THU0664 THE ABSOLUTE RISK OF CLINICALLY DIAGNOSED GOUT BY CLUSTERS OF GOUT ASSOCIATED COMORBIDITIES AND LIFESTYLE FACTORS – RESULTS FROM 30 YEARS FOLLOW-UP OF THE MALMÖ PREVENTIVE PROJECT COHORT IN SOUTHERN SWEDEN
Authors: Tahzeeb Fatima, Peter Nilsson, Carl Turesson, Mats Dehlin, Nicola Dalbeth, Lennart T.H. Jacobsson, Meliha C Kapetanovic
Journal: Annals of the Rheumatic Diseases (2019)

<h3>Background:</h3> Clinical gout is predicted by a number of factors. Several of these (comorbidities and lifestyle) have been shown to cluster in gout patients<sup>1,2</sup> indicating several different pathophysiological pathways, but it is not known if and to what extent such clusters predict gout. <h3>Objectives:</h3> To examine; 1) the prevalence of comorbidity clusters in the population and 2) the long-term absolute and relative risks for developing gout in these clusters defined at baseline in subjects of Malmö Preventive Project (MPP). <h3>Methods:</h3> The MPP is a screening program for cardiovascular risk factors, alcohol abuse and breast cancer in Malmö, Sweden. A total of 33,346 individuals (67% male, age=45.7 years, mean follow-up=28.2 years), screened between 1974 and 1992, participated. Date of gout diagnosis was defined as the first visit with gout (using ICD-codes) to physicians within primary or specialized care, through linkage of the MPP cohort with regional and national health care registers. End of follow-up was defined as the date of first gout diagnosis, death, moving from the area or December 31<sup>st</sup>, 2014. Hierarchical clustering was performed in a subset of 22,057 individuals (screening period: 1975-1992) to group observations. Variables clustered included obesity, renal dysfunction, diabetes mellitus (DM), hypertension, cardiovascular disease (CVD), dyslipidemia, pulmonary dysfunction (PD), smoking and use of diuretics. For the five identified clusters, their population prevalence as well as the incidence and hazard ratio (HR) for gout were computed using cox-proportional hazard analysis in Rv3.5.2. <h3>Results:</h3> Cluster-1 (C1) with "few comorbidities" was by far the most common in the population (73%), followed by cluster-4 (C4) characterized by "obesity and hyperlipidaemia" (17%). These two pathways included 86% of incident gout cases during the follow-up. The four clusters (C2-5) identified by different comorbidity patterns all resulted in a 2-3-fold increased risk for incident gout. The highest incidence for gout was seen for cluster-2 (C2) characterized by "renal dysfunction" and cluster-3 (C3) characterized by "cardiovascular disease" (Table). <h3>Conclusion:</h3> In a population-based study, we identified five clusters based on gout-related comorbidities. Most gout cases occurred in the cluster characterized by few comorbidities. Such cases may have predisposing factors not captured by comorbidity patterns, e.g. genetics related to serum urate. In addition, we identified four pathways each defined by different comorbidity patterns and contributing to increased risk for gout, pathways that may require partly individual interventional strategies. <h3>References:</h3> [1] Richette P, et al. Ann Rheum Dis 2015; 74(1):142-47. [2] Megan B, et al. Rheumatol 2018; 57(8):1358-63. <h3>Disclosure of Interests:</h3> Tahzeeb Fatima: None declared, Peter Nilsson: None declared, Carl Turesson: None declared, Mats Dehlin: None declared, Nicola Dalbeth Grant/research support from: Amgen, AstraZeneca, Consultant for: Horizon, Hengrui, Kowa, Speakers bureau: Pfizer, Horizon, Janssen, AbbVie, Lennart T.H. Jacobsson Consultant for: LJ has received lecture and consulting fees from Pfizer, Abbvie, Novartis, Eli-Lily and Janssen, Meliha C Kapetanovic: None declared