Method and device for screening medicine for treating non-alcoholic fatty liver disease and medicine application

Abstract

The invention discloses a method and a device for screening drugs for treating non-alcoholic fatty liver diseases and drug application. The method comprises the following steps: step S101, acquiring expression quantitative trait locus eQTLs data of drug target genes of antihypertensive drugs as exposure factor data, and acquiring whole genome association analysis GWAS data of the non-alcoholic fatty liver diseases as outcome variable data; s102, setting screening conditions according to a relevance hypothesis, an exclusiveness hypothesis, an independence hypothesis and a target spot relevance hypothesis, and extracting and screening associated SNPs from the eQTLs data according to the set screening conditions to serve as tool variables; s103, extracting the associated SNPs (Single Nucleotide Polymorphisms) which are screened out from the GWAS data; and S104, verifying the causal relationship between the antihypertensive drugs and the non-alcoholic fatty liver disease by adopting a Mendel randomization analysis method so as to select the antihypertensive drugs effective for the non-alcoholic fatty liver disease (NAFLD). Therefore, a new clinical existing medicine which can be used for treating the NAFLD can be obtained from antihypertensive medicines, and more treatment choices are provided for NAFLD patients.

Description

Technical Field

[0001] The present invention belongs to the technical field of molecular biology, and specifically relates to a method for screening drugs for treating non-alcoholic fatty liver disease, a device and drug application. Background Art

[0002] Nonalcoholic fatty liver disease (NAFLD) refers to a series of abnormal histological changes in the liver, from simple fatty degeneration with no or only mild inflammation and no signs of liver cell damage to nonalcoholic fatty hepatitis, liver fibrosis, cirrhosis, and even liver cancer. In addition to liver lesions, it is also accompanied by many extrahepatic organ lesions, such as dyslipidemia, coronary heart disease, chronic kidney disease, etc., which seriously affect the patient's prognosis. At present, the diagnosis and treatment of NAFLD are still mainly based on dietary restrictions and lifestyle changes. The only drugs listed for NAFLD are Saroglitazar, a PPAR (peroxisome proliferator-activated receptor) agonist approved for marketing in India, and Resmetirom, a selective thyroid hormone receptor β (THRβ) agonist approved for marketing in the United States this year for non-cirrhotic NASH patients with moderate to severe liver fibrosis. However, the above drugs have a narrow treatment spectrum and are expensive. They are difficult for NAFLD patients in my country to obtain, and their efficacy and safety need further evaluation. Therefore, it is crucial to develop new and accessible NAFLD drugs.

[0003] The development of new drugs is time-consuming and labor-intensive, and developing new uses for existing clinical drugs has become an effective way to discover new drugs. Studies have found that hypertension and NAFLD have common pathophysiological risk factors, and the onset of hypertension is causally related to the increased risk of NAFLD. In addition, studies have shown that antihypertensive drugs can alleviate NAFLD through various pathways. Therefore, antihypertensive drugs may become a potential treatment option for NAFLD. However, there are many types of antihypertensive drugs, and not every antihypertensive drug is effective for NAFLD or liver disease. Therefore, further research on the specific efficacy, targets and application safety of antihypertensive drugs for NAFLD is necessary. At present, most studies on the pathogenesis of NAFLD and the therapeutic effects of antihypertensive drugs are observational, which limits the ability to infer their causal relationship. In addition, epidemiological studies on drug side effects are easily interfered by indications or time deviations, resulting in inaccurate research results. Therefore, developing more effective research methods to explore the causal relationship between antihypertensive drugs and NAFLD and drug safety is an important direction of current research. Summary of the invention

[0004] The present invention aims to solve one of the technical problems in the related art at least to a certain extent. To this end, the purpose of the present invention is to provide a method, device and drug application for screening drugs for treating non-alcoholic fatty liver disease.

[0005] To achieve the above object, according to the first aspect of the present invention, the present invention provides a method for screening drugs for treating non-alcoholic fatty liver disease, comprising the steps of:

[0006] S101. Obtain the expression quantitative trait loci eQTLs data of drug target genes of antihypertensive drugs as exposure factor data, and the genome-wide association analysis GWAS data of non-alcoholic fatty liver disease as outcome variable data;

[0007] S102, setting screening conditions according to the association hypothesis, the exclusivity hypothesis, the independence hypothesis and the target correlation hypothesis, and extracting the screened associated SNPs from the expression quantitative trait locus eQTLs data of the drug target gene as instrumental variables according to the set screening conditions;

[0008] S103, extracting and screening associated SNPs from genome-wide association analysis GWAS data of non-alcoholic fatty liver disease;

[0009] S104. Use Mendelian randomization analysis to verify the causal relationship between antihypertensive drugs and non-alcoholic fatty liver disease, so as to select antihypertensive drugs that are effective for non-alcoholic fatty liver disease (NAFLD).

