Genetic association of lipids and lipid-lowering drug targets with the risk of type 1 diabetes and its complications

Genetic association of lipids and lipid-lowering drug targets with the risk of type 1 diabetes and its complications | 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 Genetic association of lipids and lipid-lowering drug targets with the risk of type 1 diabetes and its complications Haocheng Wang, Zirui Liu, Zhengkai Yang, Yu Lu, Cao Zou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4537908/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 Purpose To evaluate causal effects of lipid traits and lipid-lowering drug targets on the risk of type 1 diabetes (T1D) and its complications. Methods Our study conducted two-sample and drug-target Mendelian randomization (MR) to assess the genetic association of lipid traits and lipid-lowering drug targets with the type 1 diabetes risk, respectively. For significant lipid-modifying drug targets, data for expressions in tissues and colocalization provided extra evidence for causality. We also explored underlying mechanisms through mediation MR. Results The two-sample MR analyses detected no causal association between lipid traits and T1D. In the drug-target MR analyses, ANGPTL3 inhibitor was associated with a decreased risk of T1D (OR = 0.668, 95% CI: 0.511–0.874, P = 3.21*10 − 3 ), of which BMI mediated 5.71% of the total effect. This was validated through multiple sensitivity analyses, replication dataset and tissue sample data. Moreover, ANGPTL3 inhibitor was also found to reduce the risk of diabetic kidney diseases. Although HMGCR inhibitor reduced the risk of T1D in the primary dataset, it was not validated in the replication dataset, and HMGCR inhibitor showed adverse effects on diabetic retinopathy and neuropathy. Conclusion Circulating lipids are not causally associated with the risk of T1D. ANGPTL3 inhibitor, a novel lipid-lowering drug, may be a promising candidate for treating T1D and its renal complication, with BMI probably mediating part of the effect. Dyslipidemias lipid-lowering drug therapy type 1 diabetes mendelian randomization expression quantitative trait locus colocalization Figures Figure 1 Figure 2 Figure 3 1. Introduction Type 1 diabetes (T1D) is a chronic T-cell mediated autoimmune disease [1], the major pathophysiological hallmark of which refers to complete insulin deficiency due to the destruction of beta cells in the pancreatic islet [2]. T1D is one of the most prevalent autoimmune diseases, affecting over 9 million people around the world. In Europe, T1D accounts for approximately 10 percent of overall diabetes and its incidence rate has been increasing annually [3]. Uncontrolled patients tend to suffer from ketosis and permanent hyperglycemia. Progressive ketosis may trigger fatal ketoacidosis and persistent hyperglycemia is likely to contribute to a series of complications including retinal, renal, vascular and neurological lesions, bringing about a substantial decline in survival and the quality of life [4]. To date, although several new medications and therapies for T1D have been experimented with, insulin replacement therapy is still the first-line and primary treatment [5, 6]. However, due to low compliance, potential risk of hypoglycemia and the incapacity to forestall complications [7, 8], injected insulin may fail to replace physiological insulin secretion completely, which emphasizes the need for new therapies. Previous observational studies have investigated the relationship between dyslipidemia and T1D, suggesting that abnormal lipid metabolism may be related to an increase in the T1D risk [9–12]. Due to the potential confounding bias and reverse causation in observational studies, the causal association between circulating lipids and T1D is elusive and remains to be verified. Moreover, considering the underlying effects of dyslipidemia on the risk of T1D, it may be worthy of discussion if lipid-lowering drugs can be utilized as new repurposing candidates for T1D. Statin and other novel lipid-modifying drugs are extensively used for the prevention and treatment of cardiovascular complications in T1D patients [13] whereas few studies have focused on their efficiency in T1D. Concerns are also raised that the utilization of Statin may deteriorate glucose control in T1D patients [14, 15], which further complicates their relationships. Mendelian randomization (MR) is a new statistical approach using genetic variants as instruments to appraise the causal association between the exposure and the outcome [16]. As the genetic variants are randomly allocated in meiosis and remain unaffected by external interference after birth, MR is capable of mimicking the randomized controlled trial (RCT) and overcoming limitations of observational studies such as reverse causation and confounding biases. Previous drug target MR studies have investigated the causal effects of lipid-lowering drugs on type 2 diabetes (T2D) [17, 18]. However, considering different pathogenic mechanisms, pathological changes and therapeutic methods between T1D and T2D [19], the relationship between lipid-lowering drugs and the T1D risk requires to be corroborated. Hence, we performed the first drug-target MR analysis to disentangle the causal effects of lipid traits on T1D and investigate whether lipid-lowering drugs are effective for the treatment of T1D and its complications. 2. Materials and Methods 2.1 Study design Figure 1 . presents the outline of our study. The MR analysis was used to evaluate the causal effects of genetic predisposition to lipid traits and genetic proxies for lipid-lowering drug targets on the risk of T1D and its complications in the European population. The MR method satisfied the following three fundamental hypotheses [20]: 1. The selected instrumental variables (IVs), namely single nucleotide polymorphisms (SNPs), should be strongly correlated with the exposure; 2. The IVs cannot be in connection with any possible confounding factors of the outcome; 3. The IVs cannot be directly associated with the outcomes (Online Resource 2 Fig S1 ). 2.2 Data source Summary GWAS statistics for lipid traits including HDL cholesterol (HDL-C), LDL cholesterol (LDL-C) and Triglycerides (TG) were obtained from Global Lipids Genetics Consortium[21], and genetic data for apolipoprotein A1 (ApoA1) and apolipoprotein B (ApoB) came from an online GWAS [22]. Summary-level data of the outcome, T1D, was extracted from the latest and largest-scale GWAS dataset meta-analyzing Finngen, UK Biobank and other 7 cohorts[23], consisting of 18,942 cases and 501,638 controls of European ancestry. Genetic statistics for T1D were also derived from another GWAS dataset for replication analyses, comprising 2,292 T1D cases and 324,074 European ancestry controls [24]. Moreover, statistical data for complications in T1D was obtained from the Finngen database (Round 9) [25], including diabetic kidney disease (DKD), diabetic retinopathy (DR) and diabetic neuropathy (DN) (Table 1 ). Table 1 Detailed information of the GWAS in the MR analysis. Trait GWAS ID PMID Author Cases(n) Controls(n) Sample size(n) HDL-C ieu-a-299 24097068 Willer CJ - - 187,167 LDL-C ieu-a-300 24097068 Willer CJ - - 173,082 TG ieu-a-302 24097068 Willer CJ - - 177,861 TC ieu-a-301 24097068 Willer CJ - - 187,365 ApoA1 met-c-842 27005778 Kettunen J - - 20,687 ApoB met-c-843 27005778 Kettunen J - - 20,690 T1DM GCST90014023 34012112 Chiou J 18,942 501,638 520,580 T1DM GCST90014465 34278373 Glanville KP 2,292 324,074 326,366 CAD ieu-a-7 26343387 Nikpay M 60,801 123,504 184,305 DKD in T1DM - 36653562 Kurki MI 1579 308280 309859 DR in T1DM - 36653562 Kurki MI 5202 308280 313482 DN in T1DM - 36653562 Kurki MI 1077 308280 309357 Abbreviations: HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TG, Triglycerides; TC, Total cholesterol; ApoA1, Apolipoprotein A1; ApoB, Apolipoprotein B; T1DM, type 1 diabetes mellitus; CAD, coronary artery disease; DKD, diabetic kidney disease; DR: diabetic retinopathy; DN: diabetic neuropathy; EUR, European. 2.3 Selection of IVs According to the fundamental hypothesis 1, SNPs robustly correlated with lipid traits (P < 5*10 − 8 ) were identified as the instrumental variables of circulating lipids and were further clumped to avoid the possible linkage disequilibrium (LD) (r 2 5*10 − 8 ) were excluded to satisfy the hypothesis 3 mentioned above. Moreover, we introduced the F-statistics to assess the strength of the association between IVs and lipid traits. IVs with low F-statistics (F < 10) were excluded from further analyses to minimize the influence of weak IV bias. According to recent reviews and guidelines for managing lipid disorders [26–28], eleven common lipid-lowering drug targets were involved in our study and were distinguished into LDL-C-lowering drug targets (LDLR, PCSK9, HMGCR, NPC1L1, APOB, ABCG5, ABCG8 and CETP) and TG-lowering drug targets (PPARA, ANGPTL3 and APOC3) based on their pharmacologic actions. The pharmacological target genes and gene region of these lipid-modifying drugs were identified through the DrugBank database and the NCBI database, respectively (Table 2 ). Table 2 Information of lipid-lowering drugs and drug targets. pharmacologic actions Drug class Target genes Gene region Chromosome Position Reduction of LDL-C PCSK9 inhibitor Proprotein convertase subtilisin/kexin type 9 (PCSK9) 1 55505221..55530525 HMGCR inhibitor HMG-CoA reductase (HMGCR) 5 74632993..74657941 Cholesterol absorption inhibitor Niemann-Pick C1-like protein 1 (NPC1L1) 7 44552134..44580929 LDL Receptor (LDLR) 19 11200139..11244496 Bile acid sequestrants ATP Binding Cassette Subfamily G Member 5/ATP Binding Cassette Subfamily G Member 8 (ABCG5/ABCG8) 2 44039611..44065978 44066110..44110127 Antisense oligonucleotides against APOB Apolipoprotein B-100 (APOB) 2 21224301..21266945 CETP inhibitor Cholesteryl ester transfer protein (CETP) 16 56995862..57017757 Reduction of TG Fibrates Peroxisome proliferator-activated receptor-a (PPARA) 22 46546429..46639653 ANGPTL3 inhibitor Angiopoietin-related protein 3 (ANGPTL3) 1 63063191..63071984 APOC3 inhibitor Apolipoprotein C-III (APOC3) 11 116700623..116703788 There are eleven lipid-lowering drug targets involved in our study including PCSK9, HMGCR, NPC1L1, LDLR, ABCG5, ACG8, APOB, CETP, PPARA, APOC3 and ANGPTL3. Abbreviations: LDL-C, low-density lipoprotein cholesterol; TG, Triglycerides. Instrumental variables for genetically proxied lipid-lowering drugs referred to the SNPs within ± 100kb windows of the relevant targeted gene regions [17]. To enhance the strength of IVs, a relatively loose exclusion criterion for LD was introduced (r 2 < 0.30 with a clumping window in 100 kb) [29]. Since no IV for PPARA satisfied the inclusion criteria, PPARA was excluded from further analyses. Considering the same IVs for ABCG5 and ABCG8, both target genes were combined. Eventually, there are 9 kinds of lipid-lowering drug targets involved in our MR study, including LDLR, HMGCR, NPC1L1, PCSK9, APOB, ABCG5/ABCG8, CETP, ANGPTL3 and APOC3. For drug targets significant to the risk of T1D, we extracted expression quantitative trait locus (eQTL) from the Genotype-Tissue Expression project (GTEx-V8) [30]. SNPs with high expression levels in correlative human tissues were identified as eQTLs with a clumping threshold of r 2 < 0.2. 