The Pharmacogenomics Journal (2015) 15, 55–62 © 2015 Macmillan Publishers Limited All rights reserved 1470-269X/15 www.nature.com/tpj
ORIGINAL ARTICLE
Genetic variation at glucose and insulin trait loci and response to glucose–insulin–potassium (GIK) therapy: the IMMEDIATE trial KL Ellis1, Y Zhou2, JR Beshansky3, E Ainehsazan1, Y Yang1, HP Selker3, GS Huggins4, LA Cupples2 and I Peter1 The mechanistic effects of intravenous glucose, insulin and potassium (GIK) in cardiac ischemia are not well understood. We conducted a genetic sub-study of the Immediate Myocardial Metabolic Enhancement During Initial Assessment and Treatment in Emergency care (IMMEDIATE) Trial to explore effects of common and rare glucose and insulin-related genetic loci on initial to 6-h and 6- to 12-h change in plasma glucose and potassium. We identified 27 NOTCH2/ADAM30 and 8 C2CD4B variants conferring a 40–57% increase in glucose during the first 6 h of infusion (P o 5.96 × 10 − 6). Significant associations were also found for ABCB11 and SLC30A8 single-nucleotide polymorphisms (SNPs) and glucose responses, and an SEC61A2 SNP with a potassium response to GIK. These studies identify genetic factors that may impact the metabolic response to GIK, which could influence treatment benefits in the setting of acute coronary syndromes (ACS). The Pharmacogenomics Journal (2015) 15, 55–62; doi:10.1038/tpj.2014.41; published online 19 August 2014
INTRODUCTION Among treatments that have shown promise in acute coronary syndromes (ACS) is acute myocardial metabolic support. Cellular, animal and human studies have suggested that if instituted at the onset of ischemia, metabolic therapy in the form of intravenous glucose–insulin–potassium (GIK), which promotes glycolysis and lowers circulating free fatty acids, helps maintain the viability of an at-risk myocardium and improves ACS outcomes.1 On the basis of animal results, administration of GIK very early in ACS is considered most effective in protecting ischemic myocardium.2 The Immediate Myocardial Metabolic Enhancement During Initial Assessment and Treatment in Emergency care (IMMEDIATE) Trial was designed to deliver this metabolic support immediately upon the patient’s presentation with ACS,3 recapitulating the timing of myocardial support shown effective in experimental studies. The trial demonstrated that among patients with suspected ACS, outof-hospital administration of GIK, compared with placebo, was associated with lower rates of cardiac arrest and in-hospital mortality, smaller infarct size and lower free fatty acid levels.4 Recent advances in DNA technology through genome-wide association studies (GWAS) have allowed the identification of hundreds of common gene variants associated with metabolic diseases and related traits, including glucose sensitivity and insulin resistance.5 Moreover, there is emerging evidence pointing toward the role of low frequency variants in metabolic traits.6 The goal of this study was to examine whether genetic variants previously associated with glucose and insulin-related traits by GWAS, or located within susceptibility loci, appear to contribute to responses to GIK among IMMEDIATE Trial patients. Identifying genetic variation that modifies the glucose, potassium and insulin
response to GIK may provide greater insight into the metabolic factors that influence the overall course of patients with ACS. MATERIALS AND METHODS Study population The IMMEDIATE Trial was a double-blind randomized controlled clinical trial conducted in 13 cities across the United States that assessed the effectiveness of intravenous GIK administered early in the course of ACS for 12 h.3 Out-of-hospital intravenous administration of GIK or placebo commenced immediately after a patient was considered to likely be having ACS. Inclusion and exclusion criteria for the IMMEDIATE Trial have been previously described,4 and can be seen in more detail in Supplementary Methods. The IMMEDIATE Genetic Ancillary Study aimed to identify genetic modifiers of response to GIK therapy. A total of 321 participants were recruited at 9 participating sites during the parent trial enrollment. All IMMEDIATE Trial participants were eligible for inclusion, provided that consent for genetic analysis was granted. This study was approved by the Icahn School of Medicine at Mount Sinai’s IRB (Institutional Review Board) in addition to the IRB at each site.
Metabolite measurements Glucose and potassium levels were measured by the study site hospital laboratory upon arrival at the emergency department and after 6 and 12 h of study drug infusion, or alternatively at the point the drug was stopped prematurely. A subset of Genetic Ancillary Study participants (N = 117) was concurrently enrolled in the Biological Mechanisms Cohort3 and had glucose and insulin levels measured initially and at 6 and 12 h at a core laboratory. Glucose levels generated by the hospital laboratories were correlated with glucose levels in the Biological Mechanisms Cohort core laboratory at all time points (r2 = 0.643–0.941, P = 1.4 × 10 − 13 to 1.50 × 10 − 52). Study site hospital measurements were used for these
1 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; 2Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA; 3Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA and 4Molecular Cardiology Research Institute Center for Translational Genomics, Tufts Medical Center, Boston, MA, USA. Correspondence: Dr I Peter, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, Box 1498, New York, NY 10029, USA. E-mail: [email protected] Received 13 February 2014; revised 29 April 2014; accepted 4 June 2014; published online 19 August 2014
Glucose and insulin loci and GIK response KL Ellis et al
56 analyses as substantially more data were available. Metabolite levels were generated by the core laboratory at Tufts Clinical and Translational Science Institute.
Genotyping and candidate variant selection Genomic DNA was extracted from whole blood using the Gentra Puregene Blood Kit (Qiagen, Valencia, CA, USA) and from saliva using the Oragene®•DNA Saliva Collection Kit (DNA Genotek, Ontario, Canada). Genotyping was carried out using the Illumina Metabochip7 and HumanExome Beadchip8 at the Children’s Hospital of Philadelphia. Single-nucleotide polymorphisms (SNPs) were clustered into genotypes using the Illumina BeadStudio software, and zCall for rare variants.9 We downloaded the GWAS catalog (accessed 30 July 2013)5 and selected all SNPs associated with fasting glucose, type-2 diabetes, glycated hemoglobin levels, diabetes-related insulin, diabetes (incident, gestational), proinsulin levels, 2-h glucose challenge, insulin resistance and/or response, and fasting insulin (Supplementary Table S1). For intergenic SNPs, we also selected variants ± 250 kb from the index GWAS SNP. Annotation documentation from Metabochip was used to further select variants that had been included for type-2 diabetes, fasting glucose, 2-h glucose tests, fasting insulin and hemoglobin A1c traits. SNPs were removed if they had call rates o95% and deviated from Hardy–Weinberg equilibrium (Po0.0001), resulting in the selection of 15 159 SNPs with a mean genotyping success rate of 99.8%. Duplicate concordance was assessed for three quality control samples assayed on Metabochip and five samples assayed on Exome Chip. Genotype concordance for each duplicate was ⩾ 98.3%. Samples were filtered for individual call rates o95% and relatedness (identical by descent). After excluding 2 samples that failed identical by descent analysis (same individuals recruited twice), and samples with a low call rate, the Genetic Ancillary Study cohort comprised 318 individuals.
Statistical analysis Phenotype derivation. Associations between gene variants and changes in non-fasting plasma glucose and potassium during 12-h infusion were investigated. Due to non-linear trends over 12 h, the change in each trait was determined between the initial and 6-h measurement (calculated as 6-h level − initial level), and between the 6-h and 12-h measurements (calculated as 12-h level − 6-h level). Glucose data exhibited skewed distributions and were log-transformed before analysis. Therefore, their 6-h log-changes are interpreted as ratios. For potassium, 6-h changes can be interpreted as absolute differences between the two measurements.
Table 1.
Correlations between the metabolites were generated using Pearson’s correlation. Population stratification. To control for population admixture, we computed principal components to be used as covariates in regression analyses. We calculated principal components in EIGENSTRAT10 on a combined set of 130 539 independent SNPs (linkage disequilibrium, LD, r2 o0.3) from Metabochip and Exome chip that passed quality control, had minor allele frequency (MAF) ⩾ 5%, a call rate of 40.98 and a Hardy–Weinberg equilibrium P-value of 40.001. Association analysis. For common variant analysis (MAF ⩾ 5%), we used a two degree of freedom (d.f.) test to detect joint significance for the main effect of the SNP and the SNP × intervention interaction in the same model.11 All models adjusted for age, sex, treatment arm, diabetes status and the first two principal components. For each trait, associations with initial to 6-h change also adjusted for the time from infusion initiation to first blood draw. For gene-based association testing, all non-synonymous variants (non-synonymous, splice variants, 3' UTR and 5' UTR), regardless of MAF, were collapsed using the ‘adjusted optimal’ method (SKAT-O) within the SNP-set (Sequence) Kernel Association Test12 shown to have the greatest statistical power to detect gene-level associations.13 Statistical analyses were carried out using the R software package (www.r-project.org). Selection of significance threshold. For association analyses of 14 389 common variants (MAF ⩾ 5%), we determined the number of uncorrelated markers to be 8387, after accounting for LD using the Li and Ji approach.14 Therefore, after adjustment for multiple hypothesis testing, a P-value threshold for statistical significance was set at 5.96 × 10 − 6. For gene-based analysis, including 208 genes (Supplementary Table S2) and represented by 1310 SNPs, a P-value threshold was set at 2.40 × 10 − 4 (0.05/208).
