{"id":16308,"date":"2017-09-25T11:58:58","date_gmt":"2017-09-25T11:58:58","guid":{"rendered":"http:\/\/biomedpharmajournal.org\/?p=16308"},"modified":"2020-04-24T11:01:58","modified_gmt":"2020-04-24T11:01:58","slug":"genetic-association-of-kcne1g38s-polymorphism-in-postoperative-atrial-fibrillation-of-north-indian-population-a-case-control-study","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol10no3\/genetic-association-of-kcne1g38s-polymorphism-in-postoperative-atrial-fibrillation-of-north-indian-population-a-case-control-study\/","title":{"rendered":"Genetic Association of KCNE1G38S Polymorphism in Postoperative Atrial Fibrillation of North Indian Population: A Case-Control Study"},"content":{"rendered":"<p><strong>Introduction<\/strong><\/p>\n<p>Atrial arrhythmias and atrial fibrillation (AF), a rhythm disorder, commonly occur postoperatively.<sup>1,2<\/sup> With recent developments in molecular biology techniques, exploration of the pathology of AF at the genetic level has become an emerging topic worldwide.\u00a0 In 2013, over 1.27 crore Indians suffered from Atrial Fibrillation, an increase by 40 lakhs from 2012 and 60 lakhs from 2011.<sup>3<\/sup> AF is increasing with advancing age, diabetes, obesity, Coronary Artery Diseases and Valvular Heart diseases. AF increases the risk of heart attack and stroke.\u00a0 Several studies of secular trends have already documented increasing prevalence of AF over the past several decades. In case of India study is limited.<sup>4<\/sup><\/p>\n<p>Previous studies done in AF showed that gene exerted great influence on its pathogenesis.\u00a0 The study done by Lai et al.<sup>5<\/sup> found that gene polymorphism in <em>KCNE1<\/em> was one of the AF risk factors in a Taiwanese population. However, in Chinese population there was no relationship between the <em>KCNE1G38S<\/em> and AF.<sup>6<\/sup> In 2006, in European populations showed that the <em>KCNE1G38S<\/em> was associated with AF.<sup> 7,8<\/sup> In Uygur population also AF was associated with genetic polymorphism.<sup>9<\/sup> Recently, more and more pieces of evidence indicated that AF is a multifactorial disease resulting from the interaction between environmental factors and genetics. Several studies demonstrated that the mutations in genes coding for ion-channels may be associated with parts of the familial AF.<sup>10,11<\/sup> Whereas, in Off-pump Coronary artery bypass graft study revealed that gene variants has role in postoperative-AF development<sup>12<\/sup><\/p>\n<p><em>KCNE1<\/em> is a potassium ion channel coding gene for humans, it is located in chromosome 21q22.1\u201321q22.2 which encodes for the \u03b2-subunit of the potassium ion-channel (IKs).<sup>13,14<\/sup> It is a slowly activating component of the delayed rectifier channel current (IKs), which plays an important role in atrial repolarization.<sup>15<\/sup> Whereas, the IKs is important for ventricular repolarization and <em>KCNE1<\/em> plays an important role in atrial repolarization.<sup>15<\/sup> Studies have shown that when there is a gain of function, early onset of AF is seen and when there is loss of function, long QT syndrome develops.<sup>16<\/sup> Several single-nucleotide polymorphisms (SNPs) have been identified in the <em>KCNE1 <\/em>gene, while the<em> KCNE1G38S <\/em>polymorphism (rs1805127 G&gt;A; G38S) is the most widely investigated variant.<sup>17<\/sup> It is well accepted that the <em>KCNE1<\/em> polymorphism results in a glycine or serine amino acid substitution at codon 38 and is responsible for stronger IKs currents and high expression of <em>KCNQ1.<\/em><sup>18,19<\/sup> This gene variant has shown to be a risk factor for\u00a0 AF in several populations studies. Till, today there is no study shown regarding genetic polymorphism in the North Indian Population.