2.4 Two-sample and drug-target MR analyses We used the two-sample and drug-target MR analysis with inverse-variance weighted (IVW), Weighted median and MR-Egger analyses to estimate the causal effects of genetically predicted lipid traits and genetically proxied lipid-modifying drugs on the risk of T1D and its complications. Bonferroni-correction was introduced for multiple testing. The causal effect was considered significant if the IVW p-value was below the Bonferroni-corrected threshold. An IVW p-value less than 0.05 but greater than the Bonferroni-corrected threshold was considered suggestively significant. To check the robustness of our results, we then performed the Cochran Q test, MR-Egger intercept test, MR-PRESSO and “leave-one-out” test as the sensitivity analyses. The Cochran Q test was conducted to evaluate the heterogeneity, where a p-value > 0.05 indicated no evidence of heterogeneity. A fixed-effect IVW (IVW-fe) analysis was identified as the major method if no heterogeneity was detected. Otherwise, a multiplicative random-effect IVW (IVW-mre) model should be chosen. The MR-Egger intercept test and MR-PRESSO analysis were subsequently performed to quantify potential horizontal pleiotropy, with a 0.05 threshold of p-value. Moreover, causal effects were re-estimated with the “leave-one-out” test to filter out the outliers among the IVs. 2.5 Positive control analyses We conducted the positive control analyses, where the causal effects of genetic mimicry of lipid-lowering drugs on the risk of coronary heart disease (CHD) were estimated, aiming to validate the efficacy of IV selection. Summary-level statistics for CHD were drawn from the CARDIoGRAMplusC4D consortium [31], comprising 60,801 CHD cases and 123,504 controls. 2.6 Mediation analysis To verify how lipid-lowering drugs increases the risk of T1D, a mediation MR analysis was conducted with the two-step method [32]. Potential risk and protective factors of T1D, including BMI, Vitamin D, C-reactive protein and Omega-3 fatty acids[11, 33, 34], were considered as the mediators. In the first step, a two-sample MR model was performed to assess the effect of drug targets on mediation(β 1 ). In the second step, another univariable MR analysis was carried out to estimate the effect of mediation on T1D (β 2 ). The indirect effect, which is the effect of exposure on outcome through mediators, is the multiplication of β 1 and β 2 with the “asymptotic normal distribution” method. The total effect is calculated as the regression estimate in the univariable MR between exposure and outcome. 2.7 Colocalization For statistically significant drug targets, we conducted the Bayesian colocalization to estimate whether drug targets and T1D share the same genetic variant within a certain genetic region. We set a 1M bp window size centering around the location of the sentinel SNP and conducted the colocalization analysis by combining eQTL data and T1D datasets. Since the hypothesis 4 of colocalization indicates that two phenotypes are significantly correlated with SNPs in a certain genomic region and are driven by the same causal variant, posterior probability for hypothesis 4 (PP.H4) > 80% was considered as the evidence of colocalization [35], and the SNP with the highest SNP.PP.H4 was identified as the causal variant. 2.8 Statistical analysis The whole MR analyses were carried out in the R software 4.3.2 (The R Foundation for Statistical Computing), of which “TwoSampleMR” [36], “MR-PRESSO”[37], “RMediation”[38] “xQTLbiolinks” [39] and “Coloc” [40] packages were used for primary MR analysis, outliers detection, mediation analysis, eQTL extraction and colocalization analysis, respectively. 3. Results 3.1 Lipid traits and the risk of T1D The univariable MR analysis indicated the serum LDL-C level was at a suggestively increased risk of T1D (OR = 1.100, 95% CI: 1.003–1.206, p = 4.26*10 − 2 ) in the primary dataset while we found no significant causal relationships between the genetic tendency for LDL-C and its risk of T1D in the replication dataset (OR = 1.000, 95% CI: 0.996–1.005, P = 9.57*10 − 1 ). No causal association was revealed between other lipid traits and T1D in either primary or replication datasets (Table 3 ). Detailed information of instrumental variables and corresponding F-statistics was listed in Online Resource 1 Table S1 and Online Resource 1 Table S2 . Table 3 Effects of lipid traits on type 1 diabetes. Exposure Outcome nSNP OR 95% CI P value LDL-C Primary 76 1.100 1.003–1.206 4.26E-02 Replication 79 1.000 0.996–1.005 9.57E-01 HDL-C Primary 83 0.889 0.786–1.005 6.08E-02 Replication 85 1.000 0.990–1.009 9.72E-01 TG Primary 54 0.936 0.810–1.082 3.72E-01 Replication 54 0.997 0.985–1.008 5.89E-01 TC Primary 81 1.024 0.930–1.127 6.35E-01 Replication 84 0.998 0.993–1.004 5.34E-01 ApoA1 Primary 11 0.865 0.736–1.017 7.95E-02 Replication 11 0.999 0.992–1.005 6.82E-01 ApoB Primary 20 1.057 0.953–1.172 2.92E-01 Replication 19 0.998 0.994–1.002 4.25E-01 P value < 8.33E-3 indicates the statistical significance. Abbreviations: LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, Triglycerides; TC, Total cholesterol; ApoA1, Apolipoprotein A1; ApoB, Apolipoprotein B; nSNP, number of SNPs; OR, odd ratio; CI: confidence interval. 3.2 Positive control analyses IVs which were used to simulate the effects of long-term use of lipid-modifying drugs were listed in the Online Resource 1 Table S3. F-statistics of instrumental variables ranged from 76.8 to 392.4, indicating the strong relationships between IVs and the exposure (Online Resource 1 Table S4). Similar to other lipid-lowering drug target MR studies[17, 41], our study indicated that 8 lipid-lowering drugs were associated with the reduced risk of CHD while only the effect of genetically predicted ANGPTL3 inhibitor on CHD was at a suggestive significance (OR = 0.836, 95%CI: 0.704–0.993, P = 4.09*10 − 2 ) (Online Resource 1 Table S5). This proved the reliability of IV selection in drug targets. 3.3 Lipid-lowering drug targets and the T1D risk Genetic mimicry of HMGCR, ANGPTL3 and CETP inhibitors was found to attenuate the risk of T1D (OR HMGCR =0.607, 95% CI HMGCR : 0.491–0.752, P HMGCR =4.59*10 − 6 ; OR ANGPTL3 =0.668, 95% CI ANGPTL3 : 0.511–0.874, P ANGPTL3 =3.21*10 − 3 , OR CETP =0.550, 95% CI CETP : 0.375–0.807, P CETP =2.24*10 − 3 ). Contrarily, a detrimental effect of genetic proxied APOC3 inhibitor on the T1D risk was revealed (OR = 1.362, 95% CI: 1.118–1.658, P = 2.15*10 − 3 ). Two alternative estimation methods showed similar results (Fig. 2 , Online Resource 2 Fig S2 ). In the sensitivity analyses, heterogeneity was identified with the Cochran Q test in the APOB-T1D and APOC3-T1D pairs, therefore, IVW-mre analyses were performed as the main method to minimize the impact of heterogeneity. The MR-Egger intercept test and MR-PRESSO indicated no evidence of horizontal pleiotropy in causal effects of HMGCR, ANGPTL3 and CETP on the risk of T1D (Fig. 2 ). Due to the potential horizontal pleiotropy in the APOC3-T1D pair (P MR−PRESSO = 0.001), we then identified rs533556 as an outlier based on MR-PRESSO analysis, and the causal effect remained significant after the exclusion of the outlier. Moreover, the “leave-one-out” test was carried out and identified rs247616 as a dominant SNP that affected the robustness of the MR result in the CETP-T1D pair (Online Resource 2 Fig S3). After excluding this high-impact SNP, the causal effect was close to null (OR = 0.615, 95% CI: 0.348–1.086, P = 9.38*10 − 2 ). In the replication analyses, the ANGPTL3 inhibitor was the only lipid-lowering drug that showed a statistically significant effect on T1D (OR = 0.938, 95% CI: 0.903–0.975, P = 1.28*10 − 3 ). A similar protective trend between genetic proximity to HMGCR inhibitor and the risk of T1D was observed although it did not reach the statistical significance (OR = 0.998, 95% CI: 0.977–1.020, P = 8.89*10 − 1 ). As for the APOC3 inhibitor, we detected an opposite trend (OR = 0.998, 95% CI: 0.990–1.006, P = 6.55*10 − 1 ) (Online Resource 1 Table S6). 3.4 Lipid-lowering drug targets and the risks of type 1 diabetic complications Our study suggested that genetic tendency for ANGPTL3 inhibitor significantly reduced the risk of DKD in T1D (OR = 0.396, 95% CI: 0.193–0.815, P = 1.15*10 − 2 ) while HMGCR inhibitor was causally associated with the greater risk of DR and DN in T1D (OR DR =2.203, 95% CI DR : 1.327–3.658, P DR =2.26*10 − 3 , OR DN =1.647, 95% CI DN : 1.238–2.192, P DN =6.16*10 − 4 ) (Fig. 3 ). These causal effects remained robust after conducting multiple sensitivity analyses. 3.5 Mediation MR For significant causal relationships, genetic predisposition to lipid-lowering drug targets may impact T1D by affecting the level of potential mediators. In the mediation MR analysis, BMI was observed to play a mediating role in the causal association between genetic mimicry of ANGPTL3 inhibitors and the T1D risk, of which the proportion of mediating effect was 5.71% (95% CI: 2.43%-17.71%) (Table 4 ). Table 4 Mediation effects in the relationship between drug targets and type 1 diabetes. Mediator Exposure Proportion of mediation effects in the total effect 95%CI BMI ANGPTL3 5.71% 2.43% − 17.71% HMGCR -7.22% -22.51% − 1.99% Vitamin D ANGPTL3 -2.73% -16.65% − 1.62% HMGCR -5.41% -16.45% − 1.07% Omega 3 fatty acids ANGPTL3 -7.69% -81.79% − 43.32% HMGCR -4.61% -36.44% − 23.90% C-reactive protein ANGPTL3 2.73% -1.31% − 12.06% HMGCR 0.00% -1.60% − 1.62% BMI mediated 5.71% of the total effect of ANGPTL3 inhibitors on T1DM. Abbreviations: BMI, body mass index; CI: confidence interval. 3.6 Gene expression in tissues In consideration of the vital roles of HMGCR and ANGPTL3 inhibitors in reducing the T1D risk mentioned earlier, we obtained genetic variants, eQTLs, of two drug targets from the GTEx-V8 database and subsequently conducted the MR analysis for validation. It was observed that the increased protein expression of HMGCR in the skeletal muscle tissue was causally associated with an increase in T1D risk (OR = 1.230, 95% CI: 1.104–1.370, P = 1.81*10 − 4 ), which was consistent with our finding mentioned before. On account of the lack of eQTLs significant to ANGPTL3 in the liver tissue, we widened the eQTL inclusive criteria to a P-value threshold of 2*10 − 5 and thus discovered the causal linkage between a 1-SD increase in ANGPTL3 expression and T1D risk (OR = 1.140, 95% CI: 1.035–1.255, P = 7.73*10 − 3 ). 