Author + information
- Received October 18, 2012
- Revision received January 16, 2013
- Accepted January 20, 2013
- Published online August 27, 2013.
- Jane F. Ferguson, PhD∗,
- Gregory J. Matthews, PhD†,
- Raymond R. Townsend, MD‡,
- Dominic S. Raj, MD§,
- Peter A. Kanetsky, PhD, MPH‖,
- Matthew Budoff, MD¶,
- Michael J. Fischer, MD, MSPH#∗∗,
- Sylvia E. Rosas, MD, MSCE‡,
- Radhika Kanthety, MD, MSHS††,
- Mahboob Rahman, MD, MS††,
- Stephen R. Master, MD, PhD‡‡,
- Atif Qasim, MD, MSCE∗,
- Mingyao Li, PhD‖,
- Nehal N. Mehta, MD, MSCE∗,
- Haiqing Shen, PhD§§,
- Braxton D. Mitchell, MPH, PhD§§,
- Jeffrey R. O'Connell, PhD§§,
- Alan R. Shuldiner, MD§§,‖‖,
- Weang Kee Ho, PhD¶¶,
- Robin Young, PhD¶¶,
- Asif Rasheed, MD##,
- John Danesh, MB, ChB, PhD¶¶,
- Jiang He, MD, PhD∗∗∗,
- John W. Kusek, PhD†††,
- Akinlolu O. Ojo, MD, PhD‡‡‡,
- John Flack, MD, MPH§§§,
- Alan S. Go, MD‖‖‖,
- Crystal A. Gadegbeku, MD¶¶¶,
- Jackson T. Wright Jr., MD, PhD###,
- Danish Saleheen, PhD∗,¶¶,##,
- Harold I. Feldman, MD, MSCE‡,‖,
- Daniel J. Rader, MD∗,
- Andrea S. Foulkes, PhD†,
- Muredach P. Reilly, MB, ChB, MSCE∗∗ (, )
- CRIC Study Principal Investigators
- ∗Cardiovascular Institute, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
- †School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, Massachusetts
- ‡Renal, Electrolyte and Hypertension Division, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
- §Medical Faculty Associates, The George Washington University, Washington, DC
- ‖Center for Clinical Epidemiology and Biostatistics, and Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
- ¶Los Angeles Biomedical Research Institute, Torrance, California
- #Department of Medicine, Jesse Brown VA Medical Center and University of Illinois Hospital and Health Sciences System, Chicago, Illinois
- ∗∗Center for Management of Complex Chronic Care, Edward Hines Jr., VA Hospital, Hines, Illinois
- ††Department of Nephrology and Hypertension, Case Western Reserve University, Cleveland, Ohio
- ‡‡Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
- §§Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland
- ‖‖Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, Maryland
- ¶¶Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- ##Center for Non-Communicable Diseases, Karachi, Pakistan
- ∗∗∗Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana
- †††National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), Bethesda, Maryland
- ‡‡‡University of Michigan School of Medicine, Ann Arbor, Michigan
- §§§Department of Medicine, Wayne State University School of Medicine, Detroit, Michigan
- ‖‖‖Division of Research, Kaiser Permanente of Northern California, Oakland, California
- ¶¶¶Department of Medicine, Section of Nephrology, Temple University School of Medicine, Philadelphia, Pennsylvania
- ###Department of Medicine, Case Western Reserve University, Cleveland, Ohio
- ↵∗Reprint requests and correspondence:
Dr. Muredach P. Reilly, Perelman School of Medicine at the University of Pennsylvania, 11-136 Translational Research Building, 3400 Civic Center Boulevard, Building 421, Philadelphia, Pennsylvania 19104.
Objectives This study sought to identify loci for coronary artery calcification (CAC) in patients with chronic kidney disease (CKD).
Background CKD is associated with increased CAC and subsequent coronary heart disease (CHD), but the mechanisms remain poorly defined. Genetic studies of CAC in CKD may provide a useful strategy for identifying novel pathways in CHD.
Methods We performed a candidate gene study (∼2,100 genes; ∼50,000 single nucleotide polymorphisms [SNPs]) of CAC within the CRIC (Chronic Renal Insufficiency Cohort) study (N = 1,509; 57% European, 43% African ancestry). SNPs with preliminary evidence of association with CAC in CRIC were examined for association with CAC in the PennCAC (Penn Coronary Artery Calcification) (N = 2,560) and AFCS (Amish Family Calcification Study) (N = 784) samples. SNPs with suggestive replication were further analyzed for association with myocardial infarction (MI) in the PROMIS (Pakistan Risk of Myocardial Infarction Study) (N = 14,885).