<\/p>\n<p><strong>Materials and Methods<\/strong><\/p>\n<p>The study population consisted of 99 haemodynamically stable patients as control and 78 AF patients as case evidenced by ECG undergoing cardiac surgery (coronary artery bypass graft and valvular heart disease surgery) from Cardiovascular and Thoracic surgery Department in SGPGIMS, Lucknow. The diagnosis of AF was based on the medical history and the diagnostic criteria of ECG for AF were: (1) absence of P-waves, (2) irregular atrial activity at a rate of 350\u2013600\/min, and (3) irregular ventricular rhythm. The exclusion criteria for AF patients included one of the following: symptomatic heart failure, cardiomyopathy, chronic obstructive pulmonary disease, acute medical illness and severe infections. Adopting a one-by-one matched case-control study design, the 99 control subjects matched with age (above 18yrs) and sex, enrolled in the study during the same period admitted to these hospitals undergoing either valvular heart surgery or coronary bypass graft surgery. All samples in our study were the residents of North India. The presence of smoking, diabetes mellitus, hypertension, type of surgery (CABG and VHD)\u00a0 were assessed on the basis of subjects\u2019 questionnaires, blood detecting indexes and hospital records. Written informed consent was obtained from all the individuals.<\/p>\n<p><strong>Molecular Analysis<\/strong><\/p>\n<p>Genomic DNA extraction was performed from peripheral blood leucocytesusing the phenol\u2013chloroform method. The genotyping of <em>KCNE1G38S<\/em> was done through PCR\u2013 RFLP analysis. The PCR reaction was conducted in a final volume of 20 \u03bcl using primers 5\u2019-GTG ACG CCC TTT CTG ACC AA-3\u2019 (primer sense) and 5\u2019-CCA GAT GGT TTT CAA CGA CA-3\u2019 (primer antisense) at an annealing temperature of 54.1\u00b0C. The 12 \u03bcl of the reaction GreentaqLucigenmix, 0.8 \u00b5l of each primers, 5.4 \u03bcl nuclease free water and 1 \u03bcl genomic DNA were used for amplification. Cycling conditions included an initial denaturation at 94\u00b0C for 3 min followed by 35 cycles with a fast denaturation at 94\u00b0C for 40 s, an annealing step at 54.1\u00b0C for 30 s and an extension step at 72\u00b0C for 30 s, with a final incubation at 72\u00b0C of 5 min. The amplification reaction was followed by a digestion with the reaction enzyme, MspA1I (NEB) at 37\u00b0C for overnight and electrophoresis on 2.0% agarose gel. The polymerase reaction product was 318 base pairs in size. Amplification product was cut with MspA1I to produce 232\u2013base pair and 86\u2013 base pair fragments.<\/p>\n<p><strong>Statistical Analysis<\/strong><\/p>\n<p>Statistical analysis was performed using the SPSS (Statistical Package forSocial Sciences, Chicago, USA) software for Windows (Version 15.0). The \u03c72 -test was used to test the deviation of genotype distribution from Hardy\u2013Weinberg equilibrium and the differences of the frequency of <em>KCNE1.<\/em> The association between the risk factors and AF was assessed using logistic regression analysis. Odds ratio (OR) with 95% confidence interval (CI) was determined. Threshold for statistical significance was a p-value of 0.05.<\/p>\n<p><strong>Results<\/strong><\/p>\n<p><strong>Clinical Characteristics of Patients<\/strong><\/p>\n<p>In total, 177 patients were recruited in the study, with a mean age of 45.78\u00b115.605 vs. 47.08\u00b116.037 in cases and controls respectively.The ratio of female was higher in cases than in controls, 40% female developed postoperative AF. Out of 177\u00a0 patients, 78 (44.00%) presented at least 1 qualifying episode of AF postcardiac surgery. Clinical and demographic characteristics of the study population are summarized in Table 1. Results of the odds ratios are shownin Table3.<\/p>\n<p><strong>Table 1: Demographic and clinical characteristic:<\/strong><\/p>\n<table style=\"width: 95%;\" border=\"1\" cellspacing=\"0\" cellpadding=\"4\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\" width=\"135\"><strong>\u00a0<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"125\"><strong>Cases<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"124\"><strong>Controls<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"177\"><strong>p-value<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"135\">Age(yrs)<\/td>\n<td style=\"text-align: center;\" width=\"125\">45.78\u00b115.605<\/td>\n<td style=\"text-align: center;\" width=\"124\">47.08\u00b116.037<\/td>\n<td style=\"text-align: center;\" width=\"177\">Matched<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"135\">Sex(male\/female)<\/td>\n<td style=\"text-align: center;\" width=\"125\">47\/31<\/td>\n<td style=\"text-align: center;\" width=\"124\">71\/28<\/td>\n<td style=\"text-align: center;\" width=\"177\">Matched<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"135\">male<\/td>\n<td style=\"text-align: center;\" width=\"125\">60%<\/td>\n<td style=\"text-align: center;\" width=\"124\">71.71%<\/td>\n<td