3.7 Colocalization The probability that ANGPTL3 expression in the liver tissue shared a causal variant with the T1D trait was 2.2%. The probability of a shared genetic variant between HMGCR expression in the muscle tissue and the T1D trait was 1.5% (Online Resource 1 Table S7). 4. Discussion We conducted the drug target MR analysis in our study, aiming to investigate the potential causal effects of lipid traits and lipid-modifying drug targets on the risk of T1D and its complications. Our study yielded four critical conclusions: (1) Both ANGPTL3 and HMGCR were found to reduce the risk of T1D remarkably; (2) ANGPTL3 inhibitor was additionally associated with the risk reduction of renal complications in T1D; (3) There was no sufficient evidence of causal relationships between lipid traits and T1D, indicating that protective effects of ANGPTL3 and HMGCR inhibitors on T1D were independent of their lipid-lowering effects. (4) BMI was likely to play a role in mediating the effect of genetically mimicked ANGPTL3 inhibitor on reducing T1D risk. Genetic mimicry of ANGPTL3 inhibitor was found to lower the risk of T1D significantly in our study. This finding was validated in the replication dataset, human tissues and a range of sensitivity analyses. Besides, HMGCR was the other lipid-lowering drug target related to the reduced risk of T1D, although the causal effect was not validated in the replication dataset. Unfortunately, our colocalization analysis did not reveal the causal variants between these two drug targets and T1D. This may be attributed to the different methodologies between MR and colocalization: MR is relatively liberal while colocalization employs skeptical priors based on the genome-wide testing practice and tends to be conservative [42]. The effects of lipid traits on T1D remained disputed. Our study suggested that genetically proxied plasma lipids were not causally related to the risk of T1D while several observational studies yielded inconsistent findings. A prospective cohort study indicated that apoA-1 reduced the T1D risk independently ( HR = 0.65, 95CI: 0.58–0.73) while the increase of TGs and ApoB levels were associated with a high risk of T1D ( HR TG = 1.49, 95 CI TG : 1.41–1.58; HR ApoB = 1.20, 95 CI ApoB : 1.08–1.34) [11]. Evidence also suggested that hyperlipidemia occurred frequently in T1D patients, especially in T1D patients with poor glycemic control, which was characterized by the increase of serum TGs and LDL-C levels [9, 10]. These findings above were not in conformity with our conclusions and the discrepancy might be explained by confounding biases, reverse causality and misclassification error among different types of diabetes. Inhibition or antibody of ANGPTL3, such as Evinacumab, is an advanced lipid-lowering drug, the fundamental pharmacological mechanism of which is to descend the expression of ANGPTL3 and additionally activate lipoprotein lipase (LPL) by blocking its coordination with ANGPTL8 [43]. The specific mechanism where T1D risk reduction was beneficial from the inhibition of ANGPTL3 remained currently unclear although it was supported at the level of animal experiments: the expression of ANGPTL3 was increased in insulin-deficient diabetic mice [44]. Evidence for no causal association between lipid traits and T1D indicated that ANGPTL3 inhibitor probably reduced the risk of T1D through other underlying mechanisms, rather than via exerting their lipid-modifying effects. Consequently, we additionally conducted the mediation MR analysis and highlighted BMI as the mediating factor in the process by which ANGPTL3 inhibitor lessened the risk of T1D. Obesity, especially childhood overweight, was found to enhance the risk of T1D [45, 46] through imposing pressure on pancreatic islet beta cells. There tends to be a higher insulin demand in obese individuals, which results in increased stress on beta cells. These beta cells in an overloaded state are vulnerable to autoimmune factors and cytokines [45], thus leading to islet autoimmunity and T1D. “Overload Hypothesis” [47] and “Accelerator Hypothesis” [48] have also been proposed and attributed the adverse effect of BMI on T1D to genetic predisposition and insulin resistance, respectively. Recent studies have discovered the linkage between ANGPTL3 and body weight control. The serum level of ANGPTL3 was observed to increase in overweight individuals compared to the lean population [49]. Another study found that MiRNA-181d, a kind of micromolecule contributing to the repression of ANGPTL3 expression, was effective in the prevention of obesity[50]. To sum up, through controlling weight, ANGPTL3 inhibitor is likely to lighten the burden of beta cells and improve islet autoimmunity, thus leading to the reduction of T1D risk. Further MR analyses indicated that genetic predicted lifetime use of ANGPTL3 inhibitor was effective in reducing the risk of DKD in T1D. DKD, also known as diabetic nephropathy, is one of the most common complications of T1D [51]. Patients with diabetic nephropathy in T1D tend to suffer from a progressive deterioration of kidney function and die of renal failure ultimately. Recent studies suggested the plasma ANGPTL3 level was increased in patients with diabetic nephropathy [52]. The effect of ANGPTL3 inhibition on diabetic nephropathy was also verified in mouse experiments, where researchers found that the knockdown of ANGPTL3 was able to improve renal function reversibly in mice with diabetic nephropathy, by improving podocyte epithelial-mesenchymal transition (EMT), changing macrophages’ polarization and suppressing inflammatory factors [53]. Our MR result also implied the possible causal relationship between inhibition of HMGCR and a decreased T1D risk at the genome-wide level, whereas it does not seem to be supported by other studies. Two MR studies involving HMGCR inhibitor and T1D did not detect the similar causation, of which Yang G discovered a potential protective trend (OR = 0.85, 95% CI: 0.46–1.56, p = 5.99*10 − 1 )[54] while Xie W did not demonstrate the significant result (OR = 1.02, 95% CI: 0.60–1.75, p = 9.28*10 − 1 )[55]. Although inhibition of HMGCR, statin, is broadly used in T1D patients with circulatory complications owing to cardiovascular benefits, its impaired effect on glycemic control has been constantly mentioned in observational and cell-based studies. It is observed that statin-treated T1D patients had higher HbA1c level [14] and lower insulin sensitivity[15] than those without statin treatment, which may be ascribed to its influence on calcium channels in pancreatic beta cells or the decrease in Glut-4 transporter translocation[56, 57]. Moreover, these findings can also explain the reason why the HMGCR inhibitor was detected to increase the risk of DR and DN in T1D in our study. Chances are that hyperglycemia and insulin resistance induced by statin treatment may lead to DR and DN in T1D through possible mechanisms mentioned above. Notably, although the phenomenon where the statin-treatment triggered DR and DN in T2D was discussed in many studies [58, 59], few have explored the statin-induced DR and DN in T1D [60], which emphasizes the need for further studies. Our findings may provide insights into fundamental researches and clinical practice. Given that ANGPTL3 inhibitor, a novel lipid-modifying drug, not only reduces the T1D risk, but also provides extra benefits to treat renal complications, it may be a promising candidate for treating T1D patients, especially those with renal complications. Consequently, it is of great worth conducting further fundamental researches and randomized controlled trials to investigate specific mechanisms and ascertain effectiveness. In contrast, although there is a consensus about the excellent efficacy of HMGCR inhibitor in diabetic cardiovascular lesions, we found that its protective effect on T1D is not clear enough and the impaired effect on glucose control may lead to an increased risk of DR and DN in T1D. Further studies are necessarily required to reevaluate its priority for T1D treatment among lipid-lowering drugs. There are some noteworthy strengths in our study. To the best of our knowledge, this is the first drug-target MR study aiming to evaluate the causal effects of lipid traits and lipid-lowering drug targets on the risk of T1D and its complications. Based on the randomized allocation of alleles, MR is highly effective in mitigating confounding biases and exploring causality. In our study, we extracted genetic statistics from the latest and largest-scale GWAS data to minimize the impact of the “winner’s curse”. Both the MR-Egger intercept test and MR-PRESSO were performed to detect the potential evidence of pleiotropy and maintain the robustness of the results. Through combining eQTL data of drug targets and T1D GWAS in MR and colocalization, our study provided additional evidence for causal associations. However, there are still several limitations. First, IVs of drug targets selected in our study represented the lifetime use of drugs, therefore, the short-term effects of lipid-modifying drugs in clinical practice may not correspond with our findings. Second, although the MR-Egger intercept test and MR-PRESSO were introduced in our study, the influence of horizontal pleiotropy cannot be eliminated completely. Third, our study specifically evaluated the effects of drug targets, rather than the off-target effects. On top of that, since our study specifically focused on people of European ancestry, additional attention should be paid to verifying the generalizability of our findings to other populations. In conclusion, circulating lipids are not causally associated with the risk of T1D at the genome-wide level. ANGPTL3 inhibitor, a novel lipid-modifying drug, was identified as a promising candidate for treating T1D and its renal complication, of which BMI may play a mediating role. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author contributions Conceptualization: C. Z.; Methodology: H. W.; Formal analysis: H. W.; Writing - original draft preparation: H. W., Z. L., Z. Y., Y. L.; Writing review and editing: C. Z., H. W.; Resources: C. Z.; Supervision: C. Z.; All authors commented on previous versions of the manuscript; All authors read and approved the final manuscript. Guarantor: C. Z. Ethics approval Since the original data sources in the European Bioinformatics Institute database, the FinnGen consortium and the GTEx project for MR were all ethically approved and consented by participants, no additional ethical approval was required for this study after consultation with the Ethics Committee of Soochow University. Consent to participate Not applicable. 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Abu-Farha M, Cherian P, Al-Khairi I, et al.:Reduced miR-181d level in obesity and its role in lipid metabolism via regulation of ANGPTL3. Sci Rep 9(1): 11866.(2019). 