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. Keywords: Gout, Myocardial infarction, Association, Risk, Elderly, Cardiac outcomes Background and postmenopausal status . Lately, novel risk factors for Myocardial infarction (MI) is the most common, acute CAD have been identified, such as obesity, lack of physical manifestation of coronary artery disease (CAD), which is activity, and stress . Recognition of novel risk factors for the most common cardiovascular disease . Although MI MI can improve our understanding of the disease as well as incidence decreased slightly from 1999 to 2008 , 790,000 offer new therapeutic targets, in addition to the currently Americans have MI each year . MI is associated with effective strategies for primary and secondary prevention high mortalityrate and significant health care costs . and treatment of MI. Thus, MI is a significant public health problem with a huge Gout was associated with 3-fold higher prevalence of burden on society and the health care system. Traditional the metabolic syndrome . Gout was also associ- risk factors for CAD are well known and include hyperten- ated with a higher prevalence of risk factors for MI, sion, hyperlipidemia, diabetes, smoking, family history, age, namely hyperlipidemia, hypertension, obesity, and dia- betes . Gout was associated with a 1.6-fold higher risk of incident CAD, after adjusting for systolic blood pres- * Correspondence: Jassingh@uab.edu; Jasvinder.firstname.lastname@example.org Medicine Service, VA Medical Center, 510, 20th Street South, FOT 805B, sure, total cholesterol, alcohol intake, body mass index, Birmingham, AL 35233, USA and diabetes ; but in a case–control study, gout was Department of Medicine at School of Medicine, University of Alabama at not significantly associated with incident CAD after Birmingham, 1720 Second Ave. South, Birmingham, AL 35294-0022, USA Full list of author information is available at the end of the article © The Author(s). 2018 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. Singh and Cleveland Arthritis Research & Therapy (2018) 20:109 Page 2 of 8 matching for age, sex, and medical practice, and the Classification of Diseases, ninth revision, common modifi- relative risk (RR) of incident CAD was 0.98 . In a cation (ICD-9-CM) code of 274.xx, a validated approach study of a British Columbia linked database of people with specificity and sensitivity of ≥ 90% . The gout diag- with no history of ischemic disease, gout was associated nosis had to present before the diagnosis of MI. Thus, all with significantly increased relative risk of MI in women prevalent cases of gout at the beginning of the study win- (RR 1.39 (95% CI 1.20, 1.61)), but not in men (RR 1.11; dow were included in the gout group and new gout 95% CI 0.99, 1.23; p for interaction = 0.003); these ana- cases during the study period were included, as long lyses were adjusted for age, comorbidities, and prescrip- as the gout diagnosis preceded the diagnosis of MI. tion drug use . In men from the MRFIT study, gout was associated with an adjusted odds ratio (OR) of 1.26 Independent variable/outcome of interest for MI . In a study of an all-England national linked The outcome of interest was incident MI, identified by dataset of hospital admissions and death records from the occurrence of two claims at least 4 weeks apart, each 1999 to 2011, compared to people without gout, gout with an ICD-9-CM code for MI (410.x1), with no claims was associated with a RR of 1.82 (95% CI 1.78, 1.85) for for MI in the baseline period of at least 1 year (excluding MI; findings were replicated in a similar dataset in the Ox- people with ICD-9-CM codes of 410 or 412 at baseline, ford Record Linkage Study spanning 1963–1998 . In 1/1/2015 to 12/31/2015), as described previously [21, Taiwanese population, gout was associated with a hazard 22]. This ICD-9 code-based approach has been shown to ratio (HR) of 1.23 for MI, after adjustment for age, sex, and be valid, with positive predictive values > 90% [21, 23]. history of diabetes mellitus, hypertension, coronary heart disease, stroke, and end-stage renal disease . Thus, gout Study covariates and confounders seems to be associated with a higher risk of MI, although We included