RESULTS Patient characteristics The characteristics of the participants in the IMMEDIATE Genetic Ancillary Study (Tables 1 and 2) show no significant differences in basic demographics, including age, gender and medical history of myocardial infarction, diabetes or heart failure between randomization groups (P40.05). The average age at time of recruitment was 63.4 years (31–96 years), and 74.8% of participants were male. Participants were predominantly of White ethnicity (87.7%), 7.6% were African-Americans, 1.6% were Asian-Americans, 3.2%
Demographics, pre-hospital clinical characteristics and medical history by treatment group for the IMMEDIATE Genetic Ancillary Study
Demographics/clinical characteristics Age (years) Gender, n = Male (%) Ethnicity, n (%) White Black Asian Other Hispanic, n (%) Initial out of hospital blood pressure (mm Hg) Systolic Diastolic BMI (kg m − 2) Time for symptom onset until infusion initiation (hours) Medical history, n (%) Previous myocardial infarction (yes) History of diabetes (yes) Previous heart failure (yes)
n
GIK
n
Placebo
P-valuea
157 157 157
63.8 ± 12.5 117 (74.5)
160 160 160
63.0 ± 13.6 120 (75.0)
0.60 0.92 0.07
157
132 (84.1) 17 (10.8) 4 (2.5) 4 (2.5) 15 (9.5)
159
146 (91.3) 7 (4.4) 1 (0.6) 6 (3.8) 25 (15.7)
0.01
157 155 150 128
141.9 ± 35.4 84.9 ± 24.6 28.6 ± 5.5 2.7 ± 3.9
159 157 152 141
144.3 ± 30.3 86.8 ± 21.9 28.8 ± 6.6 2.3 ± 2.7
0.52 0.47 0.77 0.33
157 157 157
55 (35.0) 49 (31.2) 27 (17.2)
160 160 160
57 (35.6) 38 (23.8) 24 (15.0)
0.91 0.14 0.38
Abbreviations: BMI, body mass index; GIK, glucose–insulin–potassium; IMMEDIATE, Immediate Myocardial Metabolic Enhancement During Initial Assessment and Treatment in Emergency care. Data are presented as mean ± s.d. and percentages. aStatistical significance Po 0.05.
The Pharmacogenomics Journal (2015), 55 – 62
© 2015 Macmillan Publishers Limited
Glucose and insulin loci and GIK response KL Ellis et al
57 Table 2.
In-hospital clinical characteristics and analyte measurements by treatment group for the IMMEDIATE Genetic Ancillary Study
Clinical characteristics Duration of treatment (hours) Time from treatment initiation until first blood draw—Standard of Care (hours) Time from treatment initiation until first blood draw—Biological Mechanisms Cohorts (hours)
n
GIK
n
Placebo
P-valueb
157 156
8.4 ± 4.8 0.64 ± 0.76
132 159
9.1 ± 4.5 0.55 ± 0.61
0.17 0.28
50
2.21 ± 1.20
55
2.63 ± 1.27
0.09
Non-fasting plasma glucose levels (mg dl − 1)a Initial blood glucose 6-h blood glucose 12-h blood glucose Initial to 6-h fold change in glucose 6- to 12-h fold change in glucose
156 188.7 (177.7–202.4) 159 92 188.6 (167.3–214.9) 114 137 146.9 (131.6–162.4) 142 91 1.01 (0.91–1.13) 113 90 0.74 (0.66–0.83) 113
Non-fasting plasma potassium levels (mmol l − 1) Initial potassium 6-h potassium 12-h potassium Initial to 6-h change in potassium 6- to 12-h change in potassium
155 91 146 89 84
Non-fasting plasma Insulin Levels (μU ml − 1)a Initial insulin 6-h insulin 12-h insulin Initial to 6-h fold change in insulin 6- to 12-h fold change in insulin
3.99 ± 0.64 4.35 ± 0.52 4.43 ± 0.58 0.42 ± 0.53 0.17 ± 0.52
159 112 152 111 107
50 107.8 (83.9–137.0) 49 154.5 (115.6–206.4) 51 100.5 (70.8–141.2) 48 1.45 (1.10–1.91) 49 0.68 (0.53–0.86)
55 58 61 52 58
154.5 148.4 142.6 0.97 0.95
(145.5–165.7) (138.4–159.2) (134.3–151.4) (0.92–1.02) (0.91–1.00)
3.93 ± 0.55 4.08 ± 0.46 3.97 ± 0.46 0.17 ± 0.53 − 0.13 ± 0.38 18.3 22.4 21.1 1.16 0.89
(13.5–25.0) (16.6–30.3) (15.5–29.4) (0.93–1.43) (0.72–1.10)
3.10 × 10 − 5 3.70 × 10 − 4 0.64 0.47 1.64 × 10 − 5 0.37 9.30 × 10 − 5 3.95 × 10 − 13 0.001 5.62 × 10 − 6 5.30 × 0 − 14 6.02 × 10 − 15 1.43 × 10 − 9 0.20 0.09
Abbreviations: GIK, glucose–insulin–potassium; IMMEDIATE, Immediate Myocardial Metabolic Enhancement During Initial Assessment and Treatment in Emergency care. Data are presented as unadjusted means ± s.d. or percentages unless otherwise stated. aData were log-transformed before analysis. Data are presented as geometric means and 95% confidence intervals. Ratios are presented for initial to 6-h, and 6- to 12-h changes for logged data. bStatistical significance P o0.05.
were classified as ‘other’ and 12.7% identified as Hispanic race (Table 1). Initial measurements were carried out on average 35 min after the initiation of infusion for glucose and potassium (Table 2). As expected, glucose, potassium and insulin levels were higher in the GIK group than in placebo (Table 2). Plasma glucose was increased with GIK initially and at 6 h (P ⩽ 3.70 × 10 − 4); however, at 12 h glucose levels were similar to those observed in the placebo group (P = 0.64). Insulin levels reflecting endogenous and exogenous insulin from GIK were greater in the GIK group at all three measurements (P ⩽ 1.43 × 10 − 9). No differences in potassium levels were observed with intervention at the initial measurement, but modestly higher levels were detected with GIK at both 6 and 12 h (P ⩽ 9.30 × 10 − 5). Treatment-stratified and treatment-combined correlations of all study metabolites at different time points are presented in Supplementary Table S3. Glucose and potassium levels, as well as initial to 6-h and 6- to 12-h changes, were representative of the overall IMMEDIATE Trial. There were no significant differences in glucose or potassium levels when comparing Genetic Ancillary Study participants with all other individuals recruited into the IMMEDIATE Trial (P40.05, data not shown). Summary of genetic data The SNP selection criteria resulted in the inclusion of 15 159 SNPs for analysis. Of those, 14 389 were common variants (MAF ⩾ 0.05), including 165 non-synonymous, 110 synonymous, 9262 intronic, 4181 intergenic, 206 3' UTR, 39 5' UTR and 2 splice variants. There were 770 rare variants (MAFo 0.05) selected for genebased analysis. Of these, 585 were non-synonymous, 6 were splice variants, 164 were located in the 3' UTR and 15 in the 5' UTR. © 2015 Macmillan Publishers Limited
Association analysis of glucose traits There were 37 SNPs exceeding the corrected threshold for statistical significance. Specifically, 27 common variants (MAF 10–12%) in strong LD (r2 ⩾ 0.85) at the NOTCH2/ADAM30 locus on chromosome 1 modified the glucose response to GIK (Figure 1a; Supplementary Table S4). In GIK-treated individuals, each copy of ‘C’ allele for the lead SNP, rs7534586, was associated with a 57% increase in plasma glucose between the initial measurement and 6 h, compared with a 9% increase in the placebo group (2 d.f. test’s P, P2d.f. = 2.33 × 10 − 6; Table 3, Figure 1b). Among the associated variants was rs10923931 (P2d.f. = 3.80 × 10 − 6) identified by GWAS as associated with type-2 diabetes.15 Variants with the smallest P-value at each locus are shown in Table 3. The C2CD4B region on chromosome 1 also conferred a larger change in glucose in the first 6 h of infusion in GIK patients compared with placebo (Table 3) with eight variants (MAF ⩾ 0.22) in strong LD with the lead SNP rs1406227 (LD ⩾ 0.95), demonstrating associations with initial response to GIK (Figure 2a; Supplementary Table S4). For each copy of the minor ‘A’ allele of rs1406227, a 41% increase in glucose was observed with GIK treatment compared with 4% in placebo-treated patients (P2d.f. = 2.63 × 10 − 6; Figure 2b). There were two variants that modified response to GIK between 6 and 12 h. The intronic variant, rs34453330 (MAF = 0.05) in the ABCB11 gene, was associated with a 48% decrease in glucose per ‘A’ allele in GIK-treated individuals, however no difference was observed in the placebo group (P2d.f. = 5.65 × 10 − 7; Figure 3a). The minor ‘G’ allele of SLC30A8 rs10105491 (MAF = 0.25) interacted with treatment to increase glucose by 37% between 6 and 12 h with GIK but not with placebo (P2d.f. = 5.55 × 10 − 6; Figure 3b). Other suggestive genetic associations (P2d.f. o0.001) for initial to The Pharmacogenomics Journal (2015), 55 – 62
Glucose and insulin loci and GIK response KL Ellis et al
58
Figure 1. Association between NOTCH2/ADAM30 locus and initial to 6-h change in glucose: (a) Regional plots of the top association signals ± 250 kb. The X axis shows the chromosome and physical distance (kb), the left Y axis shows the negative base ten logarithm of the P-value and the right Y axis shows recombination activity (cM/Mb) as a blue line. The linkage disequilibrium of surrounding single-nucleotide polymorphisms (SNPs) with the leading SNP is indicated by a scale of intensity of red color filling as shown in the legend at the upper righthand corner of each plot. The genome-wide association studies (GWAS) SNP P-value is shown in blue, and is annotated with the SNP identifier. Positions, recombination rates and gene annotations are according to the NCBI’s build 36 (hg18). (b) Fold change by treatment arm and genotype. Data are presented as the geometric mean ± s.e.m. after the adjustment for age, sex, treatment arm, diabetes, PC1, PC2 and time from infusion initiation until initial glucose measurement. The P-value is from the 2 degree of freedom (d.f.) test. GIK, glucose–insulin–potassium.
