Dental findings and identification of undiagnosed hyperglycemia

J Dent Res. 2013 Oct;92(10):888-92. doi: 10.1177/0022034513502791. Epub 2013 Aug 26.

Abstract

A change in the American Diabetes Association guidelines added hemoglobin A1c (HbA1c) to the assays for diabetes diagnosis, but evidence suggests that glucose vs. HbA1c criteria may identify different segments of the affected population. We previously demonstrated that oral findings offer an opportunity for the detection of undiagnosed abnormal fasting plasma glucose (FPG) among dental patients who present with diabetes risk factors. In this new cross-sectional study, we sought to extend these observations. The first goal, using data from 591 new participants, was to assess our previously identified hyperglycemia detection models when HbA1c is used for case definition. The second goal, using data from our total cohort of 1,097 participants, was to evaluate the models' performance regardless of whether an FPG or an HbA1c is used for diagnosis. The presence of ≥ 26% teeth with deep pockets or ≥ 4 missing teeth correctly identified 72% of pre-diabetes or diabetes cases in the HbA1c sample and 75% in the total population. The addition of a point-of-care HbA1c ≥ 5.7% increased correct identification to 87% and 90%, respectively. These results demonstrate the validity of our prediction models regardless of the test used for diabetes or pre-diabetes diagnosis in the clinical setting and underscore the contribution dentists can make.

Keywords: diabetes; periodontal disease(s)/periodontitis; periodontal medicine; pre-diabetes; prevention; screening.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Adult
  • Blood Glucose / analysis
  • Chi-Square Distribution
  • Cohort Studies
  • Cross-Sectional Studies
  • Diabetes Mellitus, Type 2 / complications
  • Diabetes Mellitus, Type 2 / diagnosis*
  • Female
  • Glycated Hemoglobin A / analysis
  • Humans
  • Hyperglycemia / complications
  • Hyperglycemia / diagnosis*
  • Logistic Models
  • Male
  • Middle Aged
  • Prediabetic State / complications
  • Prediabetic State / diagnosis*
  • Predictive Value of Tests
  • ROC Curve
  • Risk Factors
  • Sensitivity and Specificity
  • Tooth Loss / complications*

Substances

  • Blood Glucose
  • Glycated Hemoglobin A