several covariates and potential confounders two studies reported no association overall or in men [10, in this study. Demographic variables included age (in years) 11]. , gender, and race (White, Black, other), obtained from the Very few data exist with regards to the risk of MI associ- Medicare denominator file and the beneficiary summary ated with gout in people aged 65 years or older. This is a file. Medical comorbidity was assessed using the Charlson– population at high risk of MI and poor outcome from MI. Romano index, obtained from the inpatient and outpatient More than a third of all MIs occur in people aged > 70 years Medicare claim files. The Charlson–Romano index is a vali- [15, 16] and more than 80% of deaths from MI occur in dated weighted comorbidity index, developed for claims those 65 years or older . The US population that is aged data ; it was treated as a continuous score in the main 65 years or older is predicted to grow from 34.4 million in model. We obtained data on the common cardiovascular 2000 to > 70 million in 2030 . Given the public health drugs (statins, beta-blockers, diuretics, and angioten- burden of MI and associated mortality in the adults aged sin converting enzyme (ACE) inhibitors) and gout 65 years and older, potential cardiac risk associated with drugs (allopurinol, febuxostat) by including all pre- gout needs to be examined. Therefore, we aimed to assess scription claims from the Medicare part D file. We whether gout was associated independently with a higher included these drugs as surrogates for conditions they risk of incident MI in the elderly, and whether this treat and/or disease severity and their protective or association varied by CAD risk factors, including potentially protective effects related to the risk of MI. hypertension, hyperlipidemia, and diabetes. Statistical analyses Methods We compared characteristics of people with and Data sources and study sample without MI during the follow-up and calculated crude We used the claims data from the 5% Medicare sample incidence rates per 1000 person-years. Patients from 2006 to 2012 . Study eligibility criteria were contributed time to the control population prior to being a Medicare beneficiary enrolled in Medicare fee- the diagnosis of gout. We assessed the association of for-service (Parts A, B), and not enrolled in the Medi- gout with incident MI in multivariable-adjusted Cox care Advantage Plan, and being resident in the USA regression analyses, that included all covariates during the study period, 2006–2012. The study was ap- already described, i.e., demographics, comorbidity, proved by the University of Alabama at Birmingham’s and the commonly used cardiovascular and gout med- Institutional Review Board. ications. Hazard ratios (HRs) and 95% confidence in- tervals (CIs) were calculated. Predictor of interest Sensitivity analyses were performed by: replacing the con- A current diagnosis of gout was our main independent vari- tinuous Charlson–Romano index score with a categorical able of interest. We required the presence of at least two score (model 2) or individual comorbidities (model 3; also claims at least 4 weeks apart with an International included hypertension, hyperlipidemia, and coronary artery Singh and Cleveland Arthritis Research & Therapy (2018) 20:109 Page 3 of 8 disease); additionally adjusting model 3 for obesity (ICD-9- Discussion CM code, 278.0); and expanding the diagnostic code for in- We noted a strong, robust, independent association of cident MI to 410 or 412 for incident MI, excluding people gout with incident MI in adults aged 65 years or older, with code 410 or 412 at baseline (models 4–6), replicating confirmed in multiple sensitivity analyses for the main models 1–3 for this more sensitive, less specific definition analysis (models 2–3); confirmed further when we ex- for incident MI. We also performed subgroup analyses by panded the ICD-9 code from 410.x1 (models 1-3) to a race, gender, and age as well as by MI risk factors. more sensitive definition using ICD-9 codes 410 or 412 (models 4–6). This is an important finding that Results merits further discussion. In previous studies in the gen- Among 1,733,613 eligible people, 14,279 developed incident eral population, gout