Abstract
Objective
Interleukin-1B (IL1B) modulates C-reactive protein (CRP) expression. However, whether IL1B genetic variation is associated with CRP level is unknown. Further, obesity, a state of low-grade inflammation that influences cellular IL-1 functions may modify this association.
Methods and results
Study participants (N = 3289), 48% blacks and 52% whites, had CRP level measurements at year 7 and year 15 examinations as part of the CARDIA study. Ten tag single nucleotide polymorphisms (SNPs) that characterize common IL1B gene variation were genotyped. In SNP analysis, no significant associations with either level or change in time CRP were observed after multiple testing adjustments. However, global ILIB gene variation was associated with year 7 to year 15 change in CRP (global nominal p = 0.004, multiple testing corrected p = 0.048) among obese blacks. Compared to the commonest haplotype, a common haplotype that includes the SNP rs1143642 was associated with greater increases in CRP from year 7 to year 15 among obese blacks and whites while another common haplotype that includes the SNP rs3917356 was associated with decreased change in CRP from year 7 to year 15 among obese blacks. The rare alleles of ILIB SNPs, SNP 7114 (rs1143642) and SNP 3298 (rs3917356), were associated with greater increases and decreases in CRP from year 7 to year 15 among blacks, respectively, compared to their common variants.
Conclusion
IL1B genetic variation may have a role in CRP level regulation and this association may be modified by obesity.
Keywords: Interleukin-1B, Plasma C-reactive protein, Obesity, Genetic variation, Young adults
1. Introduction
Inflammation is related to the development and progression of atherosclerosis [1]. Candidate genes (and their variations) regulating C-reactive protein (CRP), a marker of systemic inflammation associated with cardiovascular diseases have not been fully described [2]. Interleukin-1Beta (IL1B) gene, part of a cluster of genes on chromosome 2 coding for a family of IL-1 proteins, has been shown to be an important modulator of inflammatory pathways, with potential involvement in the pathogenesis of atherosclerosis and other cardiovascular diseases [3].
Associations of IL1B gene single nucleotide polymor-phisms (SNP) with IL1beta levels [4] and cardiovascular diseases [5,6] have been documented. IL1B levels regulate plasma CRP directly through CRP gene regulation and indirectly through the production of inflammatory mediators such as IL-6 [7–13]. Findings from previous association studies of IL1B SNPs and CRP levels have been inconsistent [6,12–15]. These studies did not evaluate the associations of whole gene common IL1B variation with CRP levels, using multiple SNPs and haplotype analyses, and the study populations were typically clinically ill patients, rather than healthy individuals. Further, while the effect of IL1B on some cellular functions appears to depend on obesity [11], and obesity is strongly related to CRP levels [16], the effect of obesity on the association between IL1B genetic variation and CRP levels has not been examined. We evaluated the associations of common IL1B genetic variation with CRP levels and with 8-year change in CRP levels in a population of black and white young adults participating in the Coronary Artery Risk Development in Young Adults (CARDIA) study. Further, we examined whether these associations are modified by obesity.
2. Materials and methods
2.1. Study population
This study was conducted as part of an ancillary study to CARDIA, an NHLBI-funded multi-center cohort study. The Inflammation Genomics and Atherosclerosis Prevention (IGAP) ancillary study was initially designed to investigate associations of common patterns of variations in genes involved in inflammation and thrombosis with variations in CRP and fibrinogen levels. CARDIA study participants were 18–30-year-old black and white men and women recruited from four geographic locations by community-based sampling (Birmingham, AL, Chicago, IL, and Minneapolis, MN) and from a prepaid health plan (Oakland, CA) in 1985–1986. 5115 (51% of eligible) were enrolled in the study. Participants were contacted every 6 months and were re-examined at five follow-up examinations at year 2 (1987–1988), year 5 (1990–1991), year 7 (1992–1993), year 10 (1995–1996) and year 15 (2000–2001) [17]. A subset of CARDIA participants provided DNA samples at year 10 examinations (N = 3950) and 3600 attended both year 7 and year 15 examinations. Information on IL1B tag SNP genotypes was available for 3289 participants who attended both examinations.
2.2. Data collection
Information on socio-demographic characteristics, lifestyle, physiologic and metabolic risk factors, including body mass index (BMI), was assessed at each examination (Table 1). BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m2). CRP levels were measured in stored blood obtained from CARDIA participants during the year 7 and year 15 examinations using the Latex method, which used particle enhanced technology (BN II nephelometer, Dade Behring, Deerfield, Illinois).
Table 1.
