Study population
In the current study, a cohort of 375, women aged 18 to 48, with a body mass index (BMI) ≥25 kg/m2, were recruited from Health Centers and Nutrition Clinics in Tehran, Iran between July 2017 and April 2019. The Medical Research Ethics Committee of Tehran University of Medical Sciences approved the study with the following identification IR.TUMS.VCR.REC.1395.1597. All participants expressed their willingness to participate in the study by providing informed written consent. The overall exclusion criteria were as follows: any history of acute and chronic disease including hypertension, diabetes mellitus, cardiovascular disease, and impaired renal and liver function, as well as regular use of medicine (other than birth control medication), pregnant or lactating, intake of alcohol, smoking, and menopause. In addition, participants who had been following any arbitrary special dietary regimen, as well as those with chronic disease(s) affecting their diet, or if their daily energy intake was <800 kcal or >4200 kcal (36),were excluded.
Dietary assessment
Dietary intake was assessed using a validated semi quantitative food frequency questionnaire (FFQ) with 147- food items(37). All FFQs were completed by a trained dietitian, who asked participants to designate their intake frequency for each food item consumed during the past year, on a daily, weekly, or monthly basis. Finally, we converted portion sizes of foods to grams by using household measures (38). NUTRITIONIST-IV (version 7.0; N Squared Computing, Salem, OR, USA) was used to assess nutrient and energy intakes.
Assessment of dietary acid load
Dietary acid-base load was evaluated via 3 indexes: PRAL, NEAP, and DAL which were calculated by using nutrients derived from the FFQ with the following formulas:
1) PRAL (mEq/d) = 0.4888 × dietary protein (g/d) + 0.0366 × dietary phosphorus (mg/d) - 0.0205 × dietary potassium (mg/d) - 0.0125 × calcium (mg/d) - 0.0263 × magnesium (mg/d) (8)
2) NEAP = 54.5 ×dietary protein (g/d) / dietary potassium (mg/d) -10.2 (14)
3) DAL (mEq/ day) = PRAL + (body surface area (m2) ×41 (mEq / day)/1.73 m2) (39)
Body surface area (BSA) was calculated using the Du Bois formula: 0.007184 × height 0.725 × weight 0.425 (16, 40). We adjusted both PRAL and NEAP for total energy intake, employing the residual method. Higher values of PRAL, DAL, and NEAP were considered to represent a higher acidic dietary acid-base load. Medians of diet dependent acid load scores were used in statistical analysis.
Anthropometric assessments
Weight was determined on a digital scale, where participants wore light indoor clothing, without shoes, and was recorded to the nearest 100 g. Height was measured to the nearest 0.5 cm while participants were in the normal standing position, without shoes. Waist circumference (WC) an hip circumference were measured to the nearest 0.5cm, according to standard procedures. Subsequently, waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), and BMI were calculated according to standard formulae. Overweight and obesity were defined as 25≤BMI≤29.9 kg/m 2 and BMI≥30 kg/m2, respectively. Neck circumference (NC) was measured below the laryngeal prominence and perpendicular to the long axis of the neck, and the minimal circumference was recorded to the nearest 0.1 cm(41)
Assessment of other variables
A demographic questionnaire to discern information on age, marital status, education, and economic status was collected by researchers.
Blood pressure assessment
Blood pressure measurements were taken using a standard mercury sphygmomanometer; where the participant sat for 10–15 min before two consecutive measurements were taken.
Physical activity
The Short form of the International Physical Activity Questionnaire (IPAQ) (42) was applied to evaluate physical activity and according to the frequency and time of common activities of daily life over the past year. Physical activity levels were expressed as metabolic equivalent hours per week (METs h/week)(43).
Measurement of biochemical parameters
Blood samples were collected after 10-12 hours fasting, and collected into tubes containing 0.1% Ethylenediaminetetraacetic acid (EDTA).Then, they were centrifuged for 10 min at 3000 rpm, aliquoted into 1 ml tubes, and stored at – 70oC until analysis. Samples were analyzed by using an autoanalyzer (Selectra 2; Vital Scientific, Spankeren, Netherlands).
