Association Between the Dietary Inflammatory Index and Resting Metabolic Rate per Kilogram of Fat-Free Mass in Overweight and Obese Women

Document Type: Original article


Department of Community Nutrition, Faculty of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences (TUMS), Tehran, Iran


Background: A low Resting Metabolic Rate (RMR) as a risk factor for weight gain can be affected by many factors. However, the role of Dietary Inflammatory Index (DII) in RMR is still unknown. This study was designed to examine the association between resting metabolic rate per kilogram of fat-free mass and DII.
Methods: This cross-sectional study was conducted among 304 Iranian overweight and obese women aged 18-50 years. RMR was measured by indirect calorimetry. Bioelectrical Impedance Analysis (BIA) was used to determine Fat Free Mass (FFM) and other body composition parameters. A validated 147-item FFQ was used to compute dietary inflammatory index.
Results: Participants that consumed more pro-inflammatory foods, compared to those with lower intake had significantly a lower odds ratio for increased RMR/FFM (OR: 0.40; 95% CI: 0.16-0.91; p=0.01). Moreover, after adjusting for potential confounders, a significant inverse association between DII and RMR/FFM was observed in overweight women (OR: 0.31; 95% CI: 0.09-0.98; p=0.04).
Conclusion: In this study, an inverse association between DII score and RMR/FFM in overweight women was observed. Further studies, in particular prospective cohorts with long-term follow-up, are required to confirm these findings.


Obesity is a major health problem in developed countries and a growing one in developing countries with prevalence of nearly 26% around the world. It may increase the risk of diabetes, heart disorders, fatty liver and many types of cancer (1). The understanding of the pathophysiology of excess adiposity and preventing obesity are of the utmost importance. Obesity can be induced by some risk factors such as high-energy food intake, low physical activity, sedentary behavior, genetics, and low resting metabolic rate (2).
Resting Metabolic Rate (RMR) is an important part of daily energy consumption and is accounted for 60~75% of total energy expenditure (3). A low RMR may lead to obesity or weight gain, and on the other hand, obesity may be associated with RMR via inflammation (4,5). Obesity is a chronic and systemic inflammatory disease, in which adipose tissue plays an important role through the production of numerous inflammatory cytokines, which regulate carbohydrate and lipid metabolism and immune function (6).
Alongside obesity, some foods such as red meat, high-fat dairy products, and refined grains have been shown to have inflammatory potential (7). Dietary Inflammatory Index (DII) was developed to characterize an individual’s diet on a continuum from maximally anti- to pro-inflammatory to determine the effect of diet on inflammation (8). The current study was designed to examine the association between DII and RMR/Fat Free Mass (FFM) in overweight and obese participants.

Materials and Methods
Study population
This cross-sectional study included 304 overweight and obese adult women, aged 18 to 56 years, referred to health centres in Tehran, Iran. Participants were in good general health with Body Mass Index (BMI) ranged between 25 to 50 kg/m2. The study protocol was approved by the Ethics Commission of Tehran University of Medical Sciences (IR.TUMS.VCR.REC.1395.1480), and all participants signed a written informed consent. In the current study, exclusion criteria were history of hypertension, kidney or liver disorders, cardiovascular diseases, diabetes mellitus, menopausal status, alcohol consumption, smoking, and pregnancy or lactation period. In addition, those women who had been following special diets were excluded.
Energy expenditure measurements
RMR was measured by indirect calorimetry (METALYZERR 3B-R3). An airtight mask covering nose and mouth was used for measuring respiratory gases with the spirometer MetaLyzer 3B-R3 (Cortex Biophysik GmbH, Leipzig, Germany). Participants were asked to refrain from alcohol or caffeine consumption and vigorous exercise for a day and 12 hour fasting before RMR measurements was required. The RMR was measured in the morning after a restful night’s sleep in a silent room with an ambient temperature of 24-26 °C. After achieving steady state in the supine position in a quiet and darkened atmosphere, the RMR was measured for 30 min. Gas exchange and ventilation were recorded continuously via breath-by-breath gas analysis. The oxygen uptake (VO2) and respiratory exchange ratio were analyzed within the last 20 min of the resting period and during a minimum of 5 consecutive minutes in steady-state conditions. The RMR was measured by calculating the amount of CO2 produced and O2 consumed through a one way valve between the subject and the analyzer (9).

