Circulating Levels of Interleukin 1-Receptor Antagonist and Risk of Cardiovascular Disease
Interleukin (IL)-1β represents a key cytokine in the development of cardiovascular disease (CVD). IL-1β is counter-regulated by IL-1 receptor antagonist (IL-1RA), an endogenous inhibitor. This study aimed to identify population-based studies on circulating IL-1RA and incident CVD in a systematic review, estimate the association between IL-1RA and incident CVD in a meta-analysis, and to test whether the association between IL-1RA and incident CVD is explained by other inflammation-related biomarkers in the MONICA/KORA Augsburg case–cohort study (Multinational Monitoring of Trends and Determinants in Cardiovascular Disease/Cooperative Health Research in the Region of Augsburg).
Approach and Results—
We performed a systematic literature search and identified 5 cohort studies on IL-1RA and incident CVD in addition to the MONICA/KORA Augsburg case–cohort study for a meta-analysis based on a total of 1855 CVD cases and 18 745 noncases with follow-up times between 5 and 16 years. The pooled standardized hazard ratio (95% confidence interval) for incident CVD was 1.11 (1.06–1.17) after adjustment for age, sex, anthropometric, metabolic, and lifestyle factors (P<0.0001). There was no heterogeneity in effect sizes (I2=0%; P=0.88). More detailed analyses in the MONICA/KORA study showed that the excess risk for CVD was attenuated by ≥10% after additional separate adjustment for serum levels of high-sensitivity C-reactive protein, IL-6, myeloperoxidase, soluble E-selectin, or soluble intercellular adhesion molecule-1.
Serum IL-1RA levels were positively associated with risk of CVD after adjustment for multiple confounders in a meta-analysis of 6 population-based cohorts. This association may at least partially reflect a response to triggers inducing subclinical inflammation, oxidative stress, and endothelial activation.
Proinflammatory processes are important pathophysiological mechanisms involved in the development of cardiovascular disease (CVD).1 Interleukin 1β (IL-1β) plays a crucial role in this context because it represents one of the most potent inducers of innate immunity and acts as an upstream regulator in the inflammatory cascade.2,3 Experimental and clinical studies have demonstrated that IL-1β contributes to the risk of atherosclerosis and cardiovascular events.4 Thus, IL-1β has also emerged as a promising therapeutic target, for example, in the CANTOS (Canakinumab Anti-Inflammatory Thrombosis Outcome Study), that uses a neutralizing monoclonal anti-IL-1β antibody to reduce the risk of recurrent cardiovascular events.3,5
Interleukin-1 receptor antagonist (IL-1RA) counter-regulates IL-1β as an endogenous inhibitor in vivo by blocking the binding site for IL-1β at the IL-1 receptor I.2 IL-1β induces the production of IL-1RA to contain immune activation, and the balance between IL-1RA and IL-1β has been implicated as an important control mechanism to prevent inflammation-related tissue damage and reduce cardiometabolic risk.5,6
The investigation of IL-1β as a potential biomarker for the prediction of CVD in the general population is complicated by the extremely low systemic levels of this cytokine that preclude reliable quantification.7 In contrast, circulating levels of IL-1RA are measurable with great precision.7,8 Systemic IL-1RA levels are elevated in individuals with cardiometabolic risk factors, such as obesity, insulin resistance, and type 2 diabetes mellitus,9–12 and have been interpreted to reflect higher IL-1β activity in these individuals.10
There is preliminary evidence from 2 population-based cohorts using multimarker approaches that higher IL-1RA levels may also be associated with higher risk of CVD, whereas 2 others failed to observe such an association.13,14 Therefore, it is not clear yet whether circulating IL-1RA is associated with CVD risk independent of established cardiovascular risk factors, whether IL-1RA could be used as a novel biomarker to improve the prediction of CVD, and what the underlying mechanisms are that mediate the upregulation of IL-1RA in the context of cardiometabolic risk.