was associated with a higher risk/ MI during the study follow-up. Of these, 13,030 cases oc- hazard ratio of incident MI ranging from 1.23-fold to 1. curred in people without gout (n = 1,639,534) and 1249 in 82-fold in some studies [11–14], but not associated with those with gout (n = 94,809) (Table 1), with corresponding higher risk in others [10, 25]. Studies differed in setting crude incident rates of 1.3 (13,030 cases/10,005,276 person- (cohort vs record-linkage), population (men vs women years) vs 4.1 (1249 cases/304,192 person-years) per 1000 vs both), and confounders adjusted (cardiovascular risk person-years. Mean (standard deviation (SD)) time from factors included vs not). Our hazard ratio estimates in gout diagnosis to the occurrence of incident MI was 2. models 1–3 ranging from 1.79 to 2.08 for adults aged 3 years (1.7); median 1.9 years (interquartile range 0.8–3.5). 65 years or older are slightly higher than previous esti- When we compared to people without MI, those who had mates, since we were examining an older patient MI were older, more likely to be male, white, and have population. higher medical comorbidity, including a higher prevalence Our study examined men and women 65 aged years or of cardiovascular disease, diabetes, connective tissue older in the USA, controlling for cardiovascular risk fac- disease, and other comorbidities (Table 1). tors among other important factors. Estimates were ro- In multivariable-adjusted analyses, gout was signifi- bust in sensitivity analyses. The implications of our cantly associated with higher hazard of incident MI, with study are several. Using a representative US patient HR of 2.08 (95% CI 1.95, 2.21), which was minimally population, we provided estimates for gout and incident attenuated in sensitivity analyses that replaced the con- MI, which adds clarity to this important clinical ques- tinuous Charlson–Romano score with a categorical score tion. An increased risk of incident MI in people with or individual comorbidities (models 2–3; Table 2). In gout raises a question regarding the role of chronic in- addition, older age, male gender, white race, and higher flammation and IL-1β pathways (via NLRP3 inflamma- comorbidity were each associated with a higher hazard some activation) and CRP, hallmarks of gout [26, 27], in of incident MI (Table 2). the pathogenesis of MI. The IL-1β pathway is shown to Sensitivity analyses additionally adjusting model 3 for be important in the pathogenesis of MI [28–35] and as a obesity led to minimal/no attenuation of HR from 1.79 downstream effect increases IL-6 levels, a potential (95% CI 1.69, 1.91) to 1.80 (95% CI 1.69, 1.91). Sensitivity causal pathway for atherothrombosis [36, 37]. Various analyses expanding the ICD-9-CM code for incident MI to pathogenic mechanisms common in people with CAD amoresensitiveandlessspecificdiagnostic codealgorithm risk factors, such as cholesterol crystals, tissue hypoxia, (code 410 or 412) revealed slightly lower HRs of incident and abnormal arterial flow patterns, can promote the ac- CAD related to gout in models corresponding to the three tivation of the NLRP3 inflammasome [38–41], which models as showninTable 2 above: 1.85 (95% CI 1.79, 1.92; then activates IL-1β. CRP is elevated in CAD and con- model 4), 1.83 (95% CI 1.77, 1.89; model 5), and 1.59 (95% tributes directly to atherosclerosis via leukocyte activa- CI 1.54, 1.65; model 6). tion and endothelial dysfunction [42–44]. Our study In subgroup analyses, hazard ratios of gout with incident findings generate the hypothesis that inflammatory path- MI were higher in the absence of hypertension, hyperlip- ways may be activated in the atherosclerotic plaque, idemia, diabetes, or heart failure (ranging from 2.2 to 3.0) which may then lead to MI. People with gout have up- vs hazard ratios in those with each of these comorbidities regulation of these inflammatory pathways, which might (ranging from 1.6 to 1.7) (Table 3), differences that were explain the increased risk of MI in gout, at least par- both statistically significant and seemed clinically mean- tially. The role of key mechanisms of increased athero- ingful. Similarly, the hazard ratios for MI with gout were sclerosis in gout needs to be examined