Potential Therapeutic Targets for Type 2 Diabetes: A Multi-Center Data Analysis Based on Proteomics

Potential Therapeutic Targets for Type 2 Diabetes: A Multi-Center Data Analysis Based on Proteomics | 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 Potential Therapeutic Targets for Type 2 Diabetes: A Multi-Center Data Analysis Based on Proteomics Yue-Yang Zhang, Qin Wan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5897046/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Despite the advent of numerous pharmacological agents designed to enhance the management of type 2 diabetes, treating this condition continues to pose significant challenges. Consequently, this study aims to identify potential therapeutic targets for type 2 diabetes through Mendelian randomization(MR). Methods This research primarily utilizes publicly accessible data from extensive genome-wide association studies and protein quantitative trait locus studies. The study predominantly adopts an MR framework to estimate the causal effects of specific proteins on the risk of developing type 2 diabetes. To corroborate the findings, multiple validation methodologies are employed, including summary data-based Mendelian randomization (SMR), external validation cohorts, bayesian colocalization analysis, single-cell analysis, protein-protein interaction (PPI) networks, pathways derived from KEGG 2021 Human and DsigDB. Results Our study identifies 25 proteins potentially associated with the risk of type 2 diabetes. SMR and external validation affirmed that 14 proteins, including ARG1, PAM, WWOX, and SHBG, may function as viable therapeutic targets for type 2 diabetes. Single-cell analysis illuminated the expression patterns of six proteins predominantly in pancreatic islets. Colocalization analysis and PPI networks elucidated the potential roles of these proteins in the pathogenesis of type 2 diabetes. The KEGG 2021 Human database indicated that the glycan degradation pathway may mediate the effects of MANBA and HEXB on the development of type 2 diabetes. DsigDB identified 41 potential therapeutic drugs for the treatment of type 2 diabetes. Conclusion These findings provide novel insights and directions for drug development aimed at the prevention and treatment of type 2 diabetes. proteomics type 2 diabetes Mendelian randomization bayesian co-location single-cell analysis Figures Figure 1 Figure 2 Figure 3 Highlights The global burden of type 2 diabetes is increasing and more and more people will suffer from type 2 diabetes in the future; Although there are a large number of medications available for the treatment of type 2 diabetes, there is still no cure for type 2 diabetes; The development of high-throughput assays has provided new directions for exploring the association between proteomics and disease; Fourteen plasma proteins, including ARG1, PAM, WWOX and SHBG, may serve as potential therapeutic targets for type 2 diabetes mellitus. Introduction Diabetes is a metabolic disorder marked by insulin resistance or impaired insulin secretion, standing as one of the most prevalent and critical diseases within the endocrine system 1 . Over the past few decades, the global burden of type 2 diabetes has steadily escalated, with incidence rates showing a particularly marked increase, nearly doubling every thirty years. By 2021, the global incidence of type 2 diabetes had surged to 9.8% 2 . According to projections from the International Diabetes Federation (IDF), by 2045, one in eight adults worldwide will be diagnosed with diabetes, and the global diabetic population is projected to skyrocket to an astonishing 783 million 3 . These staggering figures underscore the escalating burden that type 2 diabetes imposes on global public health, with this trend anticipated to persist in the foreseeable future 4 . Consequently, numerous scholars have suggested that type 2 diabetes be recognized as an emerging epidemic 5 . Despite the development of numerous pharmacological treatments for type 2 diabetes, an effective cure remains elusive, largely due to the complex and poorly understood pathogenesis of the disease. Extensive observational studies have demonstrated that a range of factors—including genetic predisposition, unhealthy dietary patterns, obesity, and sedentary lifestyles—significantly increase susceptibility to type 2 diabetes 6 , 7 . Notably, a family history of diabetes is widely recognized as a critical factor in the diagnostic criteria for type 2 diabetes 8 . Therefore, to further elucidate the genetic underpinnings of type 2 diabetes, researchers have launched large-scale genome-wide association studies (GWAS) 9 . Additionally, with the continued advancement of high-throughput techniques for serum protein detection and quantification, researchers have increasingly utilized proteomics to investigate the intricate relationship between proteins and diseases, aiming to deepen our understanding of the molecular mechanisms driving disease pathogenesis 10 . A prior Mendelian randomization study identified 47 plasma proteins significantly associated with type 2 diabetes from a cohort of 1886 plasma proteins 11 . In our earlier research, we identified 23 plasma protein-to-protein ratios that were closely linked to type 2 diabetes 12 . Mendelian randomization (MR) has emerged as a compelling alternative to randomized controlled trials, employing genetic instruments to supplant the conventional exposure-disease framework. Based on Mendelian principles, genetic information is randomly allocated at conception, predating the onset of any disease, thereby minimizing confounding bias 13 . Consequently, this study aims to investigate the potential association between plasma proteins and type 2 diabetes through Mendelian randomization, leveraging GWAS data from multiple extensive cohorts. Methods Study Design This study is fundamentally anchored in the Mendelian randomization (MR) framework, leveraging genetic data from several extensive cohorts, including Decode and FinnGen, to investigate the associations between 4,907 proteins and type 2 diabetes, with the overarching goal of identifying potential therapeutic targets for the condition. Moreover, summary-level Mendelian randomization (SMR), external validation cohorts, Bayesian colocalization analysis, protein-protein interaction (PPI) networks, DsigDB, and KEGG 2021 Human pathways were employed for rigorous multi-layer validation, ensuring the robustness and reliability of the findings. Figure 1 provides a comprehensive depiction of the primary framework of this investigation. Data Sources All data utilized in this study are derived from large-scale genome-wide association study (GWAS) cohorts conducted in European populations, sourced from publicly available summary-level datasets (Table S1 ). We leveraged the most recent genetic association data on type 2 diabetes from the FinnGen DF11 cohort, which comprised 71,728 individuals diagnosed with type 2 diabetes and 369,007 controls. The FinnGen study represents an extensive genomic initiative originating from Finland, designed to analyze over 500,000 samples from the Finnish biobank, with the goal of elucidating associations between genetic variants and health outcomes, thereby advancing a comprehensive understanding of disease etiology and susceptibility factors 14 . The proteomic genetic data were sourced from the Decode cohort, consisting of 35,559 Icelandic participants, covering a comprehensive set of 4,907 proteins 15 . The cis-eQTL data for summary-level Mendelian randomization (SMR) were obtained from the phase I eQTLGen project, an initiative of the eQTLGen Consortium, which analyzed whole blood samples from 31,684 individuals across diverse global population cohorts, encompassing 10,317 trait-associated single nucleotide polymorphisms (SNPs) 16 . For eQTL information absent in phase I of eQTLGen, alternative methods were employed for supplementary validation. SHBG data were extracted from a genome-wide association study (GWAS) conducted on the UK Biobank cohort (N = 425,097) 17 . For other missing protein data, genetic information related to type 2 diabetes from a meta-analysis was utilized as an external validation cohort 18 . Instrument selection To ensure the robustness of the study findings, we implemented rigorous selection criteria for protein quantitative trait loci (pQTL). Initially, the study concentrated on cis-pQTL, yielding a total of 1,614 proteins that were available for subsequent analyses. The significance threshold for SNPs was established at 5×10 − 8 , with relatively independent SNPs being filtered utilizing the European 1000 Genomes panel, applying a linkage disequilibrium threshold (r²) of less than 0.001 across a 10,000 kb genomic span. Furthermore, the F-statistic was utilized to evaluate the statistical power of all SNPs, with those exhibiting an F-statistic of less than 10 being excluded to mitigate bias associated with weak instrumental variables. Mendelian randomization and validation processes Initially, leveraging the established framework of two-sample MR, we evaluated the association between candidate proteins and type 2 diabetes employing inverse variance weighting (IVW) or Wald ratio methodologies to calculate the overall effect size. Concurrently, we utilized four sensitivity analysis methods—the weighted median, simple mode, weighted mode, and MR-Egger test—to bolster the reliability of the IVW findings. The false discovery rate (FDR) was employed to mitigate bias associated with multiple testing, while Cochran's Q test was utilized to assess heterogeneity (P < 0.05), and the MR-Egger intercept test was conducted to evaluate horizontal pleiotropy (P < 0.05). Reverse MR was employed to elucidate potential reverse causal associations. Bayesian colocalization analysis was conducted to evaluate the probability that two traits share causal variants, thereby ensuring that the observed association is not attributable to potential genetic confounding factors. Genes exhibiting a posterior probability (PP4) ≥ 0.8 were regarded as having strong evidence supporting colocalization, whereas those with PP4 ≥ 0.5 were deemed to possess moderate evidence for colocalization 19 . Subsequently, supplementary validation of the two-sample MR analysis results was conducted through SMR or external cohorts to corroborate the association between candidate proteins and type 2 diabetes 20 . All statistical analyses were executed utilizing the TwoSampleMR package (version 0.5.6) and the coloc package (version 5.2.3) within the R software environment (version 4.3.3). Statistical significance was determined using stringent criteria for two-tailed tests. Single-cell RNA sequencing elucidation, network analysis, and drug prediction We elucidated the protein expression profile within the pancreatic microenvironment through the application of single-cell analysis data 21 . We integrated the results of MR analysis with PPI by utilizing databases such as STRING and DrugBank, aiming to investigate the potential interactions of candidate proteins and their therapeutic implications 22 – 24 . Furthermore, we predicted the pathways and potential pharmacological agents associated with the mechanisms linking candidate proteins to type 2 diabetes, employing relevant data from the KEGG 2021 Human database and DsigDB 25 , 26 . Results Instrument selection As illustrated in Table S2 , we meticulously selected between 3 and 226 SNPs based on stringent screening criteria to examine the relationship between 1,614 proteins and type 2 diabetes. The F-statistic for all selected genetic instruments exceeded 10, indicating that this study significantly mitigates bias associated with weak instrumental variables. Identification of type 2 diabetes-associated proteins through proteome analysis Initially, we identified 276 proteins potentially associated with type 2 diabetes (P-IVW < 0.05); however, following false discovery rate (FDR) correction, only 25 proteins remained significantly associated with type 2 diabetes (FDR < 0.05). Among these, ten proteins, including WW domain-containing oxidoreductase (WWOX) and glutathione synthetase (GSS), exhibited a positive correlation with