Biochemical parameters
Fasting blood samples were taken and the serum was separated by centrifuge and stored at a temperature of -80°C until the analysis was carried out. Fasting plasma glucose and lipid was measured by using commercial kits (Pars Azmoon, Iran). Serum insulin concentrations were analyzed through the Enzyme-Linked Immunosorbent Assay (ELISA) (Human insulin ELISA kit, DRG Pharmaceuticals, GmbH, USA). Serum hypersensitive C-reactive protein (hs-CRP) was measured by an immunoturbidimetric assay. The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) standardization for liver enzymes (SGOT, SGPT) was used. All measurements were done at the Endocrinology & Metabolism Research Institute (EMRI) of Bionanotechnology laboratory at Tehran University of Medical Sciences.

Dietary inflammatory index
The purpose of creating the DII is to provide a tool to measure the diet’s inflammatory potential on a continuum from maximally anti-inflammatory to maximally pro-inflammatory. A higher DII score (more positive) presents a  diet with more inflammation-causing food and a lower DII score (more negative) presents a diet with less inflammation-causing food. Usual dietary intake was evaluated by using a 148-item semi-quantitative Food Frequency Questionnaire (FFQ) and its reliability and validity had been already approved in Iran (10). It was used to compute DII according to the method introduced by Shivappa et al and Esfahani et al (11,12). The FFQ included 147 foods commonly consumed by Iranians and standard serving sizes for each food item was defined. FFQ was collected through face-to-face interviews by trained interviewers at the health centers in Tehran. Software program Nutritionist IV was used for nutrient analysis which was modified for Iranian foods.
To calculate DII for all participants, first, the Z-score was computed by subtracting the global standard mean from the amount reported by participants and dividing the difference by the global standard deviation. Then, the Z-score was converted to a centered percentile score. The centered percentile score of participants for each food parameter was multiplied by the food parameter effect score, to obtain a DII score for an individual. After that, to create the overall DII score, all of the food parameter-specific DII scores were summed.
Finally, DII score was categorized into Tertile (T) including T1 (more anti-inflammatory foods), T2 (intermediate group) and T3 (more pro-inflammatory foods). Thirty food items of the theoretically possible list of 45 food parameters that were used in the present study were anti- inflammatory foods (dietary fiber, n-6 fatty acids, n-3 fatty acids, mono-unsaturated fatty acids, poly-unsaturated fatty acids, thiamin, riboflavin, niacin, vitamin B-6, folate, vitamin A, β-carotene, vitamin D, vitamin C, vitamin E, zinc, magnesium, selenium, onion, tea, and garlic) and pro- inflammatory foods (trans fatty acids, saturated fatty acids, protein, vitamin B-12, iron, total fat, energy intake, carbohydrate, and cholesterol).

Assessment of other variables
Physical activity level was measured by a validated International Physical Activity Questionnaire (IPAQ) including leisure, activities at work, commuting, and housework. The level of physical activity of participants was calculated as Met.h/d (13). Body composition including weight, BMI, body fat mass, body fat percentage, fat-free mass, visceral fat and waist to hip ratio was measured using a multi-frequency bioelectrical impedance analyzer InBody 770 scanner (Inbody Co., Seoul, Korea). According to the manufacturer’s instructions, first, participants removed their shoes, coats, and sweaters, then they stood on the scale and kept the handles of the machines. Also, height was measured using a calibrated height gauge with a precision of 0.5 cm (in the standing position without shoes to the nearest 0.01 m). Obesity and overweight were defined as BMI≥30 kg/m2 and 25≤BMI≤29.9 kg/m2, respectively.

