Novel Insights into the Relationship Between Glucose-Lowering Drugs and Nonalcoholic Fatty Liver Disease and liver function: a Mendelian Randomization Study

Novel Insights into the Relationship Between Glucose-Lowering Drugs and Nonalcoholic Fatty Liver Disease and liver function: a Mendelian Randomization Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Novel Insights into the Relationship Between Glucose-Lowering Drugs and Nonalcoholic Fatty Liver Disease and liver function: a Mendelian Randomization Study Gang Lei, Chibing Dai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4759170/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives The potential risk of nonalcoholic fatty liver disease (NAFLD) and liver toxicity attributed to glucose-lowering medications is uncertain. The objective of this study was to explore the causal relationship between these factors through the implementation of a Mendelian randomization (MR) analysis. Methods Two-sample MR, summary-data-based MR (SMR), and colocalization analysis were utilized to investigate the association between ten drug reduce glucose targets (PPARG, DPP4, GLP1R, INSR, SLC5A2, ABCC8, KCNJ11, ETFDH, GPD2, and PRKAB1) to reduce NAFLD and liver function tests (LFTs) levels, including aspartate aminotransferase (AST), gamma glutamyl transferase (GGT), alkaline phosphatase (ALP), alanine aminotransferase (ALT), and bilirubin. Results DPP4 is closely associated with GGT and ALT. PPARG is significantly associated with NAFLD and correlated with various liver enzymes GGT, AST, ALT, ALP, total bilirubin, and direct bilirubin. PRKAB1 is linked to total and direct bilirubin levels, while SLC5A2 is associated with total and direct bilirubin levels, ALP levels, and NAFLD risk. Limited evidence suggests that genetic variants in PRKAB1, GLP1R, INSR, GPD2, DPP4, and ABCC8/KCNJ11 are correlated with GGT, ALT, bilirubin, and NAFLD levels. Additional validation through SMR and colocalization analysis further confirmed the causal effects of these findings. Conclusions Specific glucose-lowering medications have been associated with an elevated risk of NAFLD and abnormal LFTs results, potentially offering innovative strategies for the management of NAFLD and LFTs abnormalities. Drug target Glucose-lowering drugs Nonalcoholic fatty liver disease Liver function tests Mendelian randomization Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION The prevalence of nonalcoholic fatty liver disease (NAFLD) has been increasing, affecting more than 30% of the adult population in the United States. 1 Furthermore, between 1999 and 2018, individuals with NAFLD experienced higher rates of decompensated cirrhosis, hepatocellular carcinoma, and liver-related mortality compared to those with viral hepatitis. 2 Elevated liver enzymes are crucial markers of liver damage in NAFLD among the overall population. Deciphering liver enzyme levels can be challenging due to the influence of metabolic processes beyond the liver. Because of their involvement in inflammation and oxidative stress, elevated levels of these enzymes could be linked to a higher likelihood of mortality. 3 At present, the main criterion for conditional approval of drugs is histopathological assessment of liver biopsy. Owing to the wide range and variability of invasive histopathological evaluations, this unique requirement poses a significant barrier to the industry, resulting in a very high rate of screening failure in clinical trials. Currently, 4 progress in potent medications with minimal side effects is limited. 5 Previous research has shown connections between targets of drugs that lower glucose levels and different health conditions, such as atrial fibrillation, cardiorenal metabolic syndrome, and prostate cancer, underscoring the significance of using glucose-lowering medications to manage diseases. 6 – 8 Metformin, a medication commonly used to lower blood sugar levels in individuals with Type 2 diabetes mellitus (T2DM), has been found to have positive effects on decreasing the prevalence of NAFLD and liver function tests. 9 Glucagon-like peptide-1 agonist drugs also show inhibitory effects on NAFLD and LFTs. 10 However, there is a certain degree of concern about the potential NAFLD risk and hepatotoxic effects of some glucose-lowering drugs, partly because preclinical studies of some glucose-lowering drugs have found potential adverse associations with liver damage. 11 Therefore, these data are critical to assess the risks that such studies may bring to participants. As an epidemiological method, Mendelian randomization (MR) has a strong ability to infer causal relationships. 12 Utilizing genetic variations in MR improve the ability to draw causal conclusions about the relationship between exposure and outcome by reducing the impact of confounding factors and ruling out reverse causation. 13 Many studies have explored the association between glucose-lowering drugs and NAFLD and liver function tests (LFTs). Because these studies were observational in nature, they inevitably faced confounding bias and reverse causation. Owing to the lengthy progression of NAFLD and LFTs development, the utilization of drug-target MR analysis has proven to be essential in assessing the viability of drug repurposing and anticipating potential adverse reactions. Currently, there is a scarcity of thorough MR studies that have extensively investigated the association between glucose-lowering medication targets and the likelihood of developing NAFLD and LFTs. Hence, this study utilized two-sample MR and SMR analyses to investigate the possible links between the targets of glucose-lowering medications and the likelihood of NAFLD and LFTs, such as aspartate aminotransferase (AST), gamma glutamyl transferase (GGT), alkaline phosphatase (ALP), alanine aminotransferase (ALT), and bilirubin. 2. MATERIALS AND METHODS 2. 1 Study design The structure of this study was guided by the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. 14 The association between glucose-lowering drug targets and NAFLD and LFTs was demonstrated by genetic analyses of two samples using MR and SMR. MR analyses' causal estimation validation depended on three key assumptions: genetic instrumental variables (IVs) were linked to glucose-lowering drug targets, genetic IVs were independent from confounding factors, and genetic IVs had no direct effects on NAFLD and LFTs risk aside from through the drug targets. The inverse-variance weighted (IVW) or Wald ratio method was the primary approach used in the two-sample MR analysis to investigate the connections between drug targets for lowering glucose levels and the likelihood of NAFLD and LFTs. Concurrently, we conducted SMR analysis, utilizing expression quantitative trait loci (eQTLs) to investigate and confirm the causal connection between exposure and outcome on a genetic expression level. Furthermore, Bayes factor analysis was utilized to conduct colocalization analysis within a ± 500 KB window around the gene coding region of each individual glucose-lowering drug target to determine the likelihood of the connection between exposure and outcome. The detailed flow of this study is illustrated in Fig.1. Summary-level statistics were provided by the Genome-wide association study (GWAS) studies utilized in Table 1. Approval for these studies was obtained from the appropriate institutional review boards in the respective countries, following the guidelines of the Declaration of Helsinki, and all participants provided informed consent. 2. 2 Identifying and choosing instrumental variables Ultimately, there were 10 targets affected by seven distinct medications with lower glucose levels. We used the ten target Single Nucleotide Polymorphism (SNPs) summarized by Yang et al. 15 as genetic tools for glucose-lowering drug targets. The ChEMBL (https://www.ebi.ac.uk/chembl) and DrugBank (https://go.drugbank.com/) databases were used to identify the relevant gene and protein targets with pharmacological activity (Supplementary Table S1). Given that a decrease in HbA1C levels is the main indicator for evaluating the body's reactions to drugs that lower glucose levels, identified genetic variations near the glucose loci boundaries within a 500 kb range that were linked to HbA1C levels with a P value < 5 × 10 –8 (considered genome-wide significant) as substitutes for the main physiological responses to glucose-lowering drug inhibition at these specific sites. 16 clustered glucose-lowering drug variants at linkage disequilibrium (LD) r 2 1%) associated with the expression of DPP4, GPD2, ETFDH, GLP1R, INSR, KCNJ11, PPARG, PRKAB1, and SLC5A2 in blood tissues. These eQTLs were identified from the eQTLGen Consortium and Genotype Tissue Expression (GTEx) Consortium Version 8.0, with significant association ( P < 5 × 10 − 8 ) IVs. As no significant eQTLs for ABCC8 was found in blood tissues, we collected IVs associated with ABCC8 expression, particularly in muscle and bone tissues. 2. 3 Validation of instrumental variables To prevent potential bias from weak IVs, the F-statistic approach was employed, with the requirement that the F value be greater than 10. 17 T2DM is the original indication for glucose-lowering drugs, which ultimately affects patients’ blood glucose levels. Previous meta-analyses have shown that some glucose-lowering drugs, such as sulfonylureas, insulin analogs, and thiazolidinediones, cause weight gain, whereas GLP-1 analogs cause weight loss, suggesting that weight is another unique phenotype affected by glucose-lowering drugs 18 . Hence, body mass index (BMI), blood sugar levels, and T2DM from a comprehensive dataset were chosen as benchmarks to confirm the significant link between IVs and genetically influenced glucose-lowering medication targets. 2. 4 Study outcome In the discovery phase, Sun et al. conducted a GWAS based on a large cohort from the UK Biobank. In this study, participants with alcoholic disorders, harmful alcohol use, confirmed hemochromatosis, viral hepatitis, Wilson's disease, and users of liver-damaging medications were excluded from the analysis. 19 We collected LFTs from the Pan UK Biobank Project, including ALT, AST, ALP, GGT, and bilirubin. 20 In the replication phase, GWAS data for NAFLD (2,568 cases and 409,613 controls) were obtained from the FinnGen r10 consortium. 21 Similarly, The GWAS summary datasets for replication LFTs were obtained from two studies, Sakaue S et al. 22 and Dennis JK et al. 23 , and were independent of the GWAS datasets in the discovery phase. The characteristics associated with NAFLD were used as endpoints for MR examination in this study. The appropriate institutional review board approved and informed consent was obtained for this study, which relied on previously published works and public databases. 2. 5 Colocalization analysis Significant findings from the MR analysis were further assessed through Bayesian colocalization analysis to determine the likelihood that a specific genetic variant influenced both the glucose-lowering drug target and NAFLD and LFTs, rather than simply being linked by chance through LD. 24 Colocalization was assessed using the 'COLORC' R package available at https://github.com/chr1swallace/coloc. 