Skip to main content
Download PDF

Gout and the risk of myocardial infarction in older adults: a study of Medicare recipients

Arthritis Research & Therapy volume 20, Article number: 109 (2018) Cite this article

Abstract

Background

Current evidence suggests that gout is independently associated with a higher risk of myocardial infarction (MI), but data in older adults at the highest risk of MI are lacking. Our objective was to examine whether gout is associated with a higher risk of incident MI in older adults.

Methods

We assessed the 2006–2012 Medicare 5% claims data for the association of gout at baseline with the occurrence of a new (incident) MI during follow-up (no diagnosis of MI in the baseline period of at least 1 year), adjusting for patient demographics, medical comorbidity (Charlson–Romano index), and commonly used cardiovascular and gout medications, in a Cox proportional hazards model. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated.

Results

In a cohort of 1,733,613 eligible people, 14,279 developed incident MI: 13,029 MIs in people without gout and 1250 MIs in those with gout, with crude incident rates of 1.3 vs 4.1 per 1000 person-years, respectively. In multivariable-adjusted analyses, gout was significantly associated with a higher hazard of incident MI, with HR of 2.08 (95% CI 1.95, 2.21). Risk was minimally attenuated in sensitivity analyses that replaced the continuous Charlson–Romano index score with a categorical score or individual comorbidities, or expanding to a more sensitive diagnostic algorithm for incident MI, or additionally adjusting for obesity.

Conclusions

Gout was independently associated with a higher risk of MI in the elderly, aged 65 years or older. The role of inflammatory and other pathways need to be explored as underlying mechanisms for this association.

Background

Myocardial infarction (MI) is the most common, acute manifestation of coronary artery disease (CAD), which is the most common cardiovascular disease [1]. Although MI incidence decreased slightly from 1999 to 2008 [2], 790,000 Americans have MI each year [3]. MI is associated with high mortality rate [4] and significant health care costs [5]. Thus, MI is a significant public health problem with a huge burden on society and the health care system. Traditional risk factors for CAD are well known and include hypertension, hyperlipidemia, diabetes, smoking, family history, age, and postmenopausal status [6]. Lately, novel risk factors for CAD have been identified, such as obesity, lack of physical activity, and stress [6]. Recognition of novel risk factors for MI can improve our understanding of the disease as well as offer new therapeutic targets, in addition to the currently effective strategies for primary and secondary prevention and treatment of MI.

Gout was associated with 3-fold higher prevalence of the metabolic syndrome [7]. Gout was also associated with a higher prevalence of risk factors for MI, namely hyperlipidemia, hypertension, obesity, and diabetes [8]. Gout was associated with a 1.6-fold higher risk of incident CAD, after adjusting for systolic blood pressure, total cholesterol, alcohol intake, body mass index, and diabetes [9]; but in a case–control study, gout was not significantly associated with incident CAD after matching for age, sex, and medical practice, and the relative risk (RR) of incident CAD was 0.98 [10]. In a study of a British Columbia linked database of people with no history of ischemic disease, gout was associated with significantly increased relative risk of MI in women (RR 1.39 (95% CI 1.20, 1.61)), but not in men (RR 1.11; 95% CI 0.99, 1.23; p for interaction = 0.003); these analyses were adjusted for age, comorbidities, and prescription drug use [11]. In men from the MRFIT study, gout was associated with an adjusted odds ratio (OR) of 1.26 for MI [12]. In a study of an all-England national linked dataset of hospital admissions and death records from 1999 to 2011, compared to people without gout, gout was associated with a RR of 1.82 (95% CI 1.78, 1.85) for MI; findings were replicated in a similar dataset in the Oxford Record Linkage Study spanning 1963–1998 [13]. In Taiwanese population, gout was associated with a hazard ratio (HR) of 1.23 for MI, after adjustment for age, sex, and history of diabetes mellitus, hypertension, coronary heart disease, stroke, and end-stage renal disease [14]. Thus, gout seems to be associated with a higher risk of MI, although two studies reported no association overall or in men [10, 11].

