CN110317868B - Analysis method for predicting weight gain caused by schizophrenia treated by second-generation antipsychotic drugs based on polygene combination interaction - Google Patents
Analysis method for predicting weight gain caused by schizophrenia treated by second-generation antipsychotic drugs based on polygene combination interaction Download PDFInfo
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Abstract
The invention discloses an analysis method for predicting weight gain of a second generation antipsychotic drug for treating schizophrenia based on polygene combination interaction, which comprises the following steps of preparing a sample, collecting peripheral blood of a patient with weight gain caused by the second generation antipsychotic drug for treating schizophrenia; extracting a genome DNA sample by using a hypotonic salting-out method; MODLI-TOF flight mass spectrometry detection method for 5-hydroxytryptamine 2C receptor (5-HT2CR) gene, histamine l receptor gene, oxytocin gene, NPY/R gene, Leptin gene and adiponectin gene, FGF21 gene and FGF23 gene genotyping; and analyzing and extracting data, aligning original data of the instrument, obtaining data which has no noise interference and can be used for statistical analysis, developing gene-gene interaction of quantitative characters by a generalized multi-factor dimensionality reduction method, further applying cross validation, and predicting weight gain by using the gene interaction. The method adopts a generalized multi-factor dimension reduction method to process the continuous outcome variable and incorporates the covariates, thereby having the advantage of greatly improving the application range and the prediction accuracy.
Description
Technical Field
The invention relates to the technical field of medicine, in particular to an analysis method for predicting weight gain caused by schizophrenia treated by a second generation antipsychotic drug based on polygene combination interaction.
Background
The second generation of anti-schizophrenia drugs for treating schizophrenia are often prone to abnormal glycolipid metabolism, and obesity and other estrogen-like side effects such as lactation, small throat and erectile dysfunction appear, so that the medication compliance and the quality of life of patients are reduced, excessive weight gain can increase the morbidity and mortality rate related to cardiovascular diseases and diabetes, and the risk of schizophrenia suffering from metabolic syndrome is increased.
At present, a plurality of genetic polymorphisms of genes are found to be related to weight gain caused by the second generation antipsychotic at home and abroad, and for predicting weight gain in the process of treating chronic schizophrenia by the second generation antipsychotic, a single gene or a single gene genetic polymorphism is generally used, but the weight gain in the process belongs to a complex disease and is the result of multi-gene superposition and interaction.
Disclosure of Invention
The invention aims to provide an analysis method for predicting weight gain caused by schizophrenia treated by a second generation antipsychotic drug based on polygene combination interaction, which adopts a generalized multi-factor dimensionality reduction method to process continuous outcome variables and incorporates covariates, and has the advantage of greatly improving the application range and the prediction accuracy of the continuous outcome variables, thereby solving the problems that a model cannot truly and effectively reflect genetic polymorphism information and the screening of markers has deviation.
In order to achieve the purpose, the invention provides the following technical scheme: an assay for predicting weight gain due to schizophrenia treated with a second generation antipsychotic drug based on polygene combination interactions, comprising the steps of:
s1: sample preparation peripheral blood from patients with weight gain due to schizophrenia treated with the second generation antipsychotic drugs was collected;
s2: extracting a genome DNA sample by using a hypotonic salting-out method;
s3: MODLI-TOF flight mass spectrometry for genotyping 5-hydroxytryptamine 2C receptor (5-HT2CR) gene, histamine l receptor (H1R) gene, Oxytocin (OT) gene, NPY/R gene, Leptin gene and Adiponectin (Adiponectin), FGF21 and FGF23 gene;
s4: analyzing and extracting data and aligning original data of the instrument to obtain data which has no noise interference and can be used for statistical analysis, developing quantitative gene-gene interaction through a generalized multi-factor dimensionality reduction method (GMDR), further applying cross validation, and predicting weight gain by using the gene interaction;
step 1, adopting case matching contrast research, collecting relevant clinical data of patients with schizophrenia treated by second-generation antipsychotic drugs, taking the patients with schizophrenia with weight gain more than 7% within 1 year after the second-generation antipsychotic drugs as research groups and the other patients as disease contrast groups, measuring the weight, Waist circumference, hip circumference and height of all the patients before and after treatment, calculating BMI (Body Mass Index) and WHR (Waist/hip-ratio) clinical phenotypes, and finishing Russell smoking reason questionnaire and nicotine dependence severity scale (FNTD) because smoking affects appetite and weight to a certain extent;
step 2: extracting 3ml of peripheral blood of the sample, extracting genomic DNA, genotyping SNP, structural mutation and insertion deletion genetic variation of 5-HT2C, H1R, NPY/R, oxyytocin, Leptin and adipinect candidate genes by using MODLI-TOF flight mass spectrometry through genbank and hapmap, and finding out specific genetic polymorphism of genes with statistically induced weight gain of second-generation antipsychotic drugs;
and step 3: and (3) comparing and selecting the difference of specific genetic polymorphisms between the research group and the control group by using frequency distribution and haplotype analysis, association analysis and quantitative trait analysis, screening out the specific genetic polymorphisms of the genes of the research group, constructing an intergenic interaction model by using a generalized multi-factor dimensionality reduction method (GMDR) and cross validation, combining clinical phenotype data of the research group, and performing GMDR and cross validation analysis again to finish the analysis method for predicting the weight gain caused by the second-generation antipsychotic drug treatment schizophrenia through multi-gene combination interaction.