Characteristics of CARDIA study population at year 15 (2000–2001)
| Characteristic | Blacks |
Whites |
||
|---|---|---|---|---|
| Men (N = 614) | Women (N = 895) | Men (N = 846) | Women (N = 934) | |
| Age (years) | 39.5 (3.7) | 39.6 (3.9) | 40.6 (3.3) | 40.6 (3.4) |
| BMI (kg/m2) | 29.0 (6.1) | 31.6 (7.9) | 27.7 (4.8) | 26.7 (6.8) |
| Waist circumference (cm) | 93.8 (14.3) | 90.3 (15.8) | 93.9 (12.4) | 81.6 (14.4) |
| SBP (mmHg) | 118.7 (15.7) | 116.0 (16.1) | 113.7 (12.8) | 106.3 (12.3) |
| DBP (mmHg) | 78.9 (12.5) | 75.5 (12.3) | 75.4 (10.1) | 69.5 (9.5) |
| Triglycerides (mg/dl) | 106.1 (66.9) | 83.3 (56.3) | 141.0 (148) | 94.5 (59.4) |
| Total cholesterol (mg/dl) | 185.1 (40.6) | 179.6 (33.2) | 191.2 (38.2) | 182.4 (31.9) |
| HDL cholesterol (mg/dl) | 47.6 (13.7) | 54.0 (13.6) | 43.0 (11.6) | 56.0 (14.8) |
| LDL cholesterol (mg/dl) | 115.6 (35.7) | 108.9 (31.1) | 121.4 (32.8) | 107.2 (29.1) |
| Fasting glucose (mg/dl) | 88.2 (17.7) | 86.9 (26.6) | 89.3 (18.6) | 82.6 (13.7) |
| Year 7 CRP (μg/ml) | 2.7 (6.2) | 4.7 (6.2) | 1.7 (2.6) | 2.6 (5.2) |
| Year 15 CRP (μg/ml) | 3.1 (5.1) | 4.8 (6.4) | 2.0 (4.7) | 2.8 (4.3) |
Notes. All values are mean (S.D.). Abbreviations: BMI = body mass index; SBP = systolic blood pressure; DBP = diastolic blood pressure; HDL = high-density lipoprotein; LDL = low-density lipoprotein.
2.3. SNP selection
TagSNPs were selected based on publicly available data generated by the SeattleSNPs Variation Discovery Resource (http://pga.gs.washington.edu). Briefly, SeattleSNPs performed resequencing of the IL1B gene region on 47 unrelated individuals; 24 of African American ancestry (from the Coriell AA100 panel) and 23 of European American ancestry (from the CEPH pedigrees) [17]. Detailed protocols for PCR and sequencing are available at the SeattleSNPs web site. Bins of common SNPs (minor allele frequency > 10%) were determined by grouping SNPs in linkage disequilibrium (LD) (r2 > 0.64) separately in each population using LDselect v1.0 [18]. One tagSNP from each bin was selected to capture the genetic variation in the two populations (Table 2).
Table 2.
Interleukin-1Beta (IL1B) tagSNPs and haplotypes in CARDIA
| TagSNPs | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SNP IDa | 302 | 2143 | 3298 | 4006 | 5277 | 7114 | 8234 | 8546 | 12885 | 15235 | Blacksb | Whitesb |
| MAF c | 11.3 | 46.6 | 27.9 | 27.1 | 12.8 | 21.7 | 14.0 | 34.0 | 16.4 | 40.9 | - | - |
| Blacks (N) | (1788) | (1772) | (1695) | (1753) | (1782) | (1775) | (1723) | (1781) | (1777) | (1786) | - | - |
| MAF c | 26.3 | 33.7 | 45.0 | 7.0 | 22.9 | 7.9 | 22.7 | 8.3 | 36.3 | 8.7 | - | - |
| Whites (N) | (1855) | (1885) | (1827) | (1910) | (1890) | (1904) | (1842) | (1913) | (1918) | (1908) | ||
|
Reference
Number |
rs1143625 | rs1143629 | rs3917356 | rs1143630 | rs1143634 | rs1143642 | rs1071676 | rs3917363 | rs3917368 | rs3917375 | ||
| Haplotype 1 | A | T | A | C | C | C | G | C | A | C | 14.9 | 33.6 |
| Haplotype 2 | G | C | G | C | C | C | G | C | G | C | 9.4 | 21.7 |
| Haplotype 3 | A | T | G | C | T | C | C | C | G | C | 4.5 | 18.6 |
| Haplotype 4 | A | C | G | A | C | T | G | T | G | G | 16.8 | 5.8 |
| Haplotype 5 | A | T | A | C | C | C | G | C | G | C | 6.2 | 8.0 |
| Haplotype 6 | A | T | G | C | C | C | G | C | G | C | 8.8 | - |
| Haplotype 7 | A | C | G | A | T | C | C | C | G | C | 5.0 | - |
| Haplotype 8 | A | T | A | C | C | C | G | T | G | G | 4.9 | - |
| Haplotype 9 | A | C | G | C | C | C | G | T | G | G | 4.6 | - |
Notes. Shaded squares represent minor alleles at each SNP position represented by the SNP identifier (SNP ID).
SNP IDs are locations with respect to nucleotide position in GenBank accession AY137079.
Haplotype frequencies calculated using weighed probabilities among blacks and whites.
SNP minor allele frequencies (MAF) and number genotyped (N) among blacks and whites.
2.4. Genotyping and haplotype inference
Genotyping of ten selected tagSNPs, 302 (rs1143625), 2143 (rs1143629), 3298 (rs3917356), 4006 (rs1143630), 5277 (rs1143634), 7114 (rs1143642), 8234 (rs1071676), 8546 (rs3917363), 12885 (rs3917368) and 15235 (rs3917375) was performed using TaqMan Assays By Design (ABI). SNP IDs represent locations of each tagSNP with respect to nucleotide position in GenBank accession AY137079. An algorithm based on a Bayesian method of inferring haplotypes was used to estimate haplotype frequencies in the sample, using Phase (v2.0) [19]. In subsequent regression models, uncertainty in haplotype estimation was handled by including probability estimates [20].
