Nuts

The FFQ, originally developed for the TLGS, was a Willett-format questionnaire modified based on Iranian food items²⁵ and contains questions about average consumption and frequency for 168 food items during the past year.⁷ The food items were chosen according to the most frequently consumed items in the national food consumption survey in Iran.²⁵ Because different recipes are used for food preparation, the FFQ was based on food items rather than dishes, eg, beans, different meats and oils, and rice. Subjects indicated their food consumption frequencies on a daily basis (eg, for bread), weekly basis (eg, for rice and meat), monthly basis (eg, for fish), yearly basis (eg, for organ meats), or a never/seldom basis according to portion sizes that were provided in the FFQ. For each food item on the FFQ, a portion size was specified using USDA serving sizes (eg, bread, 1 slice; apple, 1 medium; dairy, 1 cup) whenever possible; if this was not possible, household measures (eg, beans, 1 tablespoon; chicken meat, 1 leg, breast, or wing; rice, 1 large, medium, or small plate) were chosen. Table 1shows food items and portion sizes used in the FFQ. Trained dietary interviewers with at least 3 of experience in the Nationwide Food Consumption Survey project²⁵ or TLGS^{26 (link)} administered the FFQs and 24-hour DRs during face-to-face interviews. The interviewer read out the food items on the FFQ, and recorded their serving size and frequency. The interview session took about 45 minutes. The interviewer for FFQ1 and FFQ2 was the same for each participant. Daily intakes of each food item were determined based on the consumption frequency multiplied by the portion size or household measure for each food item.²⁷ The weight of seasonal foods, like some fruits, was estimated according to the number of seasons when each food was available.
Dietary data were also collected monthly by means of twelve 24-hour DRs that lasted for 20 minutes on average. For all subjects, 2 formal weekend day (Thursday and Friday in Iran) and 10 weekdays were recalled. All recall interviews were performed at subjects’ homes to better estimate the commonly used household measures and to limit the number of missing subjects. Detailed information about food preparation methods and recipe ingredients were considered by interviewers. To prevent subjects from intentionally altering their regular diets, participants were informed of the recall meetings with dietitians during the evening before the interview. All recalls were checked by investigators, and ambiguities were resolved with the subjects. Mixed dishes in 24-hour DRs were converted into their ingredients according to the subjects’ report on the amount of the food item consumed, thus taking into account variations in meal preparation recipes. For instance, broth or soup ingredients—usually vegetables (carrot or green beans), noodles, barley, etc.—differed according to subjects’ meal preparation. Because the only available Iranian food composition table (FCT)²⁸ analyzes a very limited number of raw food items and nutrients, we used the USDA FCT²⁹ as the main FCT; the Iranian FCT was used as an alternative for traditional Iranian food items, like kashk, which are not included in the USDA FCT.
The food items on the FFQ and DR were grouped according to their nutrient contents, based on other studies,^{30 (link)} and modified according to our dietary patterns. Seventeen food groups were thus obtained, as follows: 1) whole grains, 2) refined grains, 3) potatoes, 4) dairy products, 5) vegetables, 6) fruits, 7) legumes, 8) meats, 9) nuts and seeds, 10) solid fat, 11) liquid oil, 12) tea and coffee, 13) salty snacks, 14) simple sugars, 15) honey and jams, 16) soft drinks, and 17) desserts and snacks (Table 1). The 168 food items on the FFQ were allocated to these 17 food groups, and the amounts in grams of each item were summed to obtain the daily intake of each food group.

