Novel insights into causal effects of lipid and lipid-lowering targets with autoimmune thyroid disease: A Mendelian randomization study

Novel insights into causal effects of lipid and lipid-lowering targets with autoimmune thyroid disease: 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 causal effects of lipid and lipid-lowering targets with autoimmune thyroid disease: A Mendelian randomization study Chang Su, Juan Tian, Xueqing He, Xiaona Chang, Guang Wang, Jia Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4428352/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 Background: Dyslipidemia has been implicated in the pathogenesis of several diseases, including thyroid dysfunction and immune disorders. However, whether circulating lipids and long-term use of lipid-lowering drugs influence the development of autoimmune thyroid disease (AITD) remains unclear. Methods: Two-sample and two-step Mendelian randomization (MR) studies were performed to assess the causal relationships between circulating lipids (LDL-C, TC, TG, and ApoB) and seven lipid-lowering drug targets ( ApoB , CETP , HMGCR , LDLR , NPC1L1 , PCSK9, and PPARα ) with AITD. Mediation analyses were conducted to explore potential mediating factors. Results: There was no clear causality between circulating lipids (ApoB, LDL-C, TC, and TG) and AITD ( p > 0.05). ApoB inhibition is related to a reduced risk of autoimmune thyroiditis (AT) (OR = 0.462, p = 0.046), while PCSK9 inhibition is related to reduced Graves' disease (GD) risk (OR = 0. 551, p = 0.033). Moreover, PCSK9 inhibition (OR = 0.735, p = 0.003), LDLR inhibition (OR = 0.779, p = 0.027), and NPC1L1 inhibition (OR = 0.599, p = 0.016) reduced the risk of autoimmune hypothyroidism (AIH). Mediation analysis showed that NPC1L1 inhibition and PCSK9 inhibition exerted effects on AIH through IL-4 and FGF-19 levels. And the effect of PCSK9 inhibition on GD through TNF-β levels. Conclusions: There was no clear causality between circulating lipids (ApoB, LDL-C, TC, and TG) and AITD. Lipid-lowering drug target gene inhibitors reduced the AITD risk by modulating inflammatory factors. Mendelian randomization Drug targeting Thyroid autoimmune disease Lipid trait Inflammatory factors Figures Figure 1 Figure 2 Figure 3 Introduction Autoimmune thyroid disease (AITD) is one of the most common autoimmune diseases,[ 1 ] including Graves' disease (GD) and autoimmune thyroiditis(AT). AITD has been listed as a major cause of abnormal thyroid function, and the latter further leads to lipid metabolic disorder.[ 2 , 3 ] Interestingly, several recent studies indicated that lipotoxicity correlated with increased risk of hypothyroidism.[ 4 , 5 ] Moreover, Graves' ophthalmopathy (GO), one of the most serious complications of GD, has been proven to be related with dyslipidemia.[ 6 ] Lipid-lowering agents are the mainstay of treatment for dyslipidemia, and many studies have proven their anti-inflammatory and antioxidant properties, besides their lipid-lowering effects.[ 7 , 8 ] Based on the clinical correlation of the interplay between dyslipidemia and AITD, the association between lipid and lipid-lowering drugs with AITD deserves further exploration. Mendelian randomization (MR) stands as an analytical approach that utilizes genetic variations in humans to study the causal impacts of modifiable disease exposures. Due to the random segregation of alleles of a single nucleotide polymorphism (SNP) following Mendelian laws, MR presents an advantage in mitigating confounding factors compared to other research methods.[ 9 ] Drug target MR analysis has emerged as a potent technique for assessing the influence of drugs, antagonists, agonists or inhibitors targeting protein-coding genes on disease risk, which can be an important aid in addressing the potential for drug therapy. [ 10 ] Therefore, this study aims to comprehensively investigate the causal relationships between circulating lipids (low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglyceride (TG) and apolipoprotein B (ApoB)) and seven lipid-lowering drug targets (Apolipoprotein B ( ApoB ), Cholesteryl Ester Transfer Protein ( CETP ), 3-hydroxy-3-methylglutaryl coenzyme A reductase ( HMGCR ), Low-Density Lipoprotein Receptor ( LDLR ), Niemann-Pick C1-Like1 ( NPC1L1 ), Proprotein Convertase Subtilisin/Kexin Type 9 ( PCSK9 ) and Peroxisome Proliferator Activated Receptor-alpha ( PPARα )) with AITD using MR analysis. This study will provide novel insights into the risk of AITD associated with lipid traits and lipid-lowering drugs. Materials and methods Study design Figure 1 shows the flowchart of this study. Firstly, we performed two-sample univariable MR (UVMR) analyses to investigate the causal effects of circulating lipid traits on AITD using genetically predicted LDL-C, TC, TG, and ApoB levels as exposures and AITD including GD, GO, AT, and autoimmune hypothyroidism (AIH) as outcomes. Secondly, multiple drug target MR analysis was conducted to investigate the association between lipid-lowering drug targets and AITD. Seven drug target genes were included in the analysis: ApoB , HMGCR , NPC1L1 , PCSK9 , CETP , LDLR , and PPARα . The effectiveness of lipid-lowering drug targets was verified by their impact on coronary heart disease (CHD). Thirdly, mediation MR analysis was used to explore the potential mediation effect of inflammatory factors on the association between lipid-lowering drug targets with AITD. The reporting of this study adhered to the guidelines outlined in Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization (STROBE-MR).[ 11 ] This MR study employed publicly accessible summary statistics for its analysis, and the ethical approval on it can be traced back to the original article. Selection of genetic instruments To construct instrumental variables (IVs) representing lipid traits, we included GWAS data of 4 lipoproteins, including ApoB, LDL-C, TC, and TG, from a large-scale study that included up to 249 metabolic biomarkers in 88,329 European individuals.[ 12 ] We extracted full-gene significant variations of 4 lipid traits using p < 5×10 − 8 and linkage disequilibrium (LD) r 2 ≤ 0.001 and actual distance ≥ 10 Mb as extraction criteria. The characteristics of each GWAS dataset are detailed in Table 1 . Table 1 Genome-wide association study summary data and expression quantitative trait loci studies’ data information. Characteristic Resource Ancestor Sample size ApoB GWAS Catalog European 88329 LDL-C Global Lipids Genetics Consortium European 1320658 LDL-C GWAS Catalog European 88329 TC GWAS Catalog European 88329 TG Global Lipids Genetics Consortium European 1320658 TG GWAS Catalog European 88329 GD FinnGen consortium R10 European 412181 GO FinnGen consortium R10 European 412181 AT FinnGen consortium R10 European 350256 AIH FinnGen consortium R10 European 344168 CHD IEU GWAS database European 184305 Abbreviations: ApoB, Apolipoprotein B; LDL-C, Low-Density Lipoprotein Cholesterol; TC, Total cholesterol; TG, Total triglyceride; GD, Graves’ disease; GO, Graves' ophthalmopathy; AT, Autoimmune thyroiditis; AIH, Autoimmune hypothyroidism; CHD, Coronary heart disease. Based on the dyslipidemia management guidelines, we identified commonly prescribed lipid-lowering drugs,[ 13 ] and queried their respective target genes through Drugbank ( https://go.drugbank.com/ ). We then identified SNPs located within the target genes that were significantly associated with LDL-C and TG, respectively (Table 2 ). These genetic instruments were derived from the Global Lipids Genetics Consortium (GLGC) GWAS data on LDL-C, TG, which includes 1,320,658 European individuals.[ 14 ] We selected SNPs within each target gene that exhibited genome-wide significant associations with LDL-C, TG ( p < 5×10 − 8) and the LD parameter was set at r 2 < 0.2 within a range of 100 kb. We removed SNPs with palindromic structures to ensure the reliability of the results. The IVs we obtained were significantly associated with exposure ( p < 5×10 − 8 ) in cis -expression Quantitative Trait Loci ( cis -eQTL). Table 2 Summary Information of lipid-lowering Drug Classes, Targets, and Encoding Genes. Drug class Drug target (Drug Bank) Encoding genes Gene region (in GRCh37 from Ensembl) Drug substance ASO targeting ApoB mRNA mRNA of ApoB-100 ApoB chr 2:21225354–21266932 Mipomersen ASO targeting CETP mRNA Cholesteryl ester transfer protein CETP chr 16:56996104–57017662 Torcetrapib HMGCR inhibitors HMG-CoA reductase HMGCR chr 5:74632193–74657918 Atorvastatin Rosuvastatin etc. Key Modulator LDL Receptor LDLR chr 19:11200139–11244496 - TC absorption inhibitors Niemann-Pick C1-Like 1 (NPC1L1) protein NPC1L1 chr 7:44553349–44580706 Ezetimibe PCSK9 inhibitors proprotein convertase subtilisin/kexin type 9 PCSK9 chr 1:55505371–55530503 Evolocumab Alirocumab Fibrates Peroxisome Proliferator-Activated Receptor-alpha PPARα chr 22:46546429–46639653 Fenofibrate Gemfibrozil Abbreviations: ApoB , Apoprotein B; CETP , Cholesteryl Ester Transfer Protein; HMGCR , 3-hydroxy-3-methylglutaryl coenzyme A reductase; LDLR , Low-Density Lipoprotein Receptor; NPC1L1 , Niemann-Pick C1-like 1; PCKS9 , Proprotein Convertase Subtilisin/Kexin Type 9; PPARα , Peroxisome ProliferatorActivated Receptor-alpha. We calculated the F -statistic for selected IVs and excluded SNPs with an F -statistic 0.80) using SNiPA ( https://snipa.helmholtz-muenchen.de/snipa3/index.php ). If no suitable surrogate SNP was available, it was discarded. Genetic instruments for inflammatory factors We have chosen 91 inflammatory factors from an analysis of 11 cohorts, encompassing 14,824 individuals of European descent, the original publications detailed the entire procedure for measuring inflammatory factors.[ 15 ] Complete per-protein GWAS summary statistics can be downloaded at https://www.phpc.cam.ac.uk/ceu/proteins and the EBI GWAS Catalog (accession numbers GCST90274758 to GCST90274848). Outcome data Taking into account the well-established benefits of lipid-lowering drugs on coronary heart disease, we performed a positive control analysis using coronary heart disease as the outcome data. The GWAS data for CHD were sourced from the IEU GWAS database (60,801 cases and 123,504 controls). Then, we selected GD, GO, AT, and AIH as the primary outcomes of our study. In our research, GWAS data for GD (3176 cases and 409,005controls), GO (598 cases and 411,583 controls), AT (539 cases and 349,717 controls) and AIH (45,321 cases and 298,847 controls) were obtained from the FinnGen database (Release 10), as described in Table 1 . Statistical analysis MR analysis to estimate the effects of lipid traits targets on AITD We applied UVMR to assess the effects of lipid traits on AITD. The main analysis method is inverse variance weighting (IVW).