6-h, and 6- to 12-h change in glucose are presented in Supplementary Tables S4 and S5. When analysis was carried out on Whites only, the largest population sub-group, similar associations between each of these variants and glucose response to GIK were observed (data not shown). Association analysis of potassium traits An association between rs74474517 and change in potassium between 6 and 12 h was identified (Table 3). This intronic variant located within the SEC61A2 gene (MAF = 0.06) modified response to intervention, resulting in a decrease in potassium between 6 and 12 h in the GIK arm but not in the placebo (P2d.f. = 2.28 × 10 − 6; βGIK = − 0.68 mmol l − 1, βPlacebo = − 0.05 mmol l − 1; Figure 4). A similar association was observed when Whites were analyzed separately (P = 4.12 × 10 − 6). No significant genetic associations with initial to 6-h change in potassium were observed; however, a number of suggestive associations within or near the KCNQ1, CDKN2B, SLC2A2 and DGKB genes were observed (P2d.f. = 4.07 × 10 − 5 to 0.001). Suggestive associations are shown in Supplementary Tables S6 and S7. Gene-based analysis of glucose and potassium Gene-based analysis of common and rare functional variants in 208 genes identified a study-wise significant association (P o 2.40 × 10 − 4) between ZNF750 (P = 6.99 × 10 − 5), FAM180B (P = 1.29 × 10 − 4), CELF1 (P = 1.35 × 10 − 4) and initial to 6-h change in glucose (Table 4). Suggestive associations were identified including NOTCH2 (P = 0.002) and ADAM30 (P = 0.017, data not shown), the top region identified in the single-point common variant analysis (Table 3; Figures 1 and 2). A nominal association between NOTCH2 was also observed for 6- to 12-h change in glucose (P = 0.03) (data not shown). For potassium, gene-based testing did not provide substantial evidence for novel association signals beyond those already implicated in the single-point common variant analysis. Top gene-based associations for each trait are shown in Table 4. In several cases, different genes from the same region showed signals for association with the same or related traits. That is, The Pharmacogenomics Journal (2015), 55 – 62
ZNF750, the strongest association with the first 6-h change in glucose, is located near to FN3KRP, which was also suggestively associated with the same phenotype. Similarly, both FAM180B and CELF1 were associated with the first 6-h change in glucose. The LRP2 and SPC25 genes located ~ 250 kb apart were suggestively associated with 6- to 12-h change in potassium and initial to 6-h change in glucose, respectively. DISCUSSION This study was the first to investigate genetic modifiers of response to GIK infusion in individuals presenting with likely ACS. We investigated the association between genetic variation at glucose and insulin-related loci and short-term response to GIK infusion, assessed as initial to 6-h, and 6- to 12-h change in plasma glucose and potassium. We observed significant associations for 27 common variants at the NOTCH2-ADAM30 locus, all in strong LD, and the initial to 6-h glucose response to therapy. Participants treated with GIK, who carried a minor allele at the NOTCH2-ADAM30 region, exhibited a 40–57% greater increase in glucose in the first 6 h of infusion compared with those without a variant. In the placebo group, these same alleles conferred a 4–9% larger change in plasma glucose between the initial and 6 h measurement. In addition, a gene-based approach that combined the effects of 14 rare and common functional variants further suggested that NOTCH2 has a role in modifying response to GIK. The Diabetes Genetics Replication and Meta-Analysis consortium identified multiple signals within the NOTCH2-ADAM30 locus that were strongly associated with type-2 diabetes.15 Within this region, the ‘A’ allele of the NOTCH2 variant, rs10923931 was associated with increased risk of diabetes, and in our study this same allele was associated with a higher increase in glucose levels in response to GIK. The role of NOTCH2 in the development of type-2 diabetes has been proposed to occur through effects on pancreatic β-cell function.16 A greater increase in glucose between the initial measurement and 6 h was also observed for eight variants near the C2CD4B gene. Expression of the C2CD4B gene, which encodes the nuclear localized factor 2 in the pancreas, is stimulated by © 2015 Macmillan Publishers Limited
Abbreviations: d.f., degree of freedom; GIK, glucose–insulin–potassium; MAF, minor allele frequency; SNPs, single-nucleotide polymorphisms. One variant with the smallest P-value (2 d.f.) at each locus is presented. Included in the model are age, sex, randomization group, PC1, PC2 and history of diabetes. For initial to 6-h change in glucose, time from infusion initiation to initial blood measurement was also adjusted for. FC = fold-change between the initial to 6-h, or 6-h to 12-h measurements. bFold change. cAbsolute change.
2.28 × 10 − 06 0.0005 0.69 12182121 10 6 to 12-h potassium rs74474517
© 2015 Macmillan Publishers Limited
a
− 0.68c − 0.04c SEC61A2 Intron 0.06
0.94 0.74 0.52b 1.37b 1.01b 0.98b ABCB11 SLC30A8 (28318) MED30 (315694) Intron Intergenic 0.05 0.25 A G 169781530 118217271 Log 6-h to log 12-h glucose rs34453330 2 rs10105491 8
A
5.65 × 10 − 07 5.55 × 10 − 06 6.5 × 10 − 5 9.0 × 10 − 5
2.33 × 10 − 06 2.63 × 10 − 06 0.002 0.001 0.28 0.52 1.57b 1.41b 1.09b 1.04b NOTCH2 C2CD4B (2132) MGC15885 (469757) Intron Intergenic 0.11 0.23 C A 120496127 62459614 Log initial to log 6-h glucose rs7534586 1 rs1406227 15
Gene (distance from closest gene, bp) Function MAF Minor allele Position Chr SNP
Table 3.
Significant associations between the top candidate SNPs and metabolite traitsa
Placebo change per allele
GIK change per allele
P (SNP)
P (SNP × treatment interaction)
P (2 d.f.)
