Oral healthcare practitioners can detect diabetes and prediabetes
MedWire News: Using a combination of dental variables, oral healthcare professionals (OHPs) can detect patients with diabetes or prediabetes and refer them on to a physician for further care.
Diabetes is an established risk factor for periodontal disease and can complicate treatment outcomes. However, the ability for periodontal disease to predict for diabetes has not been confirmed.
As 70% of US adults see a dentist at least once a year, OHPs are ideally placed to detect diabetes in patients.
In the current study, Evanthia Lalla (College of Dental Medicine, Columbia University, New York, USA) and colleagues recruited 601 individuals who reported for care at a dental clinic. Patients were greater than or equal to 40 years of age if nonHispanic White, and greater than or equal to 30 years of age if Hispanic or non-White. None of the patients had ever been told that they had diabetes or prediabetes.
Patients were questioned on four risk factors for the development of diabetes: family history of diabetes, hypertension, high cholesterol, overweight/obesity. All patients with at least one of these risk factors (n=535) received a periodontal examination and hemoglobin (Hb)A1c test in the clinic.
All patients were invited back to the clinic for a second appointment following an overnight fast for a fasting plasma glucose test (FPG): 506 (95.6%) returned.
In total 182 individuals had an abnormal FPG (greater than 100 mg/dl), 21 of whom were potentially diabetic (FPG >126 mg/dl; 4.2%) and 161 of whom were prediabetic (FPG 100-125 mg/dl; 31.8%).
Individuals with an abnormal FPG had significantly more missing teeth (8.9 vs 6.2) than those with a normal FPG, more deep pockets with a probing depth greater than 5 mm (14.0 vs 9.1), and a higher level of HbA1c (6.1 vs 5.6%).
Various models were developed using the risk factors as variables. The model with the greatest success at detecting diabetes or prediabetes included the percentage of deep pockets, the number of missing teeth, and the HbA1c test, giving an area under the curve value of 0.79.
The use of cut-off values increased the predictive power of the models such that the presence of at least 26% of deep pockets or at least four missing teeth correctly identified 73% of individuals with previously unrecognized diabetes or prediabetes in the study population. The use of these two dental variables gave a predictive power that was similar to that of the HbA1c test.
Reporting in the Journal of Dental Research, the authors conclude that "the predictive models identified in the present study portend that dental professionals... have an unrealized capability to assume an active role in patients at risk for, or impacted by, diabetes, and to direct the to receive appropriate care."
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By Iain Bartlett