This study aims (1) to identify population-based studies on circulating IL-1RA and incident CVD by a systematic literature search, (2) to quantify the association between IL-1RA and incident CVD in a meta-analysis, and (3) to identify other inflammation-related biomarkers, which could partially explain this association based on data from the MONICA (Multinational Monitoring of Trends and Determinants in Cardiovascular Disease)/KORA (Cooperative Health Research in the Region of Augsburg) Augsburg case–cohort study.
Materials and Methods
The study is based on data from the MONICA/KORA Augsburg case–cohort study and on additional population-based cohort studies identified in a systematic review. The primary outcome was defined as incident CVD (fatal and nonfatal myocardial infarction, sudden death, cardiovascular mortality; however, studies with combined end points, including unstable angina, coronary revascularization, or ischemic stroke, were also eligible). For the meta-analysis, we used inverse-variance fixed effect and DerSimonian–Laird random effects modeling based on study-level data; the DerSimonian–Laird estimator was used to test for heterogeneity. We visually inspected the funnel plot and performed Egger’s regression test of funnel plot asymmetry to check for possible publication bias. For all analyses, a P value <0.05 was considered to be statistically significant. Materials and Methods are available in more details in the online-only Data Supplement.
Meta-Analysis of the Association Between IL-1RA and Incident CVD
The literature search for the systematic review using MEDLINE and Embase identified 4202 publications after removal of duplicates (Figure 1). We assessed 21 full-text articles for eligibility and excluded 16 articles because no IL-1RA data were reported and 3 articles15–17 because the study samples were not population based. The 2 publications that were eligible13,14 contained data from 3 cohort studies, the FINRISK 1997, the Belfast PRIME (Prospective Epidemiological Study of Myocardial Infarction) Men, and the Rotterdam studies. We contacted the authors of both publications13,14 to adapt the selection of confounding variables to the MONICA/KORA analysis, to reanalyze data based on extended follow-up times, if possible, and to obtain additional unpublished data that fulfilled our inclusion criteria. Through this contact, we identified the Finnish HEALTH 2000 and FINRISK 2007 surveys (data unpublished) as additional eligible studies for the meta-analysis. Therefore, the meta-analysis included the MONICA/KORA Augsburg study data (see Figure I in the online-only Data Supplement for study design, Table I in the online-only Data Supplement for baseline characteristics, and Table II and Figure II in the online-only Data Supplement for associations between IL-1RA and CVD risk), data from the 3 cohort studies identified through the systematic review (FINRISK 1997, Belfast PRIME Men cohort, Rotterdam Study; updated analyses replaced the data published previously13,14), and novel unpublished data from a further 2 studies identified through personal contact as result of the systematic review (HEALTH 2000 and FINRISK 2007; Figure 1).
Material and Methods and Table III in the online-only Data Supplement provide an overview of all 6 study cohorts, including information on sample sizes, end point definitions, follow-up times, and standardized hazard ratios (HRs), for the association between IL-1RA and incident CVD that were used for the meta-analysis. Numbers of cases ranged between 65 and 803 per study, while total sample sizes were between 839 and 6393. Follow-up times ranged from 4.9 to 16.0 years. Standardized HRs were between 1.057 and 1.165 based on the same adjustment strategy for multiple confounders (Table III in the online-only Data Supplement) and showed significantly increased risk of CVD in 3 cohorts.
The meta-analyzed estimate is based on a total of 1855 cases and 18 745 noncases. The pooled standardized and adjusted HR for incident CVD was 1.11 (95% confidence interval [CI], 1.06–1.17) in both fixed effect and random effects models (P<0.0001; Figure 2). There was no evidence for heterogeneity with I2 (percentage of variance attributable to study heterogeneity) of 0% (95% CI, 0%–0%; P=0.88). Egger’s linear regression test for publication bias yielded no evidence of funnel plot asymmetry (P=0.67; Figure III in the online-only Data Supplement).
