Study design
A total of 850 healthy adult men and women, aged 18 to 59, who were willing to participate in this cross-sectional study, were recruited from health care centers of Tehran, via a two-stage cluster sampling using advertisement, spreading of flyers in common places and information sessions at health care centers about the goal and the benefit of the examination. First, the city was split into five regions north, east, south, west, and center. A list of all existing health care centers was provided and then eight health centers were randomly chosen from each region for a tally of forty health centers. Ultimately, the sample size (n = 850) was divided by 40 to get the number of subjects in each health center. To be noted that we recruited people from the health centers of Tehran affiliated to the Health Bureau of the Municipality of Tehran. Indeed, Tehran is the Capital of Iran and has a multiethnic population, and in health research in Iran, population of Tehran are considered as a representative of Iran.
Based on the prevalence of obesity and
overweight in the adults of Tehran (65%), an error coefficient of d=0.04 and at
α level of 0.05, a sample size of 546 people was calculated \(\left(n=\frac{(z^2-p(1-p)}{d^2}=\frac{(1.96{)^2\;}\ast\;0.65\;\ast\;0.35}{\left(0.04^2\right)}\right)\). Due to
the potential exclusion of participants, the sample size was multiplied by 1.5
which included the total number of 850 subjects.
Data collection
During the first visit, subjects completed a questionnaire designed to assess the participants' demographics including age (year), gender, body weight, height, waist circumference,body mass index (BMI), physical activity (low active, moderate active, extremely active), educational level (illiterate, under diploma = uncompleted primary or secondary education, diploma = completed secondary education, educated = bachelor's degree or higher), marital status (single, divorced, dead spouse, married), job status (employed, retired, house-keeper, or unemployed), and smoking status (never smoked, former smoker, current smoker).
Anthropometric measurements
Body weight was measured using a standard body weight scale (Seca 707; Seca GmbH & Co. KG., Hamburg, Germany, measurement accuracy ± 100 g). Participant’s height was measured using a wall stadiometer with a precision of 1 cm (Seca, Germany). The BMI was calculated as weight in kilograms divided by the square of height in meters. Waist circumference was measured with a tape measure to the nearest 0.1 cm between the iliac crest and the lowest rib during exhalation. Hip circumference was recorded at maximal point, over light clothing, using a non-stretch tape measure and without exerting any pressure on body surface. Obesity was defined as BMI ≥ 30 kg/m2 [24].
Dietary assessment
Dietary intakes of participants were assessed by using three 24-h dietary recalls. Trained dietitians completed the first 24-h dietary recall by face-to-face interview at the first visit at each health center. The other two 24-h dietary recalls were completed at random days including one weekend, by telephone interviews. All 24-h dietary recall interviews were carried out by same trained dietitians. For dietary analysis, daily intakes of all food items obtained from 24-h dietary recalls were computed and then were converted into grams by using household measures [25]. The 3-day dietary intakes were summed and then averaged over the three days. Dietary intakes were expressed as food groups including total grains, fruit, vegetables, green-leafy vegetables, red/yellow vegetables, legumes, nuts, red meat, processed meat, poultry, fish, low- and high-fat dairy products, egg, soft drinks, salty snack, and solid and liquid oils. Liquid oil includes vegetable oils that were liquid in room temperature. Solid oil includes animal fat and hydrogenated vegetable oils that were solid in room temperature.
Meals definition
Breakfast was defined as a meal eaten between 05:00 and 11:00 [26].
Lunch was predefined as a large meal eaten between 12:00 and 16:00 [26].
Dinner was defined as a large meal eaten between 17:00 and 23:00 [26].
Physical activity assessment
Physical activity was assessed by using a validated short form of the International Physical Activity Questionnaire (IPAQ) [27]. Accordingly, IPAQ scores were categorized as ‘low physical activity’ (point score < 600 MET-min/week), ‘moderate physical activity’ (point score between 600 and 3000 MET-min/week) and ‘high physical activity’ (point score > 3000 MET-min/week) [28].
DQI-I construction
Meal-based Diet quality was assessed based on the DQI-I that included four major dietary components [14]. The first component was variety, which included the overall variety of different food groups (meats and meat products, fish and shellfish, eggs, pulses and pulse products; milks and milk products; vegetables; fruits; grains) and the within-group variety of protein sources (meats and meat products, fishes and shellfishes, eggs, pulses and pulse products, milks and milk products), with a score ranging from 0 to 20 points. The second component was adequacy of intake (amounts of vegetables, fruits, grains, fiber, protein, Fe, Ca, and vitamin C), with a score ranging from 0 to 40 points. The third component was moderation (total fat, saturated fat, cholesterol, sodium, and empty calorie foods), with a score ranging from 0 to 30 points. The fourth component was overall balance (macronutrient ratio and fatty acid ratio), with a score ranging from 0 to 10 points. The total DQI-I score ranged from 0 to 100, with higher scores denoting better diet quality.
Statistical analysis
Participants were categorized based on tertiles of DQI-I. Higher tertiles of DQI-I demonstrate higher diet quality compared to lower tertiles. The general characteristics and Dietary intakes of study subjects among tertiles of DQI-I were examined using analysis of variance (ANOVA) for continuous variables, and Chi-squared (χ2) for categorical variables. We used analysis of covariance (ANCOVA) to compare adjusted means of BMI across the DQI-I tertiles. The multiple linear regression analysis was used to evaluate the association between DQI-I with BMI based on sex in each meal after adjusting for possible confounders. Furthermore, odds ratio (OR) and 95% confidence interval (CI) of obesity were estimated through binary logistic regression analysis in two models. Model I adjusted for age, physical activity, socioeconomic status, and smoking. Model II was adjusted for confounders in Model I plus energy intake. All statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS version 16; SPSS Inc.). P value less than 0.05 was considered statistically significant.