[ 16 ] Heterogeneity testing was used to determine whether to choose a random effects model or a fixed effects model for IVW. Specifically, when heterogeneity was observed (Q_ pval 50%), the random effects model was selected as it provides more precise estimates and confidence interval (CI) than the fixed effects IVW method, and was tested using Cochran's Q, Otherwise, a fixed effects model is used. In addition to the IVW method, we also used four MR methods as supplementary analysis, namely MR-Egger, weighted median, weighted mode, and simple mode. Furthermore, to assess relative pleiotropy, the MR-Egger intercept test and MR pleiotropy residuals and outliers (MR-PRESSO) were used. Outlier SNPs were detected using the MR-PRESSO outlier test using a level p index of 0.05. The MR results were evaluated using a leave-one-out approach to check their robustness. MR analysis to estimate the effects of lipid-lowering drug targets on AITD First, we used CHD as a control outcome to evaluate the reliability of the extracted SNPs as alternatives to lipid-lowering drugs. IVW was also used for estimating the impact of genetic tools and lipid-lowering drugs on CHD. Subsequently, we continued to use IVW as the primary method, with the above 4 methods as complementary methods to determine the association between validated IVs and the risk of AITD. MR-Egger intercept test MR pleiotropy residuals and MR-PRESSO assessed pleiotropy. In addition to this, MR.RAPS provides our results with robust estimates corrected for systematic and idiosyncratic pleiotropies.[ 17 ] Mediation MR analysis linking lipid-lowering drug targets with AITD via inflammatory factors To evaluate the mediating role of 91 inflammatory factors on the relationship between lipid-lowering drug targets and AITD, we performed two-step MR (Fig. 1 ). First, we used UVMR to estimate the impacts of lipid-lowering drug targets on 91 inflammatory factors ( β1 ). We selected cis -eQTL genetic variation as IV, gene expression as exposure, and 91 inflammatory factors as outcomes for MR analysis. Then, we selected inflammatory factors significantly correlated with gene expression as exposures to conduct MR analysis ( β2 ) on AT, GD, and AIH respectively. Since the number of SNPs in some inflammatory factors is small, we selected SNPs that were significantly associated with inflammatory factors at the genome-wide level ( p < 1×10 − 5 ) as the corresponding IVs. The LD parameter was set to r 2 < 0.001 within 100 kb. To note, the IVs variables of the two-step MR analysis cannot be repeated, so the IVs used in the second step need to exclude those used in the first step. Finally, the mediating proportion of each inflammatory factor in the association between the lipid-lowering drug target and AITD was calculated as the product of β1 and β2 divided by the total effect of the lipid-lowering drug targets on AITD. The 95% CI for the mediation proportion was calculated using the delta method.[ 18 ] Sensitivity analysis We used the intercept term of the MR-Egger regression to represent the mean pleiotropy of IVs, and the likelihood of horizontal pleiotropy was estimated using MR-Egger regression. In addition, we used MR-PRESSO as a supplement to assess horizontal pleiotropy.[ 19 ] The purpose of detecting horizontal multivariate validity, correcting horizontal multivariate validity by removing outliers, and determining whether the causal effects have substantially changed before and after removing outliers in MR analysis can all be achieved through MR-PRESSO. To improve the accuracy and robustness of the genetic instrument, we quantified heterogeneity using Cochran's Q statistic, where p > 0.05 indicates no effect heterogeneity. Results Selection and validation of genetic instruments By applying the thresholds we set in the method analysis, among 88,329 European individuals, 44 SNPs represented ApoB, 46 SNPs represented LDL-C, 49 SNPs represented TC, and 55 SNPs represented TG. In addition, we selected 12 SNPs proxied ApoB , 6 SNPs proxied CETP , 6 SNPs proxied HMGCR , 13 SNPs proxied LDLR , 5 SNPs proxied NPC1L1 , 11 SNPs proxied PCSK9 and 3 SNPs proxied PPARα in 1,320,658 European individuals. Among the IVs studied, F -statistics ranged from 29.9 to 9740.0, suggesting that weak instrumental bias has little impact on our analysis. We then performed UVMR analyses of lipid-lowering drug targets and CHD using gene proxies with CHD as positive controls. All lipid-lowering drug target genes involved in this study showed significant associations with CHD risk. No significant heterogeneity or multiple effects were observed in the results, suggesting that these genetic tools are effective. Details of all included SNPs can be found in Supplementary Tables 1 and 2. Association of lipid traits with genetic proxies for AITD We conducted a two-sample MR analysis on the association between lipid traits (including ApoB, LDL-C, TG, and TC) and AITD. Although no evidence of pleiotropy was detected in our results, the presence of heterogeneity was observed. Therefore, the IVW model was conducted using random effects. We found that there was no clear causality between ApoB, LDL-C, TC, TG, and AITD ( p > 0.05) (Supplementary Table 3). Association of lipid-lowering drugs targets with genetic proxies for AITD In our preliminary analyses using the IVW approach, we observed strong evidence that LDL-C-derived ApoB inhibition (OR = 0.462, 95% CI = 0.216,0.986; p = 0.046) reduced the risk of AT and that PCSK9 inhibition (OR = 0.551. 95% CI = 0.319,0.953; p = 0.033) reduced the risk of GD, while PCSK9 inhibition (OR = 0.735, 95% CI = 0.598,0.903; p = 0.003) were also found to reduce the risk of AIH. In addition to this, we found that both LDLR inhibition (OR = 0.779, 95% CI = 0.624,0.972; p = 0.027) and NPC1L1 inhibition (OR = 0.599, 95% CI = 0.412,0.872; p = 0.016) similarly reduced the risk of AIH (Table 3 ). No pleiotropy or heterogeneity was found for any of the above gene inhibitors ( p > 0.05) (Table 4 ). Table 3 MR analyses of lipid-lowering drugs on AITD by different methods. Exposure Outcome IVW MR.RAPS p OR(95%CI) p OR(95%CI) ApoB AT 0.046 0.462 (0.216,0.986) 0.046 0.461 (0.216,0.986) PCSK9 GD 0.033 0.551 (0.319,0.953) 0.034 0.551 (0.318,0.955) PCSK9 AIH 0.003 0.735 (0.598,0.903) < 0.001 0.734 (0.624,0.863) LDLR AIH 0.027 0.779 (0.624,0.972) 0.009 0.778 (0.644,0.939) NPC1L1 AIH 0.007 0.599 (0.412,0.872) 0.008 0.597 (0.409,0.874) Abbreviations:MR, Mendelian randomization; OR, odds ratio; CI, confidence interval; MR.RAPS, Mendelian randomization robust adjusted profile score. OR, 95% CI, and p -values were calculated for the respective method of MR analysis. Table 4 Multiple tests of ApoB, PCSK9, LDLR, NPC1L1 gene inhibition, and AITD Exposure(inhibition) Outcome Cochran Q value Q- pval MR-Egger Intercept p -Value MR-PRESSO p- value ApoB AT 10.259 0.507 2.13E-02 0.046 0.493 PCSK9 GD 7.751 0.653 3.41E-02 0.033 0.743 PCSK9 AIH 16.274 0.092 1.14E-02 0.003 0.111 LDLR AIH 15.367 0.166 1.74E-03 0.027 0.177 NPC1L1 AIH 9.782 0.460 2.55E-02 0.016 0.486 The heterogeneity test in the IVW method was performed using Cochran’s Q statistic and the global test for the MR-PRESSO method. p < 0.05 was considered significant IVW, inverse–variance weighted; p -heterogeneity, p- value for heterogeneity test; p -intercept, p- value for the intercept of MR-Egger regression. Mediation MR of lipid-lowering drug targets, inflammatory factors and AITD We estimated the impacts of lipid-lowering drug targets on 91 inflammatory factors and observed that a total of 30 inflammatory factors were significantly associated with ApoB inhibition, NPC1L1 inhibition, and PCSK9 inhibition, respectively (Supplemental Table 4). We did not observe a significant correlation of inflammatory factors on LDLR inhibition. We further estimated the effects of 30 inflammatory factors significantly associated with lipid-lowering drug targets on AITD and found that 3 inflammatory factors were significantly associated with AIH and one inflammatory factor was significantly associated with GD (Supplementary Table 5). We observed a significant correlation between Interleukin-4 (IL-4) levels (OR = 1.119, 95% CI = 1.020,1.228; p = 0.018), Osteoprotegerin levels (OR = 0.924, 95% CI = 0.862,0.992; p = 0.029), Fibroblast growth factor-19 (FGF-19) levels (OR = 0.934, 95% CI = 0.890,0.980; p = 0.006) and AIH; Tumor necrosis factor-beta (TNF-β) levels (OR = 1.142, 95% CI = 1.029,1.268; p = 0.012) was significantly associated with GD. There was no evidence of horizontal pleiotropy, and although some of the results were heterogeneous, we used a random effects IVW approach for analysis.[ 20 ] The IVs for the 30 inflammatory factors were all strong ( F -statistics > 38.55) (Supplemental Table 6). We found that NPC1L1 inhibition through IL-4 levels had an indirect effect on AIH, with a mediated proportion of the total effect of 25.64% (95% CI = 0.139%,64.579%, p = 0.008); PCSK9 inhibition through FGF- 19 levels have an indirect effect on AIH, and the mediated proportion of the total effect is -6.84% (95% CI =-16.141%,-0.477%, p = 0.036); PCSK9 inhibition has an indirect effect on GD through TNF-β levels, and the mediated proportion of the total effect of 9.72% (95% CI = 0.047%,24.340%, p = 0.045). However, we observed that although Osteoprotegerin levels were significantly related to AIH, the 95% CI crossed the invalid line (95% CI = 0.003%, -24.471%, p = 0.625), indicating that the mediating effect of this result was not established (Fig. 3 ). Discussion In this study, we systematically evaluated the causal relationship between 4 blood lipid traits,7 lipid-lowering gene inhibitors, 91 inflammatory factors, and the risk of AITD through drug-targeted MR analysis and mediation MR analysis. There was no clear causality between circulating lipids and AITD. ApoB inhibition is related to a reduced risk of AT, while PCSK9 inhibition is related to reduced GD risk. Moreover, PCSK9 inhibition, LDLR inhibition, and NPC1L1 inhibition reduced the risk of AIH. Mediation analysis indicated that the effect of NPC1L1 inhibition and PCSK9 inhibition on AIH through IL-4 and FGF-19 levels. And the effect of PCSK9 inhibition on GD through TNF-β levels. Thyroid hormone plays a crucial role in the modulation of energy metabolism.[ 21 ] The causal relationship that thyroid dysfunction caused dyslipidemia is a well-accepted clinical finding.[ 22 ] Interestingly, some recent studies have demonstrated that lipotoxicity resulted in the pathogenesis of multiple diseases, including thyroid dysfunction and immune disorders.[ 4 ] A prospective cohort study showed that the subclinical hypothyroid patients with hypercholesterolemia were more vulnerable to developing overt hypothyroidism during a 3-year follow-up.[ 23 ] Statins, the most commonly used of the lipid-lowering drugs, have been observed to be correlate with reduced GO risk in patients with Graves' hyperthyroidism.