At baseline, registered dietitians completed a 14-item Mediterranean Diet adherence screener (Table 1) in a face-to-face interview with the participant [21] (link), [27] –[29] (link). The dietitians had been previously trained and certified to implement the PREDIMED protocol and had been hired to work full-time for the trial. The 14-item tool was developed in a Spanish case-control study of myocardial infarction [30] (link), where the best cut-off points for discriminating between cases and controls were selected for each food or food group. With this first step, 9 of the 14 items were obtained [31] (link). Five additional items that were felt to be especially relevant to assess adherence to the traditional Mediterranean diet were subsequently added. Two of these items used short questions to inquire on food habits: Do you use olive oil as the principal source of fat for cooking? and Do you prefer to eat chicken, turkey or rabbit instead of beef, pork, hamburgers or sausages? The other 3 items inquired on frequency of consumption of nuts, soda drinks and a typical Mediterranean sauce (“sofrito”): How many times do you consume nuts per week? How many carbonated and/or sugar-sweetened beverages do you consume per day? How many times per week do you consume boiled vegetables, pasta, rice, or other dishes with a sauce (“sofrito”) of tomato, garlic, onion, or leeks sauteed in olive oil?[26] (link).
The baseline 14-item questionnaire (Table 1) was the primary measure used in this study to appraise adherence of participants to the Mediterranean diet. In addition, a full-length 137-item validated FFQ [32] (link) was also administered to all participants. We obtained information about total energy intake and alcohol intake (only with descriptive purposes) from this FFQ. In the validation study, the score obtained with brief 14-item questionnaire correlated significantly with that obtained from the full-length FFQ score (Pearson correlation coefficient (r) = 0.52; intraclass correlation coefficient = 0.51). Associations in the anticipated directions for the different dietary intakes reported on the FFQ were found [26] (link). Significant inverse correlations of the 14-item tool with fasting glucose, total:HDL cholesterol ratio, triglycerides and the 10-y estimated coronary artery disease risk also supported the validity of this brief Mediterranean diet adherence screener [26] (link).
Also a general medical questionnaire, and the validated Spanish version of the Minnesota Leisure-Time Physical Activity Questionnaire [33] (link)–[34] (link) were collected by the dietitians in the personal interview with each participant [21] (link). Weight, height and WC were directly measured by registered nurses who had been previously trained and certified to implement the PREDIMED protocol and were hired to work full-time for this trial, as previously described [21] (link), [27] –[29] (link). The WHtR was calculated as WC divided by height, both in centimeters.

We created an overall plant-based diet index (PDI), a healthful plant-based diet index (hPDI), and an unhealthful plant-based diet index (uPDI). The procedure we used to create these indices is similar to the one used by Martínez-González et al. [13 (link)]; their “provegetarian food pattern” is similar in composition to our PDI. Frequencies of consumption of each food were converted into servings consumed per day. Then the number of servings of foods that belonged to each of 18 food groups were added up. The 18 food groups were created on the basis of nutrient and culinary similarities, within larger categories of animal foods and healthy and less healthy plant foods. We distinguished between healthy and less healthy plant foods using existing knowledge of associations of the foods with T2D, other outcomes (CVD, certain cancers), and intermediate conditions (obesity, hypertension, lipids, inflammation). Plant foods not clearly associated in one direction with several health outcomes, specifically alcoholic beverages, were not included in the indices. We also excluded margarine from the indices, as its fatty acid composition has changed over time from high trans fat to high unsaturated fat. We controlled for alcoholic beverages and margarine consumption in the analysis.
Healthy plant food groups included whole grains, fruits, vegetables, nuts, legumes, vegetable oils, and tea/coffee, whereas less healthy plant food groups included fruit juices, sugar-sweetened beverages, refined grains, potatoes, and sweets/desserts. Animal food groups included animal fats, dairy, eggs, fish/seafood, meat (poultry and red meat), and miscellaneous animal-based foods.
S1 Table details examples of foods constituting the food groups. The 18 food groups were divided into quintiles of consumption, and each quintile was assigned a score between 1 and 5. For PDI, participants received a score of 5 for each plant food group for which they were above the highest quintile of consumption, a score of 4 for each plant food group for which they were above the second highest quintile but below the highest quintile, and so on, with a score of 1 for consumption below the lowest quintile (positive scores). On the other hand, participants received a score of 1 for each animal food group for which they were above the highest quintile of consumption, a score of 2 for each animal food group for which they were between the highest and second highest quintiles, and so on, with a score of 5 for consumption below the lowest quintile (reverse scores). For hPDI, positive scores were given to healthy plant food groups, and reverse scores to less healthy plant food groups and animal food groups. Finally, for uPDI, positive scores were given to less healthy plant food groups, and reverse scores to healthy plant food groups and animal food groups. The 18 food group scores for an individual were summed to obtain the indices, with a theoretical range of 18 (lowest possible score) to 90 (highest possible score). The observed ranges at baseline were 24–85 (PDI), 28–86 (hPDI), and 27–90 (uPDI) across the cohorts. The indices were analyzed as deciles, with energy intake adjusted at the analysis stage.