Results Of 268 SNPs reaching p < 5 × 10−4 for CAC in CRIC, 28 SNPs in 23 loci had nominal support (p < 0.05 and in same direction) for CAC in PennCAC or AFCS. Besides chr9p21 and COL4A1, known loci for CHD, these included SNPs having reported genome-wide association study association with hypertension (e.g., ATP2B1). In PROMIS, 4 of the 23 suggestive CAC loci (chr9p21, COL4A1, ATP2B1, and ABCA4) had significant associations with MI, consistent with their direction of effect on CAC.
Conclusions We identified several loci associated with CAC in CKD that also relate to MI in a general population sample. CKD imparts a high risk of CHD and may provide a useful setting for discovery of novel CHD genes and pathways.
- candidate genes
- chronic kidney disease (CKD)
- Chronic Renal Insufficiency Cohort Study (CRIC)
- coronary artery calcification (CAC)
- myocardial infarction (MI)
- risk factors
- single nucleotide polymorphisms (SNPs)
Atherosclerotic coronary heart disease (CHD) is a major heritable cause of death and morbidity worldwide. Recent genome-wide association studies (GWAS) have provided novel insights into the genetic basis of CHD (1–3). However, these discoveries explain only a small proportion of disease heritability, suggesting that further clinical and genomic strategies are required to explore the genetic basis of the disease and to advance clinical translation.
One strategy to enhance genetic discovery in CHD is to focus efforts on unique clinical populations at an increased risk of disease. Patients with chronic kidney disease (CKD), representing over 20 million Americans (4), are at high risk for CHD. Although both traditional and nontraditional CHD risk factors are common in patients with CKD (5), the mechanistic basis for the observed accelerated atherosclerosis and CHD (6,7) remains poorly defined. Thus, genetic studies of atherosclerosis in CKD provide a strategy for identification of novel CHD loci that may also be relevant to the general population.
Noninvasive measurement of coronary artery calcification (CAC) is an indicator of subclinical coronary atherosclerosis before emergence of clinically evident CHD in persons with CKD (8,9), and is one of the few identifiable CHD predictors after controlling for traditional risk factors and Framingham risk score. A recent GWAS of CAC scores in community-based cohort studies of European ancestry (EA) identified 2 CAC loci, 9p21 and PHACTR1 (10), also known for their association with CAD and myocardial infarction (MI) (3,11). Because the burden of CAC is increased substantially in persons with CKD, this patient population may provide specific insights into mechanisms of atherosclerosis and vascular diseases (12,13).
We performed the first systematic study to examine candidate genes for CAC in persons with CKD enrolled in the CRIC (Chronic Renal Insufficiency Cohort) study. Initial validation of CRIC findings was accomplished in 2 general population cohorts with CAC data. SNPs with suggestive replication were further analyzed for association with MI in the PROMIS (Pakistan Risk of Myocardial Infarction Study).
Discovery Sample: The CRIC Study
Our CKD study sample was derived from the CRIC study (N = 3,939), a multicenter, prospective, observational cohort study of renal and cardiovascular outcomes in patients with moderate CKD (14). Ethnically diverse adults (21 to 74 years; 46% female; 45% European ancestry [EA], 46% African ancestry [AA], 5% Hispanic, 4% Asian/Pacific Islander/Native American; ∼50% with diabetes mellitus) with mild to moderate CKD (target estimated glomerular filtration rate [eGFR] 20 to 70 ml/min/1.73 m2) were enrolled from 7 clinical centers in the United States between 2003 and 2006 (14,15). In-person follow-up visits are conducted annually. A nonrandom sample of 2,026 underwent computed tomography for quantification of CAC. This paper focuses on genetic associations with CAC in the CRIC EA and AA subsample in which CAC data and consent for genetic studies were available (N = 1,509). The CRIC study protocol was approved by the institutional review boards of all participating institutions, and study participants provided written informed consent. Multiple clinical, biochemical, and imaging variables were assessed on an annual basis as described in the Online Appendix, Feldman et al. (14), and Lash et al. (15).
