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The Journal of Nutrition
Nutritional Epidemiology
Development and Validation of an Empirical
Dietary Inflammatory Index
1–3
Fred K Tabung,
4,5
* Stephanie A Smith-Warner,
4,5
Jorge E Chavarro,
4–7
Kana Wu,
4
Charles S Fuchs,
6–8
Frank B Hu,
4–7
Andrew T Chan,
6,9,10
Walter C Willett,
4–7
and Edward L Giovannucci
4–7
4
Department of Nutrition and
5
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA;
6
Channing
Division of Network Medicine, Brigham and WomenÕs Hospital, Boston, MA;
7
Department of Medicine, Harvard Medical School,
Boston, MA;
8
Department of Medical Oncology, Dana–Farber Cancer Institute, Boston, MA; and
9
Division of Gastroenterology and
10
Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA
Abstract
Background: Knowledge on specific biological pathways mediating disease occurrence (e.g., inflammation) may be
utilized to construct hypotheses-driven dietary patterns that take advantage of current evidence on disease-related
hypotheses and the statistical methods of a posteriori patterns.
Objective: We developed and validated an empirical dietary inflammatory index (EDII) based on food groups.
Methods: We entered 39 pre-defined food groups in reduced rank regression models followed by stepwise linear
regression analyses in the Nurses’ Health Study (NHS, n= 5230) to identify a dietary pattern most predictive of 3 plasma
inflammatory markers: interleukin-6 (IL-6), C-reactive protein (CRP), and tumor necrosis factor areceptor 2 (TNFaR2). We
evaluated the construct validity of the EDII in 2 independent samples from NHS-II (n= 1002) and Health Professionals
Follow-up Study (HPFS, n= 2632) using multivariable-adjusted linear regression models to examine how well the EDII
predicted concentrations of IL-6, CRP, TNFaR2, adiponectin, and an overall inflammatory marker score combining all
biomarkers.
Results: The EDII is the weighted sum of 18 food groups; 9 are anti-inflammatory and 9 proinflammatory. In NHS-II and
HPFS, the EDII significantly predicted concentrations of all biomarkers. For example, the relative concentrations
comparing extreme EDII quintiles in NHS-II were: adiponectin, 0.88 (95% CI, 0.80, 0.96), P-trend = 0.003; and CRP, 1.52
(95% CI, 1.18, 1.97), P-trend = 0.002. Corresponding associations in HPFS were: 0.87 (95% CI, 0.82, 0.92), P-trend <
0.0001; and 1.23 (95% CI, 1.09, 1.40), P-trend = 0.002.
Conclusion: The EDII represents, to our knowledge, a novel, hypothesis-driven, empirically derived dietary pattern that
assesses diet quality based on its inflammatory potential. Its strong construct validity in independent samples of women
and men indicates its usefulness in assessing the inflammatory potential of whole diets. Additionally, the EDII may be
calculated in a standardized and reproducible manner across different populations thus circumventing a major limitation of
dietary patterns derived from the same study in which they are applied. J Nutr 2016;146:1560–70.
Keywords: hypothesis-driven, dietary patterns, dietary inflammatory potential, inflammatory markers, inflammation
Introduction
Dietary patterns capture multiple dietary factors and provide a
comprehensive assessment of diet, which may account for the
complex interactions between nutrients and foods. Derived
dietary patterns thus may be more predictive of diet–disease
associations than are analyses that use single foods or nutrients
(1, 2). The 2 main approaches for creating dietary patterns are
the a priori or index-based approach and the a posteriori or
data-driven approach. A priori pattern scores are developed on
the basis of current scientific evidence with respect to the relation
between diet and disease, e.g., the Alternative Healthy Eating
Index (3), or current dietary guidelines or recommendations,
3
Supplemental Tables 1–4 are available from the ‘‘Online Supporting Material’’
link in the online posting of the article and from the same link in the online table of
contents at http://jn.nutrition.org.
*To whom correspondence should be addressed. E-mail: ftabung@hsph.harvard.
edu.
11
Abbreviations used: CRP, C-reactive protein; EDII, empirical dietary inflamma-
tory index; HPFS, Health Professionals Follow-Up Study; NHS, Nurses’ Health Study;
NHS-II, NursesÕHealth Study II; NSAID, nonsteroidal anti-inflammatory drug; RRR,
reduced rank regression; TNFaR2, TNF-areceptor 2.
1
JEC and FBH were supported by NIH grants P30DK046200 and U54
CA155426. The Health Professionals Follow-Up Study, NursesÕHealth Study,
and NursesÕHealth Study II cohorts are supported by NIH grants UM1 CA
167552, UM1 CA 186107, and UM1 CA 176726, respectively.
2
Author disclosures: FK Tabung, SA Smith-Warner, JE Chavarro, K Wu, CS
Fuchs, FB Hu, AT Chan, WC Willet, and EL Giovannucci, no conflicts of interest.
ã2016 American Society for Nutrition.
1560 Manuscript received January 4, 2016. Initial review completed February 6, 2016. Revision accepted May 23, 2016.
First published online June 29, 2016; doi:10.3945/jn.115.228718.
e.g., the dietary index based on adherence to the 2007 World
Cancer Research Fund/American Institute for Cancer Research
recommendations for cancer prevention (4). In contrast, the a
posteriori approach is based on statistical exploratory methods
such as factor analysis or principal components analysis (5–7),
and the dietary pattern derived is not necessarily based on any
disease-related hypothesis. Knowledge of specific biological
pathways mediating disease occurrence (e.g., inflammation) may
be harnessed to construct hypothesis-driven dietary patterns that
take advantage of both current scientific evidence for disease-
related hypotheses and the statistical exploratory methods of a
posteriori dietary patterns. Hypothesis-driven dietary patterns
then can be applied in a more standardized manner across differ-
ent populations in a manner similar to a priori patterns.
Chronic inflammation plays an important role in the develop-
ment of many chronic diseases (8–10), and some dietary patterns
have been shown to be associated with inflammation. Higher scores
on a priori–defined dietary patterns such as the Healthy Eating
Index and the Mediterranean diet are associated with lower
concentrations of inflammatory markers (11–13), although the
development of these indexes was not focused on inflammation. A
posteriori–defined patterns suchastheWesterndietarypattern
have been associated with higher concentrations of inflammatory
markers, whereas higher consumption of the prudent pattern is
linked with lower concentrations of inflammatory markers (11,
14). However, the evidence of the association between dietary
patterns and inflammation is still inconsistent, especially for dietary
patterns derived with the use of a posteriori methods (11, 15).
Approaches to develop hypothesis-driven dietary patterns that
can be applied across different populations could enhance between-
study comparability and utility of study findings (2, 7). In addition,
developing standardized patterns on the basis of specific disease
mechanisms such as inflammation that mediates the risk of many
chronic diseases could elucidate biological mechanisms relating
dietary patterns to disease development or progression. This could
be achieved with the use of reduced rank regression (RRR)
11
,ana
posteriori statistical method that determines linear functions of
predictors (e.g., food groups in the current study) by maximizing
the explained variation in the responses (e.g., inflammatory markers
in the current study) (16–18). Unlike other widely used statistical
exploratory methods such as principal components analysis or
factor analysis, which derive dietary patterns based on the covari-
ance structure of foods, RRR uses information on the response
variables to derive the dietary patterns (16, 17).