24 A posterior probability exceeding 0.75 for hypothesis 4 indicated compelling support for colocalization. 25 It should be mentioned that Bayesian colocalization has a limitation in assuming a single common cause SNPs, whereas in actuality, a genetic locus may have multiple causal SNPs. 2. 6 Sensitivity Analysis In the sensitivity analysis, if there was heterogeneity observed across various instruments using Cochran's Q test, a weighted median approach was utilized to account for potential ineffectiveness of up to 50% of the instruments. 26 Likewise, if heterogeneity was detected at multiple levels by the MR-Egger method tropism, MR-Egger would be employed because it has the ability to address instrument pleiotropy. Furthermore, a sensitivity analysis was conducted using SMR testing with multiple SNPs for each target drug (multi-SNPs-SMR) to enhance the significance of the evidence obtained from the initial analysis. The HEIDI test, which utilizes dependent instrumental heterogeneity, was employed to differentiate between pleiotropic and linkage models. In HEIDI studies with P values greater than 0.01 there was no evidence that linkage disequilibrium mediated the relationship between glucose-lowering drug targets and the risk of NAFLD and LFTs. 27 The analysis was conducted using the SMR software tool version 1.3.1. 2. 7 Statistical Analysis This research utilized MR with 'TwoSampleMR' (https://github.com/MRCIEU/TwoSampleMR) by incorporating 10 antidiabetic drug target genes as variables, NAFLD, and 6 LFTs as results; to prevent bias, a significance level of P < 0.0007 (0.05 divided by 70, including 10 antidiabetic drug targets, NAFLD, and 6 LFTs) was adjusted using the Bonferroni correction. In the replication phase, significant causal relationships for antidiabetic drug target genes were identified, and replication analyses were performed using GWAS summary data from the replication phase (P < 0.05). P values below 0.05 were deemed suggestive, while values above 0.05 suggested no statistical link between the antidiabetic medication target and NAFLD and LFTs. 3. RESULTS 3. 1 Positive control analysis To prevent potential bias from weak independent variables, the F value was calculated for each IVs, ensuring that the F value did not exceed 10 (Supplementary Table S2). Positive controls, such as BMI, blood glucose measurements, and T2DM, were used to test the IVs in the two-sample MR and SMR analyses, showing reliable associations between IVs and BMI, blood glucose measurements, and T2DM, demonstrating the effectiveness of IVs (Supplementary Tables S3–S6). 3. 2 Two-sample MR analysis Fig.2 and Table 2 display the findings of the two-sample MR analysis of antidiabetic medication targets and their association with NAFLD and LFTs. Analytical evidence showed that DPP4 was associated with GGT (β = 10.567; 95% confidence interval (95% CI): 4.829,16.306; P = 3E-4), ALT (β = 5.619; 95% CI: 2.504,8.733; P = 4E- 4). The evidence highlights the association between PPARG and NAFLD (OR = 1.106; 95% CI: 1.064,1.149; P = 3E-7), GGT (β = 8.846; 95% CI: 6.785,10.908; P = 4E-17), AST (β = 3.111; 95% CI: 2.539,3.682; P = 1E-26), ALT (β = 4.257; 95% CI: 3.507,5.007; P = 9E-29) and ALP (β = 8.271; 95% CI: 7.128,9.415; P = 1E-45), however, there was a substantial association with total bilirubin (β = -1.145; 95% CI: -1.296,-0.994; P = 5E-50) and direct bilirubin (β = -0.217; 95% CI: -0.250,-0.183; P = 2E-37).Similarly, PRKAB1 was temporally associated with total bilirubin (β = -1.535; 95% CI: -2.055,-1.016; P = 7E- 09) and direct bilirubin (β = -0.224; 95% CI: -0.319, -0.129; P = 4E-06) was associated with reduced risk. However, SLC5A2 was associated with total bilirubin (β = -1.375; 95% CI: -1.701, -1.050; P = 1E-16), Direct bilirubin (β = -0.261; 95% CI: -0.324,-0.197; P = 6E-16), and NAFLD (OR = 0.778; 95% CI: 0.723,0.837; P = 2E-11), but the risk was higher for ALP (β = 3.484; 95% CI: 1.779, 5.189; P = 6E-05). Evidence indicates that PRKAB1, GLP1R, INSR, GPD2, DPP4, and ABCC8/KCNJ11 are related to GGT, ALT, bilirubin, and NAFLD, and that there is no correlation between ABCC8/KCNJ11, INSR, ETFDH, and GPD2 and the risk of NAFLD and LFTs. In the replication phase, nine significant antidiabetic drug targets (P < 0.05) were successfully validated in the replicated GWAS dataset using the Wald ratio or IVW method. During the replication phase, one significant antidiabetic drug target (P < 0.05) was successfully validated in the NAFLD FinnGen dataset based on the Wald ratio or IVW method. Replication analysis of LFTs revealed similar results for eight significant antidiabetic drug targets. 3. 3 SMR analysis Supplementary Table S6 shows the results of SMR analysis of antidiabetic drug targets and the risk of NAFLD and LFTs. SMR analysis revealed genetic associations between AST risk and KCNJ11 (β = -0.452; 95% CI: -0.786, -0.117; P = 0.008) and GPD2 (β = 0.132; 95% CI: 0.004, 0.259; P = 0.044). Direct bilirubin risk was associated with DPP4 (β = 0.046; 95% CI: 0.011, 0.081; P = 0.01), PPARG (β = -0.029; 95% CI: -0.047, -0.012; P = 0.001) and INSR (β = -0.073; 95% CI: -0.136, -0.009; P = 0.025). GGT risk was associated with ABCC8 (β = 0.349; 95% CI: 0.059, 0.640; P = 0.018). Total bilirubin risk was associated with DPP4 (β = 0.303; 95% CI: 0.135, 0.472; P = 4E-4) and PPARG (β = -0.127; 95% CI: -0.210, -0.045; P = 0.003). Furthermore, ABCC8 (β = 0.209; 95% CI: 0.028, 0.390; P = 0.024), GPD2 (β = -0.409; 95% CI: -0.726, -0.092; P = 0.011), PPARG (β = -0.878; 95 % CI: -1.375, -0.380; P = 0.001), PRKAB1 (β = -0.619; 95% CI: -0.909, -0.329; P = 3E-5), and DPP4 (β = -1.312; 95% CI:-2.313, -0.311; P = 0.01) were associated with the risk of ALP. However, there is no evidence that the ten antidiabetic drug targets are associated with NAFLD and ALT levels. 3. 4 Colocalization analysis A study was conducted to demonstrate the likelihood of a common hypoglycemic drug target causal variant being shared, and its association with NAFLD and LFTs risk (Fig.3、 Supplementary Table S7). PPARG was observed to share a genetic region with ALP risk (PP4 = 75.2%). There was strong evidence of colocalization between DPP4 and ALP, AST, and ALT, respectively (PP4 = 90.3%, 93.8%, and 99.4%). 3. 4 Examining the impact of variations As a result of the SMR analysis, all HEIDI P values exceeded 0.01 (Supplementary Table S6). Furthermore, the two-sample MR analysis revealed no heterogeneity or pleiotropy in either NAFLD or LFTs, as indicated by the results of the heterogeneity and pleiotropy tests. Efficacy, P values were all > 0.05, indicating a minimal impact of horizontal pleiotropy due to the chain scenario. In complementary MR techniques, estimates typically align in terms of both size and direction; however, MR Egger and Weighted median estimates tend to be less accurate than MR IVW (Supplementary Tables S8-S9). 4. DISCUSSION This study thoroughly examined how 10 antidiabetic medications affect the likelihood of developing NAFLD and LFTs. Two-sample MR analysis showed that PPARG, SLC5A2, PRKAB1, and DPP4 were strongly associated with ALP, ALT, AST, GGT, total bilirubin, direct bilirubin and NAFLD. PRKAB1, GLP1R, INSR, GPD2, DPP4, and ABCC8/KCNJ11 are potentially associated with ALT, GGT, total bilirubin, direct bilirubin, and NAFLD. In SMR analysis, DPP4, GPD2, INSR, PPARG, PRKAB1, and ABCC8/KCNJ11 were associated with GGT, ALP, AST, total, and direct bilirubin. Replicate MR analysis verified significant causal relationships for 9 of these 15 candidate antidiabetic drug targets. Furthermore, based on the colocalization analysis, PPARG and DPP4 shared common causal variants in ALP, AST, and ALT. NAFLD is the primary cause of long-term liver disease. Some anti-diabetic drugs have been shown to induce NAFLD. The effectiveness of antidiabetic medications for NAFLD is currently uncertain, with some drugs being linked to an increased risk of LFTs. 11 Research has indicated that metformin provides significant advantages in lowering the overall occurrence of NAFLD, along with decreasing levels of AST, GGT, ALP, ALT, total, and direct bilirubin. 9 Metformin could potentially lower hepatic enzymes, although this impact may vary among individuals. Hence, a comprehensive examination was performed of the three drug targets associated with metformin (ETFDH, GPD2, and PRKAB1). We also found that PRKAB1 and GPD2 reduced total and direct bilirubin levels, whereas ETFDH was not correlated with NAFLD and LFTs. These results indicated that metformin does not uniformly increase all LFTs. In clinical practice, it is prudent to enhance treatment strategies by integrating the appropriate drug targets. In their study, Barchetta et al. discovered a higher likelihood of NAFLD in individuals with T2DM who were prescribed gliclazide and glyburide. 28 Our research supports the idea that ABCC8/KCNJ11 is linked to an increased likelihood of developing NAFLD. SMR analysis identified that ABCC8/KCNJ11 had a promoting effect on ALP and AST levels. These findings indicate that sulfonylureas should not be used to treat T2DM patients with NAFLD because of their potential to exacerbate NAFLD and lead to liver injury. In both two-sample MR and SMR analyses, DPP4 was associated with a decreased likelihood of direct bilirubin. Conversely, the two-sample MR analyses showed that it was linked to an increased risk of ALT, GGT, and NAFLD. This finding indicates that utilizing DPP4 inhibitors for managing blood sugar in diabetic patients at risk for NAFLD should be discouraged due to the potential of DPP4 to contribute to the development of NAFLD. 29 Previous research has also indicated a connection between thiazolidinediones and the likelihood of NAFLD and LFTs, supporting our conclusion that PPARG is linked to a higher risk of NAFLD, ALT, and GGT. 30 Therefore, it is advisable to steer clear thiazolidinediones in the clinical management of diabetic patients who are susceptible to NAFLD. Previous research has indicated that glucagon-like peptide-1 (GLP-1) analogs are associated with liver function test risk, supporting our conclusion that GLP1R is associated with higher chances of elevated ALT and GGT levels. 31 Therefore, it is recommended to steer clear GLP-1 analogs in the treatment of diabetic patients with liver impairment in clinical settings. SLC5A2 was linked to a decrease in direct bilirubin levels and a lower risk of NAFLD, consistent with findings from a previous study conducted by Ibrahim and colleagues. 