Glucose and insulin loci and GIK response KL Ellis et al
pro-inflammatory cytokines.17,18 A meta-analysis of 21 GWA studies, and follow-up investigation in 76 558 subjects, associated C2CD4B with fasting glucose homeostasis.19 The region has also been associated with an increase in type-2 diabetes risk,18 plasma glucose 2 h after an oral glucose load,20 diminished insulin release21 and diminished glucose-stimulated insulin response.22 On average, glucose levels decreased in the final 6 h of infusion in both treatment arms. In this study, we identified two genes that modified the glucose response to GIK during this time. The minor ‘A’ allele of rs34453330, located in intron 26 of the ATP-binding cassette sub-family B, member 11 (ABCB11) gene, was associated with a 48% larger decrease in glucose between 6 and 12 h than ‘T’ allele carriers. Interestingly, the effect of this variant was limited to GIK-treated subjects. In a meta-analysis of two GWA studies, ABCB11, which encodes the ABC transporter BSEP (bile slat export pump), and neighboring G6PC2 (glucose-6-phosphatase) genes were associated with elevated plasma glucose. Variation at this locus has also been associated with increased basal hepatic glucose production, increased insulin release23 and a reduced risk of type-2 diabetes.24 This protective effect plausibly occurs via a hyper-response to postprandial elevation in circulating glucose levels.23 The second region associated with 6- to 12-h change in glucose was near SLC30A8, which encodes an islet-specific zinc transporter connected to insulin granule function.25 In our, study, the ‘G’ allele of a variant near SLC30A8, rs10105491, was associated with a smaller decrease in glucose between 6 and 12 h in GIK-treated individuals, but no real difference with placebo. SLC30A8 was first linked to type-2 diabetes by GWAS in 2007, with the index variant, rs13266634, conferring an 18% increased risk for the ‘A’ allele.26 In a prospective study, genetic variation within SLC30A8 was also shown to result in impaired beta-cell function over time,27 and in a study of non-diabetics was associated with either abnormal insulin processing or secretion.28 In our study, the rs13266634 ‘A’ allele was associated with a larger 6- to 12-h decrease in the GIK group (P = 0.03), but not in placebo. We identified one intronic variant (rs74474517) in SEC61A2 that was associated with 6- to 12-h change in potassium levels. The ‘A’ allele was associated with a larger decrease in potassium levels (−0.64 mmol l − 1 per allele) in GIK-treated patients than in placebo (−0.05 mmol l − 1 per allele). SEC61A2 belongs to a family of proteins that mediate the translocation and insertion of membrane proteins, including potassium channels, in the endoplasmic reticulum.29 The observed association may indicate that SEC61mediated differences in potassium channel number or structure modify the response to GIK therapy. Moreover, SEC61A2 is located o150 kb away from the CDC123/CAMK1D locus. A study investigating expression quantitative trait loci and co-expression networks of top type-2 diabetes associated gene variants detected that the expression of SEC61A2 was highly correlated with CDC123/CAMK1D rs12779790 genotype,30 previously associated with type-2 diabetes15 and insulin-related traits.16 Strengths of this study include the random assignment of GIK to individuals at high risk of ACS. Moreover, availability of repeated measurements during the infusion provided additional information about the dynamics of the response. The dense genomic coverage of the candidate regions provided by the Metabochip and Exome chip has also allowed us to examine the role of the functional variants potentially responsible for the GWAS signals in gene-based analysis. Importantly, most of the top associations detected in our study did not involve previously reported GWAS SNPs, even when the analyses were limited to Whites only. Instead, other variants within the susceptibility loci were identified, suggesting that a better coverage used in our analyses could improve the accuracy of the detection or the involvement of different variants in GIK response. Moreover, gene-based analysis indicated that different genes in close proximity to each other were associated with correlated traits, further emphasizing the The Pharmacogenomics Journal (2015), 55 – 62
59
Glucose and insulin loci and GIK response KL Ellis et al
60
Figure 2. Association between the C2CD4B locus and initial to 6-h change in glucose: (a) Regional plots of the top association signals and (b) fold change by treatment arm and genotype. See Figure 1 for plot description. GIK, glucose–insulin–potassium.
Figure 3. Genetic association for 6- to 12-h change in glucose by treatment arm and genotype: (a) For ABCB11 rs34453330 and (b) for SLC30A8 rs10105491. Data are presented as the geometric mean ± s.e.m. and adjustment for age, sex, treatment arm, diabetes, PC1 and PC2. The P-value is from the 2 degree of freedom (d.f.) test. GIK, glucose–insulin–potassium.
Figure 4. Genetic association for 6- to 12-h change in potassium by treatment arm and genotype for SEC61A2. Data are presented as the arithmetic mean ± s.e.m. Data have been adjusted for age, sex, treatment arm, diabetes, PC1 and PC2. The P-value is from the 2 degree of freedom (d.f.) test. GIK, glucose–insulin–potassium. The Pharmacogenomics Journal (2015), 55 – 62
importance of fine mapping and functional studies to determine causal genes/variants that alter phenotypic traits. We acknowledge several limitations of our study, including the lack of pre-treatment and fasting metabolite measurements, and the sample size of this ancillary study, which limited the statistical power to detect modest effects. Also of note, in spite of the large glucose load in the GIK infusion, endogenous insulin secretion did not vary significantly over the 12-h infusion as indicated by mostly unchanged C-peptide levels (data not shown). This suggests that the relative levels of glucose and insulin during GIK infusions were balanced in regard to the impact on blood glucose levels. However, due to the lack of fasting glucose and insulin (or C-peptide) levels, a formal estimation of insulin resistance, as assessed by the homeostasis model assessment index, HOMA2-IR, was not possible. Novel regions not queried in the present analysis may also contribute to GIK response, leaving the possibility that genome-wide studies may prove fruitful. Finally, given the interventional nature of this study and unavailability of DNA samples from other GIK trials, replication of our findings was not possible. In summary, this is the first study to examine genetic modifiers of GIK therapy in patients with likely ACS using 15 149 genetic variants previously linked to glucose and insulin-related traits. We identified significant associations between variants within or near NOTCH2-ADAM30, C2CD4B, ABCB11, SLC30A8, SEC61A2 and glucose and potassium traits during 12-h infusion with GIK. Findings from © 2015 Macmillan Publishers Limited
Glucose and insulin loci and GIK response KL Ellis et al
61 Table 4.
Top genes (Po0.01) from gene-based analysis of non-coding variants
Gene
Chr.
Start position
End position
No of SNPs
P-value
NOTCH2 SPC25 LRP2 ZFAND3 SLC30A8 IDE ABCC8 CELF1 FAM180B MTNR1B NCKAP1L SPPL3 FN3KRP ZNF750 QPCTL
1 2 2 6 8 10 11 11 11 11 12 12 17 17 19
120455095 169732622 169985338 38084405 118184783 94211444 17414570 47487740 47609867 92714749 54902264 121201156 80676855 80788899 46201812
120506308 169732622 170177325 38084405 118188536 94333827 17450177 47574716 47610271 92715916 54928894 121202664 80685655 80790442 46206425
14 1 33 1 13 7 3 9 2 7 3 4 10 10 3
0.002 0.001 0.006 0.008 4.56 × 10 − 4 0.007 0.006 1.35 × 10 − 4 1.29 × 10 − 4 0.007 0.006 0.005 0.004 6.99 × 10 − 5 0.002
Phenotype Log initial to log 6-h change in glucose Log initial to log 6-h change in glucose 6 to 12-h change in potassium Log 6 to log 12-h change in glucose Log initial to log 6-h change in glucose Log initial to log 6-h change in glucose Initial to 6-h change in potassium Log initial to log 6-h change in glucose Log initial to log 6-h change in glucose 6- to 12-h change in potassium Log 6-h to log 12-h change in glucose Log initial to log 6-h change in glucose Log initial to log 6-h change in glucose Log initial to log 6-h change in glucose 6- to 12-h change in potassium
Abbreviation: SNPs, single-nucleotide polymorphisms.
this study suggest that genetic factors have an important role in determining the response to GIK treatment and may provide new insights into GIK mechanisms. CONFLICT OF INTEREST The authors declare no conflict of interest.
ACKNOWLEDGMENTS The Genetic Ancillary Study was funded by the National Institutes of Health (NIH) grant from National Heart, Lung and Blood Institute (NHLBI) (R01HL090997). This work was also supported by National Center for Research Resources Grant Number UL1RR025752, now the National Center for Advancing Translational Sciences, NIH Grant Number Ul1 TR000073. The IMMEDIATE Trial was funded by the NIH cooperative agreement from NHLBI (U01HL077821, U01HL077823 and U01HL077826).
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10 Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006; 38: 904–909. 11 Manning AK, LaValley M, Liu CT, Rice K, An P, Liu Y et al. Meta-analysis of geneenvironment interaction: Joint estimation of snp and snp x environment regression coefficients. Genet Epidemiol 2011; 35: 11–18. 12 Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence Kernel association test. Am J Hum Genet 2011; 89: 82–93. 13 Lee S, Emond MJ, Bamshad MJ, Barnes KC, Rieder MJ, Nickerson DA et al. Optimal unified approach for rare-variant association testing with application to smallsample case-control whole-exome sequencing studies. Am J Hum Genet 2012; 91: 224–237. 14 Li J, Ji L. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity (Edinb) 2005; 95: 221–227. 15 Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 2008; 40: 638–645. 16 Grarup N, Andersen G, Krarup NT, Albrechtsen A, Schmitz O, Jorgensen T et al. Association testing of novel type 2 diabetes risk alleles in the jazf1, cdc123/ camk1d, tspan8, thada, adamts9, and notch2 loci with insulin release, insulin sensitivity, and obesity in a population-based sample of 4,516 glucose-tolerant middle-aged Danes. Diabetes 2008; 57: 2534–2540. 17 Warton K, Foster NC, Gold WA, Stanley KK. A novel gene family induced by acute inflammation in endothelial cells. Gene 2004; 342: 85–95. 18 Yamauchi T, Hara K, Maeda S, Yasuda K, Takahashi A, Horikoshi M et al. A genome-wide association study in the Japanese population identifies susceptibility loci for type 2 diabetes at ube2e2 and c2cd4a-c2cd4b. Nat Genet 2010; 42: 864–868. 19 Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 2010; 42: 105–116. 20 Saxena R, Hivert MF, Langenberg C, Tanaka T, Pankow JS, Vollenweider P et al. Genetic variation in gipr influences the glucose and insulin responses to an oral glucose challenge. Nat Genet 2010; 42: 142–148. 21 Boesgaard TW, Grarup N, Jorgensen T, Borch-Johnsen K, Hansen T, Pedersen O. Variants at dgkb/tmem195, adra2a, glis3 and c2cd4b loci are associated with reduced glucose-stimulated beta cell function in middle-aged Danish people. Diabetologia 2010; 53: 1647–1655. 22 Grarup N, Overvad M, Sparso T, Witte DR, Pisinger C, Jorgensen T et al. The diabetogenic vps13c/c2cd4a/c2cd4b rs7172432 variant impairs glucosestimulated insulin response in 5,722 non-diabetic Danish individuals. Diabetologia 2011; 54: 789–794. 23 Rose CS, Grarup N, Krarup NT, Poulsen P, Wegner L, Nielsen T et al. A variant in the g6pc2/abcb11 locus is associated with increased fasting plasma glucose, increased basal hepatic glucose production and increased insulin release after oral and intravenous glucose loads. Diabetologia 2009; 52: 2122–2129. 24 Prokopenko I, Langenberg C, Florez JC, Saxena R, Soranzo N, Thorleifsson G et al. Variants in mtnr1b influence fasting glucose levels. Nat Genet 2009; 41: 77–81.
