This pilot investigation was conducted at the diet outpatient clinic of a state hospital in Malatya/Turkey from November 2021 to May 2022.

Sample of the study

This study was conducted on 176 individuals (103 women, 73 men) who applied to the diet outpatient clinic of a public hospital in Turkey between November 2021 and May 2022. Based on the number of applicants, a programme called ‘Sample Size Calculator’ was used and the sample size was determined as 121 with a 95% confidence interval and a 5% margin of error. During the research process, 52 of the participants refused to participate in the study, resulting in 124 individuals successfully completing the study.

Eligibility for the study was confined to individuals aged 19–65 years with a Body Mass Index (BMI) of 30–40 kg/m2, free from chronic conditions such as diabetes, hypertension, dyslipidemia, cancer, or chronic renal failure. Exclusion criteria encompassed recent dietary program adherence, nutritional supplement intake, inflammatory disease diagnosis, or severe psychiatric disorders. Pregnancy and breastfeeding were also exclusion criteria. Essential for inclusion was voluntary participation without recent engagement in dietary interventions or supplement consumption.

Data collection instruments

Data Collection Form: In this form consisting of four sections, socio-demographic information, sleep quality through Pittsburgh Sleep Quality Index and three-day dietary intake record were collected. It was administered through direct interviews. Dietary intake record and anthropometric measurements were collected by the researcher, while routine biochemical measurements were obtained from existing participant records.

Dietary intake records

To evaluate dietary patterns and calculate DII scores, participants recorded their food intake over three days, including one weekend day. Detailed instructions and examples were provided for accurate record-keeping.

Pittsburgh Sleep Quality Index (PSQI)

The PSQI, adapted for Turkish populations by Ağargün et al. (1996), assessed sleep quality over the past month. It includes 24 questions, with 19 self-reported and 5 completed by a spouse or roommate. Each item is scored from 0 to 3, with a maximum total score of 21. A score above five indicates poor sleep quality [13].

Biochemical measurements

Specific biochemical tests were routinely ordered for participants by an internist. These included fasting blood glucose, triglycerides, total cholesterol, LDL-cholesterol, and HDL-cholesterol. Additionally, inflammatory markers such as CRP, leukocyte, lymphocyte, monocyte, neutrophil, and eosinophil levels were measured. These parameters were analyzed using spectrophotometry on the Beckman Coulter AU640, with LDL cholesterol calculated using the Friedewald formula [14].

Computation of the Dietary Inflammatory Index (DII)

The DII, developed by Shivappa et al. (2014), evaluates the inflammatory potential of diets. It involves calculating z-scores for consumed nutrients, based on mean daily intakes and standard deviations established from national nutritional research datasets across various populations (Shivappa et al., 2014). The process entailed transforming z-scores to percentile scores, which were then adjusted (doubled and decremented by one) for symmetric distribution. These adjusted values were multiplied by customised effect scores for each nutrient. The DII is obtained by summing these values, representing the inflammatory potential of the diet [10]. In this study, data for 32 nutrient components was available for DII calculation. The customised effect scores, global average daily intakes, and standard deviation (SD) values pertinent to the DII computation for these nutrients are delineated in Table 1.

Table 1 Nutrient Parameters for Dietary Inflammatory Index Calculation: Customised Effect Scores, Global Average Daily Intakes and Standard Deviations (Shivappa et al. 2014).Statistical analysis

Data analysis was conducted using IBM SPSS software (Version 22.0), with dietary consumption records assessed via the BeBIS 8.2 program. Descriptive statistics, including arithmetic mean, standard deviation, range, frequency, and percentage, were computed. DII scores were categorised into quartiles, with Q1 being the most anti-inflammatory and Q4 being the most pro-inflammatory.

The normality of data distribution was evaluated using histograms, Q-Q plots, and the Kolmogorov-Smirnov test. Categorical variables were analyzed with the Pearson chi-square test. The t-test and Analysis of Variance (ANOVA) were applied for independent group differences when parametric test assumptions were met. For non-parametric conditions, the Mann-Whitney U and Kruskal Wallis tests were employed. Variance homogeneity was assessed using Levene’s test. Statistical significance was set at a p-value threshold of less than 0.05.