2.5. Data analysis
Allele frequencies were estimated by direct gene counting. Hardy Weinberg equilibrium was assessed, using a chi-square test. Raw CRP levels were right skewed, so log-transformed values were used. We examined the associations of genetic variation in IL1B with CRP, both cross-sectional year 7 and year 15 and year 7 to year 15 change in CRP. In addition, potential interaction of IL1B genetic variation and obesity on CRP levels and change were examined. In SNP analysis, separate models for whites and blacks were fit for each outcome variable (cross-sectional and change in ln(CRP)) that included terms for each SNP (additive effect of SNPs coded 0, 1 and 2 for none, one or two copies of the rare allele, respectively), sex, age, recruitment center, BMI change between year 7 and year 15 and year 7 ln(CRP) (the latter two covariates for the analysis of change in ln(CRP)). For each outcome, in race stratified models that included terms for sex, age and recruitment center, each haplotype was represented by an indicator variable marking whether a person had or did not have one or more copies. Indicators for eight haplotypes (haplotypes 2–9) among blacks and four haplotypes (haplotypes 2, 3, 4, and 5) among whites were included in each model along with an indicator for haplotype O, representing rare haplotypes (frequencies < 4.5%). Haplotype 1, the most common haplotype overall, was used as the referent. Effect modification by BMI (at the year 15 examination) was assessed using stratified analysis where the strata were defined by race and BMI in the non-obese range (BMI < 30 kg/m2) and obese range (BMI ≥ 30 kg/m2) and tests of interaction [21].
Exponentiated coefficients and their 95% confidence intervals from GEE models (for year 7 and year 15 ln(CRP) levels and log linear regression models (for the change from year 7 to year 15 ln(CRP) levels), denotes the contrast between presence of one copy of the rare allele (in SNP analysis) or one or more copy of each haplotype (in haplotype analysis) to the referent; the common allele or haplotype 1, respectively. These values correspond to percent decreases or increases in CRP associated with each copy of the rare allele or presence of one or more copy of each haplotype, compared to the referent group. p-Values of global tests of association correspond to associations of any haplotype with level or change in CRP. Statistical analyses were performed using STATA software version 8.0 (STATA, College Station, TX). Confidence intervals were calculated at the 1-alpha = 95% level. Adjustment for multiple testing was performed using the conservative Bonferroni correction of p-values. At the SNP level, adjustments were made using number of tests conducted (20) assessing each outcome. At the haplotype level, adjustments were made using the total number of all global haplotype tests (12).
3. Results
Characteristics of the study population at the year 15 examinations are presented in Table 1. Fifty-six percent were female and 46% were black. Blacks tended to have both higher BMI and mean CRP levels at year 7 and year 15, compared with whites. Female participants had generally higher CRP levels compared with their male counterparts.
All tag-SNPs selected were synonymous (Table 2). No significant departures from HWE were observed. Overall, minor allele frequency (MAF) differences were observed for various SNPs between blacks and whites. For example, MAF for SNPs 7114 (rs1143642), 15235 (rs3917375) and 8546 (rs3917363) were two- to fivefold higher among blacks compared to whites. Considerable genetic variation was observed among blacks, where nine haplotypes (inferred from tagSNPs) with frequencies above 4.5% accounted for 75% of the observed haplotypes. Among whites, only five haplotypes with frequencies above 4.5% accounted for 88% of the observed haplotypes. Although haplotype 4 was the most frequent among blacks, for consistency, haplotype 1, the most frequent haplotype overall and among whites, was chosen as a referent in subsequent analyses.
Results of race-specific GEE models fit to evaluate associations between specific IL1B SNPs and CRP (year 7 and year 15 ln(CRP)) as well as race specific regression models fit to evaluate associations between specific IL1B SNPs and change in years 7–15 ln(CRP) level are presented in Table 3. None of the SNPs were associated cross-sectionally with CRP levels in either race group. Among blacks, the rare allele of SNP 7114 (rs1143642) was associated with 13% increase in ln(CRP) levels between year 7 and year 15 (nominal p-value; 0.010) and the rare variant of SNP 3298 (rs3917356) was associated with a 9% decrease in CRP levels from year 7 to year 15 (nominal p-value = 0.030). However, these associations were not statistically significant, after correction for multiple testing. No significant associations were seen among whites.
Table 3.