Our objectives in the current study were 3-fold: 1) to use
RRR to develop an empirical dietary inflammatory index (EDII)
with the use of dietary and inflammatory markers data from the
NursesÕHealth Study (NHS); 2) to evaluate the construct
validity of the EDII in 2 independent samples of women and
men in the NursesÕHealth Study II (NHS-II) and the Health
Professionals Follow-Up Study (HPFS), respectively; and 3)to
conduct sensitivity analyses with the use of potential alternative
versions of the EDII.
Methods
Study populations. The NHS, NHS-II, and HPFS are ongoing
prospective cohorts established in 1976, 1989, and 1986, respectively.
The NHS (n= 121,701) enrolled female registered nurses aged 30–55 y,
whereas the NHS-II (n= 116,430) enrolled younger female registered
nurses 25–42 y of age (19). The HPFS (n= 51,529) enrolled male health
professionals aged 40–75 y. Blood samples were collected from subpop-
ulations of the 3 cohorts that were free of diagnosed cancer, diabetes,
heart disease or stroke as follows: NHS (n= 32,826) from 1989 to 1990,
NHS-II (n= 29,611) from 1996 to 1999, and HPFS (n= 18,225) from
1993 to 1994 (20). Blood collection was conducted with the use of
similar protocols for all cohorts. The procedures, including collection,
handling, and storage, have been summarized previously (21). In the
current study, we used data from previous matched case-control studies
nested within each of the 3 cohorts that measured plasma concentrations
of IL-6, C-reactive protein (CRP), TNF-areceptor 2 (TNFaR2), and
adiponectin. The institutional review boards at Brigham and WomenÕs
Hospital and Harvard T.H. Chan School of Public Health approved this
study.
Assessment of inflammatory markers. Procedures for the measure-
ment of plasma inflammatory markers (IL-6, CRP, TNFaR2, and
adiponectin) in the NHS, NHS-II, and HPFS have been described
previously (20, 22–25). Briefly, concentrations of IL-6 and TNFaR2
were measured with the use of ELISAs (R&D Systems). CRP was
measured with the use of a high-sensitivity immunoturbidimetric assay
with reagents and calibrators from Denka Seiken Company. We
excluded participants with CRP concentrations >10 mg/L, because
this likely may have been due to infection or medication use (26).
Concentrations of adiponectin were measured with the use of a
competitive radioimmunoassay (Linco Research). In the nested case-
control studies in which these biomarkers were measured, samples from
cases and their matched controls were analyzed in the same batch.
Quality-control samples were interspersed randomly among the case-
control samples, and laboratory personnel were blinded to quality-
control and case-control status for all assays. The intra-assay CV from
blinded quality-control samples ranged from 2.9% to 12.8% for IL-6,
1.0% to 9.1% for CRP, 4.0% to 10.0% for TNFaR2, and 8.1% to
11.1% for adiponectin across batches. In NHS-II and HPFS, we derived
an overall inflammatory marker score by computing a zscore for each of
the 4 inflammatory markers and then summing the zscores to create a
standardized overall inflammatory marker score for each participant as
follows:
zscore ðlogIL-6Þþzscore ðlogCRPÞþzscore ðlogTNFaR2Þ
2zscore ðlogAdiponectinÞð1Þ
Assessment of dietary and nondietary data. Dietary data are
updated every 4 y in the NHS (since 1980), NHS-II (since 1991), and
HPFS (since 1986) with a semi-quantitative FFQ, the validity and
reliability of which have been reported (27–29). We used dietary data
from the questionnaires closest to the blood draw, i.e., the 1986 and
1990 FFQs for the NHS, the 1995 and 1999 FFQs for the NHS-II, and
the 1990 and 1994 FFQs for the HPFS, averaging dietary data across the
2 FFQs to reduce within-subject variability in long-term diet (30).
Participants with excessive missing items ($70) on the FFQs, implau-
sibly low or high energy intake (<600 or >3500 kcal/d for women and
<800 or >4200 kcal/d for men) were excluded (31).
All 3 cohorts collected nondietary data (e.g., medical history and
health practices) and updated the data through biennial self-administered
questionnaires. We calculated participantsÕBMIs (in kg/m
2
) with the use
of height (meters) reported at baseline for each cohort, and weight
(kilograms) reported at each biennial questionnaire cycle. Participants
reported smoking status (classified as never, former, or current), and we
calculated physical activity, expressed in metabolic equivalent task
hours per week, by summing the mean metabolic equivalent task hours
per week for each activity, which included tennis/squash/racquetball,
rowing, calisthenics, walking, jogging, running, bicycling, and swim-
ming. We averaged data for BMI and physical activity across the 2
questionnaires and replaced missing data with available corresponding
data from the previous questionnaire for all variables. Regular use of
aspirin or other nonsteroidal anti-inflammatory drugs (NSAIDs) was
defined as the use of $2 standard tablets (325 mg) of aspirin or $2
tablets of NSAIDs/wk. We derived an inflammation-related chronic
disease comorbidity score by summing the presence (1) or absence (0) of
the following chronic diseases and conditions: hypercholesterolemia,
cancer, diabetes, high blood pressure, heart disease, and rheumatoid or
other arthritis).
Development and validation of a dietary inflammatory index 1561
In the NHS, we excluded participants with missing diet and covariate
data (n= 1329), retaining a final sample of 5230 for EDII development.
For EDII validation, we excluded 217 women and 223 men with missing
diet and covariate data, leaving a final sample of 1002 women in the
NHS-II and 2632 men in the HPFS.
Development of the EDII. The goal for developing the EDII was to
create empirically a score based on food groups to assess the overall
inflammatory potential of whole diets. We based the score on food
groups rather than nutrients to approximate how people perceive diet.
We used dietary and inflammatory marker data in the NHS to develop
the EDII. We first calculated the mean daily intake of 39 previously
defined food groups (31) from the 1986 and 1990 FFQs. We then applied
RRR (16) to derive a dietary pattern associated with 3 inflammatory
markers: IL-6, CRP, and TNFaR2—inflammatory markers that have
been associated with a number of diseases and are among the most
commonly used inflammatory markers to examine disease endpoints (20,
32–34). RRR identifies linear functions of predictors (e.g., food groups)
that simultaneously explain as much variation in the responses of interest
(e.g., inflammatory markers) as possible (16, 35). The first factor
obtained by RRR was retained for subsequent analyses (we called this
the RRR dietary pattern). We then used stepwise linear regression
analyses to identify the most important component food groups contrib-
uting to the RRR dietary pattern, with the biomarker response score
(RRR dietary pattern) as the dependent variable, the 39 food groups as
independent variables, and a significance level of P= 0.05 for entry into
and retention in the model. The intake of the food groups identified in
the stepwise linear regression analyses was weighted by the regression
coefficients derived from the final stepwise linear regression model and
then summed to constitute the EDII score. Finally, the EDII was rescaled
by dividing by 1000 to reduce the magnitude of the scores and aid in
interpretation of statistical analyses. The EDII assesses the inflammatory
potential of an individualÕs diet on a continuum from maximally anti-
inflammatory to maximally proinflammatory, with higher (more posi-
tive) scores indicating more proinflammatory diets and lower (more
negative) scores indicating anti-inflammatory diets.
Sensitivity analyses. In the sensitivity analyses, we created 6 alter-
native versions of the EDII: 1) an EDII without weights; 2)anEDII
that included added nutrients, including supplements, in the food
groups; 3) an EDII that included added nutrients, but not supplements,
in the food groups; 4) an EDII from BMI-adjusted biomarkers; 5)an
EDII for nonusers of aspirin/NSAIDs; and 6)anEDIIforcontrol
subjects only. In version 3, nutrients that have been associated with
inflammatory markers (36) were selected for this sensitivity analysis.