in basic and 2.2 and 1.7 in those without vs with CAD (Table 3). We translational studies. noted minor differences by age, gender, and race which We noted that the association of gout with incident were only statistically significant, except for difference in MI was stronger in people without each CAD risk factor HR between white and black race, which also seemed (hypertension, hyperlipidemia, or diabetes) (HR 2.3–3.0) potentially clinically meaningful (2.02 vs 2.49) (Table 3). than in the presence of each CAD risk factor Singh and Cleveland Arthritis Research & Therapy (2018) 20:109 Page 4 of 8 Table 1 Demographic and clinical characteristics of people with and without myocardial infarction All episodes Myocardial infarction during follow-up p value No Yes Total, N 1,733,613 1,719,334 14,279 Age, mean (SD) 75.3 (7.6) 75.3 (7.6) 77.0 (7.4) < 0.0001 Gender, N (%) < 0.0001 Male 734,540 (42.4%) 727,667 (42.3%) 6873 (48.1%) Female 999,073 (57.6%) 991,667 (57.7%) 7406 (51.9%) Race/ethnicity, N (%) 0.18 White 1,493,475 (86.1%) 1,481,232 (86.2%) 12,243 (85.7%) Black 142,284 (8.2%) 141,052 (8.2%) 1232 (8.6%) Other/unknown 97,854 (5.6%) 97,050 (5.6%) 804 (5.6%) Charlson–Romano comorbidity score 0 913,332 (52.7%) 909,183 (52.9%) 4149 (29.1%) < 0.0001 1 174,551 (10.1%) 172,711 (10.0%) 1840 (12.9%) ≥ 2 645,730 (37.2%) 637,440 (37.1%) 8290 (58.1%) Charlson–Romano comorbidity score, mean (SD) 1.60 (2.39) 1.59 (2.38) 2.71 (2.79) < 0.0001 Charlson–Romano comorbidities Myocardial infarction 65,668 (3.8%) 64,977 (3.8%) 0 (0%) < 0.0001 Heart failure 202,190 (11.7%) 199,011 (11.6%) 3179 (22.3%) < 0.0001 Peripheral vascular disease 168,646 (9.7%) 165,601 (9.6%) 3045 (21.3%) < 0.0001 Cerebrovascular disease 168,696 (9.7%) 166,159 (9.7%) 2537 (17.8%) < 0.0001 Dementia 78,238 (4.5%) 77,698 (4.5%) 540 (3.8%) < 0.0001 Chronic pulmonary disease 270,419 (15.6%) 267,014 (15.5%) 3405 (23.8%) < 0.0001 Connective tissue disease 48,195 (2.8%) 47,571 (2.8%) 624 (4.4%) < 0.0001 Peptic ulcer disease 32,778 (1.9%) 32,371 (1.9%) 407 (2.9%) < 0.0001 Mild liver disease 8543 (0.49%) 8472 (0.49%) 71 (0.50%) 0.94 Diabetes 319,836 (18.4%) 314,635 (18.3%) 5201 (36.4%) < 0.0001 Diabetes with end organ damage 94,249 (5.4%) 92,168 (5.4%) 2081 (14.6%) < 0.0001 Hemiplegia 14,339 (0.83%) 14,164 (0.82%) 175 (1.2%) < 0.0001 Renal failure/disease 59,280 (3.4%) 58,007 (3.4%) 1273 (8.9%) < 0.0001 Any tumor, leukemia, or lymphoma 174,684 (10.1%) 172,971 (10.1%) 1713 (12.0%) < 0.0001 Moderate or severe liver disease 2002 (0.12%) 1986 (0.12%) 16 (0.11%) 0.9 Metastatic cancer 18,009 (1.0%) 17,895 (1.0%) 114 (0.80%) 0.004 AIDS 549 (0.03%) 543 (0.03%) 6 (0.04%) 0.49 Hypertension 836,875 (48.3%) 827,183 (48.1%) 9692 (67.9%) < 0.0001 Hyperlipidemia 602,546 (34.8%) 595,936 (34.7%) 6610 (46.3%) < 0.0001 Coronary artery disease 302,088 (17.4%) 297,025 (17.3%) 5063 (35.5%) < 0.0001 Obesity 36,034 (2.1%) 35,602 (2.1%) 432 (3.0%) < 0.0001 Met eligibility criteria and did not have MI in the baseline 365-day period; MI identified by the presence of two separate claims 4-weeks apart each with an ICD- 9-CM code of 410.x1 during the study period of 2006–2012, with an exclusion of any person with ICD-9-CM code of 410.xx or 412.xx during the baseline 365-day period of 1/1/2005 to 12/31/2005 Identified by the presence of ICD-9-CM code of 410.x or 412.x in the baseline 365-day period in 2005 ICD-9-CM International Classification of Diseases, ninth revision, common modification, MI myocardial infarction, SD standard deviation respectively (HR 1.6–1.7). This indicates that in patients meaningful. We also noted a similar trend in presence with known CAD/MI risk factors, gout contributes vs absence of heart failure and CAD. This observation is much less to the risk of incident MI. Most differences similar to that noted by Kuo et al. previously in a Tai- were not only statistically significant, but also clinically wanese study . We speculate two potential reasons Singh and Cleveland Arthritis Research & Therapy (2018) 20:109 Page 5 of 8 Table 2 Association of gout and other risk factors with incident myocardial infarction a a a Multivariable-adjusted model 1 Multivariable-adjusted model 2 Multivariable-adjusted model 3 HR (95% CI) p value HR (95% CI) p value HR (95% CI) p