Very few data exist with regards to the risk of MI associated with gout in people aged 65 years or older. This is a population at high risk of MI and poor outcome from MI. More than a third of all MIs occur in people aged > 70 years [15, 16] and more than 80% of deaths from MI occur in those 65 years or older [17]. The US population that is aged 65 years or older is predicted to grow from 34.4 million in 2000 to > 70 million in 2030 [18]. Given the public health burden of MI and associated mortality in the adults aged 65 years and older, potential cardiac risk associated with gout needs to be examined. Therefore, we aimed to assess whether gout was associated independently with a higher risk of incident MI in the elderly, and whether this association varied by CAD risk factors, including hypertension, hyperlipidemia, and diabetes.

Methods

Data sources and study sample

We used the claims data from the 5% Medicare sample from 2006 to 2012 [19]. Study eligibility criteria were being a Medicare beneficiary enrolled in Medicare fee-for-service (Parts A, B), and not enrolled in the Medicare Advantage Plan, and being resident in the USA during the study period, 2006–2012. The study was approved by the University of Alabama at Birmingham’s Institutional Review Board.

Predictor of interest

A current diagnosis of gout was our main independent variable of interest. We required the presence of at least two claims at least 4 weeks apart with an International Classification of Diseases, ninth revision, common modification (ICD-9-CM) code of 274.xx, a validated approach with specificity and sensitivity of ≥ 90% [20]. The gout diagnosis had to present before the diagnosis of MI. Thus, all prevalent cases of gout at the beginning of the study window were included in the gout group and new gout cases during the study period were included, as long as the gout diagnosis preceded the diagnosis of MI.

Independent variable/outcome of interest

The outcome of interest was incident MI, identified by the occurrence of two claims at least 4 weeks apart, each with an ICD-9-CM code for MI (410.x1), with no claims for MI in the baseline period of at least 1 year (excluding people with ICD-9-CM codes of 410 or 412 at baseline, 1/1/2015 to 12/31/2015), as described previously [21, 22]. This ICD-9 code-based approach has been shown to be valid, with positive predictive values > 90% [21, 23].

Study covariates and confounders

We included several covariates and potential confounders in this study. Demographic variables included age (in years), gender, and race (White, Black, other), obtained from the Medicare denominator file and the beneficiary summary file. Medical comorbidity was assessed using the Charlson–Romano index, obtained from the inpatient and outpatient Medicare claim files. The Charlson–Romano index is a validated weighted comorbidity index, developed for claims data [24]; it was treated as a continuous score in the main model. We obtained data on the common cardiovascular drugs (statins, beta-blockers, diuretics, and angiotensin converting enzyme (ACE) inhibitors) and gout drugs (allopurinol, febuxostat) by including all prescription claims from the Medicare part D file. We included these drugs as surrogates for conditions they treat and/or disease severity and their protective or potentially protective effects related to the risk of MI.

Statistical analyses

We compared characteristics of people with and without MI during the follow-up and calculated crude incidence rates per 1000 person-years. Patients contributed time to the control population prior to the diagnosis of gout. We assessed the association of gout with incident MI in multivariable-adjusted Cox regression analyses, that included all covariates already described, i.e., demographics, comorbidity, and the commonly used cardiovascular and gout medications. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated.

Sensitivity analyses were performed by: replacing the continuous Charlson–Romano index score with a categorical score (model 2) or individual comorbidities (model 3; also included hypertension, hyperlipidemia, and coronary artery disease); additionally adjusting model 3 for obesity (ICD-9-CM code, 278.0); and expanding the diagnostic code for incident MI to 410 or 412 for incident MI, excluding people with code 410 or 412 at baseline (models 4–6), replicating models 1–3 for this more sensitive, less specific definition for incident MI. We also performed subgroup analyses by race, gender, and age as well as by MI risk factors.

Results

Among 1,733,613 eligible people, 14,279 developed incident MI during the study follow-up. Of these, 13,030 cases occurred in people without gout (n = 1,639,534) and 1249 in those with gout (n = 94,809) (Table 1), with corresponding crude incident rates of 1.3 (13,030 cases/10,005,276 person-years) vs 4.1 (1249 cases/304,192 person-years) per 1000 person-years. Mean (standard deviation (SD)) time from gout diagnosis to the occurrence of incident MI was 2.3 years (1.7); median 1.9 years (interquartile range 0.8–3.5). When we compared to people without MI, those who had MI were older, more likely to be male, white, and have higher medical comorbidity, including a higher prevalence of cardiovascular disease, diabetes, connective tissue disease, and other comorbidities (Table 1).