They included thiamin, riboflavin, niacin, vitamin A, vitamin B-12,
vitamin C, vitamin D, vitamin E, selenium, b-carotene, folate, iron,
magnesium, v-3 fats, zinc, v-6 fats, vitamin B-6, total fiber, alcohol,
caffeine, carbohydrates, total cholesterol, monounsaturated fats, poly-
unsaturated fats, trans fats, protein, saturated fats, isoflavones, anthocyani-
dins, flavan-3-ols, flavanones, flavonols, and flavones. The first 17
nutrients listed also had separate variables with supplements, and all
nutrients were energy-adjusted with the use of the residual method
(37). In version 4, we also used BMI-adjusted biomarkers as responses in
the RRR model, given that BMI may mediate and/or confound the
association of the EDII with inflammatory markers. The biomarkers
were adjusted for BMI before they were used in the RRR model, i.e.,
we adjusted biomarkers for BMI by regressing each of the 3 biomarkers
on BMI in 3 separate univariate linear regression models and then used
the residuals (instead of the original biomarker) in the RRR procedure.
In version 5, we constructed the EDII for nonusers of aspirin/NSAIDs,
because in previous studies, a nutrient-based dietary inflammatory
index was not associated with inflammatory markers in NSAID users
(38–40). Finally, in version 6, we constructed the EDII for only control
participants of the nested case-control studies (although all nested case-
control studies that generated the data for the current study used
prediagnostic blood samples in individuals free from diagnosed chronic
diseases). This alternative EDII tested the robustness of the EDII to using
the entire sample of cases and controls compared with the use of only the
controls.
Statistical analysis. In the NHS, 5230 women with data on the 3
inflammatory markers (IL-6, CRP, and TNFaR2) were used to develop
the EDII, whereas in the NHS-II and HPFS, 1002 women and 2632 men,
respectively, with data on these same biomarkers plus adiponectin were
used to evaluate the construct validity of the EDII. We expected the
EDII developed without the use of adiponectin to be associated with
concentrations of adiponectin and in the expected (inverse) direction. We
described participantsÕcharacteristics with the use of means 6SDs for
continuous variables, geometric means 6CVs for log-transformed
variables, and frequencies (%) for categorical variables. Concentrations
of all 4 biomarkers were back-transformed to their original units (e
x
,
where x is the transformed biomarker value) because biomarkers were
ln-transformed before analyses. We calculated correlation coefficients
between the EDII, its potential alternative versions, and inflammatory
markers in the NHS.
In the NHS, we graphically assessed the distribution of the absolute
mean concentrations of IL-6, CRP, and TNFaR2 across quintiles of the
EDII stratified by aspirin/NSAID use (regular users compared with
nonusers) and by BMI [normal weight (<25 kg/m
2
) compared with
overweight/obese ($25 kg/m
2
)], while adjusting for the following
covariates: age at blood draw (continuous), total energy intake, physical
activity, smoking status, BMI, regular aspirin/NSAID use (when not
stratifying on these 2 covariates), case-control status, batch effects for
biomarker measurements, the inflammation-related chronic disease co-
morbidity score, and menopausal status and postmenopausal hormone
use (for women). We adjusted for case-control status in the multivariable
models, given that the data were from matched nested case-control
studies. Also, the biomarkers were determined in several batches;
therefore, we adjusted for batch number in order to minimize potential
batch effects.
In the validation phase in the NHS-II and HPFS samples, we derived
scores for the EDII and its potential alternative versions and calculated
correlations among the derived pattern scores and the construct
validators of the EDII (IL-6, CRP, TNFaR2, adiponectin, and the
overall inflammatory marker score). The association between the EDII
and biomarkers was assessed with the use of multivariable-adjusted
linear regression models to calculate relative concentrations of the
biomarkers in EDII quintiles with the lowest quintile as reference (i.e.,
the ratios of biomarker concentrations in the higher EDII quintiles to the
concentration in the lowest quintile).
All multivariable models were adjusted for the previously described
potential confounding variables. We used the continuous index values
adjusted for multiple covariates to assess the linear trend of biomarker
concentrations across quintiles of the categorized index. Potential effect
modification of the association between the EDII and inflammatory
markers by BMI and aspirin/NSAID use was assessed by including
EDII 3covariate interaction terms in the multivariable-adjusted models.
In sensitivity analyses, we applied each of the alternative versions of
the EDII (the unweighted EDII, the EDII including nutrients with
supplements, the EDII including nutrients without supplements, the EDII
from BMI-adjusted biomarkers, the EDII derived in nonusers of aspirin/
NSAIDs, and the EDII derived in control subjects only) in multivariable-
adjusted linear regression models to determine relative concentrations of
the 4 biomarkers across index quintiles. All analyses were conducted
with the use of SAS version 9.3 for UNIX. All tests were 2-sided and P<
0.05 was considered to indicate statistically significant results (including
interaction terms).
Results
Among the 39 food groups entered in the RRR model, there was
wide variation in the magnitude of associations for each of the
biomarkers (Supplemental Table 1). Eighteen food groups were
identified in the subsequent stepwise linear regression model as
significant contributors to the EDII (Table 1). The intake of fish
(other than dark-meat fish), tomatoes, processed meats, high-
energy beverages, other vegetables (i.e., vegetables other than
leafy green vegetables and dark yellow vegetables), red meats,
1562 Tabung et al.
low-energy beverages, refined grains, and organ meats was
positively related to concentrations of inflammatory markers,
whereas the intake of pizza, wine, leafy green vegetables, dark
yellow vegetables (comprising carrots, yellow squash, yams),
beer, coffee, fruit juice, snacks, and tea was inversely related to
concentrations of inflammatory markers (Table 1). Components
of the potential alternative versions of the EDII are presented in
Supplemental Table 2. The potential alternative EDII version
from BMI-adjusted biomarkers had the fewest components.
The proportion of overweight/obese participants, as well as
concentrations of IL-6, CRP, TNFaR2, and the overall inflam-
matory marker score were higher in the most proinflammatory
quintile of the EDII than in the most anti-inflammatory quintile,
whereas concentrations of adiponectin were higher in quintile
1 than in quintile 5 in all 3 cohorts. Reported physical activity
level in men was $2 times higher than in both cohorts of
women; in both women and men, activity levels were highest in
quintile 1 compared with quintile 5. The majority of older
women were postmenopausal and more than one-half of them
used postmenopausal hormones, whereas the majority of younger
women were premenopausal (Table 2). In the NHS, the 5th and
95th percentiles of the RRR dietary pattern score consisting of
all 39 food groups (servings per day) were 21.56 and 1.60,
respectively. The EDII based on 18 food groups had similar
distributions in all 3 cohorts: 20.54 to 0.41 in the NHS, 20.54
to 0.49 in the NHS-II, and 20.57 to 0.85 in the HPFS. In the
NHS, the EDII was highly correlated with its potential alterna-
tive versions, with Spearman correlation coefficients ranging
from 0.67 (the EDII from BMI-adjusted biomarkers) to 0.96
(RRR dietary pattern that included all 39 food groups). The
TABLE 1 Components of the EDII and their correlations with plasma inflammatory markers in the
NursesÕHealth Study (n= 5230, 1986–1990)
1
RRR dietary
pattern
2
EDII CRP IL-6 TNFaR2 Weights
RRR dietary pattern
2
1.00 0.96 0.21 0.19 0.15 NA
EDII
3
1.00 0.21 0.19 0.15 NA
EDII components
3
Positive associations
Processed meat 0.24 0.24 0.08 0.09 20.0001* 165.03
Red meat 0.22 0.23 0.07 0.06 20.03 140.19
Organ meat 0.08 0.07 0.06 0.05 0.01* 144.61
Other fish 0.06 0.06 0.05 20.02* 20.04 252.45
Other vegetables 0.07 0.07 0.03 20.003* 0.002* 136.14
Refined grains 0.28 0.28 0.06 0.08 0.02* 81.21
High-energy beverages 0.26 0.26 0.03 0.05 0.01* 156.85
Low-energy beverages 0.18 0.19 0.07 0.05 20.01* 94.77
Tomatoes 0.15 0.16 0.05 0.02* 20.002* 167.92
Inverse associations
Beer 20.19 20.19 20.09 20.08 20.12 2136.99
Wine 20.38 20.38 20.12 20.13 20.15 2249.70
Tea 0.02* 0.02* 0.01* 20.02 20.01* 242.25
Coffee 20.49 20.51 20.11 20.07 20.10 283.18
Dark yellow vegetables 20.13 20.14 20.02* 20.06 20.02* 2165.37
Leafy green vegetables 20.23 20.24 20.02* 20.07 20.06 2190.29
Snacks 20.06 20.06 20.004* 20.02* 20.04 245.08
Fruit juice 20.03 20.04 20.02* 20.02* 20.02* 258.95
Pizza 20.07 20.08 20.03 0.001* 20.06 21175.21
1
Values in columns 2–6 are Spearman correlation coefficients, *P.0.05. Column 7 values are regression coefficients for each EDII
component obtained from the last step of the stepwise linear regression analysis. CRP, C-reactive protein; EDII, empirical dietary
inflammatory index; NA, not applicable; RRR, reduced rank regression; TNFaR2 = TNF-areceptor 2.