[ 24 ] Based on the clinical correlation of the interplay between dyslipidemia and AITD, the association between lipid and lipid-lowering drugs with AITD deserves further exploration. The present study showed that there was no clear causality between circulating lipids and AITD, however, lipid-lowering targets reduced the AITD risk. Therefore, the underlying mechanisms may extend beyond the lipid-lowering effect. Further mediation MR analysis found the effect of NPC1L1 inhibition and PCSK9 inhibition on AIH through IL-4 and FGF-19 levels. And the effect of PCSK9 inhibition on GD through TNF-β levels. Fortunately, the anti-inflammatory effects of lipid-lowering drug target gene inhibitors have long been demonstrated in other studies. Combining ezetimibe with a statin is a more effective way to lower CRP levels.[ 25 , 26 ] PCSK9 inhibition also exerts anti-inflammatory effects and is positively correlated with levels of inflammatory biomarkers such as leukocytes, hsCRP, and fibrinogen.[ 27 ] The occurrence and development of AITD are also closely related to inflammatory factors. The key to the occurrence of AITD is the activation of T cells,[ 28 ], and the generation of IgG1 isotype response is stimulated by Th1 cytokines,[ 29 ] which is the main pathogenic TSH receptor autoantibody observed in AITD.[ 30 , 31 ] IL-4 can stimulate the expression of HLA class II antigens and oppose Th1 cell inflammatory responses through signal transducer and activator of transcription 6 ( STAT6 ).[ 32 ] A cohort study demonstrated that patients with AITD had lower overall IL-4 activity, which may contribute to the propensity to produce IgG1 autoantibodies.[ 33 ] However, in another study it was demonstrated that ectopic expression of IL-4 in thyroid tissue increases the incidence of spontaneous AT, the eventual evolution of which can lead to hypothyroidism.[ 34 ] Furthermore, FGF-19, a promising lipid modulator, exhibited a notable decrease in the serum of individuals suffering from hypothyroidism and subclinical hypothyroidism.[ 35 , 36 ] This may be related to the fact that TSH triggers hepatic sterol regulatory element-binding protein (SREBP) through its receptor to negatively regulate the transcription of FGF19 in human intestinal cells.[ 37 , 38 ] However, the impact of FGF-19 on AITD has not been completely confirmed. TNF-β exerts a crucial influence on regulating inflammatory responses, apoptosis, and immune cell activity. TNF-β alleles may mark a specific immune response state with altered immune responses to mitogens and suppressor T cells and may contribute to the development of GD in a predisposing manner. [ 39 , 40 ] Interestingly, a positive association between high levels of TNF-β and GD risk was also observed in a recent MR analysis.[ 41 ] MR analysis is a natural randomized controlled trial that studies large sample sizes, minimizes confounding and reverse causation, and provides accuracy and convenience. We are the first to examine the relationship between lipid-lowering drugs and AITD and to provide evidence of a protective effect of some lipid-lowering drugs against AITD. This concept will help expand the therapeutic use of lipid-lowering medications. However, our study has some limitations. First, drug-targeted MR analysis cannot capture the short-term effects of lipid-lowering drugs. Second, the databases we used were not stratified by sex, age, or disease severity. Finally, this study utilized European population data, raising uncertainty regarding generalizability to other ethnic groups. Further investigation through laboratory studies and clinical trials is necessary to confirm and elucidate these findings. Conclusions There was no clear causality between circulating lipids (ApoB, LDL-C, TC, and TG) and AITD. Lipid-lowering drug target gene inhibitors reduced the AITD risk by modulating inflammatory factors. Abbreviations AIH Autoimmune hypothyroidism AITD Autoimmune thyroid disease ApoB/ ApoB Apolipoprotein B AT Autoimmune thyroiditis CETP Cholesteryl Ester Transfer Protein CHD Coronary heart disease CI Confidence interval cis -eQTL cis -expression Quantitative Trait Loci FGF-19 Fibroblast growth factor-19 GD Graves' disease GLGC Global Lipids Genetics Consortium GO Graves' ophthalmopathy HMGCR 3-hydroxy-3-methylglutaryl coenzyme A reductase IL-4 Interleukin-4 IVs Instrumental variables IVW Inverse variance weighting LD Linkage disequilibrium LDL-C Low-density lipoprotein cholesterol LDLR Low-Density Lipoprotein Receptor MR Mendelian randomization MR-PRESSO Mendelian randomization pleiotropy residuals and outliers NPC1L1 Niemann-Pick C1-Like1 PCSK9 Proprotein Convertase Subtilisin/Kexin Type 9 PPARα Peroxisome Proliferator Activated Receptor-alpha SNP Single nucleotide polymorphism SREBP Sterol Regulatory Element-binding Protein STAT6 Signal transducer and activator of transcription 6 STROBE-MR Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization TC Total cholesterol TG Triglyceride TNF-β Tumor necrosis factor-beta UVMR Univariable Mendelian Randomization Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The results of this study are included in this article and supporting documents. The FinnGen database is an open access resource and researchers need to obtain approval from the FinnGen database (https://www.finngen.fi/fi). Lipid-related data come from the IEU database (https://gwas.mrcieu.ac.uk/), GWAS Catalog database (https://www.ebi.ac.uk/gwas/) and GLGC database (http:// www.lipidgenetics.org/#data-downloads-title), the above databases are all open access resources. Competing interests The authors have no conflict of interest to disclose. Funding Information This work was supported by grants from the Chinese National Natural Science Foundation (No. 82072527), Beijing Natural Science Foundation Z200019 to J.L and Beijing Hospitals Authority’s Ascent Plan (DFL20220302) to G.W. Authors’ Contribution Chang Su handled conceptualization, data collection, data curation, supervision, and visualization, conducted formal analysis, and wrote an original draft. Juan Tian oversaw data collection, data curation, and supervision. Xueqing He and Xiaona Chang were involved in data collection, data curation, and supervision. Acknowledgments The authors thank all participating researchers for their contribution to the article. The authors thank Professor Liu Jia and Professor Wang Guang for their careful guidance in this research and for reviewing and revising the article. The authors thank the Chinese National Natural Science Foundation, Beijing Natural Science Foundation, and Beijing Hospitals Authority’s Ascent Plan for funding. Author Disclosure Statement The authors have no conflict of interest to disclose. References Ferrari SM, Fallahi P, Elia G, Ragusa F, Camastra S, Paparo SR, et al. Novel therapies for thyroid autoimmune diseases: An update. 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Lin D, Zhu Y, Tian Z, Tian Y, Liang C, Peng X, et al. Causal associations between gut microbiota, gut microbiota-derived metabolites, and cerebrovascular diseases: a multivariable Mendelian randomization study. Front Cell Infect Microbiol. 2023;13:1269414. Huang A, Wu X, Lin J, Wei C, Xu W. Genetic insights into repurposing statins for hyperthyroidism prevention: a drug-target Mendelian randomization study. Front Endocrinol. 2024;15:1331031. Verbanck M, Chen C-Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8. Causal association between celiac disease and inflammatory bowel disease: A two-sample bidirectional Mendelian randomization study - PMC. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9845610/. Accessed 5 Apr 2024. Mullur R, Liu Y-Y, Brent GA. Thyroid Hormone Regulation of Metabolism. Physiol Rev. 2014;94:355–82. Duntas LH. Thyroid Disease and Lipids. Thyroid®. 2002;12:287–93. Li X, Zhen D, Zhao M, Liu L, Guan Q, Zhang H, et al. Natural history of mild subclinical hypothyroidism in a middle-aged and elderly Chinese population: a prospective study. Endocr J. 2017;64:437–47. Marinò M, Lanzolla G, Marcocci C. Statins: A New Hope on the Horizon of Graves Orbitopathy? J Clin Endocrinol Metab. 2021;106:e2819–21. Pearson T, Ballantyne C, Sisk C, Shah A, Veltri E, Maccubbin D. Comparison of effects of ezetimibe/simvastatin versus simvastatin versus atorvastatin in reducing C-reactive protein and low-density lipoprotein cholesterol levels. Am J Cardiol. 2007;99:1706–13. Pearson TA, Ballantyne CM, Veltri E, Shah A, Bird S, Lin J, et al. Pooled analyses of effects on C-reactive protein and low density lipoprotein cholesterol in placebo-controlled trials of ezetimibe monotherapy or ezetimibe added to baseline statin therapy. Am J Cardiol. 2009;103:369–74. Li S, Zhang Y, Xu R-X, Guo Y-L, Zhu C-G, Wu N-Q, et al. Proprotein convertase subtilisin-kexin type 9 as a biomarker for the severity of coronary artery disease. Ann Med. 2015;47:386–93. Weetman AP, McGregor AM. Autoimmune thyroid disease: further developments in our understanding. Endocr Rev. 1994;15:788–830. Abbas AK, Murphy KM, Sher A. Functional diversity of helper T lymphocytes. Nature. 1996;383:787–93. Weetman AP, Black CM, Cohen SB, Tomlinson R, Banga JP, Reimer CB. Affinity purification of IgG subclasses and the distribution of thyroid auto-antibody reactivity in Hashimoto’s thyroiditis. Scand J Immunol. 1989;30:73–82. Kuppers RC, Outschoorn IM, Hamilton RG, Burek CL, Rose NR. Quantitative measurement of human thyroglobulin-specific antibodies by use of a sensitive enzyme-linked immunoassay. Clin Immunol Immunopathol. 1993;67:68–77. Kelso A. Cytokines: principles and prospects. Immunol Cell Biol. 1998;76:300–17. Hunt PJ, Marshall SE, Weetman AP, Bell JI, Wass JA, Welsh KI. Cytokine gene polymorphisms in autoimmune thyroid disease. J Clin Endocrinol Metab. 2000;85:1984–8. Merakchi K, Djerbib S, Dumont J-E, Miot F, De Deken X. Severe Autoimmune Thyroiditis in Transgenic NOD.H2h4 Mice Expressing Interleukin-4 in the Thyroid. Thyroid Off J Am Thyroid Assoc. 2023;33:351–64. Crunkhorn S. Metabolic disorders: Betatrophin boosts β-cells. Nat Rev Drug Discov. 2013;12:504. Lai Y, Wang H, Xia X, Wang Z, Fan C, Wang H, et al. Serum fibroblast growth factor 19 is decreased in patients with overt hypothyroidism and subclinical hypothyroidism. Medicine (Baltimore). 2016;95:e5001. Miyata M, Hata T, Yamazoe Y, Yoshinari K. SREBP-2 negatively regulates FXR-dependent transcription of FGF19 in human intestinal cells. Biochem Biophys Res Commun. 2014;443:477–82. Yan F, Wang Q, Lu M, Chen W, Song Y, Jing F, et al. Thyrotropin increases hepatic triglyceride content through upregulation of SREBP-1c activity. J Hepatol. 2014;61:1358–64. Hashimoto S, McCombs CC, Michalski JP. Mechanism of a lymphocyte abnormality associated with HLA-B8/DR3 in clinically healthy individuals. Clin Exp Immunol. 1989;76:317–23. Ambinder JN, Chiorazzi N, Gibofsky A, Fotino M, Kunkel HG. Special characteristics of cellular immune function in normal individuals of the HLA-DR3 type. Clin Immunol Immunopathol. 