Incident myocardial infarction associated with major types of arthritis in the general population: a systematic review and meta-analysis

OBJECTIVE: To synthesise, quantify and compare risks for incident myocardial infarction (MI) across five major types of arthritis in population-based studies. METHODS: A systematic search was performed in MEDLINE, EMBASE and CINAHL databases with additional manual/hand searches for population-based cohort or case-control studies published in English of French between January 1980 and January 2015 with a measure of effect and variance for associations between incident MI and five major types of arthritis: rheumatoid arthritis (RA), psoriatic arthritis (PsA), ankylosing spondylitis (AS), gout or osteoarthritis (OA), adjusted for at least age and sex. All search screening, data abstraction quality appraisals were performed independently by two reviewers. Where appropriate, random-effects meta-analysis was used to pool results from studies with a minimum of 10 events. RESULTS: We identified a total of 4, 285 articles; 27 met review criteria and 25 criteria for meta-analyses. In studies adjusting for age and sex, MI risk was significantly increased in RA (pooled relative risk (RR): 1.69, 95% CI 1.50 to 1.90), gout (pooled RR: 1.47, 95% CI 1.24 to 1.73), PsA (pooled RR: 1.41, 95% CI 1.17 to 1.69), OA (pooled RR: 1.31, 95% CI 1.01 to 1.71) and tended towards increased risk in AS (pooled RR: 1.24, 95% CI 0.93 to 1.65). Traditional risk factors were more prevalent in all types of arthritis. MI risk was attenuated for each type of arthritis in studies adjusting for traditional risk factors and remained significantly increased in RA, PsA and gout. CONCLUSIONS: MI risk was consistently increased in multiple types of arthritis in population-based studies, and was partially explained by a higher prevalence of traditional risk factors in all types of arthritis. Findings support more integrated cardiovascular (CV) prevention strategies for arthritis populations that target both reducing inflammation and enhancing management of traditional CV risk factors