value Age (years) 65 to < 75 Ref Ref Ref 75 to < 85 1.62 (1.56, 1.68) < 0.0001 1.60 (1.54, 1.66) < 0.0001 1.55 (1.49, 1.61) < 0.0001 ≥ 85 2.51 (2.39, 2.64) < 0.0001 2.52 (2.39, 2.64) < 0.0001 2.47 (2.35, 2.60) < 0.0001 Gender Male Ref Ref Ref Female 0.75 (0.73, 0.78) < 0.0001 0.74 (0.72, 0.77) < 0.0001 0.76 (0.74, 0.79) < 0.0001 Race White Ref Ref Ref Black 1.00 (0.94, 1.06) 0.97 1.05 (0.99, 1.11) 0.10 0.95 (0.90, 1.01) 0.11 Other 0.93 (0.86, 1.00) 0.038 0.97 (0.90, 1.04) 0.35 0.89 (0.83, 0.96) 0.002 Charlson–Romano score, per unit change 1.21 (1.21, 1.22) < 0.0001 N/A N/A Charlson–Romano comorbidity score, N (%) 0 N/A Ref N/A 1 2.20 (2.08, 2.33) < 0.0001 ≥ 2 3.11 (2.99, 3.23) < 0.0001 Gout 2.08 (1.95, 2.21) < 0.0001 2.14 (2.01, 2.27) < 0.0001 1.79 (1.68, 1.90) < 0.0001 Bold data represent statistical significance, with p < 0.05 CI confidence interval, HR hazard ratio, N/A not applicable, Ref referent category Model 1 included Charlson–Romano score as a continuous variable; model 2 replaced it with categorized Charlson–Romano score; and model 3 replaced it with each of the 17 Charlson–Romano comorbidities. All models also adjusted for medications for cardiovascular diseases (statins, beta-blockers, diuretics, angiotensin converting enzyme inhibitors) and for urate-lowering therapies for gout (allopurinol, febuxostat) for this observation: gout is associated with a 3-fold elderly) and the population examined in each study higher prevalence of the metabolic syndrome  that (limited to only people aged 65 years or older vs all has features of hyperlipidemia, hyperglycemia, or obes- ages vs. women 65 years or older). ity, and gout may be an early clinical manifestation of Our study has several limitations, which must be con- the metabolic syndrome; and episodic inflammation sidered while interpreting the findings. We used data from characteristic of gout flares may increase the MI risk, es- Americans aged 65 years or older, and therefore the pecially in those without other CAD risk factors. generalizability of these findings to younger people is un- In a subgroup analysis, we found that gender made lit- certain. Diagnostic misclassification may have occurred tle difference to the association of gout with incident MI despite our use of validated algorithms; this would bias in the elderly. Previous studies found higher risk of MI our study findings toward the null, making our findings with gout in UK women compared to men (2.08 (95% conservative (i.e., we may have missed some associations). CI 2.01, 2.16) vs 1.73 (95% CI 1.69, 1.77), respectively) Residual confounding is still possible, given an observa- , considering all age groups (mean age 70 years), or a tional study design, despite the fact that we controlled for trend of higher risk of MI with gout in Canadian women several potential confounders. We adjusted for several compared to men (1.11 (95% CI 0.99, 1.23); p = 0.003 for potential confounders including cardiovascular drugs, but interaction by gender)  in the elderly with a mean we did not adjust for aspirin, nonsteroidal anti- age of 75 years. The mean age of people in our study is inflammatory drug (NSAID) use, alcohol use, smok- similar to these studies at 73 years. The reasons for dif- ing, or exercise, which may have led to some residual ferences in the findings is likely related to differences in confounding. Adjustment for aspirin and NSAID use country setting (USA vs UK vs Canada), confounders was considered but not done, since most NSAID and adjusted in the analyses (cardiovascular diseases, car- aspirin use in this age group is over the counter ra- diovascular medications, and gout medications vs nei- ther than prescription use [45, 46]. Over-the-counter ther vs cardiovascular medications only), the outcome medication use is not captured in the Medicare definition, the underlying conditions (none vs. none vs. claims data, which would introduce misclassification musculoskeletal disease), study sample (all Medicare re- bias. We are also unaware of the differential rate/pat- cipients vs. all hospital admissions for gout vs. all tern of the use of aspirin or NSAID by gout status, Singh and Cleveland Arthritis Research & Therapy (2018) 20:109 Page 6 of 8 Table 3 Association of