the risk of type 2 diabetes. In contrast, 15 proteins, such as arginase 1 (ARG1) and sex hormone-binding globulin (SHBG), were negatively correlated with this risk (Fig. 2 A, 2 B; Table 1 ; Table S3-5). Table 1 Mendelian randomization results for proteins significantly related to type 2 diabetes Protein Top SNP Effect allele Other allele OR (95% CI) P value (IVW) FDR F statistics ARG1 rs2781668 T C 0.7(0.62,0.78) 1.99E-09 1.61E-06 125.59 ICAM5 rs11575073 T C 0.96(0.95,0.98) 2.28E-09 3.68E-06 3360.57 PAM rs34596924 C A 0.96(0.95,0.98) 2.94E-08 1.58E-05 8576.63 APOE rs71352239 T C 1.15(1.09,1.21) 3.04E-08 4.90E-05 778.90 TREML2 rs9381017 C T 0.96(0.94,0.97) 5.67E-08 9.16E-05 3341.21 SVEP1 rs61751937 C G 1.04(1.03,1.06) 1.03E-07 1.67E-04 1229.44 WWOX rs11545029 A G 1.42(1.24,1.61) 1.37E-07 2.22E-04 74.40 HEXB rs13164140 A G 1.08(1.05,1.12) 3.37E-07 5.44E-04 895.86 MANBA rs227275 C A 0.94(0.92,0.96) 1.17E-06 6.28E-04 2138.18 TGFBI rs35901765 T C 0.95(0.93,0.97) 1.24E-06 2.00E-03 4215.01 GSS rs6141479 C G 1.23(1.13,1.35) 2.25E-06 1.82E-03 110.92 FCRLB rs1954172 C T 1.05(1.03,1.07) 2.48E-06 4.00E-03 1972.09 NCAN rs2228603 T C 0.85(0.79,0.91) 2.93E-06 4.73E-03 422.61 RET rs2435359 G A 1.06(1.03,1.08) 3.16E-06 5.11E-03 1363.37 TGFBR3 rs59178722 C T 1.18(1.1,1.26) 3.35E-06 5.41E-03 114.35 ERAP1 rs3822683 G A 0.98(0.98,0.99) 4.55E-06 7.34E-03 42045.66 GAS6 rs6602909 C T 0.93(0.9,0.96) 4.57E-06 7.38E-03 889.38 CHRDL2 rs61389091 T C 1.07(1.04,1.1) 6.65E-06 0.01 1157.60 ICAM1 rs5030352 G C 1.02(1.01,1.03) 7.16E-06 0.01 14199.93 VIT rs12053542 T C 0.96(0.94,0.98) 9.61E-06 0.02 2394.83 FCGR2A rs6658353 C G 0.98(0.98,0.99) 1.28E-05 0.02 96629.59 PLAU rs2227551 T G 0.94(0.92,0.97) 1.72E-05 0.03 970.20 SHBG rs858519 C T 0.87(0.81,0.93) 2.49E-05 0.04 426.49 CRTAM rs2370794 G A 0.95(0.93,0.97) 2.96E-05 0.04 687.65 IL6R rs11265611 A G 0.7(0.62,0.78) 2.99E-05 0.04 19832.37 Sensitivity analysis and co-location analysis While Cochran's Q test indicated heterogeneity in the associations of PAM, APOE, TREML2, MANBA, TGFBI, RET, ERAP1, ICAM1, SHBG, and IL6R with type 2 diabetes, the consistent direction of effects observed across sensitivity analyses—namely, the weighted median, simple mode, weighted mode, and MR-Egger test—suggests that this heterogeneity can be considered negligible. The MR-Egger intercept test identified only neurocan (NCAN) and the ret proto-oncogene (RET) as exhibiting horizontal pleiotropy (Table 2 ). Although the reverse Mendelian randomization analysis suggested reverse associations between several proteins, including ARG1, and type 2 diabetes, the limited availability of SNPs raises concerns about the potential for significant bias in the reverse MR results (Table S6). Bayesian colocalization analysis yielded compelling evidence supporting the associations of ARG1, mannosidase beta (MANBA), GSS, neurocan (NCAN), plasminogen activator (PLAU), and SHBG with type 2 diabetes. In contrast, moderate evidence was identified for intercellular adhesion molecule 5 (ICAM5), WWOX, transforming growth factor beta-induced (TGFBI), and TGF-beta receptor 3 (TGFBR3) in their association with type 2 diabetes (Fig. 2 C, Table 2 ). Table 2 Overview of Cochran's Q test, MR-Egger intercept test, Bayessian co-localisation analysis detection on twenty-five candidate target protein Protein Q(IVW) Q_df(IVW) Q_pval(IVW) Egger_intercept SE P-Value Co-localization PPH 4 ARG1 4.20 3.00 0.24 -1.55E-02 0.02 0.50 0.99 ICAM5 104.43 84.00 0.06 4.44E-03 2.39E-03 0.07 0.73 PAM 181.96 83.00 2.28E-09 -1.92E-04 2.64E-03 0.94 2.33E-04 APOE 126.61 33.00 6.82E-13 2.41E-03 0.01 0.78 4.49E-11 TREML2 143.09 90.00 3.15E-04 -2.19E-03 2.24E-03 0.33 4.53E-03 SVEP1 78.93 77.00 0.42 1.34E-03 1.88E-03 0.48 0.10 WWOX 0.45 2.00 0.80 -3.36E-04 0.02 0.99 0.78 HEXB 15.79 16.00 0.47 5.03E-03 4.85E-03 0.32 0.17 MANBA 90.64 52.00 7.26E-04 5.42E-03 3.55E-03 0.13 0.98 TGFBI 93.98 68.00 0.02 -1.35E-03 2.77E-03 0.63 0.69 GSS 19.64 9.00 0.02 -2.47E-02 0.02 0.14 0.99 FCRLB 68.61 51.00 0.05 8.04E-04 2.74E-03 0.77 0.01 NCAN 13.48 8.00 0.10 -1.62E-02 6.34E-03 0.04 0.92 RET 80.12 44.00 7.12E-04 1.02E-02 3.43E-03 4.74E-03 0.02 TGFBR3 7.88 7.00 0.34 -1.77E-02 0.01 0.11 0.50 ERAP1 179.75 136.00 0.01 -4.85E-05 2.24E-03 0.98 0.47 GAS6 19.18 25.00 0.79 -6.22E-03 4.31E-03 0.16 0.02 CHRDL2 31.99 26.00 0.19 1.24E-03 3.55E-03 0.73 0.04 ICAM1 181.82 139.00 0.01 -1.15E-03 1.93E-03 0.55 0.01 VIT 91.74 73.00 0.07 -3.01E-03 3.01E-03 0.32 3.79E-03 FCGR2A 183.16 164.00 0.15 -1.04E-04 1.74E-03 0.95 3.76E-03 PLAU 31.13 27.00 0.27 1.91E-03 4.31E-03 0.66 0.83 SHBG 48.34 16.00 4.20E-05 1.96E-02 0.01 0.12 0.99 CRTAM 37.73 38.00 0.48 -1.73E-03 2.69E-03 0.52 0.02 IL6R 190.76 115.00 1.16E-05 3.64E-04 2.15E-03 0.87 0.01 Validation analysis Through SMR, we validated the associations between 20 identified proteins and type 2 diabetes. This analysis confirmed significant associations for ARG1, ICAM5, peptidyl arginine deiminase (PAM), WWOX, MANBA, GSS, endoplasmic reticulum aminopeptidase 1 (ERAP1), ICAM1, and PLAU with type 2 diabetes (Table 3 ). Furthermore, the external validation cohort corroborated potential associations between apolipoprotein E (APOE), sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SVEP1), NCAN, cysteine-rich with EGF-like domains 2 (CHRDL2), and SHBG and type 2 diabetes (Table S7). Table 3 Validation of selected protein-type 2 diabetes correlations using summary-data-based mendelian randomization. Protein ProbeID Chromosome Location β-SMR SE-SMR P-SMR P-HEIDI ARG1 ENSG00000118520 6 131899878 -0.07 0.01 7.51E-10 1.53E-03 ICAM5 ENSG00000105376 19 10404055 -0.08 0.04 0.04 0.03 PAM ENSG00000145730 5 102228247 0.02 6.27E-03 6.74E-04 0.04 TREML2 ENSG00000112195 6 41163473 0.05 0.04 0.20 0.16 WWOX ENSG00000186153 16 78689937 0.10 0.03 2.29E-03 0.05 HEXB ENSG00000049860 5 73977160 0.03 0.02 0.10 0.34 MANBA ENSG00000109323 4 103617405 -0.16 0.03 2.22E-09 1.11E-03 TGFBI ENSG00000120708 5 135382045 3.91E-03 0.02 0.80 0.31 GSS ENSG00000100983 20 33529928 0.40 0.18 0.03 5.66E-03 FCRLB ENSG00000162746 1 161694643 6.77E-04 0.03 0.98 0.39 RET ENSG00000165731 10 43599137 0.05 0.05 0.38 0.36 TGFBR3 ENSG00000069702 1 92258897 0.03 0.08 0.66 0.76 ERAP1 ENSG00000164307 5 96120162 -0.14 0.04 1.32E-04 0.22 GAS6 ENSG00000183087 13 114545285 0.05 0.04 0.23 0.12 ICAM1 ENSG00000090339 19 10389401 0.14 0.04 8.16E-04 0.03 VIT ENSG00000205221 2 36982884 0.03 0.02 0.15 0.50 FCGR2A ENSG00000143226 1 161484511 -1.61E-03 0.07 0.98 0.26 PLAU ENSG00000122861 10 75673095 -0.11 0.03 1.49E-04 6.70E-04 CRTAM ENSG00000109943 11 122726277 8.07E-03 0.02 0.74 0.14 IL6R ENSG00000160712 1 154409797 0.06 0.06 0.32 5.67E-04 Single-cell RNA sequencing elucidation Figures S1 -S3 illustrate the results of single-cell analyses conducted within the pancreatic islets, revealing distinct expression patterns of several proteins. Specifically, PAM is predominantly expressed in gamma (PP) cells, while FCGR2A, PLAU, APOE, and hexosaminidase B (HEXB) exhibit higher expression levels in Langerhans cells. Additionally, TGFBI is primarily localized in pancreatic stellate cells. These findings underscore the potential of these proteins as promising therapeutic targets for the management of type 2 diabetes. Protein interaction networks and data prediction Figure 3 depicts the intricate interactions among various proteins associated with type 2 diabetes, highlighting key axes such as the PAM-HEXB axis, CHRDL2-GAS6 axis, and FCRLB-TREML2 axis. These interactions may represent promising intervention pathways for the treatment of type 2 diabetes. Furthermore, the KEGG 2021 Human database indicates that the Other glycan degradation pathway, mediated by MANBA and HEXB, may also serve as a viable intervention route for type 2 diabetes (refer to Table S8). Additionally, insights from DsigDB suggest 41 potential therapeutic drugs for type 2 diabetes, including arsenic, 17-hydroxyandrostan-3-one, and tosylphenylalanylmethyl ketone (as detailed in Table S9). Discussion This study successfully identified 13 potential therapeutic targets for the prevention and treatment of type 2 diabetes, including ARG1 and SHBG. Utilizing an integrative approach that encompassed proteomics, bidirectional MR, Bayesian colocalization analysis, SMR, and PPI networks, along with insights from the KEGG 2021 Human database and DsigDB, we analyzed 4,907 plasma proteins. Among the identified targets, five proteins—ARG1, MANBA, GSS, PLAU, and SHBG—received strong support through colocalization analysis. Furthermore, these plasma proteins were linked to multiple potential pathways that mediate the onset and progression of type 2 diabetes, including the Other glycan degradation pathway. The study also predicts various potential therapeutic drugs for type 2 diabetes, such as arsenic, 17-hydroxyandrostan-3-one, and tosylphenylalanylmethyl ketone. Among the potential therapeutic targets identified in this study, the associations of ARG1, SHBG, and MANBA with type 2 diabetes have been documented in other cohorts. The findings of this study further underscore their interrelationships and suggest that the data utilized in this research are representative of broader trends 27 – 29 . SHBG is critical in regulating the biological effects of sex hormones, specifically testosterone and estrogen, on peripheral tissues, including the liver, muscle, and adipose tissue, thereby playing a significant role in the pathogenesis of type 2 diabetes 30 . Notably, two previous randomized controlled trials (RCTs) have reported marked differences in the effects of transdermal estradiol and oral estrogen on diabetes risk 31 , 32 . These differences may arise from the direct antagonistic properties of SHBG against estrogen at the cellular level. Additionally, the interaction between SHBG and estrogen receptors can trigger biological responses that exhibit anti-estrogen effects, further influencing the development of type 2 diabetes 33 . PAM is an enzyme localized in neuroendocrine cells, responsible for the modification of C-terminal glycine peptides to generate peptide-amides 34 . Research indicates that reduced PAM activity adversely affects insulin supply and the dynamics of granule exocytosis, ultimately leading to β-cell dysfunction 35 . Moreover, PAM may indirectly regulate β-cell function through amidation signals released by other tissues, such as glucagon-like peptide 1 (GLP-1), an amidated peptide secreted from intestinal L cells 36 , 37 . GSS primarily catalyzes the second step of glutathione biosynthesis in the form of a homodimer, converting gamma-L-glutamyl-L-cysteine into glutathione utilizing ATP 38 . Glutathione plays a vital role in various biological processes, including protecting cells from oxidative damage caused by free radicals, detoxifying xenobiotics, and facilitating membrane transport 39 . Previous studies have demonstrated that the overexpression of glutathione peroxidase can significantly reduce oxidative phospholipids, cholesterol hydroperoxides, and other harmful compounds, thereby alleviating vascular oxidative stress and mitigating the progression of atherosclerosis 40 . Additionally, glutathione S-transferase has been implicated in the onset of type 2 diabetes and its associated complications 41 . Consequently, further exploration of the association between GSS and type 2 diabetes is warranted to elucidate its potential role in disease pathogenesis. PLAU is a well-known serine protease that plays a pivotal role in fibrinolysis by activating plasminogen and converting it into plasmin, thereby facilitating the breakdown of thrombi and extracellular matrix components. An imbalance between the coagulation and fibrinolytic systems is closely associated with the development of intravascular thrombosis in patients with type 2 diabetes mellitus 42 . Additionally, PLAU has been shown to regulate the migration and recruitment of immune cells, independently promoting the release of various inflammatory factors, and thus plays a crucial role in tissue remodeling 43 . A previous animal study indicated that weak expression of PLAU impairs insulin secretion and the regeneration of β-cells in both mouse and cellular models, resulting in hyperglycemia. Furthermore, hyperglycemia itself can downregulate PLAU