Evaluation of medical and dental visits in New York City: Opportunities to identify persons with and at risk for diabetes mellitus in dental settings

Diabetes and Oral Health: Summary of Current Scientific Evidence for Why Transdisciplinary Collaboration Is Needed

This Perspective provides a brief summary of the scientific evidence for the often two-way links between hyperglycemia, including manifest diabetes mellitus (DM), and oral health. It delivers in a nutshell examples of current scientific evidence for the following oral manifestations of hyperglycemia, along with any available evidence for effect in the opposite direction: periodontal diseases, caries/periapical periodontitis, tooth loss, peri-implantitis, dry mouth (xerostomia/hyposalivation), dysbiosis in the oral microbiome, candidiasis, taste disturbances, burning mouth syndrome, cancer, traumatic ulcers, infections of oral wounds, delayed wound healing, melanin pigmentation, fissured tongue, benign migratory glossitis (geographic tongue), temporomandibular disorders, and osteonecrosis of the jaw. Evidence for effects on quality of life will also be reported. This condensed overview delivers the rationale and sets the stage for the urgent need for delivery of oral and general health care in patient-centered transdisciplinary collaboration for early detection and management of both hyperglycemia and oral diseases to improve quality of life.

Dental Treatment Effect on Blood Glucose Level Fluctuation in Type 1 Balanced Diabetic Children

INTRODUCTION

Pediatric dentists, while treating diabetic patients, have a major concern about the occurrence of hypo- or hyperglycemia complications. So far, no dental care protocol is elaborated to help practitioners in creating a safe environment for their patients. This study aims to evaluate the blood glucose level (BGL) fluctuation in type 1 diabetic children according to the type of the delivered treatment and its duration, to outline guidance for predicting the occurrence of a hypo- or hyperglycemia complication.

MATERIALS AND METHODS

A cross-sectional approach was conducted on 70 balanced diabetic children aged between 7 years and 12 years old (32 females and 38 males) in the Department of Pediatric Dentistry at the Lebanese University in Beirut. Only the patients having a BGL between 70 mg/dL and 300 mg/dL could undergo dental treatment. The gender, the duration of the dental session (≤30 or >30 minutes), the BGLs at the baseline and the end of the session were noted. The types of the performed dental treatment were classified as simple, and unpleasant acts.

RESULTS

For the female group, only eight sessions lasted ≤30 minutes. The results of simple and unpleasant interventions >30 minutes showed that BGLs before and after treatment were almost identical (p > 0.05). In the male group, for the simple acts >30 minutes, the BGL showed a significant fluctuation (p = 0.02).

CONCLUSION

Dentists must be aware of signs of discomfort in balanced type 1 diabetic children to avoid severe complications. This study highlights a new range of BGLs (70-300 mg/dL) that allows safe dental treatment.

HOW TO CITE THIS ARTICLE

Nassif N, Noueiri B. Dental Treatment Effect on Blood Glucose Level Fluctuation in Type 1 Balanced Diabetic Children. Int J Clin Pediatr Dent 2021;14(2):212-216.

The added value of periodontal measurements for identification of diabetes among Saudi adults

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.

Prediabetes and Diabetes Screening in Dental Care Settings: NHANES 2013 to 2016

Introduction

Early recognition of prediabetes may prevent progression to diabetes, yet not all adults are aware of their prediabetes risk. To reach all adults unaware of their risk, additional risk assessment strategies are warranted.

Objectives

The objective of this study was to evaluate the potential scope of benefit from prediabetes risk assessment in the dental care setting and to identify characteristics of dental patients likely to unknowingly have prediabetes or diabetes.

Methods

Data from 10,472 adults in the National Health and Nutrition Examination Survey from 2013 to 2014 and 2015 to 2016 were analyzed for associations among prediabetes/diabetes risk factors, health care use, and hemoglobin A1C levels according to chi-square tests and multivariate logistic regression.

Results

A total of 7.73% of US adults had seen a dentist but not a medical provider in the past 12 mo. The composition of this subpopulation was significantly different from that who saw a medical provider, in ways that might affect their diabetes risk. In addition, 31.27% of this subpopulation would be identified as being at high risk for prediabetes according to the CDC Prediabetes Screening Test (Centers for Disease Control and Prevention), and 15.83% had hemoglobin A1C levels indicative of undiagnosed prediabetes or diabetes. Screening in a dental setting would have the highest odds of identifying someone unaware of his or her diabetes risk among those who were non-White, obese, or ≥45 y old.

Conclusion

Extrapolation from this analysis indicates that screening for prediabetes at dental visits has the potential to alert an estimated 22.36 million adults of their risk for prediabetes or diabetes. Incorporating prediabetes or diabetes risk assessment into routine dental visits may enable 1) those with prediabetes to take action to decrease their risk of developing diabetes and 2) those with diabetes to engage in treatment to decrease their risk of diabetes-related complications.

Knowledge Transfer Statement

Screening for prediabetes and diabetes during dental visits has the potential to raise patients' awareness of diabetes risk and prevent prediabetes from progressing to diabetes. For some patients, the dental visit may be the only point of contact with the health care system, which heightens the importance of including diabetes risk assessment for patient well-being.

Screening for Diabetes Risk Using Integrated Dental and Medical Electronic Health Record Data

Undiagnosed diabetes and prediabetes present a serious public health challenge. We previously reported that data available in the dental setting can serve as a tool for early dysglycemia identification in a primarily Hispanic, urban population. In the present study, we sought to determine how the identification approach can be recalibrated to detect diabetes or prediabetes in a White, rural cohort and whether an integrated dental-medical electronic health record (iEHR) offers further value to the process. We analyzed iEHR data from the Marshfield Clinic, a health system providing care in rural Wisconsin, for dental patients who were ≥21 y of age, reported that they had never been told they had diabetes, had an initial periodontal examination of at least 2 quadrants, and had a glycemic assessment within 3 mo of that examination. We then assessed the performance of multiple predictive models for prediabetes/diabetes. The study outcome, glycemic status, was gleaned from the medical module of the iEHR based on American Diabetes Association blood test cutoffs. The sample size was 4,560 individuals. Multivariate logistic regression revealed that the best performance was achieved by a model that took advantage of the iEHR. Predictors included age, sex, race, ethnicity, number of missing teeth, percentage of teeth with at least 1 pocket ≥5 mm from the dental EHR, and overweight/obesity, hypertension, hyperlipidemia, and smoking status from the medical EHR. The model achieved an area under the receiver operating characteristic curve of 0.71 (95% confidence interval, 0.69-0.72), yielding a sensitivity of 0.70 and a specificity of 0.62. Across a range of populations, informed by certain patient characteristics, dental care team members can play a role in helping to identify dental patients with undiagnosed diabetes or prediabetes. The accuracy of the prediction increases when dental findings are combined with information from the medical EHR. Knowledge Transfer Statement: Prediabetes and diabetes often go undiagnosed for many years. Early identification and care can lead to improved glycemic outcomes and prevent wide-ranging morbidity, including adverse oral health consequences, in affected individuals. Information available in the dental office can be used by clinicians to identify those who remain undiagnosed or are at risk; the accuracy of this prediction increases when combined with information from the medical electronic health record.