IL1B SNPs and CRP levels among blacks and whites
| SNP position | CRP levela |
Change in CRP levelb |
||
|---|---|---|---|---|
| Estimate (95% CI) | p-Value | Estimate (95% CI) | p-Value | |
| Blacks | ||||
| 8234 (G/C) | 0.97 (0.68–1.10) | 0.676 | 0.94 (0.84–1.05) | 0.262 |
| 7114 (C/T) | 1.04 (0.94–1.15) | 0.468 | 1.13 (1.03–1.23) | 0.010 * |
| 2143 (T/C) | 0.99 (0.91–1.08) | 0.815 | 1.08 (1.00–1.16) | 0.053 |
| 12885 (G/A) | 1.02 (0.91–1.14) | 0.708 | 0.95 (0.86–1.04) | 0.255 |
| 3298 (G/A) | 1.01 (0.92–1.12) | 0.768 | 0.91 (0.83–0.99) | 0.030 * |
| 5277 (C/T) | 0.99 (0.88–1.13) | 0.934 | 0.78 (0.87–1.09) | 0.653 |
| 15235 (C/G) | 0.97 (0.89–1.05) | 0.459 | 1.07 (0.99–1.16) | 0.075 |
| 8546 (C/T) | 0.96 (0.88–1.05) | 0.359 | 1.06 (0.98–1.14) | 0.169 |
| 4006 (C/A) | 1.07 (0.97–1.18) | 0.193 | 1.07 (0.98–1.17) | 0.115 |
| 302 (A/G) | 0.93 (0.81–1.05) | 0.238 | 0.95 (0.85–1.07) | 0.396 |
| Whites | ||||
| 8234 (G/C) | 1.05 (0.96–1.15) | 0.299 | 0.97 (0.90–1.05) | 0.482 |
| 7114 (C/T) | 1.02 (0.89–1.17) | 0.747 | 0.96 (0.85–1.09) | 0.517 |
| 2143 (T/C) | 1.00 (0.93–1.08) | 0.976 | 1.01 (0.95–1.09) | 0.700 |
| 12885 (G/A) | 0.98 (0.91–1.05) | 0.537 | 1.01 (0.94–1.08) | 0.831 |
| 3298 (G/A) | 0.97 (0.90–1.04) | 0.366 | 1.03 (0.97–1.11) | 0.317 |
| 5277 (C/T) | 1.03 (0.94–1.13) | 0.486 | 0.96 (0.88–1.04) | 0.295 |
| 15235 (C/G) | 1.01 (0.89–1.15) | 0.822 | 0.98 (0.87–1.10) | 0.745 |
| 8546 (C/T) | 1.04 (0.91–1.18) | 0.600 | 1.00 (0.89–1.13) | 0.964 |
| 4006 (C/A) | 1.01 (0.88–1.17) | 0.867 | 1.00 (0.88–1.14) | 0.949 |
| 302 (A/G) | 1.00 (0.92–1.09) | 0.996 | 1.03 (0.95–1.11) | 0.445 |
Estimates are exponentiated model coefficients (of additive SNP models) and their 95% confidence intervals in GEE models (years 7 and 15) adjusted for age, sex and recruitment center.
Estimates are exponentiated model coefficients (year 7 to year 15 change in ln(CRP)) of additive SNP effects and their 95% confidence intervals in log linear regression models adjusted for age, sex, recruitment center, BMI difference between year 7 and year 15, and baseline (year 7) ln(CRP).
p-Value < 0.05.
The associations of IL1B gene haplotypes with CRP levels and change are presented in Tables 4 and 5, respectively. Overall, among both black and white participants, there was little evidence of global or haplotype specific associations with level or change in CRP levels in either race group. However, after stratifying the sample by BMI level, evidence for weak associations between IL1B haplotypes and change in ln(CRP) levels was present among obese participants. A global haplotype effect on change in ln(CRP) between year 7 and year 15 was significant among obese blacks (nominal p = 0.004, multiple testing corrected p = 0.048). Among obese blacks, haplotype 4, was associated with a 23% higher change in CRP between year 7 and year 15, compared to haplotype 1. In addition, haplotype 8 was associated with a 27% lower change in CRP between year 7 and year 15 among obese blacks. Among obese whites, haplotype 4 was associated with 33% higher change in CRP between year 7 and year 15, compared to haplotype 1. However, global test of associations of IL1B haplotypes with change over time in CRP levels among obese whites was not significant. Tests of haplotype and year 15 BMI interactions were not significant among blacks and whites (p-values ranging from 0.174 to 0.965).
Table 4.
IL1B haplotypes and CRP levels among blacks and whites
| Haplotypes | CRP levelsa |
|||||
|---|---|---|---|---|---|---|
| All participants |
Non-obese |
Obese |
||||
| Estimate (95% CI) | p-Value | Estimate (95% CI) | p-Value | Estimate (95% CI) | p-Value | |
| Blacks | N = 1433 | N = 788 | N = 645 | |||
| Haplotype 1 | Referent | Referent | Referent | Referent | Referent | Referent |
| Haplotype 2 | 0.98 (0.83–1.17) | 0.853 | 1.11 (0.86–1.12) | 0.422 | 0.94 (0.79–1.10) | 0.427 |
| Haplotype 3 | 1.02 (0.85–1.22) | 0.834 | 1.02 (0.79–1.31) | 0.892 | 0.94 (0.79–1.21) | 0.817 |
| Haplotype 4 | 1.09 (0.96–1.25) | 0.171 | 1.01 (0.85–1.20) | 0.933 | 1.16 (0.99–1.34) | 0.055 |
| Haplotype 5 | 1.08 (0.96–1.22) | 0.219 | 1.13 (0.95–1.35) | 0.152 | 0.96 (0.83–1.11) | 0.560 |
| Haplotype 6 | 1.07 (0.94–1.21) | 0.323 | 1.05 (0.88–1.25) | 0.587 | 0.99 (0.86–1.15) | 0.935 |
| Haplotype 7 | 0.97 (0.83–1.14) | 0.710 | 0.95 (0.77–1.16) | 0.593 | 1.03 (0.83–1.27) | 0.792 |
| Haplotype 8 | 0.98 (0.85–1.13) | 0.823 | 1.17 (0.98–1.40) | 0.083 | 0.80 (0.66–0.97) | 0.025 |
| Haplotype 9 | 0.88 (0.75–1.04) | 0.133 | 0.88 (0.72–1.08) | 0.229 | 0.95 (0.77–1.19) | 0.672 |
| Haplotype Ob | 0.95 (0.78–1.16) | 0.632 | 0.87 (0.67–1.13) | 0.293 | 0.88 (0.69–1.13) | 0.306 |
| Overall p-value | 0.538 | 0.621 | 0.215 | |||
| Whites | N = 1673 | N = 1263 | N = 410 | |||
| Haplotype 1 | Referent | Referent | Referent | Referent | Referent | Referent |
| Haplotype 2 | 1.01 (0.92–1.10) | 0.910 | 1.01 (0.92–1.10) | 0.913 | 1.06 (0.88–1.27) | 0.536 |
| Haplotype 3 | 1.05 (0.95–1.15) | 0.318 | 0.99 (0.90–1.09) | 0.877 | 1.20 (1.00–1.44) | 0.050 |
| Haplotype 4 | 1.07 (0.91–1.25) | 0.430 | 1.10 (0.92–1.29) | 0.278 | 1.08 (0.84–1.39) | 0.536 |
| Haplotype 5 | 0.98 (0.90–1.07) | 0.656 | 0.98 (0.90–1.08) | 0.718 | 1.01 (0.84–1.21) | 0.957 |
| Haplotype Ob | 1.07 (0.93–1.23) | 0.375 | 1.03 (0.90–1.18) | 0.677 | 1.20 (0.91–1.60) | 0.197 |
| Overall p-value | 0.825 | 0.846 | 0.415 | |||
Exponentiated beta coefficients (95% confidence intervals) of GEE model comparing association between having one or more copy of each haplotype with the referent (haplotype 1) adjusted for age, sex and center.