10.1038/s41598-019-48371-2 Papadopoulou-Marketou N, Chrousos GP, Kanaka-Gantenbein C:Diabetic nephropathy in type 1 diabetes: a review of early natural history, pathogenesis, and diagnosis. Diabetes Metab Res Rev 33(2).(2017). 10.1002/dmrr.2841 Ma Q, Hu X, Liu F, et al.:A novel fusion protein consisting of anti-ANGPTL3 antibody and interleukin-22 ameliorates diabetic nephropathy in mice. Front Immunol 13: 1011442.(2022). 10.3389/fimmu.2022.1011442 Ma Y, Chen Y, Xu H, Du N:The influence of angiopoietin-like protein 3 on macrophages polarization and its effect on the podocyte EMT in diabetic nephropathy. Front Immunol 14: 1228399.(2023). 10.3389/fimmu.2023.1228399 Yang G, Schooling CM:Investigating genetically mimicked effects of statins via HMGCR inhibition on immune-related diseases in men and women using Mendelian randomization. Sci Rep 11(1): 23416.(2021). 10.1038/s41598-021-02981-x Xie W, Li J, Du H, Xia J:Causal relationship between PCSK9 inhibitor and autoimmune diseases: a drug target Mendelian randomization study. Arthritis Res Ther 25(1): 148.(2023). 10.1186/s13075-023-03122-7 Kain V, Kapadia B, Misra P, Saxena U:Simvastatin may induce insulin resistance through a novel fatty acid mediated cholesterol independent mechanism. Sci Rep 5: 13823.(2015). 10.1038/srep13823 Brault M, Ray J, Gomez Y-H, Mantzoros CS, Daskalopoulou SS:Statin treatment and new-onset diabetes: a review of proposed mechanisms. Metabolism 63(6): 735-745.(2014). 10.1016/j.metabol.2014.02.014 Tomkins-Netzer O, Niederer R, Lightman S:The role of statins in diabetic retinopathy. Trends Cardiovasc Med.(2022). 10.1016/j.tcm.2022.11.003 Hammad MA, Syed Sulaiman SA, Alghamdi S, Mangi AA, Aziz NA, Mohamed Noor DA:Statins-related peripheral neuropathy among diabetic patients. Diabetes Metab Syndr 14(4): 341-346.(2020). 10.1016/j.dsx.2020.04.005 Klein BEK, Myers CE, Howard KP, Klein R:Serum Lipids and Proliferative Diabetic Retinopathy and Macular Edema in Persons With Long-term Type 1 Diabetes Mellitus: The Wisconsin Epidemiologic Study of Diabetic Retinopathy. JAMA Ophthalmol 133(5): 503-510.(2015). 10.1001/jamaophthalmol.2014.5108 Additional Declarations No competing interests reported. Supplementary Files OnlineResource1.xlsx OnlineResource2.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-4537908","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312819384,"identity":"b4ed3f4a-7774-4cb4-abf0-19eca04c0335","order_by":0,"name":"Haocheng Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Haocheng","middleName":"","lastName":"Wang","suffix":""},{"id":312819385,"identity":"d08f90d7-6980-4b08-9561-8083c7453859","order_by":1,"name":"Zirui Liu","email":"","orcid":"","institution":"First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Zirui","middleName":"","lastName":"Liu","suffix":""},{"id":312819386,"identity":"d5f6dc68-144c-49cf-ab1a-1f1fad94af6c","order_by":2,"name":"Zhengkai Yang","email":"","orcid":"","institution":"First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Zhengkai","middleName":"","lastName":"Yang","suffix":""},{"id":312819387,"identity":"082fe4bc-b4a0-4cd4-aa22-2d9a49f89707","order_by":3,"name":"Yu Lu","email":"","orcid":"","institution":"First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Lu","suffix":""},{"id":312819388,"identity":"4704b2d9-7362-436e-ad60-e636e155f05a","order_by":4,"name":"Cao Zou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYDACHgYG5r9/bICsBCBmI1ILA29DGulaDpOgxZzn+MMPkjvO2xscT37A8KHsMAP/7Ab8Wix7G5IlDM/cTtxw5pkB44xzhxkk7hzAr8XgPMMxhgS22wkGNxIMmHnbDjMYSCQQ0sLYxnCA7Zy9wY30D8x/idJytpmNsbHtAOOGGzkGzIxEaTlzjFma4Uxy4swzbwoO9pxL55G4QVBL+sPPDBV29nzH0zc++FFmLcc/g4AWFHCAARJPo2AUjIJRMAooBQA1uUY1tuyvCwAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Cao","middleName":"","lastName":"Zou","suffix":""}],"badges":[],"createdAt":"2024-06-06 06:52:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4537908/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4537908/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58731257,"identity":"1af4d47d-ff1e-4040-aa1f-c6f36fe0a9b0","added_by":"auto","created_at":"2024-06-20 11:15:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":183277,"visible":true,"origin":"","legend":"\u003cp\u003eThe outline of our study\u003c/p\u003e\n\u003cp\u003eFigure legend: Abbreviations: HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TG, Triglycerides; TC, Total cholesterol; ApoA1, Apolipoprotein A1; ApoB, Apolipoprotein B; T1DM, type 1 diabetes mellitus; CAD, coronary artery disease; DKD, diabetic kidney disease; DR: diabetic \u003ca href=\"https://r9.finngen.fi/pheno/DM_RETINA_NOS\" target=\"_blank\"\u003eretinopathy\u003c/a\u003e; DN: diabetic neuropathy; MR: mendelian randomization.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4537908/v1/f343d8488c3e004068772f86.png"},{"id":58731693,"identity":"5c7a03ad-4ecc-4f13-b3d8-f2944dae4106","added_by":"auto","created_at":"2024-06-20 11:23:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":394715,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of genetically predicted lipid-lowering drugs on the risk of type 1 diabetes\u003c/p\u003e\n\u003cp\u003eFigure legend: P value \u0026lt; 5.56*10\u003csup\u003e-3\u003c/sup\u003e indicates the statistical significance after Bonferroni correction. Abbreviations: IVW, inverse\u0026nbsp;variance\u0026nbsp;weighted; nSNP, number of SNPs; Heterogeneity_P, p-value for Cochran Q test; Intercept_P, p-value for MR-Egger intercept test; MR_PRESSO_P, p-value for MR-PRESSO.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4537908/v1/6a818b60fb497c6a3e2dcb54.png"},{"id":58731271,"identity":"7b7fce52-2f4b-4c50-863a-a379d3b9729d","added_by":"auto","created_at":"2024-06-20 11:15:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":420458,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of genetically mimicked ANGPLT3 and HMGCR inhibitors on the risk of complications in type 1 diabetes\u003c/p\u003e\n\u003cp\u003eFigure legend: P \u0026lt; 0.017 indicates significance after Bonferroni correction. Abbreviations: DKD, diabetic kidney disease; DR: diabetic \u003ca href=\"https://r9.finngen.fi/pheno/DM_RETINA_NOS\" target=\"_blank\"\u003eretinopathy\u003c/a\u003e; DN: diabetic neuropathy; IVW, inverse variance weighted; nSNP, number of SNPs; Heterogeneity_P, p-value for Cochran Q test; Intercept_P, p-value for MR-Egger intercept test; MR_PRESSO_P, p-value for MR-PRESSO.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4537908/v1/941cdbe28abc05cf6313719c.png"},{"id":58732605,"identity":"0f606ff6-6374-4744-93ac-16a30c20a2f6","added_by":"auto","created_at":"2024-06-20 11:39:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1594793,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4537908/v1/bacc1036-9f70-4cc2-8bb9-e6083617cc47.pdf"},{"id":58731258,"identity":"94390f8b-32b7-4b9b-be9f-b548f3a36247","added_by":"auto","created_at":"2024-06-20 11:15:53","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":58111,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4537908/v1/0d0464598fee303ae2f18fd8.xlsx"},{"id":58731260,"identity":"5bc3640c-b076-49d2-bc50-70834ce5cde9","added_by":"auto","created_at":"2024-06-20 11:15:53","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":394419,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4537908/v1/9dffea4ccc88045783466cad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic association of lipids and lipid-lowering drug targets with the risk of type 1 diabetes and its complications","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eType 1 diabetes (T1D) is a chronic T-cell mediated autoimmune disease [1], the major pathophysiological hallmark of which refers to complete insulin deficiency due to the destruction of beta cells in the pancreatic islet [2]. T1D is one of the most prevalent autoimmune diseases, affecting over 9\u0026nbsp;million people around the world. In Europe, T1D accounts for approximately 10 percent of overall diabetes and its incidence rate has been increasing annually [3]. Uncontrolled patients tend to suffer from ketosis and permanent hyperglycemia. Progressive ketosis may trigger fatal ketoacidosis and persistent hyperglycemia is likely to contribute to a series of complications including retinal, renal, vascular and neurological lesions, bringing about a substantial decline in survival and the quality of life [4].\u003c/p\u003e \u003cp\u003eTo date, although several new medications and therapies for T1D have been experimented with, insulin replacement therapy is still the first-line and primary treatment [5, 6]. However, due to low compliance, potential risk of hypoglycemia and the incapacity to forestall complications [7, 8], injected insulin may fail to replace physiological insulin secretion completely, which emphasizes the need for new therapies.\u003c/p\u003e \u003cp\u003ePrevious observational studies have investigated the relationship between dyslipidemia and T1D, suggesting that abnormal lipid metabolism may be related to an increase in the T1D risk [9\u0026ndash;12]. Due to the potential confounding bias and reverse causation in observational studies, the causal association between circulating lipids and T1D is elusive and remains to be verified. Moreover, considering the underlying effects of dyslipidemia on the risk of T1D, it may be worthy of discussion if lipid-lowering drugs can be utilized as new repurposing candidates for T1D. Statin and other novel lipid-modifying drugs are extensively used for the prevention and treatment of cardiovascular complications in T1D patients [13] whereas few studies have focused on their efficiency in T1D. Concerns are also raised that the utilization of Statin may deteriorate glucose control in T1D patients [14, 15], which further complicates their relationships.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a new statistical approach using genetic variants as instruments to appraise the causal association between the exposure and the outcome [16]. As the genetic variants are randomly allocated in meiosis and remain unaffected by external interference after birth, MR is capable of mimicking the randomized controlled trial (RCT) and overcoming limitations of observational studies such as reverse causation and confounding biases. Previous drug target MR studies have investigated the causal effects of lipid-lowering drugs on type 2 diabetes (T2D) [17, 18]. However, considering different pathogenic mechanisms, pathological changes and therapeutic methods between T1D and T2D [19], the relationship between lipid-lowering drugs and the T1D risk requires to be corroborated. Hence, we performed the first drug-target MR analysis to disentangle the causal effects of lipid traits on T1D and investigate whether lipid-lowering drugs are effective for the treatment of T1D and its complications.