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genetic loci regulating glucose and insulin metabolism in humans. Diabetes 2010; 59: 1266–1275. 29 Watson HR, Wunderley L, Andreou T, Warwicker J, High S. Reorientation of the first signal-anchor sequence during potassium channel biogenesis at the sec61 complex. Biochem J 2013; 456: 297–309. 30 Kang HP, Yang X, Chen R, Zhang B, Corona E, Schadt EE et al. Integration of disease-specific single nucleotide polymorphisms, expression quantitative trait loci and coexpression networks reveal novel candidate genes for type 2 diabetes. Diabetologia 2012; 55: 2205–2213.
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ORIGINAL ARTICLE
Genetic variation at glucose and insulin trait loci and response to glucose–insulin–potassium (GIK) therapy: the IMMEDIATE trial KL Ellis1, Y Zhou2, JR Beshansky3, E Ainehsazan1, Y Yang1, HP Selker3, GS Huggins4, LA Cupples2 and I Peter1 The mechanistic effects of intravenous glucose, insulin and potassium (GIK) in cardiac ischemia are not well understood. We conducted a genetic sub-study of the Immediate Myocardial Metabolic Enhancement During Initial Assessment and Treatment in Emergency care (IMMEDIATE) Trial to explore effects of common and rare glucose and insulin-related genetic loci on initial to 6-h and 6- to 12-h change in plasma glucose and potassium. We identified 27 NOTCH2/ADAM30 and 8 C2CD4B variants conferring a 40–57% increase in glucose during the first 6 h of infusion (P o 5.96 × 10 − 6). Significant associations were also found for ABCB11 and SLC30A8 single-nucleotide polymorphisms (SNPs) and glucose responses, and an SEC61A2 SNP with a potassium response to GIK. These studies identify genetic factors that may impact the metabolic response to GIK, which could influence treatment benefits in the setting of acute coronary syndromes (ACS). The Pharmacogenomics Journal (2015) 15, 55–62; doi:10.1038/tpj.2014.41; published online 19 August 2014
INTRODUCTION Among treatments that have shown promise in acute coronary syndromes (ACS) is acute myocardial metabolic support. Cellular, animal and human studies have suggested that if instituted at the onset of ischemia, metabolic therapy in the form of intravenous glucose–insulin–potassium (GIK), which promotes glycolysis and lowers circulating free fatty acids, helps maintain the viability of an at-risk myocardium and improves ACS outcomes.1 On the basis of animal results, administration of GIK very early in ACS is considered most effective in protecting ischemic myocardium.2 The Immediate Myocardial Metabolic Enhancement During Initial Assessment and Treatment in Emergency care (IMMEDIATE) Trial was designed to deliver this metabolic support immediately upon the patient’s presentation with ACS,3 recapitulating the timing of myocardial support shown effective in experimental studies. The trial demonstrated that among patients with suspected ACS, outof-hospital administration of GIK, compared with placebo, was associated with lower rates of cardiac arrest and in-hospital mortality, smaller infarct size and lower free fatty acid levels.4 Recent advances in DNA technology through genome-wide association studies (GWAS) have allowed the identification of hundreds of common gene variants associated with metabolic diseases and related traits, including glucose sensitivity and insulin resistance.5 Moreover, there is emerging evidence pointing toward the role of low frequency variants in metabolic traits.6 The goal of this study was to examine whether genetic variants previously associated with glucose and insulin-related traits by GWAS, or located within susceptibility loci, appear to contribute to responses to GIK among IMMEDIATE Trial patients. Identifying genetic variation that modifies the glucose, potassium and insulin
response to GIK may provide greater insight into the metabolic factors that influence the overall course of patients with ACS. MATERIALS AND METHODS Study population The IMMEDIATE Trial was a double-blind randomized controlled clinical trial conducted in 13 cities across the United States that assessed the effectiveness of intravenous GIK administered early in the course of ACS for 12 h.3 Out-of-hospital intravenous administration of GIK or placebo commenced immediately after a patient was considered to likely be having ACS. Inclusion and exclusion criteria for the IMMEDIATE Trial have been previously described,4 and can be seen in more detail in Supplementary Methods. The IMMEDIATE Genetic Ancillary Study aimed to identify genetic modifiers of response to GIK therapy. A total of 321 participants were recruited at 9 participating sites during the parent trial enrollment. All IMMEDIATE Trial participants were eligible for inclusion, provided that consent for genetic analysis was granted. This study was approved by the Icahn School of Medicine at Mount Sinai’s IRB (Institutional Review Board) in addition to the IRB at each site.
Metabolite measurements Glucose and potassium levels were measured by the study site hospital laboratory upon arrival at the emergency department and after 6 and 12 h of study drug infusion, or alternatively at the point the drug was stopped prematurely. A subset of Genetic Ancillary Study participants (N = 117) was concurrently enrolled in the Biological Mechanisms Cohort3 and had glucose and insulin levels measured initially and at 6 and 12 h at a core laboratory. Glucose levels generated by the hospital laboratories were correlated with glucose levels in the Biological Mechanisms Cohort core laboratory at all time points (r2 = 0.643–0.941, P = 1.4 × 10 − 13 to 1.50 × 10 − 52). Study site hospital measurements were used for these
1 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; 2Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA; 3Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA and 4Molecular Cardiology Research Institute Center for Translational Genomics, Tufts Medical Center, Boston, MA, USA. Correspondence: Dr I Peter, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, Box 1498, New York, NY 10029, USA. E-mail: [email protected] Received 13 February 2014; revised 29 April 2014; accepted 4 June 2014; published online 19 August 2014
Glucose and insulin loci and GIK response KL Ellis et al
56 analyses as substantially more data were available. Metabolite levels were generated by the core laboratory at Tufts Clinical and Translational Science Institute.
Genotyping and candidate variant selection Genomic DNA was extracted from whole blood using the Gentra Puregene Blood Kit (Qiagen, Valencia, CA, USA) and from saliva using the Oragene®•DNA Saliva Collection Kit (DNA Genotek, Ontario, Canada). Genotyping was carried out using the Illumina Metabochip7 and HumanExome Beadchip8 at the Children’s Hospital of Philadelphia. Single-nucleotide polymorphisms (SNPs) were clustered into genotypes using the Illumina BeadStudio software, and zCall for rare variants.9 We downloaded the GWAS catalog (accessed 30 July 2013)5 and selected all SNPs associated with fasting glucose, type-2 diabetes, glycated hemoglobin levels, diabetes-related insulin, diabetes (incident, gestational), proinsulin levels, 2-h glucose challenge, insulin resistance and/or response, and fasting insulin (Supplementary Table S1). For intergenic SNPs, we also selected variants ± 250 kb from the index GWAS SNP. Annotation documentation from Metabochip was used to further select variants that had been included for type-2 diabetes, fasting glucose, 2-h glucose tests, fasting insulin and hemoglobin A1c traits. SNPs were removed if they had call rates o95% and deviated from Hardy–Weinberg equilibrium (Po0.0001), resulting in the selection of 15 159 SNPs with a mean genotyping success rate of 99.8%. Duplicate concordance was assessed for three quality control samples assayed on Metabochip and five samples assayed on Exome Chip. Genotype concordance for each duplicate was ⩾ 98.3%. Samples were filtered for individual call rates o95% and relatedness (identical by descent). After excluding 2 samples that failed identical by descent analysis (same individuals recruited twice), and samples with a low call rate, the Genetic Ancillary Study cohort comprised 318 individuals.