Haplotype O: haplotype that combines all haplotypes less than 4.5% frequency.
Table 5.
IL1B haplotypes and change in CRP levels among blacks and whites
| Haplotypes | Change in CRP levelsa |
|||||
|---|---|---|---|---|---|---|
| All participants | Non-obese | Obese | ||||
| Estimate (95% CI) | p-Value | Estimate (95% CI) | p-Value | Estimate (95% CI) | p-Value | |
| Blacks | N = 1433 | N = 788 | N = 645 | |||
| Haplotype 1 | Referent | Referent | Referent | Referent | Referent | Referent |
| Haplotype 2 | 0.99 (0.85–1.15) | 0.876 | 0.99 (0.80–1.22) | 0.926 | 1.04 (0.87–1.24) | 0.692 |
| Haplotype 3 | 1.08 (0.90–1.29) | 0.416 | 1.03 (0.80–1.32) | 0.837 | 1.14 (0.93–1.41) | 0.207 |
| Haplotype 4 | 1.12 (0.99–1.27) | 0.084 | 1.01 (0.85–1.21) | 0.889 | 1.23 (1.05–1.44) | 0.010 |
| Haplotype 5 | 1.10 (0.93–1.29) | 0.276 | 1.02 (0.81–1.29) | 0.850 | 1.15 (0.94–1.40) | 0.174 |
| Haplotype 6 | 1.03 (0.89–1.20) | 0.695 | 0.96 (0.78–1.19) | 0.734 | 1.11 (0.93–1.32) | 0.239 |
| Haplotype 7 | 1.02 (0.86–1.20) | 0.827 | 1.03 (0.83–1.28) | 0.769 | 1.04 (0.82–1.30) | 0.758 |
| Haplotype 8 | 0.89 (0.75–1.06) | 0.187 | 1.04 (0.82–1.32) | 0.747 | 0.73 (0.58–0.93) | 0.010 |
| Haplotype 9 | 1.02 (0.85–1.21) | 0.865 | 1.07 (0.84–1.36) | 0.575 | 0.95 (0.75–1.20) | 0.658 |
| Haplotype Ob | 1.16 (0.92–1.46) | 0.208 | 1.00 (0.69–1.45) | 0.986 | 1.18 (0.93–1.48) | 0.166 |
| Overall p-value | 0.429 | 1.000 | 0.004 (0.048) ‡ | |||
| Whites | N = 1673 | N = 1263 | N = 410 | |||
| Haplotype 1 | Referent | Referent | Referent | Referent | Referent | Referent |
| Haplotype 2 | 1.09 (0.98–1.21) | 0.094 | 1.08 (0.95–1.21) | 0.181 | 1.09 (0.91–1.31) | 0.336 |
| Haplotype 3 | 1.03 (0.82–1.14) | 0.621 | 1.00 (0.89–1.13) | 0.988 | 1.06 (0.88–1.28) | 0.527 |
| Haplotype 4 | 1.09 (0.92–1.28) | 0.317 | 1.05 (0.87–1.26) | 0.608 | 1.33 (1.02–1.74) | 0.034 |
| Haplotype 5 | 1.07 (0.94–1.23) | 0.285 | 1.06 (0.91–1.24) | 0.439 | 1.03 (0.83–1.26) | 0.817 |
| Haplotype Ob | 1.04 (0.82–1.32) | 0.739 | 1.05 (0.79–1.39) | 0.759 | 1.19 (0.92–1.53) | 0.183 |
| Overall p-value | 0.528 | 0.775 | 0.316 | |||
Exponentiated beta coefficients (95% confidence intervals) of log linear regression model (for change in CRP between year 7 and year 15) comparing association between having one or more copy of each haplotype with the referent (haplotype 1) adjusted for age, sex, center and baseline CRP (year 7 ln(CRP)).