1982;23:269–74. Yao Z, Guo F, Tan Y, Zhang Y, Geng Y, Yang G, et al. Causal relationship between inflammatory cytokines and autoimmune thyroid disease: a bidirectional two-sample Mendelian randomization analysis. Front Immunol. 2024;15:1334772. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4428352","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304913845,"identity":"b2664cf7-7030-41f8-8ebb-5ea32f8eb27d","order_by":0,"name":"Chang Su","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Su","suffix":""},{"id":304913846,"identity":"d19b0584-a707-448e-94be-d61217aa3f82","order_by":1,"name":"Juan Tian","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Tian","suffix":""},{"id":304913848,"identity":"e2f74853-16c5-4b49-9122-d382c1d715ca","order_by":2,"name":"Xueqing He","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xueqing","middleName":"","lastName":"He","suffix":""},{"id":304913851,"identity":"65388a40-d8f5-447c-93ec-445496e79600","order_by":3,"name":"Xiaona Chang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaona","middleName":"","lastName":"Chang","suffix":""},{"id":304913853,"identity":"0baf7fbb-da1d-47d6-9018-937a0e1ecddf","order_by":4,"name":"Guang Wang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guang","middleName":"","lastName":"Wang","suffix":""},{"id":304913854,"identity":"0c78fb62-2dcb-4274-a21e-060046f12353","order_by":5,"name":"Jia Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACPmYGBoMEBgkeNvbmAwc+VBChhQ2ixUaGj+dY4sEZZ4jRAqHSbOQkfIwP87YQo4Wdx6DgQc1hHjYJng8HeBsY5PnFDhByGI+BQcIxoBbp3g0HJHcwGM6cnUCMFjagFpmzGw4YnmFIMLhNlJZ/IIflPDiQ2EaslsS2NJAWhgMHidPCVmCQ2GfDw8ZzzOBgwxkJwn7h5z+8zfDHNwl7+fbmx5//VNjI80sT0AKyyACJI0FQOQgwPyBK2SgYBaNgFIxcAABDVz6a1P8+xQAAAABJRU5ErkJggg==","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jia","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-05-16 04:33:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4428352/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4428352/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57086871,"identity":"bf099a1b-8e79-443a-b353-3bd0502fb1cc","added_by":"auto","created_at":"2024-05-24 11:59:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":868955,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of study design and analysis strategy.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4428352/v1/67351c1cb96290dde25bc4e2.jpg"},{"id":57086870,"identity":"d3a03935-7a32-4568-99e6-e6f619641feb","added_by":"auto","created_at":"2024-05-24 11:59:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2399573,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of lipid-lowering drug targets and AITD results\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4428352/v1/774ca99c922733507cbbd233.jpg"},{"id":57086872,"identity":"a2eb097a-5465-496e-9238-2778ca750b4e","added_by":"auto","created_at":"2024-05-24 11:59:37","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":742290,"visible":true,"origin":"","legend":"\u003cp\u003eThe potential causal evidence summarized from the two- step MR analysis IL-4, FGF-19, and TNF-β\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4428352/v1/83253e57d2ed6d0268820570.jpg"},{"id":57087939,"identity":"3b4449bb-be44-4a63-aafe-7effe1a559e0","added_by":"auto","created_at":"2024-05-24 12:15:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4887889,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4428352/v1/c8bcac97-f464-4f55-a3a7-dcf9cf6933ad.pdf"},{"id":57086873,"identity":"692848e8-e1dd-4c4d-91c9-588abc287b65","added_by":"auto","created_at":"2024-05-24 11:59:37","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":420591,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4428352/v1/c3d775854690ed6c91e0b39e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Novel insights into causal effects of lipid and lipid-lowering targets with autoimmune thyroid disease: A Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAutoimmune thyroid disease (AITD) is one of the most common autoimmune diseases,[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] including Graves' disease (GD) and autoimmune thyroiditis(AT). AITD has been listed as a major cause of abnormal thyroid function, and the latter further leads to lipid metabolic disorder.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Interestingly, several recent studies indicated that lipotoxicity correlated with increased risk of hypothyroidism.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Moreover, Graves' ophthalmopathy (GO), one of the most serious complications of GD, has been proven to be related with dyslipidemia.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Lipid-lowering agents are the mainstay of treatment for dyslipidemia, and many studies have proven their anti-inflammatory and antioxidant properties, besides their lipid-lowering effects.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Based on the clinical correlation of the interplay between dyslipidemia and AITD, the association between lipid and lipid-lowering drugs with AITD deserves further exploration.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) stands as an analytical approach that utilizes genetic variations in humans to study the causal impacts of modifiable disease exposures. Due to the random segregation of alleles of a single nucleotide polymorphism (SNP) following Mendelian laws, MR presents an advantage in mitigating confounding factors compared to other research methods.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Drug target MR analysis has emerged as a potent technique for assessing the influence of drugs, antagonists, agonists or inhibitors targeting protein-coding genes on disease risk, which can be an important aid in addressing the potential for drug therapy. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Therefore, this study aims to comprehensively investigate the causal relationships between circulating lipids (low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglyceride (TG) and apolipoprotein B (ApoB)) and seven lipid-lowering drug targets (Apolipoprotein B (\u003cem\u003eApoB\u003c/em\u003e), Cholesteryl Ester Transfer Protein (\u003cem\u003eCETP\u003c/em\u003e), 3-hydroxy-3-methylglutaryl coenzyme A reductase (\u003cem\u003eHMGCR\u003c/em\u003e), Low-Density Lipoprotein Receptor (\u003cem\u003eLDLR\u003c/em\u003e), Niemann-Pick C1-Like1 (\u003cem\u003eNPC1L1\u003c/em\u003e), Proprotein Convertase Subtilisin/Kexin Type 9 (\u003cem\u003ePCSK9\u003c/em\u003e) and Peroxisome Proliferator Activated Receptor-alpha (\u003cem\u003ePPARα\u003c/em\u003e)) with AITD using MR analysis. This study will provide novel insights into the risk of AITD associated with lipid traits and lipid-lowering drugs.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the flowchart of this study. Firstly, we performed two-sample univariable MR (UVMR) analyses to investigate the causal effects of circulating lipid traits on AITD using genetically predicted LDL-C, TC, TG, and ApoB levels as exposures and AITD including GD, GO, AT, and autoimmune hypothyroidism (AIH) as outcomes. Secondly, multiple drug target MR analysis was conducted to investigate the association between lipid-lowering drug targets and AITD. Seven drug target genes were included in the analysis: \u003cem\u003eApoB\u003c/em\u003e, \u003cem\u003eHMGCR\u003c/em\u003e, \u003cem\u003eNPC1L1\u003c/em\u003e, \u003cem\u003ePCSK9\u003c/em\u003e, \u003cem\u003eCETP\u003c/em\u003e, \u003cem\u003eLDLR\u003c/em\u003e, and \u003cem\u003ePPARα\u003c/em\u003e. The effectiveness of lipid-lowering drug targets was verified by their impact on coronary heart disease (CHD). Thirdly, mediation MR analysis was used to explore the potential mediation effect of inflammatory factors on the association between lipid-lowering drug targets with AITD. The reporting of this study adhered to the guidelines outlined in Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization (STROBE-MR).[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] This MR study employed publicly accessible summary statistics for its analysis, and the ethical approval on it can be traced back to the original article.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSelection of genetic instruments\u003c/h2\u003e \u003cp\u003eTo construct instrumental variables (IVs) representing lipid traits, we included GWAS data of 4 lipoproteins, including ApoB, LDL-C, TC, and TG, from a large-scale study that included up to 249 metabolic biomarkers in 88,329 European individuals.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] We extracted full-gene significant variations of 4 lipid traits using \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e and linkage disequilibrium (LD) \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001 and actual distance\u0026thinsp;\u0026ge;\u0026thinsp;10 Mb as extraction criteria. The characteristics of each GWAS dataset are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eGenome-wide association study summary data and expression quantitative trait loci studies\u0026rsquo; data information.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAncestor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGWAS Catalog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlobal Lipids Genetics Consortium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1320658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGWAS Catalog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGWAS Catalog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlobal Lipids Genetics Consortium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1320658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGWAS Catalog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinnGen consortium R10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e412181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinnGen consortium R10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e412181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinnGen consortium R10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e350256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinnGen consortium R10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e344168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIEU GWAS database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e184305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: ApoB, Apolipoprotein B; LDL-C, Low-Density Lipoprotein Cholesterol; TC, Total cholesterol; TG, Total triglyceride; GD, Graves\u0026rsquo; disease; GO, Graves' ophthalmopathy; AT, Autoimmune thyroiditis; AIH, Autoimmune hypothyroidism; CHD, Coronary heart disease.