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Original Article

A case–control study of rheumatoid arthritis revealed abdominal obesity and environmental risk factor interactions in northern China

, , , , &
Pages 249-257 | Received 14 Sep 2016, Accepted 14 Mar 2017, Published online: 11 Apr 2017
 

Abstract

Background: The aim of this study was to evaluate new and previously hypothesized environmental risk factors and their interaction with rheumatoid arthritis (RA).

Methods: Four hundred patients recently diagnosed with RA and 400 controls frequency-matched by gender and birth year using Propensity Score Matching (PSM) were selected from northern China. Investigation was performed using self-reported data from interviewer-administered surveys. Associations between exposure variables and risk of RA were evaluated using multifactor non-conditional logistic regression.

Results: It showed that damp localities, draft indoor, abdominal obesity (AO), and family history of RA among first-degree relatives were independent risk factors and drinking of milk was independent protective factors for RA. Besides these risk factors, in women, infrequent delivery times, early age at menopause, and late age at menarche were also independent risk factors for RA. Both the additive model and the multiplication model suggested that there was an interaction relationship between AO and damp localities (p < .001), and only the additive model suggested that there was interaction relationship between AO and no milk drinking (p < .001) in our study population. In women, there was interaction relationship between AO and damp localities (p < .001) and between AO and age at menopause (p < .001).

Conclusions: In northern China, damp localities, draft indoor, AO, family history of RA among first-degree relatives, and no milk drinking may be important risk factors of RA patients.

Conflict of interest

None.

Additional information

Funding

This work was supported by grants from Program of the National Natural Science Foundation of Liaoning province in China [grant No. 2013021062] and grants from Program of the National Natural Science Foundation of China [grant No. 81673246].

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