Haplotype O: haplotype that combines all haplotypes less than 4% frequencies.
p-Value after Bonferroni correction (for 12 global haplotype tests).
4. Discussion
Overall, we did not find significant independent associations of common IL1B genetic variation with CRP levels in the CARDIA population. However, our data suggests a weak to modest association between IL1B genetic variation and change in CRP levels among obese young adults. Obesity seems to modify the association of haplotype 4 and haplotype 8 with both the level and change in CRP among blacks and the association of haplotype 4 with change in CRP among whites. Although the p-value for the global haplotype and change in CRP level associations among obese blacks were marginal after correction for multiple testing and interaction tests were not significant, it should be noted that the Bonferroni correction is conservative and the study may not be powered enough for the interaction tests. Furthermore, the observations that a SNP that marks haplotype 4, SNP 7114 (rs1143642) also had similar suggestive individual association with increases in CRP while haplotype 8 includes a SNP, SNP 3298 (rs3917356) that also had suggestive individual associations with decreases in CRP strengthen the evidence for a possible association.
The associations of IL1B genetic variation and IL1B levels with CRP levels, inflammation and atherosclerosis has been a subject of numerous experimental and population studies [14,5,22–25]. To date, these studies have included only three IL1B SNPs [22–25], denoted in the literature as −511 C > T, +3953 C > T and +3954 C > T. Determining the corresponding SNPs in the SeattleSNPs resource is imperfect because only for the −511 C > T SNP has the genomic context been published [14]. Assuming that +3954 is relative to the same landmark and that +3953 is the same SNP as +3954, −511 corresponds to SeattleSNPs position number 794 while +3954 corresponds to SeattleSNPs position number 5277. SNP 794 was not in the final set of tag-SNPs that were typed, nor was it in bins tagged by the selected tag-SNPs.
Among participants with or without angiographic evidence of coronary artery disease, individuals with IL1B +3954 T/T genotype had two- to threefold higher CRP levels, as compared with individuals with IL1B +3954 C/C genotype (p < 0.001) after adjustment for gender, age and smoking [24]. In a cross-sectional analysis, Latkovskis et al. reported that among patients with CAD, carrier status for IL1B +3954 T allele correlated with higher log-CRP values (p < 0.01) [25]. However, other studies of genetic variations in the IL1B gene and CRP levels have not demonstrated such associations [26]. Ray et al. reported no significant association between IL1B −511, IL1B +3953 and absolute levels or change between 24 and 48 h of CRP among patients with acute coronary syndrome [26]. In our study, SNP 5277 (corresponding to SNPs +3953 and +3954 in previous studies) was not associated with CRP level. Observed inconsistencies between study findings could result from differences in study population that relate to population stratification or disease/health status. Several other potential IL1B SNPs among blacks and whites were identified in our study that should be investigated in relation to CRP level variations in other populations.
Based on the role of IL1B gene on adipose tissue regulation, investigators have explored potential interrelationships between obesity, IL1B genotype and CRP levels [14,15]. Um et al. demonstrated that frequency of the IL1B +3953 T allele was significantly decreased in the overweight group (BMI 25.0–29.9) compared to frequency in the lean group (BMI < 25.0) (OR: 0.20 95% CI 0.07–0.61; p-value = 0.004) and this was related to significant differences in serum IL1B concentration among obese and overweight as compared to lean groups [14]. Such IL1B SNP 5277 allele frequency differences were not observed among groups classified by BMI in our study population. Markovic et al. found that among healthy non-smoking men, CRP and triglyceride levels were positively correlated in participants with IL1B−511TT genotype and not in participants with IL1B −511CC genotype, implying that possession of a genotype that may exacerbate inflammation may tighten the association between triglycerides and CRP levels [15]. It may be hypothesized that the association between genotype (IL1B) and inflammation (CRP level) may vary by the presence or absence of another risk factor (obesity).
Several mechanisms have been postulated to account for the association between IL-1 and CRP levels [7–10]. Besides its indirect role in augmentation of production of other interleukins, such as IL-6, IL-1 directly induces CRP expression at the transcriptional level through two overlapping response elements; binding sites for CCAAT-box/enhancerbinding protein-B (C/EBP-B) and p50-nuclear factor-κB (p50-NFκB) [9,10]. Interestingly, it has been proposed that fibrates, which are PPAR-α activators, reduce formation of the complex C/EBP-B-p50-NFκB, which leads to suppression of CRP biosynthesis, suggesting that the well-known effect of fibrates on CRP levels might involve IL-1 [10].
IL1B is involved in several obesity-related functions such as adipose tissue differentiation, insulin level regulation and lipase activity [11]. Obesity has been characterized as a state of chronic low-grade inflammation with raised inflammatory markers and increased expression of inflammation-related adipokines that is characteristically different from the one observed in non-obese state [27,28]. Since IL1B has functions that are closely related to obesity and the difference in background level of inflammation among obese and non-obese individuals may alter associations of IL1B with both level and change in time CRP, similar to IL6 and CRP asso-ciations [28], obesity can play a potential effect modifier role in associations of IL1B genetic variations with CRP levels and change in CRP levels.