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. presents the outline of our study. The MR analysis was used to evaluate the causal effects of genetic predisposition to lipid traits and genetic proxies for lipid-lowering drug targets on the risk of T1D and its complications in the European population. The MR method satisfied the following three fundamental hypotheses [20]: 1. The selected instrumental variables (IVs), namely single nucleotide polymorphisms (SNPs), should be strongly correlated with the exposure; 2. The IVs cannot be in connection with any possible confounding factors of the outcome; 3. The IVs cannot be directly associated with the outcomes (Online Resource 2 Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data source\u003c/h2\u003e \u003cp\u003eSummary GWAS statistics for lipid traits including HDL cholesterol (HDL-C), LDL cholesterol (LDL-C) and Triglycerides (TG) were obtained from Global Lipids Genetics Consortium[21], and genetic data for apolipoprotein A1 (ApoA1) and apolipoprotein B (ApoB) came from an online GWAS [22]. Summary-level data of the outcome, T1D, was extracted from the latest and largest-scale GWAS dataset meta-analyzing Finngen, UK Biobank and other 7 cohorts[23], consisting of 18,942 cases and 501,638 controls of European ancestry. Genetic statistics for T1D were also derived from another GWAS dataset for replication analyses, comprising 2,292 T1D cases and 324,074 European ancestry controls [24]. Moreover, statistical data for complications in T1D was obtained from the Finngen database (Round 9) [25], including diabetic kidney disease (DKD), diabetic retinopathy (DR) and diabetic neuropathy (DN) (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\u003eDetailed information of the GWAS in the MR analysis.\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=\"left\" 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=\"left\" 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\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGWAS ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePMID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCases(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eControls(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSample size(n)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eieu-a-299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24097068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWiller CJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e187,167\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\u003eieu-a-300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24097068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWiller CJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e173,082\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\u003eieu-a-302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24097068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWiller CJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e177,861\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\u003eieu-a-301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24097068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWiller CJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e187,365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emet-c-842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27005778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKettunen J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20,687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emet-c-843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27005778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKettunen J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20,690\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCST90014023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34012112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChiou J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18,942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e501,638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e520,580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCST90014465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34278373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlanville KP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e324,074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e326,366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eieu-a-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26343387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNikpay M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60,801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e123,504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e184,305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDKD in T1DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36653562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKurki MI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e308280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e309859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDR in T1DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36653562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKurki MI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e308280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e313482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDN in T1DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36653562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKurki MI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e308280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e309357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TG, Triglycerides; TC, Total cholesterol; ApoA1, Apolipoprotein A1; ApoB, Apolipoprotein B; T1DM, type 1 diabetes mellitus; CAD, coronary artery disease; DKD, diabetic kidney disease; DR: diabetic retinopathy; DN: diabetic neuropathy; EUR, European.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Selection of IVs\u003c/h2\u003e \u003cp\u003eAccording to the fundamental hypothesis 1, SNPs robustly correlated with lipid traits (P\u0026thinsp;\u0026lt;\u0026thinsp;5*10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) were identified as the instrumental variables of circulating lipids and were further clumped to avoid the possible linkage disequilibrium (LD) (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, kb\u0026thinsp;=\u0026thinsp;10,000). SNPs strongly related to the outcomes (p\u0026thinsp;\u0026gt;\u0026thinsp;5*10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) were excluded to satisfy the hypothesis 3 mentioned above. Moreover, we introduced the F-statistics to assess the strength of the association between IVs and lipid traits. IVs with low F-statistics (F\u0026thinsp;\u0026lt;\u0026thinsp;10) were excluded from further analyses to minimize the influence of weak IV bias.\u003c/p\u003e \u003cp\u003eAccording to recent reviews and guidelines for managing lipid disorders [26\u0026ndash;28], eleven common lipid-lowering drug targets were involved in our study and were distinguished into LDL-C-lowering drug targets (LDLR, PCSK9, HMGCR, NPC1L1, APOB, ABCG5, ABCG8 and CETP) and TG-lowering drug targets (PPARA, ANGPTL3 and APOC3) based on their pharmacologic actions. The pharmacological target genes and gene region of these lipid-modifying drugs were identified through the DrugBank database and the NCBI database, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eInformation of lipid-lowering drugs and drug targets.\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=\"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 \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\u003epharmacologic actions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDrug class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTarget genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eGene region\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReduction of LDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePCSK9 inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProprotein convertase subtilisin/kexin type 9 (PCSK9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e55505221..55530525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHMGCR inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHMG-CoA reductase (HMGCR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e74632993..74657941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCholesterol absorption inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNiemann-Pick C1-like protein 1 (NPC1L1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e44552134..44580929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLDL Receptor (LDLR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e11200139..11244496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBile acid sequestrants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATP Binding Cassette Subfamily G Member 5/ATP Binding Cassette Subfamily G Member 8 (ABCG5/ABCG8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e44039611..44065978\u003c/p\u003e \u003cp\u003e44066110..44110127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAntisense oligonucleotides against APOB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApolipoprotein B-100 (APOB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e21224301..21266945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCETP inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCholesteryl ester transfer protein (CETP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e56995862..57017757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReduction of TG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFibrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeroxisome proliferator-activated receptor-a (PPARA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e46546429..46639653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANGPTL3 inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAngiopoietin-related protein 3 (ANGPTL3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e63063191..63071984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAPOC3 inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApolipoprotein C-III (APOC3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e116700623..116703788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eThere are eleven lipid-lowering drug targets involved in our study including PCSK9, HMGCR, NPC1L1, LDLR, ABCG5, ACG8, APOB, CETP, PPARA, APOC3 and ANGPTL3. Abbreviations: LDL-C, low-density lipoprotein cholesterol; TG, Triglycerides.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInstrumental variables for genetically proxied lipid-lowering drugs referred to the SNPs within \u0026plusmn;\u0026thinsp;100kb windows of the relevant targeted gene regions [17]. To enhance the strength of IVs, a relatively loose exclusion criterion for LD was introduced (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.30 with a clumping window in 100 kb) [29]. Since no IV for PPARA satisfied the inclusion criteria, PPARA was excluded from further analyses. Considering the same IVs for ABCG5 and ABCG8, both target genes were combined. Eventually, there are 9 kinds of lipid-lowering drug targets involved in our MR study, including LDLR, HMGCR, NPC1L1, PCSK9, APOB, ABCG5/ABCG8, CETP, ANGPTL3 and APOC3.