Investigation of the association between habitual dietary FODMAP intake, metabolic parameters, glycemic status, and anthropometric features among apparently healthy overweight and obese individuals

Two recent projects were previously conducted in the Tabriz and Tehran cities of Iran [24, 25]. Individuals were selected from outpatient clinics through public declaration and the dissemination of posters. The inclusion criteria for this study were individuals aged between 20 to 50 and a BMI of 25 kg/m² and more. Individuals with specific conditions, including pregnancy, breastfeeding, menopause, recent bariatric surgery, a history of cardiovascular disease (CVD), cancer, hepatic or renal disease, diabetes mellitus, and taking any drugs and medications that affect weight, were excluded from the study. Participants who had been on a weight-loss regimen or taking supplements for at least three months before participating were excluded from the study.

We gathered sociodemographic information, including age, gender, smoking status, educational level, marital status, employment, past medical history, and family size, by asking the participants to fulfill a questionnaire. The socioeconomic status (SES) score was then computed. Then, we categorized participants’ education level using ordered categorical variables: illiterate: 0, less than a diploma: 1, diploma and associate degree: 2, bachelor’s degree: 3, master’s degree: 4, and higher: 5. The occupational status was also recorded similarly: housewife: 1, a worker: 2, student: 3, freelancers:4 and more: 5 for females; And without a job: 1, rancher, farmer, and worker: 2, extras: 3, employee: 4, and independently employed: 5 for men. Additionally, individuals were assigned scores of 1, 2, or 3 to indicate whether they had a family size of 3, 4–5, or 6, respectively. They also received a score of 1 if they did not own a house and a score of 2 if they did. The body composition was determined using bioelectrical impedance analysis (BIA) (Tanita, BC-418 MA, Tokyo, Japan). A wall-mounted stadiometer and a Seca scale (Seca Co., Hamburg, Germany) were used to measure height and weight to the nearest 0.5 cm and 0.1 kg, respectively. The hip circumference (HC) was measured across the broadest part of the buttocks just upon the greater trochanters. The waist circumference (WC) was measured using tape to the nearest 0.1 cm at the midpoint of the lowest costal border and the iliac crest. We also calculated the waist-to-hip ratio (WHR) and the body mass index (BMI). Using a standard, calibrated mercury sphygmomanometer (Riester, Diplomat 1002, Jungingen, Germany), blood pressure was measured twice in the same arm after at least 15 min of rest. The mean of the two measurements was used for analysis. The US National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria defined MetS [26]. The short form of the International Physical Activity Questionnaire (IPAQ) was used to measure the physical activity levels among participants [27‐29].

A validated semi-quantitative Food Frequency Questionnaire (FFQ) with 168 questions was used to collect dietary data for the Iranian population [30]. Participants kept diaries detailing how often and how much of each food item they consumed each day, week, month, and year. The amount of food consumed was converted to grams per day using the standard common portion size, cooking yield factors, and edible portions of foods found in the Iranian household measures manual [31].

The NUTRITIONIST IV software (N Squared Computing, California, USA) was utilized to analyze daily dietary intakes. The validity and dependability of the FFQ used in this study were previously evaluated [30]. Based on the estimated validity coefficients, reasonable relative validity was obtained. Men and women had nearly identical correlation coefficient values for various nutrients. The food groups were specified as follows: Whole grains, refined grains, potatoes, dairy products, vegetables, fruits, legumes, meats, nuts and seeds, solid fat, liquid oil, tea and coffee, salty snacks, simple sugars, honey and jam, soft drinks, and desserts and snacks. The frequency with which people added salt or salty sauce to food while it was being prepared or cooked, before or during eating, and the frequency of consuming processed foods with a high salt content was used to assess dietary salt consumption [32].