The GOD/PAP (glucose oxidase phenol 4-Aminoantipyrine Peroxidase) method was used for the measurements of fasting blood sugar (FBS) and triglyceride (TG) levels, and cholesterol levels were evaluated using the cholesterol oxidase Phenol 4-Aminoantipyrine Peroxidase (CHOD-PAP) method. Total cholesterol (TC) levels and direct high-density lipoprotein (HDL) were evaluated by the Immunoinhibition assay. Aspartate aminotransferase (AST) and Serum alanine aminotransferase (ALT) were specified by the International Federation of Clinical Chemistry and Laboratory Medicine method. Serum high-sensitivity C-reactive protein (hs- CRP) was determined using a high-sensitivity immunoturbidimetric assay (Hitachi 902 analyser; Hitachi Ltd., Tokyo, Japan).
We used the homeostasis model assessment method to compute insulin resistance based on the following Homeostatic Model Assessment-Insulin Resistance (HOMA-IR) formula: fasting serum insulin (mIU/L) × FBS (mmol/l)/22.5.
Resting metabolic rate assessment
RMR measurement was performed by professional nutritionists using a standard protocol (33), and was measured by indirect calorimetry (spirometer METALYZERR 3B-R3, Cortex Biophysik GmbH, Leipzig, Germany). Based on the manufacturer’s instructions, prior to each test, gas exchange and ventilation were calibrated. Fitmate is a desktop device designed for providing accurate RMR for all fields dealing with obesity and malnutrition with a mask to cover the nose and mouth. The device is designed to measure oxygen consumption and energy expenditure during rest and exercise. It uses a turbine flow meter to measure ventilation and a galvanic fuel cell oxygen sensor for analyzing the proportion of oxygen in expiration gases. Moreover, it uses a patent-pending sampling method that allows the analyzer to monitor the performance of a metabolic cart in a standard mixing chamber. RMR was calculated using information such as oxygen consumption, a fixed respiratory quotient of 0.85, and estimated quantity of urinary nitrogen using a modified Weir equation below.
Weir equation: RMR = (O2 consumed [liter] × 3.941 + produced CO2 [liter] ×1.11) × 1440 min/d.
The RMR was evaluated in the morning, after a night’s sleep and following 10 to 12 hours of overnight fasting. Participants were asked to avoid vigorous exercise and alcohol or caffeine consumption for a day before RMR assessment. The RMR was measured for 30 min in a quiet room and participants in fixed, comfortable position. The respiratory exchange ratio and oxygen uptake (VO2) were investigated within the average 30 min of the resting period. However, the first 5 minutes were not included, and only the last 15 minutes were used to calculate RMR. Predictive RMR was determined using the Harris-Benedict equation, which considers age, weight, and height for individuals.
Complete body composition analysis
Body composition of subjects was evaluated via Body Composition Analyzer BC-418MA- In Body (United Kingdom), in accordance with manufacturer guidelines. To avoid possible discrepancies in the measured values, participants were asked not to exercise vigorously, to not use any electrical devices, and not consume excessive fluid or food before evaluating their body composition.
Statistical analysis
The Kolmogorov–Smirnov test was used to assess the distribution of the data. Continuous variables were represented by mean ± Standard Deviations (SDs) and mean ± standard Errors (SEs), and categorical information was represented by percentage and number. To avoid classification errors, we calculated the energy-adjusted PRAL, NEAP, and DAL using the residual method, and then based on the median, dichotomized participants into low or high dietary acid load. Baseline characteristics of participants were compared by independent samples t test between the median of PRAL, NEAP, and DAL score and chi square(χ2) tests for categorical variables. Also, we adjusted variables for confounders, such as age, energy intake, BMI, physical activity, and economic status, in addition further adjustment for free fat mass (FFM), using Analysis of covariance (ANCOVA). General linear regression (GLM) was used to assess the association of PRAL, NEAP, and DAL score with body composition, biochemical variables and RMR per kg. Results in crude and adjusted models were presented as beta(β) and 95% confidence intervals (CIs). Data were analyzed using IBM SPSS version 25.0(SPSS, Chicago, IL, USA) and p-values less than 0.05 were considered, a priori, to represent statistical significance.