CAC Replication and Extension Samples
We selected all SNPs associated with CAC score in CRIC at a threshold of p < 5 × 10−4 (as a suggestive first-stage threshold, given the modest size of our discovery sample) and examined their associations with CAC phenotypes in the PennCAC (Penn Coronary Artery Calcification) sample (EA, n = 2,058; and AA, n = 502) and the AFCS (Amish Family Calcification Study) (n = 784), as described in the Online Appendix, Post et al. (16), and Shen et al. (17). The PROMIS (Pakistan Risk of Myocardial Infarction Study) is a case-control study of acute MI in South Asians as described in the Online Appendix and in Saleheen et al. (18). In support of our use of PROMIS, genetic variants found in association with major lipids and CHD risk in Europeans have been previously replicated in PROMIS (19).
Genotyping (see also the Online Appendix) in the CRIC, PennCAC, and AFCS studies was performed using the HumanCVD BeadChip v2 ITMAT/Broad/CARe (IBC) array (Illumina, San Diego, California). This gene-centric SNP array includes ∼50,000 SNPs in ∼2,100 candidate genes, and was specifically designed to cover genes for cardiovascular, metabolic, and inflammatory diseases (20). Genotypes were called using Birdseed v2 as described (21). Samples from the CRIC study were excluded if any of the following were present: 1) a sample call rate <0.97; 2) reduced or excess heterozygosity (inbreeding, |F| <0.2); or 3) cryptic relatedness (PI_HAT identity-by-descent <0.2). SNPs were excluded within each race separately if the call rate was <90%; minor allele frequency (MAF) was <1%; or Hardy-Weinberg equilibrium p value was <0.0001. As described (17,22), sample and SNP filtering criteria were similar in PennCAC and AFCS. Genotyping in PROMIS was conducted on the Illumina 660Quad platform.
Data are reported as mean ± SD for continuous variables and as proportions for categorical variables. All analyses were conducted stratified by race. A principal component (PC) analysis plot for EA and AA samples in the CRIC study is provided in Online Figure 1. In CRIC, CAC was analyzed using several CAC traits and associated modeling techniques, as per published literature (8), including: 1) CACRes: linear regression of inverse normally transformed CAC residuals, where residuals were generated by a) stratifying by sex, b) regressing log (CAC+1) on age, c) calculating the residuals as the difference between the observed and predicted values, and d) combining the residuals across sexes; 2) LogCAC: linear regression of logCAC for individuals with CAC >0; and 3) separate logistic regressions for each of 3 clinically relevant CAC cut points (>0, CAC0; >100, CAC100; and >300, CAC300). Due to the exploratory nature of our analyses, we did not use a Bonferroni adjustment for all models tested. In all models, we adjusted for CRIC study site and the first 10 PCs derived using all available SNPs, to account for population substructure, whereas for #2 and #3, we additionally adjusted for age, age2, sex, and interactions for age-by-sex and age2-by-sex. Separate models were fit for each SNP, and tests of association were based on the Wald test. First, in order to assess the generalizability of the CRIC sample, we examined associations with top established CAD and CAC loci (3,10), using a nominal significance threshold of p < 0.05. All SNPs having suggestive signal (2-sided Wald test p < 5 × 10−4) with CAC in CRIC were interrogated for association with CAC phenotypes in PennCAC and AFCS.
In PennCAC, CAC phenotypes were defined and modeled in an identical manner to CRIC with the exception of a term for study site. In the family based AFCS, the Mixed Model Analysis for Pedigree software (17) was used to estimate the effects of genotype on CAC score for age and sex. The score was defined as log(CAC+1) and logCAC (for individuals with CAC >0), and the model also included an additional random polygenic component to account for relatedness in the sample. The lambda statistic of genomic control inflation (23) was calculated in all models for CRIC-EA (1.00 to 1.04), CRIC-AA (1.04 to 1.09), PennCAC-EA (1.04 to 1.07), PennCAC-AA (0.96 to 1.03), and AFCS-EA (1.04 to 1.05) (Online Figs. 2A to 2E). A SNP was considered suggestive if the associated p value corresponding to a test of no association, versus the 1-sided alternative that the corresponding coefficient is different than 0 and in the same direction as observed in CRIC, was >0.05. Top CAC-associated SNPs, or best proxies if SNP data were not available (linkage disequilibrium [LD] r2 > 0.6 using SNAP version 2.2) (24), were analyzed for their association with MI in PROMIS using logistic regression models that included age, sex, and the first 10 PCs. Because MI is a different trait from CAC, a 2-sided p value <0.05 was considered statistically meaningful.
