Development of Empirical Dietary Inflammatory Pattern (EDIP) scores with Different Food Groups and Biomarkers
Guyonnet et al_2021.PDF

How to Cite

Guyonnet, E., Amankwah, M., Chen, Y., Martini, R. ., Davis, M., & Newman, L. (2021). Development of Empirical Dietary Inflammatory Pattern (EDIP) scores with Different Food Groups and Biomarkers. Columbia Undergraduate Research Journal, 5(1).


Background: The empirical dietary inflammatory pattern (EDIP) is a hypothesis-driven dietary pattern used to assess the inflammatory potential of diet in the US population. Food-frequency questionnaire responses are used to build regression models comparing this dietary information to circulating inflammatory profiles, to help determine which food groups have more or less inflammatory potential on specific individuals. We will eventually use this tool in a cancer patient intervention to modify inflammation and improve chances of survival.

Methods: EDIP scores were calculated for 4 models from 24hr recalls reported by 67 women noncancer controls that had signed an informed consent prior to participation. The Luminex Human Chemokine Multiplex Assay was used to measure 11 chemokines and cytokines. As seen in previous studies, we first derived a model, EDIP-Limited (EDIP-L), by using a reduced rank regression model of all 17 food groups followed by a multivariable regression analysis to identify a dietary pattern that predicts concentrations of two inflammatory biomarkers: IL-6 and TNF-a. We derived a secondary EDIP score using a new model, EDIP-All Inclusive (EDIP-AI), which included the same 17 food groups to predict all 11 circulating biomarkers in our panel. Lastly, we developed two additional EDIP models to test how the biomarker predication may change when we regrouped our food variables from 17 to 14 groupings. EDIP-Limited New (EDIP-LN) used 14 new food groups derived from the same 24hr recalls, only predicting IL-6 and TNF-a. EDIP-All New (EDIP-AN) used those same 14 food groups with all 11 biomarkers.

Results: In this study, we optimize models for EDIP and report the differences in EDIP scores based on the inflammatory biomarkers and food groups used in analysis. Briefly, the components of EDIP-L were not significant. After including all the biomarkers, the components of EDIP-AI were: “fruit juice” (p = 0.0009), “snacks” (p = 0.0008), “leafy green vegetables” (p = 0.0074), “low-energy beverages” (p = 0.0098), “red meat” (p = 0.0038), “fruit” (p = 0.0002) and “whole wheat grains” (p = 0.0138). Similarly, after reorganizing our food items into 14 food groups, the components of EDIP-LN were not significant. However, components of EDIP-AN were: “fruit juice” (p = 0.0107), “snacks” (p = 0.0116) and “fruit” (p = 0.0026).

Conclusions: Findings demonstrate the EDIP scores differ based on the inflammatory biomarkers and food groups used in the analysis on the same noncancer controls. Depending on the methods used, an individual’s diet may be considered more pro- or anti-inflammatory. This study provides insight into the inflammatory potential of an individual’s diet and the factors that may affect how we calculate this potential.
Guyonnet et al_2021.PDF
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright (c) 2021 Emma Guyonnet, Millicent Amankwah, Yalei Chen, Rachel Martini, Melissa Davis, Lisa Newman