Cardiovascular assessment warranted at diagnosis, especially with other risk factors

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by Wayne Kuznar
Contributing Writer

At the index date of gout diagnosis, a significantly greater percentage of gout patients had Afib compared with controls (7.42% versus 2.98%, P<0.001). The prevalence of Afib at the index date was significantly higher in both men (6.69% versus 2.49%) and women (9.36% versuss 3.71%) with gout than in their respective controls.

“Gout is probably an independent risk factor for Afib, despite it also being associated with many comorbidities that also contribute to development of Afib,” Weiya Zhang, PhD, of the University of Nottingham, and colleagues wrote online Dec. 17 inRheumatology.

Results from their analysis support “the case for a clinical cardiovascular assessment and inclusion of an electrocardiogram as a part of the initial assessment of gout patients at diagnosis and close observation … for the occurrence of incident Afib, especially for the elderly and those having other Afib risk factors,” they noted.

Using both retrospective (prior to diagnosis) and prospective case control study (after diagnosis) designs within the Clinical Practice Research Data-link, a database containing primary care data prospectively collected by general practitioners in the U.K., the investigators sought to examine the risk of Afib at the time of first diagnosis of gout in 45,378 patients compared with 45,378 age- and sex-matched controls, and followed incident gout patients and their matched controls after diagnosis to assess their subsequent risks of Afib.

The median observation periods before and after the index date were 15 and 9 years, respectively.

Gout patients consumed more cigarettes and alcohol and had a higher body mass index and Charlson comorbidity score than controls. Hypertension, ischemic heart disease, cerebrovascular disease, heart failure, and valvular heart disease were all significantly more prevalent in gout patients than controls.

The unadjusted OR for Afib in gout patients was 2.89 (95% CI 2.70-3.09). After adjustment for covariates, gout was still associated with an increased odds overall for Afib (adjusted OR 1.45, 95% CI 1.29-1.62), as well as in men (adjusted OR 1.43, 95% CI 1.23-1.67) and women (adjusted OR 1.41, 95% CI 1.13-1.78) separately.

The cumulative probability for incident Afib was significantly higher in gout patients than in controls at all times since the index date (P<0.001). The cumulative probability of Afib in gout patients after the index date was 1.08% at 1 year, 2.03% at 2 years, 4.77% at 5 years and 9.68% at 10 years. In controls, the cumulative probability of Afib was 0.43%, 1.08%, 2.95%, and 6.33%, respectively (log-rank test P<0.001).

Within 5 years of diagnosis, about 12% of gout patients developed Afib compared with only 6% of matched controls.

After a median follow-up of 9 years, 3,534 gout patients and 2,322 matched controls developed Afib after the index date (adjusted HR 1.09, 95% CI 1.03-1.16). The adjusted HR estimates for men and women separately were 1.09 (95% CI 1.01-1.16) and 1.12 (95% CI 1.02-1.24), respectively.

“Currently, there is no explicit explanation for the link between gout and Afib,” wrote Zhang and colleagues. “The potential mechanism underlying the increased risk of Afib in gout patients is hyperuricemia. Increasing evidence suggests that uric acid participates in the atrial remodeling process that enhances the risk of Afib.”

Prospective studies supporting a link between hyperuricemia and Afib include theAtherosclerosis Risk in Communities study, which found that a 1 standard deviation increase in serum uric acid levels was associated with a HR of 1.56 for Afib in black Americans (but no significant association was identified for white Americans), and astudy following 400 patients with type 2 diabetes, which found that a 1 standard deviation increment in serum uric acid level was associated with 2.5-fold increased risk of incident Afib.

Potential limitations to the study include the possibility of misclassification bias since gout patients were identified by physician diagnosis rather than according to classification criteria or urate crystal identification, and the potential misclassification of Afib. In addition, differential ascertainment bias between incident gout patients and controls cannot be excluded entirely. Finally, not all factors in an Afib risk prediction model were available in the dataset, with the potential to bias the results.

• Reviewed by Henry A. Solomon, MD, FACP, FACC Clinical Associate Professor, Weill Cornell Medical College and Dorothy Caputo, MA, BSN, RN, Nurse Planner

LAST UPDATED 01.04.2016