2
The RRR dietary pattern was the first factor obtained from RRR with all 36 food groups. It was then retained for subsequent analyses as
the dependent variable in the stepwise linear regression analyses to identify the most important food groups contributing to this pattern
(i.e., the 18 EDII components).
3
The food groups (including serving size per day for specific foods) retained were defined as follows: other fish [3–5 oz (70–117 g) canned
tuna, shrimp, lobster, scallops, fish, or other seafood other than dark-meat fish], tomatoes [1 fresh tomato, 1 small glass of tomato juice,
or 1/2 cup (115 g) tomato sauce], high-energy beverages (1 glass, 1 bottle, or 1 can cola with sugar; other carbonated beverages with sugar;
or fruit punch drinks), red meat [4–6 oz (113–170 g) beef, pork, or lamb, or 1 hamburger patty], low-energy beverages (1 glass, 1 bottle, or
1 can low-energy cola; other low-energy carbonated beverages), refined grains [1 slice white bread, 1 English muffin, 1 bagel or roll,
1 muffin or biscuit, 1 cup (250 g) white rice, 1 cup (140 g) pasta, or 1 serving of pancakes or waffles], organ meat [4 oz (113 g) beef, calf, or
pork liver; 1 oz (28.3 g) chicken or turkey liver], 2 slices pizza, wine [4-oz (113-g) glass red or white wine], leafy green vegetables (1/2 cup
spinach, 1 serving of iceberg or head lettuce, or 1 serving of romaine or leaf lettuce), dark yellow vegetables [1/2 cup carrots, 1/2 cup yellow
(winter) squash, or 1/2 cup (100 g) yams or sweet potatoes], beer (1 bottle, 1 glass, or 1 can), 1 cup coffee, fruit juices (1 small glass apple
juice or cider, orange juice, grapefruit juice, or other fruit juice), snacks [1 small bag or 1 oz (28.3 g) potato chips, corn chips, or popcorn; or
1 cracker], 1 cup tea (not herbal), processed meat (1 piece or 1 slice processed meats, 2 slices bacon, or 1 hot dog), 1 pat margarine, whole
fruit [1 oz or small pack raisins, 1/2 cup grapes, 1 avocado, 1 banana, 1/4 cantaloupe, 1 slice watermelon, 1 orange, 1 fresh apple or pear, 1/2
cup (112 g) canned grapefruit, 1/2 cup (100 g) strawberries or blueberries, 1 fresh or 1/2 cup (112 g) canned peaches, or 1 fresh or 1/2 cup
(95 g) canned apricots or plums (1 oz = 28.3 g; 1/2 cup = 50 g)], 1 egg, and other vegetables [4-inch (10.2-cm) stick celery, 1/2 cup fresh
or cooked or 1 can mushrooms, 1/2 green pepper, 1 ear or 1/2 cup (90 g) frozen or canned corn, 1/2 cup (75 g) mixed vegetables, 1 eggplant,
1/2 cup (90 g) zucchini, 1/2 cup (16 g) alfalfa sprouts, or 1/4 cucumber].
Development and validation of a dietary inflammatory index 1563
EDII, its components food groups, and potential alternative
versions had low to moderate correlations with biomarkers
(Tabl e 3 ).
In multivariable-adjusted models in the NHS, the EDII was
significantly associated with concentrations of all 3 biomarkers
(IL-6, CRP, and TNFaR2) used in the development of the EDII.
The tests for linear trend were significant for each biomarker
across quintiles of the EDII (P-trend < 0.0001 for all biomarkers)
(Table 4). In the stratified analyses, there were no significant
differences in biomarker concentrations by aspirin/NSAID use
(P-interaction = 0.11, 0.36, and 0.21 for IL-6, CRP, and TNFaR2,
respectively). However, within each stratum of aspirin/NSAID
use, there were significant trends of increasing biomarker con-
centrations across EDII quintiles (P-trend < 0.0001 for all
biomarkers) (Figure 1).
In the validation phase with the use of NHS-II and HPFS
data, the EDII was significantly associated with concentrations
of all 3 biomarkers that were used in its development in the
NHS, plus concentrations of adiponectin and an overall inflam-
matory marker score (not involved in its development). There
were significant linear trends of higher concentrations of all
biomarkers in EDII quintiles. For example, in the NHS-II, the
relative concentrations (95% CIs) for the highest compared
with the lowest EDII quintile were 0.88 (95% CI: 0.80, 0.96),
P-trend = 0.003 for adiponectin and 3.18 (95% CI: 1.93, 5.26),
P-trend = 0.002 for the overall inflammatory marker score.
Corresponding associations in the HPFS were 0.87 (95% CI:
0.82, 0.92), P-trend < 0.0001 for adiponectin and 2.19, (95%
CI: 1.70, 2.82), P-trend < 0.0001 for the overall inflamma-
tory marker score (Tab l e s 5 and 6).
Associations between alternative versions of the EDII and the
4 biomarkers in sensitivity analyses in the NHS-II and HPFS
are presented in Supplemental Table 3. Associations between
the unweighted EDII and all biomarkers were significant al-
though smaller in magnitude than were those obtained with the
weighted EDII. The potential alternative version of the EDII that
included nutrients with supplements and the version that
included nutrients without supplements were both associated
with concentrations of all 4 biomarkers in women and men. The
EDII version derived in nonusers of aspirin/NSAIDs in the NHS
and applied in nonusers of aspirin/NSAIDs in the NHS-II (n=
812) and HPFS (n= 2174) was associated with concentrations of
all 4 biomarkers. The alternative version derived in control
subjects in the NHS-II (n= 594) and HPFS (n= 1606) was
significantly associated with concentrations of biomarkers
except for IL-6, CRP, and TNFaR2 in the HPFS (Supplemental
Table 3).
