genotype patients, 11.4% of olanzapine users experienced a weight gain of >7%, and these significant weight changes all occurred after more than 28 consecutive days of administration. 20% of TT genotype patients experienced a weight gain of >7% in risperidone users, while 15.4% of TT genotype patients experienced a weight loss of >7% in aripiprazole users. In contrast, CC genotype patients were less prone to weight changes when treated with these antipsychotic drugs, with only one quetiapine user experiencing a weight gain of >7%. Amisulpride and ziprasidone had little impact on the weight of patients across all genotypes, with no changes exceeding 7%.

[0074] Therefore, when using this kit, drugs such as amisulpride, aripiprazole, ziprasidone, olanzapine, quetiapine, and risperidone can be selected under the guidance of the c.102T>C locus genotype of the HTR2A gene, which can not only improve the treatment effect but also maintain a stable weight.

[0075] Example 2

[0076] Weight, blood lipids, blood glucose, and liver function were monitored in 100 subjects. Patients with the GG genotype at the rs1414334C>G locus of the HTR2C gene were observed to experience weight gain >7% when using antipsychotic drugs such as aripiprazole, aripiprazole, ziprasidone, olanzapine, quetiapine, and risperidone. Except for patients using aripiprazole, ziprasidone, and aripiprazole, who did not show weight gain >7%, weight gain in the other drug users was mainly concentrated in this genotype, and 47.1% of patients experienced varying degrees of adverse events after treatment. Abnormalities were found in single or multiple indicators, including fasting total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), fasting plasma glucose (FPG), alanine aminotransferase (ALT), and aspartate aminotransferase (AST). The most common patients were those treated with olanzapine and risperidone. Three patients treated with quetiapine and aripiprazole experienced weight loss >7%, and these three patients also exhibited varying degrees of abnormal liver function and apolipoprotein levels. In contrast, the rate of abnormalities in weight and metabolic syndrome indicators was only 6.7% in patients with the rs1414334C>G locus CC genotype of the HTR2C gene.

[0077] Therefore, when using this kit, drugs such as amisulpride, aripiprazole, ziprasidone, olanzapine, quetiapine, and risperidone can be selected under the genotype guidance of the rs1414334C>G site of the HTR2C gene, which can prevent significant weight changes and the occurrence of metabolic syndrome.

[0078] Example 3

[0079] Among 100 subjects, those with the TT genotype at the -1131T>C locus of the APOA5 gene exhibited strong lipid metabolism. A retrospective analysis of their medical records and medication usage revealed that these subjects had very low rates of abnormalities in fasting total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) before treatment, making them less likely to experience abnormal blood lipid levels or metabolic syndrome due to the use of antipsychotic medications. Conversely, subjects with the C genotype at the -1131T>C locus of the APOA5 gene had weaker lipid metabolism, and their lipid metabolism remained relatively stable after treatment. In Example 1, among the 119 identical subjects treated with antipsychotic drugs, one patient developed elevated fasting LDL-C and TG levels, along with significant weight gain (>7%); three patients developed elevated fasting TC levels, with weight gain of 3%-6%. A retrospective analysis of medical records and drug usage revealed that patients with the combined effects of the two genes mentioned in Examples 1 and 2, who subsequently used antipsychotic drugs such as amisulpride, aripiprazole, clozapine, olanzapine, quetiapine, paliperidone, risperidone, and lithium, were more prone to developing metabolic syndrome. The above symptoms were even more pronounced in subjects possessing the APOA5 gene-1131T>C locus (CC gene).

[0080] Example 4

[0081] The AA genotype at the rs1000940A>G site of the RABEP1 gene indicates a weaker glucose metabolism capacity. Individuals with this genotype are more prone to abnormal blood glucose levels (26.9%) when using antipsychotic drugs such as amisulpride, aripiprazole, clozapine, olanzapine, quetiapine, paliperidone, risperidone, and lithium. In contrast, the AG and GG genotypes have relatively lower blood glucose concentrations and can metabolize glucose normally. Among multiple subjects, those with the G genotype at the rs1000940A>G site of the RABEP1 gene exhibited normal glucose metabolism capacity and were less likely to experience abnormal blood glucose levels when using antipsychotic drugs (19.2%, compared to the A genotype, P<0.05). Therefore, subjects with the AA genotype at the rs1000940A>G site of the RABEP1 gene should choose low-risk antipsychotic drugs, such as aripiprazole. Furthermore, patients with this type of mental disorder need to closely monitor their blood sugar levels and rationally control their sugar intake to avoid developing diabetes.

[0082] Example 5

[0083] A comprehensive analysis of 114 patients who underwent testing at all four loci revealed that over 60% of patients simultaneously possessed the following genotypes: HTR2A gene c.102T>C mutation (TT or CT), HTR2C gene rs1414334C>G locus (GG), APOA5 gene -1131T>C locus (GG), and RABEP1 gene rs1000940A>G locus (AG). These patients also exhibited clinically significant increases or decreases in glucose and lipid metabolism (>7%). Furthermore, over 70% of these patients showed abnormalities in total glucose and lipid metabolism (TC, TG, and LDL-C). Therefore, a kit combining these four loci can be used to assess the potential impact of medications on metabolism and weight before administration, mitigating abnormalities in glucose and lipid metabolism and significant weight changes.

