Author + information
- Jared L. Christensen, MD,
- Wenzheng Yu, BA,
- Sydney Tan, BA,
- Alice Chu, BS,
- Fabian Vargas, MD,
- Maen Assali, MD,
- Nishant R. Shah, MD, MPH,
- Anthony M. Reginato, MD, PhD,
- Wen-Chih Wu, MD,
- Gaurav Choudhary, MD and
- Alan R. Morrison, MD, PhD∗ ()
- ↵∗Providence VA Medical Center, Research (151), 830 Chalkstone Avenue, Providence, Rhode Island 02908
Gout is an inflammatory arthropathy with increasing global incidence and association with cardiovascular disease and mortality (1). Smoking is a well-established risk factor for coronary artery calcium (CAC) and adverse cardiovascular outcomes, yet smoking has been associated with reduced serum uric acid and decreased gout incidence, albeit in mostly young men (1). The impact of gout on CAC and cardiovascular outcomes in an older population with more significant smoking history is unknown.
This was a single-center, retrospective analysis of 1,033 U.S. veterans who underwent U.S. Preventive Services Task Force guideline–recommended (30-pack-year smokers, 55 to 80 years of age, active or quit <15 years previously) lung cancer screening computed tomography between October 1, 2013, and May 31, 2014 (2). Patients were excluded if they had lung cancer or prior percutaneous intervention with stent implantation, which affects CAC quantification.
Lung cancer screening computed tomographic examinations were not electrocardiographically gated and were performed using a 128-slice computed tomographic scanner (Siemens Healthcare, Erlangen, Germany), using 128 × 0.6 mm collimation, 0.5-s rotation, pitch of 0.84, a 380-mm field of view, and a matrix size of 512 × 512 pixels. Tube voltage and tube current were 120 kV and 40 mA. Image reconstruction thickness was 1.0 to 1.25 mm. The minimum area required to identify calcium was 0.55 mm2. Agatston CAC score (CACS) was quantified using a semiautomated imaging workstation with readers blinded to patient data (3). The kappa value for interobserver agreement was 0.92 (0.89 to 0.95).
The U.S. Department of Veterans Affairs electronic medical record was searched for patient demographics, cardiovascular risk factors, serum uric acid, and diagnosis of gout. Coronary artery disease (CAD) was defined as a history of myocardial infarction (MI), coronary artery bypass graft surgery, or abnormalities on cardiac testing (exercise treadmill testing, echocardiography, myocardial perfusion, cardiac computed tomography, or coronary angiography). Gout diagnosis was defined as International Classification of Diseases-Ninth Revision and International Classification of Diseases-Tenth Revision codes of 274.XX/M10.XX or the use of gout-related medications.
The primary outcome was CACS. The secondary outcome was major adverse cardiac events (MACE), defined as the composite endpoint of electronic medical record–documented cardiovascular death, nonfatal MI, and nonfatal cerebral vascular accident (4). Cardiovascular death included sudden cardiac death or death from MI, heart failure, cerebrovascular accident, cardiovascular procedure, or other cardiovascular causes. MI was defined as ST-segment elevation myocardial infarction or non–ST-segment elevation myocardial infarction. Cerebrovascular accident included ischemic stroke and transient ischemic attack. Adjudicators of clinical outcomes were blinded to CACS.
Comparisons included normally distributed continuous variables (Student’s t-test), continuous variables not normally distributed (Mann-Whitney U test), and categorical variables (chi-square test). CACS between patients with and those without gout were compared using linear regression adjusted for covariates with p values <0.20 in the univariate model. MACE between patients with and those without gout were compared using Cox regression, adjusted for age and CAD or for CACS.
The median age of this mostly Caucasian (94%), male (96%) population was 65 years (interquartile interval [IQI]: 61 to 68 years), and 9.3% had gout. The median atherosclerotic cardiovascular disease score was 18% (IQI: 12% to 28%). Fifty-nine percent of patients had hypertension, 73% had dyslipidemia, 28% had diabetes, 19% had stage 3 or higher chronic kidney disease (glomerular filtration rate <60 ml/min), and 17% had CAD. The overall median CACS was 452 Agatston units (AU) (IQI: 90 to 1,345 AU). During the 48 months after lung cancer screening computed tomography, MACE occurred in 9.7% of the patients.
Patients with gout had higher median CACS than those without (768 AU [IQI: 253 to 1,966 AU] vs. 437 AU [IQI: 87 to 1,261 AU]; p = 0.001). Gout was associated with higher CACS (β = 736; 95% confidence interval [CI]: 446 to 1,027; p < 0.001), and this association remained significant after adjustment for age, sex, body mass index, diabetes mellitus, hypertension, hyperlipidemia, chronic kidney disease, statin use, and CAD (β = 437; 95% CI: 173 to 701; p = 0.001). Gout was also associated with higher serum uric acid (7.0 mg/dl [IQI: 5.7 to 8.6 mg/dl] vs. 5.8 mg/dl [IQI: 4.9 to 6.9 mg/dl]; p < 0.001).
MACE occurred in 18 patients with gout (19%) and 83 gout-free patients (9%) (Figure 1A). When patients were stratified by CACS, gout was associated with increased annualized MACE at high CACS, though median CACS was significantly increased for gout in that category (Figure 1B). By Cox regression, gout was associated with MACE (hazard ratio: 2.23; 95% CI: 1.35 to 3.74; p = 0.002), and this association remained significant after adjustment for age and CAD (hazard ratio: 1.79; 95% CI: 1.07 to 2.99; p = 0.026). After adjustment for CACS, the association between gout and MACE did not remain significant (hazard ratio: 1.50; 95% CI: 0.86 to 2.62; p = 0.152), supporting CAC as a possible mechanism for the increased MACE.
Our study shows, for the first time, that gout is independently associated with increased CACS and worsening cardiovascular outcomes, likely due to increased CAC.
This study was limited by selection biases inherent to the study design. This particular population of veterans consisted mostly of older, white men with significant smoking histories. Our definition of gout may be less accurate than American College of Rheumatology/European League Against Rheumatism classification criteria. Diagnosis codes and events may be underestimated because of limitations inherent in extracting data or because care outside the Department of Veterans Affairs was not fully captured.
Additional studies are required to assess the mechanistic relationship among uric acid, gouty inflammation, and calcific atherosclerosis leading to increased events in this population and whether this population would derive particular benefit from more aggressive gout- or inflammation-targeted clinical strategies to reduce cardiovascular events.
Please note: †Drs. Christensen and Yu are joint first authors. Research reported in this publication was supported by Research Project Grants R01HL139795 (to Dr. Morrison), R01HL128661 (to Dr. Choudhary), and R01HL148727 (to Dr. Choudhary) from the National Heart, Lung, and Blood Institute; Institutional Development Award P20GM103652 (to Dr. Morrison) from the National Institute of General Medical Sciences; grant T35HL094308 (to Dr. Chu) from the National Heart, Lung, and Blood Institute; Career Development Award 7IK2BX002527 (to Dr. Morrison) from the U.S. Department of Veterans Affairs Biomedical Laboratory Research and Development Program; Veterans Affairs Merit Award I01CX001892 (to Dr. Choudhary); and Agency for Healthcare Research and Quality Award 5K12HS022998 (to Dr. Shah). The authors have reported that they have no relationships relevant to the contents of this paper to disclose.
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