No Improvement of CVD Risk Models by IL-1RA
Clinical risk models based on the covariates listed in Table III in the online-only Data Supplement for each study yielded areas under the receiver-operating characteristic curve (AUC) between 0.6760 and 0.8639 (Table). When IL-1RA levels were added to the respective risk models, the AUC values were not significantly improved, with differences in AUC between 0.0005 and 0.0049 across the 6 cohorts.
|Cohort||AUC for Clinical Model||AUC for Clinical Model+IL-1RA||Difference (95% CI)|
|MONICA/KORA||0.8210||0.8224||0.0014 (−0.0018 to 0.0034)|
|FINRISK 1997||0.8562||0.8564||0.0003 (−0.0010 to 0.0015)|
|FINRISK 2007||0.8567||0.8576||0.0008 (−0.0013 to 0.0030)|
|HEALTH 2000||0.8639||0.8644||0.0005 (−0.0008 to 0.0017)|
|Belfast PRIME Men||0.6760||0.6796||0.0035 (−0.0017 to 0.0126)|
|Rotterdam||0.7003||0.7051||0.0049 (−0.0083 to 0.0181)|
Impact of Inflammation on the Association Between IL-1RA and CVD Risk
We used a panel of 15 additional biomarkers of subclinical inflammation in the MONICA/KORA case–cohort study to test the hypothesis that the association between IL-1RA and incident CVD may be partially explained by these biomarkers. In models that were adjusted for all aforementioned covariates (model 2) and one of each of these biomarkers, the attenuation of excess risk by IL-1RA was >10% after additional adjustment for high-sensitivity C-reactive protein, IL-6, myeloperoxidase, soluble intercellular adhesion molecule-1, or soluble (s)E-selectin, whereas the HRs based on the additional adjustment for the other biomarkers were rather similar to the HRs in model 2 (Figure 3).
This study has 3 main findings: (1) CVD risk increased by 11% per 1-SD increase in serum IL-1RA based on data from >20 000 study participants from 6 population-based cohort studies; (2) this increased CVD risk did not translate into an improved risk prediction over and above classical risk factors as assessed by AUC; and (3) the association between IL-1RA and CVD risk was partially explained by biomarkers of subclinical inflammation, oxidative stress, and cell adhesion.
This study advances our knowledge because the combination of 3 previously published studies (FINRISK 1997, Belfast PRIME Men cohort, and Rotterdam Study)13,14 and novel data from the MONICA/KORA, FINRISK 2007, and HEALTH 2000 cohorts allowed the assessment of CVD risk based on circulating IL-1RA concentrations in 1855 cases within a total sample of 20 600 individuals. Our meta-analysis yielded a pooled HR of 1.11 (95% CI, 1.06–1.17) based on 6 studies that used the same adjustment strategy for the main cardiovascular risk factors (ie, age, sex, body mass index, blood pressure, total and high-density lipoprotein cholesterol levels, smoking, and prevalent diabetes mellitus). Thus, the effect size was similar to the standardized HR that has been reported for the association between lipoprotein-associated phospholipase A2 and incident CVD,18 but slightly lower than that for total cholesterol or triglycerides and CVD risk.19
We identified 3 further relevant studies in our systematic review that we had to exclude because the samples were not population based. The first study was a nested case–control study for 42 candidate biomarkers in individuals with type 2 diabetes mellitus.16 The sample comprised 1123 cases with incident CVD and 1187 controls from 5 European centers and revealed an odds ratio of 1.13 (95% CI, 1.02–1.25) per SD of IL-1RA.16 The second study reported a standardized HR (95% CI) of 1.14 (1.03–1.27) for incident myocardial infarction in 3199 study participants with a history of coronary artery disease.17 The third study investigated 73 consecutive patients undergoing percutaneous coronary intervention.15 After 18 months of follow-up, IL-1RA levels in the highest quarter were associated with a higher risk of major adverse cardiac events compared with IL-1RA levels in the lowest quarter (19% versus 0%; adjusted P=0.032). Taken together, the 3 studies suggest that the positive association between IL-1RA and CVD risk may be similar in the general population compared with that in patients with type 2 diabetes mellitus or preexisting CVD.