32 Since sodium-glucose cotransporter inhibitors may have double benefits when treating diabetic patients with NAFLD, they may be considered for the treatment of this condition. Observational studies have suggested that insulin use may increase susceptibility to LFTs, 33 indirectly supporting the idea that a reduction in direct bilirubin levels may be attributed to insulin. This study has several strengths. Initially, a thorough analysis using two-sample MR and SMR was conducted to explore the connection between drug targets for lowering glucose levels and the likelihood of developing NAFLD and LFTs. In addition, the validity of the chosen independent variables was confirmed through positive control analysis, further demonstrating the suitability of these variables as effective factors. Furthermore, the individuals involved in this study were of European descent, which helps to reduce the risk of bias in population stratification. In addition, certain constraints were associated with this study. Owing to the lack of an effective eQTL for ABCC8 in blood tissues, we were unable to explore the correlation between ABCC8 expression in the blood and the likelihood of NAFLD and LFTs. Additionally, this research solely forecasts the intended impact of glucose-reducing medications by incorporating protein targets that have adequate documentation, but are unable to identify unintended effects. Furthermore, the research was carried out on individuals of European descent, prompting questions about the generalizability of these findings to other racial or ethnic populations. Expanding data collection in non-European populations is essential for validating targets and informing drug development. The extended duration of NAFLD and LFTs development poses a significant obstacle in converting genotype-phenotype connections into effective treatment plans, presenting a major challenge. Furthermore, Bayesian colocalization analysis was conducted to determine whether the observed MR results could be linked to linkage disequilibrium. As Bayesian colocalization necessitates specific underlying assumptions, this study might overstate the significant MR findings resulting from LD. Although this study utilized a dataset that is accessible to the public, it may lack originality, as it does not offer fresh and exclusive data sources for research. Nevertheless, by conducting thorough data processing and analyzing public databases, this study offers valuable new insights for current research. Conclusion In summary, our findings suggest a correlation between glucose-lowering medication targets, NAFLD, and LFTs, which may provide novel perspectives for pharmaceutical advancements in NAFLD therapy and enhancement of liver function. Additional investigations are warranted to elucidate the underlying mechanisms and assess the effects of glucose-lowering medications on the incidence of NAFLD and LFTs through comprehensive basic and clinical trials. Declarations AUTHOR CONTRIBUTIONS Gang Lei: Project administration (equal); acquisition, analysis, and interpretation of research data; writing – original draft (lead). Chibing Dai: Project administration (equal); writing, reviewing, and editing (equal). ACKNOWLEDGMENTS The authors thank the participants and investigators for providing publicly available summary statistics. CONFLICT OF INTEREST STATEMENT The authors have no conflict of interest. DATA AVAILABILITY STATEMENT All raw data and code are available upon request. FUNDING INFORMATION This study was supported by the Scientific Research Project of the Hubei Provincial Health Commission, China (WJ2021F060). ETHICS STATEMENT Since this study was based on existing publications and public databases, both ethical approval and informed consent were received by each relevant institutional review committee. Data availability Glucose-Lowering Drugs instrumental variables from the published article by Yang et al. (Yang 2024; https://doi.org/10.1186/s13578-024-01214-8). All GWAS summary statistics data in this study are publicly available for download by qualified researchers. References Younossi ZM, Golabi P, Paik JM, Henry A, Van Dongen C: Henry L:The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review. 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Barchetta I, Cimini FA, Dule S: Cavallo MG:Dipeptidyl Peptidase 4 (DPP4) as A Novel Adipokine: Role in Metabolism and Fat Homeostasis. Biomedicines 2022, 10. Niu L, Geyer PE, Wewer Albrechtsen NJ, Gluud LL, Santos A, Doll S, Treit PV, Holst JJ, Knop FK, Vilsbøll T, et al:Plasma proteome profiling discovers novel proteins associated with non-alcoholic fatty liver disease. Molecular systems biology 2019, 15: e8793. Yang Z, Wen J, Li Q, Tao X, Ye Z, He M, Zhang W, Huang Y, Chen L, Ling C, et al:PPARG gene Pro12Ala variant contributes to the development of non-alcoholic fatty liver in middle-aged and older Chinese population. Molecular and cellular endocrinology 2012, 348: 255-9. Ibrahim DM, Shaaban ESE: Fouad TA:Circulating Resistin Is Associated with Plasma Glucagon-Like Peptide-1 in Cirrhotic Patients with Hepatitis C Virus Genotype-4 Infection. Endocrine research 2020, 45: 17-23. Golovina EL, Vaizova OE, Meleshko MV, Samoilova IG, Podchinenova DV, Borozinets AA, Matveeva MV: Kudlay DA:[Clinical effectiveness and pharmacokinetics of gliflozin from the point of view of individual genetic characteristics: A review]. Terapevticheskii arkhiv 2023, 95: 706-709. Bao S, Wu YL, Wang X, Han S, Cho S, Ao W: Nan JX:Agriophyllum oligosaccharides ameliorate hepatic injury in type 2 diabetic db/db mice targeting INS-R/IRS-2/PI3K/AKT/PPAR-γ/Glut4 signal pathway. Journal of ethnopharmacology 2020, 257: 112863. Tables Table 1 Information of exposure and outcome data used in the Mendelian randomization analyses. Characteristic Resource/Author Sample Size Population PubMed ID Download Source eQTLs data eQTLs for DPP4, GPD2, ETFDH, GLP1R, INSR, KCNJ11, PPARG, PRKAB1 and SLC5A2 eQTLGen Consortium 31,684 Predominantly European 34475573 https://www.eqtlgen.org eQTLs for ABCC8 GTExV8 863 Predominantly European 29022597 https://www.gtexportal.org/home GWAS summary data HbA1c measurement Joelle Mbatchou et al. 389,889 European 34017140 https://www.ebi.ac.uk/gwas/ Body Mass Index Ben Elsworth et al. 461,460 European NA https://gwas.mrcieu.ac.uk/ Glucose measurement Alison R Barton et al. 400,458 European 34226706 https://www.ebi.ac.uk/gwas/ Type 2 diabetes Angli Xue et al. 655,666 European 30054458 https://gwas.mrcieu.ac.uk/ Discovery stage nonalcoholic fatty liver disease Sun Z et al. 32,941 European 37235137 https://www.ebi.ac.uk/gwas/ Aspartate aminotransferase Neale lab 342,990 European NA https://gwas.mrcieu.ac.uk/ Total bilirubin Neale lab 342,990 European NA https://gwas.mrcieu.ac.uk/ Gamma glutamyltransferase Neale lab 344,104 European NA https://gwas.mrcieu.ac.uk/ Direct bilirubin Neale lab 342,990 European NA https://gwas.mrcieu.ac.uk/ Alkaline phosphatase Neale lab 344,392 European NA https://gwas.mrcieu.ac.uk/ Alanine aminotransferase Neale lab 344,136 European NA https://gwas.mrcieu.ac.uk/ Replication stage nonalcoholic fatty liver disease Kurki, MI et al. 412,181 European 36653562 https://www.finngen.fi/en/access_results Aspartate aminotransferase Sakaue S et al. 342,990 European 34594039 https://www.ebi.ac.uk/gwas/ Total bilirubin Dennis JK et al. 56,577 European 33441150 https://www.ebi.ac.uk/gwas/ Gamma glutamyltransferase Sakaue S et al. 344,104 European 34594039 https://www.ebi.ac.uk/gwas/ Direct bilirubin Dennis JK et al. 23,850 European 33441150 https://www.ebi.ac.uk/gwas/ Alkaline phosphatase Sakaue S et al. 344,292 European 34594039 https://www.ebi.ac.uk/gwas/ Alanine aminotransferase Sakaue S et al. 344,136 European 34594039 https://www.ebi.ac.uk/gwas/ Abbreviations: eQTL: expression quantitative trait loci; GWAS: genome-wide association study. Table 2 Glucose-lowering drugs and liver function tests risks analyzed using Mendelian randomization. Abbreviations 95% CI: 95% confidence interval Outcome Exposure Discovery stage Replication stage β (95% CI) p β (95% CI) p Alanine aminotransferase ABCC8/ KCNJ11 -1.545 (-3.653,0.562) 0.151 -0.158 (-0.326,0.009) 0.063 DPP4 5.619 (2.504,8.733) 4.00E-04 0.431 (0.169,0.693) 0.001 ETFDH 1.334 (-9.538,12.205) 0.81 0.305 (-0.000,0.610) 0.05 GLP1R 2.190 (0.408,3.973) 0.016 0.079 (-0.026,0.183) 0.139 GPD2 -1.144 (-5.802,3.514) 0.63 0.151 (-0.148,0.450) 0.322 INSR -2.661 (-6.328,1.005) 0.155 -0.160 (-0.433,0.114) 0.252 PPARG 4.257 (3.507,5.007) 9.00E-29 0.386 (0.332,0.440) 4.00E-44 PRKAB1 -4.081 (-6.623,-1.540) 0.002 -0.353 (-0.574,-0.132) 0.002 SLC5A2 0.717 (-0.541,1.974) 0.264 -0.020 (-0.100,0.059) 0.617 Alkaline phosphatase ABCC8/ KCNJ11 -3.062 (-9.525,3.401) 0.353 -0.118 (-0.391,0.155) 0.397 DPP4 -0.452 (-5.782,4.879) 0.868 0.003 (-0.207,0.213) 0.977 ETFDH -0.578 (-9.963,8.806) 0.904 0.187 (-0.134,0.509) 0.253 GLP1R 1.911 (-1.475,5.297) 0.269 0.047 (-0.059,0.152) 0.385 GPD2 4.666 (-23.765,33.097) 0.748 0.228 (-0.589,1.044) 0.585 INSR -6.595 (-17.852,4.662) 0.251 -0.278 (-0.614,0.058) 0.105 PPARG 8.271 (7.128,9.415) 1.00E-45 0.312 (0.264,0.359) 1.00E-37 PRKAB1 2.149 (-0.190,4.488) 0.072 0.058 (-0.039,0.154) 0.241 SLC5A2 3.484 (1.779,5.189) 6.00E-05 0.133 (0.071,0.195) 3.00E-05 Aspartate aminotransferase ABCC8/ KCNJ11 -1.108 (-2.743,0.528) 0.184 -0.182 (-0.377,0.012) 0.066 DPP4 0.476 (-1.225,2.177) 0.583 -0.041 (-0.231,0.149) 0.671 ETFDH -0.241 (-6.704,6.222) 0.942 0.146 (-0.172,0.463) 0.369 GLP1R 0.198 (-0.927,1.322) 0.73 0.031 (-0.070,0.133) 0.544 GPD2 -1.635 (-5.241,1.971) 0.374 -0.059 (-0.361,0.243) 0.7 INSR -2.198 (-5.829,1.434) 0.236 -0.120 (-0.321,0.080) 0.241 PPARG 3.111 (2.539,3.682) 1.00E-26 0.363 (0.305,0.422) 4.00E-34 PRKAB1 -0.533 (-1.785,0.719) 0.404 -0.128 (-0.276,0.021) 0.092 SLC5A2 -0.138 (-0.848,0.572) 0.703 -0.104 (-0.167,-0.041) 0.001 Direct bilirubin ABCC8/ KCNJ11 -0.112 (-0.236,0.011) 0.074 -0.143 (-0.668,0.382) 0.594 DPP4 -0.082 (-0.160,-0.003) 0.042 -0.067 (-0.436,0.301) 0.721 ETFDH -0.128 (-0.454,0.197) 0.44 1.063 (-0.684,2.811) 0.233 GLP1R 0.049 (-0.036,0.134) 0.257 0.580 (0.108,1.052) 0.016 GPD2 -0.324 (-0.636,-0.013) 0.041 -0.783 (-2.119,0.552) 0.25 INSR -0.212 (-0.410,-0.014) 0.036 -1.336 (-2.585,-0.087) 0.036 PPARG -0.217 (-0.250,-0.183) 2.00E-37 -0.149 (-0.297,0.000) 0.05 PRKAB1 -0.224 (-0.319,-0.129) 4.00E-06 -0.140 (-0.543,0.263) 0.495 SLC5A2 -0.261 (-0.324,-0.197) 6.00E-16 0.033 (-0.351,0.416) 0.868 Gamma glutamyl transferase ABCC8/ KCNJ11 0.281 (-5.340,5.903) 0.922 0.015 (-0.102,0.132) 0.799 DPP4 10.567 (4.829,16.306) 3.00E-04 0.436 (0.227,0.646) 4.00E-05 ETFDH -1.342 (-16.319,13.636) 0.861 0.180 (-0.125,0.485) 0.247 GLP1R 4.877 (0.962,8.793) 0.015 0.056 (-0.048,0.159) 0.292 GPD2 12.738 (-8.634,34.109) 0.243 0.467 (-0.083,1.017) 0.096 INSR -7.408 (-18.398,3.582) 0.186 -0.105 (-0.360,0.149) 0.416 PPARG 8.846 (6.785,10.908) 4.00E-17 0.411 (0.355,0.467) 2.00E-47 PRKAB1 7.530 (3.137,11.923) 0.001 0.126 (-0.064,0.315) 0.193 SLC5A2 1.942 (-2.950,6.834) 0.437 0.033 (-0.107,0.172) 0.646 Total bilirubin ABCC8/ KCNJ11 -0.536 (-1.282,0.210) 0.159 0.028 (-0.245,0.301) 0.84 DPP4 -0.011 (-0.389,0.366) 0.953 -0.035 (-0.250,0.180) 0.749 ETFDH -0.587 (-2.785,1.611) 0.601 -0.648 (-1.669,0.372) 0.213 GLP1R -0.437 (-0.842,-0.031) 0.035 0.124 (-0.128,0.375) 0.336 GPD2 -1.521 (-3.006,-0.035) 0.045 0.220 (-0.572,1.011) 0.586 INSR -1.134 (-2.077,-0.192) 0.018 -0.067 (-0.793,0.659) 0.857 PPARG -1.145 (-1.296,-0.994) 5.00E-50 -0.267 (-0.354,-0.180) 2.00E-09 PRKAB1 -1.535 (-2.055,-1.016) 7.00E-09 -0.204 (-0.440,0.032) 0.09 SLC5A2 -1.375 (-1.701,-1.050) 1.00E-16 -0.146 (-0.369,0.077) 0.199 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4759170","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":339107785,"identity":"8c8b836f-c168-4a22-9861-6c8ae3f75d2c","order_by":0,"name":"Gang Lei","email":"","orcid":"","institution":"Renhe Hospital Affiliated to Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Lei","suffix":""},{"id":339107792,"identity":"3b594aa1-c318-4eeb-9c0d-34f11f45e081","order_by":1,"name":"Chibing Dai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACAxCRUMAgx8befoAULQYMxnw8ZxJI0AIkE+dJOBgQp8VcIv3xhwcGteltEgwJDD8qthHWYjkjIcEgweB4bpt04wHGnjO3iXDYjYQDQE3HcttkDiQwM7YRpSWx4QBQSzqbRIIBsVqSGRsSDGoSSNBy5hkzMJAPGLYBA/kgcX45nv7444+KOnn59vaDD35UEKGFQSABRB4Gsw8QoR4I+MHq6ohTPApGwSgYBSMTAAAIRj7VYmJtCQAAAABJRU5ErkJggg==","orcid":"","institution":"Renhe Hospital Affiliated to Three Gorges University","correspondingAuthor":true,"prefix":"","firstName":"Chibing","middleName":"","lastName":"Dai","suffix":""}],"badges":[],"createdAt":"2024-07-18 01:26:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4759170/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4759170/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62338753,"identity":"e458e664-ecad-434c-89f1-ca6a3bbe23b9","added_by":"auto","created_at":"2024-08-13 05:58:15","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":331881,"visible":true,"origin":"","legend":"\u003cp\u003eAn overview of the study's flow chart.