In the NHS, we observed stronger associations between the
EDII and IL-6 and CRP in overweight/obese women than in
normal-weight women (P-interaction = 0.07, 0.04, and 0.39 for
IL-6, CRP, and TNFaR2, respectively), although there were
significant trends of higher biomarker concentrations in both
normal-weight and overweight/obese women across EDII
quintiles (Figure 2 and Supplemental Table 4). In the multivar-
iable models, associations with adjustment for BMI, although
significant, were weaker than those without adjustment for BMI
(data not shown). In the NHS-II and HPFS, associations were
attenuated and mostly statistically nonsignificant in both
women and men when the EDII was used to predict BMI-
adjusted biomarkers, except for TNFaR2, adiponectin, and the
overall inflammatory marker score in the HPFS (Supplemen-
tal Table 3). In the NHS-II, there was no statistical evidence
for effect modification by BMI status for any biomarker except
for TNFaR2 (P-interaction = 0.95, 0.58, 0.007, 0.88, and
0.60 for IL-6, CRP, TNFaR2, adiponectin, and overall inflam-
matory marker score, respectively). Also, in the HPFS, with
1142 normal-weight men and 1490 overweight/obese men, there
was no significant effect modification by BMI (P-interaction =
0.25, 0.85, 0.17, 0.99, and 0.57 for IL-6, CRP, TNFaR2,
adiponectin, and overall inflammatory marker score, respec-
tively). However, we found stronger associations between the
EDII and IL-6, CRP, and overall inflammatory marker score in
normal weight men than in overweight/obese men (Supplemen-
tal Table 4).
Discussion
Using RRR and stepwise linear regression analyses in a large
cohort of women, we developed a hypothesis-driven index of
dietary inflammatory potential (the EDII) based on the intake of
18 food groups, and evaluated its construct validity in 2
independent cohorts of women and men. The construct valida-
tion of this index showed robust associations between the EDII
and 3 plasma inflammatory markers: IL-6, CRP, and TNFaR2,
and additional markers, adiponectin and an overall inflamma-
tory marker score, which were not included in its development.
These associations were also robust to several sensitivity analyses.
The EDII thus may be derived in a standardized manner across
different populations and used to examine associations with
diseases hypothesized to have chronic inflammation as a major
pathogenesis pathway. A previously developed literature-derived
nutrient-based dietary inflammatory index (36), whose validity
has been evaluated (38, 42), has shown strong associations with
disease risk, e.g., with colorectal cancer risk (39, 43) and pancreatic
cancer risk (44). Both dietary indexes assess the inflammatory
potential of an individualÕs diet, but differ in conception and
design. Dietary patterns based on food groups, such as the EDII,
are most directly related to dietary guidelines for health promotion
and disease prevention.
The EDII is similar to 2 dietary patterns derived previously in
the NHS by using the RRR procedure. Schulze et al. (45) used
soluble TNFaR2, IL-6, CRP, E-selectin, the soluble intracellular
cell adhesion molecule, and the soluble vascular cell adhesion
molecule 1 as responses in the RRR procedure to develop a
dietary pattern associated with type 2 diabetes that comprised 9
food groups (all except cruciferous vegetables included in the
EDII). They found positive associations between higher scores of
this pattern and risk of type 2 diabetes (OR: 3.09; 95% CI: 1.99,
4.79). More recently, Lucas et al. (46) derived a similar dietary
pattern with the use of CRP, IL-6, and soluble TNFaR2 as
responses in the RRR procedure, and examined its association
with the risk of depression in women. They identified a pattern
comprising 11 food groups (all included in the EDII), which was
significantly associated with risk of depression in the NHS (RR:
1.41; 95% CI: 1.22, 1.63), by comparing extreme quintiles of
the dietary pattern. Unlike the previous studies, the EDII focuses
on the inflammatory potential of diet more generally rather than
on specific diseases, and its validity was assessed in 2 indepen-
dent cohorts of women and men. In addition, we constructed
several potential alternative versions of the EDII in the NHS
sample, applied them in the 2 independent validation samples,
and found the EDII to be robust to these alternative hypotheses.
Among the 18 EDII components, fish (other than dark-meat
fish) and tomatoes were positively associated with inflammatory
markers, whereas pizza was inversely related. This may likely
reflect fish preparation methods, but this information was not
collected. For example, well-done or browned fried, grilled, or
1564 Tabung et al.
TABLE 2 Participant characteristics across quintiles of the empirical dietary inflammatory index for all 3 cohorts
1
NursesÕHealth Study (n= 5230; 1986–1990) NursesÕHealth Study II (n= 1002; 1995–1999)
Health Professionals Follow-Up Study
(n= 2632; 1990–1994)
Quintile 1
(22.27 to ,20.28)
Quintile 3
(20.12 to ,0.004)
Quintile 5
(0.16–1.49)
Quintile 1
(21.26 to ,20.28)
Quintile 3
(20.12 to ,0.03)
Quintile 5
(0.22–1.18)
Quintile 1
(22.67 to ,20.24)
Quintile 3
(20.03 to ,0.17)
Quintile 5
(0.41–2.08)
Age, y 57.8 66.6 58.6 66.9 57.4 67.3 42.6 64.3 41.9 64.5 40.7 64.6 59.1 69.2 60.1 610.7 62.3 69.1
BMI, kg/m
2
24.0 63.8 25.6 64.5 28.1 65.9 24.3 64.8 25.2 65.7 27.5 66.6 25.6 63.0 25.7 63.8 26.5 63.5
Overweight/obese ($25 kg/m
2
), % 30.7 46.6 64.6 31.0 37.5 57.0 54.9 53.0 63.7
Physical activity, MET-h/wk 17.2 622.4 16.6 623.8 13.3 623.6 20.2 621.3 16.6 616.9 16.3 622.5 38.4 635.9 36.6 634.1 33.8 633.1
Current smokers, % 15.9 11.8 12.3 16.5 6.0 8.5 8.0 6.1 7.0
Alcohol,
2
servings/d 0.9 61.1 0.4 60.6 0.2 60.5 0.8 60.9 0.2 60.4 0.1 60.2 2.5 62.0 1.0 61.3 0.8 61.3
Plasma CRP,
3
mg/L 1.2 61.4 1.8 61.5 2.5 61.5 0.7 61.7 0.8 61.7 1.1 61.7 0.5 61.7 0.6 61.7 0.7 61.6
Plasma CRP .3 mg/L, % 17.3 27.3 40.4 13.5 16.5 22.5 9.1 11.0 13.1
Plasma IL-6,
3
pg/mL 1.2 61.1 1.5 61.1 1.6 61.1 1.0 61.0 1.1 61.0 1.2 61.0 1.2 61.1 1.3 61.1 1.5 61.1
Plasma TNFaR2,
3
ng/mL 2.5 60.6 2.5 60.6 2.7 60.6 2.0 60.5 2.2 60.5 2.2 60.6 2.5 60.6 2.7 60.6 2.7 60.6
Plasma adiponectin,
3
μg/mL NA NA NA 6.7 60.8 6.7 60.8 5.5 60.8 7.4 61.0 7.4 61.0 6.7 60.9
Regular aspirin/NSAID users, % 34.0 34.8 39.3 21.5 20.5 16.5 20.5 15.2 16.0
Chronic diseases or conditions comorbidity score,
4
%
0 chronic diseases or conditions 50.6 41.6 35.5 73.5 72.5 68.0 43.2 47.0 40.5
1 chronic disease or condition 31.5 34.0 34.8 22.0 23.5 21.5 32.7 32.1 33.5
2 chronic diseases or conditions 14.7 17.8 19.3 4.5 3.5 7.5 17.1 13.9 18.4
$3 chronic diseases or conditions 3.3 6.6 10.4 0.0 0.5 3.0 7.0 7.0 7.6
Postmenopausal women, % 85.7 85.9 82.6 27.5 24.5 17.0 NA NA NA
Postmenopausal hormone users, % 55.4 55.0 51.1 36.5 38.5 32.5 NA NA NA
1
Values are means 6SDs (geometric means 6CVs for all inflammatory markers), or percentages. The Quan-Zhang formula (41), CV = (e
SD
21)
1/2
, was used to calculate CVs. NursesÕHealth Study, n= 1046 for each quintile; NursesÕHealth
Study II, n= 200 for each quintile; Health Professionals Follow-Up Study, n= 526 for each quintile. CRP, C-reactive protein; MET-h, metabolic equivalent task hours; NA, not applicable; NSAID, nonsteroidal anti-inflammatory drug; TNFaR2, TNF-a
receptor 2.