style=\"text-align: center;\" width=\"177\"><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"135\">female<\/td>\n<td style=\"text-align: center;\" width=\"125\">40%<\/td>\n<td style=\"text-align: center;\" width=\"124\">28.29%<\/td>\n<td style=\"text-align: center;\" width=\"177\"><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"135\">Smoking<\/td>\n<td style=\"text-align: center;\" width=\"125\">5(6.4%)<\/td>\n<td style=\"text-align: center;\" width=\"124\">9(9.1%)<\/td>\n<td style=\"text-align: center;\" width=\"177\">0.512<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"135\">Hypertension<\/td>\n<td style=\"text-align: center;\" width=\"125\">27(34.6% )<\/td>\n<td style=\"text-align: center;\" width=\"124\">41(41.4%)<\/td>\n<td style=\"text-align: center;\" width=\"177\">0.355<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"135\">Diabetes<\/td>\n<td style=\"text-align: center;\" width=\"125\">8(10.3%)<\/td>\n<td style=\"text-align: center;\" width=\"124\">25(25.3%)<\/td>\n<td style=\"text-align: center;\" width=\"177\">0.011<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"135\">Valvular heart surgery<\/td>\n<td style=\"text-align: center;\" width=\"125\">77.5%<\/td>\n<td style=\"text-align: center;\" width=\"124\">52.52%<\/td>\n<td style=\"text-align: center;\" width=\"177\">0.001<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"135\">Coronary artery bypass graft<\/td>\n<td style=\"text-align: center;\" width=\"125\">22.5%<\/td>\n<td style=\"text-align: center;\" width=\"124\">47.48%<\/td>\n<td style=\"text-align: center;\" width=\"177\">0.001<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>Table 2: Genotype &amp; Allele frequencies of cases and controls<\/strong><\/p>\n<table style=\"width: 95%;\" border=\"1\" cellspacing=\"0\" cellpadding=\"4\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\" width=\"76\"><strong>\u00a0<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"63\"><strong>AA<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"63\"><strong>AG<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"63\"><strong>GG<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"63\"><strong>\u03c72<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"51\"><strong>p<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"85\"><strong>A<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"83\"><strong>G<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"83\"><strong>p<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"76\">Case<\/td>\n<td style=\"text-align: center;\" width=\"63\">15<\/td>\n<td style=\"text-align: center;\" width=\"63\">44<\/td>\n<td style=\"text-align: center;\" width=\"63\">19<\/td>\n<td style=\"text-align: center;\" width=\"63\">0.668<\/td>\n<td style=\"text-align: center;\" width=\"51\">0.716<\/td>\n<td style=\"text-align: center;\" width=\"85\">74<\/td>\n<td style=\"text-align: center;\" width=\"83\">82<\/td>\n<td style=\"text-align: center;\" width=\"83\">0.247<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"76\"><\/td>\n<td style=\"text-align: center;\" width=\"63\">19.2%<\/td>\n<td style=\"text-align: center;\" width=\"63\">56.4%<\/td>\n<td style=\"text-align: center;\" width=\"63\">24.4%<\/td>\n<td style=\"text-align: center;\" width=\"63\"><\/td>\n<td style=\"text-align: center;\" width=\"51\"><\/td>\n<td style=\"text-align: center;\" width=\"85\">47.44%<\/td>\n<td style=\"text-align: center;\" width=\"83\">52.56%<\/td>\n<td style=\"text-align: center;\" width=\"83\"><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"76\"><\/td>\n<td style=\"text-align: center;\" width=\"63\"><\/td>\n<td style=\"text-align: center;\" width=\"63\"><\/td>\n<td style=\"text-align: center;\" width=\"63\"><\/td>\n<td style=\"text-align: center;\" width=\"63\"><\/td>\n<td style=\"text-align: center;\" width=\"51\"><\/td>\n<td style=\"text-align: center;\" width=\"85\"><\/td>\n<td style=\"text-align: center;\" width=\"83\"><\/td>\n<td style=\"text-align: center;\" width=\"83\"><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"76\">Control<\/td>\n<td style=\"text-align: center;\" width=\"63\">23<\/td>\n<td style=\"text-align: center;\" width=\"63\">56<\/td>\n<td style=\"text-align: center;\" width=\"63\">20<\/td>\n<td style=\"text-align: center;\" width=\"63\"><\/td>\n<td style=\"text-align: center;\" width=\"51\"><\/td>\n<td style=\"text-align: center;\" width=\"85\">102<\/td>\n<td style=\"text-align: center;\" width=\"83\">96<\/td>\n<td style=\"text-align: center;\" width=\"83\"><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"76\"><\/td>\n<td style=\"text-align: center;\" width=\"63\">23.2%<\/td>\n<td style=\"text-align: center;\" width=\"63\">56.6%<\/td>\n<td style=\"text-align: center;\" width=\"63\">20.2%<\/td>\n<td style=\"text-align: center;\" width=\"63\"><\/td>\n<td style=\"text-align: center;\" width=\"51\"><\/td>\n<td style=\"text-align: center;\" width=\"85\">45.69%<\/td>\n<td style=\"text-align: center;\" width=\"83\">54.31%<\/td>\n<td style=\"text-align: center;\" width=\"83\"><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>Table 3: Multivariable analysis for KCNE1 polymorphism according to conditional logistic regression model in North Indian Population<\/strong><\/p>\n<table style=\"width: 95%;\" border=\"1\" cellspacing=\"0\" cellpadding=\"4\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\" width=\"108\"><strong>Variable<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"73\"><strong>\u03b2<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"91\"><strong>SE<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"84\"><strong>Wald<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"165\"><strong>OR (95 % CI)<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"104\"><strong>p-value<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"108\">Smoking<\/td>\n<td style=\"text-align: center;\" width=\"73\">0.072<\/td>\n<td style=\"text-align: center;\" width=\"91\">0.624<\/td>\n<td style=\"text-align: center;\" width=\"84\">0.013<\/td>\n<td style=\"text-align: center;\" width=\"165\">1.075(0.317-3.647)<\/td>\n<td style=\"text-align: center;\" width=\"104\">0.908<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"108\">Hypertension<\/td>\n<td style=\"text-align: center;\" width=\"73\">0.158<\/td>\n<td style=\"text-align: center;\" width=\"91\">0.332<\/td>\n<td style=\"text-align: center;\" width=\"84\">0.227<\/td>\n<td style=\"text-align: center;\" width=\"165\">1.172(0.611-2.248)<\/td>\n<td style=\"text-align: center;\" width=\"104\">0.634<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"108\">Diabetes<\/td>\n<td style=\"text-align: center;\" width=\"73\">1.035<\/td>\n<td style=\"text-align: center;\" width=\"91\">0.464<\/td>\n<td style=\"text-align: center;\" width=\"84\">4.971<\/td>\n<td style=\"text-align: center;\" width=\"165\">2.814(1.133-6.987)<\/td>\n<td style=\"text-align: center;\" width=\"104\">0.026<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\" width=\"108\">KCNE1<\/td>\n<td style=\"text-align: center;\" width=\"73\">0.408<\/td>\n<td style=\"text-align: center;\" width=\"91\">0.474<\/td>\n<td style=\"text-align: center;\" width=\"84\">0.384<\/td>\n<td style=\"text-align: center;\" width=\"165\">1.272(0.594-2.726)<\/td>\n<td style=\"text-align: center;\" width=\"104\">0.389<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>Results of Allele, Genotype Frequencies, and Hardy-Weinberg Equilibrium<\/strong><\/p>\n<p>A 318-bp fragment of the coding sequence of <em>KCNE1<\/em> gene was observed by PCR-RFLP genotyping. Genotype and allele frequencies in the study&#8217;s population are summarized in Table 2. Observed allele and genotype frequencies were in accordance with expected frequencies by the Hardy-Weinberg equilibrium in the total cohort study.<\/p>\n<p><strong>The <em>KCNE1G38S<\/em> Polymorphism and Incidence of Postoperative AF<\/strong><\/p>\n<p>The frequency of G allele was observed same in the postoperative AF group compared with the group without postoperative AF (52.56% vs. 54.31%, respectively, p = 0.247). The genotype frequencies did not deviated from Hardy-Weinberg law of equilibrium. The results of the multivariate regression analysis revealed that only diabetic patients had relation with AF while smoking, hypertension and type of surgery (CABG and VHD surgery) were not significant as shown in Table 3.<\/p>\n<p><strong>Discussion<\/strong><\/p>\n<p>AF shows differential incidence rates among different ethnic groups.\u00a0 The\u00a0 first study by Lai et.al. emphasized on polymorphism study in <em>KCNE1G38S\u00a0<\/em>gene variant in AF subjects. In 2002, for the first time, he reported that the <em>KCNE1G38S<\/em> is related to AF in a Taiwanese population. Therefore,<em>KCNE1G38S<\/em>polymorphism has been considered as one of the risk factors for AF.