The main finding from this meta-analysis also extends observations from the Interleukin-1 Genetics Consortium that investigated the association between gene variants upstream of IL1RN, the gene encoding IL-1RA, and coronary heart disease.20 That study found that each minor allele in a gene score comprising 2 common IL1RN gene variants was associated with an increase in circulating IL-1RA of 0.22 SD and with an odds ratio (95% CI) for coronary heart disease of 1.03 (1.02–1.04). Therefore, the odds ratio per 1 SD of genetically upregulated IL-1RA can be calculated as 1.14 (95% CI, 1.09–1.18), which is similar to our pooled estimate.
Despite the agreement of this meta-analysis of prospective, population-based studies, the nested case–control study in individuals with type 2 diabetes mellitus, and the aforementioned genetic data regarding the effect size, the interpretation of the positive association between circulating IL-1RA and incident CVD is less straightforward.21 First, higher IL-1RA levels in the circulation could have direct detrimental effects and contribute causally to the development of atherosclerosis and CVD. Alternatively, higher IL-1RA levels could reflect a response to proatherogenic processes mediated by IL-1β and other proinflammatory biomarkers and, therefore, serve as an indirect indicator of cardiovascular risk.
The first explanation is mainly supported by the aforementioned genetic study,20 but it is currently not possible to rule out pleiotropic effects of these IL-1RN gene variants on other phenotypes beyond IL-1RA levels that might be responsible for the positive association. The same study also demonstrated increased serum lipid levels (LDL and total cholesterol and triglycerides) associated with the IL-1RA-raising alleles, which could explain 20% to 40% of the excess cardiovascular risk. Importantly, all cohorts that were meta-analyzed in our study adjusted for cholesterol levels, so that the pooled effect estimate was corrected for this potential mediator.
The second explanation that sees IL-1RA as response to proinflammatory triggers is supported by the observation that IL-1RA has no agonist activity on its own and does not induce IL-1 receptor I–mediated signaling in humans at levels that are 1 000 000-fold greater than those of IL-1α or IL-1β.2 In addition, experimental studies have demonstrated that IL-1RA deficiency fueled arterial inflammation and atherosclerosis,22–24 whereas IL-1RA treatment was atheroprotective.25,26 It is possible that IL-1RA is directly upregulated by the same proinflammatory stimuli that increase the expression of IL-1α, IL-1β, and other proteins that have been characterized as risk indicators or risk factors of CVD (eg, high-sensitivity C-reactive protein, IL-6, myeloperoxidase, soluble intercellular adhesion molecule-1, and soluble (s)E-selectin27–31). In addition (and not mutually exclusive), IL-1RA could also be upregulated in response to these aforementioned proteins. Thus, the positive association between circulating IL-1RA levels and risk of CVD would at least partially be based on the coordinated regulatory control of atherogenic immune mediators and IL-1RA as an anti-inflammatory protein. The interpretation of the physiological upregulation of IL-1RA to antagonize proinflammatory stimuli would be facilitated by the ability to precisely measure levels of IL-1 cytokines concomitantly with IL-1RA levels in the circulation or preferably at the sites of atheroma formation. Ultimately, stronger evidence favoring either a direct atherogenic or indirect proinflammatory pathway will emerge from population studies that have repeated measures of these biomarkers with long-term follow-up and a more sophisticated approach to mediation analysis.32
Our study indicates that the addition of IL-1RA to CVD risk scores consisting of established cardiovascular risk factors failed to improve their accuracy as assessed by the comparison of the respective AUC. It is well known that the AUC of good clinical risk scores can only be increased by a fairly small degree by single biomarkers,18,33 which is in line with our observation that the increase in AUC, albeit not statistically significant, was largest for the risk scores with the lowest baseline AUC. However, this meta-analysis points toward the role of inflammatory processes in the pathogenesis of CVD and does not preclude a therapeutic value of IL-1 inhibition that can only be assessed with confidence in large randomized controlled trials. The large CANTOS trial will be able to answer the question of whether selective targeting of IL-1β can prevent recurrent major cardiovascular events in patients with stable coronary artery disease and a systemic proinflammatory state.5
Our study has several strengths. The large sample size and the absence of heterogeneity between studies allowed us to precisely quantify the association between circulating IL-1RA and CVD risk. The comparable level of adjustment for important confounders helped us to obtain an effect estimate that was not affected by established cardiovascular risk factors, although residual or unmeasured confounding cannot be ruled out. Nevertheless, our meta-analytic approach counters the common criticism of effect size inflation and vibration commonly levied against single studies of biomarkers.34 The availability of additional 15 biomarkers of inflammation in MONICA/KORA enabled the identification of proinflammatory biomarkers that partially explain the excess cardiovascular risk linked to IL-1RA. Finally, our meta-analysis included both published and previously unpublished data.