2
Total servings per day of wine [4-oz (113.4-g) glass], beer (1 bottle, can, or glass), and liquor (1 drink or shot).
3
Geometric means 6CVs are presented for the biomarkers because all 4 biomarkers were log-transformed before analyses.
4
Chronic diseases or conditions included in the score were hypercholesterolemia, cancer, diabetes, high blood pressure, heart disease, and rheumatoid or other arthritis.
Development and validation of a dietary inflammatory index 1565
barbecuedfish may be more proinflammatory and associated with
a higher risk ofchronic diseases (47). The oils used for deepfrying
have low amounts of n–3 FAs because of the oxidation of these
acids (48), and, until the regulation of trans fats in the United
States, they also contained high amounts of trans fats, which are
proinflammatory. Three trials investigated the effect of tomato
intake on concentrations of inflammatory markers with conflict-
ing findings (49–51). One trial found a significantly reduced
concentration of adiponectin (49), but others found no effect on
IL-6, CRP, and other inflammatory markers (50, 51). Indeed, at
the end of the intervention, one trial reported significantly higher
concentrations of inflammatory markers in the intervention group
TABLE 3 Spearman correlation coefficients between the EDII, its potential alternative versions, and
plasma inflammatory markers in all 3 cohorts
1
EDII CRP IL-6 TNFaR2 Adiponectin
Overall inflammatory
marker score
2
Nurses' Health Study (n= 5230; 1986–1990)
EDII 1.00 0.21 0.19 0.15 NA NA
Unweighted EDII 0.88 0.18 0.17 0.13 NA NA
EDII with nutrients (including supplements) 0.90 0.22 0.20 0.16 NA NA
EDII with nutrients (no supplements) 0.87 0.23 0.20 0.16 NA NA
EDII from BMI-adjusted biomarkers 0.67 0.12 0.11 0.15 NA NA
EDII in nonusers of aspirin/NSAIDs 0.94 0.21 0.17 0.15 NA NA
EDII in control subjects 0.91 0.28 0.22 0.17 NA NA
Nurses' Health Study-II (n= 1002; 1995–1999)
EDII 1.00 0.10 0.14 0.11 20.14 0.18
Unweighted EDII 0.88 0.08 0.13 0.10 20.10 0.16
EDII with nutrients (including supplements) 0.88 0.13 0.16 0.11 20.12 0.19
EDII with nutrients (no supplements) 0.84 0.13 0.15 0.09 20.10 0.17
EDII from BMI-adjusted biomarkers 0.67 20.01* 0.02* 0.06 20.09 0.06
EDII in nonusers aspirin/NSAIDs 0.94 0.08 0.16 0.14 20.15 0.19
EDII in control subjects 0.90 0.10 0.12 0.13 20.07* 0.16
Health Professionals Follow-Up Study
(n= 2632; 1990–1994)
EDII 1.00 0.05 0.10 0.13 20.06 0.14
Unweighted EDII 0.89 0.03* 0.07 0.11 20.09 0.12
EDII with nutrients (including supplements) 0.71 0.09 0.13 0.15 20.05 0.17
EDII with nutrients (no supplements) 0.73 0.07 0.12 0.12 20.04 0.14
EDII from BMI-adjusted biomarkers 0.53 20.002* 0.07 0.13 0.01* 0.07
EDII in nonusers aspirin/NSAIDs 0.85 0.07 0.07 0.12 20.09 0.14
EDII in control subjects 0.87 0.03* 0.06 0.08 20.05 0.08
1
All values are Spearman correlation coefficients, *P.0.05. CRP, C-reactive protein; EDII, empirical dietary inflammatory index; NA, not
applicable; NSAID, nonsteroidal anti-inflammatory drug; TNFaR2, TNF-areceptor 2.
2
Computed by summing the zscores of all 4 biomarkers for each participant.
TABLE 4 Relative concentrations of plasma inflammatory markers across quintiles of the EDII in the NursesÕHealth Study (n= 5230)
1
Quintile 1
(22.27 to ,20.28; most
anti-inflammatory diets)
Quintile 2
(20.28 to ,20.12)
Quintile 3
(20.12 to ,0.004)
Quintile 4
(0.004 to ,0.16)
Quintile 5
(0.16–1.49;
most proinflammatory diets) P-trend
2
IL-6
Age-adjusted 1 1.10 (1.03, 1.17) 1.22 (1.14, 1.30) 1.31 (1.23, 1.40) 1.47 (1.38, 1.58) ,0.0001
Multivariable-adjusted
3
1 1.09 (1.03, 1.16) 1.20 (1.12, 1.27) 1.25 (1.17, 1.33) 1.36 (1.28, 1.45) ,0.0001
CRP
Age-adjusted 1 1.29 (1.16, 1.42) 1.41 (1.28, 1.56) 1.62 (1.46, 1.79) 2.09 (1.89, 2.31) ,0.0001
Multivariable-adjusted
3
1 1.27 (1.15, 1.39) 1.38 (1.26, 1.52) 1.52 (1.38, 1.57) 1.82 (1.65, 2.01) ,0.0001
TNFaR2
Age-adjusted 1 1.03 (1.01, 1.06) 1.05 (1.03, 1.08) 1.09 (1.06, 1.12) 1.14 (1.11, 1.17) ,0.0001
Multivariable-adjusted
3
1 1.03 (1.00, 1.06) 1.05 (1.03, 1.08) 1.09 (1.06, 1.11) 1.13 (1.10, 1.16) ,0.0001
1
Values are relative concentrations (95% CIs) of biomarkers in higher EDII quintiles relative to quintile 1 as the reference quintile (e.g., the ratio of the concentration in quintile
5 to that in quintile 1), n= 1046 in each quintile. All values were back-transformed (e
x
) because biomarker data were ln-transformed before analyses. CRP, C-reactive protein; EDII,
empirical dietary inflammatory index; TNFaR2, TNF-areceptor 2.
2
The P-value of the dietary index as a continuous variable adjusted for all covariates listed in footnote 3.
3
Adjusted for age, physical activity, smoking status, case-control status, batch effects for biomarker measurements, regular aspirin/nonsteroidal anti-inflammatory drug use,
menopausal status, postmenopausal hormone use, and an inflammation-related chronic disease comorbidity score. Chronic diseases and conditions included in the score were
hypercholesterolemia, cancer, diabetes, high blood pressure, heart disease, and rheumatoid or other arthritis.
1566 Tabung et al.
than in the control group (50). It is therefore possible that the
mechanisms of the potential benefit of a tomato-rich diet may not
be related directly to the inflammation process. Tomato paste
contains 2.5- to 4-fold higher bioavailable lycopene than fresh
tomatoes (52), and lycopene has shown anti-inflammatory proper-
ties (53), which could explain the inverse association of pizza with
inflammatory markers.
