Polymorphisms and Gene-Gene Interaction in AGER/IL6 Pathway are Associated with Diabetic Ischemic Heart Disease

Polymorphisms and Gene-Gene Interaction in AGER/IL6 Pathway are Associated with Diabetic Ischemic Heart Disease | 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 Polymorphisms and Gene-Gene Interaction in AGER/IL6 Pathway are Associated with Diabetic Ischemic Heart Disease Kuo Liu, Yunyi Xie, Qian Zhao, Wenjuan Peng, Chunyue Guo, Jie Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-147920/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background: The aim of the present study is to demonstrate the association of AGER and IL6R gene polymorphisms with diabetic ischemic heart disease (IHD), and to investigate the effect of gene-gene interaction on disease risk. Methods: Our study included 204 ischemic heart disease cases who have previously been diagnosed as diabetes before the diagnoses of IHD, and 882 health controls. Polygenic risk score (PRS) was calculated by summing the number of risk alleles of all the candidate single nucleotide polymorphisms (SNPs). Logistic regression was used to find the association of candidate SNPs and PRS with diabetic ischemic heart disease. Generalized multifactor dimensionality reduction (GMDR) was used to illustrate gene-gene interaction. Haplotypes were identified and analyzed via Haploview and Plink software. Results: The rs184003 and rs2070600 in AGER gene were significantly associated with the risk of diabetic ischemic heart disease ( P additive =0.005; P additive =0.025, respectively). For IL6R rs4845625, CT and TT genotype were associated with lower risk of the disease comparing with CC genotype (OR=0.692, P =0.045; OR=0.503, P =0.003, respectively). After adjustment for covariates, the association of rs4845625 with disease remained statistically significant. Haplotypes in AGER gene (rs184003-rs1035798-rs2070600-rs1800624) and IL6R gene (rs7529229-rs4845625-rs4129267-rs7514452-rs4072391) were both significantly associated with diabetic ischemic heart disease ( P =0.008; P =0.007). PRS was associated with the disease (OR=1.106, P =0.020) after adjusting for covariates. The GMDR analysis suggested that rs184003 and rs4845625 was the best interaction model after permutation testing ( P =0.001) with a cross-validation consistency of 10/10. Conclusions: SNPs and haplotypes in AGER and IL6R gene and the interaction of rs184003 in AGER with rs4845625 in IL6R were significantly associated with diabetic ischemic heart disease. Medical Genetics Molecular Genetics Population Genetics ischemic heart disease (IHD) Generalized multifactor dimensionality reduction (GMDR) Figures Figure 1 Introduction Cardiovascular disease (CVD) is the leading cause of mortality in people with type 2 diabetes mellitus (T2DM). About 68% of deaths in type 2 diabetic patients are caused by cardiac complications [ 1 ] . However, it is not clear how diabetes promotes cardiac dysfunction. The widely acceptable hypothesis is that many signaling cascades, ultimately resulting in pro-inflammatory reaction, oxidative stress or thrombotic pathways, and subsequently leading to vascular inflammation [ 2 ] . It has been demonstrated that advanced glycation end products (AGER) / interleukin-6 (IL-6) pathway plays an important role in the physiological mechanism of diabetic cardiovascular complication. High glucose level can trigger neutrophil to release S100 calcium-binding protein A8/A9 (S100A8/A9), which binds to AGER on Kupffer cells, and leads to IL-6 secretion [ 3 ] . IL-6 / IL-6R complex activate JAK2 / STAT3 pathway, which can mediate pro-inflammatory response and increase platelet thrombopoiesis by inducing thrombopoietin (TPO) production [ 4 , 5 ] . Several single nucleotide polymorphisms (SNPs) in AGER gene have been reported to be associated with diabetes or its complications. The Atherosclerosis Risk in Communities Study showed an association between rs2070600 and an approximate 50% reduction in soluble AGER levels [ 6 ] . A meta-analysis has highlighted a significant association of rs2070600 with the risk of diabetic nephropathy development [ 7 ] . However, the association between rs2070600 and diabetic cardiovascular disease are under-reported. On IL6R gene, the rs1800624 and rs1800625 are in absolute or strong linkage disequilibrium, and were reported to be protective factors for cardiovascular disease [ 8 , 9 ] . However, the effect of them on the vascular complications in T2DM remains inconsistent [ 10 , 11 ] , In addition, the gene-gene interactions on the increased risk of the disease requires further clarification . The current study aimed at illustrating the association of AGER and IL6R gene polymorphisms with the risk of diabetic cardiovascular disease, and assess the modulatory effect of gene-gene interaction between these variants on disease risk. The result would provide evidence on the precise prevention of ischemic heart disease in diabetes. Method Study design and population A total of 204 diabetic ischemic heart disease cases and 882 health controls were enrolled from communities in Beijing. All subjects gave written informed consent. This study was approved by the Ethics Committee of Capital Medical University (No:2016SY24). Inclusion criteria for the cases were as follows: (1) T2DM patients diagnosed according to American Diabetes Association Criteria [ 12 ] , or receiving pharmaceutical treatment on T2DM; (2) Ischemic heart disease defined by clinical history, including acute myocardium infarction, angina pectoris and/or ischemic electrocardiographic alterations; (3) T2DM was diagnosed earlier than ischemic heart disease. (4) The medical records or copies should be provided to verify the diagnose of diseases. Inclusion criteria for the controls were as follows: (1) Subjects had not been diagnosed as T2DM before, and fasting blood glucose was less than 5.6 mmol/L in the current survey. (2) Subjects did not have cardiovascular disease, which included ischemic heart disease, ischemic stroke, or cerebral hemorrhage. (3) Subjects did not have chronic kidney disease. (4) Subjects were not in the acute phase of infection. Measurements Life style risk factors were obtained from structured questionnaire. Smoking status was categorized as: “currently smoking” and “past / never smoking”. Current smoking was defined as at least 1 cigarette per day, lasting for more than 1 year. Those who have never smoked before or have not smoked for at least 3 months were defined as past / never smoking. Alcohol drinking was categorized as “currently alcohol drinking” and “past / never alcohol drinking”. Currently drinking was defined as at least drink once per week and still drank at that frequency in the previous month. Those who never drink alcohol or have not drank alcohol for at least one month were defined as never / past alcohol drinking. Blood pressure (BP) was measured in the morning before participants use anti-hypertensive medication. Participants were asked to rest for at least 30 minutes before BP measurement if they had just smoked or had caffeinated products. BP (mmHg) was measured for three times at sitting positions by mercury sphygmomanometer. The average of the last two measurements was used for data analysis. Serum markers After an overnight fasting, all participants underwent fasting blood sampling. Fasting blood samples are collected and restored in 2% EDTA vacutainer for each participant. After centrifuging, plasma and blood cell samples are separated into two cryovials. Fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDLC), low-density lipoprotein cholesterol (LDLC) were tested using the Beckman coulter chemistry analyzer AU5800 in the clinical laboratory of Beijing Hepingli Hospital. Venous blood samples were obtained and stored in 4 o C refrigerator. All the hematological analysis was done within 8 hours. Serum glucose and biochemical determinations were measured by an enzymatic method using a chemistry analyzer (Beckman LX20, Beckman, Brea, CA, USA) at the central laboratory of the hospital. Genotyping Important functional SNPs and previously reported susceptible SNPs were selected as candidate SNPs. Five SNPs (rs1035798, rs1800624, rs1800625, rs184003 and rs2070600) in AGER gene and seven SNPs (rs2228144, rs4072391, rs4129267, rs4537545, rs4845625, rs7514452 and rs7529229) in IL6R gene were selected in the current study. Genomic DNA was extracted from 1 ml of peripheral blood cell using TIANGEN DNA kit (TIANGEN Biotech, China, DP319-01) according to the manufacturer’s protocol. Primers were designed by the AssayDesigner3.1 software, and they were synthesized by Thermo Fisher Scientific Co., Ltd. Detailed information of the primers were shown in supplementary table S1 . A Sequenom MassARRAY® matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) platform (Sequenom Inc., San Diego, CA, USA) were used to genotype SNPs. Definition of diseases and recommend level of their risk factors T2DM was defined as FPG ≥ 7.0 mmol/L or self-reported physician-diagnosed diabetes and/or on use of antidiabetes agents, according to American Diabetes Association Criteria [ 12 ] . Ischemic heart disease (IHD) was defined as non-fatal ischemic heart disease, including acute myocardial infarction and angina pectoris. The incident of ischemic heart disease in T2DM patients were defined as diabetic ischemic heart disease. Hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg and/or on current antihypertensive medication. Participants with TG ≥ 2.3 mmol/L, or TC ≥ 6.2 mmol/L, or LDLC ≥ 4.1 mmol/L, or HDLC ≤ 1.0 mmol/L were defined as dyslipidemia according to the criteria of the 2016 Chinese guidelines for the management of dyslipidemia in adults [ 13 ] . Statistical analysis Continuous variables with normal distribution are expressed as means ± standard deviations (SDs). Categorical variables were expressed as number (percentage). Student’s t test was used to compare the difference of each continuous variables. Polygenic risk score (PRS) was calculated by summing the number of risk alleles of all the eleven candidate SNPs. Logistic regression was used to evaluate the association of the diabetic ischemic heart disease with candidate SNPs and PRS. SPSS25.0 software (SPSS Inc., Chicago, IL, USA) was used for all abovementioned statistical analysis. Generalized multifactor dimensionality reduction (GMDR) method was used to estimate the gene-gene interaction. For the adjustment for multiple testing, a permutation test with 1000 replications was performed. Haplotypes were identified and visualized by Haploview software. The association between haplotypes and diabetic cardiovascular disease were demonstrated by using Plink software. A two-sided P ≤ 0.05 was considered statistically significant. Result General characteristics of the studied participants A total of 882 health controls and 204 diabetic cardiovascular disease cases were included in the current study. DBP, TG and FPG were significantly higher in cardiovascular disease cases than controls ( P < 0.001). SBP, TC, LDLC and HDLC were significantly higher in controls compared with cases ( P < 0.001). According to recommendation of “2017 Guidelines for the prevention and treatment of type 2 diabetes in China”, the percentage of SBP, DBP, HDLC, LDLC, TG and TC in ideal range were significantly higher in control group compared with cases ( P < 0.001). In people with diabetic cardiovascular disease, the proportion of current smoker or alcohol drinker was significantly lower than controls ( P < 0.001). Details were show in Table 1 . Table 1 Demographic and biochemical characteristics of the participants Controls T2DM + CHD P value Age (years) 61.83 ± 10.55 63.97 ± 9.43 0.058 Male 488(55.3) 119(58.3) 0.436 SBP (mmHg) 136.79 ± 18.15 131.24 ± 11.78 < 0.001** DBP (mmHg) 78.74 ± 10.73 81.02 ± 8.34 0.001** BMI (kg/m 2 ) 25.89 ± 3.47 25.67 ± 3.18 0.428 TC (mmol/L) 5.16 ± 1.07 4.76 ± 1.26 < 0.001** HDLC (mmol/L) 1.39 ± 0.38 1.26 ± 0.40 < 0.001** LDLC (mmol/L) 3.06 ± 0.88 2.73 ± 0.86 < 0.001** TG (mmol/L) 1.62 ± 1.01 2.54 ± 1.38 < 0.001** FPG (mmol/L) 5.87 ± 2.43 7.82 ± 2.91 < 0.001** Current smoking 194(22.0) 18(8.8) < 0.001** Current drinking 295(33.5) 32(15.7) < 0.001** AGEs (mmol/L) 35.31 ± 15.91 37.76 ± 17.13 0.151 IL-6 (mmol/L) 137.75 ± 41.25 135.94 ± 35.65 0.675 BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, FPG fasting plasma glucose, TG triglyceride, TC total cholesterol, LDL-C low density lipoprotein cholesterol, HDL-C high density lipoprotein cholesterol, AGEs advanced glycation end products, IL-6 interleukin 6. ** P < 0.01. Association of AGER, IL6R polymorphisms with diabetic cardiovascular disease All polymorphisms were in Hardy-Weinberg equilibrium (all P -values were more than 0.05). For AGER rs184003, participants with CA and AA genotype have significantly higher risk of diabetic cardiovascular disease compared with CC genotype (OR = 1.435, P = 0.039; OR = 2.525, P = 0.030, respectively). The A allele is associated with an increased risk of diabetic cardiovascular disease by 50% in additive and dominant models ( P = 0.005; P = 0.013, respectively). For AGER rs2070600, the T allele is associated with a lower risk of diabetic cardiovascular disease by 30% in additive and dominant models ( P = 0.025; P = 0.030, respectively). However, after adjusting for potential confounders, the association between the above two SNPs and disease were null. For IL6R rs4845625, participants with CT and TT genotype have significantly lower risk of diabetic cardiovascular disease compared with CC genotype (OR = 0.692, P = 0.045; OR = 0.503, P = 0.003, respectively). The T allele can significantly decrease the risk of diabetic cardiovascular disease in additive and dominant models (OR = 0.707, P = 0.005; OR = 0.632, P = 0.013, respectively). The association between rs4845625 and disease was still significant after adjusting for potential confounders. Details were shown in Table 2 . Polygenic risk score is also associated with an increased risk of diabetic cardiovascular disease by 10% (OR = 1.098, 95% CI : 1.041 ~ 1.160, P = 0.001). After adjusting for dyslipidemia, hypertension, smoking, and drinking status, PRS was consistently associated with the disease (OR = 1.106, 95% CI : 1.016 ~ 1.205, P = 0.020). Table 2 Associations of gene polymorphisms with the risk of diabetic cardiovascular disease Genotype Crude OR £ (95%CI) Crude P value Adjusted OR ¤ (95%CI) Adjusted P value rs1035798 GG Ref Ref Ref Ref AG 1.156 (0.817, 1.637) 0.413 1.200 (0.781, 1.845) 0.406 AA 0.887 (0.360, 2.183) 0.794 0.767 (0.237, 2.481) 0.658 additive 1.069 (0.805, 1.420) 0.645 1.072 (0.750, 1.531) 0.704 dominant 1.124 (0.804, 1.570) 0.495 1.149 (0.758, 1.742) 