\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4759170/v1/4e488d399a441c7d498c89e2.jpg"},{"id":62338750,"identity":"2b5ea547-c2a2-4b25-a30e-8a1b1cd5cb7b","added_by":"auto","created_at":"2024-08-13 05:58:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":174208,"visible":true,"origin":"","legend":"\u003cp\u003eGlucose-lowering drugs and nonalcoholic fatty liver disease risks analyzed using Mendelian randomization. Abbreviations 95% CI: 95% confidence interval; OR: odds ratio\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4759170/v1/445c42397879704d5ac54f6f.jpg"},{"id":62338754,"identity":"2208d38e-eb09-48db-aa3d-728ca169351b","added_by":"auto","created_at":"2024-08-13 05:58:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":113138,"visible":true,"origin":"","legend":"\u003cp\u003eAnalyses of colocalization of glucose-lowering drug targets with NAFLD and liver function tests. Abbreviations NAFLD: nonalcoholic fatty liver disease; AST: Aspartate aminotransferase; GGT: Gamma glutamyl transferase; ALP: Alkaline phosphatase; ALT: Alanine aminotransferase\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4759170/v1/6cf7e1488633fec4127b72a8.jpg"},{"id":64314505,"identity":"947783ee-d74c-4fe1-9a44-0745a7a1bbf9","added_by":"auto","created_at":"2024-09-11 14:14:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1321035,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4759170/v1/1a786d9f-d563-42c5-81e4-60652f83acad.pdf"},{"id":62338751,"identity":"4ebe3202-6a65-4ac6-afe3-99bf5f26a0c5","added_by":"auto","created_at":"2024-08-13 05:58:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":109467,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4759170/v1/e50beeae83e124c9df4d2d2e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Novel Insights into the Relationship Between Glucose-Lowering Drugs and Nonalcoholic Fatty Liver Disease and liver function: a Mendelian Randomization Study","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe prevalence of nonalcoholic fatty liver disease (NAFLD) has been increasing, affecting more than 30% of the adult population in the United States.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Furthermore, between 1999 and 2018, individuals with NAFLD experienced higher rates of decompensated cirrhosis, hepatocellular carcinoma, and liver-related mortality compared to those with viral hepatitis.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Elevated liver enzymes are crucial markers of liver damage in NAFLD among the overall population. Deciphering liver enzyme levels can be challenging due to the influence of metabolic processes beyond the liver. Because of their involvement in inflammation and oxidative stress, elevated levels of these enzymes could be linked to a higher likelihood of mortality.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e At present, the main criterion for conditional approval of drugs is histopathological assessment of liver biopsy. Owing to the wide range and variability of invasive histopathological evaluations, this unique requirement poses a significant barrier to the industry, resulting in a very high rate of screening failure in clinical trials. Currently,\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e progress in potent medications with minimal side effects is limited.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePrevious research has shown connections between targets of drugs that lower glucose levels and different health conditions, such as atrial fibrillation, cardiorenal metabolic syndrome, and prostate cancer, underscoring the significance of using glucose-lowering medications to manage diseases.\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Metformin, a medication commonly used to lower blood sugar levels in individuals with Type 2 diabetes mellitus (T2DM), has been found to have positive effects on decreasing the prevalence of NAFLD and liver function tests.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Glucagon-like peptide-1 agonist drugs also show inhibitory effects on NAFLD and LFTs.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e However, there is a certain degree of concern about the potential NAFLD risk and hepatotoxic effects of some glucose-lowering drugs, partly because preclinical studies of some glucose-lowering drugs have found potential adverse associations with liver damage.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Therefore, these data are critical to assess the risks that such studies may bring to participants.\u003c/p\u003e \u003cp\u003eAs an epidemiological method, Mendelian randomization (MR) has a strong ability to infer causal relationships.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Utilizing genetic variations in MR improve the ability to draw causal conclusions about the relationship between exposure and outcome by reducing the impact of confounding factors and ruling out reverse causation.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Many studies have explored the association between glucose-lowering drugs and NAFLD and liver function tests (LFTs). Because these studies were observational in nature, they inevitably faced confounding bias and reverse causation. Owing to the lengthy progression of NAFLD and LFTs development, the utilization of drug-target MR analysis has proven to be essential in assessing the viability of drug repurposing and anticipating potential adverse reactions. Currently, there is a scarcity of thorough MR studies that have extensively investigated the association between glucose-lowering medication targets and the likelihood of developing NAFLD and LFTs. Hence, this study utilized two-sample MR and SMR analyses to investigate the possible links between the targets of glucose-lowering medications and the likelihood of NAFLD and LFTs, such as aspartate aminotransferase (AST), gamma glutamyl transferase (GGT), alkaline phosphatase (ALP), alanine aminotransferase (ALT), and bilirubin.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cp\u003e2. 1 Study design\u003c/p\u003e\n\u003cp\u003eThe structure of this study was guided by the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.\u003csup\u003e14\u003c/sup\u003e The association between glucose-lowering drug targets and NAFLD and LFTs was demonstrated by genetic analyses of two samples using MR and SMR. MR analyses\u0026apos; causal estimation validation depended on three key assumptions: genetic instrumental variables (IVs) were linked to glucose-lowering drug targets, genetic IVs were independent from confounding factors, and genetic IVs had no direct effects on NAFLD and LFTs risk aside from through the drug targets. The inverse-variance weighted (IVW) or Wald ratio method was the primary approach used in the two-sample MR analysis to investigate the connections between drug targets for lowering glucose levels and the likelihood of NAFLD and LFTs. Concurrently, we conducted SMR analysis, utilizing expression quantitative trait loci (eQTLs) to investigate and confirm the causal connection between exposure and outcome on a genetic expression level. Furthermore, Bayes factor analysis was utilized to conduct colocalization analysis within a \u0026plusmn; 500 KB window around the gene coding region of each individual glucose-lowering drug target to determine the likelihood of the connection between exposure and outcome. The detailed flow of this study is illustrated in Fig.1. Summary-level statistics were provided by the Genome-wide association study (GWAS) studies utilized in Table 1. Approval for these studies was obtained from the appropriate institutional review boards in the respective countries, following the guidelines of the Declaration of Helsinki, and all participants provided informed consent.\u003c/p\u003e\n\u003cp\u003e2. 2 Identifying and choosing instrumental variables\u003c/p\u003e\n\u003cp\u003eUltimately, there were 10 targets affected by seven distinct medications with lower glucose levels. We used the ten target Single Nucleotide Polymorphism (SNPs) summarized by Yang et al.\u003csup\u003e15\u003c/sup\u003e as genetic tools for glucose-lowering drug targets. The ChEMBL (https://www.ebi.ac.uk/chembl) and DrugBank (https://go.drugbank.com/) databases were used to identify the relevant gene and protein targets with pharmacological activity (Supplementary Table S1). Given that a decrease in HbA1C levels is the main indicator for evaluating the body\u0026apos;s reactions to drugs that lower glucose levels, identified genetic variations near the glucose loci boundaries within a 500 kb range that were linked to HbA1C levels with a \u003cem\u003eP\u003c/em\u003e value \u0026lt; 5 \u0026times; 10\u003csup\u003e\u0026ndash;8\u003c/sup\u003e (considered genome-wide significant) as substitutes for the main physiological responses to glucose-lowering drug inhibition at these specific sites.\u003csup\u003e16\u003c/sup\u003e clustered glucose-lowering drug variants at linkage disequilibrium (LD) r\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.01. The SMR analysis revealed common eQTLs (with minor allele frequency \u0026gt; 1%) associated with the expression of DPP4, GPD2, ETFDH, GLP1R, INSR, KCNJ11, PPARG, PRKAB1, and SLC5A2 in blood tissues. These eQTLs were identified from the eQTLGen Consortium and Genotype Tissue Expression (GTEx) Consortium Version 8.0, with significant association (\u003cem\u003eP\u003c/em\u003e \u0026lt; 5 \u0026times; 10 \u003csup\u003e\u0026minus; 8\u003c/sup\u003e) IVs. As no significant eQTLs for ABCC8 was found in blood tissues, we collected IVs associated with ABCC8 expression, particularly in muscle and bone tissues.\u003c/p\u003e\n\u003cp\u003e2. 