Predictors for Clinically Diagnosed Gout – Results from 30 Years Follow-up of the Malmö Preventive Project Cohort in Southern Sweden

Meliha C. Kapetanovic¹, Peter M Nilsson², Carl Turesson³, Nicola Dalbeth⁴, Martin Englund⁵, Lieke E.J.M. Scheepers⁶ and Lennart TH Jacobsson⁶, ¹Lund University, Skane University Hospital, Department of Rheumatology, Lund, Sweden, Lund, Sweden, ²Department of Clinical Sciences, Lund University, Skåne University Hospital Malmö, Sweden., Lund, Sweden, ³Department of Rheumatology, Skåne University Hospital, Malmö, Sweden, ⁴University of Auckland, Auckland, New Zealand, ⁵Clinical Sciences Lund, Orthopedics, Clinical Epidemiology Unit, Lund University, Lund, Sweden, ⁶Department of Rheumatology and Inflammation Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

Meeting: 2017 ACR/ARHP Annual Meeting

Date of first publication: September 18, 2017

Keywords: gout, hyperuricemia and risk assessment

Session Information

Date: Tuesday, November 7, 2017

Title: Metabolic and Crystal Arthropathies Poster II

Session Type: ACR Poster Session C

Session Time: 9:00AM-11:00AM

Background/Purpose: Gout is the most common form of arthritis worldwide. Hyperuricemia is a crucial risk factor resulting in accumulation of uric acid (s-UA) crystals in tissues and in a subset development of clinical gout. The relative importance of other risk factors, some of which are associated with s-UA levels, is slightly more controversial. The aim of this preliminary analysis, were to identify such predictors for clinical gout in a population survey, the Malmö Preventive Project (MPP)- a large-scale screening and case-finding program for cardiovascular risk factors, alcohol abuse and breast cancer in the city of Malmö, Sweden.

Methods: Overall, 33.346 individuals (67% male, mean age 45.7 years at inclusion, mean follow up 28.2 years) participated. The study population was screened between 1974 and 1992. A baseline health screening included: 1. Questionnaire with 260 questions (socioeconomic factors, alcohol consumption, smoking, physical activity, dietary habits, history of gout and other co-morbidities) 2. Physical examination (including weight, height, BMI, and blood pressure) and 3. Laboratory tests (serum urate, fasting glucose, s-creatinine). Endpoint was defined as the date of first gout diagnosis, death, moving from the area, or December 31^st 2014. In order to identify all gout diagnoses (using ICD-codes) given at visits to physicians in primary care, in specialized in-patient (from 1974) and out-patient specialized care (from 2001); MPP cohort was linked to reginal health care register and to the national patient register, respectively. Individuals with a history of gout before the inclusion in MPP (n=11) were excluded from the analysis. Possible baseline predictors of developing gout were analysed using Cox-regression model.

Results: Of 33.346 individuals participating in MPM project, 1275 (3.8%) were diagnosed with clinically gout over 30 years follow up. Subjects with higher s-UA at baseline (age- and sex-adjusted) had a significantly increased risk of developing gout. In addition, results from the multivariate analysis identified higher age, male sex, higher baseline BMI, systolic blood pressure, current smoking for ≥ 10 years and daily alcohol drinking as independent predictors of incident gout (Table).

Conclusion: In addition to hyperuricemia, increased age, male sex, hypertriglyceridemia, concomitant hypertension, smoking and daily drinking were independent predictors for development of gout in this large cohort of middle-aged individuals.

Table: Baseline predictors of development of gout over 30 years

	Mean (SD)	HR (95% CI) (age/sex adjusted)	HR (95% CI)* (multivariate analysis)
Age	45.7 (7.4)	1.5 (1.4-1.6)	1.4 (1.3-1.6)
s-UA (µmol/L)	300.8 (70.1)	2.0 (1.9-2.0)	1.8 (1.8-1.9)
s-creatinine (µmol/L)	87.6 (18.7)	1.1 (1.0-1.1)	1.0 (1.0-1.1)
BMI (kg/m²)	24.6 (3.6)	1.5 (1.4-1.6)	1.3 (1.2-1.3)
s-glucose (mmol/L)	4.97 (1.0)	1.1 (1.1-1.2)	0.8 (0.7-0.9)
s-cholesterol (mmol/L)	5.7 (1.1)	1.2 (1.1-1.2)	1.1 (1.0-1.1)
s-triglycerides(mmol/L)	1.4 (0.9)	1.1(1.1-1.2)	1.1 (1.0-1.1)
Systolic blood pressure (mm Hg)	129.8 (17.1)	1.3 (1.3-1.4)	1.2 (1.1-1.2)
Sex (% male)	67.3%	1.7 (1.4-1.9)	1.4 (1.2-1.7)
Daily alcohol drinking (yes/no)	0.7%	1.8 (1.1-3.1)	1.4 (1.1-1.9)
Smoking for >10 years (yes/no)	53.1%	1.3 (1.1-1.4)	1.3 (1.2-1.5)
Treatment for hypertension (yes/no)	2.2%	1.9 (1.4-2.6)	1.1 (0.9-1.4)
Treatment with diuretics (yes/no)	0.6%	1.4 (0.8-2.7)	0.8 (0.6-1.2)

*HR is calculated per 1 SD or for dichotomous covariates (yes vs. no).

Disclosure: M. C. Kapetanovic, None; P. M. Nilsson, None; C. Turesson, None; N. Dalbeth, Abbott Laboratories, 8; M. Englund, None; L. E. J. M. Scheepers, None; L. T. Jacobsson, Abbvie, Celegen, MSD, Novartis and UCB, 5.

To cite this abstract in AMA style:

Kapetanovic MC, Nilsson PM, Turesson C, Dalbeth N, Englund M, Scheepers LEJM, Jacobsson LT. Predictors for Clinically Diagnosed Gout – Results from 30 Years Follow-up of the Malmö Preventive Project Cohort in Southern Sweden [abstract]. Arthritis Rheumatol. 2017; 69 (suppl 10). https://acrabstracts.org/abstract/predictors-for-clinically-diagnosed-gout-results-from-30-years-follow-up-of-the-malmo-preventive-project-cohort-in-southern-sweden/. Accessed .

« Back to 2017 ACR/ARHP Annual Meeting

ACR Meeting Abstracts - https://acrabstracts.org/abstract/predictors-for-clinically-diagnosed-gout-results-from-30-years-follow-up-of-the-malmo-preventive-project-cohort-in-southern-sweden/