0.514 recessive 0.852 (0.348, 2.086) 0.725 0.726 (0.226, 2.333) 0.591 rs1800624 AA Ref Ref Ref Ref AT 1.105 (0.776, 1.572) 0.580 1.168 (0.756, 1.806) 0.484 TT 0.987 (0.446, 2.183) 0.974 0.941 (0.347, 2.549) 0.905 additive 1.054 (0.799, 1.389) 0.711 1.077 (0.764, 1.518) 0.673 dominant 1.088 (0.778, 1.521) 0.622 1.136 (0.749, 1.721) 0.549 recessive 0.961 (0.437, 2.114) 0.921 0.900 (0.335, 2.419) 0.835 rs1800625 AA Ref Ref Ref Ref AG 1.071 (0.746, 1.538) 0.709 1.387 (0.884, 2.177) 0.150 GG 2.324 (0.672, 8.041) 0.183 1.992 (0.440, 9.022) 0.371 additive 1.157 (0.835, 1.601) 0.381 1.393 (0.934, 2.077) 0.104 dominant 1.117 (0.785, 1.589) 0.539 1.418 (0.914, 2.201) 0.119 recessive 2.286 (0.663, 7.885) 0.191 1.842 (0.410, 8.286) 0.426 rs184003 CC Ref Ref Ref Ref CA 1.435 (1.019, 2.020) 0.039* 1.306 (0.840, 2.032) 0.236 AA 2.525 (1.092, 5.837) 0.030* 1.399 (0.486, 4.027) 0.533 additive 1.491 (1.125, 1.976) 0.005** 1.255 (0.880, 1.789) 0.210 dominant 1.518 (1.093, 2.017) 0.013* 1.317 (0.864, 2.007) 0.200 recessive 2.282 (0.993, 5.241) 0.052 1.312 (0.459, 3.752) 0.613 rs2070600 CC Ref Ref Ref Ref CT 0.713 (0.496, 1.024) 0.067 0.842 (0.541, 1.310) 0.445 TT 0.536 (0.237, 1.211) 0.134 0.690 (0.230, 2.071) 0.508 additive 0.721 (0.542, 0.960) 0.025* 0.838 (0.584, 1.202) 0.336 dominant 0.684 (0.485, 0.964) 0.030* 0.823 (0.539, 1.256) 0.266 recessive 0.587 (0.261, 1.320) 0.198 0.724 (0.243, 2.158) 0.562 rs2228144 GG Ref Ref Ref Ref AG 1.301 (0.889), 1.904 0.175 1.127 (0.685, 1.854) 0.637 AA 1.679 (0.520, 5.425) 0.386 1.747 (0.429, 7.122) 0.437 additive 1.300 (0.935, 1.806) 0.118 1.184 (0.776, 1.807) 0.433 dominant 1.326 (0.917, 1.917) 0.134 1.171 (0.726, 1.889) 0.519 recessive 1.594 (0.495, 5.135) 0.435 1.709 (0.421, 6.943) 0.454 rs4072391 CC Ref Ref Ref Ref CT 1.149 (0.777, 1.698) 0.487 1.258 (0.765, 2.069) 0.367 TT 0.675 (0.081, 5.644) 0.717 1.172 (0.121, 10.178) 0.891 additive 1.300 (0.935, 1.806) 0.118 1.142 (0.711, 1.835) 0.583 dominant 1.129 (0.767, 1.662) 0.537 1.208 (0.736, 1.982) 0.455 recessive 0.658 (0.079, 5.493) 0.658 1.128 (0.116, 10.918) 0.917 rs4129267 CC Ref Ref Ref Ref CT 1.327 (0.937, 1.877) 0.111 1.224 (0.791, 1.895) 0.364 TT 1.430 (0.898, 2.279) 0.132 1.559 (0.863, 2.818) 0.141 additive 1.217 (0.973, 1.521) 0.085 1.224 (0.993, 1.660) 0.137 dominant 1.351 (0.970, 1.880) 0.075 1.294 (0.853, 1.963) 0.226 recessive 1.208 (0.799, 1.825) 0.371 1.377 (0.814, 2.330) 0.232 rs4537545 CC Ref Ref Ref Ref CT 1.395 (0.984, 1.978) 0.062 1.439 (0.924, 2.239) 0.107 TT 1.386 (0.866, 2.220) 0.174 1.603 (0.877, 2.929) 0.125 additive 1.217 (0.973, 1.521) 0.085 1.294 (0.969, 1.727) 0.081 dominant 1.393 (0.998, 1.944) 0.051 1.474 (0.964, 2.253) 0.073 recessive 1.132 (0.747, 1.716) 0.558 1.276 (0.751, 2.168) 0.368 rs4845625 CC Ref Ref Ref Ref CT 0.692 (0.483, 0.991) 0.045* 0.572 (0.362, 0.904) 0.017* TT 0.503 (0.318, 0.795) 0.003** 0.541 (0.311, 0.940) 0.029* additive 0.707 (0.563, 0.888) 0.003** 0.723 (0.545, 0.960) 0.025* dominant 0.632 (0.448, 0.889) 0.009** 0.562 (0.365, 0.866) 0.009** recessive 0.644 (0.434, 0.955) 0.028 0.784 (0.491, 1.252) 0.308 rs7514452 TT Ref Ref Ref Ref CT 1.113 (0.751, 1.650) 0.594 1.193 (0.789, 1.805) 0.403 CC 0.671 (0.080, 5.610) 0.712 1.166 (0.120, 11.317) 0.895 additive 1.069 (0.739, 1.544) 0.724 1.108 (0.686, 1.789) 0.675 dominant 1.095 (0.742, 1.616) 0.647 1.170 (0.710, 1.928) 0.539 recessive 0.658 (0.079, 5.493) 0.699 1.128 (0.116, 10.918) 0.917 rs7529229 TT Ref Ref Ref Ref CT 1.289 (0.912, 1.822) 0.151 1.223 (0.790, 1.893) 0.366 CC 1.403 (0.881, 2.234) 0.153 1.557 (0.861, 2.815) 0.143 additive 1.069 (0.739, 1.544) 0.724 1.243 (0.932, 1.659) 0.138 dominant 1.315 (0.946, 1.829) 0.103 1.292 (0.852, 1.960) 0.228 recessive 1.206 (0.798, 1.822) 0.375 1.377 (0.814, 2.328) 0.233 £ No variables were adjusted in logistic regression model ¤ Dyslipidemia, hypertension, smoking, and drinking were adjusted in the logistic regression model. * P < 0.05, ** P < 0.01. Association between haplotypes and diabetic cardiovascular disease Four out of five SNPs in AGER gene (Block1: rs184003-rs1035798-rs2070600-rs1800624) and five out of seven SNPs in IL6R gene (Block2: rs7529229-rs4845625-rs4129267-rs7514452-rs4072391) showed linkage disequilibrium, see Fig. 1 . These two blocks were both significantly associated with diabetic cardiovascular disease (Block1: P = 0.008; Block2: P = 0.007). Four haplotypes were constructed in block 1, and two of them associated with diabetic ischemic heart disease (CGTA: P = 0.018; AGCA: P = 0.004). Four haplotypes were constructed in block 2, and two of them associated with diabetic cardiovascular disease (TCCTC: P = 0.033; TTCTC: P = 0.001). Details of haplotype analysis were shown in Table 3 . Table 3 Haplotype analysis for blocks in AGER and IL6R genes haplotypes F_U £ F_A ¤ Chi-square df P value Block1 § Omnibus test - - 11.750 3 0.008** CACT 0.162 0.170 0.162 1 0.687 CGTA 0.202 0.150 5.575 1 0.018* AGCA 0.140 0.197 8.229 1 0.004** CGCA 0.497 0.482 0.247 1 0.620 Block2 ¢ Omnibus test - - 11.99 3 0.007** TTCCT 0.093 0.100 0.227 1 0.634 CCTTC 0.387 0.431 2.639 1 0.104 TCCTC 0.009 0.131 4.551 1 0.033 TTCTC 0.426 0.338 10.32 1 0.001** § Block1: rs184003-rs1035798-rs2070600-rs1800624; ¢ Block2: rs7529229-rs4845625-rs4129267-rs7514452-rs4072391; £ F_U: minor allele frequency in controls; ¤ F_A: minor allele frequency in cases; * P < 0.05; ** P < 0.01 The effect of gene-gene interaction on diabetic cardiovascular disease GMDR analysis were performed to assess the gene-gene interaction on diabetic cardiovascular disease risk, after adjustment for dyslipidemia, hypertension, smoking, and drinking. The GMDR analysis suggested that rs184003 in AGER gene and rs4845625 in IL-6R gene was the best model in terms of statistical significance after permutation testing ( P = 0.001). The two-locus models had a cross-validation consistency of 10/10, and had a testing accuracy of 0.597. Logistic regression was subsequently used to obtain the odds ratios (ORs) and 95% confidence intervals (CI) for the interaction between rs184003 and rs4845625. In additive model, the joint effect of rs184003 and rs4845625 is associated with an increased risk of diabetic cardiovascular disease by 38% (OR = 1.38, 95% CI : 1.13–1.69, P = 0.002). Discussion Individuals with T2DM are with an increased risk of CVD which cannot be fully explained by elevated glucose [ 14 ] . Genetic risk factors contribute a lot to the pathogenesis of diabetic macrovascular complications, but its role has not been fully illustrated yet. In the present community-based case-control study, rs4845625 in IL-6R gene, and the interaction of rs184003 in AGER gene and rs4845625 in IL-6R were significantly associated with diabetic ischemic heart disease. Polygenic risk score calculated by summing the number of risk alleles of the SNPs located in the above two genes were also associated with the elevated risk of diabetic ischemic heart disease. AGER is a multiligand cell surface receptor. Advanced glycation end products (AGEs) which is produced after high glucose exposure can bind to AGER. Their interaction has been implicated in the pathogenesis of atherosclerosis. In addition, HMGB1 (high-mobility group protein 1) and neutrophil-derived S100 calcium-binding family members (S100A8/A9/A11/A12, and S100B) were also ligands of AGER. After ligand binding, proinflammatory and procoagulant pathways will be activated. The rs2070600 was found to be significantly associated with diabetic ischemic heart disease in the current study. But after adjustments for covariates, the associations became null. The rs2070600 is located in ligand-binding V domain of the AGER gene, often referred to as Gly82Ser [ 15 ] . Genome-wide association studies (GWAS) showed that rs2070600 were strongly and dose-dependently correlated with sRAGE level in whites and blacks from Atherosclerosis Risk in Communities Study and Chinese population [ 6 , 16 ] . Interestingly, although soluble-RAGE levels were found to be associated with diabetic complications in many researches, the association between rs2070600 and cardiovascular disease or other diabetic complications were not consistent. In Atherosclerosis Risk in Communities Study, the rs2070600 was not significantly associated with incident coronary heart disease or diabetes in both whites and blacks with a median follow-up of 20 years [ 6 ] . Gao et al. has found a significant association between rs2070600 and coronary arterial disease in 175 cases and 170 controls [ 17 ] . Meta-analysis found that the discrepancy may be attributable to ethnicity, subjects with rs2070600 risk allele were at higher risk of coronary arterial disease (CAD) in the Chinese population, rather than non-Chinese population. However, our study found the association between rs2070600 and diabetic ischemic heart disease was null. Another research also found rs2070600 was associated with the circulating levels of esRAGE but not with CAD in Chinese patients with T2DM [ 18 ] . These results might indicate that the association between rs2070600 and CAD may also be different in general population and T2DM patients. Only few studies had demonstrated the association between rs184003 and cardiovascular disease. A hospital-based case-control study found rs184003 can significantly increase the risk of coronary artery disease (OR = 1.23, P = 0.008), and haplotypes C-T-G-G and T-A-G-T in AGER gene (rs1800625-rs1800624-rs2070600-rs184003) were associated with significant increases in risk for CAD [ 19 ] . In the current study, we also haplotypes C-G-T-A and A-G-C-A in AGER gene (rs184003-rs1035798-rs2070600-rs1800624) were significantly associated with diabetic ischemic heart disease. Although the rs184003 was significantly associated with diabetic ischemic heart disease in the current study, the associations became null after adjustments for covariates. To our knowledge, few studies illustrated the relationship between rs184003 and diabetic macrovascular complications. More researches are still need to validate our results. Given the fact sRAGE level were found to be significantly associated with CAD [ 20 , 21 ] in many researches, the null association between AGER polymorphisms and diabetic ischemic heart disease in the current study indicated that sRAGE level could be served a marker of CAD, but not the a potential intervention targeting of reducing the burden of CAD. Mendelian randomization analysis illustrated that IL6R signaling might have a causal role in development of coronary heart disease [ 22 ] . Previous meta-analysis demonstrated that rs2228145 and rs7529229 in IL6R could significantly reduce the risk of coronary heart disease [ 22 , 23 ] . Although the meta-analysis constituting a large sample size, the data from Asian is insufficient. Chen et al. did not find an association of rs2228145 with coronary stenosis or acute myocardial infarction in the Chinese Han population [ 24 ] . Likewise, our current study, showed no association between rs2228145 and diabetic ischemic heart disease in Chinese population. The haplotype T-T-C-T-C (rs7529229-rs4845625-rs4129267-rs7514452-rs4072391) in IL6R gene and the rs4845625 was associated with diabetic cardiovascular disease in our study, and the association held after adjusting for potential confounders. The rs4845625 was found to be significantly associated with hypertriglyceridemia in Japanese population [ 25 ] , and the T allele was associated with lower serum concentration of creatinine and increased eGFR [ 26 ] . Hypertriglyceridemia and chronic kidney disease (CKD) have common pathway leading to metabolic cardiovascular disease, like endothelial dysfunction, dyslipidemia, and inflammation [ 27 ] . Although there was seldom any study focus on the association between rs4845625 and diabetic heart disease, its association with triglyceride and kidney function might indicate the potential mechanisms of rs4645625 on diabetic ischemic heart disease. It has been found that, in response to hyperglycemia, AGER will be activated by S100A8/A9 on hepatic Kupffer cells, leading to the secretion of IL-6. IL-6 would subsequently bind to its receptor (IL6R) on hepatocytes to enhance the production of thrombopoietin, thereby regulating platelet production and resulting diabetes-induced thrombocytosis [ 28 ] . In the current study, we found that gene-gene interaction between AGER and IL6R would increase the risk of diabetic ischemic heart disease. We subsequently used GeneMANIA to construct gene network and predict gene function. IL6R and AGER have physical interactions with each other, and several pathways including NF-kB /RelA and JAK/STAT are involved in these interactions. Details were shown in Supplementary Figure S1 . These interactions illustrated that the interaction of SNPs in IL6R and AGER was not only a statistical interaction, but also a biological interaction. To our knowledge, this is the first study aimed to identify interaction of AGER and IL6R gene, and our results provided a genetic evidence on the physiological mechanism of diabetic macrovascular complications. Whether the main effect and gene-gene interaction in these two genes could be used to predict the risk of diabetic macrovascular complications are still need to be validated by cohort study in the future. Although we found the significant interaction of AGER gene and IL6R gene, the association between circulating IL-6 and diabetic ischemic heart disease was null. This result indicates that the role of circulating IL-6 in the pathogenesis and development of T2DM cardiovascular complications is complex. The most common hypothesis is that local IL-6 production and dynamics of sIL-6R which indicated the activation of IL-6 trans-signaling pathway were more likely to affect the TPO production and macrovascular complications [ 28 , 29 ] . In the current study, SBP, TC, LDLC level and the proportion of people with smoking and drinking habits were significantly lower in cases than in controls, which is not consistent with other researches. According to “2017 Guidelines for the prevention and treatment of type 2 diabetes in China”, diabetes patients have more stringent standards on blood pressure (BP) and blood lipid compared with health population, and diabetes patients with ischemic heart disease should quit smoking and drinking [ 30 ] . Diabetes patients might change their lifestyles and medication to maintain their BP or blood lipid at a lower level. Due to the case-control study design of the current study, we were not able to collect the lifestyle risk factors and blood sample before the incident of diabetic ischemic heart disease. However, the percentage of SBP, DBP, HDLC, LDLC, TG and TC in ideal range were significantly higher in control group compared with cases ( P < 0.001, supplementary table S2 ). Due to the above limitation of our study, more longitudinal researches are still needed to demonstrate whether genetic variants will increase the incident of diabetic macrovascular complications. What is more, medication information was not included in the investigation. Given the fact that some antidiabetic medication, like SGLT-2 inhibitor [ 31 ] , will reduce the risk of cardiovascular disease in diabetes patients, future researches considering antidiabetic medication are still needed to validate the genetic effect on diabetic macrovascular complications. List of abbreviations CVD, Cardiovascular disease T2DM, Type 2 diabetes mellitus AGER, Advanced glycation end products receptors AGEs, Advanced glycation end products IL-6, Interleukin-6 S100A8/A9, S100 calcium-binding protein A8/A9 FPG, Fasting plasma glucose TC, Total cholesterol TG, Triglycerides HDLC, High-density lipoprotein cholesterol LDLC, Low-density lipoprotein cholesterol IHD, Ischemic heart disease SBP, Systolic blood pressure DBP, Diastolic blood pressure PRS, Polygenic risk score GMDR, Generalized multifactor dimensionality reduction GWAS, Genome-wide association studies Declarations Abbreviations CVD, Cardiovascular disease T2DM, Type 2 diabetes mellitus AGER, Advanced glycation end products receptors AGEs, Advanced glycation end products IL-6, Interleukin-6 S100A8/A9, S100 calcium-binding protein A8/A9 FPG, Fasting plasma glucose TC, Total cholesterol TG, Triglycerides HDLC, High-density lipoprotein cholesterol LDLC, Low-density lipoprotein cholesterol IHD, Ischemic heart disease SBP, Systolic blood pressure DBP, Diastolic blood pressure PRS, Polygenic risk score GMDR, Generalized multifactor dimensionality reduction GWAS, Genome-wide association studies Declarations Ethical Approval and consent to participate: This study was approved by the Ethics Committee of Capital Medical University (No:2016SY24). All participants enrolled in this study have signed informed consent. Consent for publication: Not applicable Availability of data and materials: The datasets generated and/or analyzed during the current study are not publicly available due to the regulations of the people's Republic of China on the administration of human genetic resources, but part of the dataset is available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: This study was supported by grants from National Science Foundation of China (81602908), and National key research and development program of China (2016YFC0900600/2016YFC0900603). The funding sources had no involvement in the study design, data collection, analysis and interpretation of data, writing of the manuscript, and the decision to submit the article for publication. Author contributions: LK designed the study and wrote the manuscript, XY analyzed data and visualized the interaction diagram. ZQ provided the statistical plan and helped to revise the manuscript. PW contributed to the verification of diabetic ischemic heart diseases in case group. GC and ZJ contributed to the management of blood sample and DNA extraction. ZL contributed to the collection of controls and participated in the study design. Submission declaration and verification: Submission of an article implies that the work described has not been published previously. This article is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, it will not be published elsewhere in the same form, in English or in any other language, including electronically without the written consent of the copyright holder. 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Lu W, Feng B, Xie G, Liu F. Association of AGER gene G82S polymorphism with the severity of coronary artery disease in Chinese Han population. Clin Endocrinol (Oxf). 2011;75(4):470–4. Lim SC, Dorajoo R, Zhang X, Wang L, Ang SF, Tan CSH, et al. Genetic variants in the receptor for advanced glycation end products (RAGE) gene were associated with circulating soluble RAGE level but not with renal function among Asians with type 2 diabetes: a genome-wide association study. Nephrol Dial Transplant. 2017;32(10):1697–704. Gao J, Shao Y, Lai W, Ren H, Xu D. Association of polymorphisms in the RAGE gene with serum CRP levels and coronary artery disease in the Chinese Han population. J Hum Genet. 2010;55(10):668–75. Peng WH, Lu L, Wang LJ, Yan XX, Chen QJ, Zhang Q, et al. RAGE gene polymorphisms are associated with circulating levels of endogenous secretory RAGE but not with coronary artery disease in Chinese patients with type 2 diabetes mellitus. Arch Med Res. 2009;40(5):393–8. Yu X, Liu J, Zhu H, Xia Y, Gao L, Li Z, et al. An interactive association of advanced glycation end-product receptor gene four common polymorphisms with coronary artery disease in northeastern Han Chinese. PLoS One. 2013;8(10):e76966. Ligthart S, Sedaghat S, Ikram MA, Hofman A, Franco OH, Dehghan A. EN-RAGE: a novel inflammatory marker for incident coronary heart disease. Arterioscler Thromb Vasc Biol. 2014;34(12):2695–9. Reichert S, Triebert U, Santos AN, Hofmann B, Schaller HG, Schlitt A, et al. Soluble form of receptor for advanced glycation end products and incidence of new cardiovascular events among patients with cardiovascular disease. Atherosclerosis. 2017;266:234–9. Interleukin-6 Receptor Mendelian Randomisation Analysis. Swerdlow DI, Holmes MV, Kuchenbaecker KB, Engmann JE, Shah T, et al Sattar N, Hingorani AD, Casas JP. The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis. Lancet. 2012;379(9822): 1214–24. Collaboration IRGCERF, Sarwar N, Butterworth AS, Freitag DF, Gregson J, Willeit P, et al. Interleukin-6 receptor pathways in coronary heart disease: a collaborative meta-analysis of 82 studies. Lancet. 2012;379(9822):1205–13. Chen Z, Qian Q, Tang C, Ding J, Feng Y, Ma G. Association of two variants in the interleukin-6 receptor gene and premature coronary heart disease in a Chinese Han population. Mol Biol Rep. 2013;40(2):1021–6. Abe S, Tokoro F, Matsuoka R, Arai M, Noda T, Watanabe S, et al. Association of genetic variants with dyslipidemia. Mol Med Rep. 2015;12(4):5429–36. Horibe H, Fujimaki T, Oguri M, Kato K, Matsuoka R, Abe S, et al. Association of a polymorphism of the interleukin 6 receptor gene with chronic kidney disease in Japanese individuals. Nephrology (Carlton). 2015;20(4):273–8. Gajjala PR, Sanati M, Jankowski J. Cellular and Molecular Mechanisms of Chronic Kidney Disease with Diabetes Mellitus and Cardiovascular Diseases as Its Comorbidities. Front Immunol. 2015;6:340. Kraakman MJ, Lee MK, Al-Sharea A, Dragoljevic D, Barrett TJ, Montenont E, et al. Neutrophil-derived S100 calcium-binding proteins A8/A9 promote reticulated thrombocytosis and atherogenesis in diabetes. J Clin Invest. 2017;127(6):2133–47. Qu D, Liu J, Lau CW, Huang Y. IL-6 in diabetes and cardiovascular complications. Br J Pharmacol. 2014;171(15):3595–603. Society CD. Guidelines for the prevention and control of type 2 diabetes in China (2017 Edition). Chinese Journal of Practical Internal Medicine. 2018;38(4):292–344. Scheen AJ. Cardiovascular Effects of New Oral Glucose-Lowering Agents: DPP-4 and SGLT-2 Inhibitors. Circ Res. 2018;122(10):1439–59. Supplementary Files FigureS1.jpeg Figure S1 The gene-gene interaction network between AGER and IL6R CVD, cardiovascular disease; T2DM, type 2 diabetes mellitus; AGER, advanced glycation end products; IL-6, interleukin-6; TPO, thrombopoietin; SNP, single nucleotide polymorphism; BP, blood pressure; TC, total cholesterol; TG, triglycerides; HDLC, high-density lipoprotein cholesterol; LDLC, low-density lipoprotein cholesterol; FPG, fasting plasma glucose; IHD, ischemic heart disease; SBP, systolic blood pressure; DBP, diastolic blood pressure; GMDR, generalized multifactor dimensionality reduction; tableS1.docx tableS2.docx Cite Share Download PDF Status: Under Review Version 1 posted Review # 2 received at journal 08 Mar, 2021 Editorial decision: Major revision 08 Mar, 2021 Reviewer # 3 agreed at journal 01 Mar, 2021 Reviewer # 2 agreed at journal 15 Feb, 2021 Review # 1 received at journal 01 Feb, 2021 Reviewers invited by journal 25 Jan, 2021 Reviewer # 1 agreed at journal 25 Jan, 2021 Editor assigned by journal 11 Jan, 2021 Submission checks completed at journal 11 Jan, 2021 Editor invited by journal 11 Jan, 2021 First submitted to journal 21 Dec, 2020 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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T2DM, type 2 diabetes mellitus; AGER, advanced glycation end products; IL-6, interleukin-6; TPO, thrombopoietin; SNP, single nucleotide polymorphism; BP, blood pressure; TC, total cholesterol; TG, triglycerides; HDLC, high-density lipoprotein cholesterol; LDLC, low-density lipoprotein cholesterol; FPG, fasting plasma glucose; IHD, ischemic heart disease; SBP, systolic blood pressure; DBP, diastolic blood pressure; GMDR, generalized multifactor dimensionality reduction;\n","description":"","filename":"FigureS1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-147920/v1/c7b1147a27e343dd61ea97b5.jpeg"},{"id":5090917,"identity":"84dd3c9b-76a3-4e98-ae49-af44fe923d28","added_by":"auto","created_at":"2021-01-19 17:00:13","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20109,"visible":true,"origin":"","legend":"","description":"","filename":"tableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-147920/v1/0c6b5bc46c4329b05e85c9fb.docx"},{"id":5091186,"identity":"0a36a07a-c9a6-47c4-b1dd-85fac380c120","added_by":"auto","created_at":"2021-01-19 17:03:13","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19209,"visible":true,"origin":"","legend":"","description":"","filename":"tableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-147920/v1/06b01feb1357ebf43cb1a163.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003ePolymorphisms and Gene-Gene Interaction in AGER/IL6 Pathway are Associated with Diabetic Ischemic Heart Disease\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eCardiovascular disease (CVD) is the leading cause of mortality in people with type 2 diabetes mellitus (T2DM). About 68% of deaths in type 2 diabetic patients are caused by cardiac complications\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. However, it is not clear how diabetes promotes cardiac dysfunction. The widely acceptable hypothesis is that many signaling cascades, ultimately resulting in pro-inflammatory reaction, oxidative stress or thrombotic pathways, and subsequently leading to vascular inflammation\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. It has been demonstrated that advanced glycation end products (AGER) / interleukin-6 (IL-6) pathway plays an important role in the physiological mechanism of diabetic cardiovascular complication. High glucose level can trigger neutrophil to release S100 calcium-binding protein A8/A9 (S100A8/A9), which binds to AGER on Kupffer cells, and leads to IL-6 secretion\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. IL-6 / IL-6R complex activate JAK2 / STAT3 pathway, which can mediate pro-inflammatory response and increase platelet thrombopoiesis by inducing thrombopoietin (TPO) production\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeveral single nucleotide polymorphisms (SNPs) in \u003cem\u003eAGER\u003c/em\u003e gene have been reported to be associated with diabetes or its complications. The Atherosclerosis Risk in Communities Study showed an association between rs2070600 and an approximate 50% reduction in soluble AGER levels\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. A meta-analysis has highlighted a significant association of rs2070600 with the risk of diabetic nephropathy development\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. However, the association between rs2070600 and diabetic cardiovascular disease are under-reported. On \u003cem\u003eIL6R\u003c/em\u003e gene, the rs1800624 and rs1800625 are in absolute or strong linkage disequilibrium, and were reported to be protective factors for cardiovascular disease\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. However, the effect of them on the vascular complications in T2DM remains inconsistent\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, In addition, the gene-gene interactions on the increased risk of the disease requires further clarification .\u003c/p\u003e \u003cp\u003eThe current study aimed at illustrating the association of \u003cem\u003eAGER\u003c/em\u003e and \u003cem\u003eIL6R\u003c/em\u003e gene polymorphisms with the risk of diabetic cardiovascular disease, and assess the modulatory effect of gene-gene interaction between these variants on disease risk. The result would provide evidence on the precise prevention of ischemic heart disease in diabetes.\u003c/p\u003e "},{"header":"Method","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eA total of 204 diabetic ischemic heart disease cases and 882 health controls were enrolled from communities in Beijing. All subjects gave written informed consent. This study was approved by the Ethics Committee of Capital Medical University (No:2016SY24).\u003c/p\u003e \u003cp\u003eInclusion criteria for the cases were as follows: (1) T2DM patients diagnosed according to American Diabetes Association Criteria \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, or receiving pharmaceutical treatment on T2DM; (2) Ischemic heart disease defined by clinical history, including acute myocardium infarction, angina pectoris and/or ischemic electrocardiographic alterations; (3) T2DM was diagnosed earlier than ischemic heart disease. (4) The medical records or copies should be provided to verify the diagnose of diseases.\u003c/p\u003e \u003cp\u003eInclusion criteria for the controls were as follows: (1) Subjects had not been diagnosed as T2DM before, and fasting blood glucose was less than 5.6\u0026nbsp;mmol/L in the current survey. (2) Subjects did not have cardiovascular disease, which included ischemic heart disease, ischemic stroke, or cerebral hemorrhage. (3) Subjects did not have chronic kidney disease. (4) Subjects were not in the acute phase of infection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasurements\u003c/h2\u003e \u003cp\u003eLife style risk factors were obtained from structured questionnaire. Smoking status was categorized as: \u0026ldquo;currently smoking\u0026rdquo; and \u0026ldquo;past / never smoking\u0026rdquo;. Current smoking was defined as at least 1 cigarette per day, lasting for more than 1\u0026nbsp;year. Those who have never smoked before or have not smoked for at least 3\u0026nbsp;months were defined as past / never smoking. Alcohol drinking was categorized as \u0026ldquo;currently alcohol drinking\u0026rdquo; and \u0026ldquo;past / never alcohol drinking\u0026rdquo;. Currently drinking was defined as at least drink once per week and still drank at that frequency in the previous month. Those who never drink alcohol or have not drank alcohol for at least one month were defined as never / past alcohol drinking.