3 Validation of instrumental variables\u003c/p\u003e\n\u003cp\u003eTo prevent potential bias from weak IVs, the F-statistic approach was employed, with the requirement that the F value be greater than 10.\u003csup\u003e17\u003c/sup\u003e T2DM is the original indication for glucose-lowering drugs, which ultimately affects patients\u0026rsquo; blood glucose levels. Previous meta-analyses have shown that some glucose-lowering drugs, such as sulfonylureas, insulin analogs, and thiazolidinediones, cause weight gain, whereas GLP-1 analogs cause weight loss, suggesting that weight is another unique phenotype affected by glucose-lowering drugs\u003csup\u003e18\u003c/sup\u003e. Hence, body mass index (BMI), blood sugar levels, and T2DM from a comprehensive dataset were chosen as benchmarks to confirm the significant link between IVs and genetically influenced glucose-lowering medication targets.\u003c/p\u003e\n\u003cp\u003e2. 4 Study outcome\u003c/p\u003e\n\u003cp\u003eIn the discovery phase, Sun et al. conducted a GWAS based on a large cohort from the UK Biobank. In this study, participants with alcoholic disorders, harmful alcohol use, confirmed hemochromatosis, viral hepatitis, Wilson\u0026apos;s disease, and users of liver-damaging medications were excluded from the analysis.\u003csup\u003e19\u003c/sup\u003e We collected LFTs from the Pan UK Biobank Project, including ALT, AST, ALP, GGT, and bilirubin.\u003csup\u003e20\u003c/sup\u003e In the replication phase, GWAS data for NAFLD (2,568 cases and 409,613 controls) were obtained from the FinnGen r10 consortium.\u003csup\u003e21\u003c/sup\u003e Similarly, The GWAS summary datasets for replication LFTs were obtained from two studies, Sakaue S et al.\u003csup\u003e22\u003c/sup\u003e and Dennis JK et al.\u003csup\u003e23\u003c/sup\u003e, and were independent of the GWAS datasets in the discovery phase. The characteristics associated with NAFLD were used as endpoints for MR examination in this study. The appropriate institutional review board approved and informed consent was obtained for this study, which relied on previously published works and public databases.\u003c/p\u003e\n\u003cp\u003e2. 5 Colocalization analysis\u003c/p\u003e\n\u003cp\u003eSignificant findings from the MR analysis were further assessed through Bayesian colocalization analysis to determine the likelihood that a specific genetic variant influenced both the glucose-lowering drug target and NAFLD and LFTs, rather than simply being linked by chance through LD.\u003csup\u003e24\u003c/sup\u003e Colocalization was assessed using the \u0026apos;COLORC\u0026apos; R package available at https://github.com/chr1swallace/coloc.\u003csup\u003e24\u003c/sup\u003e A posterior probability exceeding 0.75 for hypothesis 4 indicated compelling support for colocalization.\u003csup\u003e25\u003c/sup\u003e It should be mentioned that Bayesian colocalization has a limitation in assuming a single common cause SNPs, whereas in actuality, a genetic locus may have multiple causal SNPs.\u003c/p\u003e\n\u003cp\u003e2. 6 Sensitivity Analysis\u003c/p\u003e\n\u003cp\u003eIn the sensitivity analysis, if there was heterogeneity observed across various instruments using Cochran\u0026apos;s Q test, a weighted median approach was utilized to account for potential ineffectiveness of up to 50% of the instruments.\u003csup\u003e26\u003c/sup\u003e Likewise, if heterogeneity was detected at multiple levels by the MR-Egger method tropism, MR-Egger would be employed because it has the ability to address instrument pleiotropy. Furthermore, a sensitivity analysis was conducted using SMR testing with multiple SNPs for each target drug (multi-SNPs-SMR) to enhance the significance of the evidence obtained from the initial analysis. The HEIDI test, which utilizes dependent instrumental heterogeneity, was employed to differentiate between pleiotropic and linkage models. In HEIDI studies with P values greater than 0.01 there was no evidence that linkage disequilibrium mediated the relationship between glucose-lowering drug targets and the risk of NAFLD and LFTs.\u003csup\u003e27\u003c/sup\u003e The analysis was conducted using the SMR software tool version 1.3.1.\u003c/p\u003e\n\u003cp\u003e2. 7 Statistical Analysis\u003c/p\u003e\n\u003cp\u003eThis research utilized MR with \u0026apos;TwoSampleMR\u0026apos; (https://github.com/MRCIEU/TwoSampleMR) by incorporating 10 antidiabetic drug target genes as variables, NAFLD, and 6 LFTs as results; to prevent bias, a significance level of \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0007 (0.05 divided by 70, including 10 antidiabetic drug targets, NAFLD, and 6 LFTs) was adjusted using the Bonferroni correction. In the replication phase, significant causal relationships for antidiabetic drug target genes were identified, and replication analyses were performed using GWAS summary data from the replication phase (P \u0026lt; 0.05). P values below 0.05 were deemed suggestive, while values above 0.05 suggested no statistical link between the antidiabetic medication target and NAFLD and LFTs.\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e3. 1\u0026nbsp;Positive control analysis\u003c/p\u003e\n\u003cp\u003eTo prevent potential bias from weak independent variables, the F value was calculated for each IVs, ensuring that the F value did not exceed 10 (Supplementary Table S2). Positive controls, such as BMI, blood glucose measurements, and T2DM, were used to test the IVs in the two-sample MR and SMR analyses, showing reliable associations between IVs and BMI, blood glucose measurements, and T2DM, demonstrating the effectiveness of IVs (Supplementary Tables S3\u0026ndash;S6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. 2\u0026nbsp;Two-sample MR analysis\u003c/p\u003e\n\u003cp\u003eFig.2 and Table 2 display the findings of the two-sample MR analysis of antidiabetic medication targets and their association with NAFLD and LFTs. Analytical evidence showed that DPP4 was associated with GGT (\u0026beta; = 10.567; 95% confidence interval (95% CI): 4.829,16.306; \u003cem\u003eP\u003c/em\u003e = 3E-4), ALT (\u0026beta; = 5.619; 95% CI: 2.504,8.733; \u003cem\u003eP\u003c/em\u003e = 4E- 4). The evidence highlights the association between PPARG and NAFLD (OR = 1.106; 95% CI: 1.064,1.149; \u003cem\u003eP\u003c/em\u003e = 3E-7), GGT (\u0026beta; = 8.846; 95% CI: 6.785,10.908; \u003cem\u003eP\u003c/em\u003e = 4E-17), AST (\u0026beta; = 3.111; 95% CI: 2.539,3.682; \u003cem\u003eP\u003c/em\u003e = 1E-26), ALT (\u0026beta; = 4.257; 95% CI: 3.507,5.007; \u003cem\u003eP\u003c/em\u003e = 9E-29) and ALP (\u0026beta; = 8.271; 95% CI: 7.128,9.415; \u003cem\u003eP\u003c/em\u003e = 1E-45), however, there was a substantial association with total bilirubin (\u0026beta; = -1.145; 95% CI: -1.296,-0.994; \u003cem\u003eP\u003c/em\u003e = 5E-50) and direct bilirubin (\u0026beta; = -0.217; 95% CI: -0.250,-0.183; \u003cem\u003eP\u003c/em\u003e = 2E-37).Similarly, PRKAB1 was temporally associated with total bilirubin (\u0026beta; = -1.535; 95% CI: -2.055,-1.016; \u003cem\u003eP\u003c/em\u003e = 7E- 09) and direct bilirubin (\u0026beta; = -0.224; 95% CI: -0.319, -0.129; \u003cem\u003eP\u003c/em\u003e = 4E-06) was associated with reduced risk. However, SLC5A2 was associated with total bilirubin (\u0026beta; = -1.375; 95% CI: -1.701, -1.050; \u003cem\u003eP\u003c/em\u003e = 1E-16), Direct bilirubin (\u0026beta; = -0.261; 95% CI: -0.324,-0.197; \u003cem\u003eP\u003c/em\u003e = 6E-16), and NAFLD (OR = 0.778; 95% CI: 0.723,0.837; \u003cem\u003eP\u003c/em\u003e = 2E-11), but the risk was higher for ALP (\u0026beta; = 3.484; 95% CI: 1.779, 5.189; \u003cem\u003eP\u003c/em\u003e = 6E-05). Evidence indicates that PRKAB1, GLP1R, INSR, GPD2, DPP4, and ABCC8/KCNJ11 are related to GGT, ALT, bilirubin, and NAFLD, and that there is no correlation between ABCC8/KCNJ11, INSR, ETFDH, and GPD2 and the risk of NAFLD and LFTs. In the replication phase, nine significant antidiabetic drug targets (P \u0026lt; 0.05) were successfully validated in the replicated GWAS dataset using the Wald ratio or IVW method. During the replication phase, one significant antidiabetic drug target (P \u0026lt; 0.05) was successfully validated in the NAFLD FinnGen dataset based on the Wald ratio or IVW method. Replication analysis of LFTs revealed similar results for eight significant antidiabetic drug targets.\u003c/p\u003e\n\u003cp\u003e3. 3\u0026nbsp;SMR analysis\u003c/p\u003e\n\u003cp\u003eSupplementary Table S6 shows the results of SMR analysis of antidiabetic drug targets and the risk of NAFLD and LFTs. SMR analysis revealed genetic associations between AST risk and KCNJ11 (\u0026beta; = -0.452; 95% CI: -0.786, -0.117; \u003cem\u003eP\u003c/em\u003e = 0.008) and GPD2 (\u0026beta; = 0.132; 95% CI: 0.004, 0.259; \u003cem\u003eP\u003c/em\u003e = 0.044). Direct bilirubin risk was associated with DPP4 (\u0026beta; = 0.046; 95% CI: 0.011, 0.081; \u003cem\u003eP\u003c/em\u003e = 0.01), PPARG (\u0026beta; = -0.029; 95% CI: -0.047, -0.012; P = 0.001) and INSR (\u0026beta; = -0.073; 95% CI: -0.136, -0.009; \u003cem\u003eP\u003c/em\u003e = 0.025). GGT risk was associated with ABCC8 (\u0026beta; = 0.349; 95% CI: 0.059, 0.640; \u003cem\u003eP\u003c/em\u003e = 0.018). Total bilirubin risk was associated with DPP4 (\u0026beta; = 0.303; 95% CI: 0.135, 0.472; \u003cem\u003eP\u003c/em\u003e = 4E-4) and PPARG (\u0026beta; = -0.127; 95% CI: -0.210, -0.045; \u003cem\u003eP\u003c/em\u003e = 0.003). Furthermore, ABCC8 (\u0026beta; = 0.209; 95% CI: 0.028, 0.390; \u003cem\u003eP\u003c/em\u003e = 0.024), GPD2 (\u0026beta; = -0.409; 95% CI: -0.726, -0.092; \u003cem\u003eP\u003c/em\u003e = 0.011), PPARG (\u0026beta; = -0.878; 95 % CI: -1.375, -0.380; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.001), PRKAB1 (\u0026beta; = -0.619; 95% CI: -0.909, -0.329; \u003cem\u003eP\u003c/em\u003e = 3E-5), and DPP4 (\u0026beta; = -1.312; 95% CI:-2.313, -0.311; \u003cem\u003eP\u003c/em\u003e = 0.01) were associated with the risk of ALP. However, there is no evidence that the ten antidiabetic drug targets are associated with NAFLD and ALT levels.\u003c/p\u003e\n\u003cp\u003e3. 4\u0026nbsp;Colocalization analysis\u003c/p\u003e\n\u003cp\u003eA study was conducted to demonstrate the likelihood of a common hypoglycemic drug target causal variant being shared, and its association with NAFLD and LFTs risk (Fig.3、 Supplementary Table S7). PPARG was observed to share a genetic region with ALP risk (PP4 = 75.2%). There was strong evidence of colocalization between DPP4 and ALP, AST, and ALT, respectively (PP4 = 90.3%, 93.8%, and 99.4%).\u003c/p\u003e\n\u003cp\u003e3. 4\u0026nbsp;Examining the impact of variations\u003c/p\u003e\n\u003cp\u003eAs a result of the SMR analysis, all HEIDI P values exceeded 0.01 (Supplementary Table S6). Furthermore, the two-sample MR analysis revealed no heterogeneity or pleiotropy in either NAFLD or LFTs, as indicated by the results of the heterogeneity and pleiotropy tests. Efficacy, \u003cem\u003eP\u003c/em\u003e values were all \u0026gt; 0.05, indicating a minimal impact of horizontal pleiotropy due to the chain scenario. In complementary MR techniques, estimates typically align in terms of both size and direction; however, MR Egger and Weighted median estimates tend to be less accurate than MR IVW (Supplementary Tables S8-S9).