Statistical analysis Phenotype derivation. Associations between gene variants and changes in non-fasting plasma glucose and potassium during 12-h infusion were investigated. Due to non-linear trends over 12 h, the change in each trait was determined between the initial and 6-h measurement (calculated as 6-h level − initial level), and between the 6-h and 12-h measurements (calculated as 12-h level − 6-h level). Glucose data exhibited skewed distributions and were log-transformed before analysis. Therefore, their 6-h log-changes are interpreted as ratios. For potassium, 6-h changes can be interpreted as absolute differences between the two measurements.
Table 1.
Correlations between the metabolites were generated using Pearson’s correlation. Population stratification. To control for population admixture, we computed principal components to be used as covariates in regression analyses. We calculated principal components in EIGENSTRAT10 on a combined set of 130 539 independent SNPs (linkage disequilibrium, LD, r2 o0.3) from Metabochip and Exome chip that passed quality control, had minor allele frequency (MAF) ⩾ 5%, a call rate of 40.98 and a Hardy–Weinberg equilibrium P-value of 40.001. Association analysis. For common variant analysis (MAF ⩾ 5%), we used a two degree of freedom (d.f.) test to detect joint significance for the main effect of the SNP and the SNP × intervention interaction in the same model.11 All models adjusted for age, sex, treatment arm, diabetes status and the first two principal components. For each trait, associations with initial to 6-h change also adjusted for the time from infusion initiation to first blood draw. For gene-based association testing, all non-synonymous variants (non-synonymous, splice variants, 3' UTR and 5' UTR), regardless of MAF, were collapsed using the ‘adjusted optimal’ method (SKAT-O) within the SNP-set (Sequence) Kernel Association Test12 shown to have the greatest statistical power to detect gene-level associations.13 Statistical analyses were carried out using the R software package (www.r-project.org). Selection of significance threshold. For association analyses of 14 389 common variants (MAF ⩾ 5%), we determined the number of uncorrelated markers to be 8387, after accounting for LD using the Li and Ji approach.14 Therefore, after adjustment for multiple hypothesis testing, a P-value threshold for statistical significance was set at 5.96 × 10 − 6. For gene-based analysis, including 208 genes (Supplementary Table S2) and represented by 1310 SNPs, a P-value threshold was set at 2.40 × 10 − 4 (0.05/208).
RESULTS Patient characteristics The characteristics of the participants in the IMMEDIATE Genetic Ancillary Study (Tables 1 and 2) show no significant differences in basic demographics, including age, gender and medical history of myocardial infarction, diabetes or heart failure between randomization groups (P40.05). The average age at time of recruitment was 63.4 years (31–96 years), and 74.8% of participants were male. Participants were predominantly of White ethnicity (87.7%), 7.6% were African-Americans, 1.6% were Asian-Americans, 3.2%
Demographics, pre-hospital clinical characteristics and medical history by treatment group for the IMMEDIATE Genetic Ancillary Study
Demographics/clinical characteristics Age (years) Gender, n = Male (%) Ethnicity, n (%) White Black Asian Other Hispanic, n (%) Initial out of hospital blood pressure (mm Hg) Systolic Diastolic BMI (kg m − 2) Time for symptom onset until infusion initiation (hours) Medical history, n (%) Previous myocardial infarction (yes) History of diabetes (yes) Previous heart failure (yes)
n
GIK
n
Placebo
P-valuea
157 157 157
63.8 ± 12.5 117 (74.5)
160 160 160
63.0 ± 13.6 120 (75.0)
0.60 0.92 0.07
157
132 (84.1) 17 (10.8) 4 (2.5) 4 (2.5) 15 (9.5)
159
146 (91.3) 7 (4.4) 1 (0.6) 6 (3.8) 25 (15.7)
0.01
157 155 150 128
141.9 ± 35.4 84.9 ± 24.6 28.6 ± 5.5 2.7 ± 3.9
159 157 152 141
144.3 ± 30.3 86.8 ± 21.9 28.8 ± 6.6 2.3 ± 2.7
0.52 0.47 0.77 0.33
157 157 157
55 (35.0) 49 (31.2) 27 (17.2)
160 160 160
57 (35.6) 38 (23.8) 24 (15.0)
0.91 0.14 0.38
Abbreviations: BMI, body mass index; GIK, glucose–insulin–potassium; IMMEDIATE, Immediate Myocardial Metabolic Enhancement During Initial Assessment and Treatment in Emergency care. Data are presented as mean ± s.d. and percentages. aStatistical significance Po 0.05.
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Glucose and insulin loci and GIK response KL Ellis et al
57 Table 2.
In-hospital clinical characteristics and analyte measurements by treatment group for the IMMEDIATE Genetic Ancillary Study
Clinical characteristics Duration of treatment (hours) Time from treatment initiation until first blood draw—Standard of Care (hours) Time from treatment initiation until first blood draw—Biological Mechanisms Cohorts (hours)
n
GIK
n
Placebo
P-valueb
157 156
8.4 ± 4.8 0.64 ± 0.76
132 159
9.1 ± 4.5 0.55 ± 0.61
0.17 0.28
50
2.21 ± 1.20
55
2.63 ± 1.27
0.09
Non-fasting plasma glucose levels (mg dl − 1)a Initial blood glucose 6-h blood glucose 12-h blood glucose Initial to 6-h fold change in glucose 6- to 12-h fold change in glucose
156 188.7 (177.7–202.4) 159 92 188.6 (167.3–214.9) 114 137 146.9 (131.6–162.4) 142 91 1.01 (0.91–1.13) 113 90 0.74 (0.66–0.83) 113
Non-fasting plasma potassium levels (mmol l − 1) Initial potassium 6-h potassium 12-h potassium Initial to 6-h change in potassium 6- to 12-h change in potassium
155 91 146 89 84
Non-fasting plasma Insulin Levels (μU ml − 1)a Initial insulin 6-h insulin 12-h insulin Initial to 6-h fold change in insulin 6- to 12-h fold change in insulin
3.99 ± 0.64 4.35 ± 0.52 4.43 ± 0.58 0.42 ± 0.53 0.17 ± 0.52
159 112 152 111 107
50 107.8 (83.9–137.0) 49 154.5 (115.6–206.4) 51 100.5 (70.8–141.2) 48 1.45 (1.10–1.91) 49 0.68 (0.53–0.86)
55 58 61 52 58
154.5 148.4 142.6 0.97 0.95
(145.5–165.7) (138.4–159.2) (134.3–151.4) (0.92–1.02) (0.91–1.00)
3.93 ± 0.55 4.08 ± 0.46 3.97 ± 0.46 0.17 ± 0.53 − 0.13 ± 0.38 18.3 22.4 21.1 1.16 0.89
(13.5–25.0) (16.6–30.3) (15.5–29.4) (0.93–1.43) (0.72–1.10)
3.10 × 10 − 5 3.70 × 10 − 4 0.64 0.47 1.64 × 10 − 5 0.37 9.30 × 10 − 5 3.95 × 10 − 13 0.001 5.62 × 10 − 6 5.30 × 0 − 14 6.02 × 10 − 15 1.43 × 10 − 9 0.20 0.09
Abbreviations: GIK, glucose–insulin–potassium; IMMEDIATE, Immediate Myocardial Metabolic Enhancement During Initial Assessment and Treatment in Emergency care. Data are presented as unadjusted means ± s.d. or percentages unless otherwise stated. aData were log-transformed before analysis. Data are presented as geometric means and 95% confidence intervals. Ratios are presented for initial to 6-h, and 6- to 12-h changes for logged data. bStatistical significance P o0.05.