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the dyslipidemia management guidelines, we identified commonly prescribed lipid-lowering drugs,[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and queried their respective target genes through Drugbank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://go.drugbank.com/\u003c/span\u003e\u003cspan address=\"https://go.drugbank.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We then identified SNPs located within the target genes that were significantly associated with LDL-C and TG, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These genetic instruments were derived from the Global Lipids Genetics Consortium (GLGC) GWAS data on LDL-C, TG, which includes 1,320,658 European individuals.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] We selected SNPs within each target gene that exhibited genome-wide significant associations with LDL-C, TG (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;8) and the LD parameter was set at \u003cem\u003er\u003c/em\u003e2\u0026thinsp;\u0026lt;\u0026thinsp;0.2 within a range of 100 kb. We removed SNPs with palindromic structures to ensure the reliability of the results. The IVs we obtained were significantly associated with exposure (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) in \u003cem\u003ecis\u003c/em\u003e-expression Quantitative Trait Loci (\u003cem\u003ecis\u003c/em\u003e-eQTL).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSummary Information of lipid-lowering Drug Classes, Targets, and Encoding Genes.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrug target (Drug Bank)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEncoding genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGene region (in GRCh37 from Ensembl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDrug substance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASO targeting ApoB mRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emRNA of ApoB-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eApoB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003echr 2:21225354\u0026ndash;21266932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMipomersen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASO targeting CETP mRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCholesteryl ester transfer protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCETP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003echr 16:56996104\u0026ndash;57017662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTorcetrapib\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHMGCR\u003c/em\u003e inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHMG-CoA reductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHMGCR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003echr 5:74632193\u0026ndash;74657918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAtorvastatin Rosuvastatin etc.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKey Modulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLDL Receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLDLR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003echr 19:11200139\u0026ndash;11244496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC absorption inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNiemann-Pick C1-Like 1 (NPC1L1) protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNPC1L1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003echr 7:44553349\u0026ndash;44580706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEzetimibe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePCSK9\u003c/em\u003e inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eproprotein convertase subtilisin/kexin type 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePCSK9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003echr 1:55505371\u0026ndash;55530503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEvolocumab Alirocumab\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeroxisome Proliferator-Activated Receptor-alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePPARα\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003echr 22:46546429\u0026ndash;46639653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eFenofibrate Gemfibrozil\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: \u003cem\u003eApoB\u003c/em\u003e, Apoprotein B; \u003cem\u003eCETP\u003c/em\u003e, Cholesteryl Ester Transfer Protein; \u003cem\u003eHMGCR\u003c/em\u003e, 3-hydroxy-3-methylglutaryl coenzyme A reductase; \u003cem\u003eLDLR\u003c/em\u003e, Low-Density Lipoprotein Receptor; \u003cem\u003eNPC1L1\u003c/em\u003e, Niemann-Pick C1-like 1; \u003cem\u003ePCKS9\u003c/em\u003e, Proprotein Convertase Subtilisin/Kexin Type 9; \u003cem\u003ePPARα\u003c/em\u003e, Peroxisome ProliferatorActivated Receptor-alpha.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe calculated the \u003cem\u003eF\u003c/em\u003e-statistic for selected IVs and excluded SNPs with an \u003cem\u003eF\u003c/em\u003e-statistic\u0026thinsp;\u0026lt;\u0026thinsp;10 that represents minimal weak instrument bias. If the SNP was not present in the resultant GWAS, it was replaced with a surrogate SNP in the high LD (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.80) using SNiPA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://snipa.helmholtz-muenchen.de/snipa3/index.php\u003c/span\u003e\u003cspan address=\"https://snipa.helmholtz-muenchen.de/snipa3/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). If no suitable surrogate SNP was available, it was discarded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenetic instruments for inflammatory factors\u003c/h2\u003e \u003cp\u003eWe have chosen 91 inflammatory factors from an analysis of 11 cohorts, encompassing 14,824 individuals of European descent, the original publications detailed the entire procedure for measuring inflammatory factors.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Complete per-protein GWAS summary statistics can be downloaded at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.phpc.cam.ac.uk/ceu/proteins\u003c/span\u003e\u003cspan address=\"https://www.phpc.cam.ac.uk/ceu/proteins\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and the EBI GWAS Catalog (accession numbers GCST90274758 to GCST90274848).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcome data\u003c/h2\u003e \u003cp\u003eTaking into account the well-established benefits of lipid-lowering drugs on coronary heart disease, we performed a positive control analysis using coronary heart disease as the outcome data. The GWAS data for CHD were sourced from the IEU GWAS database (60,801 cases and 123,504 controls). Then, we selected GD, GO, AT, and AIH as the primary outcomes of our study. In our research, GWAS data for GD (3176 cases and 409,005controls), GO (598 cases and 411,583 controls), AT (539 cases and 349,717 controls) and AIH (45,321 cases and 298,847 controls) were obtained from the FinnGen database (Release 10), as described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eMR analysis to estimate the effects of lipid traits targets on AITD\u003c/h2\u003e \u003cp\u003eWe applied UVMR to assess the effects of lipid traits on AITD. The main analysis method is inverse variance weighting (IVW).[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Heterogeneity testing was used to determine whether to choose a random effects model or a fixed effects model for IVW. Specifically, when heterogeneity was observed (Q_\u003cem\u003epval\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;50%), the random effects model was selected as it provides more precise estimates and confidence interval (CI) than the fixed effects IVW method, and was tested using Cochran's Q, Otherwise, a fixed effects model is used. In addition to the IVW method, we also used four MR methods as supplementary analysis, namely MR-Egger, weighted median, weighted mode, and simple mode. Furthermore, to assess relative pleiotropy, the MR-Egger intercept test and MR pleiotropy residuals and outliers (MR-PRESSO) were used. Outlier SNPs were detected using the MR-PRESSO outlier test using a level \u003cem\u003ep\u003c/em\u003e index of 0.05. The MR results were evaluated using a leave-one-out approach to check their robustness.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMR analysis to estimate the effects of lipid-lowering drug targets on AITD\u003c/h2\u003e \u003cp\u003eFirst, we used CHD as a control outcome to evaluate the reliability of the extracted SNPs as alternatives to lipid-lowering drugs. IVW was also used for estimating the impact of genetic tools and lipid-lowering drugs on CHD. Subsequently, we continued to use IVW as the primary method, with the above 4 methods as complementary methods to determine the association between validated IVs and the risk of AITD. MR-Egger intercept test MR pleiotropy residuals and MR-PRESSO assessed pleiotropy. In addition to this, MR.RAPS provides our results with robust estimates corrected for systematic and idiosyncratic pleiotropies.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMediation MR analysis linking lipid-lowering drug targets with AITD via inflammatory factors\u003c/h2\u003e \u003cp\u003eTo evaluate the mediating role of 91 inflammatory factors on the relationship between lipid-lowering drug targets and AITD, we performed two-step MR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). First, we used UVMR to estimate the impacts of lipid-lowering drug targets on 91 inflammatory factors (\u003cem\u003eβ1\u003c/em\u003e). We selected \u003cem\u003ecis\u003c/em\u003e-eQTL genetic variation as IV, gene expression as exposure, and 91 inflammatory factors as outcomes for MR analysis. Then, we selected inflammatory factors significantly correlated with gene expression as exposures to conduct MR analysis (\u003cem\u003eβ2\u003c/em\u003e) on AT, GD, and AIH respectively. Since the number of SNPs in some inflammatory factors is small, we selected SNPs that were significantly associated with inflammatory factors at the genome-wide level (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) as the corresponding IVs. The LD parameter was set to \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 within 100 kb. To note, the IVs variables of the two-step MR analysis cannot be repeated, so the IVs used in the second step need to exclude those used in the first step. Finally, the mediating proportion of each inflammatory factor in the association between the lipid-lowering drug target and AITD was calculated as the product of \u003cem\u003eβ1\u003c/em\u003e and \u003cem\u003eβ2\u003c/em\u003e divided by the total effect of the lipid-lowering drug targets on AITD. The 95% CI for the mediation proportion was calculated using the delta method.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eWe used the intercept term of the MR-Egger regression to represent the mean pleiotropy of IVs, and the likelihood of horizontal pleiotropy was estimated using MR-Egger regression. In addition, we used MR-PRESSO as a supplement to assess horizontal pleiotropy.