gout with MI, in predefined subgroup analyses Multivariable-adjusted model 1 Multivariable-adjusted model 1 Multivariable-adjusted model 1 HR (95% CI) p value HR (95% CI) p value HR (95% CI) p value Black White Other race Gout 2.49 (2.10, 2.96) < 0.0001 2.02 (1.89, 2.16) < 0.0001 2.05 (1.59, 2.65) < 0.0001 Female Male Gout 2.13 (1.94, 2.35) < 0.0001 2.04 (1.89, 2.21) < 0.0001 Age 65–75 years Age 75–85 years Age > 85 years Gout 2.09 (1.90, 2.31) < 0.0001 1.89 (1.73, 2.08) < 0.0001 2.50 (2.15, 2.91) < 0.0001 No hypertension Hypertension Gout 3.00 (2.65, 3.40) < 0.0001 1.73 (1.61, 1.85) < 0.0001 No diabetes Diabetes Gout 2.35 (2.17, 2.55) < 0.0001 1.64 (1.49, 1.80) < 0.0001 No hyperlipidemia Hyperlipidemia Gout 2.45 (2.24, 2.69) < 0.0001 1.74 (1.60, 1.90) < 0.0001 No heart failure Heart failure Gout 2.20 (2.04, 2.36) < 0.0001 1.73 (1.55, 1.93) < 0.0001 No CAD CAD Gout 2.18 (2.01, 2.37) < 0.0001 1.74 (1.59, 1.91) < 0.0001 Race × gout, p = 0.10 Age × gout, p < 0.05 Gender × gout, p = 0.017 Hypertension × gout, p < 0.0001 Diabetes × gout, p < 0.0001 Heart failure × gout, p < 0.0001 Hyperlipidemia × gout, p < 0.0001 CAD × gout, p < 0.0001 Bold data represent significant HRs with p < 0.05 HR hazard ratio, CI confidence interval, CAD coronary artery disease for primary or secondary prevention of CAD. Our and evaluate to what extent this association is due to study has several strengths. Inclusion of medications chronic inflammation versus other potential pathways. for cardiovascular disease and gout strengthens the Abbreviations analyses, since these medications might be imperfect ACE: Angiotensin converting enzyme; CAD: Coronary artery disease; CRP: surrogates of disease severity which Medicare data C-reactive protein; ICD-9-CM: International Classification of Diseases, ninth revision, common modification; MI: Myocardial infarction; SD: Standard lack and have independent protective effects related deviation to MI risk, but also in some cases may be indicative ofthepresenceofadiseaseintheabsenceofan Acknowledgements ICD-9-CM code and may reduce misclassification 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 bias. We used a representative sample of US adults study to answer the question. aged 65 years or older, had an adequate number of events for analyses, and conducted multiple sensitivity Funding analyses to test the robustness of findings. 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, Conclusions Birmingham, AL, USA. The funding body did not play any role in design; in This study showed an association of gout with incident the collection, analysis, and interpretation of data; in the writing of the manuscript; and in the decision to submit the manuscript for publication. MI in adults aged 65 years or older, independent of the traditional CAD risk factors. The MI risk associated with Availability of data and materials gout was stronger in people without CAD risk factors The authors are ready to share the data with colleagues, after obtaining compared to people with CAD risk factors, and the risk appropriate permissions from the University of Alabama at Birmingham (UAB) Ethics Committee, related to HIPAA and privacy policies. was increased 2-fold or higher in both groups. Chronic inflammation, a hallmark of gout, is implicated in the Authors’ contributions pathogenesis of incident MI . Future studies should JAS designed the study, developed the study protocol, reviewed analyses, evaluate the mechanisms for this disease association, and wrote the first draft of the article. JDC performed the data abstraction Singh and Cleveland Arthritis Research & Therapy (2018) 20:109 Page 7 of 8 and data analyses. Both authors made revisions to the manuscript, read, and 11. De Vera MA, Rahman MM, Bhole V, Kopec JA, Choi HK. Independent impact approved the final manuscript. of gout on the risk of acute myocardial infarction among elderly women: a population-based study. Ann Rheum Dis. 2010;69(6):1162–4. 12. Krishnan E, Baker JF, Furst DE, Schumacher HR. Gout and the risk of acute Ethics approval and consent to participate myocardial infarction. Arthritis Rheum. 