Table 1 Demographic and clinical characteristics of people with and without myocardial infarction
Full size table

In multivariable-adjusted analyses, gout was significantly associated with higher hazard of incident MI, with HR of 2.08 (95% CI 1.95, 2.21), which was minimally attenuated in sensitivity analyses that replaced the continuous Charlson–Romano score with a categorical score or individual comorbidities (models 2–3; Table 2). In addition, older age, male gender, white race, and higher comorbidity were each associated with a higher hazard of incident MI (Table 2).

Table 2 Association of gout and other risk factors with incident myocardial infarction
Full size table

Sensitivity analyses additionally adjusting model 3 for obesity led to minimal/no attenuation of HR from 1.79 (95% CI 1.69, 1.91) to 1.80 (95% CI 1.69, 1.91). Sensitivity analyses expanding the ICD-9-CM code for incident MI to a more sensitive and less specific diagnostic code algorithm (code 410 or 412) revealed slightly lower HRs of incident CAD related to gout in models corresponding to the three models as shown in Table 2 above: 1.85 (95% CI 1.79, 1.92; model 4), 1.83 (95% CI 1.77, 1.89; model 5), and 1.59 (95% CI 1.54, 1.65; model 6).

In subgroup analyses, hazard ratios of gout with incident MI were higher in the absence of hypertension, hyperlipidemia, diabetes, or heart failure (ranging from 2.2 to 3.0) vs hazard ratios in those with each of these comorbidities (ranging from 1.6 to 1.7) (Table 3), differences that were both statistically significant and seemed clinically meaningful. Similarly, the hazard ratios for MI with gout were 2.2 and 1.7 in those without vs with CAD (Table 3). We noted minor differences by age, gender, and race which were only statistically significant, except for difference in HR between white and black race, which also seemed potentially clinically meaningful (2.02 vs 2.49) (Table 3).

Table 3 Association of gout with MI, in predefined subgroup analyses
Full size table

Discussion

We noted a strong, robust, independent association of gout with incident MI in adults aged 65 years or older, confirmed in multiple sensitivity analyses for the main analysis (models 2–3); confirmed further when we expanded the ICD-9 code from 410.x1 (models 1-3) to a more sensitive definition using ICD-9 codes 410 or 412 (models 4–6). This is an important finding that merits further discussion. In previous studies in the general population, gout was associated with a higher risk/hazard ratio of incident MI ranging from 1.23-fold to 1.82-fold in some studies [11,12,13,14], but not associated with higher risk in others [10, 25]. Studies differed in setting (cohort vs record-linkage), population (men vs women vs both), and confounders adjusted (cardiovascular risk factors included vs not). Our hazard ratio estimates in models 1–3 ranging from 1.79 to 2.08 for adults aged 65 years or older are slightly higher than previous estimates, since we were examining an older patient population.

Our study examined men and women 65 aged years or older in the USA, controlling for cardiovascular risk factors among other important factors. Estimates were robust in sensitivity analyses. The implications of our study are several. Using a representative US patient population, we provided estimates for gout and incident MI, which adds clarity to this important clinical question. An increased risk of incident MI in people with gout raises a question regarding the role of chronic inflammation and IL-1β pathways (via NLRP3 inflammasome activation) and CRP, hallmarks of gout [26, 27], in the pathogenesis of MI. The IL-1β pathway is shown to be important in the pathogenesis of MI [28,29,30,31,32,33,34,35] and as a downstream effect increases IL-6 levels, a potential causal pathway for atherothrombosis [36, 37]. Various pathogenic mechanisms common in people with CAD risk factors, such as cholesterol crystals, tissue hypoxia, and abnormal arterial flow patterns, can promote the activation of the NLRP3 inflammasome [38,39,40,41], which then activates IL-1β. CRP is elevated in CAD and contributes directly to atherosclerosis via leukocyte activation and endothelial dysfunction [42,43,44]. Our study findings generate the hypothesis that inflammatory pathways may be activated in the atherosclerotic plaque, which may then lead to MI. People with gout have upregulation of these inflammatory pathways, which might explain the increased risk of MI in gout, at least partially.  The role of key mechanisms of increased atherosclerosis in gout needs to be examined in basic and translational studies.