expression in the pancreas, creating a vicious cycle that exacerbates the onset and progression of type 2 diabetes 44 . This research presents several advantages compared to existing studies. First, although a previous MR study utilized similar proteomic data, the genetic information for type 2 diabetes employed in this study derives from the latest publicly available FinnGen data, which boasts a larger sample size and a greater number of available SNPs, rendering it more representative. Second, this study employed various validation methods, including Bayesian colocalization and SMR, which not only bolstered the reliability of the results but also yielded distinct conclusions from prior research, identifying several previously undiscovered therapeutic targets for type 2 diabetes. Third, all selected SNPs in this study exhibited F-statistics exceeding 10, thereby maximizing the avoidance of bias related to weak instrumental variables. Fourth, the study primarily utilized cis-expression quantitative trait loci (eQTL) and pQTL, which can effectively minimize bias stemming from horizontal pleiotropy. However, while interpreting our findings, several limitations must be acknowledged. First, it is crucial to recognize the inherent limitations of MR, such as trait heterogeneity and developmental compensation issues, which may influence the accuracy and generalizability of our results 45 . Second, the GWAS data utilized in our analysis originate from various large-scale sequencing studies, and differences in study design across these cohorts may introduce bias. Third, as our study primarily focuses on European populations, caution should be exercised when extrapolating the findings to more ethnically diverse populations, such as Asian cohorts. Lastly, although this study has unveiled numerous potential associations between plasma proteins and type 2 diabetes, our understanding of the underlying mechanisms remains incomplete, highlighting the need for further foundational research to elucidate these complex pathways. Conclusions In conclusion, this study presents a comprehensive proteomic analysis within a MR framework, integrated with a variety of robust validation methods, including SMR, external validation cohorts, Bayesian colocalization analysis, single-cell analysis, PPI networks, and pathways from KEGG 2021 Human and DsigDB. This multifaceted approach elucidates the potential associations between specific protein levels and the risk of developing type 2 diabetes, particularly highlighting the roles of ARG1, MANBA, GSS, PLAU, and SHBG. The results underscore the necessity for further foundational research to deepen our understanding of the intricate relationships between these proteins and the pathophysiology of type 2 diabetes. Moreover, these findings pave the way for novel therapeutic strategies, offering new avenues for the development of more precise and effective pharmacological interventions for type 2 diabetes. Declarations Ethics approval and consent to participate This study is based on publicly available summarized data. Ethical approval and informed consent had been obtained in all original studies. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding The Ministry of Science and Technology of China and Sichuan Provincial Health Commission provided funding for this study through grants 2016YFC0901200 and 23LCYJ026. Author Contribution YYZ made the most contributions to this work performing the statistical analysis, interpreting the results, and writing the paper. QW proposed the ideas. All authors contributed to the article and approved the submitted version. Acknowledgement We are grateful to the authors and participants of all GWASs for the summary statistics used. Data Availability The datasets supporting the conclusions of this article are included within the article. References Jb M. The Genetic Epidemiology of Type 2 Diabetes: Opportunities for Health Translation. Curr Diab Rep. 2019;19(8). 10.1007/s11892-019-1173-y . Global regional, national burden of diabetes. from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Lond Engl. 2023;402(10397). 10.1016/S0140-6736(23)01301-6 . H S, P S, S K, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183. 10.1016/j.diabres.2021.109119 Y Z, Sh L, Fb H. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14(2). 10.1038/nrendo.2017.151 . U MF. Population-wide weight loss and regain in relation to diabetes burden and cardiovascular mortality in Cuba 1980–2010: repeated cross sectional surveys and ecological comparison of secular trends. BMJ. 2013;346. 10.1136/bmj.f1515 . Ra H. Type 2 diabetes mellitus and psychological stress - a modifiable risk factor. Nat Rev Endocrinol. 2017;13(9). 10.1038/nrendo.2017.64 . Y W. Risk factors contributing to type 2 diabetes and recent advances in the treatment and prevention. Int J Med Sci. 2014;11(11). 10.7150/ijms.10001 . Mj SCKK. Type 2 diabetes. Lancet Lond Engl. 2017;389(10085). 10.1016/S0140-6736(17)30058-2 . D AM. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet. 2018;50(11). 10.1038/s41588-018-0241-6 . Jm MV, Ja K. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat Genet. 2020;52(7). 10.1038/s41588-020-0637-y . Yuan S, Xu F, Li X, et al. Plasma proteins and onset of type 2 diabetes and diabetic complications: Proteome-wide Mendelian randomization and colocalization analyses. Cell Rep Med. 2023;4(9). 10.1016/j.xcrm.2023.101174 . Zhang YY, Chen BX, Yang Q, Wan Q. The causal relationship between plasma protein-to‐protein ratios and type 2 diabetes and its complications: Proteomics mendelian randomization study. 10.1111/dom.15792 Da L, Rm H, Ja S, N T. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27(8). 10.1002/sim.3034 . Mi K, J K. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613(7944). 10.1038/s41586-022-05473-8 . Ferkingstad E, Sulem P, Atlason BA, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 2021;53(12):1712–21. 10.1038/s41588-021-00978-w . Võsa U, Claringbould A, Westra HJ, et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet. 2021;53(9):1300–10. 10.1038/s41588-021-00913-z . Ks R, Fr D. Using human genetics to understand the disease impacts of testosterone in men and women. Nat Med. 2020;26(2). 10.1038/s41591-020-0751-5 . M SS. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021;53(10). 10.1038/s41588-021-00931-x . J L, J Z, Y X. Potential drug targets for multiple sclerosis identified through Mendelian randomization analysis. Brain J Neurol. 2023;146(8). 10.1093/brain/awad070 Zhu Z, Zhang F, Hu H, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48(5):481–7. 10.1038/ng.3538 . Franzén O, Gan LM, Björkegren JLM. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Accessed October 15, 2024. https://dx.doi.org/10.1093/database/baz046 Al DS, Kc G. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1). 10.1093/nar/gkaa1074 . R DS. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51(D1). 10.1093/nar/gkac1000 . A DS. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43(Database issue). 10.1093/nar/gku1003 . Mv K, Mr J, Ad R, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44(W1). 10.1093/nar/gkw377 . Xie Z, Bailey A, Kuleshov MV et al. Gene Set Knowledge Discovery with Enrichr. 10.1002/cpz1.90 Mr R, Jb JC. Proteomic Predictors of Incident Diabetes: Results From the Atherosclerosis Risk in Communities (ARIC) Study. Diabetes Care. 2023;46(4). 10.2337/dc22-1830 . L SY. Genetically predicted sex hormone levels and health outcomes: phenome-wide Mendelian randomization investigation. Int J Epidemiol. 2022;51(6). 10.1093/ije/dyac036 . F G, N Y, Jb MY. Connecting Genomics and Proteomics to Identify Protein Biomarkers for Adult and Youth-Onset Type 2 Diabetes: A Two-Sample Mendelian Randomization Study. Diabetes. 2022;71(6). 10.2337/db21-1046 . Ding EL, Song Y, Manson JE, et al. Sex Hormone–Binding Globulin and Risk of Type 2 Diabetes in Women and Men. N Engl J Med. 2009;361(12):1152. 10.1056/NEJMoa0804381 . Kl M, De B, Rj R, et al. Effect of oestrogen plus progestin on the incidence of diabetes in postmenopausal women: results from the Women’s Health Initiative Hormone Trial. Diabetologia. 2004;47(7). 10.1007/s00125-004-1448-x . De B. The effect of conjugated equine oestrogen on diabetes incidence: the Women’s Health Initiative randomised trial. Diabetologia. 2006;49(3). 10.1007/s00125-005-0096-0 . Mg NFMB. Sex hormone-binding globulin, its membrane receptor, and breast cancer: a new approach to the modulation of estradiol action in neoplastic cells. J Steroid Biochem Mol Biol. 1999;69(1–6). 10.1016/s0960-0760(99)00068-0 . Sl M, Re M, Ba E. Identification of routing determinants in the cytosolic domain of a secretory granule-associated integral membrane protein. J Biol Chem. 1996;271(29). 10.1074/jbc.271.29.17526 . Thomsen SK, Raimondo A, Hastoy B, et al. Type 2 Diabetes Risk Alleles in PAM Impact Insulin Release from Human Pancreatic Beta Cells. Nat Genet. 2018;50(8):1122. 10.1038/s41588-018-0173-1 . Pe M, W EK, Mj R, Am S, Pe L, Mb W. The multiple actions of GLP-1 on the process of glucose-stimulated insulin secretion. Diabetes. 2002;51(Suppl 3). 10.2337/diabetes.51.2007.s434 . Dm B, A N. Novel Observations From Next-Generation RNA Sequencing of Highly Purified Human Adult and Fetal Islet Cell Subsets. Diabetes. 2015;64(9). 10.2337/db15-0039 . Cv B, Mt S, Wj HV, Fd G. Genetic variation in the glutathione synthesis pathway, air pollution, and children’s lung function growth. Am J Respir Crit Care Med. 2011;183(2). 10.1164/rccm.201006-0849OC . Banerjee M, Vats P. Reactive metabolites and antioxidant gene polymorphisms in Type 2 diabetes mellitus. Redox Biol. 2014;2:170. 10.1016/j.redox.2013.12.001 . A S, M S, H F, et al. Glutathione peroxidase 4 senses and translates oxidative stress into 12/15-lipoxygenase dependent- and AIF-mediated cell death. Cell Metab. 2008;8(3). 10.1016/j.cmet.2008.07.005 K O YU. Glutathione S-transferase A1 polymorphism as a risk factor for smoking-related type 2 diabetes among Japanese. Toxicol Lett. 2008;178(3). 10.1016/j.toxlet.2008.03.004 . Pj G. Diabetes mellitus as a prothrombotic condition. J Intern Med. 2007;262(2). 10.1111/j.1365-2796.2007.01824.x . B F. The urokinase system in the pathogenesis of atherosclerosis. Atherosclerosis. 2012;222(1). 10.1016/j.atherosclerosis.2011.10.044 . Wu CZ, Ou SH, Chang LC, Lin YF, Pei D, Chen JS. Deficiency of Urokinase Plasminogen Activator May Impair β Cells Regeneration and Insulin Secretion in Type 2 Diabetes Mellitus. Molecules. 2019;24(23). 10.3390/molecules24234208 . Pc H, Kh SB, C WJB. Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies. Am J Clin Nutr. 2016;103(4). 10.3945/ajcn.115.118216 . Legend. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterial.docx supplementarymaterial.xlsx 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5897046","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":407629728,"identity":"c75a07b2-17b5-4aca-8363-67a821fd6712","order_by":0,"name":"Yue-Yang Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yue-Yang","middleName":"","lastName":"Zhang","suffix":""},{"id":407629729,"identity":"435bbfa1-50cb-436e-9644-e0aabfbd7eb9","order_by":1,"name":"Qin Wan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYLCCBIYaOTb2BijvAHFajhnz88CUEqWFgYE5UXJGApFaDI6fPXjj4Q62BIObz59J3WxjkOO7kcD4uQCfljN5yRaJZ2TyDG7nmEnntjEYS95IYJaegUeL2YEcM4nENrZioBa220AtiRtuJLAx8+DTcv4NSAtz4oabx5+BtNQT1nIjB6Jl5gwGM5CWBANCWuxvvDG2SGwDBXKO+e+ccxKGM888bJbGp0WyP8fw5s82UFQef2ycU2Yjz3c8+eBnfFpAQAKNzdhAQAOqllEwCkbBKBgFmAAA28lOFt92Bd0AAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":true,"prefix":"","firstName":"Qin","middleName":"","lastName":"Wan","suffix":""}],"badges":[],"createdAt":"2025-01-24 16:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5897046/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5897046/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75006386,"identity":"6b466978-e755-429b-a86a-084a30df1e7f","added_by":"auto","created_at":"2025-01-29 10:49:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1498217,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for proteomics-based identification of novel type 2 diabetes therapeutic target proteins.