Potential limitations of our study deserve mention. Recombination can result in differences in linkage disequilibrium (LD) with unidentified loci. The differences in the MAF of some SNPs among blacks (12885, 5277, 3298, 302 and 8234) and whites (7114, 15235, 8546 and 4006) influences the statistical power to detect associations for the same SNP among blacks and whites and may account, at least in part, for the differences in SNP associations observed among the two populations. Most of the suggested potential associations related IL1B genetic variations to change in CRP levels, rather than cross-sectional CRP levels. Based on supporting evidence from previous investigations, it may be postulated that change in (or sub-acute or acute phase response) CRP levels may be dependent on other inflammatory genes such as IL-1 or IL-6 while baseline (or cross-sectional) CRP level may be highly regulated by the CRP gene [2,17,29].
Both the modest 8-year CRP change over time in this healthy population and the likelihood that IL1B genetic variation accounts for only a proportion of this change may have further limited the power of our study to detect associations. Evaluation at a later time and/or simultaneous consideration of variation in other genes, such as CRP and/or IL-6 could prove useful. Further investigation on identified SNPs and haplotypes in other populations as well as consideration of other IL1-related genes (such as IL-1A and IL-1RA) accompanied with functional studies involving IL1B genetic variation and IL1B protein levels are warranted.
Evaluating associations of IL1B variations with CRP levels will enhance the effort to understand associations of IL1beta levels with CRP levels. Since most common genetic variations are determined at conception, the direction of relationships (potentially cause and effect) between IL1B variations (and by inference IL1beta levels) and CRP levels are clear in this type of study. Further, associations of IL1B variations and CRP levels are less likely to be confounded (less noise) by other cardiovascular risk factors than associations of IL1beta levels and CRP levels.
In summary, results from this study suggest that IL1B genetic variation may have a role in the regulation of CRP levels; and the association of IL1B genetic variation with CRP levels may potentially be modified by obesity among blacks. Further studies are needed to advance understanding of the regulation of CRP levels and the interaction of genetic and environmental factors that influence the change in CRP levels over time.
Acknowledgments
This study was conducted as part of IGAP (Inflammation Genomics and Atherosclerosis Prevention), an ancillary study to CARDIA. The study was supported by the following grants from NHLBI; Cardiovascular Training Grant (1-T32-HL07902), CARDIA (N01 HC95095), IGAP (R01 HL071017) and SeattleSNPs (U01 HL66682 and U01 HL66642); additional assistance was provided by NIEHS grant P30ES07033.
References
- [1].Ross R. Atherosclerosis—an inflammatory disease. N Engl J Med. 1999;340:115–6. doi: 10.1056/NEJM199901143400207. [DOI] [PubMed] [Google Scholar]
- [2].Kluft C, de Maat MP. Genetics of C-reactive protein: new possibilities and complications. Arterioscler Thromb Vasc Biol. 2003;23:1956–9. doi: 10.1161/01.ATV.0000100113.47260.EB. [DOI] [PubMed] [Google Scholar]
- [3].Maruyama Y, Nordfors L, Stenvinkel P, et al. Interleukin-1 gene cluster polymorphisms are associated with nutritional status and inflammation in patients with end-stage renal disease. Blood Purif. 2005;23:384–93. doi: 10.1159/000087196. [DOI] [PubMed] [Google Scholar]
- [4].Hall SK, Perregaux DG, Gabel CA, et al. Correlation of polymorphic variation in the promoter region of the interleukin-1 beta gene with secretion of interleukin-1 beta protein. Arthritis Rheum. 2004;50:1976–83. doi: 10.1002/art.20310. [DOI] [PubMed] [Google Scholar]
- [5].Simon AD, Yazdani S, Wang W, Schwartz A, Rabbani LE. Circulating levels of IL1Beta, a prothrombotic cytokine, are elevated in unstable angina versus stable angina. J Thromb Thrombolysis. 2000;9:217–22. doi: 10.1023/a:1018758409934. [DOI] [PubMed] [Google Scholar]
- [6].Iacoviello L, et al. IGIGI Investigators Polymorphisms of the interleukin-1beta gene affect the risk of myocardial infarction and ischemic stroke at young age and the response of mononuclear cells to stimulation in vitro. Arterioscler Thromb Vasc Biol. 2005;25:222–7. doi: 10.1161/01.ATV.0000150039.60906.02. [DOI] [PubMed] [Google Scholar]
- [7].Galea J, Armstrong J, Gadsdon P, et al. Interleukin-1 beta in coronary arteries of patients with ischemic heart disease. Arterioscler Thromb Vasc Biol. 1996;16:1000–6. doi: 10.1161/01.atv.16.8.1000. [DOI] [PubMed] [Google Scholar]
- [8].Lytvyn OI, Kopitsa MP, Petyunina OV. Interaction between inflammation and thrombosis in acute coronary syndrome. Kardiol Pol. 2004;61:110–6. [PubMed] [Google Scholar]
- [9].Dejana E, Breviario F, Erroi A, et al. Modulation of endothelial cell functions by different molecular species of interleukin 1. Blood. 1987;69:695–9. [PubMed] [Google Scholar]