We noted that the association of gout with incident MI was stronger in people without each CAD risk factor (hypertension, hyperlipidemia, or diabetes) (HR 2.3–3.0) than in the presence of each CAD risk factor respectively (HR 1.6–1.7). This indicates that in patients with known CAD/MI risk factors, gout contributes much less to the risk of incident MI. Most differences were not only statistically significant, but also clinically meaningful. We also noted a similar trend in presence vs absence of heart failure and CAD. This observation is similar to that noted by Kuo et al. previously in a Taiwanese study [14]. We speculate two potential reasons for this observation: gout is associated with a 3-fold higher prevalence of the metabolic syndrome [7] that has features of hyperlipidemia, hyperglycemia, or obesity, and gout may be an early clinical manifestation of the metabolic syndrome; and episodic inflammation characteristic of gout flares may increase the MI risk, especially in those without other CAD risk factors.

In a subgroup analysis, we found that gender made little difference to the association of gout with incident MI in the elderly. Previous studies found higher risk of MI with gout in UK women compared to men (2.08 (95% CI 2.01, 2.16) vs 1.73 (95% CI 1.69, 1.77), respectively) [13], considering all age groups (mean age 70 years), or a trend of higher risk of MI with gout in Canadian women compared to men (1.11 (95% CI 0.99, 1.23); p = 0.003 for interaction by gender) [11] in the elderly with a mean age of 75 years. The mean age of people in our study is similar to these studies at 73 years. The reasons for differences in the findings is likely related to differences in country setting (USA vs UK vs Canada), confounders adjusted in the analyses (cardiovascular diseases, cardiovascular medications, and gout medications vs neither vs cardiovascular medications only), the outcome definition, the underlying conditions (none vs. none vs. musculoskeletal disease), study sample (all Medicare recipients vs. all hospital admissions for gout vs. all elderly) and the population examined in each study (limited to only people aged 65 years or older vs all ages vs. women 65 years or older).

Our study has several limitations, which must be considered while interpreting the findings. We used data from Americans aged 65 years or older, and therefore the generalizability of these findings to younger people is uncertain. Diagnostic misclassification may have occurred despite our use of validated algorithms; this would bias our study findings toward the null, making our findings conservative (i.e., we may have missed some associations). Residual confounding is still possible, given an observational study design, despite the fact that we controlled for several potential confounders. We adjusted for several potential confounders including cardiovascular drugs, but we did not adjust for aspirin, nonsteroidal anti-inflammatory drug (NSAID) use, alcohol use, smoking, or exercise, which may have led to some residual confounding. Adjustment for aspirin and NSAID use was considered but not done, since most NSAID and aspirin use in this age group is over the counter rather than prescription use [45, 46]. Over-the-counter medication use is not captured in the Medicare claims data, which would introduce misclassification bias. We are also unaware of the differential rate/pattern of the use of aspirin or NSAID by gout status, for primary or secondary prevention of CAD. Our study has several strengths. Inclusion of medications for cardiovascular disease and gout strengthens the analyses, since these medications might be imperfect surrogates of disease severity which Medicare data lack and have independent protective effects related to MI risk, but also in some cases may be indicative of the presence of a disease in the absence of an ICD-9-CM code and may reduce misclassification bias. We used a representative sample of US adults aged 65 years or older, had an adequate number of events for analyses, and conducted multiple sensitivity analyses to test the robustness of findings.

Conclusions

This study showed an association of gout with incident MI in adults aged 65 years or older, independent of the traditional CAD risk factors. The MI risk associated with gout was stronger in people without CAD risk factors compared to people with CAD risk factors, and the risk was increased 2-fold or higher in both groups. Chronic inflammation, a hallmark of gout, is implicated in the pathogenesis of incident MI [28]. Future studies should evaluate the mechanisms for this disease association, and evaluate to what extent this association is due to chronic inflammation versus other potential pathways.

Abbreviations

ACE:

Angiotensin converting enzyme

CAD:

Coronary artery disease

CRP:

C-reactive protein

ICD-9-CM:

International Classification of Diseases, ninth revision, common modification

MI:

Myocardial infarction

SD:

Standard deviation

References

  1. 1.

