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Abstract Number: 1863

Inflammatory Dietary Pattern and Risk of Developing Rheumatoid Arthritis in Women

Bing Lu1, Jeffrey A. Sparks1, Susan Malspeis1, Medha Barbhaiya1, Sara K. Tedeschi2, Karen H. Costenbader3 and Elizabeth Karlson3, 1Rheumatology, Immunology and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 2Division of Rheumatology, Immunology and Allergy,, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 3Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA

Meeting: 2017 ACR/ARHP Annual Meeting

Date of first publication: September 18, 2017

Keywords: Inflammation, rheumatoid arthritis (RA) and risk assessment

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Session Information

Date: Monday, November 6, 2017

Title: ACR/ARHP Combined: Epidemiology and Public Health: Prevention, Recognition, and Treatment

Session Type: ACR Concurrent Abstract Session

Session Time: 4:30PM-6:00PM

Background/Purpose: The preclinical period of rheumatoid arthritis (RA) is characterized by elevated inflammatory biomarkers but it is unclear whether inflammatory diet may contribute to RA risk. We aimed to examine whether a dietary pattern associated with inflammatory biomarkers predicts subsequent risk of RA using the Nurses’ Health Study (NHS) and NHS II.

Methods: We prospectively followed 79,988 women in NHS aged 38-63 years at 1984 and 93,585 women in NHS II aged 27-44 years at 1991, who were free from RA or other connective tissue diseases. Lifestyle, environmental, and anthropometric information were collected at baseline and updated biennially. Dietary data were obtained from validated food frequency questionnaires at baseline and approximately every 4 years during follow-up. RA cases were self-reported and confirmed by a connective tissue disease screening questionnaire and medical record review according to the 1987 ACR criteria. Seropositive RA was defined as positive rheumatoid factor or anti-citrullinated peptide antibody and was determined by medical record review. The inflammatory dietary pattern was measured by the Empirical Dietary Inflammatory Index (EDII), a weighted score including 9 pro- and anti-inflammatory food groups, which was predictive of 3 plasma inflammatory biomarkers (CRP, IL-6 and sTNFR2). Higher (more positive) scores indicate more pro-inflammatory diets and lower (more negative) scores indicate anti-inflammatory diets. We pooled the data from both cohorts to examine the association between cumulative averaged EDII and risk of RA among younger (≤55 years) and older age groups (>55 years) since diet has been shown to have different effects on RA risk according to age of onset in previous studies. The EDII was categorized according to baseline quartile cutoffs. Time-varying Cox regression models were used to calculate the hazard ratios and 95% confidence intervals (CI) after adjusting for potential confounding factors.  

Results: During 34 years of follow-up, we identified 1,188 incident RA cases. Among women ≤55 years old, a higher EDII was associated with a higher RA risk: HRs (95% CI) across increasing quartiles of EDII score were 1.00 (reference), 1.16 (0.87 to 1.53), 1.38 (1.05 to 1.81) and 1.43 (1.08 to 1.89) (p-trend 0.007). After additional adjustment for BMI, the observed results were attenuated. When further stratifying by serostatus, the significant association was observed only for seropositive RA (p-trend: 0.028) (Table). There was no significant association for RA among women aged >55 years (p for EDII-age interaction <0.01).

Conclusion: In these two large prospective cohort studies, inflammatory dietary pattern was associated with increased risk of developing RA among young and middle-aged women. The observed association may be partially mediated through BMI.

Table. Hazard ratios (95% CI) for incident RA according to EDII quartiles in Nurses’ Health Study (NHS, 1984-2014) and  Nurses’ Health Study II (NHS II, 1991-2013), (n=1188 cases)

                         Empirical Dietary Inflammatory Index (EDII) quartiles

Q1 (least inflammatory)

Q2

Q3

Q4 (most inflammatory)

p-trend ‡

Age ≤55 years

                             All RA (n=488 cases)

  Case/person-years

84/456,054

122/610,940

144/630,777

138/598,524

  Multivariable model 1 (main)*

1.00 (Ref)

1.16(0.87 to 1.53)

1.38(1.05 to 1.81)