[0010] According to one embodiment of the present invention, in step S101, genome-wide association analysis GWAS data of non-alcoholic fatty liver disease of two samples are obtained respectively, and in step S104, the causal relationship between antihypertensive drugs and non-alcoholic fatty liver disease is verified for the two samples respectively using the Mendelian randomization analysis method to obtain analysis results of the two samples;

[0011] After step S104, the method further includes the following steps:

[0012] S105. Perform a meta-analysis on the results of the two samples to obtain one or more antihypertensive drugs as protective agents for non-alcoholic fatty liver disease (NAFLD).

[0013] According to one embodiment of the present invention, the method further includes the following steps after step S105:

[0014] S106. Obtain genome-wide association analysis GWAS data of one or more adverse reactions or diseases as outcome variable data;

[0015] S107, extracting the selected associated SNPs from the genome-wide association analysis GWAS data of each disease according to the set screening conditions;

[0016] S108. Use Mendelian randomization analysis to verify the causal relationship between antihypertensive drugs as protective agents for non-alcoholic fatty liver disease (NAFLD) and various diseases.

[0017] According to one embodiment of the present invention, the Mendelian randomization analysis includes:

[0018] Any one or more of the five regression models: MR-Egger regression, random effects inverse variance weighted method IVW, weighted median method WME, weighted model or simple model.

[0019] According to one embodiment of the present invention, in step S102, the screened associated SNPs are used as instrumental variables. The method includes the following steps:

[0020] S1021, removal of weakly associated SNPs;

[0021] S1022. Remove SNPs in linkage disequilibrium.

[0022] According to one embodiment of the present invention, in step S1021, the condition for removing weakly associated SNPs is P < 1 × 10 -5 The strong correlation standard is used, and the statistic F>10 is used as an indicator to eliminate weak correlation.

[0023] According to one embodiment of the present invention, in step S1022, the linkage disequilibrium coefficient LD of the SNPs from which linkage disequilibrium is removed is:

[0024] The linkage disequilibrium relationship parameter r2=0.3, the linkage disequilibrium region width=100kb, and the minor allele frequency MAF>0.01.

[0025] According to one embodiment of the present invention, in step S102, the method of screening qualified SNPs as instrumental variables further comprises the steps of:

[0026] S1023, extract SNPs located within ±300kb of the cis-acting region of the drug target gene.

[0027] According to a second aspect of the present invention, an embodiment of the present invention provides a use of an antihypertensive drug in treating non-alcoholic fatty liver disease, characterized in that the antihypertensive drug comprises:

[0028] Any one or more of an ADRB1 antagonist, a NEU1 inhibitor, and a SLC12A1 inhibitor.

[0029] According to the third aspect of the present invention, an embodiment of the present invention provides a device for screening drugs for treating non-alcoholic fatty liver disease, wherein the device for screening drugs for treating non-alcoholic fatty liver disease comprises a computer device, wherein the computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for screening drugs for treating non-alcoholic fatty liver disease when executing the computer program.

[0030] Through the above technical scheme, the method, device and drug application for screening and treating non-alcoholic fatty liver disease of the present invention, the method comprises steps S101, obtaining the expression quantitative trait locus eQTLs data of the drug target gene of the antihypertensive drug as the exposure factor data, and the genome-wide association analysis GWAS data of non-alcoholic fatty liver disease as the outcome variable data; S102, setting the screening conditions according to the association hypothesis, the exclusive hypothesis, the independence hypothesis and the target correlation hypothesis, and extracting the selected associated SNPs from the expression quantitative trait locus eQTLs data of the drug target gene according to the set screening conditions as the instrumental variable; S103, extracting the selected associated SNPs from the genome-wide association analysis GWAS data of non-alcoholic fatty liver disease; S104, using the Mendelian randomization analysis method to verify the causal relationship between the antihypertensive drug and non-alcoholic fatty liver disease, so as to select the antihypertensive drug that is effective for non-alcoholic fatty liver disease NAFLD. In this way, new clinical existing drugs that may treat NAFLD can be obtained from antihypertensive drugs, providing more treatment options for NAFLD patients.