Statistical analyses
Descriptive analysis was used to describe the baseline characteristics of study population. The chi-square test and one way ANOVA were used to investigate categorical variables and continuous variables, respectively. In addition, to analyze the association between DII tertiles and RMR/FFM, logistic regression was utilized after adjusting for confounders including total energy intake, age, BMI, physical activity, and smoking. Also, p-values less than 0.05 were considered statistically significant. Statistical analyses of the present study were performed using IBM SPSS software (version 22.0; IBM Corp., Armonk, NY, USA).

Study population characteristics
A total of 304 overweight and obese adult women, aged 18 to 56 years took part in the study. The mean and standard deviation of age, weight, and BMI among participants were 36.49±8.38 years, 80.89±2.45 kg, and 31.04±4.30 kg/m2, respectively.
The mean and standard deviation of DII, RMR and RMR/ FFM were -0.003±152.36, 1720.38±1.70 and 33.76±4.47, respectively. The anthropometric and biochemical characteristics of participants are shown in table 1. In addition, participant’s characteristics among DII tertiles are presented in table 2. Individuals with high DII score compared to those with low DII score had higher RMR /FFM, soft lean mass, basal metabolic rate and lower body fat. However, there were no significant differences in other characteristics of participants among DII tertiles.

Association between DII and RMR/FFM
The association between DII score and RMR/FFM was analyzed in the total population and also in sub-grouped population based on obesity degree in crude and adjusted binary regression model (Table 3). In total population, a marginally significant inverse association was observed between DII score and RMR/FFM in crude (OR T3 vs. T1= 0.58, 95% CI= 0.32-1.08, p-value=0.06) and adjusted models (OR T3 vs. T1= 0.63, 95% CI= 0.30-1.01, p-value =0.05).
It has been shown that overweight women with high DII score (T3) compared to low DII score (T1) had 60 and 69% lower odds of high RMR/FFM, respectively in crude (OR T3 vs. T1= 0.40, 95% CI= 0.16-0.98, p-value =0.01) and adjusted models (OR T3 vs. T1= 0.31, 95% CI= 0.09-0.98, p-value =0.04). However, women with obesity and morbid obesity had shown no statistically significant association between DII score and RMR/FFM.

Table 1. Characteristics of participants






Age (year)





Weight ) kg)





Height) cm)





BMI (kg/m²)





RMR )kcal/24h)





RMR/FFM )kcal/24h/m2 )





Physical activity (Met.h/d)





FFMI (kg/m2)





FMI (kg/m2)





Soft lean mass (kg)





Body fat (%)





BMR (kcal/24h)





TC (mg/dl)





TG (mg/dl)





HDL (mg/dl)





LDL (mg/dl)




















DII score











BMI-Body Mass Index, BMR-Basal Metabolic Rate, RMR-Resting Metabolic Rate, FFMI-Fat Free Mass Index, FMI-Fat Mass Index, SGOT-Serum Glutamic Oxaloacetic Transaminase, SGPT-Serum Glutamic Pyruvic Transaminase, HDL-High Density Lipoprotein, LDL-Low Density Lipoprotein, DII- Dietary Inflammatory Index, RQ-Respiratory Quotient, TG-Triglyceride, CRP-high–sensitivity c-reactive protein, TC-Total Cholesterol, RMR/FFM- Resting Metabolic Rate/Fat Free Mass

Table 2. Characteristics of study group sub-grouped by dietary inflammatory index



n = 96





* p-value    

Age (year)

35.34 ±7.22

37.17 ± 8.60

36.69 ± 9.15


Weight) kg)


80.24 ± 10.36

82.25 ± 13.57               


Height )cm)



162.90± 6.27


BMI (kg/m²)





RMR )kcal/24h)





RMR/ FFM )kcal/24h/m2 )

33.72±  4.45           

33.35±  4.20           

34.40 ±4.80

< 0.001

FFMI (kg/m2)