1.43(1.08 to 1.89)

0.007

  Multivariable model 2†

1.00 (Ref)

1.12(0.85 to 1.49)

1.30(0.99 to 1.72)

1.29(0.97 to 1.71)

0.055

                      Seropositive RA (n=317 cases)

  Case/person-years

52/455,275

77/609,996

104/629,737

84/597,345

  Multivariable model 1 (main)*

1.00

1.17(0.82 to 1.67)

1.61(1.15 to 2.26)

1.40(0.98 to 2.00)

0.028

  Multivariable model 2†

1.00

1.13(0.79 to 1.61)

1.49(1.06 to 2.09)

1.22(0.85 to 1.75)

0.172

                     Seronegative RA (n=171 cases)

  Case/person-years

32/454,727

45/609,397

40/628,983

54/596,946

  Multivariable model 1 (main)*

1.00

1.14(0.72 to 1.80)

1.02(0.63 to 1.63)

1.48(0.94 to 2.33)

0.107

  Multivariable model 2†

1.00

1.12(0.71 to 1.77)

0.98(0.61 to 1.57)

1.41(0.89 to 2.23)

0.173

Age >55 years

                             All RA (n=700 cases)

  Case/person-years

169/508,885

247/714,074

183/595,845

101/338,346

  Multivariable model 1 (main)*

1.00

1.09(0.89 to 1.32)

0.97(0.79 to 1.20)

0.90(0.70 to 1.16)

0.341

  Multivariable model 2†

1.00

1.08(0.88 to 1.31)

0.96(0.78 to 1.19)

0.90(0.69 to 1.16)

0.343

                     Seropositive RA (n=426 cases)

  Case/person-years

100/508,034

155/713,078

118/594,949

53/337,799

  Multivariable model 1 (main)*

1.00

1.17(0.90 to 1.50)

1.08(0.82 to 1.41)

0.82(0.58 to 1.15)

0.322

  Multivariable model 2†

1.00

1.17(0.90 to 1.50)

1.08(0.83 to 1.42)

0.83(0.59 to 1.17)

0.374

                     Seronegative RA (n=274 cases)

  Case/person-years

69/507,773

92/712,715

65/594,703

48/337,824

  Multivariable model 1 (main)*

1.00

0.97(0.71 to 1.33)

0.83(0.59 to 1.17)

1.03(0.70 to 1.50)

0.780

  Multivariable model 2†

1.00

0.94(0.69 to 1.30)

0.80(0.56 to 1.13)

0.98(0.67 to 1.44)

0.612

Hazard ratios were calculated using time-varying Cox proportional hazards models

*Adjustment for age, cohort, questionnaire period, household income, smoking (never, past, current 1-14 cigarettes/d, current ≥15 cigarettes/d), age at menarche (<12, 12, >12 years), parity and breast feeding (nulliparous, parous/no breastfeeding, parous/1–12 months breastfeeding, parous/ >12 months breastfeeding), hormone use (pre-menopausal, post-menopausal with never use, current use and past use) and total energy (quintiles)

†Additional adjustment for BMI (<20, 20-22.9, 23-24.9, 25-29.9, ≥30kg/m2)

‡p for trend was derived from tests of linear trend across categories of EDII using the median value of each category as a continuous variable


Disclosure: B. Lu, None; J. A. Sparks, None; S. Malspeis, None; M. Barbhaiya, None; S. K. Tedeschi, None; K. H. Costenbader, Glaxo Smith Kline, 5,Merck Pharmaceuticals, 2,Biogen Idec, 5,AstraZeneca, 5; E. Karlson, None.

To cite this abstract in AMA style:

Lu B, Sparks JA, Malspeis S, Barbhaiya M, Tedeschi SK, Costenbader KH, Karlson E. Inflammatory Dietary Pattern and Risk of Developing Rheumatoid Arthritis in Women [abstract]. Arthritis Rheumatol. 2017; 69 (suppl 10). https://acrabstracts.org/abstract/inflammatory-dietary-pattern-and-risk-of-developing-rheumatoid-arthritis-in-women/. Accessed .
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