Meta-analysis of summary statistics across race or study in CRIC and PennCAC applied a weighted z-score method using METAL (25), as we have previously described (1,22). All analysis, with the exception of the pedigree analysis for AFCS, was performed using PLINK version 1.06 or R version 2.14.1 (26).
Baseline characteristics of the CRIC sample
Baseline clinical and demographic characteristics of the CRIC study CAC genetic subsample by ancestry and sex are presented in Table 1. The average age was 57 years, and did not vary significantly by ancestry or sex; 43% were AA, 47% were female, and 40% had diabetes. The median eGFR was 48 ml/min/1.73 m2. Compared with expectations for similar age distributions in the general population, the CRIC study sample was more likely to be overweight and have hypertension (HTN), increased levels of triglycerides, fibrinogen, and C-reactive protein, and a high proportion had cardiovascular diseases at enrollment. Mean CAC scores and the distribution by 3 cut points (>0, >100, >300) in each of the ancestry and sex groups are presented in Table 2. In agreement with prior reports in the CRIC study (12) and the general population (27), CAC scores tended to be higher in men and EA. Median CAC scores and the prevalence of CAC >0 (66%), >100 (39%), and >300 (25%) were substantially higher than reported for population samples of similar age and ethnicity (8). Thus, compared with the general population, this CRIC study sample displayed increased prevalence of traditional and nontraditional CHD risk factors as well as a greater burden of subclinical and clinical atherosclerosis.
Association of established CAC and CAD loci with CAC in CRIC
The top published GWAS 9p21 allele (rs1333049C) for CAC (10) was associated in the same direction with CAC traits in CRIC (e.g., z = 2.81, p = 0.005 for CAC residual in meta-analysis of AA and EA; p = 0.03 in EA, p = 0.07 in AA). Similarly, rs4977574G, a top 9p21 GWAS allele for CAD (3), was also associated in the same direction with CAC in CRIC (e.g., z = 3.18, p = 0.001 for CAC residual in meta-analysis of AA and EA; p = 0.04 in EA, p = 0.01 in AA).
Rare variants in LPA (rs3798220) (3,28) and PCSK9 (rs11591147/R46L) (29) are associated with CHD risk. The IBC array included these variants or proxies (LD r2 > 0.6). Despite limited power to detect associations with SNPs of low frequency, there was suggestive evidence of CAC associations with these rare variants in the expected direction of effect. These findings were generally consistent across CAC traits; the strongest association for rs3798220 in LPA was with LogCAC in EA (beta = 1.1, p = 0.02, MAF = 0.02) and for rs11591147 in PCSK9 was with CAC0 in EA (odds ratio [OR]: 3.6, p = 0.14, MAF = 0.009).
Strongest IBC loci for CAC in CRIC
In order to maximize identification of candidate loci for CAC in the CRIC sample, we tested IBC SNP associations across multiple CAC trait definitions within each race separately as well as in a race-combined meta-analysis. For ∼45,000 SNPs examined, Online Table 1 shows those SNPs (n = 268) that were associated at p < 5 × 10−4 with any CAC trait within either race or in their meta-analysis. As might be expected with our relatively small sample size, none of these SNPs reached the Bonferroni-corrected threshold for the estimated number of independent SNPs tested on the IBC array (p < 3 × 10−6) (30). Regional plots including recombination rate, LD, and p values for SNPs at selective top CAC loci in CRIC are presented in Online Figures 3A to 3D.
Next, we examined these top CRIC CAC SNPs for their association with CAC in PennCAC interrogating the same CAC trait and race (or meta-analysis) combinations evaluated in the CRIC sample. We also tested these SNPs for their associations in AFCS, but in this case, the strongest available CAC phenotype association is presented because the AFCS family structure and analysis did not permit an interrogation of the identical CAC traits and race as those in CRIC. Summary data for each study are shown in Table 3 for the subset of SNPs that had nominal evidence (effect in the same direction, 1-sided p < 0.05) for similar effects in PennCAC or AFCS. Overall, 28 SNPs representing 23 independent loci met these suggestive replication criteria and included known CAD and CAC loci (9p21 and COL4A1) (3,10,31), known HTN and diabetes loci not previously associated with coronary atherosclerosis (HNF4A, ATP2B1, ADIPOR2 [32–34]), as well as several loci not previously reported to be associated with CAC, CAD, or CHD risk factors. In exploratory meta-analyses of the CRIC and PennCAC data for these 28 SNPs (Online Table 2), SNPs at 2 loci (ABCA4 and HNF4A) reached p < 2.38 × 10−5, the Bonferroni-corrected threshold for the number of genes tested. No locus met the more stringent IBC array SNP-wide Bonferroni correction (p < 3 × 10−6) (30).