The association between the inflammatory potential of diet
and concentrations of inflammatory markers may be con-
founded by BMI, mediated through BMI, and/or modified by
BMI. That is, BMI or weight gain has been associated with the
quality of dietary intake (54) and with inflammatory markers
(55), and it is possible also that overweight/obesity, a state of
low-grade chronic inflammation (56, 57), may partly mediate
the association between dietary inflammatory potential and
concentrations of inflammatory markers or chronic disease
outcomes. In sensitivity analyses in the NHS-II and HPFS with
the EDII from BMI-adjusted biomarkers, associations with all
biomarkers were attenuated, and most became nonsignificant
(Supplemental Table 3), which may reflect more mediation
than confounding. Adjusting for BMI (and other potential
confounders) is important for etiologic purposes to identify the
independent association between the inflammatory potential
of diet and concentrations of inflammatory markers, but the
FIGURE 1 Adjusted mean (95% CI)
plasma inflammatory marker concen-
trations in quintiles of the EDII in
regular users (A) and nonusers (B) of
aspirin/NSAIDs (NursesÕHealth Study;
n= 5230; 1986–1990). Values are
mean concentrations of biomarkers
adjusted for age at blood draw, phys-
ical activity, smoking status, BMI,
menopausal status, postmenopausal
hormone use, case-control status,
batch effects for biomarker measure-
ments, and an inflammation-related
chronic disease comorbidity score.
Chronic diseases and conditions in-
cluded in the score were hypercholes-
terolemia, cancer, diabetes, high blood pressure, heart disease, and rheumatoid or other arthritis. All tests were 2-sided and all 95% CIs were
statistically significant (i.e., did not include 1). All biomarker concentrations were back-transformed (e
x
), and all P-trends ,0.0001. Pvalues for
the interaction of EDII and aspirin/NSAIDs were as follows: IL-6 = 0.11; CRP = 0.36; TNFaR2 = 0.21. Sample sizes in EDII quintiles were as
follows—nonusers of aspirin/NSAIDs: Q1 = 668, Q2 = 669, Q3 = 669, Q4 = 669, and Q5 = 669; regular aspirin/NSAID users: Q1 = 377, Q2 = 377,
Q3 = 378, Q4 = 377, and Q5 = 377. CRP, C-reactive protein; EDII, empirical dietary inflammatory index; NSAID, nonsteroidal anti-inflammatory
drug; Q, quintile; TNFaR2, TNF-areceptor 2.
TABLE 5 Relative concentrations of plasma inflammatory markers across quintiles of the EDII in the Nurses’ Health Study II
(n= 1002; 1995–1999)
1
Quintile 1
(21.26 to ,20.28;
most anti-inflammatory diets)
Quintile 2
(20.28 to ,20.12)
Quintile 3
(20.12 to ,0.03)
Quintile 4
(0.03 to ,0.22)
Quintile 5
(0.22–1.18;
most proinflammatory diets) P-trend
2
IL-6
Age-adjusted 1 0.97 (0.84, 1.11) 1.07 (0.93, 1.23) 1.09 (0.95, 1.25) 1.24 (1.08, 1.42) ,0.0001
Multivariable-adjusted
3
1 0.96 (0.84, 1.09) 1.07 (0.93, 1.22) 1.02 (0.89, 1.18) 1.17 (1.02, 1.34) 0.001
CRP
Age-adjusted 1 1.22 (0.94, 1.60) 1.21 (0.93, 1.60) 1.36 (1.04, 1.77) 1.74 (1.33, 2.27) ,0.0001
Multivariable-adjusted
3
1 1.24 (0.97, 1.60) 1.20 (0.93, 1.54) 1.24 (0.96, 1.60) 1.52 (1.18, 1.97) 0.002
TNFaR2
Age-adjusted 1 1.02 (0.97, 1.06) 1.04 (0.99, 1.08) 1.06 (1.01, 1.10) 1.10 (1.05, 1.15) ,0.0001
Multivariable-adjusted
3
1 1.02 (0.98, 1.07) 1.04 (0.99, 1.09) 1.06 (1.01, 1.10) 1.09 (1.04, 1.14) 0.0003
Adiponectin
Age-adjusted 1 1.01 (0.92, 1.11) 1.01 (0.92, 1.11) 0.93 (0.84, 1.02) 0.83 (0.75, 0.91) ,0.0001
Multivariable-adjusted
3
1 1.01 (0.92, 1.11) 1.03 (0.94, 1.13) 0.97 (0.87, 1.07) 0.88 (0.80, 0.96) 0.003
Overall inflammatory marker score
4
Age-adjusted 1 1.19 (0.71, 1.99) 1.45 (0.87, 2.43) 2.09 (1.25, 3.50) 4.49 (2.67, 7.54) ,0.0001
Multivariable-adjusted
3
1 1.18 (0.72, 1.92) 1.39 (0.85, 2.28) 1.62 (0.99, 2.67) 3.18 (1.93, 5.26) ,0.0001
1
Values are relative concentrations (95% CIs) of biomarkers in higher EDII quintiles relative to quintile 1 as the reference quintile (e.g., ratio of concentration in quintile 5 to
concentration in quintile 1). All values were back-transformed (e
x
) because biomarker data were ln-transformed before analysis. Quintile 1: n= 200, quintile 2: n= 201, quintile 3:
n= 200, quintile 4: n= 201, and quintile 5: n= 200. CRP, C-reactive protein; EDII, empirical dietary inflammatory index; TNFaR2, TNF-areceptor 2.
2
The Pvalue of the dietary index as a continuous variable adjusted for all covariates listed in footnote 3.
3
Adjusted for age, physical activity, smoking status, case-control status, batch effects for biomarker measurements, regular aspirin/nonsteroidal anti-inflammatory drug use, and
an inflammation-related chronic disease comorbidity score. Chronic diseases or conditions included in the score were hypercholesterolemia, cancer, diabetes, high blood pressure,
heart disease, and rheumatoid or other arthritis, with additional adjustment for menopausal status and postmenopausal hormone use in women.
4
Computed by summing the zscores of all 4 biomarkers for each participant.
Development and validation of a dietary inflammatory index 1567
proportion of this association mediated by BMI is important to
inform the design of public health interventions. Evidence for
mediation is strengthened by findings from well-designed meta-
analyses that combine data from highly powered prospective
studies addressing dietary determinants of long-term weight
gain and randomized clinical trials evaluating the short-term
effects of specific dietary factors on weight changes (58–60).
For example, replacing sugar-sweetened beverage intake with
water, coffee, or tea is inversely associated with weight gain
(60). In the current study, we found the same direction of
association between coffee and inflammatory markers. Also,
other evidence shows that weight loss leads to changes in
concentrations of inflammatory markers (61, 62). In our large
NHS sample, we found effect modification by BMI of the
association between higher EDII scores and IL-6 and CRP
concentrations, with higher concentrations in overweight/
obese women than in normal-weight women, but it is not
clear why there were differences between women and men.
Perhaps the multiplicative effects of overweight/obesity may
be more dominant than those of diet alone and thus explain
the stronger associations we found in normal-weight men
than overweight/obese men.
Our approach in developing the EDII in the NHS was based
on the relatively large sample with inflammatory marker data.
