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Mediators of Inflammation
Volume 2016, Article ID 3082820, 8 pages
http://dx.doi.org/10.1155/2016/3082820
Research Article

Smoking Functions as a Negative Regulator of IGF1 and Impairs Adipokine Network in Patients with Rheumatoid Arthritis

Department of Rheumatology and Inflammation Research, Sahlgrenska Medical Academy, Gothenburg University, 40583 Gothenburg, Sweden

Received 14 September 2015; Revised 3 February 2016; Accepted 7 February 2016

Academic Editor: Oreste Gualillo

Copyright © 2016 Malin C. Erlandsson et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objectives. Smoking is pathogenic for rheumatoid arthritis (RA) being tightly connected to the genetic and serological risk factors for this disease. This study aims to understand connections between cigarette smoking and serum levels of IGF1 and adipokines in RA. Methods. Serum levels of IGF1 and adipokines leptin, adiponectin, resistin, and visfatin were measured in two independent cohorts of RA patients from Gothenburg () and Leiden (). An association of these parameters with smoking was tested in a direct comparison and proved by bivariate correlation analysis. The obtained associations were further tested in multivariate regression models where the confounders (age, gender, disease duration, and BMI) were controlled. Results. The smokers had significantly lower serum levels of IGF1, adiponectin, and leptin compared to never smokers. In regression analysis, smoking and low leptin, but not adiponectin, were associated and predicted low IGF1. Additionally, high disease activity and high BMI increased the probability of low leptin. Conclusions. The study indicates cigarette smoking as an important cause of a relative IGF1 and leptin deficiency in RA patients. This novel association between smoking and hypoleptinemia may be of importance for long-term prognosis of RA and for prediction of comorbidities.