[0084] It can help patients with mental disorders choose medications individually, which is beneficial for long-term medication adherence to this chronic disease and the patient's individual health status. It allows for targeted selection of appropriate drugs for treatment, preventing self-discontinuation of medication due to significant changes in lipid metabolism and weight, and avoiding relapse of mental disorders.

[0085] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. The application of a primer-probe set in the preparation of a detection product for the effects of antipsychotic drug metabolism, characterized in that, include: A probe set for the specific detection of nucleic acid fragments at the c.102T>C site of the HTR2A gene, the rs1414334C>G site of the HTR2C gene, the -1131T>C site of the APOA5 gene, and the rs1000940A>G site of the RABEP1 gene; Primer set for amplifying the c.102T>C site of the HTR2A gene, the rs1414334C>G site of the HTR2C gene, the -1131T>C site of the APOA5 gene, and the rs1000940A>G site of the RABEP1 gene.

2. The application according to claim 1, characterized in that, The primer set contains primers with nucleotide sequences as shown in SEQ ID NO:9 to SEQ ID NO:

16.

3. The application according to claim 1, characterized in that, The probe set contains probes with nucleotide sequences as shown in SEQ ID NO:1 to SEQ ID NO:

8.

4. The application of a reagent kit in the preparation of a product for detecting the metabolic effects of antipsychotic drugs, characterized in that, include: A probe set for the specific detection of nucleic acid fragments at the c.102T>C site of the HTR2A gene, the rs1414334C>G site of the HTR2C gene, the -1131T>C site of the APOA5 gene, and the rs1000940A>G site of the RABEP1 gene, wherein the probe set contains probes with nucleotide sequences as shown in SEQ ID NO:1 to SEQ ID NO:8, and the probes are immobilized on a solid-phase support; Primer sets for amplifying the c.102T>C site of the HTR2A gene, the rs1414334C>G site of the HTR2C gene, the -1131T>C site of the APOA5 gene, and the rs1000940A>G site of the RABEP1 gene, wherein the primer sets contain primers with nucleotide sequences as shown in SEQ ID NO:9 to SEQ ID NO:

16.

5. The application according to claim 4, characterized in that, The solid support is selected from nylon membrane, nitrocellulose membrane, quartz glass sheet or polystyrene.

6. The application according to claim 4, characterized in that, The kit also includes a hybridization solution and an elution solution, wherein the hybridization solution contains 0.3–0.8 mol / L formamide and 0.001–0.003 mol / L sodium dodecyl sulfate.

7. A metabolic risk assessment system for antipsychotic drugs, characterized in that, Includes the following modules: (1) Gene detection module, wherein the gene detection module is configured to input user gene samples and output genotype data of four SNP sites in the sample: HTR2A gene c.102T>C, HTR2C gene rs1414334C>G, APOA5 gene -1131T>C and RABEP1 gene rs1000940A>G; (2) Dynamic database module, which receives the genotype data and patient clinical monitoring data, and outputs real-time optimized SNP risk contribution value and indicator threshold parameters; (3) Risk calculation module, which receives the genotype data output by the gene detection module, retrieves the risk contribution value of each SNP site in the dynamic database module, and calculates the risk contribution value based on the proportion of abnormal biochemical indicators and clinical indicators. The risk contribution values ​​of the four SNP sites are integrated by equal weighting and output an initial risk score ranging from 0 to 12. (4) Risk assessment and output module, which receives the genotype data and initial risk score, and outputs a glucose and lipid metabolism risk level report and targeted medication recommendations.

8. The system according to claim 7, characterized in that, The risk assessment and output module includes components that perform the following functions: The initial risk score is mapped to a four-level risk level: low risk (0-3 points), medium risk (4-6 points), medium-high risk (7-9 points), and high risk (10-12 points). When the genotype meets the following conditions simultaneously, "high risk of abnormal glucose and lipid metabolism" is directly output: HTR2Ac.102T>C is of type TT or CT, HTR2C rs1414334C>G is of type GG, APOA5-1131T>C is of type CC, and RABEP1 rs1000940A>G is of type AG.

9. The system according to claim 7, characterized in that, The risk assessment and output module includes components that perform the following functions: The targeted medication recommendations include: a) When HTR2Ac.102T>C is detected as having the CC genotype, output "Olanzapine is recommended to improve drug response rate"; b) When HTR2Ac.102T>C is detected as the TT genotype, output "Recommended treatment period ≥28 days to improve drug response rate"; c) When HTR2C rs1414334C>G is detected as having the GG genotype, output "Avoid using olanzapine or risperidone to prevent weight gain >7% and metabolic syndrome"; d) When APOA5-1131T>C is detected as having the CC genotype, output "Avoid using olanzapine, quetiapine, or risperidone to prevent lipid metabolism abnormalities"; e) When RABEP1 rs1000940A>G is detected as having the AA genotype, output "Aripiprazole is recommended to reduce the risk of abnormal blood sugar, and blood sugar levels should be monitored and sugar intake controlled".

10. The system according to claim 7, characterized in that, The dynamic database module is configured with a SNP weight database, which contains the association mapping relationship between each genotype and abnormal glucose and lipid metabolism phenotype, as well as a database of abnormal indicator thresholds. The risk weight parameters are dynamically updated based on the genotype and clinical indicator data of newly added patients. The dynamic update iterates the risk contribution value through a statistical method of equal weight summation.

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