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FRI0687 Body mass index, smoking, socioeconomic status and the risk of giant cell arteritis
  1. K Jakobsson1,2,
  2. LT Jacobsson1,3,
  3. AJ Mohammad4,5,
  4. KJ Warrington6,
  5. EL Matteson6,
  6. C Turesson1,2
  1. 1Department of Clinical Sciences, Lunds University
  2. 2Department of Rheumatology, Skåne University Hospital, Malmö
  3. 3Department of Rheumatology & Inflammation Research, The Sahlgrenska Academy, University of Gothenburg, Institute of Medicine, Gothenburg
  4. 4Department of Rheumatology, Skåne University Hospital, Lund, Sweden
  5. 5Vasculitis and Lupus Clinic, Addenbrooke's Hospital, Cambridge, United Kingdom
  6. 6Division of Rheumatology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, United States


Background Few studies have examined predictors of giant cell arteritis (GCA). A negative association between body mass index (BMI) and development of GCA has been reported (1, 2). There is limited information on the impact of smoking and socioeconomic factors on the risk of GCA.

Objectives To further investigate the relation between BMI and GCA, and also assess the role of smoking and socioeconomic factors in this context.

Methods Two population based health-surveys, The Malmö Preventive Medicine Program (MPMP) and the Malmö Diet Cancer Study (MDCS), performed in the same catchment area between 1974 and 1996. In the MPMP, 33346 subjects (33% women), and in the MDCS, 30447 subjects (60% women) were included. Both surveys included standard physical examinations and self-administered questionnaires. Subjects were classified as blue-collar workers or white-collar workers, using the Socioeconomic Index (SEI) based on self-reported job titles in the Swedish national censuses.

Individuals who developed GCA after inclusion in the two health surveys were identified by linking the health survey databases to local and national patient registers. A structured review of the medical records was performed. Four controls for every validated case, matched for sex, year of birth and year of screening, were selected from the corresponding databases. Potential predictors of GCA were examined in conditional logistic regression models. This is an extension of a previous study, adding more incident cases (2).

Results A total of 138 cases with a confirmed clinical diagnosis of GCA (median age at diagnosis 71 years; 72% female; 66% biopsy-positive; 94% fulfilled the ACR criteria for GCA) were included. The median time from screening to diagnosis was 15 years (range 0–32).

The cases who subsequently developed GCA had significantly lower BMI at baseline (24.3 vs 25.3 kg/m2, odds ratio (OR) 0.91 per kg/m2; 95% confidence interval (CI) 0.86–0.97) and were less likely to be current smokers when entering the health survey (OR 0.56; 95% CI 0.33–0.94). There was no difference in the proportion with low level of formal education between cases and controls (OR 1.27; 95% CI 0.66–2.44). Blue-collar workers tended to be less likely to develop GCA than white-collar workers (OR 0.53; 95% CI 0.28–1.00). This association reached statistical significance in women (OR 0.32; 95% CI 0.13–0.81) but not in men (OR 0.87; 95% CI 0.35–2.16). In multivariate analysis, including both variables and the SEI, the impact of BMI (OR 0.85; 95% CI 0.74–0.96) and smoking (OR 0.26; 95% CI 0.12–0.60) remained significant.

Conclusions In this study, the negative association between BMI and subsequent GCA was confirmed, and there was an independent protective effect of smoking. Socioeconomic status, reflected by occupation later in life rather than level of formal education, may also influence the risk of developing GCA.


  1. Larsson K et al Ann Rheum Dis 2006; 65:529–532.

  2. Jakobsson K et al Rheumatology 2015; 54:433–440.


Disclosure of Interest None declared

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