\u003c/p\u003e \u003cp\u003eBlood pressure (BP) was measured in the morning before participants use anti-hypertensive medication. Participants were asked to rest for at least 30 minutes before BP measurement if they had just smoked or had caffeinated products. BP (mmHg) was measured for three times at sitting positions by mercury sphygmomanometer. The average of the last two measurements was used for data analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSerum markers\u003c/h2\u003e \u003cp\u003eAfter an overnight fasting, all participants underwent fasting blood sampling. Fasting blood samples are collected and restored in 2% EDTA vacutainer for each participant. After centrifuging, plasma and blood cell samples are separated into two cryovials. Fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDLC), low-density lipoprotein cholesterol (LDLC) were tested using the Beckman coulter chemistry analyzer AU5800 in the clinical laboratory of Beijing Hepingli Hospital.\u003c/p\u003e \u003cp\u003eVenous blood samples were obtained and stored in 4\u003csup\u003eo\u003c/sup\u003eC refrigerator. All the hematological analysis was done within 8 hours. Serum glucose and biochemical determinations were measured by an enzymatic method using a chemistry analyzer (Beckman LX20, Beckman, Brea, CA, USA) at the central laboratory of the hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGenotyping\u003c/h2\u003e \u003cp\u003eImportant functional SNPs and previously reported susceptible SNPs were selected as candidate SNPs. Five SNPs (rs1035798, rs1800624, rs1800625, rs184003 and rs2070600) in \u003cem\u003eAGER\u003c/em\u003e gene and seven SNPs (rs2228144, rs4072391, rs4129267, rs4537545, rs4845625, rs7514452 and rs7529229) in \u003cem\u003eIL6R\u003c/em\u003e gene were selected in the current study.\u003c/p\u003e \u003cp\u003eGenomic DNA was extracted from 1\u0026nbsp;ml of peripheral blood cell using TIANGEN DNA kit (TIANGEN Biotech, China, DP319-01) according to the manufacturer\u0026rsquo;s protocol. Primers were designed by the AssayDesigner3.1 software, and they were synthesized by Thermo Fisher Scientific Co., Ltd. Detailed information of the primers were shown in \u003cb\u003esupplementary table S1\u003c/b\u003e. A Sequenom MassARRAY\u0026reg; matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) platform (Sequenom Inc., San Diego, CA, USA) were used to genotype SNPs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of diseases and recommend level of their risk factors\u003c/h2\u003e \u003cp\u003eT2DM was defined as FPG\u0026thinsp;\u0026ge;\u0026thinsp;7.0\u0026nbsp;mmol/L or self-reported physician-diagnosed diabetes and/or on use of antidiabetes agents, according to American Diabetes Association Criteria\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Ischemic heart disease (IHD) was defined as non-fatal ischemic heart disease, including acute myocardial infarction and angina pectoris. The incident of ischemic heart disease in T2DM patients were defined as diabetic ischemic heart disease. Hypertension was defined as systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;140\u0026nbsp;mmHg and/or diastolic blood pressure (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;90\u0026nbsp;mmHg and/or on current antihypertensive medication. Participants with TG\u0026thinsp;\u0026ge;\u0026thinsp;2.3\u0026nbsp;mmol/L, or TC\u0026thinsp;\u0026ge;\u0026thinsp;6.2\u0026nbsp;mmol/L, or LDLC\u0026thinsp;\u0026ge;\u0026thinsp;4.1\u0026nbsp;mmol/L, or HDLC\u0026thinsp;\u0026le;\u0026thinsp;1.0\u0026nbsp;mmol/L were defined as dyslipidemia according to the criteria of the 2016 Chinese guidelines for the management of dyslipidemia in adults\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables with normal distribution are expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SDs). Categorical variables were expressed as number (percentage). Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e test was used to compare the difference of each continuous variables. Polygenic risk score (PRS) was calculated by summing the number of risk alleles of all the eleven candidate SNPs. Logistic regression was used to evaluate the association of the diabetic ischemic heart disease with candidate SNPs and PRS. SPSS25.0 software (SPSS Inc., Chicago, IL, USA) was used for all abovementioned statistical analysis. Generalized multifactor dimensionality reduction (GMDR) method was used to estimate the gene-gene interaction. For the adjustment for multiple testing, a permutation test with 1000 replications was performed. Haplotypes were identified and visualized by Haploview software. The association between haplotypes and diabetic cardiovascular disease were demonstrated by using Plink software. A two-sided \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e "},{"header":"Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eGeneral characteristics of the studied participants\u003c/h2\u003e\n\u003cp\u003eA total of 882 health controls and 204 diabetic cardiovascular disease cases were included in the current study. DBP, TG and FPG were significantly higher in cardiovascular disease cases than controls (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). SBP, TC, LDLC and HDLC were significantly higher in controls compared with cases (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). According to recommendation of \u0026ldquo;2017 Guidelines for the prevention and treatment of type 2 diabetes in China\u0026rdquo;, the percentage of SBP, DBP, HDLC, LDLC, TG and TC in ideal range were significantly higher in control group compared with cases (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In people with diabetic cardiovascular disease, the proportion of current smoker or alcohol drinker was significantly lower than controls (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Details were show in Table\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDemographic and biochemical characteristics of the participants\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eControls\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eT2DM\u0026thinsp;+\u0026thinsp;CHD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61.83\u0026thinsp;\u0026plusmn;\u0026thinsp;10.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.97\u0026thinsp;\u0026plusmn;\u0026thinsp;9.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.058\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e488(55.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e119(58.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.436\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSBP (mmHg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e136.79\u0026thinsp;\u0026plusmn;\u0026thinsp;18.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e131.24\u0026thinsp;\u0026plusmn;\u0026thinsp;11.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDBP (mmHg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78.74\u0026thinsp;\u0026plusmn;\u0026thinsp;10.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81.02\u0026thinsp;\u0026plusmn;\u0026thinsp;8.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.89\u0026thinsp;\u0026plusmn;\u0026thinsp;3.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.428\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTC (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDLC (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLDLC (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTG (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.87\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurrent smoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e194(22.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18(8.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurrent drinking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e295(33.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32(15.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAGEs (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.31\u0026thinsp;\u0026plusmn;\u0026thinsp;15.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.76\u0026thinsp;\u0026plusmn;\u0026thinsp;17.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.151\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIL-6 (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e137.75\u0026thinsp;\u0026plusmn;\u0026thinsp;41.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e135.94\u0026thinsp;\u0026plusmn;\u0026thinsp;35.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.675\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003e\u003cem\u003eBMI\u003c/em\u003e body mass index, \u003cem\u003eSBP\u003c/em\u003e systolic blood pressure, \u003cem\u003eDBP\u003c/em\u003e diastolic blood pressure, \u003cem\u003eFPG\u003c/em\u003e fasting plasma glucose, \u003cem\u003eTG\u003c/em\u003e triglyceride, \u003cem\u003eTC\u003c/em\u003e total cholesterol, \u003cem\u003eLDL-C\u003c/em\u003e low density lipoprotein cholesterol, \u003cem\u003eHDL-C\u003c/em\u003e high density lipoprotein cholesterol, \u003cem\u003eAGEs\u003c/em\u003e advanced glycation end products, \u003cem\u003eIL-6\u003c/em\u003e interleukin 6. **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of\u003c/strong\u003e \u003cspan class=\"BoldItalic\"\u003eAGER, IL6R\u003c/span\u003e \u003cstrong\u003epolymorphisms with diabetic cardiovascular disease\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll polymorphisms were in Hardy-Weinberg equilibrium (all \u003cem\u003eP\u003c/em\u003e-values were more than 0.05). For \u003cem\u003eAGER\u003c/em\u003e rs184003, participants with CA and AA genotype have significantly higher risk of diabetic cardiovascular disease compared with CC genotype (OR\u0026thinsp;=\u0026thinsp;1.435, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039; OR\u0026thinsp;=\u0026thinsp;2.525, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030, respectively). The A allele is associated with an increased risk of diabetic cardiovascular disease by 50% in additive and dominant models (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013, respectively). For \u003cem\u003eAGER\u003c/em\u003e rs2070600, the T allele is associated with a lower risk of diabetic cardiovascular disease by 30% in additive and dominant models (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030, respectively). However, after adjusting for potential confounders, the association between the above two SNPs and disease were null.\u003c/p\u003e\n\u003cp\u003eFor \u003cem\u003eIL6R\u003c/em\u003e rs4845625, participants with CT and TT genotype have significantly lower risk of diabetic cardiovascular disease compared with CC genotype (OR\u0026thinsp;=\u0026thinsp;0.692, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045; OR\u0026thinsp;=\u0026thinsp;0.503, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, respectively). The T allele can significantly decrease the risk of diabetic cardiovascular disease in additive and dominant models (OR\u0026thinsp;=\u0026thinsp;0.707, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; OR\u0026thinsp;=\u0026thinsp;0.632, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013, respectively). The association between rs4845625 and disease was still significant after adjusting for potential confounders. Details were shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Polygenic risk score is also associated with an increased risk of diabetic cardiovascular disease by 10% (OR\u0026thinsp;=\u0026thinsp;1.098, 95%\u003cem\u003eCI\u003c/em\u003e: 1.041\u0026thinsp;~\u0026thinsp;1.160, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). After adjusting for dyslipidemia, hypertension, smoking, and drinking status, PRS was consistently associated with the disease (OR\u0026thinsp;=\u0026thinsp;1.106, 95%\u003cem\u003eCI\u003c/em\u003e: 1.016\u0026thinsp;~\u0026thinsp;1.205, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAssociations of gene polymorphisms with the risk of diabetic cardiovascular disease\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGenotype\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCrude OR\u003csup\u003e\u0026pound;\u003c/sup\u003e (95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCrude \u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAdjusted OR\u003csup\u003e\u0026curren;\u003c/sup\u003e (95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAdjusted \u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers1035798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.156 (0.817, 1.637)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.413\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.200 (0.781, 1.845)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.406\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.887 (0.360, 2.183)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.794\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.767 (0.237, 2.481)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.658\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eadditive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.069 (0.805, 1.420)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.645\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.072 (0.750, 1.531)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.704\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edominant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.124 (0.804, 1.570)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.495\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.149 (0.758, 1.742)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.514\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003erecessive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.852 (0.348, 2.086)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.725\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.726 (0.226, 2.333)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.591\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers1800624\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.105 (0.776, 1.572)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.580\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.168 (0.756, 1.806)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.484\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.987 (0.446, 2.183)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.974\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.941 (0.347, 2.549)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.905\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eadditive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.054 (0.799, 1.389)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.711\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.077 (0.764, 1.518)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.673\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edominant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.088 (0.778, 1.521)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.622\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.136 (0.749, 1.721)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.549\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003erecessive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.961 (0.437, 2.114)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.921\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.900 (0.335, 2.419)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.835\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers1800625\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.071 (0.746, 1.538)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.709\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.387 (0.884, 2.177)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.150\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.324 (0.672, 8.041)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.183\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.992 (0.440, 9.022)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.371\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eadditive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.157 (0.835, 1.601)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.381\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.393 (0.934, 2.077)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.104\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edominant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.117 (0.785, 1.589)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.539\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.418 (0.914, 2.201)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.119\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003erecessive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.286 (0.663, 7.885)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.191\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.842 (0.410, 8.286)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.426\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers184003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.435 (1.019, 2.020)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.039*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.306 (0.840, 2.032)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.236\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.525 (1.092, 5.837)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.030*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.399 (0.486, 4.027)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.533\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eadditive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.491 (1.125, 1.976)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.255 (0.880, 1.789)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.210\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edominant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.518 (1.093, 2.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.317 (0.864, 2.007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.200\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003erecessive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.282 (0.993, 5.241)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.052\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.312 (0.459, 3.752)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.613\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2070600\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.713 (0.496, 1.024)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.842 (0.541, 1.310)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.445\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.536 (0.237, 1.211)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.690 (0.230, 2.071)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.508\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eadditive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.721 (0.542, 0.960)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.838 (0.584, 1.202)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.336\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edominant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.684 (0.485, 0.964)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.030*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.823 (0.539, 1.256)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.266\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003erecessive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.587 (0.261, 1.320)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.198\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.724 (0.243, 2.158)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.562\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers2228144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.301 (0.889), 1.904\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.175\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.127 (0.685, 1.854)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.637\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.679 (0.520, 5.425)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.386\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.747 (0.429, 7.122)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.437\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eadditive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.300 (0.935, 1.806)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.184 (0.776, 1.807)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.433\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edominant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.326 (0.917, 1.917)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.171 (0.726, 1.889)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.519\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003erecessive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.594 (0.495, 5.135)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.435\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.709 (0.421, 6.943)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.454\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers4072391\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.149 (0.777, 1.698)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.487\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.258 (0.765, 2.069)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.367\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.675 (0.081, 5.644)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.717\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.172 (0.121, 10.178)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.891\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eadditive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.300 (0.935, 1.806)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.142 (0.711, 1.835)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.583\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edominant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.129 (0.767, 1.662)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.537\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.208 (0.736, 1.982)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.455\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003erecessive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.658 (0.079, 5.493)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.658\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.128 (0.116, 10.918)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.917\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers4129267\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.327 (0.937, 1.877)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.224 (0.791, 1.895)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.364\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.430 (0.898, 2.279)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.559 (0.863, 2.818)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.141\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eadditive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.217 (0.973, 1.521)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.224 (0.993, 1.660)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.137\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edominant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.351 (0.970, 1.880)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.294 (0.853, 1.963)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.226\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003erecessive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.208 (0.799, 1.825)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.377 (0.814, 2.330)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.232\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers4537545\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.395 (0.984, 1.978)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.062\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.439 (0.924, 2.239)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.107\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.386 (0.866, 2.220)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.174\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.603 (0.877, 2.929)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.125\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eadditive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.217 (0.973, 1.521)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.294 (0.969, 1.727)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.081\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edominant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.393 (0.998, 1.944)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.051\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.474 (0.964, 2.253)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.073\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003erecessive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.132 (0.747, 1.716)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.558\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.276 (0.751, 2.168)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.368\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers4845625\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.692 (0.483, 0.991)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.045*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.572 (0.362, 0.904)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.017*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.503 (0.318, 0.795)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.541 (0.311, 0.940)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.029*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eadditive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.707 (0.563, 0.888)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.723 (0.545, 0.960)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.025*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edominant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.632 (0.448, 0.889)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.562 (0.365, 0.866)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003erecessive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.644 (0.434, 0.955)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.784 (0.491, 1.252)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.308\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers7514452\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.113 (0.751, 1.650)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.594\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.193 (0.789, 1.805)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.403\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.671 (0.080, 5.610)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.712\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.166 (0.120, 11.317)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.895\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eadditive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.069 (0.739, 1.544)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.724\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.108 (0.686, 1.789)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.675\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edominant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.095 (0.742, 1.616)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.647\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.170 (0.710, 1.928)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.539\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003erecessive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.658 (0.079, 5.493)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.699\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.128 (0.116, 10.918)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.917\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ers7529229\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.289 (0.912, 1.822)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.151\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.223 (0.790, 1.893)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.366\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.403 (0.881, 2.234)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.153\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.557 (0.861, 2.815)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.143\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eadditive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.069 (0.739, 1.544)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.724\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.243 (0.932, 1.659)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.138\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edominant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.315 (0.946, 1.829)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.292 (0.852, 1.960)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.228\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003erecessive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.206 (0.798, 1.822)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.375\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.377 (0.814, 2.328)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.233\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u0026pound;\u003c/sup\u003e No variables were adjusted in logistic regression model\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u0026curren;\u003c/sup\u003e Dyslipidemia, hypertension, smoking, and drinking were adjusted in the logistic regression model.