2006;54(8):2688–96. The University of Alabama at Birmingham’s Institutional Review Board 13. Seminog OO, Goldacre MJ. Gout as a risk factor for myocardial infarction approved this study and all investigations were conducted in conformity and stroke in England: evidence from record linkage studies. Rheumatology with ethical principles of research. The IRB waived the need for informed (Oxford). 2013;52(12):2251–9. consent for this database study. 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- Competing interests based study. Rheumatology (Oxford). 2013;52(1):111–7. JAS has received research grants from Takeda and Savient and consultant fees 15. Alexander KP, Roe MT, Chen AY, Lytle BL, Pollack CV Jr, Foody JM, Boden from Savient, Takeda, Regeneron, Merz, Iroko, Bioiberica, Fidia, Crealta/Horizon and WE, Smith SC Jr, Gibler WB, Ohman EM, et al. Evolution in cardiovascular Allergan pharmaceuticals, WebMD, UBM LLC, and National Institute of Health and care for elderly patients with non-ST-segment elevation acute coronary the American College of Rheumatology. JAS serves as the principal investigator syndromes: results from the CRUSADE National Quality Improvement for an investigator-initiated study funded by Horizon pharmaceuticals through a Initiative. J Am Coll Cardiol. 2005;46(8):1479–87. grant to DINORA, Inc., a 501 (c)(3) entity. JAS is a member of the executive of 16. Rask-Madsen C, Jensen G, Kober L, Melchior T, Torp-Pedersen C, Hildebrand OMERACT, an organization that develops outcome measures in rheumatology P. Age-related mortality, clinical heart failure, and ventricular fibrillation in and receives arms-length funding from 36 companies; a member of the American 4259 Danish patients after acute myocardial infarction. Eur Heart J. 1997; College of Rheumatology’s (ACR) Annual Meeting Planning Committee (AMPC); 18(9):1426–31. Chair of the ACR Meet-the-Professor, Workshop and Study Group Subcommittee; 17. Gurwitz JH, Col NF, Avorn J. The exclusion of the elderly and women from and a member of the Veterans Affairs Rheumatology Field Advisory Committee. clinical trials in acute myocardial infarction. JAMA. 1992;268(11):1417–22. JAS is the editor and Director of the UAB Cochrane Musculoskeletal Group 18. US Census Bureau. National population projections. I. Summary files. Total Satellite Center on Network Meta-analysis. JDC has no relevant financial conflicts. population by age, sex, race and Hispanic origin. (NP-T4-F). Projections of There are no nonfinancial competing interests for either author. 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- Publisher’sNote summary-tables/np-t4-f.pdf. Accessed 28 Feb 2018. Springer Nature remains neutral with regard to jurisdictional claims in 19. CMS fee information. https://www.resdac.org/resconnect/articles/123. published maps and institutional affiliations. Accessed 28 Feb 2018. 20. Singh JA, Hodges JS, Toscano JP, Asch SM. Quality of care for gout in the Author details US needs improvement. Arthritis Rheum. 2007;57(5):822–9. Medicine Service, VA Medical Center, 510, 20th Street South, FOT 805B, 21. Kiyota Y, Schneeweiss S, Glynn RJ, Cannuscio CC, Avorn J, Solomon DH. Birmingham, AL 35233, USA. Department of Medicine at School of Accuracy of Medicare claims-based diagnosis of acute myocardial infarction: Medicine, University of Alabama at Birmingham, 1720 Second Ave. South, estimating positive predictive value on the basis of review of hospital Birmingham, AL 35294-0022, USA. Division of Epidemiology at School of records. Am Heart J. 2004;148(1):99–104. Public Health, University of Alabama at Birmingham, 1720 Second Ave. 22. Singh JA, Ramachandaran R, Yu S, Yang S, Xie F, Yun H, Zhang J, Curtis JR. Is South, Birmingham, AL 35294-0022, USA. University of Alabama at gout a risk equivalent to diabetes for stroke and myocardial infarction? A Birmingham, Faculty Office Tower 805B, 510 20th Street S, Birmingham, AL retrospective claims database study. Arthritis Res Ther. 2017;19(1):228. 35294-0022, USA. 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 Received: 7 March 2018 Accepted: 24 April 2018 factors. Med Care. 2005;43(5):480–5. 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 References 25. Gelber AC, Klag MJ, Mead LA, Thomas J, Thomas DJ, Pearson TA, Hochberg 1. Centers for Disease Control and Prevention. Heart disease. http://www.cdc. MC. Gout and risk for subsequent coronary heart disease. The Meharry- gov/heartdisease/. Accessed 28 Feb 2018. Hopkins Study. Arch Intern Med. 1997;157(13):1436–40. 