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5897046/v1/37d3be86fd774c59d7c2573a.png"},{"id":75006388,"identity":"3a61600e-4f3c-47c0-925e-f413e29c9c34","added_by":"auto","created_at":"2025-01-29 10:49:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1490528,"visible":true,"origin":"","legend":"\u003cp\u003eA. Volcano plots of the MR results; B. Forest plots of the MR results; C. Correlation heatmap of Bayesian co-localisation results\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5897046/v1/afd82ba45b00a0cf2f7ed553.png"},{"id":75006395,"identity":"15ac10c7-f3f5-41db-9f39-44f6c2b73984","added_by":"auto","created_at":"2025-01-29 10:49:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5663127,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network between the potentially target proteins for type 2 diabetes\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5897046/v1/8eaed5d0260dea3849047d14.png"},{"id":75008456,"identity":"2acb1ea2-6c3f-461e-ac3e-aafe4977d03d","added_by":"auto","created_at":"2025-01-29 11:05:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9732228,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5897046/v1/9b11da60-9b5f-4c75-9e0e-89e4a7e17502.pdf"},{"id":75006389,"identity":"86d2b95a-4cef-4ce6-8de7-a7fa7860fdc7","added_by":"auto","created_at":"2025-01-29 10:49:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1542266,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5897046/v1/2de6b12dbc9417cc6cf7cd80.docx"},{"id":75006394,"identity":"88c72083-2350-4ee6-9dae-5f2ecbd8ebdb","added_by":"auto","created_at":"2025-01-29 10:49:36","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1693665,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5897046/v1/17304634e96dcaf996178558.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Potential Therapeutic Targets for Type 2 Diabetes: A Multi-Center Data Analysis Based on Proteomics","fulltext":[{"header":"Highlights","content":"\u003cp\u003eThe global burden of type 2 diabetes is increasing and more and more people will suffer from type 2 diabetes in the future;\u003c/p\u003e\n\u003cp\u003eAlthough there are a large number of medications available for the treatment of type 2 diabetes, there is still no cure for type 2 diabetes;\u003c/p\u003e\n\u003cp\u003eThe development of high-throughput assays has provided new directions for exploring the association between proteomics and disease;\u003c/p\u003e\n\u003cp\u003eFourteen plasma proteins, including ARG1, PAM, WWOX and SHBG, may serve as potential therapeutic targets for type 2 diabetes mellitus.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eDiabetes is a metabolic disorder marked by insulin resistance or impaired insulin secretion, standing as one of the most prevalent and critical diseases within the endocrine system\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Over the past few decades, the global burden of type 2 diabetes has steadily escalated, with incidence rates showing a particularly marked increase, nearly doubling every thirty years. By 2021, the global incidence of type 2 diabetes had surged to 9.8%\u003csup\u003e2\u003c/sup\u003e. According to projections from the International Diabetes Federation (IDF), by 2045, one in eight adults worldwide will be diagnosed with diabetes, and the global diabetic population is projected to skyrocket to an astonishing 783 million\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. These staggering figures underscore the escalating burden that type 2 diabetes imposes on global public health, with this trend anticipated to persist in the foreseeable future\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Consequently, numerous scholars have suggested that type 2 diabetes be recognized as an emerging epidemic\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the development of numerous pharmacological treatments for type 2 diabetes, an effective cure remains elusive, largely due to the complex and poorly understood pathogenesis of the disease. Extensive observational studies have demonstrated that a range of factors\u0026mdash;including genetic predisposition, unhealthy dietary patterns, obesity, and sedentary lifestyles\u0026mdash;significantly increase susceptibility to type 2 diabetes\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Notably, a family history of diabetes is widely recognized as a critical factor in the diagnostic criteria for type 2 diabetes\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Therefore, to further elucidate the genetic underpinnings of type 2 diabetes, researchers have launched large-scale genome-wide association studies (GWAS)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Additionally, with the continued advancement of high-throughput techniques for serum protein detection and quantification, researchers have increasingly utilized proteomics to investigate the intricate relationship between proteins and diseases, aiming to deepen our understanding of the molecular mechanisms driving disease pathogenesis\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. A prior Mendelian randomization study identified 47 plasma proteins significantly associated with type 2 diabetes from a cohort of 1886 plasma proteins\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In our earlier research, we identified 23 plasma protein-to-protein ratios that were closely linked to type 2 diabetes\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) has emerged as a compelling alternative to randomized controlled trials, employing genetic instruments to supplant the conventional exposure-disease framework. Based on Mendelian principles, genetic information is randomly allocated at conception, predating the onset of any disease, thereby minimizing confounding bias\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Consequently, this study aims to investigate the potential association between plasma proteins and type 2 diabetes through Mendelian randomization, leveraging GWAS data from multiple extensive cohorts.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis study is fundamentally anchored in the Mendelian randomization (MR) framework, leveraging genetic data from several extensive cohorts, including Decode and FinnGen, to investigate the associations between 4,907 proteins and type 2 diabetes, with the overarching goal of identifying potential therapeutic targets for the condition. Moreover, summary-level Mendelian randomization (SMR), external validation cohorts, Bayesian colocalization analysis, protein-protein interaction (PPI) networks, DsigDB, and KEGG 2021 Human pathways were employed for rigorous multi-layer validation, ensuring the robustness and reliability of the findings. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a comprehensive depiction of the primary framework of this investigation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Sources\u003c/h3\u003e\n\u003cp\u003eAll data utilized in this study are derived from large-scale genome-wide association study (GWAS) cohorts conducted in European populations, sourced from publicly available summary-level datasets (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe leveraged the most recent genetic association data on type 2 diabetes from the FinnGen DF11 cohort, which comprised 71,728 individuals diagnosed with type 2 diabetes and 369,007 controls. The FinnGen study represents an extensive genomic initiative originating from Finland, designed to analyze over 500,000 samples from the Finnish biobank, with the goal of elucidating associations between genetic variants and health outcomes, thereby advancing a comprehensive understanding of disease etiology and susceptibility factors\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The proteomic genetic data were sourced from the Decode cohort, consisting of 35,559 Icelandic participants, covering a comprehensive set of 4,907 proteins\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The cis-eQTL data for summary-level Mendelian randomization (SMR) were obtained from the phase I eQTLGen project, an initiative of the eQTLGen Consortium, which analyzed whole blood samples from 31,684 individuals across diverse global population cohorts, encompassing 10,317 trait-associated single nucleotide polymorphisms (SNPs)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. For eQTL information absent in phase I of eQTLGen, alternative methods were employed for supplementary validation. SHBG data were extracted from a genome-wide association study (GWAS) conducted on the UK Biobank cohort (N\u0026thinsp;=\u0026thinsp;425,097)\u003csup\u003e17\u003c/sup\u003e. For other missing protein data, genetic information related to type 2 diabetes from a meta-analysis was utilized as an external validation cohort\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eInstrument selection\u003c/h3\u003e\n\u003cp\u003eTo ensure the robustness of the study findings, we implemented rigorous selection criteria for protein quantitative trait loci (pQTL). Initially, the study concentrated on cis-pQTL, yielding a total of 1,614 proteins that were available for subsequent analyses. The significance threshold for SNPs was established at 5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, with relatively independent SNPs being filtered utilizing the European 1000 Genomes panel, applying a linkage disequilibrium threshold (r\u0026sup2;) of less than 0.001 across a 10,000 kb genomic span. Furthermore, the F-statistic was utilized to evaluate the statistical power of all SNPs, with those exhibiting an F-statistic of less than 10 being excluded to mitigate bias associated with weak instrumental variables.\u003c/p\u003e\n\u003ch3\u003eMendelian randomization and validation processes\u003c/h3\u003e\n\u003cp\u003eInitially, leveraging the established framework of two-sample MR, we evaluated the association between candidate proteins and type 2 diabetes employing inverse variance weighting (IVW) or Wald ratio methodologies to calculate the overall effect size. Concurrently, we utilized four sensitivity analysis methods\u0026mdash;the weighted median, simple mode, weighted mode, and MR-Egger test\u0026mdash;to bolster the reliability of the IVW findings. The false discovery rate (FDR) was employed to mitigate bias associated with multiple testing, while Cochran's Q test was utilized to assess heterogeneity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the MR-Egger intercept test was conducted to evaluate horizontal pleiotropy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Reverse MR was employed to elucidate potential reverse causal associations. Bayesian colocalization analysis was conducted to evaluate the probability that two traits share causal variants, thereby ensuring that the observed association is not attributable to potential genetic confounding factors. Genes exhibiting a posterior probability (PP4)\u0026thinsp;\u0026ge;\u0026thinsp;0.8 were regarded as having strong evidence supporting colocalization, whereas those with PP4\u0026thinsp;\u0026ge;\u0026thinsp;0.5 were deemed to possess moderate evidence for colocalization\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Subsequently, supplementary validation of the two-sample MR analysis results was conducted through SMR or external cohorts to corroborate the association between candidate proteins and type 2 diabetes\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. All statistical analyses were executed utilizing the TwoSampleMR package (version 0.5.6) and the coloc package (version 5.2.3) within the R software environment (version 4.3.3). Statistical significance was determined using stringent criteria for two-tailed tests.