Study population
The current study was performed within the framework of the Tehran Lipid and Glucose Study (TLGS), a population-based prospective study that was conducted to determine the risk factors for chronic diseases among a representative urban population of Tehran, including 15,005 participants, aged ≥ 3 years [16]. The first survey of TLGS (a cross-sectional survey) is initiated in March 1999 and data collection, conducted prospectively at 3 years intervals, is ongoing; the details of the TLGS have been reported previously [16].
In the fourth examination of the TLGS (2009–2011), from 12,823 participants, 7956 randomly selected, agreed to complete the dietary assessment. For the current study, a total of 6560 individuals, aged ≥ 20 years old, with complete data in the fourth examination of TLGS, as a baseline examination, were enrolled. Subjects with underreporting or over-reporting dietary intakes (less than 800 kcal/d or more than 4500 kcal/d, respectively), (n = 459) or on hyperglycemic diets (n = 205); those with a history of myocardial infarction, cerebral vascular accident, and cancers (n = 60); those with diabetes (n = 552); those with non-normal body mass index (lower than 18.5 and upper than 40), (n = 207), and lactating and pregnant women (n = 116) were excluded. Some individuals fell into more than one exclusion category. Finally, 5142 participants were followed-up until the sixth phase of TLGS (2015-18), over a mean period of 6.2 years. After excluding the participants who were missed to follow up (n = 1408), final analyses was conducted on 3734 adult subjects (Fig. 1).
Physical activity assessment
The physical activity levels of participants were assessed using the modifiable activity, which has previously been modified and validated among the Iranians population [17]; individuals were asked to report and identify the frequency and time spent on activities of light, moderate, hard, and very hard intensity, during the past 12 months, according to a list of common activities of daily life; physical activity levels were expressed as metabolic equivalent hours per week(Met.h.wk).
Clinical and biological measurements
A pretested questionnaire was used by trained interviewers to collect the information of participants on age, sex, medical history, medication use, and smoking habits. The participant’s weight was measured and recorded in light clothing, without shoes or socks, using a digital scale (model 707, Seca, Hamburg, Germany) with an accuracy of up to 100. Height was measured in a standing position without shoes, using a stadiometer to the nearest 0.1 cm (model 208 Portable Body Meter Measuring Device; Seca). Body mass index (BMI) was computed as weight (kg) divided by height (m2). Waist circumference (WC) was measured to the nearest 0.1 cm using an un-stretched tape meter, at the level of the umbilicus, over light clothing, without any pressure on the body surface.
Blood samples were taken and transferred into vacutainer tubes between 7:00 and 9:00 a.m, after a 12–14-h overnight fast, while subjects were in sitting position. Blood samples were centrifuged within 30 to 45 min of collection. All biochemical analyses were performed using a Selectra 2 auto-analyzer at the TLGS research laboratory, on the day of blood collection. Fasting blood sugar (FBS) was measured using an enzymatic colorimetric method with glucose oxidase. Inter- and intra-assay CVs were both 2.2 % for FBS [16]. The 2-h oral glucose tolerance test was performed using a 82.5 g of glucose monohydrate solution (equivalent to 75 g anhydrous glucose), which administered orally to the all individuals aged > 20 years, except diabetic patients on anti-diabetic drug therapy based on prescription of endocrinologist. Triglyceride (TGs) levels were measured using the enzymatic colorimetric method with glycerol phosphate oxidase. Inter- and intra-assay CVs for TGs were 0.6 and 1.6 %, respectively. Serum high-density lipoprotein-cholesterol (HDL-C) was measured after precipitation of the apolipoprotein B-containing lipoproteins with phosphotungstic acid. Enzymatic colorimetric tests were used to assay total cholesterol (TC) with cholesterol esterase and cholesterol oxidase. Inter- and intra-assay CVs for both TC and HDL-C were 0.5 and 2 %, respectively. Analyses were performed using commercial kits (Pars Azmoon Inc., Tehran, Iran).