\u003c/p\u003e \u003cp\u003eFor drug targets significant to the risk of T1D, we extracted expression quantitative trait locus (eQTL) from the Genotype-Tissue Expression project (GTEx-V8) [30]. SNPs with high expression levels in correlative human tissues were identified as eQTLs with a clumping threshold of r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Two-sample and drug-target MR analyses\u003c/h2\u003e \u003cp\u003eWe used the two-sample and drug-target MR analysis with inverse-variance weighted (IVW), Weighted median and MR-Egger analyses to estimate the causal effects of genetically predicted lipid traits and genetically proxied lipid-modifying drugs on the risk of T1D and its complications. Bonferroni-correction was introduced for multiple testing. The causal effect was considered significant if the IVW p-value was below the Bonferroni-corrected threshold. An IVW p-value less than 0.05 but greater than the Bonferroni-corrected threshold was considered suggestively significant.\u003c/p\u003e \u003cp\u003eTo check the robustness of our results, we then performed the Cochran Q test, MR-Egger intercept test, MR-PRESSO and \u0026ldquo;leave-one-out\u0026rdquo; test as the sensitivity analyses. The Cochran Q test was conducted to evaluate the heterogeneity, where a p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicated no evidence of heterogeneity. A fixed-effect IVW (IVW-fe) analysis was identified as the major method if no heterogeneity was detected. Otherwise, a multiplicative random-effect IVW (IVW-mre) model should be chosen. The MR-Egger intercept test and MR-PRESSO analysis were subsequently performed to quantify potential horizontal pleiotropy, with a 0.05 threshold of p-value. Moreover, causal effects were re-estimated with the \u0026ldquo;leave-one-out\u0026rdquo; test to filter out the outliers among the IVs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Positive control analyses\u003c/h2\u003e \u003cp\u003eWe conducted the positive control analyses, where the causal effects of genetic mimicry of lipid-lowering drugs on the risk of coronary heart disease (CHD) were estimated, aiming to validate the efficacy of IV selection. Summary-level statistics for CHD were drawn from the CARDIoGRAMplusC4D consortium [31], comprising 60,801 CHD cases and 123,504 controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Mediation analysis\u003c/h2\u003e \u003cp\u003eTo verify how lipid-lowering drugs increases the risk of T1D, a mediation MR analysis was conducted with the two-step method [32]. Potential risk and protective factors of T1D, including BMI, Vitamin D, C-reactive protein and Omega-3 fatty acids[11, 33, 34], were considered as the mediators. In the first step, a two-sample MR model was performed to assess the effect of drug targets on mediation(β\u003csub\u003e1\u003c/sub\u003e). In the second step, another univariable MR analysis was carried out to estimate the effect of mediation on T1D (β\u003csub\u003e2\u003c/sub\u003e). The indirect effect, which is the effect of exposure on outcome through mediators, is the multiplication of β\u003csub\u003e1\u003c/sub\u003e and β\u003csub\u003e2\u003c/sub\u003e with the \u0026ldquo;asymptotic normal distribution\u0026rdquo; method. The total effect is calculated as the regression estimate in the univariable MR between exposure and outcome.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Colocalization\u003c/h2\u003e \u003cp\u003eFor statistically significant drug targets, we conducted the Bayesian colocalization to estimate whether drug targets and T1D share the same genetic variant within a certain genetic region. We set a 1M bp window size centering around the location of the sentinel SNP and conducted the colocalization analysis by combining eQTL data and T1D datasets. Since the hypothesis 4 of colocalization indicates that two phenotypes are significantly correlated with SNPs in a certain genomic region and are driven by the same causal variant, posterior probability for hypothesis 4 (PP.H4)\u0026thinsp;\u0026gt;\u0026thinsp;80% was considered as the evidence of colocalization [35], and the SNP with the highest SNP.PP.H4 was identified as the causal variant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe whole MR analyses were carried out in the R software 4.3.2 (The R Foundation for Statistical Computing), of which \u0026ldquo;TwoSampleMR\u0026rdquo; [36], \u0026ldquo;MR-PRESSO\u0026rdquo;[37], \u0026ldquo;RMediation\u0026rdquo;[38] \u0026ldquo;xQTLbiolinks\u0026rdquo; [39] and \u0026ldquo;Coloc\u0026rdquo; [40] packages were used for primary MR analysis, outliers detection, mediation analysis, eQTL extraction and colocalization analysis, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Lipid traits and the risk of T1D\u003c/h2\u003e \u003cp\u003eThe univariable MR analysis indicated the serum LDL-C level was at a suggestively increased risk of T1D (OR\u0026thinsp;=\u0026thinsp;1.100, 95% CI: 1.003\u0026ndash;1.206, p\u0026thinsp;=\u0026thinsp;4.26*10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) in the primary dataset while we found no significant causal relationships between the genetic tendency for LDL-C and its risk of T1D in the replication dataset (OR\u0026thinsp;=\u0026thinsp;1.000, 95% CI: 0.996\u0026ndash;1.005, P\u0026thinsp;=\u0026thinsp;9.57*10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). No causal association was revealed between other lipid traits and T1D in either primary or replication datasets (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Detailed information of instrumental variables and corresponding F-statistics was listed in Online Resource 1 Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Online Resource 1 Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\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\u003eEffects of lipid traits on type 1 diabetes.\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\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\u003enSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.003\u0026ndash;1.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.26E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReplication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.996\u0026ndash;1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.57E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.786\u0026ndash;1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.08E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReplication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.990\u0026ndash;1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.72E-01\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\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.810\u0026ndash;1.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.72E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReplication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.985\u0026ndash;1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.89E-01\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\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.930\u0026ndash;1.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.35E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReplication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.993\u0026ndash;1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.34E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.736\u0026ndash;1.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.95E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReplication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.992\u0026ndash;1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.82E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.953\u0026ndash;1.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.92E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReplication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.994\u0026ndash;1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.25E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eP value\u0026thinsp;\u0026lt;\u0026thinsp;8.33E-3 indicates the statistical significance. Abbreviations: LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, Triglycerides; TC, Total cholesterol; ApoA1, Apolipoprotein A1; ApoB, Apolipoprotein B; nSNP, number of SNPs; OR, odd ratio; CI: confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Positive control analyses\u003c/h2\u003e \u003cp\u003eIVs which were used to simulate the effects of long-term use of lipid-modifying drugs were listed in the Online Resource 1 Table S3. F-statistics of instrumental variables ranged from 76.8 to 392.4, indicating the strong relationships between IVs and the exposure (Online Resource 1 Table S4). Similar to other lipid-lowering drug target MR studies[17, 41], our study indicated that 8 lipid-lowering drugs were associated with the reduced risk of CHD while only the effect of genetically predicted ANGPTL3 inhibitor on CHD was at a suggestive significance (OR\u0026thinsp;=\u0026thinsp;0.836, 95%CI: 0.704\u0026ndash;0.993, P\u0026thinsp;=\u0026thinsp;4.09*10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) (Online Resource 1 Table S5). This proved the reliability of IV selection in drug targets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Lipid-lowering drug targets and the T1D risk\u003c/h2\u003e \u003cp\u003eGenetic mimicry of HMGCR, ANGPTL3 and CETP inhibitors was found to attenuate the risk of T1D (OR \u003csub\u003eHMGCR\u003c/sub\u003e=0.607, 95% CI \u003csub\u003eHMGCR\u003c/sub\u003e: 0.491\u0026ndash;0.752, P \u003csub\u003eHMGCR\u003c/sub\u003e=4.59*10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e; OR \u003csub\u003eANGPTL3\u003c/sub\u003e=0.668, 95% CI \u003csub\u003eANGPTL3\u003c/sub\u003e: 0.511\u0026ndash;0.874, P \u003csub\u003eANGPTL3\u003c/sub\u003e=3.21*10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, OR \u003csub\u003eCETP\u003c/sub\u003e =0.550, 95% CI \u003csub\u003eCETP\u003c/sub\u003e: 0.375\u0026ndash;0.807, P \u003csub\u003eCETP\u003c/sub\u003e=2.24*10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). Contrarily, a detrimental effect of genetic proxied APOC3 inhibitor on the T1D risk was revealed (OR\u0026thinsp;=\u0026thinsp;1.362, 95% CI: 1.118\u0026ndash;1.658, P\u0026thinsp;=\u0026thinsp;2.15*10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). Two alternative estimation methods showed similar results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Online Resource 2 Fig \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the