Development and validation of a screening model for diabetes mellitus in patients with periodontitis in dental settings

Abstract

Objectives To identify predictors in patient profiles and to develop, internally validate, and calibrate a screening model for diabetes mellitus (DM) in patients with periodontitis in dental settings Materials and methods The study included 204 adult patients with periodontitis. Patients’ socio-demographic characteristics, general health status, and periodontal status were recorded as potential predictors. The diabetic status was considered the outcome, classified into no DM, prediabetes (pre-DM), or DM. Multinomial logistic regression analysis was used to develop the model. The performance and clinical values of the model were determined. Results Seventeen percent and 47% of patients were diagnosed with DM and pre-DM, respectively. Patients’ age, BMI, European background, cholesterol levels, previous periodontal treatment, percentage of the number of teeth with mobility, and with gingival recession were significantly associated with the diabetic status of the patients. The model showed a reasonable calibration and moderate to good discrimination with area under the curve (AUC) values of 0.67 to 0.80. The added predictive values for ruling in the risk of DM and pre-DM were 0.42 and 0.11, respectively, and those for ruling it out were 0.05 and 0.17, respectively. Conclusions Predictors in patient profiles for screening of DM and pre-DM in patients with periodontitis were identified. The calibration, discrimination, and clinical values of the model were acceptable. Clinical relevance The model may well assist clinicians in screening of diabetic status of patients with periodontitis. The model can be used as a reliable screening tool for DM and pre-DM in patients with periodontitis in dental settings.

Can periodontal measures assist in the identification of adults with undiagnosed hyperglycaemia? A systematic review

“…Some studies also collected self‐reported measures (Herman et al, 2015), radiographic bone loss (Holm et al, 2016; Heji et al, 2020) and salivary biomarkers (Grigoriadis et al, 2021). Six studies used clinical periodontitis case definitions; namely the CDC/AAP (Holm et al, 2016; Su et al, 2020), the 2017 World Workshop (Grigoriadis et al, 2021) and ad hoc definitions (Herman et al, 2015; Hegde et al, 2019; Montero et al, 2021). All studies used multiple regression for model development, except for one study which used machine learning (Hegde et al, 2019).…”

“…All studies but one (Lalla et al, 2011) had less than 10 events per variable. In addition, four studies did not include all participants in the analysis (Herman et al, 2015; Hegde et al, 2019; Heji et al, 2020; Su et al, 2020), three dichotomized periodontal predictors using arbitrary thresholds (Herman et al, 2015; Holm et al, 2016; Montero et al, 2021), three used an inappropriate method to select predictors for inclusion in the model (Holm et al, 2016; Grigoriadis et al, 2021; Montero et al, 2021) and one did not account for the complexities in the data, namely sampling fractions in a case–control study (Hegde et al, 2019). Finally, the two studies that reported a PPM (Lalla et al, 2011; Montero et al, 2021) did not assess model performance in terms of both discrimination and calibration, did not account for model overfitting and optimism and did not report the weights assigned to predictors for clinical use.…”

“…Seven studies were conducted in Western countries, namely three in the United States (Lalla et al, 2011; Herman et al, 2015; Hegde et al, 2019) and one each in Denmark (Holm et al, 2016), Greece (Grigoriadis et al, 2021), the Netherlands (Su et al, 2020) and Spain (Montero et al, 2021). The remaining study was from Saudi Arabia (Heji et al, 2020).…”

“…In the participant domain, five studies had high risk of bias. Of them, three had an inappropriate design, either non‐nested case–control (Hegde et al, 2019) or cross‐sectional in which diabetes was not determined in all participants (Herman et al, 2015; Montero et al, 2021), and five recruited any dental patients (Herman et al, 2015; Holm et al, 2016; Hegde et al, 2019; Su et al, 2020; Montero et al, 2021), thus not reflecting the intended target population of dental patients at risk of diabetes (on whom the prediction model will be used). In the predictors domain, seven studies were at high risk of bias.…”

Diabetes and Oral Health: Summary of Current Scientific Evidence for Why Transdisciplinary Collaboration Is Needed

“…PreDM was found in 9.9% of unaware dental patients in Sweden (242), 28.7% in the US (235), and 46.6% among patients with Dutch periodontal (243). Such screening in dental offices is well-accepted by patients (245)(246)(247), dentists (246)(247)(248)(249)(250) and physicians (251), and their professional organizations (252), and can lead to positive lifestyle changes and decreased HbA1c level (253,254).…”