Our study has several limitations that should be considered. As our systematic review identified only cohorts from Europe, the results of our meta-analysis are not generalizable to populations with other ethnic backgrounds. We cannot exclude that we may have missed studies because of language restrictions (English, German, Dutch, and French) in our literature search. The difficulty in reliably measuring systemic levels of IL-1α and IL-1β means that we were unable to assess the balance of IL-1 cytokines and IL-1RA as potential risk factors for CVD. In addition, we did not have data on cell surface or circulating levels of the decoy receptor IL-1 receptor II or the IL-1 receptor accessory protein, 2 further members of the IL-1 cytokine and receptor family that downregulate IL-1 activity. Finally, CVD outcome definitions were not identical across studies as outlined in the Materials and Methods section. However, this did not lead to heterogeneity in effect estimates.
We conclude that higher serum levels of IL-1RA are positively associated with incident CVD in the general population. Our findings are in line with the hypothesis that the upregulation of circulating IL-1RA before an incident CVD event may at least partially reflect a response to proinflammatory triggers that also induce subclinical inflammation, oxidative stress, and endothelial activation. The potential clinical relevance of targeting IL-1 cytokines to reduce the risk of CVD is currently being tested in a large randomized clinical trial (CANTOS).
area under the receiver-operating characteristic curve
Canakinumab Anti-Inflammatory Thrombosis Outcome Study
interleukin-1 receptor antagonist
gene encoding IL-1RA
Cooperative Health Research in the Region of Augsburg
Multinational Monitoring of Trends and Determinants in Cardiovascular Disease
Prospective Epidemiological Study of Myocardial Infarction
We are grateful to the participants of all studies included in this meta-analysis. We also thank the staff involved with the Rotterdam Study and the participating general practitioners and pharmacists.
Sources of Funding
This study was supported in part by the Else Kröner-Fresenius-Stiftung and by a grant from the German Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZD e.V.). The measurement of serum IL-1RA in the MONICA/KORA study (Multinational Monitoring of Trends and Determinants in Cardiovascular Disease/Cooperative Health Research in the Region of Augsburg) was funded by Tethys Bioscience Inc. The KORA study was initiated and financed by the Helmholtz Zentrum München–German Research Center for Environmental Health, which is funded by the BMBF and the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ. The German Diabetes Center is funded by the German Federal Ministry of Health (BMG) and the Ministry of Innovation, Science, Research and Technology (MIWF) of the State North Rhine-Westphalia. This work has also been supported by the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement No HEALTH-F2-2011–278913 (BiomarCaRE). C. Huth is supported by intramural funding for Translational & Clinical Projects of Helmholtz Zentrum München–German Research Center for Environmental Health (HMGU). V. Salomaa was supported by the Finnish Foundation for Cardiovascular Research. The funders had no role in study design, data collection, data analysis, data interpretation, writing of the report, and decision to publish the article.
J. Sudduth-Klinger and D. Peretz were employed by Tethys Bioscience Inc. The other authors declare that they have no conflict of interest.