17.68 ±1.45           

19.16 ±13.01           

18.19 ±1.45


FMI (kg/m2)

13.64± 3.39

12.99 ±3.31

12.85 ±3.39


Soft lean mass (kg)

42.79± 5.02

43.47± 4.89

45.69 ±5.68

< 0.001

Body fat (%)

42.98± 5.048

41.37 ±5.05

40.42± 6.05


Basal metabolic rate (kcal/24h)          

1350.30± 114.86  

1372.70 ±108.59   



TC (mg/dl)

183.01 ±32.96        

186.66 ±33.42 

185.22 ±41.21


TG (mg/dl)

112.19 ±63.14

129.43 ± 70.74

124.13± 73.25


HDL (mg/dl)

46.79± 10.48

45.85 ±11.53

47.06± 10.87


LDL (mg/dl)

95.06 ± 24.56       

97.39 ± 23.05        

92.30± 24.32


SGOT (µKat/L)

16.26± 6.17

18.53± 7.98

19.21± 8.48


SGPT (µKat/L)

16.87 ±11.06

19.70 ±14.07

21.57± 14.50


CRP (mg/l)

4.62± 4.52                

3.84± 4.48                

4.64 ±4.91



*p-value resulted from ANOVA analysis

BMI-Body Mass Index, RMR-Resting Metabolic Rate, FFMI-Fat Free Mass Index, FMI-Fat Mass Index, SGOT-Serum Glutamic Oxaloacetic Transaminase, SGPT-Serum Glutamic Pyruvic Transaminase, HDL-High Density Lipoprotein, LDL-Low Density Lipoprotein, DII-Dietary Inflammatory Index, RQ-Respiratory Quotient, TG-Triglyceride, CRP-C-reactive protein , TC-Total Cholesterol


Table 3. Association between DII and RMR/FFM in participants

Total participants


Crude model

OR (95% CI)


Adjustment model

OR (95% CI)


DII (T1) ¥





DII (T2)                                                            

0.92 (0.52 - 1.65)


1.24 (0.49 -  3.14)


DII (T3)                                                               

0.58 (0.32 - 1.08)               


0.63 (0.30 - 1.01)


*Based on obesity degree


Crude model

OR (95% CI)


Adjustment model

OR (95% CI)



DII (T1) ¥





DII (T2)                            

0.93 (0.39 - 2.19)


0.92 (0.22 - 3.81)               


DII (T3)                            

0.40 (0.16 - 0.98)


0.31 (0.09 - 0.98)



DII (T1) ¥





DII (T2)

0.68 (0.27 - 1.71)


1.31 (0.37 - 2.56)


DII (T3)

1.07 (0.41 - 2.77)


1.75 (0.58 - 3.09)             


Morbid obesity

DII (T1) ¥





DII (T2)

1.11 (0.26 - 4.71)


1.67 (0.27 - 2.74)


DII (T3)

0.66 (0.15 - 2.90)


0.88 (0.08 - 1.47)



∞ based on binary regression model in crude and adjustment models

 ¥ Tertile 1 considered as reference group

* Overweight (BMI of 25-29.9 kg/m2), Obesity (BMI of 30-39.9 kg/m2), Morbid obesity (BMI greater than or equal to 40 kg/m2)