\u003c/p\u003e\n\u003ch3\u003eSingle-cell RNA sequencing elucidation, network analysis, and drug prediction\u003c/h3\u003e\n\u003cp\u003eWe elucidated the protein expression profile within the pancreatic microenvironment through the application of single-cell analysis data\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. We integrated the results of MR analysis with PPI by utilizing databases such as STRING and DrugBank, aiming to investigate the potential interactions of candidate proteins and their therapeutic implications\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Furthermore, we predicted the pathways and potential pharmacological agents associated with the mechanisms linking candidate proteins to type 2 diabetes, employing relevant data from the KEGG 2021 Human database and DsigDB\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e "},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eInstrument selection\u003c/h2\u003e \u003cp\u003eAs illustrated in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, we meticulously selected between 3 and 226 SNPs based on stringent screening criteria to examine the relationship between 1,614 proteins and type 2 diabetes. The F-statistic for all selected genetic instruments exceeded 10, indicating that this study significantly mitigates bias associated with weak instrumental variables.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eIdentification of type 2 diabetes-associated proteins through proteome analysis\u003c/h3\u003e\n\u003cp\u003eInitially, we identified 276 proteins potentially associated with type 2 diabetes (P-IVW\u0026thinsp;\u0026lt;\u0026thinsp;0.05); however, following false discovery rate (FDR) correction, only 25 proteins remained significantly associated with type 2 diabetes (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among these, ten proteins, including WW domain-containing oxidoreductase (WWOX) and glutathione synthetase (GSS), exhibited a positive correlation with the risk of type 2 diabetes. In contrast, 15 proteins, such as arginase 1 (ARG1) and sex hormone-binding globulin (SHBG), were negatively correlated with this risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table S3-5).\u003c/p\u003e \u003cp\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\u003eMendelian randomization results for proteins significantly related to type 2 diabetes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTop SNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffect allele\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOther allele\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value (IVW)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF statistics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers2781668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7(0.62,0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.99E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.61E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e125.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICAM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers11575073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96(0.95,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.28E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.68E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3360.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers34596924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96(0.95,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.94E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.58E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8576.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers71352239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.15(1.09,1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.04E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.90E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e778.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTREML2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers9381017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96(0.94,0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.67E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.16E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3341.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVEP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers61751937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.04(1.03,1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.67E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1229.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWWOX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers11545029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.42(1.24,1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.37E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.22E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e74.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHEXB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers13164140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.08(1.05,1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.37E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.44E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e895.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMANBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers227275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94(0.92,0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.28E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2138.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGFBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers35901765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95(0.93,0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.24E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4215.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers6141479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.23(1.13,1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.25E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.82E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e110.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCRLB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1954172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.05(1.03,1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.48E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1972.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers2228603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85(0.79,0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.93E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.73E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e422.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers2435359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.06(1.03,1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.16E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.11E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1363.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGFBR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers59178722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.18(1.1,1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.35E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.41E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e114.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eERAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers3822683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98(0.98,0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.55E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.34E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e42045.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers6602909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.93(0.9,0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.57E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.38E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e889.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHRDL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers61389091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.07(1.04,1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.65E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1157.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICAM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers5030352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.02(1.01,1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.16E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14199.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers12053542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96(0.94,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.61E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2394.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCGR2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers6658353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98(0.98,0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.28E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e96629.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLAU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers2227551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94(0.92,0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.72E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e970.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHBG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers858519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87(0.81,0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.49E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e426.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRTAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers2370794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95(0.93,0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.96E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e687.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL6R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers11265611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7(0.62,0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.99E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19832.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis and co-location analysis\u003c/h2\u003e \u003cp\u003eWhile Cochran's Q test indicated heterogeneity in the associations of PAM, APOE, TREML2, MANBA, TGFBI, RET, ERAP1, ICAM1, SHBG, and IL6R with type 2 diabetes, the consistent direction of effects observed across sensitivity analyses\u0026mdash;namely, the weighted median, simple mode, weighted mode, and MR-Egger test\u0026mdash;suggests that this heterogeneity can be considered negligible. The MR-Egger intercept test identified only neurocan (NCAN) and the ret proto-oncogene (RET) as exhibiting horizontal pleiotropy (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although the reverse Mendelian randomization analysis suggested reverse associations between several proteins, including ARG1, and type 2 diabetes, the limited availability of SNPs raises concerns about the potential for significant bias in the reverse MR results (Table S6). Bayesian colocalization analysis yielded compelling evidence supporting the associations of ARG1, mannosidase beta (MANBA), GSS, neurocan (NCAN), plasminogen activator (PLAU), and SHBG with type 2 diabetes. In contrast, moderate evidence was identified for intercellular adhesion molecule 5 (ICAM5), WWOX, transforming growth factor beta-induced (TGFBI), and TGF-beta receptor 3 (TGFBR3) in their association with type 2 diabetes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, 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\u003eOverview of Cochran's Q test, MR-Egger intercept test, Bayessian co-localisation analysis detection on twenty-five candidate target protein\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ(IVW)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ_df(IVW)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ_pval(IVW)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEgger_intercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCo-localization PPH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.55E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICAM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e104.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.44E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.39E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e181.