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. Author manuscript; available in PMC: 2016 Jun 1.
Published in final edited form as: Ann Epidemiol. 2015 Mar 19;25(6):398–405. doi: 10.1016/j.annepidem.2015.03.009

Table 5.

Associations between the FFQ-derived dietary inflammatory index and biomarkers of inflammation in subgroups of BMI (kg/m2), in non-users of NSAIDs (n=1290)

Quintiles (Q) of the DII: Estimate1 (95% confidence interval)
Inflammatory
Biomarker
Q1 (−6.230,
< −3.126)
Q2 (−3.126, < −
1.833)
Q3 (−1.833, < −
0.023)
Q4 (−0.023,
<2.147)
Q5 (2.147, 5.224) Ptrend2
IL-63
Normal weight (<25) referent 0.93 (0.81, 1.10) 0.98 (0.87, 1.12) 1.05 (0.93, 1.20) 1.10 (0.93, 1.29) 0.10
Overweight (25 – <30) referent 1.02 (0.87, 1.17) 0.98 (0.85, 1.15) 1.05 (0.91, 1.23) 1.15 (1.02, 1.32)* 0.01
Obese (≥30) referent 1.10 (0.95, 1.26) 0.95 (0.93, 1.17) 1.05 (0.93, 1.17) 1.12 (0.99, 1.29) 0.02
hs-CRP model (hs-CRP dichotomous; odds ratios)4
Normal weight (<25) referent 0.94 (0.39, 2.23) 1.04 (0.45, 2.43) 1.30 (0.61, 2.79) 1.08 (0.46, 2.52) 0.60
Overweight (25 – <30) referent 2.14 (1.01, 4.56)* 1.10 (0.51, 2.37) 1.51 (0.70, 3.23) 1.77 (0.86, 3.68) 0.33
Obese (≥30) referent 2.63 (1.28, 5.42)* 2.38 (1.20, 4.71)* 2.50 (1.27, 4.93)* 2.34 (1.17, 4.64)* 0.11
TNFα-R2
Normal weight (<25) referent 2.38 (−148.28,
153.05)
−45.15(−192.48,
102.18)
93.12 (−40.84,
227.07)
11.59 (−136.84,
160.02)
0.30
Overweight (25 – <30) referent −26.13 (−170.83,
118.57)
−22.57 (−162.88,
117.83)
45.63(−98.32,
189.58)
35.44 (−91.46,
162.33)
0.42
Obese (≥30) referent −37.76 (−209.02,
133.51)
−132.84(−286.05,
20.37)
110.87(−38.02,
259.75)
282.63(126.71,
438.56)*
<0.0001
Overall inflammatory biomarker score3,5
Normal weight (<25) referent 0.08 (−0.22, 0.39) 02 (−0.28, 0.32) 0.25 (−0.03, 0.53) 0.14 (−0.17, 0.44) 0.18
Overweight (25 – <30) referent 0.29 (−0.12, 0.57) 0.17 (−0.17, 0.52) 0.29 (−0.06, 0.64) 0.30 (−0.40, 0.63) 0.12
Obese (≥30) referent 0.39 (0.30, 0.75)* 0.18 (−0.16, 0.53) 0.54 (0.20, 0.88)* 0.74 (0.39, 1.09)* <0.0001
*

Statistically significant association;

1

Beta parameters;

2

The median of each quintile was assigned to all participants in the quintile and the variable introduced into models as an ordinal variable and its p-value interpreted as the p-value for tend;

3

Backtransformed (10x) estimates since the data were log transformed to the base 10, prior to analyses;

4

Dichotomous hs-CRP (>3 mg/L, ≤3 mg/L) modeled the probability that hs-CRP >3 mg/L;

5

The overall inflammatory score was computed by summing z-scores of all three biomarkers for each participant. All models were adjusted for age, race, educational level, smoking status, physical activity, inflammation-related co-morbidity, regular use of antidepressants, and statins use