    Centers for Disease Control and Prevention. Heart disease. http://www.cdc.gov/heartdisease/. Accessed 28 Feb 2018.

  2. 2.

    Yeh RW, Sidney S, Chandra M, Sorel M, Selby JV, Go AS. Population trends in the incidence and outcomes of acute myocardial infarction. N Engl J Med. 2010;362(23):2155–65.

    Article  PubMed  CAS  Google Scholar 

  3. 3.

    Centers for Disease Control and Prevention. Heart attack. https://www.cdc.gov/heartdisease/heart_attack.htm. Accessed 28 Feb 2018.

  4. 4.

    Roger VL. Epidemiology of myocardial infarction. Med Clin North Am. 2007;91(4):537–52. ix

    Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Kauf TL, Velazquez EJ, Crosslin DR, Weaver WD, Diaz R, Granger CB, McMurray JJ, Rouleau JL, Aylward PE, White HD, et al. The cost of acute myocardial infarction in the new millennium: evidence from a multinational registry. Am Heart J. 2006;151(1):206–12.

    Article  PubMed  Google Scholar 

  6. 6.

    Roeters van Lennep JE, Westerveld HT, Erkelens DW, van der Wall EE. Risk factors for coronary heart disease: implications of gender. Cardiovasc Res. 2002;53(3):538–49.

    Article  PubMed  CAS  Google Scholar 

  7. 7.

    Choi HK, Ford ES, Li C, Curhan G. Prevalence of the metabolic syndrome in patients with gout: the Third National Health and Nutrition Examination Survey. Arthritis Rheum. 2007;57(1):109–15.

    Article  PubMed  Google Scholar 

  8. 8.

    Zhu Y, Pandya BJ, Choi HK. Comorbidities of gout and hyperuricemia in the US general population: NHANES 2007-2008. Am J Med. 2012;125(7):679–87. e671

    Article  PubMed  Google Scholar 

  9. 9.

    Abbott RD, Brand FN, Kannel WB, Castelli WP. Gout and coronary heart disease: the Framingham Study. J Clin Epidemiol. 1988;41(3):237–42.

    Article  PubMed  CAS  Google Scholar 

  10. 10.

    Janssens HJ, van de Lisdonk EH, Bor H, van den Hoogen HJ, Janssen M. Gout, just a nasty event or a cardiovascular signal? A study from primary care. Fam Pract. 2003;20(4):413–6.

    Article  PubMed  CAS  Google Scholar 

  11. 11.

    De Vera MA, Rahman MM, Bhole V, Kopec JA, Choi HK. Independent impact of gout on the risk of acute myocardial infarction among elderly women: a population-based study. Ann Rheum Dis. 2010;69(6):1162–4.

    Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Krishnan E, Baker JF, Furst DE, Schumacher HR. Gout and the risk of acute myocardial infarction. Arthritis Rheum. 2006;54(8):2688–96.

    Article  PubMed  CAS  Google Scholar 

  13. 13.

    Seminog OO, Goldacre MJ. Gout as a risk factor for myocardial infarction and stroke in England: evidence from record linkage studies. Rheumatology (Oxford). 2013;52(12):2251–9.

    Article  Google Scholar 

  14. 14.

    Kuo CF, Yu KH, See LC, Chou IJ, Ko YS, Chang HC, Chiou MJ, Luo SF. Risk of myocardial infarction among patients with gout: a nationwide population-based study. Rheumatology (Oxford). 2013;52(1):111–7.

    Article  Google Scholar 

  15. 15.

    Alexander KP, Roe MT, Chen AY, Lytle BL, Pollack CV Jr, Foody JM, Boden WE, Smith SC Jr, Gibler WB, Ohman EM, et al. Evolution in cardiovascular care for elderly patients with non-ST-segment elevation acute coronary syndromes: results from the CRUSADE National Quality Improvement Initiative. J Am Coll Cardiol. 2005;46(8):1479–87.

    Article  PubMed  Google Scholar 

  16. 16.

    Rask-Madsen C, Jensen G, Kober L, Melchior T, Torp-Pedersen C, Hildebrand P. Age-related mortality, clinical heart failure, and ventricular fibrillation in 4259 Danish patients after acute myocardial infarction. Eur Heart J. 1997;18(9):1426–31.