The foods were categorized based on their FODMAP content into high, moderate, and low FODMAP groups using the classification system provided by the Monash University Android app [33]. The Monash Uni low FODMAP diet was developed by nutritionists who coined the term FODMAP and is regularly updated and accessible globally [34]. Iranian foods were adopted with the list of high FODMAP (E.g., wheat, garlic, onion, fruit, vegetables, legumes and pulses, sweeteners, and other grains), moderate FODMAP (E.g., avocado, sweet potato, broccoli, cabbage, canned pumpkin) and low FODMAP (E.g., eggs and meat, almond milk, grains like rice, quinoa and oats, vegetables like eggplant, potatoes, tomatoes, cucumbers and zucchini, fruits such as grapes, oranges, strawberries, blueberries, and pineapple) foods. Then the consumed amounts of low, moderate, and high FODMAP foods for each participant were calculated using the FFQ described before. In order to calculate the gram of high, moderate, and low FODMAP food intake, we used a semi-quantitative food frequency questionnaire in which every food item each participant consumed was converted into grams. The sum of all foods with high, moderate, or low FODMAP content that each participant consumed was calculated in grams separately. Then, these final values were categorized into tertiles. Then we categorized it into three tertiles (based on the amount of consumption). Higher tertiles denote higher consumption of dietary FODMAP.

A total of 10 ml of fasting venous blood were obtained from all participants for the biochemical analysis. A commercial kit (Pars Azmoon, Tehran, Iran) was utilized to determine total serum cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and fasting blood glucose (FBG). Plasma and serum samples were divided by centrifugation at 4,500 rpm for ten minutes at four degrees Celsius. Aliquots were frozen at 70 degrees Celsius prior to the analysis. Moreover, to calculate the portion of low-density lipoprotein cholesterol (LDL-C), the Friedewald equation was applied [35]. Enzyme-linked immunosorbent assay (ELISA) kits (Bioassay Technology Laboratory, Shanghai Korean Biotech, Shanghai City, China) were utilized to determine insulin levels in the blood. The Quantitative Insulin Sensitivity Check Index (QUICKI) and the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) were calculated by dividing fasting insulin (IU/ml) by 22.5 fasting glucose (mmol/l). 1/[log insulin (U/mL) + log glucose (mmol/L) during fasting].

The SPSS (IBM SPSS version 26.0) software was used to analyze the data at a significance level of 0.05. Categorical variables and continuous variables were described as frequency (percentage) and mean [standard deviation (SD)], respectively. Analysis of variance (ANOVA) was utilized to evaluate the association between low, moderate, and high FODMAP foods and metabolic syndrome. Analysis of covariance (ANCOVA) was used to control the effect of confounding variables (including age, sex, BMI, and total energy intake) on the association of low, moderate, and high FODMAP foods and metabolic syndrome. Multinomial logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the presence of cardiometabolic risk factors across the FODMAP tertiles in two multivariable-adjusted models. The risk was described in three models (Model I: crude, Model II: adjusted for age and sex, Model III: adjusted for age, BMI, sex, SES, and energy intake). The G-power software was utilized to determine the minimum sample size required for the study, considering a correlation coefficient (r) of 0.25, a significance level of 0.05, and a power of 80%, which resulted in a prediction of 315 participants. However, based on previous studies, considering a 10% drop-out [36, 37]. The sample size was calculated with α = 0.05 and β = 0.2. Therefore, the power was 80%. According to the power of 80%, categorizing of the high, moderate, and low FODMAP groups into tertiles was the best choice to avoid false positives due to multiple comparisons and false negatives due to inadequate power [38, 39]. The final sample size for the study was 347 individuals, 58.2% male and 41.8% female [40]. The sampling was performed in three months.

This study was a cross-sectional study to assess the association between FODMAP diet and metabolic syndrome. Several limitations of this study should be addressed; first of all, the causality inference is impossible due to the study’s observational design. Therefore, performing clinical trials is suggested which reduces insulin sensitivity and increase dress this issue. Also, using FFQ as a subjective tool to collect dietary information is a matter of recall bias. However, it should be noted that our semi-quantitative FFQ was a valid and reliable tool adopted by the Iranian population.

In conclusion, this study evaluated the association of foods with different amounts of FODMAP and metabolic syndrome in the Iranian population. The findings of this study revealed that consuming low and moderate FODMAP foods is associated with lower WHR, higher FFM, and SBP. Conversely, consuming higher amounts of high-FODMAP foods is associated with insulin resistance. It can be suggested that high-FODMAP foods might be harmful to people with metabolic syndrome. These findings highlight the potential role of FODMAP in managing metabolic syndrome and open a new field of research.