\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThis study thoroughly examined how 10 antidiabetic medications affect the likelihood of developing NAFLD and LFTs. Two-sample MR analysis showed that PPARG, SLC5A2, PRKAB1, and DPP4 were strongly associated with ALP, ALT, AST, GGT, total bilirubin, direct bilirubin and NAFLD. PRKAB1, GLP1R, INSR, GPD2, DPP4, and ABCC8/KCNJ11 are potentially associated with ALT, GGT, total bilirubin, direct bilirubin, and NAFLD. In SMR analysis, DPP4, GPD2, INSR, PPARG, PRKAB1, and ABCC8/KCNJ11 were associated with GGT, ALP, AST, total, and direct bilirubin. Replicate MR analysis verified significant causal relationships for 9 of these 15 candidate antidiabetic drug targets. Furthermore, based on the colocalization analysis, PPARG and DPP4 shared common causal variants in ALP, AST, and ALT.\u003c/p\u003e\n\u003cp\u003eNAFLD is the primary cause of long-term liver disease. Some anti-diabetic drugs have been shown to induce NAFLD. The effectiveness of antidiabetic medications for NAFLD is currently uncertain, with some drugs being linked to an increased risk of LFTs.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Research has indicated that metformin provides significant advantages in lowering the overall occurrence of NAFLD, along with decreasing levels of AST, GGT, ALP, ALT, total, and direct bilirubin.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Metformin could potentially lower hepatic enzymes, although this impact may vary among individuals. Hence, a comprehensive examination was performed of the three drug targets associated with metformin (ETFDH, GPD2, and PRKAB1). We also found that PRKAB1 and GPD2 reduced total and direct bilirubin levels, whereas ETFDH was not correlated with NAFLD and LFTs. These results indicated that metformin does not uniformly increase all LFTs. In clinical practice, it is prudent to enhance treatment strategies by integrating the appropriate drug targets. In their study, Barchetta et al. discovered a higher likelihood of NAFLD in individuals with T2DM who were prescribed gliclazide and glyburide.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Our research supports the idea that ABCC8/KCNJ11 is linked to an increased likelihood of developing NAFLD. SMR analysis identified that ABCC8/KCNJ11 had a promoting effect on ALP and AST levels. These findings indicate that sulfonylureas should not be used to treat T2DM patients with NAFLD because of their potential to exacerbate NAFLD and lead to liver injury. In both two-sample MR and SMR analyses, DPP4 was associated with a decreased likelihood of direct bilirubin. Conversely, the two-sample MR analyses showed that it was linked to an increased risk of ALT, GGT, and NAFLD. This finding indicates that utilizing DPP4 inhibitors for managing blood sugar in diabetic patients at risk for NAFLD should be discouraged due to the potential of DPP4 to contribute to the development of NAFLD.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Previous research has also indicated a connection between thiazolidinediones and the likelihood of NAFLD and LFTs, supporting our conclusion that PPARG is linked to a higher risk of NAFLD, ALT, and GGT.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Therefore, it is advisable to steer clear thiazolidinediones in the clinical management of diabetic patients who are susceptible to NAFLD. Previous research has indicated that glucagon-like peptide-1 (GLP-1) analogs are associated with liver function test risk, supporting our conclusion that GLP1R is associated with higher chances of elevated ALT and GGT levels.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Therefore, it is recommended to steer clear GLP-1 analogs in the treatment of diabetic patients with liver impairment in clinical settings. SLC5A2 was linked to a decrease in direct bilirubin levels and a lower risk of NAFLD, consistent with findings from a previous study conducted by Ibrahim and colleagues.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Since sodium-glucose cotransporter inhibitors may have double benefits when treating diabetic patients with NAFLD, they may be considered for the treatment of this condition. Observational studies have suggested that insulin use may increase susceptibility to LFTs,\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e indirectly supporting the idea that a reduction in direct bilirubin levels may be attributed to insulin.\u003c/p\u003e\n\u003cp\u003eThis study has several strengths. Initially, a thorough analysis using two-sample MR and SMR was conducted to explore the connection between drug targets for lowering glucose levels and the likelihood of developing NAFLD and LFTs. In addition, the validity of the chosen independent variables was confirmed through positive control analysis, further demonstrating the suitability of these variables as effective factors. Furthermore, the individuals involved in this study were of European descent, which helps to reduce the risk of bias in population stratification.\u003c/p\u003e\n\u003cp\u003eIn addition, certain constraints were associated with this study. Owing to the lack of an effective eQTL for ABCC8 in blood tissues, we were unable to explore the correlation between ABCC8 expression in the blood and the likelihood of NAFLD and LFTs. Additionally, this research solely forecasts the intended impact of glucose-reducing medications by incorporating protein targets that have adequate documentation, but are unable to identify unintended effects. Furthermore, the research was carried out on individuals of European descent, prompting questions about the generalizability of these findings to other racial or ethnic populations. Expanding data collection in non-European populations is essential for validating targets and informing drug development. The extended duration of NAFLD and LFTs development poses a significant obstacle in converting genotype-phenotype connections into effective treatment plans, presenting a major challenge. Furthermore, Bayesian colocalization analysis was conducted to determine whether the observed MR results could be linked to linkage disequilibrium. As Bayesian colocalization necessitates specific underlying assumptions, this study might overstate the significant MR findings resulting from LD. Although this study utilized a dataset that is accessible to the public, it may lack originality, as it does not offer fresh and exclusive data sources for research. Nevertheless, by conducting thorough data processing and analyzing public databases, this study offers valuable new insights for current research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our findings suggest a correlation between glucose-lowering medication targets, NAFLD, and LFTs, which may provide novel perspectives for pharmaceutical advancements in NAFLD therapy and enhancement of liver function. Additional investigations are warranted to elucidate the underlying mechanisms and assess the effects of glucose-lowering medications on the incidence of NAFLD and LFTs through comprehensive basic and clinical trials.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGang Lei: Project administration (equal); acquisition, analysis, and interpretation of research data; writing \u0026ndash; original draft (lead). Chibing Dai: Project administration (equal); writing, reviewing, and editing (equal).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the participants and investigators for providing publicly available summary statistics.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw data and code are available upon request.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Scientific Research Project of the Hubei Provincial Health Commission, China (WJ2021F060).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eETHICS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince this study was based on existing publications and public databases, both ethical approval and informed consent were received by each relevant institutional review committee.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlucose-Lowering Drugs instrumental variables from the published article by Yang et al. (Yang 2024; https://doi.org/10.1186/s13578-024-01214-8). All GWAS summary statistics data in this study are publicly available for download by qualified researchers.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYounossi ZM, Golabi P, Paik JM, Henry A, Van Dongen C: Henry L:The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review. Hepatology (Baltimore, Md.) 2023, 77: 1335-1347.\u003c/li\u003e\n\u003cli\u003eZhou J, Zhou F, Wang W, Zhang XJ, Ji YX, Zhang P, She ZG, Zhu L, Cai J: Li H:Epidemiological Features of NAFLD From 1999 to 2018 in China. Hepatology (Baltimore, Md.) 2020, 71: 1851-1864.\u003c/li\u003e\n\u003cli\u003eAlbhaisi S: Qayyum R:The association between serum liver enzymes and cancer mortality. 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PLoS medicine 2008, 5: e177.\u003c/li\u003e\n\u003cli\u003eSkrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N, et al:Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. Jama 2021, 326: 1614-1621.\u003c/li\u003e\n\u003cli\u003eYang Y, Chen B, Zheng C, Zeng H, Zhou J, Chen Y, Su Q, Wang J, Wang J, Wang Y, et al:Association of glucose-lowering drug target and risk of gastrointestinal cancer: a mendelian randomization study. Cell \u0026amp; bioscience 2024, 14: 36.\u003c/li\u003e\n\u003cli\u003eMbatchou J, Barnard L, Backman J, Marcketta A, Kosmicki JA, Ziyatdinov A, Benner C, O\u0026apos;Dushlaine C, Barber M, Boutkov B, et al:Computationally efficient whole-genome regression for quantitative and binary traits. Nature genetics 2021, 53: 1097-1103.\u003c/li\u003e\n\u003cli\u003eYuan S, Mason AM, Carter P, Vithayathil M, Kar S, Burgess S: Larsson SC:Selenium and cancer risk: Wide-angled Mendelian randomization analysis. International journal of cancer 2022, 150: 1134-1140.\u003c/li\u003e\n\u003cli\u003eLiu SC, Tu YK, Chien MN: Chien KL:Effect of antidiabetic agents added to metformin on glycaemic control, hypoglycaemia and weight change in patients with type 2 diabetes: a network meta-analysis. Diabetes, obesity \u0026amp; metabolism 2012, 14: 810-20.\u003c/li\u003e\n\u003cli\u003eSun Z, Pan X, Tian A, Surakka I, Wang T, Jiao X, He S, Song J, Tian X, Tong D, et al:Genetic variants in HFE are associated with non-alcoholic fatty liver disease in lean individuals. JHEP reports : innovation in hepatology 2023, 5: 100744.\u003c/li\u003e\n\u003cli\u003eNagai A, Hirata M, Kamatani Y, Muto K, Matsuda K, Kiyohara Y, Ninomiya T, Tamakoshi A, Yamagata Z, Mushiroda T, et al:Overview of the BioBank Japan Project: Study design and profile. Journal of epidemiology 2017, 27: S2-s8.\u003c/li\u003e\n\u003cli\u003eKurki MI, Karjalainen J, Palta P, Sipil\u0026auml; TP, Kristiansson K, Donner KM, Reeve MP, Laivuori H, Aavikko M, Kaunisto MA, et al:FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 2023, 613: 508-518.