    Article  PubMed  CAS  Google Scholar 

  17. 17.

    Gurwitz JH, Col NF, Avorn J. The exclusion of the elderly and women from clinical trials in acute myocardial infarction. JAMA. 1992;268(11):1417–22.

    Article  PubMed  CAS  Google Scholar 

  18. 18.

    US Census Bureau. National population projections. I. Summary files. Total population by age, sex, race and Hispanic origin. (NP-T4-F). Projections of the total redodent population by 5-year age groups, race and Hispanic origin with special age categories: middle series. 2025 to 2045. https://www2.census.gov/programs-surveys/popproj/tables/2000/2000-national-summary-tables/np-t4-f.pdf. Accessed 28 Feb 2018.

  19. 19.

    CMS fee information. https://www.resdac.org/resconnect/articles/123. Accessed 28 Feb 2018.

  20. 20.

    Singh JA, Hodges JS, Toscano JP, Asch SM. Quality of care for gout in the US needs improvement. Arthritis Rheum. 2007;57(5):822–9.

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Kiyota Y, Schneeweiss S, Glynn RJ, Cannuscio CC, Avorn J, Solomon DH. Accuracy of Medicare claims-based diagnosis of acute myocardial infarction: estimating positive predictive value on the basis of review of hospital records. Am Heart J. 2004;148(1):99–104.

    Article  PubMed  Google Scholar 

  22. 22.

    Singh JA, Ramachandaran R, Yu S, Yang S, Xie F, Yun H, Zhang J, Curtis JR. Is gout a risk equivalent to diabetes for stroke and myocardial infarction? A retrospective claims database study. Arthritis Res Ther. 2017;19(1):228.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. 23.

    Birman-Deych E, Waterman AD, Yan Y, Nilasena DS, Radford MJ, Gage BF. Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors. Med Care. 2005;43(5):480–5.

    Article  PubMed  Google Scholar 

  24. 24.

    Romano PS, Roos LL, Jollis JG. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol. 1993;46(10):1075–9. discussion 1081-1090

    Article  PubMed  CAS  Google Scholar 

  25. 25.

    Gelber AC, Klag MJ, Mead LA, Thomas J, Thomas DJ, Pearson TA, Hochberg MC. Gout and risk for subsequent coronary heart disease. The Meharry-Hopkins Study. Arch Intern Med. 1997;157(13):1436–40.

    Article  PubMed  CAS  Google Scholar 

  26. 26.

    Martinon F, Petrilli V, Mayor A, Tardivel A, Tschopp J. Gout-associated uric acid crystals activate the NALP3 inflammasome. Nature. 2006;440(7081):237–41.

    Article  PubMed  CAS  Google Scholar 

  27. 27.

    Cronstein BN, Terkeltaub R. The inflammatory process of gout and its treatment. Arthritis Res Ther. 2006;8(Suppl 1):S3.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. 28.

    Libby P, Ridker PM, Maseri A. Inflammation and atherosclerosis. Circulation. 2002;105(9):1135–43.

    Article  PubMed  CAS  Google Scholar 

  29. 29.

    Libby P, Ordovas JM, Auger KR, Robbins AH, Birinyi LK, Dinarello CA. Endotoxin and tumor necrosis factor induce interleukin-1 gene expression in adult human vascular endothelial cells. Am J Pathol. 1986;124(2):179–85.

    PubMed  PubMed Central  CAS  Google Scholar 

  30. 30.

    Dinarello CA, Simon A, van der Meer JW. Treating inflammation by blocking interleukin-1 in a broad spectrum of diseases. Nat Rev Drug Discov. 2012;11(8):633–52.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. 31.

    Fuster JJ, MacLauchlan S, Zuriaga MA, Polackal MN, Ostriker AC, Chakraborty R, Wu CL, Sano S, Muralidharan S, Rius C, et al. Clonal hematopoiesis associated with TET2 deficiency accelerates atherosclerosis development in mice. Science. 2017;355(6327):842–7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. 32.

    Sager HB, Heidt T, Hulsmans M, Dutta P, Courties G, Sebas M, Wojtkiewicz GR, Tricot B, Iwamoto Y, Sun Y, et al. Targeting interleukin-1beta reduces leukocyte production after acute myocardial infarction. Circulation. 2015;132(20):1880–90.

