This cross-sectional study was conducted at the Nutrition Clinics of Kafr Shokr Specialized Hospital, Egypt. A total of 380 patients with T2DM and periodontitis were recruited. The sample size was determined using a power analysis based on previous studies investigating the association between periodontitis and systemic conditions [6]. The required sample size was determined to be 378 participants using G*Power software (version 3.1.9.7), assuming a medium effect size (Cohen’s d = 0.5), a significance level of 0.05, and a power of 80%.

Participants were included if they were aged 18 years or older, diagnosed with type 2 diabetes mellitus (T2DM), exhibited clinical signs of periodontitis (probing pocket depth ≥ 4 mm, clinical attachment loss ≥ 3 mm, and bleeding on probing), and met the criteria for obesity (body mass index [BMI] ≥ 30 kg/m²).

Exclusion criteria included patients with type 1 diabetes, those taking medications known to influence periodontal conditions (e.g., immunosuppressants, long-term antibiotics) and individuals with severe renal impairment. Individuals who had undergone periodontal treatment in the last three months were also excluded.

Each participant underwent a thorough and structured evaluation to obtain a comprehensive understanding of their overall health, particularly in relation to systemic conditions and oral health. This assessment comprised three key components: medical history, anthropometric measurements, and an oral and periodontal examination. Each aspect was meticulously conducted to ensure the accuracy and reliability of the collected data.

As part of the medical history assessment, detailed information was gathered regarding the duration of diabetes, the specific medications used for its management, and the participant’s overall systemic health. This step was crucial in establishing a background for further analysis, helping to identify potential links between systemic health and oral conditions.

Anthropometric measurements were also recorded to evaluate the participants’ physical health. The Body Mass Index (BMI) was calculated by dividing weight (kg) by the square of height (m²), providing an essential indicator of overall body composition. Additionally, waist circumference (WC) was measured at the midpoint between the lower rib and the iliac crest to assess central adiposity. Visceral Fat Area (VFA) was determined using bioelectrical impedance analysis with the Tanita MC-980MA device (Tanita Corporation, Japan), offering further insights into fat distribution and metabolic health risks.

The oral and periodontal examination involved a comprehensive assessment of periodontal health. Probing Pocket Depth (PPD) and Bleeding on Probing (BOP) were measured at six sites per tooth using a pocket probe (PCP106, Hu-Friedy®). Oral hygiene was assessed using the Silness and Löe plaque index, which provided insight into the level of plaque accumulation. To quantify periodontal inflammation, the Periodontal Inflamed Surface Area (PISA) was calculated through a standardized computational approach.

The PISA calculation followed a systematic process consisting of four key steps. First, PPD measurements for all six sites per tooth were entered into a spreadsheet, which computed the mean PPD for each individual tooth. Next, this mean PPD value was used in a formula to derive the Periodontal Epithelium Surface Area (PESA), representing the total root surface area (in mm²) covered by pocket epithelium. Since not all pocket epithelium contributes to active inflammation, the PESA value was adjusted based on the proportion of sites exhibiting BOP. For example, if three out of six sites displayed BOP, the PESA for that tooth was multiplied by 3/6 to yield its specific PISA value. Finally, the PISA values of all teeth were summed to determine the total inflamed periodontal surface area in the participant’s mouth.

To ensure consistency and accuracy, the PISA calculation spreadsheet used in this study is available at http://www.parsprototo.info/docs/PISA_CAL.xls. By employing this systematic methodology, the study aimed to provide a detailed assessment of periodontal inflammation, allowing for a more precise evaluation of the relationship between periodontal disease and systemic health conditions.

To ensure consistency in measurements, intra- and inter-examiner calibration was performed prior to data collection. Ten patients were examined twice by the same examiner (intra-examiner reliability) and by a second examiner (inter-examiner reliability). The intraclass correlation coefficient (ICC) for PPD, CAL, and BOP measurements exceeded 0.85, indicating excellent agreement. Separate ICC analysis of calculated PISA/PESA metrics in a 20-patient subsample similarly showed excellent reliability (PISA: intra-rater ICC = 0.92, inter-rater ICC = 0.88; PESA: intra-rater ICC = 0.94, inter-rater ICC = 0.90).

Statistical analysis was performed using SPSS version 16 (SPSS Inc., Chicago, IL, USA). The association between PISA or PESA and obesity or diabetes was analyzed using obesity parameters, including BMI, VFA, and waist circumference, and diabetes parameters such as HbA1c and fasting plasma glucose (FPG). BMI was applied as a continuous or categorical variable with a threshold of 25 kg/m², while VFA had a threshold of 100 cm², indicating obesity-related cardiovascular risk. HbA1c and FPG were also used as continuous or categorical variables with thresholds of 6.5% and 126 mg/dl, respectively. Quantitative data were expressed as mean ± SD or median and interquartile range, while qualitative data were presented as frequency and percentage. The Mann-Whitney U test was used for comparisons, and Spearman’s correlation assessed relationships between variables.

Multivariable linear regression was used to assess independent associations between PISA/PESA and metabolic parameters (HbA1c, BMI, waist circumference, VFA), adjusting for age, gender, smoking status, and oral hygiene. Assumptions of linearity, homoscedasticity, and normality of residuals were verified.