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] The purpose of detecting horizontal multivariate validity, correcting horizontal multivariate validity by removing outliers, and determining whether the causal effects have substantially changed before and after removing outliers in MR analysis can all be achieved through MR-PRESSO. To improve the accuracy and robustness of the genetic instrument, we quantified heterogeneity using Cochran's Q statistic, where \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicates no effect heterogeneity.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSelection and validation of genetic instruments\u003c/h2\u003e \u003cp\u003eBy applying the thresholds we set in the method analysis, among 88,329 European individuals, 44 SNPs represented ApoB, 46 SNPs represented LDL-C, 49 SNPs represented TC, and 55 SNPs represented TG. In addition, we selected 12 SNPs proxied \u003cem\u003eApoB\u003c/em\u003e, 6 SNPs proxied \u003cem\u003eCETP\u003c/em\u003e, 6 SNPs proxied \u003cem\u003eHMGCR\u003c/em\u003e, 13 SNPs proxied \u003cem\u003eLDLR\u003c/em\u003e, 5 SNPs proxied \u003cem\u003eNPC1L1\u003c/em\u003e, 11 SNPs proxied \u003cem\u003ePCSK9\u003c/em\u003e and 3 SNPs proxied \u003cem\u003ePPARα\u003c/em\u003e in 1,320,658 European individuals. Among the IVs studied, \u003cem\u003eF\u003c/em\u003e-statistics ranged from 29.9 to 9740.0, suggesting that weak instrumental bias has little impact on our analysis. We then performed UVMR analyses of lipid-lowering drug targets and CHD using gene proxies with CHD as positive controls. All lipid-lowering drug target genes involved in this study showed significant associations with CHD risk. No significant heterogeneity or multiple effects were observed in the results, suggesting that these genetic tools are effective. Details of all included SNPs can be found in Supplementary Tables\u0026nbsp;1 and 2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of lipid traits with genetic proxies for AITD\u003c/h2\u003e \u003cp\u003eWe conducted a two-sample MR analysis on the association between lipid traits (including ApoB, LDL-C, TG, and TC) and AITD. Although no evidence of pleiotropy was detected in our results, the presence of heterogeneity was observed. Therefore, the IVW model was conducted using random effects. We found that there was no clear causality between ApoB, LDL-C, TC, TG, and AITD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of lipid-lowering drugs targets with genetic proxies for AITD\u003c/h2\u003e \u003cp\u003eIn our preliminary analyses using the IVW approach, we observed strong evidence that LDL-C-derived \u003cem\u003eApoB\u003c/em\u003e inhibition (OR\u0026thinsp;=\u0026thinsp;0.462, 95% CI\u0026thinsp;=\u0026thinsp;0.216,0.986; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046) reduced the risk of AT and that \u003cem\u003ePCSK9\u003c/em\u003e inhibition (OR\u0026thinsp;=\u0026thinsp;0.551. 95% CI\u0026thinsp;=\u0026thinsp;0.319,0.953; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033) reduced the risk of GD, while \u003cem\u003ePCSK9\u003c/em\u003e inhibition (OR\u0026thinsp;=\u0026thinsp;0.735, 95% CI\u0026thinsp;=\u0026thinsp;0.598,0.903; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) were also found to reduce the risk of AIH. In addition to this, we found that both \u003cem\u003eLDLR\u003c/em\u003e inhibition (OR\u0026thinsp;=\u0026thinsp;0.779, 95% CI\u0026thinsp;=\u0026thinsp;0.624,0.972; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) and \u003cem\u003eNPC1L1\u003c/em\u003e inhibition (OR\u0026thinsp;=\u0026thinsp;0.599, 95% CI\u0026thinsp;=\u0026thinsp;0.412,0.872; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) similarly reduced the risk of AIH (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). No pleiotropy or heterogeneity was found for any of the above gene inhibitors (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMR analyses of lipid-lowering drugs on AITD by different methods.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR.RAPS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eApoB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.462 (0.216,0.986)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003cp\u003e(0.216,0.986)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePCSK9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.551 (0.319,0.953)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003cp\u003e(0.318,0.955)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePCSK9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.735 (0.598,0.903)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003cp\u003e(0.624,0.863)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLDLR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.779 (0.624,0.972)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003cp\u003e(0.644,0.939)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNPC1L1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.599 (0.412,0.872)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003cp\u003e(0.409,0.874)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations:MR, Mendelian randomization; OR, odds ratio; CI, confidence interval; MR.RAPS, Mendelian randomization robust adjusted profile score. OR, 95% CI, and \u003cem\u003ep\u003c/em\u003e-values were calculated for the respective method of MR analysis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple tests of \u003cem\u003eApoB, PCSK9, LDLR, NPC1L1\u003c/em\u003e gene inhibition, and AITD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure(inhibition)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCochran Q value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ-\u003cem\u003epval\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR-Egger Intercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMR-PRESSO \u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eApoB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.13E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePCSK9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.41E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePCSK9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLDLR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.74E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNPC1L1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.55E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe heterogeneity test in the IVW method was performed using Cochran\u0026rsquo;s Q statistic and the global test for the MR-PRESSO method. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant IVW, inverse\u0026ndash;variance weighted; \u003cem\u003ep\u003c/em\u003e-heterogeneity, \u003cem\u003ep-\u003c/em\u003evalue for heterogeneity test; \u003cem\u003ep\u003c/em\u003e-intercept, \u003cem\u003ep-\u003c/em\u003evalue for the intercept of MR-Egger regression.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMediation MR of lipid-lowering drug targets, inflammatory factors and AITD\u003c/h2\u003e \u003cp\u003eWe estimated the impacts of lipid-lowering drug targets on 91 inflammatory factors and observed that a total of 30 inflammatory factors were significantly associated with \u003cem\u003eApoB\u003c/em\u003e inhibition, \u003cem\u003eNPC1L1\u003c/em\u003e inhibition, and \u003cem\u003ePCSK9\u003c/em\u003e inhibition, respectively (Supplemental Table\u0026nbsp;4). We did not observe a significant correlation of inflammatory factors on \u003cem\u003eLDLR\u003c/em\u003e inhibition.\u003c/p\u003e \u003cp\u003eWe further estimated the effects of 30 inflammatory factors significantly associated with lipid-lowering drug targets on AITD and found that 3 inflammatory factors were significantly associated with AIH and one inflammatory factor was significantly associated with GD (Supplementary Table\u0026nbsp;5). We observed a significant correlation between Interleukin-4 (IL-4) levels (OR\u0026thinsp;=\u0026thinsp;1.119, 95% CI\u0026thinsp;=\u0026thinsp;1.020,1.228; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018), Osteoprotegerin levels (OR\u0026thinsp;=\u0026thinsp;0.924, 95% CI\u0026thinsp;=\u0026thinsp;0.862,0.992; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029), Fibroblast growth factor-19 (FGF-19) levels (OR\u0026thinsp;=\u0026thinsp;0.934, 95% CI\u0026thinsp;=\u0026thinsp;0.890,0.980; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and AIH; Tumor necrosis factor-beta (TNF-β) levels (OR\u0026thinsp;=\u0026thinsp;1.142, 95% CI\u0026thinsp;=\u0026thinsp;1.029,1.268; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) was significantly associated with GD. There was no evidence of horizontal pleiotropy, and although some of the results were heterogeneous, we used a random effects IVW approach for analysis.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] The IVs for the 30 inflammatory factors were all strong (\u003cem\u003eF\u003c/em\u003e-statistics\u0026thinsp;\u0026gt;\u0026thinsp;38.55) (Supplemental Table\u0026nbsp;6).\u003c/p\u003e \u003cp\u003eWe found that \u003cem\u003eNPC1L1\u003c/em\u003e inhibition through IL-4 levels had an indirect effect on AIH, with a mediated proportion of the total effect of 25.64% (95% CI\u0026thinsp;=\u0026thinsp;0.139%,64.579%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008); \u003cem\u003ePCSK9\u003c/em\u003e inhibition through FGF- 19 levels have an indirect effect on AIH, and the mediated proportion of the total effect is -6.84% (95% CI =-16.141%,-0.477%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036); \u003cem\u003ePCSK9\u003c/em\u003e inhibition has an indirect effect on GD through TNF-β levels, and the mediated proportion of the total effect of 9.72% (95% CI\u0026thinsp;=\u0026thinsp;0.047%,24.340%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045). However, we observed that although Osteoprotegerin levels were significantly related to AIH, the 95% CI crossed the invalid line (95% CI\u0026thinsp;=\u0026thinsp;0.003%, -24.471%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.625), indicating that the mediating effect of this result was not established (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we systematically evaluated the causal relationship between 4 blood lipid traits,7 lipid-lowering gene inhibitors, 91 inflammatory factors, and the risk of AITD through drug-targeted MR analysis and mediation MR analysis. There was no clear causality between circulating lipids and AITD. \u003cem\u003eApoB\u003c/em\u003e inhibition is related to a reduced risk of AT, while \u003cem\u003ePCSK9\u003c/em\u003e inhibition is related to reduced GD risk. Moreover, \u003cem\u003ePCSK9\u003c/em\u003e inhibition, \u003cem\u003eLDLR\u003c/em\u003e inhibition, and \u003cem\u003eNPC1L1\u003c/em\u003e inhibition reduced the risk of AIH. Mediation analysis indicated that the effect of \u003cem\u003eNPC1L1\u003c/em\u003e inhibition and \u003cem\u003ePCSK9\u003c/em\u003e inhibition on AIH through IL-4 and FGF-19 levels. And the effect of \u003cem\u003ePCSK9\u003c/em\u003e inhibition on GD through TNF-β levels.