Preferably, the grouping criteria in S4 are: (1) the age is 18-55 years; (2) han nationality; (3) the patient himself and his family members or guardians give informed consent to the study and sign an informed consent form; (4) study group and disease control group (treatment time difference less than 2 months); and the difference between the study group and the control group in terms of age and sex is not significant (P is more than 0.05).
Preferably, the exclusion criteria in S4 are: (1) patients with severe organic diseases of the heart, liver and kidney (such as severe liver and kidney diseases, kidney stones, diabetes, angle closure glaucoma) and history of significant drug allergy or other mental diseases including alcohol dependence; (2) other medicines which have influence on the body weight are taken for a long time or within 3 months before the patients are put into the group; (3) pregnant or lactating women.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention adopts a generalized multi-factor dimensionality reduction method to process continuous outcome variables and incorporates covariates, so that the application range and the prediction accuracy are greatly improved, GMDR is not only suitable for the continuous variables, but also can process the condition that more than 2 potential groups exist in data, the prediction accuracy of a model after incorporating the covariates is better than the analysis of the uninvolved covariates, statistical inspection is carried out by taking the prediction accuracy of an inspection sample in an interactive verification process as an index of more than p < 0.5, GMDR and cross verification are applied to the research of predicting the weight gain caused by the second-generation antipsychotic drug therapy schizophrenia, a prediction model is established, a gene combination interactive effect model is confirmed and verified through the correlation analysis of the concentration of the second-generation antipsychotic drug and the psychotic score, and thus, the multi-gene interactive effect marker of the weight gain caused by the second-generation antipsychotic drug therapy schizophrenia is comprehensively examined, and reasonable screening of the prediction model is realized.
2. The invention applies a generalized multi-factor dimensionality reduction method (GMDR) to develop gene-gene interaction of quantitative characters, further uses cross validation to predict disease phenotype, and develops new application of a multi-gene combination interaction to predict a weight gain analysis method for treating schizophrenia by using a second generation antipsychotic drug.