Hansson GK, Hermansson A. The immune system in atherosclerosis.Nat Immunol. 2011; 12:204–212. doi: 10.1038/ni.2001.CrossrefMedlineGoogle Scholar
Dinarello CA. Immunological and inflammatory functions of the interleukin-1 family.Annu Rev Immunol. 2009; 27:519–550. doi: 10.1146/annurev.immunol.021908.132612.CrossrefMedlineGoogle Scholar
Ridker PM. From C-reactive protein to interleukin-6 to interleukin-1: moving upstream to identify novel targets for atheroprotection.Circ Res. 2016; 118:145–156. doi: 10.1161/CIRCRESAHA.115.306656.LinkGoogle Scholar
Herder C, Dalmas E, Böni-Schnetzler M, Donath MY. The IL-1 pathway in type 2 diabetes and cardiovascular complications.Trends Endocrinol Metab. 2015: 26:551–563. doi: 10.1016/j.tem.2015.08.001.CrossrefMedlineGoogle Scholar
Ridker PM, Thuren T, Zalewski A, Libby P. Interleukin-1β inhibition and the prevention of recurrent cardiovascular events: rationale and design of the Canakinumab Anti-inflammatory Thrombosis Outcomes Study (CANTOS).Am Heart J. 2011; 162:597–605. doi: 10.1016/j.ahj.2011.06.012.CrossrefMedlineGoogle Scholar
Arend WP, Gabay C. Physiologic role of interleukin-1 receptor antagonist.Arthritis Res. 2000; 2:245–248. doi: 10.1186/ar94.CrossrefMedlineGoogle Scholar
Dinarello CA. Interleukin-1 in the pathogenesis and treatment of inflammatory diseases.Blood. 2011; 117:3720–3732. doi: 10.1182/blood-2010-07-273417.CrossrefMedlineGoogle Scholar
Herder C, Nuotio ML, Shah S,. Genetic determinants of circulating interleukin-1 receptor antagonist levels and their association with glycemic traits.Diabetes. 2014; 63:4343–4359. doi: 10.2337/db14-0731.CrossrefMedlineGoogle Scholar
Meier CA, Bobbioni E, Gabay C, Assimacopoulos-Jeannet F, Golay A, Dayer JM. IL-1 receptor antagonist serum levels are increased in human obesity: a possible link to the resistance to leptin?J Clin Endocrinol Metab. 2002; 87:1184–1188. doi: 10.1210/jcem.87.3.8351.CrossrefMedlineGoogle Scholar
Herder C, Brunner EJ, Rathmann W, Strassburger K, Tabák AG, Schloot NC, Witte DR. Elevated levels of the anti-inflammatory interleukin-1 receptor antagonist precede the onset of type 2 diabetes: the Whitehall II study.Diabetes Care. 2009; 32:421–423. doi: 10.2337/dc08-1161.CrossrefMedlineGoogle Scholar
Grossmann V, Schmitt VH, Zeller T,. Profile of the Immune and Inflammatory Response in Individuals With Prediabetes and Type 2 Diabetes.Diabetes Care. 2015; 38:1356–1364. doi: 10.2337/dc14-3008.CrossrefMedlineGoogle Scholar
Herder C, Færch K, Carstensen-Kirberg M, Lowe GD, Haapakoski R, Witte DR, Brunner EJ, Roden M, Tabák AG, Kivimäki M, Vistisen D. Biomarkers of subclinical inflammation and increases in glycaemia, insulin resistance and beta-cell function in non-diabetic individuals: the Whitehall II study.Eur J Endocrinol. 2016; 175:367–377. doi: 10.1530/EJE-16-0528.CrossrefMedlineGoogle Scholar
Blankenberg S, Zeller T, Saarela O, Havulinna AS, Kee F, Tunstall-Pedoe H, Kuulasmaa K, Yarnell J, Schnabel RB, Wild PS, Münzel TF, Lackner KJ, Tiret L, Evans A, Salomaa V; MORGAM Project. Contribution of 30 biomarkers to 10-year cardiovascular risk estimation in 2 population cohorts: the MONICA, risk, genetics, archiving, and monograph (MORGAM) biomarker project.Circulation. 2010; 121:2388–2397. doi: 10.1161/CIRCULATIONAHA.109.901413.LinkGoogle Scholar
