Inflammatory dietary pattern and risk of developing rheumatoid arthritis in women

Clin Rheumatol. 2019 Jan;38(1):243-250. doi: 10.1007/s10067-018-4261-5. Epub 2018 Aug 14.


Our objective was to investigate whether a dietary pattern derived using inflammatory biomarkers is associated with rheumatoid arthritis (RA) risk. We prospectively followed 79,988 women in the Nurses' Health Study (NHS, 1984-2014) and 93,572 women in the NHSII (1991-2013); incident RA was confirmed by medical records. Food frequency questionnaires (FFQ) were completed at baseline and approximately every 4 years. Inflammatory dietary pattern was assessed from FFQ data using the Empirical Dietary Inflammatory Pattern (EDIP), including 18 anti-/pro-inflammatory food/beverage groups weighted by correlations with plasma inflammatory biomarkers (interleukin-6, C-reactive protein, and tumor necrosis factor-α receptor 2). We investigated associations between EDIP and RA using Cox regression. We identified 1185 incident RA cases over 4,425,434 person-years. EDIP was not associated with overall RA risk (p trend = 0.21 across EDIP quartiles). Among women ≤ 55 years, increasing EDIP was associated with increased overall RA risk; HRs (95% CIs) across EDIP quartiles were 1.00 (reference), 1.14 (0.86-1.51), 1.35 (1.03-1.77), and 1.38 (1.05-1.83; p for trend = 0.01). Adjusting for BMI attenuated this association. Increasing EDIP was associated with increased seropositive RA risk among women ≤ 55 years (p for trend = 0.04). There was no association between EDIP and RA among women > 55 years (EDIP-age interaction, p = 0.03). An inflammatory dietary pattern was associated with increased seropositive RA risk with onset ≤ 55 years old, and this association may be partially mediated through BMI.

Keywords: Diet; Epidemiology; Inflammation; Rheumatoid arthritis.

MeSH terms

  • Adult
  • Arthritis, Rheumatoid / blood
  • Arthritis, Rheumatoid / epidemiology*
  • Biomarkers / blood
  • Diet / adverse effects*
  • Diet Surveys
  • Female
  • Humans
  • Middle Aged
  • Nurses / statistics & numerical data*
  • Prospective Studies
  • Risk Factors
  • United States / epidemiology


  • Biomarkers