    PubMed  PubMed Central  CAS  Article  Google Scholar 

  33. 33.

    Jaiswal S, Natarajan P, Silver AJ, Gibson CJ, Bick AG, Shvartz E, McConkey M, Gupta N, Gabriel S, Ardissino D, et al. Clonal hematopoiesis and risk of atherosclerotic cardiovascular disease. N Engl J Med. 2017;377(2):111–21.

    Article  PubMed  Google Scholar 

  34. 34.

    Shimokawa H, Ito A, Fukumoto Y, Kadokami T, Nakaike R, Sakata M, Takayanagi T, Egashira K, Takeshita A. Chronic treatment with interleukin-1 beta induces coronary intimal lesions and vasospastic responses in pigs in vivo. The role of platelet-derived growth factor. J Clin Invest. 1996;97(3):769–76.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. 35.

    Kirii H, Niwa T, Yamada Y, Wada H, Saito K, Iwakura Y, Asano M, Moriwaki H, Seishima M. Lack of interleukin-1beta decreases the severity of atherosclerosis in ApoE-deficient mice. Arterioscler Thromb Vasc Biol. 2003;23(4):656–60.

    Article  PubMed  CAS  Google Scholar 

  36. 36.

    Collaboration IRGCERF, Sarwar N, Butterworth AS, Freitag DF, Gregson J, Willeit P, Gorman DN, Gao P, Saleheen D, Rendon A, et al. Interleukin-6 receptor pathways in coronary heart disease: a collaborative meta-analysis of 82 studies. Lancet. 2012;379(9822):1205–13.

    Article  CAS  Google Scholar 

  37. 37.

    Interleukin-6 Receptor Mendelian Randomisation Analysis Consortium, Swerdlow DI, Holmes MV, Kuchenbaecker KB, Engmann JE, Shah T, Sofat R, Guo Y, Chung C, Peasey A, et al. The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis. Lancet. 2012;379(9822):1214–24.

    Article  CAS  Google Scholar 

  38. 38.

    Duewell P, Kono H, Rayner KJ, Sirois CM, Vladimer G, Bauernfeind FG, Abela GS, Franchi L, Nunez G, Schnurr M, et al. NLRP3 inflammasomes are required for atherogenesis and activated by cholesterol crystals. Nature. 2010;464(7293):1357–61.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. 39.

    Rajamaki K, Mayranpaa MI, Risco A, Tuimala J, Nurmi K, Cuenda A, Eklund KK, Oorni K, Kovanen PT. p38delta MAPK: a novel regulator of NLRP3 inflammasome activation with increased expression in coronary atherogenesis. Arterioscler Thromb Vasc Biol. 2016;36(9):1937–46.

    Article  PubMed  CAS  Google Scholar 

  40. 40.

    Xiao H, Lu M, Lin TY, Chen Z, Chen G, Wang WC, Marin T, Shentu TP, Wen L, Gongol B, et al. Sterol regulatory element binding protein 2 activation of NLRP3 inflammasome in endothelium mediates hemodynamic-induced atherosclerosis susceptibility. Circulation. 2013;128(6):632–42.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. 41.

    Folco EJ, Sukhova GK, Quillard T, Libby P. Moderate hypoxia potentiates interleukin-1beta production in activated human macrophages. Circ Res. 2014;115(10):875–83.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. 42.

    Torzewski M, Rist C, Mortensen RF, Zwaka TP, Bienek M, Waltenberger J, Koenig W, Schmitz G, Hombach V, Torzewski J. C-reactive protein in the arterial intima: role of C-reactive protein receptor-dependent monocyte recruitment in atherogenesis. Arterioscler Thromb Vasc Biol. 2000;20(9):2094–9.

    Article  PubMed  CAS  Google Scholar 

  43. 43.

    Pasceri V, Willerson JT, Yeh ET. Direct proinflammatory effect of C-reactive protein on human endothelial cells. Circulation. 2000;102(18):2165–8.

    Article  PubMed  CAS  Google Scholar 

  44. 44.