[0031] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flow chart of a method for screening drugs for treating non-alcoholic fatty liver disease provided by an embodiment of the present invention;

[0033] Figure 2 This is a flow chart of another method for screening drugs for treating non-alcoholic fatty liver disease provided by an embodiment of the present invention;

[0034] Figure 3 This is another flow chart of a method for screening drugs for treating non-alcoholic fatty liver disease provided by an embodiment of the present invention;

[0035] Figure 4 It is an IVW analysis display diagram based on the NAFLD ebi-a-GCST90091033 data set provided by an embodiment of the present invention;

[0036] Figure 5 It is an IVW analysis display diagram based on the FinnGen NAFLD data set provided by an embodiment of the present invention;

[0037] Figure 6 The present invention performs a Meta-analysis on the results of two data sets to obtain an analysis chart that ADRB1 antagonists, NEU1 inhibitors, and SLC12A1 inhibitors are protective agents for NAFLD;

[0038] Figure 7 This is a diagram analyzing the safety of ADRB1 antagonists, NEU1 inhibitors, and SLC12A1 inhibitors according to an embodiment of the present invention;

[0039] Figure 8 It is a schematic diagram of the structure of a device for screening drugs for treating non-alcoholic fatty liver disease provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to facilitate the understanding of the present invention, the present invention will be described more fully below. Preferred embodiments of the present invention are given below. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0042] On the one hand, see Figure 1 The present invention provides a method for screening drugs for treating non-alcoholic fatty liver disease, comprising the steps of:

[0043] S101. Obtain the expression quantitative trait loci (eQTLs, gene expression) data of the drug target genes of antihypertensive drugs as exposure factor data, and the genome-wide association study data (GWAS data, genome-wide association analysis data) of non-alcoholic fatty liver disease as outcome variable data;

[0044] S102, setting screening conditions according to the association hypothesis, the exclusivity hypothesis, the independence hypothesis and the target correlation hypothesis, and extracting the screened associated SNPs from the expression quantitative trait locus eQTLs data of the drug target gene as instrumental variables according to the set screening conditions;

[0045] S103. Extract and screen associated SNPs (single nucleotide polymorphisms) from genome-wide association analysis GWAS data of non-alcoholic fatty liver disease;

[0046] S104. Use Mendelian randomization analysis to verify the causal relationship between antihypertensive drugs and non-alcoholic fatty liver disease, so as to select antihypertensive drugs that are effective for non-alcoholic fatty liver disease (NAFLD).

[0047] Specifically, in step S101, the drug target genes of 58 antihypertensive drugs can be collected through literature, and the gene proxy drugs of the 58 antihypertensive drug targets can be used. For example, the GWAS data of 18 possible adverse reactions such as expression quantitative trait loci eQTLs of 37 drug target genes, non-alcoholic fatty liver disease (ebi-a-GCST90091033), acute renal failure (ebi-a-GCST90018790), acute renal failure (ebi-a-GCST003374), and hypokalemia (finn-b-E4_HYPOKALAEMIA) can be obtained from the IEU OpenGWAS project (https: / / gwas.mrcieu.ac.uk / ) website. The GWAS data of non-alcoholic fatty liver disease NAFLD were obtained from the FinnGen (https: / / www.finngen.fi / en) website, and the non-alcoholic fatty liver disease data set NAFLD was used for the verification of the GWAS data set. Among them, the drug target genes of 21 antihypertensive drugs did not find corresponding GWAS data in the IEU OpenGWAS project. Therefore, the embodiment of the present invention screens drugs based on the acquired drug target gene data of 37 antihypertensive drugs. That is, the gene proxy drugs of the acquired 37 antihypertensive drug targets are used for screening.

