Development and External Validation of a Multivariable Prediction Model to Identify Nondiabetic Hyperglycemia and Undiagnosed Type 2 Diabetes: Diabetes Risk Assessment in Dentistry Score (DDS)

Abstract

The aim of this study was to develop and externally validate a score for use in dental settings to identify those at risk of undiagnosed nondiabetic hyperglycemia (NDH) or type 2 diabetes (T2D). The Studies of Health in Pomerania (SHIP) project comprises 2 representative population-based cohort studies conducted in northeast Germany. SHIP-TREND-0, 2008 to 2012 (the development data set) had 3,339 eligible participants, with 329 having undiagnosed NDH or T2D. Missing data were replaced using multiple imputation. Potential covariates were selected for inclusion in the model using backward elimination. Heuristic shrinkage was used to reduce overfitting, and the final model was adjusted for optimism. We report the full model and a simplified paper-based point-score system. External validation of the model and score employed an independent data set comprising 2,359 participants with 357 events. Predictive performance, discrimination, calibration, and clinical utility were assessed. The final model included age, sex, body mass index, smoking status, first-degree relative with diabetes, presence of a dental prosthesis, presence of mobile teeth, history of periodontal treatment, and probing pocket depths ≥5 mm as well as prespecified interaction terms. In SHIP-TREND-0, the model area under the curve (AUC) was 0.72 (95% confidence interval [CI] 0.69, 0.75), calibration in the large was −0.025. The point score AUC was 0.69 (95% CI 0.65, 0.72), with sensitivity of 77.0 (95% CI 76.8, 77.2), specificity of 51.5 (95% CI 51.4, 51.7), negative predictive value of 94.5 (95% CI 94.5, 94.6), and positive predictive value of 17.0 (95% CI 17.0, 17.1). External validation of the point score gave an AUC of 0.69 (95% CI 0.66, 0.71), sensitivity of 79.2 (95% CI 79.0, 79.4), specificity of 49.9 (95% CI 49.8, 50.00), negative predictive value 91.5 (95% CI 91.5, 91.6), and positive predictive value of 25.9 (95% CI 25.8, 26.0). A validated prediction model involving dental variables can identify NDH or undiagnosed T2DM. Further studies are required to validate the model for different European populations.

Association between periodontal diseases and cardiovascular diseases, diabetes and respiratory diseases: Consensus report of the Joint Workshop by the European Federation of Periodontology (EFP) and the European arm of the World Organization of Family Doctors (WONCA Europe)

“…Optimal cut‐off points were determined as the presence of ≥26% deep pockets or ≥4 missing teeth, and these correctly identified 73% of true cases of pre‐diabetes/NDH or diabetes, and the inclusion of a POC HbA1C test value of ≥5.7% further improved correct identification (Lalla et al, 2011). Recently, a risk prediction questionnaire developed specifically for dental offices, employing a large sample size (>4000) and validated internally and externally in a German population with 357 events, produced an AUROC of 0.7 and positive predictive value of 91.5% (95% CI: 91.5–91.6) (Yonel et al, 2022).…”

“…Recently, a risk prediction questionnaire developed specifically for dental offices, employing a large sample size (>4000) and validated internally and externally in a German population with 357 events, produced an AUROC of 0.7 and positive predictive value of 91.5% (95% CI: 91.5-91.6) (Yonel et al, 2022).…”

Current scientific evidence for why periodontitis should be included in diabetes management

“…Hyperglycemia screening in the dental setting is found cost-effective ( 158 , 159 ), and general physicians are receptive to receive referrals from dentists ( 160 ). Few teeth/edentulism and periodontitis are significantly associated with undiagnosed DM ( 63 , 161 ) and hence predict potential hyperglycemia.…”