    Pasceri V, Cheng JS, Willerson JT, Yeh ET. Modulation of C-reactive protein-mediated monocyte chemoattractant protein-1 induction in human endothelial cells by anti-atherosclerosis drugs. Circulation. 2001;103(21):2531–4.

    Article  PubMed  CAS  Google Scholar 

  45. 45.

    Kaufman DW, Kelly JP, Rosenberg L, Anderson TE, Mitchell AA. Recent patterns of medication use in the ambulatory adult population of the United States: the Slone survey. JAMA. 2002;287(3):337–44.

    Article  PubMed  Google Scholar 

  46. 46.

    Zhou Y, Boudreau DM, Freedman AN. Trends in the use of aspirin and nonsteroidal anti-inflammatory drugs in the general U.S. population. Pharmacoepidemiol Drug Saf. 2014;23(1):43–50.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank several patients in the gout clinic, who raised important questions about the effect of gout on the heart, that led us to design this study to answer the question.

Funding

This material is the result of work supported by research funds from the Division of Rheumatology at the University of Alabama at Birmingham and the resources and use of facilities at the Birmingham VA Medical Center, Birmingham, AL, USA. The funding body did not play any role in design; in the collection, analysis, and interpretation of data; in the writing of the manuscript; and in the decision to submit the manuscript for publication.

Availability of data and materials

The authors are ready to share the data with colleagues, after obtaining appropriate permissions from the University of Alabama at Birmingham (UAB) Ethics Committee, related to HIPAA and privacy policies.

Author information

Affiliations

  1. Medicine Service, VA Medical Center, 510, 20th Street South, FOT 805B, Birmingham, AL, 35233, USA

    Jasvinder A. Singh

  2. Department of Medicine at School of Medicine, University of Alabama at Birmingham, 1720 Second Ave. South, Birmingham, AL, 35294-0022, USA

    Jasvinder A. Singh & John D. Cleveland

  3. Division of Epidemiology at School of Public Health, University of Alabama at Birmingham, 1720 Second Ave. South, Birmingham, AL, 35294-0022, USA

    Jasvinder A. Singh

  4. University of Alabama at Birmingham, Faculty Office Tower 805B, 510 20th Street S, Birmingham, AL, 35294-0022, USA

    Jasvinder A. Singh

Authors
  1. Jasvinder A. Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John D. Cleveland
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JAS designed the study, developed the study protocol, reviewed analyses, and wrote the first draft of the article. JDC performed the data abstraction and data analyses. Both authors made revisions to the manuscript, read, and approved the final manuscript.

Corresponding author

Correspondence to Jasvinder A. Singh.

Ethics declarations

Ethics approval and consent to participate

The University of Alabama at Birmingham’s Institutional Review Board approved this study and all investigations were conducted in conformity with ethical principles of research. The IRB waived the need for informed consent for this database study.

Competing interests

JAS has received research grants from Takeda and Savient and consultant fees from Savient, Takeda, Regeneron, Merz, Iroko, Bioiberica, Fidia, Crealta/Horizon and Allergan pharmaceuticals, WebMD, UBM LLC, and National Institute of Health and the American College of Rheumatology. JAS serves as the principal investigator for an investigator-initiated study funded by Horizon pharmaceuticals through a grant to DINORA, Inc., a 501 (c)(3) entity. JAS is a member of the executive of OMERACT, an organization that develops outcome measures in rheumatology and receives arms-length funding from 36 companies; a member of the American College of Rheumatology’s (ACR) Annual Meeting Planning Committee (AMPC); Chair of the ACR Meet-the-Professor, Workshop and Study Group Subcommittee; and a member of the Veterans Affairs Rheumatology Field Advisory Committee. JAS is the editor and Director of the UAB Cochrane Musculoskeletal Group Satellite Center on Network Meta-analysis. JDC has no relevant financial conflicts. There are no nonfinancial competing interests for either author.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Singh, J.A., Cleveland, J.D. Gout and the risk of myocardial infarction in older adults: a study of Medicare recipients. Arthritis Res Ther 20, 109 (2018). https://doi.org/10.1186/s13075-018-1606-z

Download citation

Keywords

  • Gout
  • Myocardial infarction
  • Association
  • Risk
  • Elderly
  • Cardiac outcomes

Arthritis Research & Therapy

ISSN: 1478-6362

Contact us