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e*\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eAssociation between haplotypes and diabetic cardiovascular disease\u003c/h2\u003e\n\u003cp\u003eFour out of five SNPs in \u003cem\u003eAGER\u003c/em\u003e gene (Block1: rs184003-rs1035798-rs2070600-rs1800624) and five out of seven SNPs in \u003cem\u003eIL6R\u003c/em\u003e gene (Block2: rs7529229-rs4845625-rs4129267-rs7514452-rs4072391) showed linkage disequilibrium, see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. These two blocks were both significantly associated with diabetic cardiovascular disease (Block1: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008; Block2: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). Four haplotypes were constructed in block 1, and two of them associated with diabetic ischemic heart disease (CGTA: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018; AGCA: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). Four haplotypes were constructed in block 2, and two of them associated with diabetic cardiovascular disease (TCCTC: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033; TTCTC: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Details of haplotype analysis were shown in Table\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eHaplotype analysis for blocks in \u003cem\u003eAGER\u003c/em\u003e and \u003cem\u003eIL6R\u003c/em\u003e genes\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ehaplotypes\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF_U \u003csup\u003e\u0026pound;\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF_A \u003csup\u003e\u0026curren;\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eChi-square\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003edf\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlock1\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOmnibus test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.750\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCACT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.162\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.170\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.162\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.687\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCGTA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.575\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.018*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAGCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.140\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.197\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.229\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCGCA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.497\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.482\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.247\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.620\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlock2\u003csup\u003e\u0026cent;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOmnibus test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.007**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTTCCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.093\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.227\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.634\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCCTTC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.387\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.431\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.639\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.104\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTCCTC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.551\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.033\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTTCTC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.426\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.338\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e Block1: rs184003-rs1035798-rs2070600-rs1800624; \u003csup\u003e\u0026cent;\u003c/sup\u003e Block2: rs7529229-rs4845625-rs4129267-rs7514452-rs4072391; \u003csup\u003e\u0026pound;\u003c/sup\u003e F_U: minor allele frequency in controls; \u003csup\u003e\u0026curren;\u003c/sup\u003e F_A: minor allele frequency in cases; *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eThe effect of gene-gene interaction on diabetic cardiovascular disease\u003c/h2\u003e\n\u003cp\u003eGMDR analysis were performed to assess the gene-gene interaction on diabetic cardiovascular disease risk, after adjustment for dyslipidemia, hypertension, smoking, and drinking. The GMDR analysis suggested that rs184003 in \u003cem\u003eAGER\u003c/em\u003e gene and rs4845625 in \u003cem\u003eIL-6R\u003c/em\u003e gene was the best model in terms of statistical significance after permutation testing (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). The two-locus models had a cross-validation consistency of 10/10, and had a testing accuracy of 0.597. Logistic regression was subsequently used to obtain the odds ratios (ORs) and 95% confidence intervals (CI) for the interaction between rs184003 and rs4845625. In additive model, the joint effect\u003c/p\u003e\n\u003cp\u003eof rs184003 and rs4845625 is associated with an increased risk of diabetic cardiovascular disease by 38% (OR\u0026thinsp;=\u0026thinsp;1.38, 95%\u003cem\u003eCI\u003c/em\u003e: 1.13\u0026ndash;1.69, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":" \u003cp\u003eIndividuals with T2DM are with an increased risk of CVD which cannot be fully explained by elevated glucose\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Genetic risk factors contribute a lot to the pathogenesis of diabetic macrovascular complications, but its role has not been fully illustrated yet. In the present community-based case-control study, rs4845625 in \u003cem\u003eIL-6R\u003c/em\u003e gene, and the interaction of rs184003 in \u003cem\u003eAGER\u003c/em\u003e gene and rs4845625 in \u003cem\u003eIL-6R\u003c/em\u003e were significantly associated with diabetic ischemic heart disease. Polygenic risk score calculated by summing the number of risk alleles of the SNPs located in the above two genes were also associated with the elevated risk of diabetic ischemic heart disease.\u003c/p\u003e \u003cp\u003eAGER is a multiligand cell surface receptor. Advanced glycation end products (AGEs) which is produced after high glucose exposure can bind to AGER. Their interaction has been implicated in the pathogenesis of atherosclerosis. In addition, HMGB1 (high-mobility group protein 1) and neutrophil-derived S100 calcium-binding family members (S100A8/A9/A11/A12, and S100B) were also ligands of AGER. After ligand binding, proinflammatory and procoagulant pathways will be activated. The rs2070600 was found to be significantly associated with diabetic ischemic heart disease in the current study. But after adjustments for covariates, the associations became null. The rs2070600 is located in ligand-binding V domain of the AGER gene, often referred to as Gly82Ser\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Genome-wide association studies (GWAS) showed that rs2070600 were strongly and dose-dependently correlated with sRAGE level in whites and blacks from Atherosclerosis Risk in Communities Study and Chinese population \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Interestingly, although soluble-RAGE levels were found to be associated with diabetic complications in many researches, the association between rs2070600 and cardiovascular disease or other diabetic complications were not consistent. In Atherosclerosis Risk in Communities Study, the rs2070600 was not significantly associated with incident coronary heart disease or diabetes in both whites and blacks with a median follow-up of 20 years\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Gao et al. has found a significant association between rs2070600 and coronary arterial disease in 175 cases and 170 controls \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Meta-analysis found that the discrepancy may be attributable to ethnicity, subjects with rs2070600 risk allele were at higher risk of coronary arterial disease (CAD) in the Chinese population, rather than non-Chinese population. However, our study found the association between rs2070600 and diabetic ischemic heart disease was null. Another research also found rs2070600 was associated with the circulating levels of esRAGE but not with CAD in Chinese patients with T2DM \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. These results might indicate that the association between rs2070600 and CAD may also be different in general population and T2DM patients.\u003c/p\u003e \u003cp\u003eOnly few studies had demonstrated the association between rs184003 and cardiovascular disease. A hospital-based case-control study found rs184003 can significantly increase the risk of coronary artery disease (OR\u0026thinsp;=\u0026thinsp;1.23, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), and haplotypes C-T-G-G and T-A-G-T in \u003cem\u003eAGER\u003c/em\u003e gene (rs1800625-rs1800624-rs2070600-rs184003) were associated with significant increases in risk for CAD\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In the current study, we also haplotypes C-G-T-A and A-G-C-A in \u003cem\u003eAGER\u003c/em\u003e gene (rs184003-rs1035798-rs2070600-rs1800624) were significantly associated with diabetic ischemic heart disease. Although the rs184003 was significantly associated with diabetic ischemic heart disease in the current study, the associations became null after adjustments for covariates. To our knowledge, few studies illustrated the relationship between rs184003 and diabetic macrovascular complications. More researches are still need to validate our results. Given the fact sRAGE level were found to be significantly associated with CAD \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e in many researches, the null association between \u003cem\u003eAGER\u003c/em\u003e polymorphisms and diabetic ischemic heart disease in the current study indicated that sRAGE level could be served a marker of CAD, but not the a potential intervention targeting of reducing the burden of CAD.\u003c/p\u003e \u003cp\u003eMendelian randomization analysis illustrated that IL6R signaling might have a causal role in development of coronary heart disease\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Previous meta-analysis demonstrated that rs2228145 and rs7529229 in \u003cem\u003eIL6R\u003c/em\u003e could significantly reduce the risk of coronary heart disease\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Although the meta-analysis constituting a large sample size, the data from Asian is insufficient. Chen et al. did not find an association of rs2228145 with coronary stenosis or acute myocardial infarction in the Chinese Han population\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Likewise, our current study, showed no association between rs2228145 and diabetic ischemic heart disease in Chinese population. The haplotype T-T-C-T-C (rs7529229-rs4845625-rs4129267-rs7514452-rs4072391) in \u003cem\u003eIL6R\u003c/em\u003e gene and the rs4845625 was associated with diabetic cardiovascular disease in our study, and the association held after adjusting for potential confounders. The rs4845625 was found to be significantly associated with hypertriglyceridemia in Japanese population\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, and the T allele was associated with lower serum concentration of creatinine and increased eGFR\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Hypertriglyceridemia and chronic kidney disease (CKD) have common pathway leading to metabolic cardiovascular disease, like endothelial dysfunction, dyslipidemia, and inflammation\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Although there was seldom any study focus on the association between rs4845625 and diabetic heart disease, its association with triglyceride and kidney function might indicate the potential mechanisms of rs4645625 on diabetic ischemic heart disease.\u003c/p\u003e \u003cp\u003eIt has been found that, in response to hyperglycemia, AGER will be activated by S100A8/A9 on hepatic Kupffer cells, leading to the secretion of IL-6. IL-6 would subsequently bind to its receptor (IL6R) on hepatocytes to enhance the production of thrombopoietin, thereby regulating platelet production and resulting diabetes-induced thrombocytosis\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In the current study, we found that gene-gene interaction between \u003cem\u003eAGER\u003c/em\u003e and \u003cem\u003eIL6R\u003c/em\u003e would increase the risk of diabetic ischemic heart disease. We subsequently used GeneMANIA to construct gene network and predict gene function. IL6R and AGER have physical interactions with each other, and several pathways including NF-kB /RelA and JAK/STAT are involved in these interactions. Details were shown in \u003cb\u003eSupplementary Figure S1\u003c/b\u003e. These interactions illustrated that the interaction of SNPs in \u003cem\u003eIL6R\u003c/em\u003e and \u003cem\u003eAGER\u003c/em\u003e was not only a statistical interaction, but also a biological interaction. To our knowledge, this is the first study aimed to identify interaction of \u003cem\u003eAGER\u003c/em\u003e and \u003cem\u003eIL6R\u003c/em\u003e gene, and our results provided a genetic evidence on the physiological mechanism of diabetic macrovascular complications. Whether the main effect and gene-gene interaction in these two genes could be used to predict the risk of diabetic macrovascular complications are still need to be validated by cohort study in the future. Although we found the significant interaction of \u003cem\u003eAGER\u003c/em\u003e gene and \u003cem\u003eIL6R\u003c/em\u003e gene, the association between circulating IL-6 and diabetic ischemic heart disease was null. This result indicates that the role of circulating IL-6 in the pathogenesis and development of T2DM cardiovascular complications is complex. The most common hypothesis is that local IL-6 production and dynamics of sIL-6R which indicated the activation of IL-6 trans-signaling pathway were more likely to affect the TPO production and macrovascular complications\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the current study, SBP, TC, LDLC level and the proportion of people with smoking and drinking habits were significantly lower in cases than in controls, which is not consistent with other researches. According to \u0026ldquo;2017 Guidelines for the prevention and treatment of type 2 diabetes in China\u0026rdquo;, diabetes patients have more stringent standards on blood pressure (BP) and blood lipid compared with health population, and diabetes patients with ischemic heart disease should quit smoking and drinking\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Diabetes patients might change their lifestyles and medication to maintain their BP or blood lipid at a lower level. Due to the case-control study design of the current study, we were not able to collect the lifestyle risk factors and blood sample before the incident of diabetic ischemic heart disease. However, the percentage of SBP, DBP, HDLC, LDLC, TG and TC in ideal range were significantly higher in control group compared with cases (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cb\u003esupplementary table S2\u003c/b\u003e). Due to the above limitation of our study, more longitudinal researches are still needed to demonstrate whether genetic variants will increase the incident of diabetic macrovascular complications. What is more, medication information was not included in the investigation. Given the fact that some antidiabetic medication, like SGLT-2 inhibitor\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, will reduce the risk of cardiovascular disease in diabetes patients, future researches considering antidiabetic medication are still needed to validate the genetic effect on diabetic macrovascular complications.\u003c/p\u003e \u003cp\u003e \u003cb\u003eList of abbreviations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCVD, Cardiovascular disease\u003c/p\u003e \u003cp\u003eT2DM, Type 2 diabetes mellitus\u003c/p\u003e \u003cp\u003eAGER, Advanced glycation end products receptors\u003c/p\u003e \u003cp\u003eAGEs, Advanced glycation end products\u003c/p\u003e \u003cp\u003eIL-6, Interleukin-6\u003c/p\u003e \u003cp\u003eS100A8/A9, S100 calcium-binding protein A8/A9\u003c/p\u003e \u003cp\u003eFPG, Fasting plasma glucose\u003c/p\u003e \u003cp\u003eTC, Total cholesterol\u003c/p\u003e \u003cp\u003eTG, Triglycerides\u003c/p\u003e \u003cp\u003eHDLC, High-density lipoprotein cholesterol\u003c/p\u003e \u003cp\u003eLDLC, Low-density lipoprotein cholesterol\u003c/p\u003e \u003cp\u003eIHD, Ischemic heart disease\u003c/p\u003e \u003cp\u003eSBP, Systolic blood pressure\u003c/p\u003e \u003cp\u003eDBP, Diastolic blood pressure\u003c/p\u003e \u003cp\u003ePRS, Polygenic risk score\u003c/p\u003e \u003cp\u003eGMDR, Generalized multifactor dimensionality reduction\u003c/p\u003e \u003cp\u003eGWAS, Genome-wide association studies\u003c/p\u003e \u003cp\u003e \u003cb\u003eDeclarations\u003c/b\u003e \u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eCVD, Cardiovascular disease\u003c/p\u003e\n\u003cp\u003eT2DM, Type 2 diabetes mellitus\u003c/p\u003e\n\u003cp\u003eAGER, Advanced glycation end products receptors\u003c/p\u003e\n\u003cp\u003eAGEs, Advanced glycation end products\u003c/p\u003e\n\u003cp\u003eIL-6, Interleukin-6\u003c/p\u003e\n\u003cp\u003eS100A8/A9, S100 calcium-binding protein A8/A9\u003c/p\u003e\n\u003cp\u003eFPG, Fasting plasma glucose\u003c/p\u003e\n\u003cp\u003eTC, Total cholesterol\u003c/p\u003e\n\u003cp\u003eTG, Triglycerides\u003c/p\u003e\n\u003cp\u003eHDLC, High-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eLDLC, Low-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eIHD, Ischemic heart disease\u003c/p\u003e\n\u003cp\u003eSBP, Systolic blood pressure\u003c/p\u003e\n\u003cp\u003eDBP, Diastolic blood pressure\u003c/p\u003e\n\u003cp\u003ePRS, Polygenic risk score\u003c/p\u003e\n\u003cp\u003eGMDR, Generalized multifactor dimensionality reduction\u003c/p\u003e\n\u003cp\u003eGWAS, Genome-wide association studies\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and consent to participate: \u003c/strong\u003eThis study was approved by the Ethics Committee of Capital Medical University (No:2016SY24). All participants enrolled in this study have signed informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication: \u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The datasets generated and/or analyzed during the current study are not publicly available due to the regulations of the people's Republic of China on the administration of human genetic resources, but part of the dataset is available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was supported by grants from National Science Foundation of China (81602908), and National key research and development program of China (2016YFC0900600/2016YFC0900603). The funding sources had no involvement in the study design, data collection, analysis and interpretation of data, writing of the manuscript, and the decision to submit the article for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e LK designed the study and wrote the manuscript, XY analyzed data and visualized the interaction diagram. ZQ provided the statistical plan and helped to revise the manuscript. PW contributed to the verification of diabetic ischemic heart diseases in case group. GC and ZJ contributed to the management of blood sample and DNA extraction. ZL contributed to the collection of controls and participated in the study design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubmission declaration and verification: \u003c/strong\u003eSubmission of an article implies that the work described has not been published previously. This article is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, it will not be published elsewhere in the same form, in English or in any other language, including electronically without the written consent of the copyright holder.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e The authors thank all the participants and community health workers for their participation in this research effort.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShah AD, Langenberg C, Rapsomaniki E, Denaxas S, Pujades-Rodriguez M, Gale CP, et al. Type 2 diabetes and incidence of cardiovascular diseases: a cohort study in 1.9 million people. Lancet Diabetes Endocrinol. 2015;3(2):105\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnapp M, Tu X, Wu R. Vascular endothelial dysfunction, a major mediator in diabetic cardiomyopathy. Acta Pharmacol Sin. 2019;40(1):1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaugeri N, Malato S, Femia EA, Pugliano M, Campana L, Lunghi F, et al. Clearance of circulating activated platelets in polycythemia vera and essential thrombocythemia. Blood. 2011;118(12):3359\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZegeye MM, Lindkvist M, Falker K, Kumawat AK, Paramel G, Grenegard M, et al. Activation of the JAK/STAT3 and PI3K/AKT pathways are crucial for IL-6 trans-signaling-mediated pro-inflammatory response in human vascular endothelial cells. Cell Commun Signal. 2018;16(1):55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrozovsky R, Giannini S, Falet H, Hoffmeister KM. Novel mechanisms of platelet clearance and thrombopoietin regulation. Curr Opin Hematol. 2015;22(5):445\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaruthur NM, Li M, Halushka MK, Astor BC, Pankow JS, Boerwinkle E, et al. Genetics of Plasma Soluble Receptor for Advanced Glycation End-Products and Cardiovascular Outcomes in a Community-based Population: Results from the Atherosclerosis Risk in Communities Study. PLoS One. 2015;10(6):e0128452.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu W, Yang J, Sui W, Qu B, Huang P, Chen Y. Association of genetic variants in the receptor for advanced glycation end products gene with diabetic retinopathy: A meta-analysis. Med (Baltim). 2016;95(39):e4463.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZee RY, Romero JR, Gould JL, Ricupero DA, Ridker PM. Polymorphisms in the advanced glycosylation end product-specific receptor gene and risk of incident myocardial infarction or ischemic stroke. Stroke. 2006;37(7)):1686\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorres MC, Beltrame MH, Santos IC, Picheth G, Petzl-Erler ML, Pedrosa FO, et al. Polymorphisms of the promoter and exon 3 of the receptor for advanced glycation end products (RAGE) in Euro- and Afro-Brazilians. Int J Immunogenet. 2012;39(2):155\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu W, Feng B. The \u0026ndash; 374A allele of the RAGE gene as a potential protective factor for vascular complications in type 2 diabetes: a meta-analysis. Tohoku J Exp Med. 2010;220(4):291\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng F, Hu D, Jia N, Li X, Li Y, Chu S, et al. Association of four genetic polymorphisms of AGER and its circulating forms with coronary artery disease: a meta-analysis. PLoS One. 2013;8(7):e70834.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2014;37(Suppl 1):81\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoint committee for guideline. 2016 Chinese guidelines for the management of dyslipidemia in adults. J Geriatr Cardiol. 2018; \u003cb\u003e15\u003c/b\u003e(1): 1\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirkman MS, Mahmud H, Korytkowski MT. Intensive Blood Glucose Control and Vascular Outcomes in Patients with Type 2 Diabetes Mellitus. Endocrinol Metab Clin North Am. 2018;47(1):81\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu W, Feng B, Xie G, Liu F. Association of AGER gene G82S polymorphism with the severity of coronary artery disease in Chinese Han population. Clin Endocrinol (Oxf). 2011;75(4):470\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim SC, Dorajoo R, Zhang X, Wang L, Ang SF, Tan CSH, et al. Genetic variants in the receptor for advanced glycation end products (RAGE) gene were associated with circulating soluble RAGE level but not with renal function among Asians with type 2 diabetes: a genome-wide association study. Nephrol Dial Transplant. 2017;32(10):1697\u0026ndash;704.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao J, Shao Y, Lai W, Ren H, Xu D. Association of polymorphisms in the RAGE gene with serum CRP levels and coronary artery disease in the Chinese Han population. J Hum Genet. 2010;55(10):668\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng WH, Lu L, Wang LJ, Yan XX, Chen QJ, Zhang Q, et al. RAGE gene polymorphisms are associated with circulating levels of endogenous secretory RAGE but not with coronary artery disease in Chinese patients with type 2 diabetes mellitus. Arch Med Res. 2009;40(5):393\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu X, Liu J, Zhu H, Xia Y, Gao L, Li Z, et al. An interactive association of advanced glycation end-product receptor gene four common polymorphisms with coronary artery disease in northeastern Han Chinese. PLoS One. 2013;8(10):e76966.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLigthart S, Sedaghat S, Ikram MA, Hofman A, Franco OH, Dehghan A. EN-RAGE: a novel inflammatory marker for incident coronary heart disease. Arterioscler Thromb Vasc Biol. 2014;34(12):2695\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReichert S, Triebert U, Santos AN, Hofmann B, Schaller HG, Schlitt A, et al. Soluble form of receptor for advanced glycation end products and incidence of new cardiovascular events among patients with cardiovascular disease. Atherosclerosis. 2017;266:234\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInterleukin-6 Receptor Mendelian Randomisation Analysis. Swerdlow DI, Holmes MV, Kuchenbaecker KB, Engmann JE, Shah T, et al Sattar N, Hingorani AD, Casas JP. The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis. Lancet. 2012;379(9822): 1214\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollaboration IRGCERF, Sarwar N, Butterworth AS, Freitag DF, Gregson J, Willeit P, et al. Interleukin-6 receptor pathways in coronary heart disease: a collaborative meta-analysis of 82 studies. Lancet. 2012;379(9822):1205\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Z, Qian Q, Tang C, Ding J, Feng Y, Ma G. Association of two variants in the interleukin-6 receptor gene and premature coronary heart disease in a Chinese Han population. Mol Biol Rep. 2013;40(2):1021\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbe S, Tokoro F, Matsuoka R, Arai M, Noda T, Watanabe S, et al. Association of genetic variants with dyslipidemia. Mol Med Rep. 2015;12(4):5429\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoribe H, Fujimaki T, Oguri M, Kato K, Matsuoka R, Abe S, et al. Association of a polymorphism of the interleukin 6 receptor gene with chronic kidney disease in Japanese individuals. Nephrology (Carlton). 2015;20(4):273\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGajjala PR, Sanati M, Jankowski J. Cellular and Molecular Mechanisms of Chronic Kidney Disease with Diabetes Mellitus and Cardiovascular Diseases as Its Comorbidities. Front Immunol. 2015;6:340.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKraakman MJ, Lee MK, Al-Sharea A, Dragoljevic D, Barrett TJ, Montenont E, et al. Neutrophil-derived S100 calcium-binding proteins A8/A9 promote reticulated thrombocytosis and atherogenesis in diabetes. J Clin Invest. 2017;127(6):2133\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQu D, Liu J, Lau CW, Huang Y. IL-6 in diabetes and cardiovascular complications. Br J Pharmacol. 2014;171(15):3595\u0026ndash;603.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSociety CD. Guidelines for the prevention and control of type 2 diabetes in China (2017 Edition). Chinese Journal of Practical Internal Medicine. 2018;38(4):292\u0026ndash;344.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScheen AJ. Cardiovascular Effects of New Oral Glucose-Lowering Agents: DPP-4 and SGLT-2 Inhibitors. Circ Res. 2018;122(10):1439\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-genomic-data","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gtic","sideBox":"Learn more about [BMC Genomic Data](http://bmcgenet.