Dietary intake assessment
Dietary intakes of participants over the previous year were assessed using a valid and reliable semi-quantitative food frequency questionnaire (FFQ) at baseline [18]. The reliability and validity of the FFQ have been previously reported. Consumption frequency for each food item during the previous year on a daily, weekly, or monthly basis was collected during a face-to-face interview by trained and experienced dieticians. Portion sizes of consumed foods, reported in household measures were then converted into grams. Using the United States Department of Agriculture (USDA) food composition table (FCT), energy and nutrient contents were computed. The Iranian FCT was used for local food items that were not available in USDA FCT.
Calculation of indices
Dietary data derived from FFQ were used to calculate insulinemic scores. Calculating the EDIH and ELIH has been explained elsewhere ) [11]. EDIH score; calculated with 15 instead of 18 food groups including processed meat (sausage), red meat (beef, or lamb), fish (canned tuna, or fish), margarine, poultry (chicken with or without skin), French fries, high-energy beverages (cola with sugar, carbonated beverages with sugar, fruit punch drinks), tomatoes, low-fat dairy products (skimmed or low-fat milk and yogurt), and eggs (positive association) and also, coffee, green leafy vegetables (cabbage, spinach, or lettuce), whole fruits, and high-fat dairy products (whole milk, cream, cream cheese, and other cheese) )inverse association(.
ELIH score; calculated with 11 instead of 14 dietary and lifestyle factor including BMI, margarine, butter, red meat, and fruit juice (apple juice, cantaloupe juice, orange juice, or other fruit juice) with positive association and coffee, whole fruit, physical activity, high-fat dairy products, snacks, and salad dressing with the inverse association.
Since consumption of alcoholic drinks such as wine and liquor is unusual in the Iranian population due to religious considerations and wasn’t reported in the TLGS study, we don’t include them in the calculation of indices. As we had not any food items as low energy beverages and cream soup in our FFQ we excluded them in the calculation.
For weighting, the daily intakes of each food group (serving size) and lifestyle factors values multiplied by specific proposed regression coefficients. Finally, to calculate total scores, all values of weighted food group and lifestyle factors were summed and then divided by 1000 to decline the magnitude of the scores which ease the interpretation of the results.
EDIR score; calculated with 13 instead of 18 food Items including margarine, red meat, refined grains, processed meat, tomatoes, other vegetables, fish, fruit juice (positive association), and coffee, green leafy vegetables, high-fat dairy products, dark yellow vegetables, nuts (inverse association).
ELIR score; calculated with 14 instead of 17 dietary and lifestyle factor including BMI, refined grains, red meat, margarine, tomatoes, other vegetables, potatoes, fruit juice, processed meat, tea with positive association and coffee, green leafy vegetables, high-fat dairy products, physical activity with the inverse association.
Weighting and calculation of EDIR and ELIR were conducted similar to which was mentioned above for EDIH and ELIH.
Definitions of terms
Type 2 diabetes was defined based on the criteria of the American Diabetes Association (ADA) as FPG ≥ 126 mg/dl or 2-h post 75-gram glucose load ≥ 200 mg/dl or being on anti-diabetic medication [19].
Statistical analysis
Data were analyzed using the Statistical Package for Social Sciences (version 20.0; SPSS Inc, Chicago IL). The normality of variables was checked using histogram charts and Kolmogorov–Smirnov tests. Participants were classified into quartiles based on scores of EDIH, EDIR, ELIH, and ELIR. Baseline characteristics of participants in quartiles of EDIH and EDIR were expressed as mean ± SD or median (interquartile range (IQR)) for continuos variables, and number (percentage) for categorical variables. To test the trend of qualitative and quantitative variables across quartiles EDIH and EDIR, Chi-square and linear regression were used, respectively. The associations of EDIH, EDIR, ELIH, and ELIR with diabetes incident were assessed using multivariable logistic regression models. The odds ratios (ORs) and 95 % confidence interval (CI) was reported based on three adjusted models for confounder variables including model 1 (adjusted for age and sex), model 2 [additional adjustment for waist circumference (for EDIH and EDIR), waist adjusted BMI (for ELIH and ELIR), smoking, physical activity (for EDIH and EDIR), education level, and energy intake], and model 3 (additional adjustment for baseline values of fasting blood sugar, and TAG: HDL-cholesterol). P-values < 0.05 were considered to be statistically significant.