Original Article

The Relationship between Empirical Dietary Inflammatory Pattern with Anthropometric Measures in Women with Overweight and Obesity: A Cross-Sectional Study

Abstract

Background: The increasing trend of obesity prevalence is a serious health warning for people worldwide. Evaluation of anthropometric measures is essential for explaining individual’s health status. Studies have investigated the effect of diet on inflammation. Empirical Dietary Inflammatory Pattern (EDIP) was recently developed to empirically create a score for overall inflammatory potential of diet. This study aimed to investigate the relationship between EDIP with anthropometric measures in women with overweight and obesity.

Methods: In a cross-sectional study, the EDIP score was calculated for 301 participants from their food frequency questionnaire, who referred to health centers in Tehran, Iran in 2018. Anthropometric measures was assessed through multi-frequency bioelectrical impedance analyzer.

Results: 49% (95% CI: 40.8 - 57.2) had positive EDIP score. A significant relationship was found between EDIP quartiles with weight (P=0.004), BMI (P=0.012), FM (P=0.013), WC (P=0.003) and WHR (P=0.031). Those individuals in the lowest group of EDIP score had significantly lower weight, Body Mass Index, Fat Mass, Waist Circumference and Waist to Hip Ratio, compared to those with highest inflammation score.

Conclusion: A significant relationship was found between EDIP with anthropometric measures in women with overweight and obesity, supporting the hypothesis that an anti-inflammatory diet is associated with decreasing trend of weight, Body Mass Index, Fat Mass, Waist Circumference and Waist to Hip Ratio.

 

1. Ng M, Fleming T, Robinson M, et al (2014). Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet, 384(9945):766-81.
2. Engin A (2017). The definition and prevalence of obesity and metabolic syndrome. Adv Exp Med Biol, 960: 1-17.
3. Ezzati M, Lopez AD, Rodgers AA, et al (2004). Comparative quantification of health risks: global and regional burden of disease attributable to selected major risk factors: World Health Organization. https://apps.who.int/iris/bitstream/handle/10665/42792/9241580348_eng_Volume1.pdf?sequence=1
4. Santoro A, Guidarelli G, Ostan R, et al (2019). Gender-specific association of body composition with inflammatory and adipose-related markers in healthy elderly Europeans from the NU-AGE study. Eur Radiol,1-12.
5. Lemos T, Gallagher D (2017). Current body composition measurement techniques. Curr Opin Endocrinol Diabetes Obes,24(5):310.
6. Mancuso P (2016). The role of adipokines in chronic inflammation.Immunotargets Ther,5:47.
7. Engeda J, Mezuk B, Ratliff S,et al (2013). Association between duration and quality of sleep and the risk of pre‐diabetes: evidence from NHANES. Diabet Med,30(6):676-80.
8. Kanagasabai T, Ardern CI (2015). Inflammation, oxidative stress, and antioxidants contribute to selected sleep quality and cardiometabolic health relationships: a cross-sectional study. Mediators Inflamm, 2015:824589.
9. Christian LM, Blair LM, Porter K, et al (2016). Polyunsaturated fatty acid (PUFA) status in pregnant women: associations with sleep quality, inflammation, and length of gestation. PloS One,11(2):e0148752.
10. Pierce GL, Kalil GZ, Ajibewa T, et al (2017). Anxiety independently contributes to elevated inflammation in humans with obesity. Obesity,25(2):286-9.
11. Kessler RC, Chiu WT, Demler O, et al (2005). Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry,62(6):617-27.
12. Dohrenwend BP (2006). Inventorying stressful life events as risk factors for psychopathology: Toward resolution of the problem of intracategory variability. Psychol Bull,132(3):477.
13. Zellner DA, Loaiza S, Gonzalez Z, et al (2006). Food selection changes under stress. Physiol Behav,87(4):789-93.
14. Steptoe A, Lipsey Z, Wardle J (1998). Stress, hassles and variations in alcohol consumption, food choice and physical exercise: A diary study. Br J Health Psychol, 3(1):51-63.
15. Suarez EC (2008). Self-reported symptoms of sleep disturbance and inflammation, coagulation, insulin resistance and psychosocial distress: evidence for gender disparity. Brain Behav Immun, 22(6):960-8.
16. Tabung FK, Smith-Warner SA, Chavarro JE, et al (2016). Development and validation of an empirical dietary inflammatory index. J Nutr, 146(8):1560-70.
17. Shakeri Z, Mirmiran P, Khalili-Moghadam S, et al (2019). Empirical dietary inflammatory pattern and risk of metabolic syndrome and its components: Tehran Lipid and Glucose Study. Diabetol Metab Syndr,11(1):16.
18. Tabung FK, Smith-Warner SA, Chavarro JE, et al (2017). An empirical dietary inflammatory pattern score enhances prediction of circulating inflammatory biomarkers in adults. J Nutr,147(8):1567-77.
19. Mirmiran P, Esfahani FH, Mehrabi Y, et al (2010). Reliability and relative validity of an FFQ for nutrients in the Tehran lipid and glucose study. Public Health Nutr,13(5):654-62.
20. Ghaffarpour M, Houshiar-Rad A, Kianfar H (1999). The manual for household measures, cooking yields factors and edible portion of foods. Tehran: Nashre Olume Keshavarzy,7:213.
21. Shivappa N, Steck SE, Hurley TG, et al (2014). Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr,17(8):1689-96.
22. Ruiz-Canela M, Zazpe I, Shivappa N, et al (2015). Dietary inflammatory index and anthropometric measures of obesity in a population sample at high cardiovascular risk from the PREDIMED (PREvencion con DIeta MEDiterranea) trial. Br J Nutr,113(6):984-95.
23. Correa-Rodríguez M, Rueda-Medina B, González-Jiménez E, et al (2018). Dietary inflammatory index, bone health and body composition in a population of young adults: a cross-sectional study. Int J Food Sci Nutr,69(8):1013-9.
24. Tabung FK, Giovannucci EL, Giulianini F, et al (2018). An empirical dietary inflammatory pattern score is associated with circulating inflammatory biomarkers in a multi-ethnic population of postmenopausal women in the United States. J Nutr,148(5):771-80.
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IssueVol 51 No 6 (2022) QRcode
SectionOriginal Article(s)
DOI https://doi.org/10.18502/ijph.v51i6.9680
Keywords
Inflammation Anthropometric measures Women’s health Iran
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How to Cite
1.
Badrooj N, Keshavarz SA, Tilves C, Yekaninejad MS, Pooyan S, Ghodoosi N, Mirzaei K. The Relationship between Empirical Dietary Inflammatory Pattern with Anthropometric Measures in Women with Overweight and Obesity: A Cross-Sectional Study. Iran J Public Health. 2022;51(6):1348-1354.