[0048] Since Mendelian randomization MR is an experimental design method, random grouping experiments are used to eliminate the influence of potential other factors on the experimental results. The idea of ​​Mendelian randomization can be used to deal with the problem of weak instrumental variables. Through random grouping experiments, we can obtain instrumental variables with higher external validity, thereby solving the problem of weak instrumental variables. Mendelian randomization analysis uses genetic variation as an instrumental variable, which can provide potential causal judgment evidence. It can not only overcome the influence of potential confounding factors, reverse causal associations, etc. on the results in traditional observational epidemiological studies, but also make up for the inability to conduct randomized controlled trials due to various reasons. Therefore, in step S102, the screening conditions are set according to the association hypothesis, exclusivity hypothesis, independence hypothesis and target correlation hypothesis. That is to say, Mendelian randomization analysis has four assumptions: Association hypothesis: Assumption 1, SNPs sites are strongly associated with exposure factors; Exclusivity hypothesis: Assumption 2, SNPs sites can only affect outcome variables by affecting exposure factors, but cannot affect outcome variables through other pathways; Independence hypothesis: Assumption 3, SNPs sites and outcome variables and confounding factors are not associated; Target relevance hypothesis: Assumption 4, the instrumental variable is within the range of ±300 of the cis-acting region of the target gene.

[0049] That is, when setting the instrumental variables of the Mendelian randomization MR method, the conditions for setting the instrumental variables are: SNPs (eQTLs) near the target protein encoding gene that have a significant effect on the biomarker, and the four core assumptions of the drug target Mendelian randomization (MR):

[0050] ① The instrumental variable is highly correlated with exposure, with P < 1×10 -5 is a strong correlation standard (association hypothesis). In addition, an F statistic greater than 10 is used as an indicator for eliminating weak instrumental variables.

[0051] ② The instrumental variable is not directly related to the outcome and only affects the outcome through exposure, that is, there is no gene pleiotropy (exclusive hypothesis). The fact that the intercept term of the Mendelian randomization MR-Egger regression is not statistically significant compared with 0 (P>0.05) and the result of the MR-PRESSO level pleiotropy test is not significant (P>0.05) indicates that there is no gene pleiotropy.

[0052] ③ The instrumental variable must be independent of confounding factors (independence assumption). Since the SNPs selected by the Mendelian randomization MR method follow the genetic principle that parental alleles are randomly assigned to offspring, the effects of the environment and acquired life are very small, that is, in theory, it can be considered that the instrumental variable is independent of environmental factors such as socioeconomic culture.

[0053] ④ Target relevance: The instrumental variable is within the range of ±300 of the cis-acting region of the target gene (target relevance assumption).

[0054] After setting the screening conditions, in steps S102 and S103, the associated SNPs are selected according to the set screening conditions. For example, in step S102, the selected associated SNPs are used as instrumental variables. The method includes the following steps:

[0055] S1021, removal of weakly associated SNPs;

[0056] S1022. Remove SNPs in linkage disequilibrium.

[0057] S1023, extract SNPs located within ±300kb of the cis-acting region of the drug target gene.

[0058] In step S1021, the condition for removing weakly associated SNPs is P < 1 × 10 -5 The strong correlation standard is used, and the statistic F>10 is used as an indicator to eliminate weak correlation.

[0059] Wherein, in step S1022, the linkage disequilibrium coefficient LD of the SNPs after removing linkage disequilibrium is:

[0060] The linkage disequilibrium relationship parameter r2=0.3, the linkage disequilibrium region width=100kb, and the minor allele frequency MAF>0.01.

[0061] P < 1 × 10 -5 As the screening conditions, the screening hypothesis ① was set as follows: the linkage disequilibrium coefficient r2 was set to 0.3, the linkage disequilibrium region width was set to 100 kb, and the minor allele frequency MAF>0.01 to ensure that each SNP was independent of each other and to eliminate the influence of linkage disequilibrium on the results; SNPs related to confounding factors and outcomes were eliminated by LDtait (https: / / ldlink.nih.gov / ?tab=ldtrait) (hypothesis ② and hypothesis ③), and those located within ±300 kb of the cis-acting region of the drug target gene were extracted (hypothesis ④), and the above-screened related instrumental variables were extracted from the eQTLs data of the drug target gene. The above-screened related SNPs were extracted from the GWAS summary data of the outcome variables (non-alcoholic fatty liver disease and adverse reactions), and the SNPs directly related to the outcome variables (non-alcoholic fatty liver disease and adverse reactions) were eliminated (P<1×10 -5 ), and MR-PRESSO was used to eliminate abnormal SNPs.

[0062] After screening under the above conditions, a total of 1484 SNPs associated with non-alcoholic fatty liver disease were obtained from the eQTLs data of drug target genes. After screening under the above conditions, a total of 1600 SNPs associated with adverse reactions were obtained from the eQTLs data of drug target genes SLC12A1, NEU1, and ADRB1.