\u003c/p\u003e \u003cp\u003eThyroid hormone plays a crucial role in the modulation of energy metabolism.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] The causal relationship that thyroid dysfunction caused dyslipidemia is a well-accepted clinical finding.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Interestingly, some recent studies have demonstrated that lipotoxicity resulted in the pathogenesis of multiple diseases, including thyroid dysfunction and immune disorders.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] A prospective cohort study showed that the subclinical hypothyroid patients with hypercholesterolemia were more vulnerable to developing overt hypothyroidism during a 3-year follow-up.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] Statins, the most commonly used of the lipid-lowering drugs, have been observed to be correlate with reduced GO risk in patients with Graves' hyperthyroidism.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Based on the clinical correlation of the interplay between dyslipidemia and AITD, the association between lipid and lipid-lowering drugs with AITD deserves further exploration. The present study showed that there was no clear causality between circulating lipids and AITD, however, lipid-lowering targets reduced the AITD risk. Therefore, the underlying mechanisms may extend beyond the lipid-lowering effect.\u003c/p\u003e \u003cp\u003eFurther mediation MR analysis found the effect of \u003cem\u003eNPC1L1\u003c/em\u003e inhibition and \u003cem\u003ePCSK9\u003c/em\u003e inhibition on AIH through IL-4 and FGF-19 levels. And the effect of \u003cem\u003ePCSK9\u003c/em\u003e inhibition on GD through TNF-β levels. Fortunately, the anti-inflammatory effects of lipid-lowering drug target gene inhibitors have long been demonstrated in other studies. Combining ezetimibe with a statin is a more effective way to lower CRP levels.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] \u003cem\u003ePCSK9\u003c/em\u003e inhibition also exerts anti-inflammatory effects and is positively correlated with levels of inflammatory biomarkers such as leukocytes, hsCRP, and fibrinogen.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] The occurrence and development of AITD are also closely related to inflammatory factors. The key to the occurrence of AITD is the activation of T cells,[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and the generation of IgG1 isotype response is stimulated by Th1 cytokines,[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] which is the main pathogenic TSH receptor autoantibody observed in AITD.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] IL-4 can stimulate the expression of HLA class II antigens and oppose Th1 cell inflammatory responses through signal transducer and activator of transcription 6 (\u003cem\u003eSTAT6\u003c/em\u003e).[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] A cohort study demonstrated that patients with AITD had lower overall IL-4 activity, which may contribute to the propensity to produce IgG1 autoantibodies.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] However, in another study it was demonstrated that ectopic expression of IL-4 in thyroid tissue increases the incidence of spontaneous AT, the eventual evolution of which can lead to hypothyroidism.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] Furthermore, FGF-19, a promising lipid modulator, exhibited a notable decrease in the serum of individuals suffering from hypothyroidism and subclinical hypothyroidism.[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] This may be related to the fact that TSH triggers hepatic sterol regulatory element-binding protein (SREBP) through its receptor to negatively regulate the transcription of FGF19 in human intestinal cells.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] However, the impact of FGF-19 on AITD has not been completely confirmed. TNF-β exerts a crucial influence on regulating inflammatory responses, apoptosis, and immune cell activity. TNF-β alleles may mark a specific immune response state with altered immune responses to mitogens and suppressor T cells and may contribute to the development of GD in a predisposing manner. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] Interestingly, a positive association between high levels of TNF-β and GD risk was also observed in a recent MR analysis.[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eMR analysis is a natural randomized controlled trial that studies large sample sizes, minimizes confounding and reverse causation, and provides accuracy and convenience. We are the first to examine the relationship between lipid-lowering drugs and AITD and to provide evidence of a protective effect of some lipid-lowering drugs against AITD. This concept will help expand the therapeutic use of lipid-lowering medications. However, our study has some limitations. First, drug-targeted MR analysis cannot capture the short-term effects of lipid-lowering drugs. Second, the databases we used were not stratified by sex, age, or disease severity. Finally, this study utilized European population data, raising uncertainty regarding generalizability to other ethnic groups. Further investigation through laboratory studies and clinical trials is necessary to confirm and elucidate these findings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThere was no clear causality between circulating lipids (ApoB, LDL-C, TC, and TG) and AITD. Lipid-lowering drug target gene inhibitors reduced the AITD risk by modulating inflammatory factors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIH \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Autoimmune hypothyroidism\u003c/p\u003e\n\u003cp\u003eAITD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Autoimmune thyroid disease\u003c/p\u003e\n\u003cp\u003eApoB/\u003cem\u003eApoB\u003c/em\u003e\u0026nbsp; \u0026nbsp;Apolipoprotein B\u003c/p\u003e\n\u003cp\u003eAT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Autoimmune thyroiditis\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCETP\u003c/em\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cholesteryl Ester Transfer Protein\u003c/p\u003e\n\u003cp\u003eCHD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Coronary heart disease\u003c/p\u003e\n\u003cp\u003eCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Confidence interval\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ecis\u003c/em\u003e-eQTL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cem\u003ecis\u003c/em\u003e-expression Quantitative Trait Loci\u003c/p\u003e\n\u003cp\u003eFGF-19\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Fibroblast growth factor-19\u003c/p\u003e\n\u003cp\u003eGD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Graves\u0026apos; disease\u003c/p\u003e\n\u003cp\u003eGLGC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Global Lipids Genetics Consortium\u003c/p\u003e\n\u003cp\u003eGO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Graves\u0026apos; ophthalmopathy\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHMGCR\u003c/em\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3-hydroxy-3-methylglutaryl coenzyme A reductase\u003c/p\u003e\n\u003cp\u003eIL-4\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Interleukin-4\u003c/p\u003e\n\u003cp\u003eIVs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Instrumental variables\u003c/p\u003e\n\u003cp\u003eIVW\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Inverse variance weighting\u003c/p\u003e\n\u003cp\u003eLD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Linkage disequilibrium\u003c/p\u003e\n\u003cp\u003eLDL-C \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Low-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eLDLR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Low-Density Lipoprotein Receptor\u003c/p\u003e\n\u003cp\u003eMR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mendelian randomization\u003c/p\u003e\n\u003cp\u003eMR-PRESSO \u0026nbsp; \u0026nbsp;Mendelian randomization pleiotropy residuals and outliers\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNPC1L1\u003c/em\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Niemann-Pick C1-Like1\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePCSK9\u003c/em\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Proprotein Convertase Subtilisin/Kexin Type 9\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePPAR\u0026alpha; \u0026nbsp;\u003c/em\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Peroxisome Proliferator Activated Receptor-alpha\u003c/p\u003e\n\u003cp\u003eSNP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Single nucleotide polymorphism\u003c/p\u003e\n\u003cp\u003eSREBP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sterol Regulatory Element-binding Protein\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSTAT6\u003c/em\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Signal transducer and activator of transcription 6\u003c/p\u003e\n\u003cp\u003eSTROBE-MR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization\u003c/p\u003e\n\u003cp\u003eTC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Total cholesterol\u003c/p\u003e\n\u003cp\u003eTG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Triglyceride\u003c/p\u003e\n\u003cp\u003eTNF-\u0026beta;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tumor necrosis factor-beta\u003c/p\u003e\n\u003cp\u003eUVMR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Univariable Mendelian Randomization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of this study are included in this article and supporting documents. The FinnGen database is an open access resource and researchers need to obtain approval from the FinnGen database (https://www.finngen.fi/fi). Lipid-related data come from the IEU database (https://gwas.mrcieu.ac.uk/), GWAS Catalog database (https://www.ebi.ac.uk/gwas/) and GLGC database (http:// www.lipidgenetics.org/#data-downloads-title), the above databases are all open access resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Chinese National Natural Science Foundation (No. 82072527), Beijing Natural Science Foundation Z200019 to J.L and Beijing Hospitals Authority\u0026rsquo;s Ascent Plan (DFL20220302) to G.W.