were classified as ‘other’ and 12.7% identified as Hispanic race (Table 1). Initial measurements were carried out on average 35 min after the initiation of infusion for glucose and potassium (Table 2). As expected, glucose, potassium and insulin levels were higher in the GIK group than in placebo (Table 2). Plasma glucose was increased with GIK initially and at 6 h (P ⩽ 3.70 × 10 − 4); however, at 12 h glucose levels were similar to those observed in the placebo group (P = 0.64). Insulin levels reflecting endogenous and exogenous insulin from GIK were greater in the GIK group at all three measurements (P ⩽ 1.43 × 10 − 9). No differences in potassium levels were observed with intervention at the initial measurement, but modestly higher levels were detected with GIK at both 6 and 12 h (P ⩽ 9.30 × 10 − 5). Treatment-stratified and treatment-combined correlations of all study metabolites at different time points are presented in Supplementary Table S3. Glucose and potassium levels, as well as initial to 6-h and 6- to 12-h changes, were representative of the overall IMMEDIATE Trial. There were no significant differences in glucose or potassium levels when comparing Genetic Ancillary Study participants with all other individuals recruited into the IMMEDIATE Trial (P40.05, data not shown). Summary of genetic data The SNP selection criteria resulted in the inclusion of 15 159 SNPs for analysis. Of those, 14 389 were common variants (MAF ⩾ 0.05), including 165 non-synonymous, 110 synonymous, 9262 intronic, 4181 intergenic, 206 3' UTR, 39 5' UTR and 2 splice variants. There were 770 rare variants (MAFo 0.05) selected for genebased analysis. Of these, 585 were non-synonymous, 6 were splice variants, 164 were located in the 3' UTR and 15 in the 5' UTR. © 2015 Macmillan Publishers Limited
Association analysis of glucose traits There were 37 SNPs exceeding the corrected threshold for statistical significance. Specifically, 27 common variants (MAF 10–12%) in strong LD (r2 ⩾ 0.85) at the NOTCH2/ADAM30 locus on chromosome 1 modified the glucose response to GIK (Figure 1a; Supplementary Table S4). In GIK-treated individuals, each copy of ‘C’ allele for the lead SNP, rs7534586, was associated with a 57% increase in plasma glucose between the initial measurement and 6 h, compared with a 9% increase in the placebo group (2 d.f. test’s P, P2d.f. = 2.33 × 10 − 6; Table 3, Figure 1b). Among the associated variants was rs10923931 (P2d.f. = 3.80 × 10 − 6) identified by GWAS as associated with type-2 diabetes.15 Variants with the smallest P-value at each locus are shown in Table 3. The C2CD4B region on chromosome 1 also conferred a larger change in glucose in the first 6 h of infusion in GIK patients compared with placebo (Table 3) with eight variants (MAF ⩾ 0.22) in strong LD with the lead SNP rs1406227 (LD ⩾ 0.95), demonstrating associations with initial response to GIK (Figure 2a; Supplementary Table S4). For each copy of the minor ‘A’ allele of rs1406227, a 41% increase in glucose was observed with GIK treatment compared with 4% in placebo-treated patients (P2d.f. = 2.63 × 10 − 6; Figure 2b). There were two variants that modified response to GIK between 6 and 12 h. The intronic variant, rs34453330 (MAF = 0.05) in the ABCB11 gene, was associated with a 48% decrease in glucose per ‘A’ allele in GIK-treated individuals, however no difference was observed in the placebo group (P2d.f. = 5.65 × 10 − 7; Figure 3a). The minor ‘G’ allele of SLC30A8 rs10105491 (MAF = 0.25) interacted with treatment to increase glucose by 37% between 6 and 12 h with GIK but not with placebo (P2d.f. = 5.55 × 10 − 6; Figure 3b). Other suggestive genetic associations (P2d.f. o0.001) for initial to The Pharmacogenomics Journal (2015), 55 – 62
Glucose and insulin loci and GIK response KL Ellis et al
58
Figure 1. Association between NOTCH2/ADAM30 locus and initial to 6-h change in glucose: (a) Regional plots of the top association signals ± 250 kb. The X axis shows the chromosome and physical distance (kb), the left Y axis shows the negative base ten logarithm of the P-value and the right Y axis shows recombination activity (cM/Mb) as a blue line. The linkage disequilibrium of surrounding single-nucleotide polymorphisms (SNPs) with the leading SNP is indicated by a scale of intensity of red color filling as shown in the legend at the upper righthand corner of each plot. The genome-wide association studies (GWAS) SNP P-value is shown in blue, and is annotated with the SNP identifier. Positions, recombination rates and gene annotations are according to the NCBI’s build 36 (hg18). (b) Fold change by treatment arm and genotype. Data are presented as the geometric mean ± s.e.m. after the adjustment for age, sex, treatment arm, diabetes, PC1, PC2 and time from infusion initiation until initial glucose measurement. The P-value is from the 2 degree of freedom (d.f.) test. GIK, glucose–insulin–potassium.
6-h, and 6- to 12-h change in glucose are presented in Supplementary Tables S4 and S5. When analysis was carried out on Whites only, the largest population sub-group, similar associations between each of these variants and glucose response to GIK were observed (data not shown). Association analysis of potassium traits An association between rs74474517 and change in potassium between 6 and 12 h was identified (Table 3). This intronic variant located within the SEC61A2 gene (MAF = 0.06) modified response to intervention, resulting in a decrease in potassium between 6 and 12 h in the GIK arm but not in the placebo (P2d.f. = 2.28 × 10 − 6; βGIK = − 0.68 mmol l − 1, βPlacebo = − 0.05 mmol l − 1; Figure 4). A similar association was observed when Whites were analyzed separately (P = 4.12 × 10 − 6). No significant genetic associations with initial to 6-h change in potassium were observed; however, a number of suggestive associations within or near the KCNQ1, CDKN2B, SLC2A2 and DGKB genes were observed (P2d.f. = 4.07 × 10 − 5 to 0.001). Suggestive associations are shown in Supplementary Tables S6 and S7. Gene-based analysis of glucose and potassium Gene-based analysis of common and rare functional variants in 208 genes identified a study-wise significant association (P o 2.40 × 10 − 4) between ZNF750 (P = 6.99 × 10 − 5), FAM180B (P = 1.29 × 10 − 4), CELF1 (P = 1.35 × 10 − 4) and initial to 6-h change in glucose (Table 4). Suggestive associations were identified including NOTCH2 (P = 0.002) and ADAM30 (P = 0.017, data not shown), the top region identified in the single-point common variant analysis (Table 3; Figures 1 and 2). A nominal association between NOTCH2 was also observed for 6- to 12-h change in glucose (P = 0.03) (data not shown). For potassium, gene-based testing did not provide substantial evidence for novel association signals beyond those already implicated in the single-point common variant analysis. Top gene-based associations for each trait are shown in Table 4. In several cases, different genes from the same region showed signals for association with the same or related traits. That is, The Pharmacogenomics Journal (2015), 55 – 62
ZNF750, the strongest association with the first 6-h change in glucose, is located near to FN3KRP, which was also suggestively associated with the same phenotype. Similarly, both FAM180B and CELF1 were associated with the first 6-h change in glucose. The LRP2 and SPC25 genes located ~ 250 kb apart were suggestively associated with 6- to 12-h change in potassium and initial to 6-h change in glucose, respectively. DISCUSSION This study was the first to investigate genetic modifiers of response to GIK infusion in individuals presenting with likely ACS. We investigated the association between genetic variation at glucose and insulin-related loci and short-term response to GIK infusion, assessed as initial to 6-h, and 6- to 12-h change in plasma glucose and potassium. We observed significant associations for 27 common variants at the NOTCH2-ADAM30 locus, all in strong LD, and the initial to 6-h glucose response to therapy. Participants treated with GIK, who carried a minor allele at the NOTCH2-ADAM30 region, exhibited a 40–57% greater increase in glucose in the first 6 h of infusion compared with those without a variant. In the placebo group, these same alleles conferred a 4–9% larger change in plasma glucose between the initial and 6 h measurement. In addition, a gene-based approach that combined the effects of 14 rare and common functional variants further suggested that NOTCH2 has a role in modifying response to GIK. The Diabetes Genetics Replication and Meta-Analysis consortium identified multiple signals within the NOTCH2-ADAM30 locus that were strongly associated with type-2 diabetes.15 Within this region, the ‘A’ allele of the NOTCH2 variant, rs10923931 was associated with increased risk of diabetes, and in our study this same allele was associated with a higher increase in glucose levels in response to GIK. The role of NOTCH2 in the development of type-2 diabetes has been proposed to occur through effects on pancreatic β-cell function.16 A greater increase in glucose between the initial measurement and 6 h was also observed for eight variants near the C2CD4B gene. Expression of the C2CD4B gene, which encodes the nuclear localized factor 2 in the pancreas, is stimulated by © 2015 Macmillan Publishers Limited
Abbreviations: d.f., degree of freedom; GIK, glucose–insulin–potassium; MAF, minor allele frequency; SNPs, single-nucleotide polymorphisms. One variant with the smallest P-value (2 d.f.) at each locus is presented. Included in the model are age, sex, randomization group, PC1, PC2 and history of diabetes. For initial to 6-h change in glucose, time from infusion initiation to initial blood measurement was also adjusted for. FC = fold-change between the initial to 6-h, or 6-h to 12-h measurements. bFold change. cAbsolute change.
2.28 × 10 − 06 0.0005 0.69 12182121 10 6 to 12-h potassium rs74474517
© 2015 Macmillan Publishers Limited
a
− 0.68c − 0.04c SEC61A2 Intron 0.06
0.94 0.74 0.52b 1.37b 1.01b 0.98b ABCB11 SLC30A8 (28318) MED30 (315694) Intron Intergenic 0.05 0.25 A G 169781530 118217271 Log 6-h to log 12-h glucose rs34453330 2 rs10105491 8
A
5.65 × 10 − 07 5.55 × 10 − 06 6.5 × 10 − 5 9.0 × 10 − 5
2.33 × 10 − 06 2.63 × 10 − 06 0.002 0.001 0.28 0.52 1.57b 1.41b 1.09b 1.04b NOTCH2 C2CD4B (2132) MGC15885 (469757) Intron Intergenic 0.11 0.23 C A 120496127 62459614 Log initial to log 6-h glucose rs7534586 1 rs1406227 15
Gene (distance from closest gene, bp) Function MAF Minor allele Position Chr SNP
Table 3.