Association of suggestive CAC loci with MI in the PROMIS sample
For SNPs with suggestive CAC association, we observed directionally consistent associations (alleles associated with greater CAC also increased odds of MI with 2-sided p < 0.05) with MI in PROMIS for 4 of 23 (17.4%) independent loci (exact binomial test p = 0.026; null proportion = 0.05 vs. 1-sided alternative that proportion is >0.05) (Table 4). Not surprisingly, the strongest signal was for SNPs at the 9p21 locus (most significant SNP rs4977574; p = 5.67 × 10−12). Three additional loci, ATP2B1 (rs11105354; p = 3.3 × 10−5), COL4A1 (rs13260; p = 9.6 × 10−4), and ABCA4 (rs3789422; p = 1.7 × 10−2), were associated with MI. Associations at both ATP2B1 and COL4A exceeded the p value Bonferroni adjustment for multiple testing of suggestive CAC SNPs (p < 0.0022; 0.05 of 23 independent loci).
We sought to identify genes for CAC in patients with CKD, a population at an increased risk of CHD. We found that previously identified loci for CAC and CAD in non-CKD populations had the expected pattern of associations with CAC in the CRIC. We also identified a group of suggestive loci for CAC in the CRIC study sample, for which associations were similar in PennCAC or AFCS datasets. In addition to chr9p21 and COL4A1, previously shown by GWAS to be associated with CAC and CAD (3,10), we identified ATP2B1 (a locus for HTN) (30) and HNF4A (a locus for high-density lipoprotein cholesterol [HDL-C] and diabetes) (32,35), previously identified by GWAS to be associated with CHD risk factors. Besides 9p21and COL4A, the suggestive CAC loci, ATP2B1 and ABCA4, were associated with MI in the PROMIS sample further supporting their potential importance in CHD.
CKD imparts a substantial increase in CHD risk (9) although the mechanisms remain incompletely understood. The CKD milieu might provide a discovery opportunity for CHD genes and pathways. Indeed, we found that SNPs at the 9p21 locus, the top GWAS signal for CAC and CHD in the general population, had the expected pattern of association with CAC in the CRIC study sample. Further, despite modest sample size, we identified trends for low frequency and rare CHD variants in LPA and PCSK9 (25,26), with the expected direction and magnitude of effect, on CAC in the CRIC study. These findings support our search for CAC loci in CKD patients and suggest that this may be 1 strategy to enhance discovery of novel genes for heart disease. Our top findings in the CRIC sample provide preliminary support for the concept that genes identified for CAC in CKD may have relevance to CHD risk in the general population. In addition to SNPs at 9p21 and COL4A1, top SNPs for CAC in the CRIC sample reside in loci (e.g., ATP2B1, HNF4A, and ABCA4) that have established genetic associations with CHD risk factors.
Several GWAS have identified ATP2B1 as a locus for HTN and blood pressure in samples of EA and AA (33,36). ATP2B1 encodes a plasma membrane calcium-transporting ATPase that plays a critical role in intracellular calcium homeostasis by removing bivalent calcium ions from eukaryotic cells. This suggests a potential role in regulation of arterial tone and vascular calcification. Indeed, mice lacking Atp2b1 in vascular smooth muscle cells had elevated blood pressure (37) suggesting a protective role in HTN and CHD. In the CRIC sample, the ATP2B1 rs11105354A allele that is associated with lower CAC (e.g., OR: 0.53; p = 2.8 × 10−5 for CAC100) is also the most significant ATP2B1 allele for lower blood pressure and reduced HTN in a large meta-analysis of EA individuals (33). This same SNP is in strong LD (r2 = 0.9) with the strongest ATP2B1 variant for HTN in Japanese (36). Furthermore, the ATP2B1 SNP related to lower CAC also had lower odds of MI (OR: 0.9, p = 3.3 × 10−5) in the PROMIS sample. Thus, our findings provide strong support for a role for ATP2B1 in coronary atherosclerosis and CHD.