\u003c/li\u003e\n\u003cli\u003eSakaue S, Kanai M, Tanigawa Y, Karjalainen J, Kurki M, Koshiba S, Narita A, Konuma T, Yamamoto K, Akiyama M, et al:A cross-population atlas of genetic associations for 220 human phenotypes. Nature genetics 2021, 53: 1415-1424.\u003c/li\u003e\n\u003cli\u003eDennis JK, Sealock JM, Straub P, Lee YH, Hucks D, Actkins K, Faucon A, Feng YA, Ge T, Goleva SB, et al:Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease. Genome medicine 2021, 13: 6.\u003c/li\u003e\n\u003cli\u003eDeng YT, Ou YN, Wu BS, Yang YX, Jiang Y, Huang YY, Liu Y, Tan L, Dong Q, Suckling J, et al:Identifying causal genes for depression via integration of the proteome and transcriptome from brain and blood. Molecular psychiatry 2022, 27: 2849-2857.\u003c/li\u003e\n\u003cli\u003eZheng J, Haberland V, Baird D, Walker V, Haycock PC, Hurle MR, Gutteridge A, Erola P, Liu Y, Luo S, et al:Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nature genetics 2020, 52: 1122-1131.\u003c/li\u003e\n\u003cli\u003eBowden J, Davey Smith G, Haycock PC: Burgess S:Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genetic epidemiology 2016, 40: 304-14.\u003c/li\u003e\n\u003cli\u003eChauquet S, Zhu Z, O\u0026apos;Donovan MC, Walters JTR, Wray NR: Shah S:Association of Antihypertensive Drug Target Genes With Psychiatric Disorders: A Mendelian Randomization Study. JAMA psychiatry 2021, 78: 623-631.\u003c/li\u003e\n\u003cli\u003eBarchetta I, Cimini FA, Dule S: Cavallo MG:Dipeptidyl Peptidase 4 (DPP4) as A Novel Adipokine: Role in Metabolism and Fat Homeostasis. Biomedicines 2022, 10.\u003c/li\u003e\n\u003cli\u003eNiu L, Geyer PE, Wewer Albrechtsen NJ, Gluud LL, Santos A, Doll S, Treit PV, Holst JJ, Knop FK, Vilsb\u0026oslash;ll T, et al:Plasma proteome profiling discovers novel proteins associated with non-alcoholic fatty liver disease. Molecular systems biology 2019, 15: e8793.\u003c/li\u003e\n\u003cli\u003eYang Z, Wen J, Li Q, Tao X, Ye Z, He M, Zhang W, Huang Y, Chen L, Ling C, et al:PPARG gene Pro12Ala variant contributes to the development of non-alcoholic fatty liver in middle-aged and older Chinese population. Molecular and cellular endocrinology 2012, 348: 255-9.\u003c/li\u003e\n\u003cli\u003eIbrahim DM, Shaaban ESE: Fouad TA:Circulating Resistin Is Associated with Plasma Glucagon-Like Peptide-1 in Cirrhotic Patients with Hepatitis C Virus Genotype-4 Infection. Endocrine research 2020, 45: 17-23.\u003c/li\u003e\n\u003cli\u003eGolovina EL, Vaizova OE, Meleshko MV, Samoilova IG, Podchinenova DV, Borozinets AA, Matveeva MV: Kudlay DA:[Clinical effectiveness and pharmacokinetics of gliflozin from the point of view of individual genetic characteristics: A review]. Terapevticheskii arkhiv 2023, 95: 706-709.\u003c/li\u003e\n\u003cli\u003eBao S, Wu YL, Wang X, Han S, Cho S, Ao W: Nan JX:Agriophyllum oligosaccharides ameliorate hepatic injury in type 2 diabetic db/db mice targeting INS-R/IRS-2/PI3K/AKT/PPAR-\u0026gamma;/Glut4 signal pathway. Journal of ethnopharmacology 2020, 257: 112863.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eInformation of exposure and outcome data used in the Mendelian randomization analyses.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\"\u003e\n \u003cp\u003e\u003cstrong\u003eResource/Author\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePubMed ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDownload Source\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eeQTLs data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003eeQTLs for DPP4, GPD2, ETFDH, GLP1R, INSR, KCNJ11, PPARG, PRKAB1 and SLC5A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\"\u003e\n \u003cp\u003eeQTLGen Consortium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e31,684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003ePredominantly European\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e34475573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://www.eqtlgen.org\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003eeQTLs for ABCC8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\"\u003e\n \u003cp\u003eGTExV8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003ePredominantly European\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e29022597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://www.gtexportal.org/home\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWAS summary data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003eHbA1c\u0026nbsp;measurement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\"\u003e\n \u003cp\u003eJoelle Mbatchou et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e389,889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e34017140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://www.ebi.ac.uk/gwas/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\"\u003e\n \u003cp\u003eBen Elsworth et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e461,460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://gwas.mrcieu.ac.uk/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003eGlucose measurement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\"\u003e\n \u003cp\u003eAlison R Barton et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e400,458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e34226706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://www.ebi.ac.uk/gwas/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003eType 2 diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\"\u003e\n \u003cp\u003eAngli Xue et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e655,666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e30054458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://gwas.mrcieu.ac.uk/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiscovery stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003enonalcoholic fatty liver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003eSun Z\u0026nbsp;et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e32,941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e37235137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://www.ebi.ac.uk/gwas/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eAspartate aminotransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003eNeale lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e342,990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://gwas.mrcieu.ac.uk/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eTotal bilirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003eNeale lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e342,990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://gwas.mrcieu.ac.uk/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eGamma glutamyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003eNeale lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e344,104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://gwas.mrcieu.ac.uk/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eDirect bilirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003eNeale lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e342,990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://gwas.mrcieu.ac.uk/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eAlkaline phosphatase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003eNeale lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e344,392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://gwas.mrcieu.ac.uk/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eAlanine aminotransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003eNeale lab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e344,136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://gwas.mrcieu.ac.uk/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReplication stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003enonalcoholic fatty liver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003eKurki, MI\u0026nbsp;et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e412,181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e36653562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://www.finngen.fi/en/access_results\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eAspartate aminotransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\"\u003e\n \u003cp\u003eSakaue S\u0026nbsp;et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e342,990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e34594039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://www.ebi.ac.uk/gwas/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eTotal bilirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003eDennis JK\u0026nbsp;et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e56,577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e33441150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://www.ebi.ac.uk/gwas/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eGamma glutamyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\"\u003e\n \u003cp\u003eSakaue S\u0026nbsp;et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e344,104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e34594039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://www.ebi.ac.uk/gwas/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eDirect bilirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\" valign=\"top\"\u003e\n \u003cp\u003eDennis JK\u0026nbsp;et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e23,850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e33441150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://www.ebi.ac.uk/gwas/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eAlkaline phosphatase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\"\u003e\n \u003cp\u003eSakaue S\u0026nbsp;et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e344,292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e34594039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://www.ebi.ac.uk/gwas/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eAlanine aminotransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.842105263157894%\"\u003e\n \u003cp\u003eSakaue S\u0026nbsp;et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e344,136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e34594039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\"\u003e\n \u003cp\u003ehttps://www.ebi.ac.uk/gwas/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eeQTL: expression quantitative trait loci; GWAS: genome-wide association study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Glucose-lowering drugs and\u0026nbsp;liver function tests\u0026nbsp;risks analyzed using Mendelian randomization.