[0063] After the associated SNPs are screened, in step S104, any one or more of the five regression models, including MR-Egger regression, random effect inverse variance weighted (IVW), weighted median estimator (WME), weighted model and simple model, can be used, and eQTLs can be used as instrumental variables to verify the causal relationship between antihypertensive drugs and non-alcoholic fatty liver disease. By verifying the causal relationship between antihypertensive drugs and non-alcoholic fatty liver disease, antihypertensive drugs that are effective for non-alcoholic fatty liver disease NAFLD can be selected.

[0064] In the embodiments of the present invention, due to the four core assumptions based on Mendelian randomization (MR) of drug targets, the influence of confounding factors such as sociodemographic, behavioral factors or health status is eliminated, and new clinical existing drugs that may treat NAFLD are obtained from antihypertensive drugs using Mendelian randomization MR analysis, providing more treatment options for NAFLD patients.

[0065] Furthermore, in one embodiment of the present invention, in step S101, genome-wide association analysis GWAS data of non-alcoholic fatty liver disease of two samples are obtained respectively, and in step S104, the causal relationship between antihypertensive drugs and non-alcoholic fatty liver disease is verified for the two samples respectively using the Mendelian randomization analysis method to obtain the analysis results of the two samples.

[0066] One of the samples used the non-alcoholic fatty liver disease (ebi-a-GCST90091033) data from the IEU OpenGWAS project (https: / / gwas.mrcieu.ac.uk / ). The other sample used the GWAS data (Finnish dataset) of non-alcoholic fatty liver disease NAFLD obtained from the FinnGen (https: / / www.finngen.fi / en) website. The non-alcoholic fatty liver disease NAFLD dataset was used to validate the GWAS dataset. Based on the NAFLD ebi-a-GCST90091033 dataset, IVW analysis showed (e.g. Figure 4As shown in ), a total of 10 genetically predicted antihypertensive drugs were associated with NAFLD, of which 5 were negatively correlated with NAFLD, namely ADRB1 antagonists, NEU1 inhibitors, prostaglandin endoperoxide synthase 2 (PTGS2) inhibitors, SLC12A1 inhibitors, and solute carrier family 12 member 3 (SLC12A3) inhibitors. In contrast, the other 5 antihypertensive drugs were positively correlated with NAFLD, including calcium voltage-gated channel auxiliary subunit alpha2delta2 (CACNA2D2) inhibitors, calcium voltage-gated channel auxiliary subunit β2 (CACNB2) inhibitors, calcium voltage-gated channel auxiliary subunit β4 (CACNB4) inhibitors, carnitine palmitoyltransferase 1A (CPT1A) inhibitors, and sodium channel epithelial 1 subunit δ (SCNN1D) inhibitors. Similarly, we conducted the same study on the relationship between antihypertensive drugs and NAFLD in the FinnGen NAFLD dataset. The IVW results of the FinnGenNAFLD dataset show (as shown in Figure 5 As shown in the data, a total of 7 antihypertensive drugs were associated with NAFLD, and all of them were associated with a reduced risk of non-alcoholic fatty liver disease NAFLD, including ADRB1 antagonists, carbonic anhydrase IV (CA4) inhibitors, carbonic anhydrase VI (CA6) inhibitors, calcium voltage-gated channel auxiliary subunit α1a (CACNA1A) inhibitors, calcium voltage-gated channel auxiliary subunit α1h (CACNA1H) inhibitors, NEU1 inhibitors, and SLC12A1 inhibitors.

[0067] See also Figure 2 , after step S104, it also includes the step: S105, performing a meta-analysis on the results of the two samples to obtain one or more antihypertensive drugs as protective agents for non-alcoholic fatty liver disease NAFLD. Meta-analysis is a statistical method used to compare and integrate research results on the same scientific issue. Whether its conclusions are meaningful depends on the quality of the included studies. It is often used in quantitative combined analysis in systematic reviews. Compared with a single study, by integrating all relevant studies, the effect of medical and health care can be more accurately estimated, and it is beneficial to explore the consistency of evidence from each study and the differences between studies. When the results of multiple studies are inconsistent or not statistically significant, Meta-analysis can be used to obtain statistical analysis results that are close to the actual situation (such as Figure 6 Meta-analysis of the results of the two datasets revealed that ADRB1 antagonists, NEU1 inhibitors, and SLC12A1 inhibitors are protective agents for non-alcoholic fatty liver disease (NAFLD).