<sup>5<\/sup> A similar study of Zhiyu et al. in a Chinese population revealed that there was no relationship between the <em>KCNE1G38S<\/em> and AF, which totally differed from the study published by former. He stated that the difference possibly resulted from the different subjectsi. eethinicity.<sup>6<\/sup> However, two studies in European populations demonstrated that the <em>KCNE1G38S<\/em> was a higher risk of AF.<sup>20<\/sup> Due to the relationship between the <em>KCNE1G38S<\/em> and AF is different among different ethnicities. The <em>KCNE1G38S<\/em> variant was associated with increased risk of AF among Uygur people. Yao et al. in 2011 showed that the <em>KCNE1<\/em> gene (rs1805127) polymorphism increases the AF risk in Xinjiang Uygur individuals, which still remained significant after adjustment for related risk factors.<sup>21<\/sup> The results manifested that the Uygur exhibited more European features in heredity than the Asian population. Hence, they inferred that this is one of the possible reasons why results showed that the distribution of <em>KCNE1G38S<\/em> genotype and allele frequency among Uygur AF individuals was similar to that in European AF subjects instead of Chinese people.<\/p>\n<table style=\"width: 70%;\" border=\"1\" cellpadding=\"5\">\n<tbody>\n<tr>\n<td><img decoding=\"async\" class=\"alignnone size-thumbnail wp-image-16317\" src=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2017\/08\/Vol10No3_Gen_Sur_fig1-150x150.jpg\" alt=\"Figure 1: Gel picture of KCNE1G38S gene polymorphism\" width=\"150\" height=\"150\" srcset=\"https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2017\/08\/Vol10No3_Gen_Sur_fig1-150x150.jpg 150w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2017\/08\/Vol10No3_Gen_Sur_fig1.jpg 506w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/><\/td>\n<td>\n<p style=\"text-align: left;\"><strong>Figure 1: Gel picture of KCNE1G38S gene polymorphism<\/strong><\/p>\n<p style=\"text-align: left;\"><a href=\"http:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2017\/08\/Vol10No3_Gen_Sur_fig1.jpg\" target=\"_blank\">Click here to View figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>As far our findings suggests that the <em>KCNE1G38S<\/em> gene polymorphism confers \u00a0no susceptibility to AF in the North Indian Population. \u00a0The allele and genotype frequencies did not deviated from Hardy-weingberg law of equilibrium. The G allele in cases came 52.56% and in controls 54.31% which is almost similar. The genotype frequency in cases of AG+GG came 80.80% and in controls it is 76.80%. Here, also difference is less i.e. 4%. The results suggests that\u00a0 <em>KCNE1<\/em> gene variant has no\u00a0 association with postoperative AF in north Indian population.<\/p>\n<p>The study has some limitations. Firstly, few risk factors of AF were selected. Secondly, we cannot\u00a0 completely exclude the presence of asymptomatic AF in the control group\u00a0 though the standard interview were done. Thirdly, we relied on a clinical history, ECG and obtained evidence in the hospitals to assess coronary artery disease and valvular heart diesease. However, the symptoms and ECG are sufficient to diagnose the most of the patients in clinics. The experiment is not a large-scale study. Therefore, what the results mean cannot support general consideration on the genetic background of the whole population it may vary. Lastly, AF episodes that occurred after hospital discharge were missed.\u00a0 In conclusion, we found that the <em>KCNE1G38S<\/em> was not a risk factor for post-AF in an north Indian\u00a0 population. The KCNE1G38S might have different impact on AF in different ethnicities. Further researches among different parts of India may potentially reveal new avenues for explaining the pathogenesis of the important disease.Understanding the risk factors for atrial fibrillation would promote the development of improved therapies and preventive measures to lessen this public-health burden.<\/p>\n<p><strong>Conflict of Interest<\/strong><\/p>\n<p>The authors declared that there is no conflict of interest.<\/p>\n<p><strong>Financial Support<\/strong><\/p>\n<p>There was no financial support from any agencies.<\/p>\n<p><strong>References<\/strong><\/p>\n<ol>\n<li>Maesen B., Nijs J., Maessen J., et al. 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