Ligthart S, Sedaghat S, Ikram MA, Hofman A, Franco OH, Dehghan A. EN-RAGE: a novel inflammatory marker for incident coronary heart disease.Arterioscler Thromb Vasc Biol. 2014; 34:2695–2699. doi: 10.1161/ATVBAHA.114.304306.LinkGoogle Scholar
Patti G, Di Sciascio G, D’Ambrosio A, Dicuonzo G, Abbate A, Dobrina A. Prognostic value of interleukin-1 receptor antagonist in patients undergoing percutaneous coronary intervention.Am J Cardiol. 2002; 89:372–376.CrossrefMedlineGoogle Scholar
Looker HC, Colombo M, Agakov F,; SUMMIT Investigators. Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes.Diabetologia. 2015; 58:1363–1371. doi: 10.1007/s00125-015-3535-6.CrossrefMedlineGoogle Scholar
Blankenberg S, McQueen MJ, Smieja M, Pogue J, Balion C, Lonn E, Rupprecht HJ, Bickel C, Tiret L, Cambien F, Gerstein H, Münzel T, Yusuf S; HOPE Study Investigators. Comparative impact of multiple biomarkers and N-Terminal pro-brain natriuretic peptide in the context of conventional risk factors for the prediction of recurrent cardiovascular events in the Heart Outcomes Prevention Evaluation (HOPE) Study.Circulation. 2006; 114:201–208. doi: 10.1161/CIRCULATIONAHA.105.590927.LinkGoogle Scholar
- 18. Lp-PLA(2) Studies Collaboration;
Thompson A, Gao P, Orfei L, Watson S, Di Angelantonio E, Kaptoge S, Ballantyne C, Cannon CP, Criqui M, Cushman M, Hofman A, Packard C, Thompson SG, Collins R, Danesh J. Lipoprotein-associated phospholipase A(2) and risk of coronary disease, stroke, and mortality: collaborative analysis of 32 prospective studies.Lancet. 2010; 375:1536–1544. doi: 10.1016/S0140-6736(10)60319-4.CrossrefMedlineGoogle Scholar
Di Angelantonio E, Gao P, Pennells L,; Emerging Risk Factors Collaboration. Lipid-related markers and cardiovascular disease prediction.JAMA. 2012; 307:2499–2506. doi: 10.1001/jama.2012.6571.MedlineGoogle Scholar
- 20. Interleukin 1 Genetics Consortium. Cardiometabolic effects of genetic upregulation of the interleukin 1 receptor antagonist: a Mendelian randomisation analysis.Lancet Diabetes Endocrinol. 2015; 3:243–253. doi: 10.1016/S2213-8587(15)00034-0.CrossrefMedlineGoogle Scholar
Herder C, Donath MY. Interleukin-1 receptor antagonist: friend or foe to the heart?Lancet Diabetes Endocrinol. 2015; 3:228–229. doi: 10.1016/S2213-8587(15)00035-2.CrossrefMedlineGoogle Scholar
Nicklin MJ, Hughes DE, Barton JL, Ure JM, Duff GW. Arterial inflammation in mice lacking the interleukin 1 receptor antagonist gene.J Exp Med. 2000; 191:303–312.CrossrefMedlineGoogle Scholar
Isoda K, Shiigai M, Ishigami N, Matsuki T, Horai R, Nishikawa K, Kusuhara M, Nishida Y, Iwakura Y, Ohsuzu F. Deficiency of interleukin-1 receptor antagonist promotes neointimal formation after injury.Circulation. 2003; 108:516–518. doi: 10.1161/01.CIR.0000085567.18648.21.LinkGoogle Scholar
Merhi-Soussi F, Kwak BR, Magne D, Chadjichristos C, Berti M, Pelli G, James RW, Mach F, Gabay C. Interleukin-1 plays a major role in vascular inflammation and atherosclerosis in male apolipoprotein E-knockout mice.Cardiovasc Res. 2005; 66:583–593. doi: 10.1016/j.cardiores.2005.01.008.CrossrefMedlineGoogle Scholar
Elhage R, Maret A, Pieraggi MT, Thiers JC, Arnal JF, Bayard F. Differential effects of interleukin-1 receptor antagonist and tumor necrosis factor binding protein on fatty-streak formation in apolipoprotein E-deficient mice.Circulation. 1998; 97:242–244.LinkGoogle Scholar