[0068] See also Figure 3 In one embodiment of the present invention, after step S105, the following steps are further included:

[0069] S106. Obtain genome-wide association analysis GWAS data of one or more adverse reactions or diseases as outcome variable data;

[0070] S107, extracting the selected associated SNPs from the genome-wide association analysis GWAS data of each disease according to the set screening conditions;

[0071] S108. Use Mendelian randomization analysis to verify the causal relationship between antihypertensive drugs as protective agents for non-alcoholic fatty liver disease (NAFLD) and various diseases.

[0072] Among them, the screening method and verification of SNPs associated with adverse reactions or diseases are similar to those of the above-mentioned non-alcoholic fatty liver disease NAFLD. Both use Mendelian randomization analysis methods to verify causal relationships, and will not be repeated again.

[0073] The safety of the screened non-alcoholic fatty liver disease (NAFLD) protective agents ADRB1 antagonists, NEU1 inhibitors, and SLC12A1 inhibitors was analyzed (the results are shown in Figure 7 As shown in ), it was found that NEU1 inhibitors are relatively safe, and ADRB1 antagonists and SLC12A1 inhibitors may be associated with the occurrence of transient cerebral ischemia, suggesting that we should closely monitor when using medication.

[0074] The embodiment of the present invention is based on the original observational study, and uses genetic variation as an instrumental variable to conduct in-depth research to obtain antihypertensive drugs related to NAFLD and evaluate their safety. The study is affected by confounding factors such as social demographics, behavioral factors or health conditions, which excludes reverse causality, reduces bias and enhances the causal inference of the study. The present invention obtains three antihypertensive drugs that can be used for the treatment of NAFLD, namely adrenergic receptor (ADRB1) antagonists, neuraminidase 1 (NEU1) inhibitors and solute carrier family 12 member 1 (SLC12A1) inhibitors, and the safety evaluation of the above drugs found that NEU1 inhibitors are safe, and ADRB1 antagonists and SLC12A1 inhibitors may be associated with transient encephalopathy (TIA) attacks. The above invention provides more options for the treatment of NAFLD patients.

[0075] In this study, we first used Mendelian Randomization (MR) to conduct a two-sample MR to explore the causal association between antihypertensive drugs and NAFLD. Secondly, we performed a drug target MR analysis to evaluate the safety of antihypertensive drugs in the treatment of NAFLD. MR studies use genetic variants as instrumental variables to exclude reverse causality, reduce bias, and enhance the causal inference power of the study. These genetic variants are fixed at conception and are not affected by any later outcomes or diseases, nor are they affected by confounding factors such as sociodemographic, behavioral factors, or health conditions. MR analysis helps to evaluate the long-term effects of drug targets on the risk of NAFLD. This will provide insights into the pathological mechanisms of antihypertensive drugs in the treatment of NAFLD, provide safer medication guidance for the clinic, and provide more treatment options for patients with these diseases.

[0076] According to the second aspect of the present invention, an embodiment of the present invention further provides an application of an antihypertensive drug in the treatment of non-alcoholic fatty liver disease, wherein the antihypertensive drug comprises any one or more of an ADRB1 antagonist, a NEU1 inhibitor, and a SLC12A1 inhibitor.

[0077] The present invention obtains three antihypertensive drugs that can be used for the treatment of NAFLD, namely, adrenergic receptor (ADRB1) antagonists, neuraminidase 1 (NEU1) inhibitors and solute carrier family 12 member 1 (SLC12A1) inhibitors, and conducts safety evaluation on the above drugs, and finds that NEU1 inhibitors are safe to use, and ADRB1 antagonists and SLC12A1 inhibitors may be associated with transient encephalopathy (TIA) attacks. The above invention provides more options for the treatment of NAFLD patients.

[0078] See also Figure 8 According to the third aspect of the present invention, an embodiment of the present invention provides a device for screening drugs for treating non-alcoholic fatty liver disease, wherein the device for screening drugs for treating non-alcoholic fatty liver disease comprises a computer device, wherein the computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for screening drugs for treating non-alcoholic fatty liver disease when executing the computer program.

[0079] The computer program may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of each of the above method embodiments may be implemented. The computer program includes computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0080] It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0081] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0082] The steps in the method of the embodiment of the present invention can be adjusted in order, combined or deleted according to actual needs.

[0083] The modules or units in the system of the embodiment of the present invention may be combined, divided or deleted according to actual needs.