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChang Su handled conceptualization, data collection, data curation, supervision, and visualization, conducted formal analysis, and wrote an original draft. Juan Tian oversaw data collection, data curation, and supervision. Xueqing He and Xiaona Chang were involved in data collection, data curation, and supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all participating researchers for their contribution to the article. The authors thank Professor Liu Jia and Professor Wang Guang for their careful guidance in this research and for reviewing and revising the article. The authors thank the Chinese National Natural Science Foundation, Beijing Natural Science Foundation, and Beijing Hospitals Authority\u0026rsquo;s Ascent Plan for funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Disclosure Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFerrari SM, Fallahi P, Elia G, Ragusa F, Camastra S, Paparo SR, et al. Novel therapies for thyroid autoimmune diseases: An update. Best Pract Res Clin Endocrinol Metab. 2020;34:101366.\u003c/li\u003e\n\u003cli\u003eHollowell JG, Staehling NW, Flanders WD, Hannon WH, Gunter EW, Spencer CA, et al. Serum TSH, T \u003csub\u003e4\u003c/sub\u003e , and Thyroid Antibodies in the United States Population (1988 to 1994): National Health and Nutrition Examination Survey (NHANES III). 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JAMA. 2021;326:1614.\u003c/li\u003e\n\u003cli\u003eDavyson E, Shen X, Gadd DA, Bernabeu E, Hillary RF, McCartney DL, et al. Metabolomic Investigation of Major Depressive Disorder Identifies a Potentially Causal Association With Polyunsaturated Fatty Acids. Biol Psychiatry. 2023;94:630\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eMach F, Baigent C, Catapano AL, Koskinas KC, Casula M, Badimon L, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J. 2020;41:111\u0026ndash;88.\u003c/li\u003e\n\u003cli\u003eWiller CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, et al. Discovery and Refinement of Loci Associated with Lipid Levels. Nat Genet. 2013;45:1274\u0026ndash;83.\u003c/li\u003e\n\u003cli\u003eZhao JH, Stacey D, Eriksson N, Macdonald-Dunlop E, Hedman \u0026Aring;K, Kalnapenkis A, et al. Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets. Nat Immunol. 2023;24:1540\u0026ndash;51.\u003c/li\u003e\n\u003cli\u003eBowden J, Del Greco M F, Minelli C, Davey Smith G, Sheehan N, Thompson J. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med. 2017;36:1783\u0026ndash;802.\u003c/li\u003e\n\u003cli\u003eLin D, Zhu Y, Tian Z, Tian Y, Liang C, Peng X, et al. Causal associations between gut microbiota, gut microbiota-derived metabolites, and cerebrovascular diseases: a multivariable Mendelian randomization study. Front Cell Infect Microbiol. 2023;13:1269414.\u003c/li\u003e\n\u003cli\u003eHuang A, Wu X, Lin J, Wei C, Xu W. Genetic insights into repurposing statins for hyperthyroidism prevention: a drug-target Mendelian randomization study. Front Endocrinol. 2024;15:1331031.\u003c/li\u003e\n\u003cli\u003eVerbanck M, Chen C-Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eCausal association between celiac disease and inflammatory bowel disease: A two-sample bidirectional Mendelian randomization study - PMC. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9845610/. Accessed 5 Apr 2024.\u003c/li\u003e\n\u003cli\u003eMullur R, Liu Y-Y, Brent GA. Thyroid Hormone Regulation of Metabolism. Physiol Rev. 2014;94:355\u0026ndash;82.\u003c/li\u003e\n\u003cli\u003eDuntas LH. Thyroid Disease and Lipids. Thyroid\u0026reg;. 2002;12:287\u0026ndash;93.\u003c/li\u003e\n\u003cli\u003eLi X, Zhen D, Zhao M, Liu L, Guan Q, Zhang H, et al. Natural history of mild subclinical hypothyroidism in a middle-aged and elderly Chinese population: a prospective study. Endocr J. 2017;64:437\u0026ndash;47.\u003c/li\u003e\n\u003cli\u003eMarin\u0026ograve; M, Lanzolla G, Marcocci C. Statins: A New Hope on the Horizon of Graves Orbitopathy? J Clin Endocrinol Metab. 2021;106:e2819\u0026ndash;21.\u003c/li\u003e\n\u003cli\u003ePearson T, Ballantyne C, Sisk C, Shah A, Veltri E, Maccubbin D. Comparison of effects of ezetimibe/simvastatin versus simvastatin versus atorvastatin in reducing C-reactive protein and low-density lipoprotein cholesterol levels. Am J Cardiol. 2007;99:1706\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003ePearson TA, Ballantyne CM, Veltri E, Shah A, Bird S, Lin J, et al. Pooled analyses of effects on C-reactive protein and low density lipoprotein cholesterol in placebo-controlled trials of ezetimibe monotherapy or ezetimibe added to baseline statin therapy. Am J Cardiol. 2009;103:369\u0026ndash;74.\u003c/li\u003e\n\u003cli\u003eLi S, Zhang Y, Xu R-X, Guo Y-L, Zhu C-G, Wu N-Q, et al. Proprotein convertase subtilisin-kexin type 9 as a biomarker for the severity of coronary artery disease. Ann Med. 2015;47:386\u0026ndash;93.\u003c/li\u003e\n\u003cli\u003eWeetman AP, McGregor AM. Autoimmune thyroid disease: further developments in our understanding. Endocr Rev. 1994;15:788\u0026ndash;830.\u003c/li\u003e\n\u003cli\u003eAbbas AK, Murphy KM, Sher A. Functional diversity of helper T lymphocytes. Nature. 1996;383:787\u0026ndash;93.\u003c/li\u003e\n\u003cli\u003eWeetman AP, Black CM, Cohen SB, Tomlinson R, Banga JP, Reimer CB. Affinity purification of IgG subclasses and the distribution of thyroid auto-antibody reactivity in Hashimoto\u0026rsquo;s thyroiditis. Scand J Immunol. 1989;30:73\u0026ndash;82.\u003c/li\u003e\n\u003cli\u003eKuppers RC, Outschoorn IM, Hamilton RG, Burek CL, Rose NR. Quantitative measurement of human thyroglobulin-specific antibodies by use of a sensitive enzyme-linked immunoassay. Clin Immunol Immunopathol. 1993;67:68\u0026ndash;77.\u003c/li\u003e\n\u003cli\u003eKelso A. Cytokines: principles and prospects. Immunol Cell Biol. 1998;76:300\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003eHunt PJ, Marshall SE, Weetman AP, Bell JI, Wass JA, Welsh KI. Cytokine gene polymorphisms in autoimmune thyroid disease. J Clin Endocrinol Metab. 2000;85:1984\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eMerakchi K, Djerbib S, Dumont J-E, Miot F, De Deken X. Severe Autoimmune Thyroiditis in Transgenic NOD.H2h4 Mice Expressing Interleukin-4 in the Thyroid. Thyroid Off J Am Thyroid Assoc. 2023;33:351\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eCrunkhorn S. Metabolic disorders: Betatrophin boosts \u0026beta;-cells. Nat Rev Drug Discov. 2013;12:504.\u003c/li\u003e\n\u003cli\u003eLai Y, Wang H, Xia X, Wang Z, Fan C, Wang H, et al. Serum fibroblast growth factor 19 is decreased in patients with overt hypothyroidism and subclinical hypothyroidism. Medicine (Baltimore). 2016;95:e5001.\u003c/li\u003e\n\u003cli\u003eMiyata M, Hata T, Yamazoe Y, Yoshinari K. SREBP-2 negatively regulates FXR-dependent transcription of FGF19 in human intestinal cells. Biochem Biophys Res Commun. 2014;443:477\u0026ndash;82.\u003c/li\u003e\n\u003cli\u003eYan F, Wang Q, Lu M, Chen W, Song Y, Jing F, et al. Thyrotropin increases hepatic triglyceride content through upregulation of SREBP-1c activity. J Hepatol. 2014;61:1358\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eHashimoto S, McCombs CC, Michalski JP. Mechanism of a lymphocyte abnormality associated with HLA-B8/DR3 in clinically healthy individuals. Clin Exp Immunol. 1989;76:317\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eAmbinder JN, Chiorazzi N, Gibofsky A, Fotino M, Kunkel HG. Special characteristics of cellular immune function in normal individuals of the HLA-DR3 type. Clin Immunol Immunopathol. 1982;23:269\u0026ndash;74.\u003c/li\u003e\n\u003cli\u003eYao Z, Guo F, Tan Y, Zhang Y, Geng Y, Yang G, et al. Causal relationship between inflammatory cytokines and autoimmune thyroid disease: a bidirectional two-sample Mendelian randomization analysis. Front Immunol. 2024;15:1334772.\u003c/li\u003e\n\u003c/ol\u003e"}],"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":"Mendelian randomization, Drug targeting, Thyroid autoimmune disease, Lipid trait, Inflammatory factors","lastPublishedDoi":"10.21203/rs.3.rs-4428352/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4428352/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eDyslipidemia has been implicated in the pathogenesis of several diseases, including thyroid dysfunction and immune disorders. However, whether circulating lipids and long-term use of lipid-lowering drugs influence the development of autoimmune thyroid disease (AITD) remains unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eTwo-sample and two-step Mendelian randomization (MR) studies were performed to assess the causal relationships between circulating lipids (LDL-C, TC, TG, and ApoB) and seven lipid-lowering drug targets (\u003cem\u003eApoB\u003c/em\u003e, \u003cem\u003eCETP\u003c/em\u003e, \u003cem\u003eHMGCR\u003c/em\u003e, \u003cem\u003eLDLR\u003c/em\u003e, \u003cem\u003eNPC1L1\u003c/em\u003e, \u003cem\u003ePCSK9,\u003c/em\u003e and \u003cem\u003ePPARα\u003c/em\u003e) with AITD. Mediation analyses were conducted to explore potential mediating factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThere was no clear causality between circulating lipids (ApoB, LDL-C, TC, and TG) and AITD (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). \u003cem\u003eApoB\u003c/em\u003e inhibition is related to a reduced risk of autoimmune thyroiditis (AT) (OR = 0.462, \u003cem\u003ep\u003c/em\u003e= 0.046), while \u003cem\u003ePCSK9\u003c/em\u003e inhibition is related to reduced Graves' disease (GD) risk (OR = 0. 551, \u003cem\u003ep \u003c/em\u003e= 0.033). Moreover, \u003cem\u003ePCSK9\u003c/em\u003e inhibition (OR = 0.735, \u003cem\u003ep\u003c/em\u003e = 0.003), \u003cem\u003eLDLR\u003c/em\u003e inhibition (OR = 0.779, \u003cem\u003ep \u003c/em\u003e= 0.027), and \u003cem\u003eNPC1L1\u003c/em\u003e inhibition (OR = 0.599, \u003cem\u003ep\u003c/em\u003e = 0.016) reduced the risk of autoimmune hypothyroidism (AIH). Mediation analysis showed that \u003cem\u003eNPC1L1\u003c/em\u003e inhibition and \u003cem\u003ePCSK9\u003c/em\u003e inhibition exerted effects on AIH through IL-4 and FGF-19 levels. And the effect of \u003cem\u003ePCSK9\u003c/em\u003einhibition on GD through TNF-β levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThere was no clear causality between circulating lipids (ApoB, LDL-C, TC, and TG) and AITD. Lipid-lowering drug target gene inhibitors reduced the AITD risk by modulating inflammatory factors.\u003c/p\u003e","manuscriptTitle":"Novel insights into causal effects of lipid and lipid-lowering targets with autoimmune thyroid disease: A Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-24 11:59:32","doi":"10.21203/rs.3.rs-4428352/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":"cbc8d5fe-5c92-454b-a881-e782ce2f6562","owner":[],"postedDate":"May 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-24T11:59:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-24 11:59:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4428352","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4428352","identity":"rs-4428352","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}