Significant associations between the top candidate SNPs and metabolite traitsa
Placebo change per allele
GIK change per allele
P (SNP)
P (SNP × treatment interaction)
P (2 d.f.)
Glucose and insulin loci and GIK response KL Ellis et al
pro-inflammatory cytokines.17,18 A meta-analysis of 21 GWA studies, and follow-up investigation in 76 558 subjects, associated C2CD4B with fasting glucose homeostasis.19 The region has also been associated with an increase in type-2 diabetes risk,18 plasma glucose 2 h after an oral glucose load,20 diminished insulin release21 and diminished glucose-stimulated insulin response.22 On average, glucose levels decreased in the final 6 h of infusion in both treatment arms. In this study, we identified two genes that modified the glucose response to GIK during this time. The minor ‘A’ allele of rs34453330, located in intron 26 of the ATP-binding cassette sub-family B, member 11 (ABCB11) gene, was associated with a 48% larger decrease in glucose between 6 and 12 h than ‘T’ allele carriers. Interestingly, the effect of this variant was limited to GIK-treated subjects. In a meta-analysis of two GWA studies, ABCB11, which encodes the ABC transporter BSEP (bile slat export pump), and neighboring G6PC2 (glucose-6-phosphatase) genes were associated with elevated plasma glucose. Variation at this locus has also been associated with increased basal hepatic glucose production, increased insulin release23 and a reduced risk of type-2 diabetes.24 This protective effect plausibly occurs via a hyper-response to postprandial elevation in circulating glucose levels.23 The second region associated with 6- to 12-h change in glucose was near SLC30A8, which encodes an islet-specific zinc transporter connected to insulin granule function.25 In our, study, the ‘G’ allele of a variant near SLC30A8, rs10105491, was associated with a smaller decrease in glucose between 6 and 12 h in GIK-treated individuals, but no real difference with placebo. SLC30A8 was first linked to type-2 diabetes by GWAS in 2007, with the index variant, rs13266634, conferring an 18% increased risk for the ‘A’ allele.26 In a prospective study, genetic variation within SLC30A8 was also shown to result in impaired beta-cell function over time,27 and in a study of non-diabetics was associated with either abnormal insulin processing or secretion.28 In our study, the rs13266634 ‘A’ allele was associated with a larger 6- to 12-h decrease in the GIK group (P = 0.03), but not in placebo. We identified one intronic variant (rs74474517) in SEC61A2 that was associated with 6- to 12-h change in potassium levels. The ‘A’ allele was associated with a larger decrease in potassium levels (−0.64 mmol l − 1 per allele) in GIK-treated patients than in placebo (−0.05 mmol l − 1 per allele). SEC61A2 belongs to a family of proteins that mediate the translocation and insertion of membrane proteins, including potassium channels, in the endoplasmic reticulum.29 The observed association may indicate that SEC61mediated differences in potassium channel number or structure modify the response to GIK therapy. Moreover, SEC61A2 is located o150 kb away from the CDC123/CAMK1D locus. A study investigating expression quantitative trait loci and co-expression networks of top type-2 diabetes associated gene variants detected that the expression of SEC61A2 was highly correlated with CDC123/CAMK1D rs12779790 genotype,30 previously associated with type-2 diabetes15 and insulin-related traits.16 Strengths of this study include the random assignment of GIK to individuals at high risk of ACS. Moreover, availability of repeated measurements during the infusion provided additional information about the dynamics of the response. The dense genomic coverage of the candidate regions provided by the Metabochip and Exome chip has also allowed us to examine the role of the functional variants potentially responsible for the GWAS signals in gene-based analysis. Importantly, most of the top associations detected in our study did not involve previously reported GWAS SNPs, even when the analyses were limited to Whites only. Instead, other variants within the susceptibility loci were identified, suggesting that a better coverage used in our analyses could improve the accuracy of the detection or the involvement of different variants in GIK response. Moreover, gene-based analysis indicated that different genes in close proximity to each other were associated with correlated traits, further emphasizing the The Pharmacogenomics Journal (2015), 55 – 62
59
Glucose and insulin loci and GIK response KL Ellis et al
60
Figure 2. Association between the C2CD4B locus and initial to 6-h change in glucose: (a) Regional plots of the top association signals and (b) fold change by treatment arm and genotype. See Figure 1 for plot description. GIK, glucose–insulin–potassium.
Figure 3. Genetic association for 6- to 12-h change in glucose by treatment arm and genotype: (a) For ABCB11 rs34453330 and (b) for SLC30A8 rs10105491. Data are presented as the geometric mean ± s.e.m. and adjustment for age, sex, treatment arm, diabetes, PC1 and PC2. The P-value is from the 2 degree of freedom (d.f.) test. GIK, glucose–insulin–potassium.
Figure 4. Genetic association for 6- to 12-h change in potassium by treatment arm and genotype for SEC61A2. Data are presented as the arithmetic mean ± s.e.m. Data have been adjusted for age, sex, treatment arm, diabetes, PC1 and PC2. The P-value is from the 2 degree of freedom (d.f.) test. GIK, glucose–insulin–potassium. The Pharmacogenomics Journal (2015), 55 – 62
importance of fine mapping and functional studies to determine causal genes/variants that alter phenotypic traits. We acknowledge several limitations of our study, including the lack of pre-treatment and fasting metabolite measurements, and the sample size of this ancillary study, which limited the statistical power to detect modest effects. Also of note, in spite of the large glucose load in the GIK infusion, endogenous insulin secretion did not vary significantly over the 12-h infusion as indicated by mostly unchanged C-peptide levels (data not shown). This suggests that the relative levels of glucose and insulin during GIK infusions were balanced in regard to the impact on blood glucose levels. However, due to the lack of fasting glucose and insulin (or C-peptide) levels, a formal estimation of insulin resistance, as assessed by the homeostasis model assessment index, HOMA2-IR, was not possible. Novel regions not queried in the present analysis may also contribute to GIK response, leaving the possibility that genome-wide studies may prove fruitful. Finally, given the interventional nature of this study and unavailability of DNA samples from other GIK trials, replication of our findings was not possible. In summary, this is the first study to examine genetic modifiers of GIK therapy in patients with likely ACS using 15 149 genetic variants previously linked to glucose and insulin-related traits. We identified significant associations between variants within or near NOTCH2-ADAM30, C2CD4B, ABCB11, SLC30A8, SEC61A2 and glucose and potassium traits during 12-h infusion with GIK. Findings from © 2015 Macmillan Publishers Limited
Glucose and insulin loci and GIK response KL Ellis et al
61 Table 4.
Top genes (Po0.01) from gene-based analysis of non-coding variants
Gene
Chr.
Start position
End position
No of SNPs
P-value
NOTCH2 SPC25 LRP2 ZFAND3 SLC30A8 IDE ABCC8 CELF1 FAM180B MTNR1B NCKAP1L SPPL3 FN3KRP ZNF750 QPCTL
1 2 2 6 8 10 11 11 11 11 12 12 17 17 19
120455095 169732622 169985338 38084405 118184783 94211444 17414570 47487740 47609867 92714749 54902264 121201156 80676855 80788899 46201812
120506308 169732622 170177325 38084405 118188536 94333827 17450177 47574716 47610271 92715916 54928894 121202664 80685655 80790442 46206425
14 1 33 1 13 7 3 9 2 7 3 4 10 10 3
0.002 0.001 0.006 0.008 4.56 × 10 − 4 0.007 0.006 1.35 × 10 − 4 1.29 × 10 − 4 0.007 0.006 0.005 0.004 6.99 × 10 − 5 0.002
Phenotype Log initial to log 6-h change in glucose Log initial to log 6-h change in glucose 6 to 12-h change in potassium Log 6 to log 12-h change in glucose Log initial to log 6-h change in glucose Log initial to log 6-h change in glucose Initial to 6-h change in potassium Log initial to log 6-h change in glucose Log initial to log 6-h change in glucose 6- to 12-h change in potassium Log 6-h to log 12-h change in glucose Log initial to log 6-h change in glucose Log initial to log 6-h change in glucose Log initial to log 6-h change in glucose 6- to 12-h change in potassium
Abbreviation: SNPs, single-nucleotide polymorphisms.
this study suggest that genetic factors have an important role in determining the response to GIK treatment and may provide new insights into GIK mechanisms. CONFLICT OF INTEREST The authors declare no conflict of interest.
ACKNOWLEDGMENTS The Genetic Ancillary Study was funded by the National Institutes of Health (NIH) grant from National Heart, Lung and Blood Institute (NHLBI) (R01HL090997). This work was also supported by National Center for Research Resources Grant Number UL1RR025752, now the National Center for Advancing Translational Sciences, NIH Grant Number Ul1 TR000073. The IMMEDIATE Trial was funded by the NIH cooperative agreement from NHLBI (U01HL077821, U01HL077823 and U01HL077826).
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