Given the established GWAS associations with HTN, the ATP2B1 locus effect on CAC may be mediated through regulation of vasomotor tone and blood pressure. ATP2B1 might also play a specific role in regulating arterial calcification in the setting of disordered calcium and phosphate metabolism that is characteristic of progressive CKD (38) although this has yet to be established. Using cross-sectional CRIC baseline data, we found a weak association of the ATP2B1 rs11105354A allele with increased serum calcium (p = 0.02), but no association with serum phosphorous, baseline blood pressure traits, or eGFR (data not shown).
Since initial submission of our paper, a GWAS in Han Chinese has demonstrated that SNPs at the ATP2B1 locus have genome-wide significant associations with clinical CAD (e.g., rs7136259; p = 5.68 × 10−10) (39). Although the lead SNP for CAD in the Han has only nominal associations with CAC in our CRIC data (rs7136259; p = 0.01 for CAC100 in CRIC EA), this SNP has low LD in EA samples with our top CAC-associated ATP2B1 SNP (rs11105354; r2 = 0.136 in CEU [Utah residents with ancestry from northern and western Europe]). However, there is high correlation between these 2 SNPs in Asian samples (r2 = 0.963 in CHBJPT [Han Chinese in Beijing, China, and Japanese in Tokyo, Japan]) (LD estimates from 1000 Genomes Project Pilot 1 data using SNAP), suggesting that the CAC and HTN association for the ATP2B1 locus in EA populations overlaps with the CAD finding in the Han population. This new report reinforces the significance of our findings in CKD and underscores the importance of this locus in CHD.
Through GWAS, the HNF4A locus has been associated with HDL-C (40), metabolic dyslipidemia (41), and type 2 diabetes (T2DM) in multiethnic populations (32,42). HNF4A encodes a nuclear transcription factor that regulates development and function of the liver, kidney, pancreas, and intestines (43) and modulates hepatic lipogenesis as well as apolipoprotein C-III and very low-density lipoprotein secretion (44). Mutations in HNF4A affect insulin secretion and have been linked to maturity onset diabetes in the young (45). In the CRIC, the HNF4A SNP that is associated with higher CAC is not in LD with variants that have published associations with lower HDL-C and higher odds of T2DM. This may reflect differences in ethnic LD structure because HNF4A SNP associations with CAC in CRIC were detected in the AA subsample, whereas cardiometabolic findings to date for the HNF4A locus have been in non-AA samples. However, evidence for HNF4A association with clinical CHD, including within PROMIS, is lacking.
Many of the suggestive loci for CAC contain genes with known associations with cardiometabolic traits and pathways. Besides ATP2B1 and HNF4A, these include the adiponectin receptor ADIPOR2 (34); PPARGC1, which regulates PPARG, adipose, and lipids (46); FOXO3, a longevity gene linked to insulin pathway signaling (47); ACSL5, which regulates fatty acid metabolism; BCL2 (48) and BCAT2, recently found to associate with T2DM; and ABCA4, a gene for Stargardt retinal disease that also has suggestive association with HDL particle number and size (49). Whether any of these loci have causal roles in atherosclerosis and CHD remains to be determined. These results do suggest, however, that loci modulating cardiometabolic risks that are exacerbated in CKD might be revealed through the study of atherosclerosis in patients with CKD.
Our study has several strengths. This is the first systematic search for candidate genes for a coronary atherosclerosis trait in patients with CKD within the CRIC. The CRIC study is a rigorously designed, multicenter, National Institutes of Health–sponsored cohort study of CKD that includes almost equal numbers of EA and AA individuals and generates resources for subclinical atherosclerosis, multiple biomarkers of CKD and CHD risk, as well as incident CHD events and CKD progression (12,14). The CRIC study genotyped the IBC array in all eligible participants, in part to facilitate comparisons with other existing IBC datasets. Thus, we were able to extend CRIC study findings by leveraging independent resources with IBC CAC datasets, as well as a large GWAS study of MI.