In the present study, a significant inverse association between DII score and RMR/FFM in overweight women and a marginally significant association in total participants were observed. However, no significant association was found between DII score and RMR/FFM in obese and morbidly obese women.
Low resting metabolic rate, as one of the risk factors for obesity, was associated with high grade inflammation in the participants. There are controversial findings regarding the role of dietary intake in changing the energy expenditure. Some studies suggested that dietary macronutrient compositions or micronutrient can change the metabolic rate (14-16); however, other studies reported no change after dietary intervention (17,18). In addition, some studies revealed the effects of dietary supplements on energy expenditure. It has been shown that dietary supplements containing bioactive food ingredients such as green tea extract, L-tyrosine, caffeine, cayenne, and calcium may increase basal metabolic rate in obese participants (19,20). This may be due to the thermogenic effect of such ingredients.
Among dietary parameters which may influence the metabolic rate, the effect of inflammation-causing foods is not well investigated. Dietary inflammatory index is associated with increased levels of various inflammatory markers which may aggravate the effect of inflammatory factors induced by adipose tissue in overweight and obese individuals. Adipose tissue is considered as a main determinant of inflammation in obesity (21), releases Free Fatty Acids (FFAs) and proinflammatory cytokines such as TNF-alpha (induced from adipose tissue as well as dietary inflammatory intake) and results in generation of Reactive Oxygen Species (ROS) in the mitochondria that may lead to mitochondrial loss and dysfunction (22).
Mitochondria have a central role in the energy metabolism. Energy is derived from the oxidation of food; NADH and FADH2 are formed in glycolysis, fatty-acid oxidation, and the citric acid cycle and they are energy-rich molecules that are eventually transformed to ATP inside mitochondria (23,24), but among this electron transfer process, generation of ROS is also probable. When mitochondria are impaired by ROS, utilization of oxygen, oxidation of food intake for production of energy-rich molecules as well as production of ATP (energy currency of the cell needed to run cellular processes) can be reduced and eventually metabolic rate may decrease (25,26).
To our knowledge, this is the first study that examined the association between DII and RMR/FFM. The strengths of the study were its fairly large same-sex population and its adjustment for some major confounders, and also utilization of advanced equipment for measuring RMR.

One of the limitations was the cross-sectional design of the study; certainly further randomized clinical trials and prospective observational studies are needed to confirm the effect of DII on resting metabolic rate.

The findings of this study show that adherence to the pro-inflammatory diet may lead to a reduction in RMR/FFM in overweight women.

Conflict of Interest
Not declared.

The authors thank the directors of the school of Nutritional and Dietetics at Tehran University of Medical Sciences for allowing them to conduct the cross-sectional study for the purpose of evaluating RMR. This study was supported by grants (ID: 95-03-161-33142 and 96-01-161-34479) from Tehran University of Medical Sciences.

1. Friedman JM. Obesity: Causes and control of excess body fat. Nature 2009;459(7245):340-2.
2. Edrisi F, Salehi M, Ahmadi A, Fararoei M, Rusta F, Mahmodianfard S. Effects of supplementation with rice husk powder and rice bran on inflammatory factors in overweight and obese adults following an energy-restricted diet: a randomized controlled trial. Eur J Nutr 2018;57(2):833-43.
3. Kim DK. Accuracy of predicted resting metabolic rate and relationship between resting metabolic rate and cardiorespiratory fitness in obese men. J Exerc Nutrition Biochem 2014;18(1):25-30.
4. Drabsch T, Holzapfel C, Stecher L, Petzold J, Skurk T, Hauner H. Associations between C-reactive protein, insulin sensitivity, and resting metabolic rate in adults: A mediator analysis. Front Endocrinol 2018;9:556.
5. Buscemi S, Verga S, Caimi G, Cerasola G. Low relative resting metabolic rate and body weight gain in adult Caucasian Italians. Int J Obes 2005;29:287-91.
6. Ellulu MS, Patimah I, Khaza’ai H, Rahmat A, Abed Y. Obesity and inflammation: the linking mechanism and the complications. Arch Med Sci 2017;13(4):851-63.
7. Nettleton JA, Steffen LM, Mayer-Davis EJ, Jenny NS, Jiang R, Herrington DM, et al. Dietary patterns are associated with biochemical markers of inflammation and endothelial activation in the Multi-Ethnic Study of Atherosclerosis (MESA). Am J Clin Nutr 2006;83(6):1369-79.
8. Kaluza J, Harris H, Melhus H, Michaelsson K, Wolk A. Questionnaire-based anti-inflammatory diet index as a predictor of low-grade systemic inflammation. Antioxid Redox Signal 2018;28(1):78-84.
9. Lam YY, Ravussin E. Indirect calorimetry: an indispensable tool to understand and predict obesity. Eur J Clin Nutr 2016;71:318-22.

