Assessment of Black Stains on Teeth in Relation to Salivary Parameters, Periodontal Health, and Body Mass Index in Children

Plain-language summary

Objective The aim of this study is to compare salivary flow rate, salivary pH, salivary buffering capacity, plaque index, gingival index, DMFT/dft indices, and body mass index (BMI) in systemically healthy children aged 5-10 years with and without black stains (BS) in the oral cavity. By evaluating the differences between these two groups, we seek to better understand the effects of black stains on oral health and assess the relationship between BMI and these parameters. The findings may contribute to improved oral health management, preventive dentistry practices, and a deeper understanding of the association between obesity and oral health in children. Methods This study will consist of two groups of systemically healthy children: those with black stains (BS group, n=50) and those without (non-BS group, n=50). Both groups will be examined to determine the d (decayed), f (filled), and t (missing) values for primary teeth (dft index) and D (decayed), M (missing), and F (filled) values for permanent teeth (DMFT index). We will assess plaque index and gingival index using Silness-Löe (1963, 1967) criteria. Saliva samples will be collected under standardized conditions (at least 2 hours post-breakfast, between 9-11 AM, after rinsing with water) in an isolated dental unit with patients seated upright. Salivary pH will be measured using pH strips (GC Saliva-Check BUFFER) from unstimulated saliva. We will categorize pH levels as: highly acidic (5.0-5.8, red), moderately acidic (6.0-6.6, yellow), or healthy (6.8-7.8, green). For salivary flow rate measurement, stimulated saliva will be collected after paraffin chewing (5 minutes) and measured in mL/min. Salivary buffering capacity will be evaluated using GC Saliva-Check BUFFER test pads and scored as: very low (0-5), low (6-9), or normal/high (10-12). We will calculate Body Mass Index (BMI) as kg/m² and interpret results using age- and sex-specific percentiles. Statistical analysis will compare all parameters between the two groups.

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