Percentages were used to report the distribution of categorized variables. The chi-square test was used to measure the association between the grouped variables in this study. In addition, multiple logistic regression was used to examine the adjusted relationships (odds ratios and 95% CI) between the study variables (household heads, parents’ education, parents’ job, household size, having a chronic disease, having a member of vulnerable groups, consumption of vegetables, fruits, meat, nuts and legumes, socioeconomic status, cost, food cost to income ratio, government financial assistance, covid-19-induced poverty) and food insecurity.
Variables were included in the model (p-value under 0.2 in the univariable analysis) if they significantly contributed to the model’s fitness using the stepwise selection method, and the final model was reported. P-value < 0.05 was considered significant. STATA14.0 software (Stata, College Station, TX, USA) was used for all the statistical analyses.

Data were collected through face-to-face interviews, anthropometric measurement, and serum ferritin analysis by trained research assistants. The questionnaire contained data on socio-economic, obstetric, maternal perception, food consumption, dietary diversity, knowledge, attitude, and practices of pregnant women. In addition, mid-upper arm circumference (MUAC) and maternal height measurements were taken. The nutritional status of the pregnant women was measured with non-stretchable MUAC tape and the reading value was taken to the nearest 0.1-cm. All measurements were performed threefold and the average value of two concordant readings was considered as the ultimate value. Pregnant women with average MUAC measurements of less than 23 cm were categorized as having “undernutrition” otherwise normal [25 , 26 (link)]. The questionnaire was initially prepared in English and translated to the local language (Afan Oromo) by individuals with good command of both languages. It was also pre-tested on 10% of the samples in Kersa District before actual implementation. Women’s hemoglobin concentration (in g/dL) was measured at each study site by well-trained medical technologists using HemoCue® Hb 301 system, according to the manufacturer’s instructions (HemoCue AB Ängelholm Sweden) which is a gold standard for fieldwork. A prick was done on the tip of the middle finger after the site was cleaned with disinfectant. The first drop of blood was cleaned off and the second drop was collected to fill the microcuvette which is then placed in the cuvette holder of the device for measuring hemoglobin concentration. Hemoglobin values were adjusted for altitude as per the Center for Disease Prevention and Control (CDC) recommendation [27 ].
As the detailed description has been given elsewhere in a previous papers [23 (link), 24 (link)], the formerly validated food frequency questionnaire (FFQ) containing 27 of the most common lists of food items consumed by the district community was used to assess the dietary diversity of the study participants [28 –33 ]. The food items in the FFQ were grouped into ten food groups, including cereal, white roots and tubers, pulse and legumes, nuts and seeds, dark green leafy vegetables, other vitamin A-rich fruits and vegetables, meat, fish and poultry, dairy and dairy product, egg, other vegetables, and other fruits. The sum of each food group pregnant women consumed over seven days was calculated to analyze the dietary diversity scores (DDS) [32 (link)]. Furthermore, the dietary diversity score was converted into tertiles, with the highest tertile labeled as a "high dietary diversity score" whereas both lower tertiles combined were defined as a “low dietary diversity score". The food variety score (FVS) is the frequency of individual food items consumed during the reference period. Therefore, it was estimated by calculating each individual’s intake of the 27 food items over seven days.