Study design and Population
The present study was performed on 280 non-postmenopausal, apparently healthy women aged 18≤ years, who had been referred to health centers in the city of Tehran, Iran, in 2018. In this study, a multistage cluster random sampling method was used to select certain regions from among all the regions of the city; 20 clusters were selected. Participants with a body mass index in the range of 25-40 kg/m2 were selected using cluster sampling. Efforts were made to include different regions with different socioeconomic statuses in the present study. Informed consent was obtained from all participants. The present study was confirmed by the local Ethics Committee of Tehran University of Medical Sciences (Ethics Number IR.TUMS.VCR.REC.1395.1597). All participants signed a written informed consent that was approved by this committee prior to the beginning of the study. The exclusion criteria for enrollment in this study were as follows: history of hypertension, cardiovascular disease, diabetes mellitus, hepatic or renal disease, alcohol usage, regular usage of medicine other than birth control pills, pregnancy or lactation, people who adhered to special dietary patterns, and people with any significant body weight fluctuations over the past year.
Dietary Measurements & DII Calculation
Dietary assessment was carried out by a 147-item semi-quantitative FFQ that administered by a trained nutritionist for last year in order to assess the average daily intake. Household measures were used to convert portion sizes of the consumed foods into grams, and an estimated average daily intake of food parameters (including micro and macronutrients) was calculated from the FFQ using NUTRITIONIST IV software (version 7.0; N-Squared Computing, Salem, OR), modified for Iranian foods. 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 [25]. 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 [26]. The centered percentile score for each food parameter for each individual was then multiplied by the respective food parameter effect score (inflammatory potential for each food parameter), which was derived from the literature review, 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 for each participant. Higher DII scores indicated a more pro-inflammatory diet; whereas lower values represented more anti-inflammatory diets [28]. A total of 29 food parameters were available from the FFQ, and therefore were used to calculate DII (namely: energy, carbohydrate, protein, total fat, monounsaturated fat, polyunsaturated fat, saturated fat, omega-3, omega-6, cholesterol, fiber, thiamin, riboflavin, niacin, vitamin B6, folic acid, vitamin B12, iron, magnesium, selenium, zinc, β carotene, vitamin A, vitamin C, vitamin D, vitamin E, tea, onion, caffeine).
Biochemical Assessment
Biochemical tests were carried out on venous blood samples extracted after 12-hour overnight fasting. Serum was separated from whole blood samples and stored at −80 °C until the assay. 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
Anthropometric measures such as body weight, body mass index, waist circumferences, and waist-hip ratio were measured, and body composition measures, including body fat mass and fat-free mass, were measured by a multi-frequency bioelectrical impedance analyzer In-body 770 scanner (In-body Co., Seoul, Korea). For height measurements, 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. Technicians were trained to provide precise anthropometric measurements according to specific guidelines, in order to reduce interpersonal variation.
Physical Activity Assessment
Physical activity status information was obtained by using an interview-based with International Physical Activity Questionnaires (IPAQ). A metabolic equivalent hour per week was considered as the units for the expression of physical activity level (METs.h/wk.) [29, 30].
Statistical Analyses
DII (dichotomous) was examined across the following characteristics: age, weight, height, body mass index, waist circumferences, waist-hip ratio, energy intake, physical activity, body fat mass, fat-free mass, visceral fat area, serum MCP-1 level, and nutrient intake. 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 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).