[0084] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic preset hardware, or in a combination of computer software and electronic preset hardware. Whether these functions are performed in preset hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0085] In the embodiments provided by the present invention, it should be understood that the disclosed apparatus / computer equipment and methods can be implemented in other ways. For example, the apparatus / computer equipment embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division. There may be other division methods in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection of the apparatus or unit, which may be electrical, mechanical or other forms.

[0086] The preferred embodiments of the present invention are described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, a variety of simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope of the present invention.

[0087] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0088] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A method for screening drugs for treating non-alcoholic fatty liver disease, characterized in that: Includes steps: S101. Obtain the expression quantitative trait loci eQTLs data of drug target genes of antihypertensive drugs as exposure factor data, and the genome-wide association analysis GWAS data of non-alcoholic fatty liver disease as outcome variable data; S102, setting screening conditions according to the association hypothesis, the exclusivity hypothesis, the independence hypothesis and the target correlation hypothesis, and extracting the screened associated SNPs from the expression quantitative trait locus eQTLs data of the drug target gene as instrumental variables according to the set screening conditions; S103, extracting and screening associated SNPs from genome-wide association analysis GWAS data of non-alcoholic fatty liver disease; S104. Use Mendelian randomization analysis to verify the causal relationship between antihypertensive drugs and non-alcoholic fatty liver disease, so as to select antihypertensive drugs that are effective for non-alcoholic fatty liver disease (NAFLD).

2. The method for screening drugs for treating non-alcoholic fatty liver disease according to claim 1, characterized in that: In step S101, the genome-wide association analysis GWAS data of non-alcoholic fatty liver disease of two samples are obtained respectively, and in step S104, the causal relationship between antihypertensive drugs and non-alcoholic fatty liver disease is verified for the two samples respectively using the Mendelian randomization analysis method to obtain the analysis results of the two samples; After step S104, the method further includes the following steps: S105. Perform a meta-analysis on the results of the two samples to obtain one or more antihypertensive drugs as protective agents for non-alcoholic fatty liver disease (NAFLD).

3. The method for screening drugs for treating non-alcoholic fatty liver disease according to claim 2, characterized in that: After step S105, the method further includes the following steps: S106. Obtain genome-wide association analysis GWAS data of one or more adverse reactions or diseases as outcome variable data; S107, extracting the selected associated SNPs from the genome-wide association analysis GWAS data of each disease according to the set screening conditions; S108. Use Mendelian randomization analysis to verify the causal relationship between antihypertensive drugs as protective agents for non-alcoholic fatty liver disease (NAFLD) and various diseases.

4. The method for screening drugs for treating non-alcoholic fatty liver disease according to claim 1, characterized in that: The Mendelian randomization analysis included: Any one or more of the five regression models: MR-Egger regression, random effects inverse variance weighted method IVW, weighted median method WME, weighted model or simple model.

5. The method for screening drugs for treating non-alcoholic fatty liver disease according to claim 1, characterized in that: In step S102, the screened associated SNPs are used as instrumental variables. The method includes the following steps: S1021, removal of weakly associated SNPs; S1022. Remove SNPs in linkage disequilibrium.

6. The method for screening drugs for treating non-alcoholic fatty liver disease according to claim 5, characterized in that: in, In step S1021, the condition for removing weakly associated SNPs is P < 1 × 10 -5 The strong correlation standard is used, and the statistic F>10 is used as an indicator to eliminate weak correlation.

7. The method for screening drugs for treating non-alcoholic fatty liver disease according to claim 5, characterized in that: in, In step S1022, the linkage disequilibrium coefficient LD of the SNPs after removing linkage disequilibrium is: The linkage disequilibrium relationship parameter r2=0.3, the linkage disequilibrium region width=100kb, and the minor allele frequency MAF>0.

01.

8. The method for screening drugs for treating non-alcoholic fatty liver disease according to claim 7, characterized in that: In step S102, the method of screening qualified SNPs as instrumental variables further comprises the steps of: S1023, extract SNPs located within ±300kb of the cis-acting region of the drug target gene.

9. A device for screening drugs for treating non-alcoholic fatty liver disease, the device comprising a computer device, the computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for screening drugs for treating non-alcoholic fatty liver disease according to any one of claims 1 to 8 is implemented.

10. Use of an antihypertensive drug in the treatment of non-alcoholic fatty liver disease, characterized in that: Blood pressure medications include: Any one or more of an ADRB1 antagonist, a NEU1 inhibitor, and a SLC12A1 inhibitor.

Images