Chamberlain J, Evans D, King A, Dewberry R, Dower S, Crossman D, Francis S. Interleukin-1beta and signaling of interleukin-1 in vascular wall and circulating cells modulates the extent of neointima formation in mice.Am J Pathol. 2006; 168:1396–1403.CrossrefMedlineGoogle Scholar
- 27. C Reactive Protein Coronary Heart Disease Genetics Collaboration (CCGC),
Wensley F, Gao P, Burgess S,. Association between C reactive protein and coronary heart disease: Mendelian randomisation analysis based on individual participant data.BMJ. 2011; 342:d548. doi: 10.1136/bmj.d548.CrossrefMedlineGoogle Scholar
- 28. Interleukin-6 Receptor Mendelian Randomisation Analysis (IL6R MR) Consortium;
Swerdlow DI, Holmes MV, Kuchenbaecker KB,. The interleukin-6 receptor as a target for prevention of coronary heart disease: a Mendelian randomisation analysis.Lancet. 2012; 379:1214–1224. doi: 10.1016/S0140-6736(12)60110-X.CrossrefMedlineGoogle Scholar
Meuwese MC, Stroes ES, Hazen SL, van Miert JN, Kuivenhoven JA, Schaub RG, Wareham NJ, Luben R, Kastelein JJ, Khaw KT, Boekholdt SM. Serum myeloperoxidase levels are associated with the future risk of coronary artery disease in apparently healthy individuals: the EPIC-Norfolk Prospective Population Study.J Am Coll Cardiol. 2007; 50:159–165. doi: 10.1016/j.jacc.2007.03.033.CrossrefMedlineGoogle Scholar
Karakas M, Koenig W, Zierer A, Herder C, Rottbauer W, Baumert J, Meisinger C, Thorand B. Myeloperoxidase is associated with incident coronary heart disease independently of traditional risk factors: results from the MONICA/KORA Augsburg study.J Intern Med. 2012; 271:43–50. doi: 10.1111/j.1365-2796.2011.02397.x.CrossrefMedlineGoogle Scholar
Blankenberg S, Barbaux S, Tiret L. Adhesion molecules and atherosclerosis.Atherosclerosis. 2003; 170:191–203.CrossrefMedlineGoogle Scholar
VanderWeele TJ. Mediation analysis: a practitioner’s guide.Annu Rev Public Health. 2016; 37:17–32. doi: 10.1146/annurev-publhealth-032315-021402.CrossrefMedlineGoogle Scholar
Herder C, Kowall B, Tabak AG, Rathmann W. The potential of novel biomarkers to improve risk prediction of type 2 diabetes.Diabetologia. 2014; 57:16–29. doi: 10.1007/s00125-013-3061-3.CrossrefMedlineGoogle Scholar
Ioannidis JP. Why most discovered true associations are inflated.Epidemiology. 2008; 19:640–648. doi: 10.1097/EDE.0b013e31818131e7.CrossrefMedlineGoogle Scholar
Cardiovascular disease risk increases by 11% (95% confidence interval, 6%–17%) per SD of serum interleukin-1 receptor antagonist based on data from >20 000 study participants from 6 population-based cohorts.
This association is not pronounced enough to result in a significant and clinically relevant improvement in CVD risk prediction.
The association between interleukin-1 receptor antagonist and CVD risk is partially explained by biomarkers of subclinical inflammation, oxidative stress, and cell adhesion.
Our data are in line with the hypothesis that the upregulation of circulating interleukin-1 receptor antagonist before the incidence of CVD may at least partially reflect a response to proatherogenic, inflammation-mediated processes.
These findings corroborate the evidence that cytokines of the interleukin-1 family are implicated in the development of CVD.