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.28E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.92E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.64E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.33E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e126.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.82E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.41E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.49E-11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTREML2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e143.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.15E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.19E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.24E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.53E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVEP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.34E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.88E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWWOX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.36E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHEXB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.03E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.85E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMANBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.26E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.42E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.55E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGFBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.35E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.77E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.47E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCRLB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.04E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.74E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.62E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.34E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.12E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.43E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.74E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGFBR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.77E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eERAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.85E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.24E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.22E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.31E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHRDL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.55E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICAM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e181.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.15E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.93E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.01E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.01E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.79E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCGR2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e183.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e164.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.04E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.74E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.76E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLAU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.91E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.31E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHBG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.20E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.96E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRTAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.73E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.69E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL6R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e190.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.64E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.15E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eValidation analysis\u003c/h2\u003e \u003cp\u003eThrough SMR, we validated the associations between 20 identified proteins and type 2 diabetes. This analysis confirmed significant associations for ARG1, ICAM5, peptidyl arginine deiminase (PAM), WWOX, MANBA, GSS, endoplasmic reticulum aminopeptidase 1 (ERAP1), ICAM1, and PLAU with type 2 diabetes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, the external validation cohort corroborated potential associations between apolipoprotein E (APOE), sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SVEP1), NCAN, cysteine-rich with EGF-like domains 2 (CHRDL2), and SHBG and type 2 diabetes (Table S7).\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\u003eValidation of selected protein-type 2 diabetes correlations using summary-data-based mendelian randomization.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProbeID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ-SMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE-SMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-SMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-HEIDI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000118520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e131899878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.51E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.53E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICAM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000105376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10404055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000145730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102228247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.27E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.74E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTREML2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000112195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41163473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWWOX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000186153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78689937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.29E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHEXB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000049860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73977160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMANBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000109323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103617405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.22E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.11E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGFBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000120708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e135382045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.91E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000100983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33529928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.66E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCRLB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000162746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e161694643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.77E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000165731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43599137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGFBR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000069702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92258897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eERAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000164307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96120162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.32E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000183087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114545285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICAM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000090339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10389401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.16E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000205221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36982884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCGR2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000143226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e161484511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.61E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLAU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000122861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75673095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.49E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.70E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRTAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000109943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122726277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.07E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL6R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000160712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e154409797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.67E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell RNA sequencing elucidation\u003c/h2\u003e \u003cp\u003eFigures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S3 illustrate the results of single-cell analyses conducted within the pancreatic islets, revealing distinct expression patterns of several proteins. Specifically, PAM is predominantly expressed in gamma (PP) cells, while FCGR2A, PLAU, APOE, and hexosaminidase B (HEXB) exhibit higher expression levels in Langerhans cells. Additionally, TGFBI is primarily localized in pancreatic stellate cells. These findings underscore the potential of these proteins as promising therapeutic targets for the management of type 2 diabetes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eProtein interaction networks and data prediction\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e depicts the intricate interactions among various proteins associated with type 2 diabetes, highlighting key axes such as the PAM-HEXB axis, CHRDL2-GAS6 axis, and FCRLB-TREML2 axis. These interactions may represent promising intervention pathways for the treatment of type 2 diabetes. Furthermore, the KEGG 2021 Human database indicates that the Other glycan degradation pathway, mediated by MANBA and HEXB, may also serve as a viable intervention route for type 2 diabetes (refer to Table S8). Additionally, insights from DsigDB suggest 41 potential therapeutic drugs for type 2 diabetes, including arsenic, 17-hydroxyandrostan-3-one, and tosylphenylalanylmethyl ketone (as detailed in Table S9).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study successfully identified 13 potential therapeutic targets for the prevention and treatment of type 2 diabetes, including ARG1 and SHBG. Utilizing an integrative approach that encompassed proteomics, bidirectional MR, Bayesian colocalization analysis, SMR, and PPI networks, along with insights from the KEGG 2021 Human database and DsigDB, we analyzed 4,907 plasma proteins. Among the identified targets, five proteins\u0026mdash;ARG1, MANBA, GSS, PLAU, and SHBG\u0026mdash;received strong support through colocalization analysis. Furthermore, these plasma proteins were linked to multiple potential pathways that mediate the onset and progression of type 2 diabetes, including the Other glycan degradation pathway. The study also predicts various potential therapeutic drugs for type 2 diabetes, such as arsenic, 17-hydroxyandrostan-3-one, and tosylphenylalanylmethyl ketone.