2. Yeh RW, Sidney S, Chandra M, Sorel M, Selby JV, Go AS. Population trends 26. Martinon F, Petrilli V, Mayor A, Tardivel A, Tschopp J. Gout-associated uric acid in the incidence and outcomes of acute myocardial infarction. N Engl J crystals activate the NALP3 inflammasome. Nature. 2006;440(7081):237–41. Med. 2010;362(23):2155–65. 27. Cronstein BN, Terkeltaub R. The inflammatory process of gout and its 3. Centers for Disease Control and Prevention. Heart attack. https://www.cdc. treatment. Arthritis Res Ther. 2006;8(Suppl 1):S3. gov/heartdisease/heart_attack.htm. Accessed 28 Feb 2018. 28. Libby P, Ridker PM, Maseri A. Inflammation and atherosclerosis. Circulation. 4. Roger VL. Epidemiology of myocardial infarction. Med Clin North Am. 2007; 2002;105(9):1135–43. 91(4):537–52. ix 29. Libby P, Ordovas JM, Auger KR, Robbins AH, Birinyi LK, Dinarello CA. 5. Kauf TL, Velazquez EJ, Crosslin DR, Weaver WD, Diaz R, Granger CB, Endotoxin and tumor necrosis factor induce interleukin-1 gene expression McMurray JJ, Rouleau JL, Aylward PE, White HD, et al. The cost of acute in adult human vascular endothelial cells. Am J Pathol. 1986;124(2):179–85. myocardial infarction in the new millennium: evidence from a multinational 30. Dinarello CA, Simon A, van der Meer JW. Treating inflammation by blocking registry. Am Heart J. 2006;151(1):206–12. interleukin-1 in a broad spectrum of diseases. Nat Rev Drug Discov. 2012; 6. Roeters van Lennep JE, Westerveld HT, Erkelens DW, van der Wall EE. Risk 11(8):633–52. factors for coronary heart disease: implications of gender. Cardiovasc Res. 31. Fuster JJ, MacLauchlan S, Zuriaga MA, Polackal MN, Ostriker AC, Chakraborty 2002;53(3):538–49. R, Wu CL, Sano S, Muralidharan S, Rius C, et al. Clonal hematopoiesis 7. Choi HK, Ford ES, Li C, Curhan G. Prevalence of the metabolic syndrome in associated with TET2 deficiency accelerates atherosclerosis development in patients with gout: the Third National Health and Nutrition Examination mice. Science. 2017;355(6327):842–7. Survey. Arthritis Rheum. 2007;57(1):109–15. 8. Zhu Y, Pandya BJ, Choi HK. Comorbidities of gout and hyperuricemia in the US 32. Sager HB, Heidt T, Hulsmans M, Dutta P, Courties G, Sebas M, Wojtkiewicz general population: NHANES 2007-2008. Am J Med. 2012;125(7):679–87. e671 GR, Tricot B, Iwamoto Y, Sun Y, et al. Targeting interleukin-1beta reduces 9. Abbott RD, Brand FN, Kannel WB, Castelli WP. Gout and coronary heart leukocyte production after acute myocardial infarction. Circulation. 2015; disease: the Framingham Study. J Clin Epidemiol. 1988;41(3):237–42. 132(20):1880–90. 10. Janssens HJ, van de Lisdonk EH, Bor H, van den Hoogen HJ, Janssen M. 33. Jaiswal S, Natarajan P, Silver AJ, Gibson CJ, Bick AG, Shvartz E, McConkey M, Gout, just a nasty event or a cardiovascular signal? A study from primary Gupta N, Gabriel S, Ardissino D, et al. Clonal hematopoiesis and risk of care. Fam Pract. 2003;20(4):413–6. atherosclerotic cardiovascular disease. N Engl J Med. 2017;377(2):111–21. Singh and Cleveland Arthritis Research & Therapy (2018) 20:109 Page 8 of 8 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. 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. 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. 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. 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. 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. 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. 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. 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. 43. Pasceri V, Willerson JT, Yeh ET. Direct proinflammatory effect of C-reactive protein on human endothelial cells. Circulation. 2000;102(18):2165–8. 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. 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. 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.
Arthritis Research & Therapy – Springer Journals
Published: Jun 1, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
All the latest content is available, no embargo periods.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud