Association between Dietary Inflammatory Index, Dietary Patterns, Plant-Based Dietary Index and the Risk of Obesity.

Yoko B Wang,James R Hébert,Amanda J Page,Tiffany K Gill,Nitin Shivappa,Yohannes Adama Melaku

doi:10.3390/nu13051536

Abstract

Evidence on the association between various dietary constructs and obesity risk is limited. This study aims to investigate the longitudinal relationship between different diet indices and dietary patterns with the risk of obesity. Non-obese participants (n = 787) in the North West Adelaide Health Study were followed from 2010 to 2015. The dietary inflammatory index (DII®), plant-based dietary index (PDI) and factor-derived dietary pattern scores were computed based on food frequency questionnaire data. We found the incidence of obesity was 7.62% at the 5-year follow up. In the adjusted model, results from multivariable log-binomial logistic regression showed that a prudent dietary pattern (RRQ5 vs. Q1 = 0.38; 95% CI: 0.15–0.96), healthy PDI (RR = 0.31; 95% CI: 0.12–0.77) and overall PDI (RR = 0.56; 95% CI: 0.23–1.33) were inversely associated with obesity risk. Conversely, the DII (RR = 1.59; 95% CI: 0.72–3.50), a Western dietary pattern (RR = 2.16; 95% CI: 0.76–6.08) and unhealthy PDI (RR = 1.94; 95% CI: 0.81–4.66) were associated with increased risk of obesity. Based on the cubic spline analysis, the association between an unhealthy PDI or diet quality with the risk of obesity was non-linear. In conclusion, an anti-inflammatory diet, healthy diet or consumption of healthy plant-based foods were all associated with a lower risk of developing obesity.

Highlights

Obesity has become a global challenge, with a devastating impact on public health, the economy and climate [1]
We developed three plant-based dietary indices, i.e., plant-based dietary index (PDI), healthy PDI and unhealthy PDI, using the approach described by Satija et al [24]
We found a significant non-linear association between diet quality and an unhealthy plant-based diet and the risk of obesity (p value non-linear

Summary

Introduction

Obesity has become a global challenge, with a devastating impact on public health, the economy and climate [1]. In the past four decades, the global prevalence of obesity has rapidly increased. According to the World Health Organization (WHO), 49% of adults globally were overweight, and 13% were obese in 2016 [2]. In 2017–2018, 67% of Australian adults were overweight and 31% were obese [3]. The escalating global prevalence of obesity begins early in life [4]. If this trend continues, it is predicted that

Objectives

Methods

Results

Conclusion