sensitivity analyses, heterogeneity was identified with the Cochran Q test in the APOB-T1D and APOC3-T1D pairs, therefore, IVW-mre analyses were performed as the main method to minimize the impact of heterogeneity. The MR-Egger intercept test and MR-PRESSO indicated no evidence of horizontal pleiotropy in causal effects of HMGCR, ANGPTL3 and CETP on the risk of T1D (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Due to the potential horizontal pleiotropy in the APOC3-T1D pair (P\u003csub\u003eMR\u0026minus;PRESSO\u003c/sub\u003e = 0.001), we then identified rs533556 as an outlier based on MR-PRESSO analysis, and the causal effect remained significant after the exclusion of the outlier. Moreover, the \u0026ldquo;leave-one-out\u0026rdquo; test was carried out and identified rs247616 as a dominant SNP that affected the robustness of the MR result in the CETP-T1D pair (Online Resource 2 Fig S3). After excluding this high-impact SNP, the causal effect was close to null (OR\u0026thinsp;=\u0026thinsp;0.615, 95% CI: 0.348\u0026ndash;1.086, P\u0026thinsp;=\u0026thinsp;9.38*10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eIn the replication analyses, the ANGPTL3 inhibitor was the only lipid-lowering drug that showed a statistically significant effect on T1D (OR\u0026thinsp;=\u0026thinsp;0.938, 95% CI: 0.903\u0026ndash;0.975, P\u0026thinsp;=\u0026thinsp;1.28*10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). A similar protective trend between genetic proximity to HMGCR inhibitor and the risk of T1D was observed although it did not reach the statistical significance (OR\u0026thinsp;=\u0026thinsp;0.998, 95% CI: 0.977\u0026ndash;1.020, P\u0026thinsp;=\u0026thinsp;8.89*10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). As for the APOC3 inhibitor, we detected an opposite trend (OR\u0026thinsp;=\u0026thinsp;0.998, 95% CI: 0.990\u0026ndash;1.006, P\u0026thinsp;=\u0026thinsp;6.55*10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Online Resource 1 Table S6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Lipid-lowering drug targets and the risks of type 1 diabetic complications\u003c/h2\u003e \u003cp\u003eOur study suggested that genetic tendency for ANGPTL3 inhibitor significantly reduced the risk of DKD in T1D (OR\u0026thinsp;=\u0026thinsp;0.396, 95% CI: 0.193\u0026ndash;0.815, P\u0026thinsp;=\u0026thinsp;1.15*10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) while HMGCR inhibitor was causally associated with the greater risk of DR and DN in T1D (OR \u003csub\u003eDR\u003c/sub\u003e=2.203, 95% CI \u003csub\u003eDR\u003c/sub\u003e: 1.327\u0026ndash;3.658, P \u003csub\u003eDR\u003c/sub\u003e=2.26*10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, OR \u003csub\u003eDN\u003c/sub\u003e=1.647, 95% CI \u003csub\u003eDN\u003c/sub\u003e: 1.238\u0026ndash;2.192, P \u003csub\u003eDN\u003c/sub\u003e=6.16*10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These causal effects remained robust after conducting multiple sensitivity analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Mediation MR\u003c/h2\u003e \u003cp\u003eFor significant causal relationships, genetic predisposition to lipid-lowering drug targets may impact T1D by affecting the level of potential mediators. In the mediation MR analysis, BMI was observed to play a mediating role in the causal association between genetic mimicry of ANGPTL3 inhibitors and the T1D risk, of which the proportion of mediating effect was 5.71% (95% CI: 2.43%-17.71%) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\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\u003eMediation effects in the relationship between drug targets and type 1 diabetes.\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMediator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion of mediation effects in the total effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANGPTL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.43% \u0026minus;\u0026thinsp;17.71%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHMGCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-22.51% \u0026minus;\u0026thinsp;1.99%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANGPTL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-16.65% \u0026minus;\u0026thinsp;1.62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHMGCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-16.45% \u0026minus;\u0026thinsp;1.07%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOmega 3 fatty acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANGPTL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-81.79% \u0026minus;\u0026thinsp;43.32%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHMGCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-36.44% \u0026minus;\u0026thinsp;23.90%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANGPTL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.31% \u0026minus;\u0026thinsp;12.06%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHMGCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.60% \u0026minus;\u0026thinsp;1.62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI mediated 5.71% of the total effect of ANGPTL3 inhibitors on T1DM. Abbreviations: BMI, body mass index; CI: confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Gene expression in tissues\u003c/h2\u003e \u003cp\u003eIn consideration of the vital roles of HMGCR and ANGPTL3 inhibitors in reducing the T1D risk mentioned earlier, we obtained genetic variants, eQTLs, of two drug targets from the GTEx-V8 database and subsequently conducted the MR analysis for validation. It was observed that the increased protein expression of HMGCR in the skeletal muscle tissue was causally associated with an increase in T1D risk (OR\u0026thinsp;=\u0026thinsp;1.230, 95% CI: 1.104\u0026ndash;1.370, P\u0026thinsp;=\u0026thinsp;1.81*10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), which was consistent with our finding mentioned before. On account of the lack of eQTLs significant to ANGPTL3 in the liver tissue, we widened the eQTL inclusive criteria to a P-value threshold of 2*10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e and thus discovered the causal linkage between a 1-SD increase in ANGPTL3 expression and T1D risk (OR\u0026thinsp;=\u0026thinsp;1.140, 95% CI: 1.035\u0026ndash;1.255, P\u0026thinsp;=\u0026thinsp;7.73*10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Colocalization\u003c/h2\u003e \u003cp\u003eThe probability that ANGPTL3 expression in the liver tissue shared a causal variant with the T1D trait was 2.2%. The probability of a shared genetic variant between HMGCR expression in the muscle tissue and the T1D trait was 1.5% (Online Resource 1 Table S7).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWe conducted the drug target MR analysis in our study, aiming to investigate the potential causal effects of lipid traits and lipid-modifying drug targets on the risk of T1D and its complications. Our study yielded four critical conclusions: (1) Both ANGPTL3 and HMGCR were found to reduce the risk of T1D remarkably; (2) ANGPTL3 inhibitor was additionally associated with the risk reduction of renal complications in T1D; (3) There was no sufficient evidence of causal relationships between lipid traits and T1D, indicating that protective effects of ANGPTL3 and HMGCR inhibitors on T1D were independent of their lipid-lowering effects. (4) BMI was likely to play a role in mediating the effect of genetically mimicked ANGPTL3 inhibitor on reducing T1D risk.\u003c/p\u003e \u003cp\u003eGenetic mimicry of ANGPTL3 inhibitor was found to lower the risk of T1D significantly in our study. This finding was validated in the replication dataset, human tissues and a range of sensitivity analyses. Besides, HMGCR was the other lipid-lowering drug target related to the reduced risk of T1D, although the causal effect was not validated in the replication dataset. Unfortunately, our colocalization analysis did not reveal the causal variants between these two drug targets and T1D. This may be attributed to the different methodologies between MR and colocalization: MR is relatively liberal while colocalization employs skeptical priors based on the genome-wide testing practice and tends to be conservative [42].\u003c/p\u003e \u003cp\u003eThe effects of lipid traits on T1D remained disputed. Our study suggested that genetically proxied plasma lipids were not causally related to the risk of T1D while several observational studies yielded inconsistent findings. A prospective cohort study indicated that apoA-1 reduced the T1D risk independently ( HR\u0026thinsp;=\u0026thinsp;0.65, 95CI: 0.58\u0026ndash;0.73) while the increase of TGs and ApoB levels were associated with a high risk of T1D ( HR \u003csub\u003eTG\u003c/sub\u003e = 1.49, 95 CI \u003csub\u003eTG\u003c/sub\u003e: 1.41\u0026ndash;1.58; HR \u003csub\u003eApoB\u003c/sub\u003e = 1.20, 95 CI \u003csub\u003eApoB\u003c/sub\u003e: 1.08\u0026ndash;1.34) [11]. Evidence also suggested that hyperlipidemia occurred frequently in T1D patients, especially in T1D patients with poor glycemic control, which was characterized by the increase of serum TGs and LDL-C levels [9, 10]. These findings above were not in conformity with our conclusions and the discrepancy might be explained by confounding biases, reverse causality and misclassification error among different types of diabetes.\u003c/p\u003e \u003cp\u003eInhibition or antibody of ANGPTL3, such as Evinacumab, is an advanced lipid-lowering drug, the fundamental pharmacological mechanism of which is to descend the expression of ANGPTL3 and additionally activate lipoprotein lipase (LPL) by blocking its coordination with ANGPTL8 [43]. The specific mechanism where T1D risk reduction was beneficial from the inhibition of ANGPTL3 remained currently unclear although it was supported at the level of animal experiments: the expression of ANGPTL3 was increased in insulin-deficient diabetic mice [44]. Evidence for no causal association between lipid traits and T1D indicated that ANGPTL3 inhibitor probably reduced the risk of T1D through other underlying mechanisms, rather than via exerting their lipid-modifying effects. Consequently, we additionally conducted the mediation MR analysis and highlighted BMI as the mediating factor in the process by which ANGPTL3 inhibitor lessened the risk of T1D.