FIGURE 2 Adjusted mean (95% CI)
plasma inflammatory marker concentra-
tions in quintiles of the EDII in normal-
weight women (BMI ,25 kg/m
2
)(A)
and overweight/obese women (BMI
$25 kg/m
2
) (B) from the NursesÕHealth
Study (n= 5230; 1986–1990). Values
are mean concentrations of biomarkers,
adjusted for age at blood draw, physical
activity, smoking status, aspirin/NSAID
use, menopausal status, postmeno-
pausal hormone use, case-control status,
batch effects for biomarker measure-
ments, and an inflammation-related
chronic disease comorbidity score.
Chronic diseases and conditions in-
cluded in the score were hypercholesterolemia, cancer, diabetes, high blood pressure, heart disease, and rheumatoid or other arthritis. All tests
were 2-sided and all 95% CIs were statistically significant (i.e., did not include 1). All biomarker concentrations were back-transformed (e
x
), and all
P-trends ,0.0001. Pvalues for interaction of EDII and aspirin/NSAIDs were as follows: CRP = 0.13, IL-6 = 0.12, and TNFaR2 = 0.43. Sample sizes in
EDII quintiles were as follows—normal-weight women: Q1 = 544, Q2 = 545, Q3 = 544, Q4 = 545, and Q5 = 544; overweight/obese women:
Q1 = 501, Q2 = 502, Q3 = 502, Q4 = 502, and Q5 = 501. CRP, C-reactive protein, EDII, empirical dietary inflammatory index; NSAID, nonsteroidal anti-
inflammatory drug; Q, quintile; TNFaR2, TNF-areceptor 2.
TABLE 6 Relative concentrations of plasma inflammatory markers across quintiles of the EDII in the Health Professionals Follow-Up
Study (n= 2632; 1990–1994)
1
Quintile 1
(22.67 to ,20.24;
most anti-inflammatory diets)
Quintile 2
(20.24 to ,20.03)
Quintile 3
(20.03 to ,0.17)
Quintile 4
(0.17 to ,0.41)
Quintile 5
(0.41–2.08;
most proinflammatory diets) P-trend
2
IL-6
Age-adjusted 1 1.13 (1.04, 1.24) 1.10 (1.01, 1.21) 1.15 (1.06, 1.26) 1.14 (1.04, 1.25) 0.01
Multivariable-adjusted
3
1 1.11 (1.02, 1.21) 1.11 (1.01, 1.21) 1.16 (1.06, 1.27) 1.14 (1.04, 1.24) 0.01
C-reactive protein
Age-adjusted 1 1.12 (0.94, 1.32) 1.07 (0.90, 1.27) 1.20 (1.01, 1.41) 1.22 (1.03, 1.45) 0.05
Multivariable-adjusted
3
1 1.15 (1.02, 1.30) 1.19 (1.06, 1.34) 1.22 (1.08, 1.38) 1.23 (1.09, 1.40) 0.002
TNF-areceptor 2
Age-adjusted 1 1.04 (1.01, 1.07) 1.06 (1.03, 1.09) 1.07 (1.03, 1.09) 1.07 (1.04, 1.11) ,0.0001
Multivariable-adjusted
3
1 1.03 (1.00, 1.07) 1.06 (1.03, 1.09) 1.05 (1.02, 1.09) 1.07 (1.04, 1.10) 0.0001
Adiponectin
Age-adjusted 1 1.02 (0.95, 1.10) 1.01 (0.93, 1.09) 0.90 (0.84, 0.98) 0.86 (0.79, 0.92) ,0.0001
Multivariable-adjusted
3
1 1.00 (0.94, 1.05) 0.97 (0.92, 1.03) 0.91 (0.86, 0.96) 0.87 (0.82, 0.92) ,0.0001
Overall inflammatory marker score
4
Age-adjusted 1 1.43 (1.07, 1.90) 1.47 (1.10, 1.96) 1.97 (1.48, 2.62) 2.28 (1.70, 3.04) ,0.0001
Multivariable-adjusted
3
1 1.44 (1.12, 1.85) 1.66 (1.29, 2.13) 1.97 (1.53, 2.53) 2.19 (1.70, 2.82) ,0.0001
1
Values are relative concentrations (95% CIs) of biomarkers in higher EDII quintiles relative to quintile 1 as the reference quintile (e.g., ratio of concentration in quintile 5 to
concentration in quintile 1). All values were back-transformed (e
x
) because biomarker data were ln-transformed before analysis. Quintile 1: n= 526, quintile 2: n= 527, quintile 3:
n= 526, quintile 4: n= 527, and quintile 5, n= 526. CRP, C-reactive protein; EDII, empirical dietary inflammatory index; TNFaR2, TNF-areceptor 2.
2
The Pvalue of the dietary index as a continuous variable adjusted for all covariates listed in footnote 3.
3
Adjusted for age, physical activity, smoking status, case-control status, batch effects for biomarker measurements, regular aspirin/nonsteroidal anti-inflammatory drug use, and
an inflammation-related chronic disease comorbidity score. Chronic diseases or conditions included in the score were hypercholesterolemia, cancer, diabetes, high blood pressure,
heart disease, and rheumatoid or other arthritis, with additional adjustment for menopausal status and postmenopausal hormone use in women.
4
Computed by summing the zscores of all 4 biomarkers for each participant.
1568 Tabung et al.
Evaluating the construct validity of the EDII in both the HPFS and
NHS-II samples was done not only to avoid the statistical over-
fitting of NHS data, but also to determine the association of EDII
scores with concentrations of inflammatory markers in indepen-
dent populations of men and women. Thus, one contribution of
the current analysis is that studies that lack inflammatory marker
data may derive EDII scores to investigate associations between
dietary inflammatory potential and disease outcomes. The com-
position of food groups may not be uniform across studies, which
may limit the ability to apply EDII scores across studies in a
standardized manner; however, investigators may be able to
create unified food groups in pooled analyses of primary data
or in multicenter studies, thus enhancing the usefulness of this
hypothesis-driven dietary pattern in large-scale epidemiologic
research. Although the component food groups of the EDII and
its potential alternative versions are not exactly the same, the
correlations between these potential alternative versions and the
EDII were quite strong, ranging from 0.67 to 0.94 in the NHS;
0.67 to 0.90 in the NHS-II, and 0.53 to 0.89 in the HPFS. The use
of repeated dietary and covariate measures is another strength of
our study design. The use of >1 measurement has been shown to
reduce measurement error and also accounts for potential changes
over a 4-y time period in dietary and lifestyle behavior.
Our study is not without limitations: the NHS, NHS-II, and
HPFS study populations are mostly white, thus warranting the need
to apply further the EDII in multiethnic/multiracial populations.
Although we used repeated dietary and covariate data, we had only
one measurement of the inflammatory markers that would tend to
underestimate validity assessed by correlation coefficients (63).
Although we adjusted for a large number of potential confounding
factors, including a history of inflammation-related chronic diseases
and conditions, these potential confounding factors were self-
reported, allowing for the possibility of residual confounding.
To our knowledge, the EDII represents a novel hypothesis-
driven dietary inflammatory index that assesses diet quality based
on its inflammatory potential. Its construct validity in independent
samples of women and men with the use of 4 different inflam-
matory markers indicates its usefulness in assessing the inflam-
matory potential of whole diets. In addition, the EDII may be
derived in a standardized and reproducible manner across different
populations, thus circumventing a major limitation of dietary
patterns derived from the same study in which they are applied.
Acknowledgments
FKT and ELG designed the research; FKT conducted the
research and performed the statistical analysis; SAS-W, JEC,
KW, CSF, FBH, ATC, and WCW analyzed and interpreted the
data and provided critical intellectual input; FKT and ELG
wrote the paper; SAS-W reviewed all results for accuracy; and
ELG provided study oversight and had primary responsibility
for the final content. All authors read and approved the final
manuscript.
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