\u003c/p\u003e \u003cp\u003eAmong the potential therapeutic targets identified in this study, the associations of ARG1, SHBG, and MANBA with type 2 diabetes have been documented in other cohorts. The findings of this study further underscore their interrelationships and suggest that the data utilized in this research are representative of broader trends\u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. SHBG is critical in regulating the biological effects of sex hormones, specifically testosterone and estrogen, on peripheral tissues, including the liver, muscle, and adipose tissue, thereby playing a significant role in the pathogenesis of type 2 diabetes\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Notably, two previous randomized controlled trials (RCTs) have reported marked differences in the effects of transdermal estradiol and oral estrogen on diabetes risk\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. These differences may arise from the direct antagonistic properties of SHBG against estrogen at the cellular level. Additionally, the interaction between SHBG and estrogen receptors can trigger biological responses that exhibit anti-estrogen effects, further influencing the development of type 2 diabetes\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePAM is an enzyme localized in neuroendocrine cells, responsible for the modification of C-terminal glycine peptides to generate peptide-amides\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Research indicates that reduced PAM activity adversely affects insulin supply and the dynamics of granule exocytosis, ultimately leading to β-cell dysfunction\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Moreover, PAM may indirectly regulate β-cell function through amidation signals released by other tissues, such as glucagon-like peptide 1 (GLP-1), an amidated peptide secreted from intestinal L cells\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGSS primarily catalyzes the second step of glutathione biosynthesis in the form of a homodimer, converting gamma-L-glutamyl-L-cysteine into glutathione utilizing ATP\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Glutathione plays a vital role in various biological processes, including protecting cells from oxidative damage caused by free radicals, detoxifying xenobiotics, and facilitating membrane transport\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Previous studies have demonstrated that the overexpression of glutathione peroxidase can significantly reduce oxidative phospholipids, cholesterol hydroperoxides, and other harmful compounds, thereby alleviating vascular oxidative stress and mitigating the progression of atherosclerosis\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Additionally, glutathione S-transferase has been implicated in the onset of type 2 diabetes and its associated complications\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Consequently, further exploration of the association between GSS and type 2 diabetes is warranted to elucidate its potential role in disease pathogenesis.\u003c/p\u003e \u003cp\u003ePLAU is a well-known serine protease that plays a pivotal role in fibrinolysis by activating plasminogen and converting it into plasmin, thereby facilitating the breakdown of thrombi and extracellular matrix components. An imbalance between the coagulation and fibrinolytic systems is closely associated with the development of intravascular thrombosis in patients with type 2 diabetes mellitus\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Additionally, PLAU has been shown to regulate the migration and recruitment of immune cells, independently promoting the release of various inflammatory factors, and thus plays a crucial role in tissue remodeling\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. A previous animal study indicated that weak expression of PLAU impairs insulin secretion and the regeneration of β-cells in both mouse and cellular models, resulting in hyperglycemia. Furthermore, hyperglycemia itself can downregulate PLAU expression in the pancreas, creating a vicious cycle that exacerbates the onset and progression of type 2 diabetes\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis research presents several advantages compared to existing studies. First, although a previous MR study utilized similar proteomic data, the genetic information for type 2 diabetes employed in this study derives from the latest publicly available FinnGen data, which boasts a larger sample size and a greater number of available SNPs, rendering it more representative. Second, this study employed various validation methods, including Bayesian colocalization and SMR, which not only bolstered the reliability of the results but also yielded distinct conclusions from prior research, identifying several previously undiscovered therapeutic targets for type 2 diabetes. Third, all selected SNPs in this study exhibited F-statistics exceeding 10, thereby maximizing the avoidance of bias related to weak instrumental variables. Fourth, the study primarily utilized cis-expression quantitative trait loci (eQTL) and pQTL, which can effectively minimize bias stemming from horizontal pleiotropy.\u003c/p\u003e \u003cp\u003eHowever, while interpreting our findings, several limitations must be acknowledged. First, it is crucial to recognize the inherent limitations of MR, such as trait heterogeneity and developmental compensation issues, which may influence the accuracy and generalizability of our results\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Second, the GWAS data utilized in our analysis originate from various large-scale sequencing studies, and differences in study design across these cohorts may introduce bias. Third, as our study primarily focuses on European populations, caution should be exercised when extrapolating the findings to more ethnically diverse populations, such as Asian cohorts. Lastly, although this study has unveiled numerous potential associations between plasma proteins and type 2 diabetes, our understanding of the underlying mechanisms remains incomplete, highlighting the need for further foundational research to elucidate these complex pathways.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study presents a comprehensive proteomic analysis within a MR framework, integrated with a variety of robust validation methods, including SMR, external validation cohorts, Bayesian colocalization analysis, single-cell analysis, PPI networks, and pathways from KEGG 2021 Human and DsigDB. This multifaceted approach elucidates the potential associations between specific protein levels and the risk of developing type 2 diabetes, particularly highlighting the roles of ARG1, MANBA, GSS, PLAU, and SHBG. The results underscore the necessity for further foundational research to deepen our understanding of the intricate relationships between these proteins and the pathophysiology of type 2 diabetes. Moreover, these findings pave the way for novel therapeutic strategies, offering new avenues for the development of more precise and effective pharmacological interventions for type 2 diabetes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis study is based on publicly available summarized data. Ethical approval and informed consent had been obtained in all original studies.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe Ministry of Science and Technology of China and Sichuan Provincial Health Commission provided funding for this study through grants 2016YFC0901200 and 23LCYJ026.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYYZ made the most contributions to this work performing the statistical analysis, interpreting the results, and writing the paper. QW proposed the ideas. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are grateful to the authors and participants of all GWASs for the summary statistics used.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets supporting the conclusions of this article are included within the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJb M. The Genetic Epidemiology of Type 2 Diabetes: Opportunities for Health Translation. Curr Diab Rep. 2019;19(8). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11892-019-1173-y\u003c/span\u003e\u003cspan address=\"10.1007/s11892-019-1173-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal regional, national burden of diabetes. from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Lond Engl. 2023;402(10397). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(23)01301-6\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(23)01301-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH S, P S, S K, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.diabres.2021.109119\u003c/span\u003e\u003cspan address=\"10.1016/j.diabres.2021.109119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY Z, Sh L, Fb H. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrendo.2017.151\u003c/span\u003e\u003cspan address=\"10.1038/nrendo.2017.151\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU MF. Population-wide weight loss and regain in relation to diabetes burden and cardiovascular mortality in Cuba 1980\u0026ndash;2010: repeated cross sectional surveys and ecological comparison of secular trends. BMJ. 2013;346. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.f1515\u003c/span\u003e\u003cspan address=\"10.1136/bmj.f1515\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRa H. Type 2 diabetes mellitus and psychological stress - a modifiable risk factor. Nat Rev Endocrinol. 2017;13(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrendo.2017.64\u003c/span\u003e\u003cspan address=\"10.1038/nrendo.2017.64\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY W. Risk factors contributing to type 2 diabetes and recent advances in the treatment and prevention. Int J Med Sci. 2014;11(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7150/ijms.10001\u003c/span\u003e\u003cspan address=\"10.7150/ijms.10001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMj SCKK. Type 2 diabetes. Lancet Lond Engl. 2017;389(10085). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(17)30058-2\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(17)30058-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD AM. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet. 2018;50(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-018-0241-6\u003c/span\u003e\u003cspan address=\"10.1038/s41588-018-0241-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJm MV, Ja K. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat Genet. 2020;52(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-020-0637-y\u003c/span\u003e\u003cspan address=\"10.1038/s41588-020-0637-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan S, Xu F, Li X, et al. Plasma proteins and onset of type 2 diabetes and diabetic complications: Proteome-wide Mendelian randomization and colocalization analyses. Cell Rep Med. 2023;4(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.xcrm.2023.101174\u003c/span\u003e\u003cspan address=\"10.1016/j.xcrm.2023.101174\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang YY, Chen BX, Yang Q, Wan Q. The causal relationship between plasma protein-to‐protein ratios and type 2 diabetes and its complications: Proteomics mendelian randomization study. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/dom.15792\u003c/span\u003e\u003cspan address=\"10.1111/dom.15792\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDa L, Rm H, Ja S, N T. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27(8). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/sim.3034\u003c/span\u003e\u003cspan address=\"10.1002/sim.3034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMi K, J K. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613(7944). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-022-05473-8\u003c/span\u003e\u003cspan address=\"10.1038/s41586-022-05473-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerkingstad E, Sulem P, Atlason BA, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 2021;53(12):1712\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-021-00978-w\u003c/span\u003e\u003cspan address=\"10.1038/s41588-021-00978-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV\u0026otilde;sa U, Claringbould A, Westra HJ, et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet. 2021;53(9):1300\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-021-00913-z\u003c/span\u003e\u003cspan address=\"10.1038/s41588-021-00913-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKs R, Fr D. Using human genetics to understand the disease impacts of testosterone in men and women. Nat Med. 2020;26(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41591-020-0751-5\u003c/span\u003e\u003cspan address=\"10.1038/s41591-020-0751-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM SS. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021;53(10). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-021-00931-x\u003c/span\u003e\u003cspan address=\"10.1038/s41588-021-00931-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ L, J Z, Y X. Potential drug targets for multiple sclerosis identified through Mendelian randomization analysis. Brain J Neurol. 2023;146(8). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/brain/awad070\u003c/span\u003e\u003cspan address=\"10.1093/brain/awad070\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Z, Zhang F, Hu H, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48(5):481\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ng.3538\u003c/span\u003e\u003cspan address=\"10.1038/ng.3538\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranz\u0026eacute;n O, Gan LM, Bj\u0026ouml;rkegren JLM. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Accessed October 15, 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dx.doi.org/10.1093/database/baz046\u003c/span\u003e\u003cspan address=\"10.1093/database/baz046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl DS, Kc G. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkaa1074\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkaa1074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR DS. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51(D1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkac1000\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkac1000\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA DS. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43(Database issue). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gku1003\u003c/span\u003e\u003cspan address=\"10.1093/nar/gku1003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMv K, Mr J, Ad R, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44(W1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkw377\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkw377\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie Z, Bailey A, Kuleshov MV et al. Gene Set Knowledge Discovery with Enrichr. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cpz1.90\u003c/span\u003e\u003cspan address=\"10.1002/cpz1.90\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMr R, Jb JC. Proteomic Predictors of Incident Diabetes: Results From the Atherosclerosis Risk in Communities (ARIC) Study. Diabetes Care. 2023;46(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/dc22-1830\u003c/span\u003e\u003cspan address=\"10.2337/dc22-1830\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL SY. Genetically predicted sex hormone levels and health outcomes: phenome-wide Mendelian randomization investigation. Int J Epidemiol. 2022;51(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ije/dyac036\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyac036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eF G, N Y, Jb MY. Connecting Genomics and Proteomics to Identify Protein Biomarkers for Adult and Youth-Onset Type 2 Diabetes: A Two-Sample Mendelian Randomization Study. Diabetes. 2022;71(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/db21-1046\u003c/span\u003e\u003cspan address=\"10.2337/db21-1046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing EL, Song Y, Manson JE, et al. Sex Hormone\u0026ndash;Binding Globulin and Risk of Type 2 Diabetes in Women and Men. N Engl J Med. 2009;361(12):1152. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa0804381\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa0804381\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKl M, De B, Rj R, et al. Effect of oestrogen plus progestin on the incidence of diabetes in postmenopausal women: results from the Women\u0026rsquo;s Health Initiative Hormone Trial. Diabetologia. 2004;47(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00125-004-1448-x\u003c/span\u003e\u003cspan address=\"10.1007/s00125-004-1448-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe B. The effect of conjugated equine oestrogen on diabetes incidence: the Women\u0026rsquo;s Health Initiative randomised trial. Diabetologia. 2006;49(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00125-005-0096-0\u003c/span\u003e\u003cspan address=\"10.1007/s00125-005-0096-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMg NFMB. Sex hormone-binding globulin, its membrane receptor, and breast cancer: a new approach to the modulation of estradiol action in neoplastic cells. J Steroid Biochem Mol Biol. 1999;69(1\u0026ndash;6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0960-0760(99)00068-0\u003c/span\u003e\u003cspan address=\"10.1016/s0960-0760(99)00068-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSl M, Re M, Ba E. Identification of routing determinants in the cytosolic domain of a secretory granule-associated integral membrane protein. J Biol Chem. 1996;271(29). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.271.29.17526\u003c/span\u003e\u003cspan address=\"10.1074/jbc.271.29.17526\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomsen SK, Raimondo A, Hastoy B, et al. Type 2 Diabetes Risk Alleles in PAM Impact Insulin Release from Human Pancreatic Beta Cells. Nat Genet. 2018;50(8):1122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-018-0173-1\u003c/span\u003e\u003cspan address=\"10.1038/s41588-018-0173-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePe M, W EK, Mj R, Am S, Pe L, Mb W. The multiple actions of GLP-1 on the process of glucose-stimulated insulin secretion. Diabetes. 2002;51(Suppl 3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/diabetes.51.2007.s434\u003c/span\u003e\u003cspan address=\"10.2337/diabetes.51.2007.s434\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDm B, A N. Novel Observations From Next-Generation RNA Sequencing of Highly Purified Human Adult and Fetal Islet Cell Subsets. Diabetes. 2015;64(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/db15-0039\u003c/span\u003e\u003cspan address=\"10.2337/db15-0039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCv B, Mt S, Wj HV, Fd G. Genetic variation in the glutathione synthesis pathway, air pollution, and children\u0026rsquo;s lung function growth. Am J Respir Crit Care Med. 2011;183(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/rccm.201006-0849OC\u003c/span\u003e\u003cspan address=\"10.1164/rccm.201006-0849OC\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanerjee M, Vats P. Reactive metabolites and antioxidant gene polymorphisms in Type 2 diabetes mellitus. Redox Biol. 2014;2:170. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.redox.2013.12.001\u003c/span\u003e\u003cspan address=\"10.1016/j.redox.2013.12.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA S, M S, H F, et al. Glutathione peroxidase 4 senses and translates oxidative stress into 12/15-lipoxygenase dependent- and AIF-mediated cell death. Cell Metab. 2008;8(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cmet.2008.07.005\u003c/span\u003e\u003cspan address=\"10.1016/j.cmet.2008.07.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK O YU. Glutathione S-transferase A1 polymorphism as a risk factor for smoking-related type 2 diabetes among Japanese. Toxicol Lett. 2008;178(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.toxlet.2008.03.004\u003c/span\u003e\u003cspan address=\"10.1016/j.toxlet.2008.03.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePj G. Diabetes mellitus as a prothrombotic condition. J Intern Med. 2007;262(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1365-2796.2007.01824.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2796.2007.01824.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB F. The urokinase system in the pathogenesis of atherosclerosis. Atherosclerosis. 2012;222(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.atherosclerosis.2011.10.044\u003c/span\u003e\u003cspan address=\"10.1016/j.atherosclerosis.2011.10.044\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu CZ, Ou SH, Chang LC, Lin YF, Pei D, Chen JS. Deficiency of Urokinase Plasminogen Activator May Impair β Cells Regeneration and Insulin Secretion in Type 2 Diabetes Mellitus. Molecules. 2019;24(23). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/molecules24234208\u003c/span\u003e\u003cspan address=\"10.3390/molecules24234208\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePc H, Kh SB, C WJB. Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies. Am J Clin Nutr. 2016;103(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3945/ajcn.115.118216\u003c/span\u003e\u003cspan address=\"10.3945/ajcn.115.118216\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLegend.\u003c/span\u003e\u003c/li\u003e\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":"proteomics, type 2 diabetes, Mendelian randomization, bayesian co-location, single-cell analysis","lastPublishedDoi":"10.21203/rs.3.rs-5897046/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5897046/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDespite the advent of numerous pharmacological agents designed to enhance the management of type 2 diabetes, treating this condition continues to pose significant challenges. Consequently, this study aims to identify potential therapeutic targets for type 2 diabetes through Mendelian randomization(MR).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research primarily utilizes publicly accessible data from extensive genome-wide association studies and protein quantitative trait locus studies. The study predominantly adopts an MR framework to estimate the causal effects of specific proteins on the risk of developing type 2 diabetes. To corroborate the findings, multiple validation methodologies are employed, including summary data-based Mendelian randomization (SMR), external validation cohorts, bayesian colocalization analysis, single-cell analysis, protein-protein interaction (PPI) networks, pathways derived from KEGG 2021 Human and DsigDB.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur study identifies 25 proteins potentially associated with the risk of type 2 diabetes. SMR and external validation affirmed that 14 proteins, including ARG1, PAM, WWOX, and SHBG, may function as viable therapeutic targets for type 2 diabetes. Single-cell analysis illuminated the expression patterns of six proteins predominantly in pancreatic islets. Colocalization analysis and PPI networks elucidated the potential roles of these proteins in the pathogenesis of type 2 diabetes. The KEGG 2021 Human database indicated that the glycan degradation pathway may mediate the effects of MANBA and HEXB on the development of type 2 diabetes. DsigDB identified 41 potential therapeutic drugs for the treatment of type 2 diabetes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusion\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThese findings provide novel insights and directions for drug development aimed at the prevention and treatment of type 2 diabetes.\u003c/p\u003e","manuscriptTitle":"Potential Therapeutic Targets for Type 2 Diabetes: A Multi-Center Data Analysis Based on Proteomics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-29 10:49:32","doi":"10.21203/rs.3.rs-5897046/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":"1066ca2c-c562-4f2d-a384-db035093cd8b","owner":[],"postedDate":"January 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-29T10:49:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-29 10:49:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5897046","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5897046","identity":"rs-5897046","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}