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gtic/default.aspx","title":"BMC Genomic Data","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ischemic heart disease (IHD),Generalized multifactor dimensionality reduction (GMDR)","lastPublishedDoi":"10.21203/rs.3.rs-147920/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-147920/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe aim of the present study is to demonstrate the association of \u003cem\u003eAGER \u003c/em\u003eand \u003cem\u003eIL6R\u003c/em\u003e gene polymorphisms with diabetic ischemic heart disease (IHD), and to investigate the effect of gene-gene interaction on disease risk. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Our study included 204 ischemic heart disease cases who have previously been diagnosed as diabetes before the diagnoses of IHD, and 882 health controls. Polygenic risk score (PRS) was calculated by summing the number of risk alleles of all the candidate single nucleotide polymorphisms (SNPs). Logistic regression was used to find the association of candidate SNPs and PRS with diabetic ischemic heart disease. Generalized multifactor dimensionality reduction (GMDR) was used to illustrate gene-gene interaction. Haplotypes were identified and analyzed via Haploview and Plink software. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe\u003cem\u003e \u003c/em\u003ers184003 and\u003cem\u003e \u003c/em\u003ers2070600 in \u003cem\u003eAGER \u003c/em\u003egene were significantly associated with the risk of diabetic ischemic heart disease (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eadditive\u003c/sub\u003e=0.005; \u003cem\u003eP\u003c/em\u003e\u003csub\u003eadditive\u003c/sub\u003e=0.025, respectively). For \u003cem\u003eIL6R \u003c/em\u003ers4845625, CT and TT genotype were associated with lower risk of the disease comparing with CC genotype (OR=0.692, \u003cem\u003eP\u003c/em\u003e=0.045; OR=0.503, \u003cem\u003eP\u003c/em\u003e=0.003, respectively). After adjustment for covariates, the association of\u003cem\u003e \u003c/em\u003ers4845625 with disease remained statistically significant. Haplotypes in\u003cem\u003e AGER \u003c/em\u003egene (rs184003-rs1035798-rs2070600-rs1800624) and\u003cem\u003e IL6R \u003c/em\u003egene (rs7529229-rs4845625-rs4129267-rs7514452-rs4072391) were both significantly associated with diabetic ischemic heart disease (\u003cem\u003eP\u003c/em\u003e=0.008; \u003cem\u003eP\u003c/em\u003e=0.007). PRS was associated with the disease (OR=1.106, \u003cem\u003eP\u003c/em\u003e=0.020) after adjusting for covariates. The GMDR analysis suggested that rs184003 and rs4845625 was the best interaction model after permutation testing (\u003cem\u003eP\u003c/em\u003e=0.001) with a cross-validation consistency of 10/10. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eSNPs and\u003cstrong\u003e \u003c/strong\u003ehaplotypes in \u003cem\u003eAGER \u003c/em\u003eand\u003cem\u003e IL6R \u003c/em\u003egene and the interaction of rs184003 in \u003cem\u003eAGER\u003c/em\u003e with rs4845625 in\u003cem\u003e IL6R \u003c/em\u003ewere significantly associated with diabetic ischemic heart disease.\u003c/p\u003e","manuscriptTitle":"Polymorphisms and Gene-Gene Interaction in AGER/IL6 Pathway are Associated with Diabetic Ischemic Heart Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-01-19 16:57:11","doi":"10.21203/rs.3.rs-147920/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-03-09T00:00:00+00:00","index":2,"fulltext":"Recommendation: Major revisions required\nForm responses:\n---\n\nComments to Author:\n---\nThe work is interesting and provide new insights into the complex issue of genetic risk for a multifactorial disorder, like ischemic heart disease associated with diabetes. However, major criticisms have to be addressed.\n1)Despite the association of AGER and IL6R polymorphisms with this disorder was found to be significant, it is very hard to understand how the AGER/IL-6 pathway might be involved in the development of this disorder provided that plasma levels of AGEs and IL-6 are similar both in controls and patients.\nThis issue may be better addressed by performing a comparison of AGEs and IL-6 levels, as well as other biomarkers analysed (glucose, LDL-C, HDL-C) stratified in both populations according to genetic background.\nAGEs and IL-6 levels should be compared in individuals having different haplotypes in order to better characterize the influence of a given haplotype on ischemic heart disease and also establish a prognostic role for different haplotyeps.\nThe same analysis should be replicated for all other relevant biomarkers.\n2) The effects of different AGER and IL6R polymorphisms on protein expression and function should be reported in detail, if known, or, at least, predicted on the basis of computational analyses.\n3) Provided that in each group the concentrations of examined biomarkers are under the ideal cut-off only in a part of the population, further statistical analyses should be carried out by comparing individuals having ideal levels and those out of range.\n4) The OR calculation only showed small effect of single AGER and IL6R polymorphisms on risk increase for ischemic heart disease. A similar calculation should be carried out for each haplotype in order to undestand the synergistic effect played by single variants of examined gene polymorphisms in the different haplotye and clearly define risk haplotypes and protective haplotypes.\n\nMinor issues\nSeveral typos are present throughout the text and have to be corrected .\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons after the final decision on the manuscript has been made. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **Yes**\n* Declaration of competing interests: **I declare that I have no competing interests.**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **Yes**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **Yes**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"decision","content":"Major revision","date":"2021-03-09T00:00:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2021-03-02T00:00:00+00:00","index":3,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2021-02-16T00:00:00+00:00","index":2,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-02-02T00:00:00+00:00","index":1,"fulltext":"Recommendation: Major revisions required\nForm responses:\n---\n\nComments to Author:\n---\nThis is a candidate gene study aimed to investigate the association of gene polymorphisms in AGER (rs1035798, rs1800624, rs1800625, rs184003 and rs2070600) and IL6R (rs2228144, rs4072391, rs4129267, rs4537545, rs4845625, rs7514452 and rs7529229) with the ischemic disease in type-2 diabetic patients.\n\nMajor Issues\nThe design of the study does not allow discriminating between associations due to type-2 diabetes (T2DM) and ischemic disease, given that the comparison is between patients with T2DM and ischemic events versus healthy controls. The inclusion of another group of comparison composed of patients with T2DM but without ischemic events would have allowed identifying associations due only to the ischemic disease. The authors should include such a group of comparison to be able to infer conclusions in diabetic ischemic disease.\n\nMinor Issues\n\nThe writing throughout the article is professional and scientific although it may benefit of the revision by a Native English. \n\nFormat\n\nAbstract\nThe abstract is missing the context of the research in the background section.\n\nTables\nTables are missing some of the extended definitions of the abbreviations to be self-explanatory, as T2DM, CHD in Tables 1, A.2, S2, etc; MAF, SNPs,… in Table S1. Please include the extended definition of all abbreviations used in each table.\nTable S1 should include the alleles involved in the gene polymorphism to easily identify the minor allele.\n\nIntroduction\n\nPAGE LINE \n4 32 The statement \"Several single nucleotide polymorphisms (SNPs) in AGER gene have…\" needs to be referenced.\n4 38 It would be useful to extend the information given for the studies referenced, especially regarding number of participants, ethnicity, etc.\n4 44 The alleles associated with the protective effect on CVD should be indicated.\n4 49 The information on inconsistent´´ effect of IL6R polymorphisms should be extended in more detail.\n4 51 There is not background information on the rationale to ´clarify gene-gene interactions´ in ischemic heart disease. Are there other approaches reported? More studies on these SNPs?\n4 57 The ´cardiovascular disease´ phenotype is an umbrella term encompassing many heart conditions. The phenotype outcome used for this study should be standardised throughout the document according to the definition and term given in \"Study design and population\".\n\nMethods\n\nStudy design and population\nPAGE LINE \n5 13 Grammar: Healthy controls\n5 44 The definition of chronic kidney disease used should be included, along with the equation used to calculate eGFR.\n5 47 The definition of acute phase of infection should be included, along with the parameters used to determine it.\n5 57 Grammar: Use of the past: HAD never smoked\n5 59 Grammar: Use of the past: HAD not smoked\n6 4 Grammar: Use of the past: who never DRANK\n6 7 Grammar: Use of the past: HAD not DRUNK\n6 13 Grammar: before participants USING\n\nSerum markers\nPAGE LINE \n6 29 Grammar: Use of the past: WERE collected\n 31 Grammar: Use of the past: WERE collected\n 46 \"haematological\" should be \"biochemical\"\n\nGenotyping\nThe background and rationale for the selection of the five SNPs in AGER and seven SNPs in IL6R should be included in the introduction.\nPAGE LINE \n7 17 Grammar: Use of the past: WAS used\n\nDefinition of diseases and recommend level of their risk factors\nThe definition of type-2 diabetes mellitus differs from the one described in the inclusion criteria for cases. Definitions should be standardised throughout the document.\nPAGE LINE \n7 29 Please rephrase \"The incident…\" (redundant)\n\nStatistical Analysis\nStudent´s t-Test is only applicable to normal distributed variables. The authors should describe the statistical analysis performed on non-normally distributed variables and change the expression of some of the demographic variables described in Table 1, which are clearly non-normal distributed, and therefore should be described as median and interquartile range. The authors should also describe the method used to account for the normal distribution of the variables.\nPAGE LINE \n7 48 Grammar: Use of the past: WERE expressed\n\nResults\n\nGeneral characteristics of the studied participants\n The inclusion of a higher proportion of participants in the normal range for lipid serums is a methodology criterion that should be included in the definition of controls in the \"Methods\" section.\n It´s not clear if the adjusted p-values shown in the tables are adjusted both by covariates and multiple comparisons or only by covariates. Please clarify this in the table legend.\n\nAssociation of AGER, IL6R polymorphisms with diabetic cardiovascular disease\nHardy-Weinberg calculations and thresholds used should be included in \"Methods\".\nPAGE LINE \n9 16 Grammar: Use of the past: HAD\n9 20,24,49 Grammar: Use of the past: WAS\n12 2 Grammar: Use of the past: WAS\n\nDiscussion\n Some of the studies showing associations in the literature that have been compared in the discussion with the present work are better powered and/or have been age- and gender-matched to the controls. These issues should be indicated and properly discussed in every case.\n The number, ethnicity and phenotype investigated in the studies compared to the present work should be detailed and properly discussed in the context of different statistical power and allele frequencies, and how they may have influenced the results.\n The rs184003 SNP has been associated with coronary artery disease (as indicated in the discussion), but also with diabetes (PMID: 22402134). These findings should be reviewed and may be worthy to be included in the discussion as context for this SNP.\n Please rephrase \"Previous meta-analysis demonstrated that rs2228145 and rs7529229 in IL6R could significantly reduce the risk of coronary heart disease\". SNPs may be associated with lower risk, but not reduce the risk. The addition of the effect allele would be more clarifying when the statement is indicating a specific direction of the effect (for instance: the T-allele is associated with lower risk of…\").\n There are multiple grammar errors in the discussion that would benefit of the revision by an English native.\n\nConclusions\nConclusions are missing in the main text.\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons after the final decision on the manuscript has been made. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. 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Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **Yes**\n* Declaration of competing interests: **I declare that I have no competing interests**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **No**\n* Are the methods sufficiently described to allow the study to be repeated?: **Yes**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **Yes**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"reviewersInvited","content":"","date":"2021-01-26T00:00:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2021-01-26T00:00:00+00:00","index":1,"fulltext":""},{"type":"editorAssigned","content":"","date":"2021-01-12T00:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-01-11T23:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-01-11T23:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2020-12-22T00:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomic-data","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gtic","sideBox":"Learn more about [BMC Genomic Data](http://bmcgenet.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gtic/default.aspx","title":"BMC Genomic Data","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2bd7eeba-5752-47aa-b0ce-25486df5e44f","owner":[],"postedDate":"January 19th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":1938971,"name":"Medical Genetics"},{"id":1938972,"name":"Molecular Genetics"},{"id":1938973,"name":"Population Genetics"}],"tags":[],"updatedAt":"2021-01-19T16:57:11+00:00","versionOfRecord":[],"versionCreatedAt":"2021-01-19 16:57:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-147920","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-147920","identity":"rs-147920","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}