This cross-sectional research performed in 2018,293 adult women aged between 18 and 56 years old who were selected by a multistage cluster random sampling method that had been referred to health centers in Tehran recruited. Participants were enrolled in the study according to inclusion and exclusion criteria. Individuals were included if they met following eligibility criteria: good general health overweight and obese women with BMI in the range of 25-40 kg/m2. The exclusion criteria for the study were as follows: regular use of medicine (including oral contraceptive pill), history of hypertension, cardiovascular diseases, diabetes mellitus, impaired renal and liver function, alcohol use, smoking, pregnancy, lactation period, and menopause. Furthermore, participants were excluded from chronic diseases affecting their diet, as well as those who had been following an arbitrary special dietary regimen, and also those with any significant body weight fluctuations over the past 1 year. Participants whose reported daily energy intakes lower than 800 kcal/d or higher than 4200 kcal/d were also excluded. The final analysis has been conducted on 293 participants. Each participant was fully informed about the study protocol and provided a written and informed consent form prior to taking part in the study.
Energy expenditure measurements
RMR was measured by indirect calorimetry (spirometer METALYZERR 3B-R3, Cortex Biophysik GmbH, Leipzig, Germany). According to the manufacturer’s instructions, gas ventilation and exchange is calibrated before each test. RMR is evaluated by measuring the amount of O2 consumed and CO2 produced. The RMR was assessed in the morning, after a comfortable night’s sleep, and following a 10-12 hour fast. Participants were asked to avoid caffeine or alcohol consumption and severe exercise for a day before RMR measurements. After reclining in a steady-state and a supine position in a quiet room, the RMR was measured for 30 minutes. The respiratory exchange ratio and oxygen uptake (VO2) were analyzed within the middle 20 min of the resting period. Predictive RMR was determined using the Harris-Benedict equation, which considers the weight, height, and age of participants.
Body composition measurement
Body composition, including weight, BMI, fat mass, and fat-free mass (FFM) were acquired using a multi-frequency bioelectrical impedance analyzer InBody 770 scanner (Inbody Co., Seoul, Korea). This electrical impedance analyzer calculates the resistance of body tissues to the flow of an electrical signal sent through both hands and feet. According to the manufacturer’s instructions, participants removed their shoes, coats, and sweaters, and stood on the balance scale in bare feet and grasped the handles of the machine. The measurements took place in approximately 20 seconds, and the output was printed.
Biochemical assessment and hormonal assay
Metabolic health was assessed using the metabolic parameters that measured following standard chemical procedures. A 12-hour fasting venous blood sample was used to measure all biochemical markers. Serum glucose was evaluated by a colorimetric method based on the GOD-PAP method. Serum insulin concentrations were analyzed by enzyme-linked immunosorbent assay (ELISA) method (Human insulin ELISA kit, Monobind Inc., Lake Forest, USA). All measurements were taken at the Nutrition and Biochemistry Laboratory of the School of Nutritional Sciences and Dietetics.
HOMA and QUICKI calculations
Insulin resistance was estimated by homeostasis model assessment (HOMA). The HOMA was calculated according to the following equation: HOMA = [Fasting Plasma Glucose (mmol/L) × Fasting Plasma Insulin (mIU/L)]/22.5 (26). Insulin sensitivity quantitative insulin sensitivity check index (ISQUICKI) was assessed by: ISQUICKI = 1/[log (fasting insulin) + log (fasting glucose) (27).
Dietary intake assessment
Dietary intake data of the past year were obtained using a validated semi-quantitative food-frequency questionnaire (FFQ)(28), comprises of 168-item a trained nutritionist administered these FFQ. The FFQ consisted of a list of foods with standard serving sizes. Participants were asked to report their frequency and 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. Portion sizes of the consumed foods were converted to grams using household measurements(29). Nutritionist IV computer software was used for the nutrient analysis of the diets. The database of this software was modified for Iranian foods. Then, all items were converted to daily intakes of a gram.
Nutrient Adequacy Ratios (NAR)
For calculating the NAR, the ratio of daily individual intakes to the standard recommended amounts for the subject's sex and age category was used. The standard recommended amounts are based on RDA (Recommended Daily Allowances) (30). We calculated the NAR for three key nutrients, including zinc, vitamin C, and riboflavin according to the above-mentioned method. The prevalence of nutrient deficiency was estimated using NAR. NAR lower than one is considered as a deficiency.
Assessment of other covariates
International Physical Activity Questionnaire (IPAQ, short form) were obtained by using an interview-based questionnaire from all participants about all the vigorous and moderate elements over the last 7 days, considering the time spent on these activities, that was used to assess physical activity of the study subjects across a variety of different domains including leisure-time, domestic, work, and transport-related physical activity and time spent on light, moderate, high and very high-intensity activities. Participant physical activity was classified as low < 600 (MET-h/week), moderate1⁄4 600-3500 (MET-h/week) and severe > 3500 (MET-h/week). according to the list of common activities of daily life over the past year, the level of physical activity of participants was calculated as Met.h/d (31). 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.
All statistical analysis was performed using the IBM SPSS software version 22.0 (SPSS, Chicago, IL, USA) and p-values less than 0.05 were considered statistically significant. Normal distribution of data was checked by the Kolmogorov-Smirnov test. An independent sample t-test was used for assessed differences between groups with the low and standard intake of nutrients. RMR/FFM was analyzed after adjusting for FFM. The differences between RMR/FFM groups and dietary intake of nutrients were assessed by the Binary logistic regression were performed to adjust for confounders effects such as age, energy intake, and physical activity (METs/d). Results were presented as odds ratios (ORs) and 95% confidence intervals (CIs) compared with the RMR groups.