Screening of hyperglycaemia in dental settings

Background + Aims

• Type 2 diabetes mellitus (T2DM) is a growing global health issue, with millions remaining undiagnosed.
• In Singapore, diabetes prevalence is among the highest in Southeast Asia, with half of those affected unaware of their condition at diagnosis.
• Early identification of undiagnosed diabetes or prediabetes can reduce complications and improve health outcomes.
• Periodontitis, a chronic inflammatory disease linked to diabetes, may serve as an early indicator of dysglycaemia.
• Studies suggest that routine dental visits provide an opportunity for screening diabetes risk, yet most research has focused on Western populations. Asians are at a higher risk of developing T2DM, with two-thirds of global cases occurring in Asia. Singapore, with its diverse Asian population, serves as a representative microcosm for the region.
• This study aimed to:
  • Assess the prevalence of undiagnosed diabetes and prediabetes in a Singaporean dental cohort.
  • Develop a clinical risk model for dentists to identify high-risk patients and improve diabetes screening within dental settings, especially in Asian populations with unique diabetes risk profiles.

Materials + Methods

• This cross-sectional study was conducted at the National Dental Centre Singapore (NDCS) from May 2016 to October 2019.
• Participants were excluded if they had existing diabetes diagnoses or conditions affecting glucose metabolism.
• The primary outcome was the confirmation of diabetes or prediabetes based on point-of-care glycated haemoglobin (HbA1c) levels, followed by physician referrals for further testing.
• Patients completed the American Diabetes Association Risk Test, providing information on demographics, medical history, and lifestyle factors.
• Periodontal parameters, including probing pocket depth (PPD), bleeding on probing (BOP), and the periodontal inflamed surface area (PISA), were measured by calibrated dentists to ensure consistency.
• Finger-prick HbA1c testing categorised participants as normoglycaemic, prediabetic, or diabetic.
• Patients with HbA1c >6.0% were referred for further medical assessment.
• Logistic regression models were used to identify factors predicting undiagnosed diabetes, with receiver operating characteristic (ROC) curves assessing model performance.

Results

• A total of 1,074 patients from NDCS without prior diabetes diagnoses were enrolled in the study:
  • 65 were diagnosed with diabetes – confirmed diabetes mellitus (CDM)
  • 83 with prediabetes – confirmed prediabetes (CPD)
  • 11 showed hyperglycaemic HbA1c levels but did not comply with follow-ups
  • The remaining 915 participants had normal glycaemic status (N).
• Patients in the CDM and CPD groups were older, had higher BMIs, and scored higher on the ADA diabetes risk test compared to the N group.
• Non-Chinese ethnicities, smoking, hypertension, and family history of diabetes were more prevalent in the CDM group.
• Periodontal health assessments revealed that severe periodontitis was significantly higher in the CDM group, with increased PPD, BOP and PISA compared to the N group.
• Patients with Stage III/IV periodontitis had a greater likelihood of undiagnosed diabetes.
• Logistic regression identified key predictors of undiagnosed diabetes, including:
  • BMI ≥23.0 kg/m²
  • ADA risk score ≥5
  • Family history of diabetes
  • Smoking
  • Severe periodontitis
  • Models combining these variables showed good predictive performance, with the best models incorporating severe periodontitis or Stage III/IV periodontitis alongside BMI, family history, and smoking, achieving area under the curve (AUC) values of 0.717–0.721.
• Point-of-care (POC) HbA1c testing alone demonstrated superior predictive ability for undiagnosed diabetes (AUC 0.954).

Limitations