10. Mirmiran P, Hosseini-Esfahanil F, Jessri M, Mahan LK, Shiva N, Azizis F. Does dietary intake by Tehranian adults align with the 2005 dietary guidelines for Americans? Observations from the Tehran lipid and glucose study. J Health Popul Nutr 2011;29(1):39-52.
11. Shivappa N, Steck SE, Hurley TG, Hussey JR, Hébert JR. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr 2014;17(8):1689-96.
12. Esfahani FH, Asghari G, Mirmiran P, Azizi F. Reproducibility and relative validity of food group intake in a food frequency questionnaire developed for the Tehran Lipid and Glucose Study. J Epidemiol 2010;20(2):150-8.
13. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003;35(8):1381-95.

14. De Jonge L, Bray GA, Smith SR, Ryan DH, De Souza RJ, Loria CM, et al. Effect of diet composition and weight loss on resting energy expenditure in the POUNDS LOST study. Obesity 2012;20(12):2384-9.
15. Nadimi H, Yousefinejad A, Djazayery A, Hosseini M, Hosseini S. Association of vegan diet with RMR, body composition and oxidative stress. Acta Sci Pol Technol Aliment 2013;12(3):311-8.
16. Dubnov-Raz G, Constantini N, Yariv H, Nice S, Shapira N. Influence of water drinking on resting energy expenditure in overweight children. Inte J Obes 2011;35(10):1295-300.
17. Tagliabue A, Bertoli S, Trentani C, Borrelli P, Veggiotti P. Effects of the ketogenic diet on nutritional status, resting energy expenditure, and substrate oxidation in patients with medically refractory epilepsy: a 6-month prospective observational study. Clin Nutr 2012;31(2):246-9.

18. Domańska I, Bawa S. [The influence of nutritional intervention on resting energy expenditure in obese adolescents aged 15-18 years]. Rocz Panstw Zakl Hig 2011;62(1):65-9. Polish.
19. Belza A, Frandsen E, Kondrup J. Body fat loss achieved by stimulation of thermogenesis by a combination of bioactive food ingredients: a placebo-controlled, double-blind 8-week intervention in obese subjects. Int J Obes (Lond) 2007;31(1):121.
20. Belza A, Jessen AB. Bioactive food stimulants of sympathetic activity: effect on 24-h energy expenditure and fat oxidation. Eur J Clin Nutr 2005;59(6):733.
21. Armellini F, Zamboni M, Mino A, Bissoli L, Micciolo R, Bosello O. Postabsorptive resting metabolic rate and thermic effect of food in relation to body composition and adipose tissue distribution. Metabolism 2000;49(1):6-10.
22. Liu J, Shen W, Zhao B, Wang Y, Wertz K, Weber P, et al. Targeting mitochondrial biogenesis for preventing and treating insulin resistance in diabetes and obesity: Hope from natural mitochondrial nutrients. Adv Drug Deliv Rev 2009;61(14):1343-52.

23. Bratic I, Trifunovic A. Mitochondrial energy metabolism and ageing. Biochimica et Biophysica Acta 2010;1797(6):961-7.
24. Ames BN, Shigenaga MK, Hagen TM. Oxidants, antioxidants, and the degenerative diseases of aging. Proc Natl Acad Sci USA 1993;90(17):7915-22.
25. Larsen FJ, Schiffer TA, Sahlin K, Ekblom B, Weitzberg E, Lundberg JO. Mitochondrial oxygen affinity predicts basal metabolic rate in humans. FASEB J 2011;25(8):2843-52.

26. Weyer C, Snitker S, Bogardus C, Ravussin E. Energy metabolism in African Americans: potential risk factors for obesity. Am J Clin Nutr 1999;70(1):13-20.