3. The invention can comprehensively reflect the novel application of the analysis method for predicting the weight gain caused by the treatment of schizophrenia by the second generation antipsychotic drugs through the multi-gene combined interaction, and can be used for providing favorable technical support for the early prediction and prognosis of the weight gain of the patients with chronic schizophrenia treated by the second generation antipsychotic drugs
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FIG. 1 is a schematic view of the structure of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution of an analysis method for predicting weight gain caused by schizophrenia treated by the second generation antipsychotic drugs based on polygene combination interaction: an assay for predicting weight gain due to schizophrenia treated with a second generation antipsychotic drug based on polygene combination interactions, comprising the steps of:
s1: sample preparation peripheral blood from patients with weight gain due to schizophrenia treated with the second generation antipsychotic drugs was collected;
s2: extracting a genome DNA sample by using a hypotonic salting-out method;
s3: MODLI-TOF flight mass spectrometry for genotyping 5-hydroxytryptamine 2C receptor (5-HT2CR) gene, histamine l receptor (H1R) gene, Oxytocin (OT) gene, NPY/R gene, Leptin gene and Adiponectin (Adiponectin), FGF21 and FGF23 gene;
s4: analyzing and extracting data and aligning original data of the instrument to obtain data which has no noise interference and can be used for statistical analysis, developing quantitative gene-gene interaction through a generalized multi-factor dimensionality reduction method (GMDR), further applying cross validation, and predicting weight gain by using the gene interaction;
step 1, adopting case matching contrast research, collecting relevant clinical data of patients with schizophrenia treated by second-generation antipsychotic drugs, taking the patients with schizophrenia with weight gain more than 7% within 1 year after the second-generation antipsychotic drugs as research groups and the other patients as disease contrast groups, measuring the weight, Waist circumference, hip circumference and height of all the patients before and after treatment, calculating BMI (Body Mass Index) and WHR (Waist/hip-ratio) clinical phenotypes, and finishing Russell smoking reason questionnaire and nicotine dependence severity scale (FNTD) because smoking affects appetite and weight to a certain extent;
grouping standard: (1) the age is 18-55 years; (2) han nationality; (3) the patient himself and his family members or guardians give informed consent to the study and sign an informed consent form; (4) study group and disease control group (treatment time difference less than 2 months); the difference between the study group and the control group in terms of age and sex is not significant (P is more than 0.05);
exclusion criteria: (1) patients with severe organic diseases of the heart, liver and kidney (such as severe liver and kidney diseases, kidney stones, diabetes, angle closure glaucoma) and history of significant drug allergy or other mental diseases including alcohol dependence; (2) other medicines which have influence on the body weight are taken for a long time or within 3 months before the patients are put into the group; (3) pregnant or lactating women;
step 2: extracting 3ml of peripheral blood of the sample, extracting genomic DNA, genotyping SNP, structural mutation and insertion deletion genetic variation of 5-HT2C, H1R, NPY/R, oxyytocin, Leptin and adipinect candidate genes by using MODLI-TOF flight mass spectrometry through genbank and hapmap, and finding out specific genetic polymorphism of genes with statistically induced weight gain of second-generation antipsychotic drugs;
and step 3: and (3) comparing and selecting the difference of specific genetic polymorphisms between the research group and the control group by using frequency distribution and haplotype analysis, association analysis and quantitative trait analysis, screening out the specific genetic polymorphisms of the genes of the research group, constructing an intergenic interaction model by using a generalized multi-factor dimensionality reduction method (GMDR) and cross validation, combining clinical phenotype data of the research group, and performing GMDR and cross validation analysis again to finish the analysis method for predicting the weight gain caused by the second-generation antipsychotic drug treatment schizophrenia through multi-gene combination interaction.
The method is a novel application of an analysis method for predicting the weight gain caused by schizophrenia treated by a second generation antipsychotic drug based on polygene combined interaction, and comprises the steps of collecting genomic DNA of patients with weight gain after chronic schizophrenia treated by the second generation antipsychotic drug, completing genetic polymorphism analysis genotyping of 5-hydroxytryptamine 2C receptor (5-HT2CR) gene, histamine l receptor (H1R) gene, Oxytocin (OT) gene, NPY/R gene, Leptin gene and Adiponectin (Adiponectin) gene, FGF21 gene and FGF23 gene of all samples, extracting and aligning original data obtained by a MODLI-TOF flight mass spectrometry method, and obtaining data which are free of noise interference and can be used for statistical analysis. And then applying a generalized multi-factor dimensionality reduction method (GMDR) to develop quantitative-trait gene-gene interaction, further using cross validation to predict disease phenotype, and developing a new application of a multi-gene combined interaction to predict a weight gain analysis method for treating schizophrenia by using a second generation antipsychotic drug. The invention can comprehensively reflect the novel application of the analysis method for predicting the weight gain caused by the treatment of the schizophrenia by the secondary antipsychotic drugs through the multi-gene combined interaction, and can be used for providing favorable technical support for the early prediction and prognosis of the weight gain of the patients with the chronic schizophrenia treated by the secondary antipsychotic drugs.
