Abstract
Background The aims of this study were to develop a prediction model for identification of individuals with diabetes based on clinical and perceived periodontal measurements; and to evaluate its added value when combined with standard diabetes screening tools. Methods The study was carried out among 250 adults attending primary care clinics in Riyadh (Saudi Arabia). The study adopted a case-control approach, where diabetes status was first ascertained, and the Finnish Diabetes Risk Score (FINDRISC), Canadian Diabetes Risk questionnaire (CANRISK), and periodontal examinations were carried out afterward. Results A periodontal prediction model (PPM) including three periodontal indicators (missing teeth, percentage of sites with pocket probing depth >= 6 mm, and mean pocket probing depth) had an area under the curve (AUC) of 0.694 (95% Confidence Interval: 0.612-0.776) and classified correctly 62.4% of participants. The FINDRISC and CANRISK tools had AUCs of 0.766 (95% CI: 0.690-0.843) and 0.821 (95% CI: 0.763-0.879), respectively. The addition of the PPM significantly improved the AUC of FINDRISC (P = 0.048) but not of CANRISK (P = 0.144), with 26.8% and 9.8% of participants correctly reclassified, respectively. Finally, decision curve analysis showed that adding the PPM to both tools would result in net benefits among patients with probability scores lower than 70%. Conclusions This study showed that periodontal measurements could play a role in identifying individuals with diabetes, and that addition of clinical periodontal measurements improved the performance of FINDRISC and CANRISK.