\u003c/p\u003e \u003cp\u003eObesity, especially childhood overweight, was found to enhance the risk of T1D [45, 46] through imposing pressure on pancreatic islet beta cells. There tends to be a higher insulin demand in obese individuals, which results in increased stress on beta cells. These beta cells in an overloaded state are vulnerable to autoimmune factors and cytokines [45], thus leading to islet autoimmunity and T1D. \u0026ldquo;Overload Hypothesis\u0026rdquo; [47] and \u0026ldquo;Accelerator Hypothesis\u0026rdquo; [48] have also been proposed and attributed the adverse effect of BMI on T1D to genetic predisposition and insulin resistance, respectively. Recent studies have discovered the linkage between ANGPTL3 and body weight control. The serum level of ANGPTL3 was observed to increase in overweight individuals compared to the lean population [49]. Another study found that MiRNA-181d, a kind of micromolecule contributing to the repression of ANGPTL3 expression, was effective in the prevention of obesity[50]. To sum up, through controlling weight, ANGPTL3 inhibitor is likely to lighten the burden of beta cells and improve islet autoimmunity, thus leading to the reduction of T1D risk.\u003c/p\u003e \u003cp\u003eFurther MR analyses indicated that genetic predicted lifetime use of ANGPTL3 inhibitor was effective in reducing the risk of DKD in T1D. DKD, also known as diabetic nephropathy, is one of the most common complications of T1D [51]. Patients with diabetic nephropathy in T1D tend to suffer from a progressive deterioration of kidney function and die of renal failure ultimately. Recent studies suggested the plasma ANGPTL3 level was increased in patients with diabetic nephropathy [52]. The effect of ANGPTL3 inhibition on diabetic nephropathy was also verified in mouse experiments, where researchers found that the knockdown of ANGPTL3 was able to improve renal function reversibly in mice with diabetic nephropathy, by improving podocyte epithelial-mesenchymal transition (EMT), changing macrophages\u0026rsquo; polarization and suppressing inflammatory factors [53].\u003c/p\u003e \u003cp\u003eOur MR result also implied the possible causal relationship between inhibition of HMGCR and a decreased T1D risk at the genome-wide level, whereas it does not seem to be supported by other studies. Two MR studies involving HMGCR inhibitor and T1D did not detect the similar causation, of which Yang G discovered a potential protective trend (OR\u0026thinsp;=\u0026thinsp;0.85, 95% CI: 0.46\u0026ndash;1.56, p\u0026thinsp;=\u0026thinsp;5.99*10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)[54] while Xie W did not demonstrate the significant result (OR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 0.60\u0026ndash;1.75, p\u0026thinsp;=\u0026thinsp;9.28*10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)[55]. Although inhibition of HMGCR, statin, is broadly used in T1D patients with circulatory complications owing to cardiovascular benefits, its impaired effect on glycemic control has been constantly mentioned in observational and cell-based studies. It is observed that statin-treated T1D patients had higher HbA1c level [14] and lower insulin sensitivity[15] than those without statin treatment, which may be ascribed to its influence on calcium channels in pancreatic beta cells or the decrease in Glut-4 transporter translocation[56, 57]. Moreover, these findings can also explain the reason why the HMGCR inhibitor was detected to increase the risk of DR and DN in T1D in our study. Chances are that hyperglycemia and insulin resistance induced by statin treatment may lead to DR and DN in T1D through possible mechanisms mentioned above. Notably, although the phenomenon where the statin-treatment triggered DR and DN in T2D was discussed in many studies [58, 59], few have explored the statin-induced DR and DN in T1D [60], which emphasizes the need for further studies.\u003c/p\u003e \u003cp\u003eOur findings may provide insights into fundamental researches and clinical practice. Given that ANGPTL3 inhibitor, a novel lipid-modifying drug, not only reduces the T1D risk, but also provides extra benefits to treat renal complications, it may be a promising candidate for treating T1D patients, especially those with renal complications. Consequently, it is of great worth conducting further fundamental researches and randomized controlled trials to investigate specific mechanisms and ascertain effectiveness. In contrast, although there is a consensus about the excellent efficacy of HMGCR inhibitor in diabetic cardiovascular lesions, we found that its protective effect on T1D is not clear enough and the impaired effect on glucose control may lead to an increased risk of DR and DN in T1D. Further studies are necessarily required to reevaluate its priority for T1D treatment among lipid-lowering drugs.\u003c/p\u003e \u003cp\u003eThere are some noteworthy strengths in our study. To the best of our knowledge, this is the first drug-target MR study aiming to evaluate the causal effects of lipid traits and lipid-lowering drug targets on the risk of T1D and its complications. Based on the randomized allocation of alleles, MR is highly effective in mitigating confounding biases and exploring causality. In our study, we extracted genetic statistics from the latest and largest-scale GWAS data to minimize the impact of the \u0026ldquo;winner\u0026rsquo;s curse\u0026rdquo;. Both the MR-Egger intercept test and MR-PRESSO were performed to detect the potential evidence of pleiotropy and maintain the robustness of the results. Through combining eQTL data of drug targets and T1D GWAS in MR and colocalization, our study provided additional evidence for causal associations.\u003c/p\u003e \u003cp\u003eHowever, there are still several limitations. First, IVs of drug targets selected in our study represented the lifetime use of drugs, therefore, the short-term effects of lipid-modifying drugs in clinical practice may not correspond with our findings. Second, although the MR-Egger intercept test and MR-PRESSO were introduced in our study, the influence of horizontal pleiotropy cannot be eliminated completely. Third, our study specifically evaluated the effects of drug targets, rather than the off-target effects. On top of that, since our study specifically focused on people of European ancestry, additional attention should be paid to verifying the generalizability of our findings to other populations.\u003c/p\u003e \u003cp\u003eIn conclusion, circulating lipids are not causally associated with the risk of T1D at the genome-wide level. ANGPTL3 inhibitor, a novel lipid-modifying drug, was identified as a promising candidate for treating T1D and its renal complication, of which BMI may play a mediating role.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization:\u0026nbsp;C. Z.; Methodology: H. W.; Formal analysis: H. W.; Writing - original draft preparation: H. W., Z. L., Z. Y., Y. L.; Writing review and editing: C. Z., H. W.; Resources: C. Z.; Supervision: C. Z.; All authors commented on previous versions of the manuscript; All authors read and approved the final manuscript. Guarantor: C. Z.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince the original data sources in the European Bioinformatics Institute database, the FinnGen consortium and the GTEx project for MR were all ethically approved and consented by participants, no additional ethical approval was required for this study after consultation with the Ethics Committee of Soochow University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenetic association information was obtained from the European Bioinformatics Institute (EBI) database, the FinnGen consortium and GTEx-V8 project. The authors thank all investigators for sharing these data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChellappan DK, Sivam NS, Teoh KX, et al.:Gene therapy and type 1 diabetes mellitus. Biomed Pharmacother 108: 1188-1200.(2018). 10.1016/j.biopha.2018.09.138\u003c/li\u003e\n\u003cli\u003eBlagov AV, Summerhill VI, Sukhorukov VN, Popov MA, Grechko AV, Orekhov AN:Type 1 diabetes mellitus: Inflammation, mitophagy, and mitochondrial function. Mitochondrion 72: 11-21.(2023). 10.1016/j.mito.2023.07.002\u003c/li\u003e\n\u003cli\u003eGreen A, Hede SM, Patterson CC, et al.:Type 1 diabetes in 2017: global estimates of incident and prevalent cases in children and adults. Diabetologia 64(12): 2741-2750.(2021). 10.1007/s00125-021-05571-8\u003c/li\u003e\n\u003cli\u003eKatsarou A, Gudbj\u0026ouml;rnsdottir S, Rawshani A, et al.:Type 1 diabetes mellitus. 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JAMA Ophthalmol 133(5): 503-510.(2015). 10.1001/jamaophthalmol.2014.5108\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Dyslipidemias, lipid-lowering drug therapy, type 1 diabetes, mendelian randomization, expression quantitative trait locus, colocalization","lastPublishedDoi":"10.21203/rs.3.rs-4537908/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4537908/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo evaluate causal effects of lipid traits and lipid-lowering drug targets on the risk of type 1 diabetes (T1D) and its complications.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eOur study conducted two-sample and drug-target Mendelian randomization (MR) to assess the genetic association of lipid traits and lipid-lowering drug targets with the type 1 diabetes risk, respectively. For significant lipid-modifying drug targets, data for expressions in tissues and colocalization provided extra evidence for causality. We also explored underlying mechanisms through mediation MR.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe two-sample MR analyses detected no causal association between lipid traits and T1D. In the drug-target MR analyses, ANGPTL3 inhibitor was associated with a decreased risk of T1D (OR\u0026thinsp;=\u0026thinsp;0.668, 95% CI: 0.511\u0026ndash;0.874, P\u0026thinsp;=\u0026thinsp;3.21*10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), of which BMI mediated 5.71% of the total effect. This was validated through multiple sensitivity analyses, replication dataset and tissue sample data. Moreover, ANGPTL3 inhibitor was also found to reduce the risk of diabetic kidney diseases. Although HMGCR inhibitor reduced the risk of T1D in the primary dataset, it was not validated in the replication dataset, and HMGCR inhibitor showed adverse effects on diabetic retinopathy and neuropathy.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCirculating lipids are not causally associated with the risk of T1D. ANGPTL3 inhibitor, a novel lipid-lowering drug, may be a promising candidate for treating T1D and its renal complication, with BMI probably mediating part of the effect.\u003c/p\u003e","manuscriptTitle":"Genetic association of lipids and lipid-lowering drug targets with the risk of type 1 diabetes and its complications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-20 11:15:48","doi":"10.21203/rs.3.rs-4537908/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":"6f1d0b7f-66e8-4eb1-90bf-7d7e2e203735","owner":[],"postedDate":"June 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-20T11:15:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-20 11:15:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4537908","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4537908","identity":"rs-4537908","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}