The working principle is as follows: the generalized multi-factor dimensionality reduction method is adopted to process continuous outcome variables, covariates are included, the application range and the prediction accuracy are greatly improved, GMDR is not only suitable for the continuous variables, but also capable of processing the situation that more than 2 potential groups exist in data, the prediction accuracy of the model after the covariates are included is better than that of the model without the covariates, statistical test is carried out by taking the prediction accuracy of a test sample in the interactive verification process as an index which is higher than p < 0.5, GMDR and cross verification are applied to the research of predicting the weight gain caused by the second-generation antipsychotic drug therapy schizophrenia, a prediction model is established, a gene combination interactive model is confirmed and verified through the correlation analysis of the concentration of the second-generation antipsychotic drug and the score of the psychotic symptoms, and therefore, the multi-gene interactive marker of the weight gain caused by the second-generation antipsychotic drug therapy schizophrenia is comprehensively examined, and reasonable screening of the prediction model is realized.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (3)
1. An assay for predicting weight gain due to schizophrenia treated with a second generation antipsychotic based on polygene combinatorial interactions, comprising: the method comprises the following steps:
s1: sample preparation peripheral blood from patients with weight gain due to schizophrenia treated with the second generation antipsychotic drugs was collected;
s2: extracting a genome DNA sample by using a hypotonic salting-out method;
s3: MODLI-TOF flight mass spectrometry for genotyping 5-hydroxytryptamine 2C receptor (5-HT2CR) gene, histamine l receptor (H1R) gene, Oxytocin (OT) gene, NPY/R gene, Leptin gene and Adiponectin (Adiponectin), FGF21 and FGF23 gene;
s4: analyzing and extracting data and aligning original data of the instrument to obtain data which has no noise interference and can be used for statistical analysis, developing quantitative gene-gene interaction through a generalized multi-factor dimensionality reduction method (GMDR), further applying cross validation, and predicting weight gain by using the gene interaction;
step 1, adopting case matching contrast research, collecting relevant clinical data of patients with schizophrenia treated by second-generation antipsychotic drugs, taking the patients with schizophrenia with weight gain more than 7% within 1 year after the second-generation antipsychotic drugs as research groups and the rest patients as disease contrast groups, measuring the weight, Waist circumference, hip circumference and height of all the patients before and after treatment, calculating BMI (BodyMass Index) and WHR (Waist/hip-ratio) clinical phenotypes, and finishing Russell smoking reason questionnaire and nicotine dependence severity scale (FNTD) because smoking affects appetite and weight to a certain extent;
step 2: extracting 3ml of peripheral blood of the sample, extracting genomic DNA, genotyping SNP, structural mutation and insertion deletion genetic variation of 5-HT2C, H1R, NPY/R, oxyytocin, Leptin and adipinect candidate genes by using MODLI-TOF flight mass spectrometry through genbank and hapmap, and finding out specific genetic polymorphism of genes with statistically induced weight gain of second-generation antipsychotic drugs;
and step 3: and (3) comparing and selecting the difference of specific genetic polymorphisms between the research group and the control group by using frequency distribution and haplotype analysis, association analysis and quantitative trait analysis, screening out the specific genetic polymorphisms of the genes of the research group, constructing an intergenic interaction model by using a generalized multi-factor dimensionality reduction method (GMDR) and cross validation, combining clinical phenotype data of the research group, and performing GMDR and cross validation analysis again to finish the analysis method for predicting the weight gain caused by the second-generation antipsychotic drug treatment schizophrenia through multi-gene combination interaction.
2. The method of claim 1, wherein the analysis is performed to predict weight gain due to schizophrenia based on polygene combination interactions as follows: the grouping criteria in S4: (1) the age is 18-55 years; (2) han nationality; (3) the patient himself and his family members or guardians give informed consent to the study and sign an informed consent form; (4) study group and disease control group; and the difference between the study group and the control group in terms of age and sex is not significant (P is more than 0.05).
3. The method of claim 1, wherein the analysis is performed to predict weight gain due to schizophrenia based on polygene combination interactions as follows: the exclusion criteria in S4: (1) patients with severe organic physical diseases of the heart, liver and kidney and a history of significant drug allergies or other psychiatric diseases including alcohol dependence; (2) other medicines which have influence on the body weight are taken for a long time or within 3 months before the patients are put into the group; (3) pregnant or lactating women.
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| CN109868311A (en) * | 2017-12-01 | 2019-06-11 | 上海市精神卫生中心 | The application of the metabolism adverse reaction of MIF and its prediction two generation antipsychotics induction |
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| CN109868311A (en) * | 2017-12-01 | 2019-06-11 | 上海市精神卫生中心 | The application of the metabolism adverse reaction of MIF and its prediction two generation antipsychotics induction |
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