Study setting and design
We used baseline data from RaNCD in this cross-sectional study, one of the sub-studies of the national Prospective Epidemiological Research Studies of IrAN (PERSIAN) cohort study [16]. Ravansar is one of Kermanshah Province’s western cities, with a population of approximately 50,000 in western Iran. Comprehensive information about the RaNCD study has been published and is accessible [17]. This study enrolled participants aged 35–65 years’ old who were in the bassline phase of RaNCD (10,000 individuals). Due to the research design, subjects with cancer and pregnant women were excluded. The final population of the study consisted of 9811 adults (Fig. 1).
Data collection
Sociodemographic data, such as age, gender, marital status, and residence location, as well as personal habits (smoking status and alcohol consumption), were collected face to face using digital questionnaires.
The socioeconomic status (SES) was determined using 18 items (housing, car price, dishwasher, freezer, washing machine, computer, laptop, internet access, motorcycle, color TV, TV type, bathroom, cell phone, vacuum cleaner, area per capita, room per capita, education level, and residence place); finally, the SES was classified into five groups from poorest to richest using the principal component analysis (PCA) method [18].
A standardized cohort study using a physical activity questionnaire (Including 22 questions) was used to assess participants’ physical activity on a met/hour per week basis. Participants were divided into three groups (light, moderate, high).
Blood samples were collected after 12 h of fasting to measure biochemical markers such as triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and total cholesterol (T-C), as well as fasting blood sugar (FBS). A BSM 370 (with 0.1 cm precision) was used to measure the height (Biospace Co, Seoul, Korea).
Weight and other anthropometric indices, including body mass index (BMI), body fat mass (BFM), and visceral fat area (VFA), were measured using a Bio-Impedance Analyzer BIA (with a precision of 0.5 kg) (InBody 770 Biospace, Korea). Standard methods were used to determine waist circumference (WC) and waist to hip ratio (WHR). Blood pressure (BP) was measured via a manometer cuff and stethoscope after resting the arm in the seated position for 10 min.
Assessment of the dietary inflammatory index
Food Frequency Questionnaire (FFQ) items were used to calculate the DII scores. Participants responded to questions about their consumption of various food groups in terms of quantity and frequency. They were shown a photo from the booklet to assist them in estimating portion sizes. At this stage, diet-related data were collected face-to-face to minimize measurement bias.
Shivappa et al. found that 45 foods were associated with one or more of the inflammatory markers Interleukin-1b (IL-1b), Interleukin-6 (IL-6), Tumor Necrosis Factor-α (TNF-α), C-reactive protein (CRP), anti-inflammatory markers Interleukin-4 (IL-4) and Interleukin-10 (IL-10). Z-scores for each parameter were determined through the method developed by Shivappa et al., using the mean and standard deviation (SD) of global intake. The Z-score was then converted to a percentile. This method was utilized to calculate the inflammatory score for each food parameter, and then the inflammatory scores for all parameters were added to obtain the total DII score. The higher the DII score, the more pro-inflammatory the diet, and the lower the DII score, the more anti-inflammatory the diet [19, 20]. DII scores were classified into four groups (quartiles) to assess associations. The first and fourth quartiles had the lowest and highest DII scores, respectively.
In the current study, DII was calculated using 31 food parameters, including carbohydrate, protein, total fat, trans fat, monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), cholesterol, saturated fat, omega-3, omega-6, vitamins A, B6, B12, C, D, E, selenium, zinc, energy, iron, magnesium, niacin, riboflavin, thiamine, beta-carotene, fibre, folic acid, caffeine, garlic, onion, and tea.
Hypertension and type 2 diabetes mellitus assessment
HTN was defined as a systolic blood pressure (SBP) of ≥140 mmHg either-or a diastolic blood pressure (DBP) of ≥90 mmHg either-or as being currently on antihypertensive medication [21]. T2DM was defined as having an FBS (fasting blood sugar) of ≥126 mg/dl either-or being on diabetes medication either-or having diabetes confirmed by a health practitioner [22].
Statistical analysis
The mean ± standard deviation was used for quantitative variables, while the frequency (percentage) was used via DII quartiles for qualitative variables. Additionally, Chi square test and one-way ANOVA compared the frequency (%) and mean ± standard deviation of basic characteristics among the quartiles of DII. Also, one-way ANOVA compared the mean ± standard deviation of anthropometric and biochemical characteristics among the four studied groups (Healthy, T2DM, HTN, T2DM & HTN). Crude and adjusted logistic regression models (Adjusted for potential confounders including age, sex, BMI, BFM, WHR, carbohydrate (%E), protein (%E) and oil/fat (%E) and physical activity) were used to determine the association between DII and hypertension and T2DM. The crude and adjusted odds ratios with 95% confidence interval were reported. P values < 0.05 were considered significant. All analyses were done with STATA software version 14.2 (Stata Corp, College Station, Tex).