Study design and Population
A cohort of 280 non-postmenopausal and healthy women aged 18≤ years, who referred to health centers in Tehran, Iran, in 2018 was recruited in this cross-sectional study. Multistage cluster random sampling method was used to select certain regions from among all the regions of the city; finally, 20 clusters were chosen to select the obese and overweight participants (BMI: 25-40 kg/m2). The exclusion criteria were as follows: a history of any acute or chronic diseases such as hypertension, CVD, diabetes mellitus, hepatic or renal disease or alcohol consumption, regular usage of medicine other than birth control pills, pregnancy, or lactation. They were excluded if adhered to special dietary patterns or had any significant body weight fluctuations over the past year.
Dietary Measurements & DII Calculation
Dietary assessment was carried out by a validated and reliable 147-item semi-quantitative food frequency question (FFQ) designed according to the Willett study that administered by a trained nutritionist to assess the average daily intake for last year (25). The FFQ consisted of a list of foods with standard serving sizes. Participants were asked to report their frequency and the amount of each food item consumed during the previous year on a daily (e.g., bread), weekly (e.g., rice, meat), or monthly (e.g., fish) basis. Household measures were used to convert portion sizes of the consumed foods into grams(26), and an estimated average daily intake of food parameters was calculated from the FFQ using NUTRITIONIST IV software (version 7.0; N-Squared Computing, Salem, OR). FFQ-derived dietary data were used to calculate DII scores for all participants. The dietary data were linked to the regionally representative world database that included food consumption from eleven populations around the world and provided a robust estimate of a mean and standard deviation for each parameter (27). In order to get z-scores, the "standard global mean" was subtracted from the actual dietary intake amount, and this value was divided by the standard deviation. Subsequently, to minimize the effect of ‘right skewing’, these z-scores were converted into a percentile – each percentile score was doubled and then subtracted by 1. The centered percentile score for each food parameter for each individual was then multiplied by the respective food parameter effect score, to obtain a food parameter-specific DII score for an individual(27). Subsequently, all of the food parameter-specific DII scores were summed together to calculate the overall DII score. Higher DII scores indicated a more pro-inflammatory diet; whereas lower values represented more anti-inflammatory diets (27). A total of 29 food parameters were available from the FFQ, were used to calculate DII (namely: energy, carbohydrate, protein, total fat, monounsaturated fat, polyunsaturated fat, saturated fat, omega-3, omega-6 fatty acids, cholesterol, fiber, thiamin, riboflavin, niacin, vitamin B6, folic acid, vitamin B12, iron, magnesium, selenium, zinc, β carotene, vitamin A, C, D, E and tea, onion, caffeine).
Biochemical Assessment
All Biochemical analyses were carried out on venous blood samples that were collected after 12 hours fasting, and the serum was centrifuged, liquated, then stored at −80 °C. Serum MCP-1 levels were measured by the enzyme-linked immunosorbent assay (ELISA) method with an appropriate kit (Zell Bio GmbH, ULM, Germany, assay range: 5ng/L-1500ng/L, sensitivity: 2.4 ng/L, inter-assay variability: CV<12%, intra-assay variability: CV<10%).
Anthropometric Assessment
Bioelectrical impedance analysis (BIA) (InBody 720, Korea) was utilized to calculate body composition measures, including body fat mass and fat-free mass. Anthropometric measures such as body weight, BMI, waist circumferences (WC), and waist-hip ratio (WHR) were measured for all participants. Height was characterized while the subjects were in a standing position without shoes, in contact with the wall with their head, shoulders, heels, and hips, and their height was recorded to the nearest 0.1 cm with Seca 206. All of the measurements were done by expert trained technicians and based on specific guidelines, in order to reduce interpersonal variation.
Physical Activity Assessment
The required information on physical activities in three levels of mild, moderate, and vigorous for research purpose was achieved by using The International Physical Activity Questionnaire (IPAQ) which can be applied by young and middle-aged adults (18–65 years). All these sub-components were summed across MET scores and MET-minutes per week (MET-min/wk) were computed and then the total physical activity from all activity categories was reported (28).
Statistical Analyses
DII (dichotomous) was examined across the quantitative characteristics. These were analyzed through an independent sample T-test. Linear regression analyses were conducted to determine the relationship between DII score with fat-free mass and serum MCP-1 levels, adjusted for potential confounding factors. The results are reported as a percentage change (β), with 95% confidence intervals (95% CI). P values of <0.05 were considered to be statistically significant. Statistical analysis was performed using SPSS version 21 (SPSS Inc., Chicago, USA).