First, the CRIC study sample size is relatively small for genetic studies of complex traits. Second, because of the exploratory focus of our analyses, we applied nonconservative statistical thresholds that did not meet criteria for genome-wide significance, and we did not attempt to perform Bonferroni correction for the full extent of multiple testing. Rather, our approach sought to identify a group of loci with suggestive evidence for CAC and CHD risk that warrant further study. Our initial findings do provide some support for the potential importance of several of these loci in cardiometabolic disease. Because our work tested many different outcome models across race and CAC phenotypes, we did not run additional models adjusting for traditional or novel (e.g., phosphate and FGF23 [50,51]) risk biomarkers for CHD in CKD. Future studies will focus on determining the role of intermediate factors in the genetic associations with CAC in CKD. We acknowledge the need for larger studies and targeted replications. Third, in using CKD as a setting to try to identify CHD loci of broader relevance, our CAC follow-up in PennCAC and AFCS was not CKD focused; the PROMIS study also was not in CKD patients and was focused on South Asians. This heterogeneity may have limited replication. In support of use of the PROMIS sample, however, most CHD loci have consistent associations with MI in European and South Asian samples (2,18). Finally, although not a direct measure, studies have shown that CAC provides a quantitative estimate of coronary atherosclerosis (52) and is a useful predictor of CHD, including in patients with CKD (8,9).
CKD, which imparts a high risk for CHD, may provide a setting for discovery of genes for heart disease. Using a CRIC study sample of patients with CKD, we identified several loci with suggestive evidence for CAC, some of which are also associated with MI in a general population sample. Our findings support the potential for discovery of novel pathways involved in CHD through focus on atherosclerosis traits in patients with CKD.
The authors thank the participants, investigators, and staff of the CRIC study for their time and commitment. The CRIC Principal Investigators are Lawrence J. Appel, MD, MPH, Harold I. Feldman, MD, MSCE, Alan S. Go, MD, Jiang He, MD, PhD, John W. Kusek, PhD, James P. Lash, MD, Akinlolu Ojo, MD, PhD, Mahboob Rahman, MD, and Raymond R. Townsend, MD.
For a supplemental methods section, figures, and tables, please see the online version of this article.
The first 2 authors contributed equally to this paper and the last 2 authors are joint senior authors of this paper.
The CRIC study is supported by cooperative agreements 5U01DK060990, 5U01DK060984, 5U01DK06102, 5U01DK061021, 5U01DK061028, 5U01DK60980, 5U01DK060963, and 5U01DK060902 from the National Institute of Diabetes and Digestive and Kidney Diseases and by grants UL1RR024134, UL1RR025005, M01RR16500, UL1RR024989, M01RR000042, UL1RR024986, UL1RR029879, RR05096, and UL1RR024131 from the National Institutes of Health (NIH). This work was supported by R01-DK071224 (to Dr. Reilly). The Amish Calcification Study was supported by National Institutes of Health Research Grants R01-HL69313, U01-HL72515, and R01-HL088119, and an American Heart Association Scientist Development Grant (0830146N). Partial funding was also provided by the Mid-Atlantic Nutrition and Obesity Research Center (P30 DK072488). Fieldwork in PROMIS was supported by grants available to investigators at the University of Cambridge and at the Center for Non-Communicable Diseases, Pakistan. Genotyping in PROMIS was supported by Wellcome Trust. Dr. Fischer is supported by a Department of Veterans Affairs Health Services Research and Development Service (Career Development Award). Dr. Foulkes is supported by R01-HL107196. PennCAC was supported by a Clinical and Translational Science Award (UL1RR024134) and a Diabetes and Endocrine Research Center (P20-DK 019525) award (both from the NIH to the University of Pennsylvania). Dr. Rosas has received grants from Abbott and Reata Laboratoriea. Dr. Danesh has received research funding from the British Heart Foundation, UK Medical Research Council, Wellcome Trust, U.S. National Institutes of Health, Fogarty Foundation, UK National Institute of Health Research, Cambridge, Biomedical Research Centre, Pfizer, and European Commission. Dr. Wright Jr. has received grant support funding and served on the advisory board for Medtronics. Dr. Reilly has received research grant support from GlaxoSmithKline and Merck Research Laboratories; and is supported by a Virginia Brown Fellowship for Aging and Stroke Research; by K24-HL107643, R01-DK090505, U01-HL108636, and R01-HL113147 from the NIH. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Abbreviations and Acronyms
- African ancestry
- coronary artery calcification
- coronary heart disease
- chronic kidney disease
- European ancestry
- estimated glomerular filtration rate
- genome-wide association study/studies
- high-density lipoprotein cholesterol
- linkage disequilibrium
- low-density lipoprotein
- minor allele frequency
- myocardial infarction
- odds ratio
- principal component
- single nucleotide polymorphism
- Received October 18, 2012.
- Revision received January 16, 2013.
- Accepted January 20, 2013.
- American College of Cardiology Foundation
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