- [10].Kleemann R, Gervois PP, Verschuren L, et al. Fibrates down-regulate IL-1-stimulated C-reactive protein gene expression in hepatocytes by reducing nuclear p50-NFkappa B-C/EBP-beta complex formation. Blood. 2003;101:545–51. doi: 10.1182/blood-2002-06-1762. [DOI] [PubMed] [Google Scholar]
- [11].Matsuki T, Horai R, Sudo K, Iwakura Y. IL-1 plays an important role in lipid metabolism by regulating insulin levels under physiological conditions. J Exp Med. 2003;198:877–88. doi: 10.1084/jem.20030299. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [12].Licastro F, Chiappelli M, Caldarera CM, et al. The concomitant presence of polymorphic alleles of interleukin-1beta, interleukin-6 and apolipoprotein E is associated with an increased risk of myocardial infarction in elderly men. Results from a pilot study. Mech Ageing Dev. 2004;125:575–9. doi: 10.1016/j.mad.2004.06.008. [DOI] [PubMed] [Google Scholar]
- [13].Balbay Y, Tikiz H, Baptiste RJ, et al. Circulating interleukin-1 beta, interleukin-6, tumor necrosis factor-alpha, and soluble ICAM-1 in patients with chronic stable angina and myocardial infarction. Angiology. 2001;52:109–14. doi: 10.1177/000331970105200204. [DOI] [PubMed] [Google Scholar]
- [14].Um JY, Chung HS, Song MY, Shin HD, Kim HM. Association of interleukin-1beta gene polymorphism with body mass index in women. Clin Chem. 2004;50:647–50. doi: 10.1373/clinchem.2003.025858. [DOI] [PubMed] [Google Scholar]
- [15].Markovic O, O’Reilly G, Fussell HM, et al. Role of single nucleotide polymorphisms of pro-inflammatory cytokine genes in the relationship between serum lipids and inflammatory parameters, and the lipid-lowering effect of fish oil in healthy males. Clin Nutr. 2004;23:1084–95. doi: 10.1016/j.clnu.2004.02.002. [DOI] [PubMed] [Google Scholar]
- [16].Bonora E. The metabolic syndrome and cardiovascular disease. Ann Med. 2006;38:64–80. doi: 10.1080/07853890500401234. [DOI] [PubMed] [Google Scholar]
- [17].Carlson CS, Aldred SF, Lee PK, et al. Polymorphisms within the C-reactive protein (CRP) promoter region are associated with plasma CRP levels. Am J Hum Genet. 2005;77:64–77. doi: 10.1086/431366. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [18].Carlson CS, Eberle MA, Rieder MJ, et al. Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. Am J Hum Genet. 2004;74:106–20. doi: 10.1086/381000. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Stephens M, Donnelly P. A comparison of Bayesian methods for haplotype reconstruction from population genotype data. Am J Hum Genet. 2003;73:1162–9. doi: 10.1086/379378. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [20].French B, Lumley T, Monks SA, et al. Simple estimates of haplo-type relative risks in case-control data. Genet Epidemiol. 2006;30:485–94. doi: 10.1002/gepi.20161. [DOI] [PubMed] [Google Scholar]
- [21].Ravussin E, Swinburn BA. Pathophysiology of obesity. Lancet. 1992;340:404–8. doi: 10.1016/0140-6736(92)91480-v. [DOI] [PubMed] [Google Scholar]
- [22].Momiyama Y, Hirano R, Taniguchi H, Nakamura H, Ohsuzu F. Effects of interleukin-1 gene polymorphisms on the development of coronary artery disease associated with Chlamydia pneumoniae infection. J Am Coll Cardiol. 2001;38:712–7. doi: 10.1016/s0735-1097(01)01438-3. [DOI] [PubMed] [Google Scholar]
- [23].Eklund C, Jahan F, Pessi T, Lehtimaki T, Hurme M. Interleukin 1B gene polymorphism is associated with baseline C-reactive protein levels in healthy individuals. Eur Cytokine Netw. 2003;14:168–71. [PubMed] [Google Scholar]
- [24].Berger P, McConnell JP, Nunn M, et al. C-reactive protein levels are influenced by common IL-1 gene variations. Cytokine. 2002;17:171–4. doi: 10.1006/cyto.2001.0974. [DOI] [PubMed] [Google Scholar]
- [25].Latkovskis G, Licis N, Kalnins U. C-reactive protein levels and common polymorphisms of the interleukin-1 gene cluster and interleukin-6 gene in patients with coronary heart disease. Eur J Immunogenet. 2004;31:207–13. doi: 10.1111/j.1365-2370.2004.00476.x. [DOI] [PubMed] [Google Scholar]
- [26].Ray KK, Camp NJ, Bennett CE, Francis SE, Crossman DC. Genetic variation at the interleukin-1 locus is a determinant of changes in soluble endothelial factors in patients with acute coronary syndromes. Clin Sci (Lond) 2002;103:303–10. doi: 10.1042/cs1030303. [DOI] [PubMed] [Google Scholar]
- [27].Trayhurn P, Wood IS. Signalling role of adipose tissue: adipokines and inflammation in obesity. Biochem Soc Trans. 2005;33:1078–81. doi: 10.1042/BST0331078. [DOI] [PubMed] [Google Scholar]
- [28].Yudkin JS, Kumari M, Humphries SE, Mohamed-Ali V. Inflammation, obesity, stress and coronary heart disease: is interleukin-6 the link? Atherosclerosis. 2000;148:209–14. doi: 10.1016/s0021-9150(99)00463-3. [DOI] [PubMed] [Google Scholar]
- [29].Szalai AJ, Wu J, Lange EM, et al. Single-nucleotide polymorphisms in the C-reactive protein (CRP) gene promoter that affect transcription factor binding, alter transcriptional activity, and associate with differences in baseline serum CRP level. J Mol Med. 2005;83:440–7. doi: 10.1007/s00109-005-0658-0. [DOI] [PubMed] [Google Scholar]