\u0026nbsp;Abbreviations\u0026nbsp;95% CI: 95% confidence interval\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"651\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.48079877112135%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eDiscovery stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.095238095238095%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eReplication stage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.30081300813008%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29268292682927%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.926829268292686%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.479674796747968%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003eAlanine aminotransferase\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eABCC8/\u003c/p\u003e\n \u003cp\u003eKCNJ11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.545 (-3.653,0.562)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.158 (-0.326,0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eDPP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.619 (2.504,8.733)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.00E-04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.431 (0.169,0.693)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eETFDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.334 (-9.538,12.205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.305 (-0.000,0.610)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eGLP1R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.190 (0.408,3.973)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.079 (-0.026,0.183)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eGPD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.144 (-5.802,3.514)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.151 (-0.148,0.450)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n 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width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eETFDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.578 (-9.963,8.806)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.187 (-0.134,0.509)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eGLP1R\u003c/p\u003e\n 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width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003ePPARG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.271 (7.128,9.415)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00E-45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.312 (0.264,0.359)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00E-37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n 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width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.108 (-2.743,0.528)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.182 (-0.377,0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eDPP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.476 (-1.225,2.177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n 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width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eGLP1R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.198 (-0.927,1.322)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.031 (-0.070,0.133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n 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valign=\"bottom\"\u003e\n \u003cp\u003e-2.198 (-5.829,1.434)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.120 (-0.321,0.080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003ePPARG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.111 (2.539,3.682)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n 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width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eDPP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.082 (-0.160,-0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.067 (-0.436,0.301)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eETFDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n 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width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.580 (0.108,1.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eGPD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.324 (-0.636,-0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.783 (-2.119,0.552)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eINSR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.212 (-0.410,-0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.336 (-2.585,-0.087)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n 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valign=\"bottom\"\u003e\n \u003cp\u003ePRKAB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.224 (-0.319,-0.129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.00E-06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.140 (-0.543,0.263)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eSLC5A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.261 (-0.324,-0.197)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.00E-16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.033 (-0.351,0.416)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003eGamma glutamyl\u003c/p\u003e\n \u003cp\u003etransferase\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eABCC8/\u003c/p\u003e\n \u003cp\u003eKCNJ11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.281 (-5.340,5.903)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.015 (-0.102,0.132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eDPP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.567 (4.829,16.306)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.00E-04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.436 (0.227,0.646)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.00E-05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eETFDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.342 (-16.319,13.636)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.180 (-0.125,0.485)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eGLP1R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.877 (0.962,8.793)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.056 (-0.048,0.159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eGPD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e12.738 (-8.634,34.109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.467 (-0.083,1.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eINSR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e-7.408 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width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.411 (0.355,0.467)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.00E-47\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003ePRKAB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.530 (3.137,11.923)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.126 (-0.064,0.315)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eSLC5A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.942 (-2.950,6.834)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.033 (-0.107,0.172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n 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width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eINSR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.134 (-2.077,-0.192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.067 (-0.793,0.659)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003ePPARG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.145 (-1.296,-0.994)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.00E-50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.267 (-0.354,-0.180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.00E-09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003ePRKAB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.535 (-2.055,-1.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.00E-09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.204 (-0.440,0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.056835637480798%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.367127496159755%\" valign=\"bottom\"\u003e\n \u003cp\u003eSLC5A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.375 (-1.701,-1.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.824884792626728%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00E-16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.88479262672811%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.146 (-0.369,0.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210445468509985%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Drug target, Glucose-lowering drugs, Nonalcoholic fatty liver disease, Liver function tests, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4759170/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4759170/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThe potential risk of nonalcoholic fatty liver disease (NAFLD) and liver toxicity attributed to glucose-lowering medications is uncertain. The objective of this study was to explore the causal relationship between these factors through the implementation of a Mendelian randomization (MR) analysis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTwo-sample MR, summary-data-based MR (SMR), and colocalization analysis were utilized to investigate the association between ten drug reduce glucose targets (PPARG, DPP4, GLP1R, INSR, SLC5A2, ABCC8, KCNJ11, ETFDH, GPD2, and PRKAB1) to reduce NAFLD and liver function tests (LFTs) levels, including aspartate aminotransferase (AST), gamma glutamyl transferase (GGT), alkaline phosphatase (ALP), alanine aminotransferase (ALT), and bilirubin.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDPP4 is closely associated with GGT and ALT. PPARG is significantly associated with NAFLD and correlated with various liver enzymes GGT, AST, ALT, ALP, total bilirubin, and direct bilirubin. PRKAB1 is linked to total and direct bilirubin levels, while SLC5A2 is associated with total and direct bilirubin levels, ALP levels, and NAFLD risk. Limited evidence suggests that genetic variants in PRKAB1, GLP1R, INSR, GPD2, DPP4, and ABCC8/KCNJ11 are correlated with GGT, ALT, bilirubin, and NAFLD levels. Additional validation through SMR and colocalization analysis further confirmed the causal effects of these findings.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eSpecific glucose-lowering medications have been associated with an elevated risk of NAFLD and abnormal LFTs results, potentially offering innovative strategies for the management of NAFLD and LFTs abnormalities.\u003c/p\u003e","manuscriptTitle":"Novel Insights into the Relationship Between Glucose-Lowering Drugs and Nonalcoholic Fatty Liver Disease and liver function: a Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-13 05:58:10","doi":"10.21203/rs.3.rs-4759170/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7c8319df-9e8d-470e-9d9a-728aeefffdf6","owner":[],"postedDate":"August 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-11T14:06:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-13 05:58:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4759170","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4759170","identity":"rs-4759170","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}