The synergistic and detrimental interplay between diabetes mellitus and periodontitis poses a significant health burden. This study aimed to rigorously evaluate the association between routinely documented clinical periodontal parameters and glycemic control (as measured by HbA1c) in a cohort of diabetic patients attending Al-Kufa University College of Dentistry over a decade. A further objective was to develop and internally validate a multivariable predictive model for estimating HbA1c based on these readily accessible markers. A retrospective cohort study design was employed, analyzing patient records from January 2014 to December 2023. Diabetic patients were identified through documented clinical diagnosis in medical records, not solely based on HbA1c values. The inclusion criteria targeted diabetic patients (Type 1 or 2) aged 18 years or older with at least one comprehensive periodontal examination and a corresponding HbA1c value (within ± 3 months). Both periodontal parameters and HbA1c values were extracted from records at the same time point within this timeframe. Data on demographics, smoking status, HbA1c levels, and detailed periodontal parameters were meticulously extracted. Statistical analyses included descriptive statistics, Pearson correlation coefficients, and multivariable linear regression, considering alternative variable selection methods to construct the HbA1c predictive model. The dataset was split for internal validation (70% training, 30% testing). Out of 987 screened records, 350 diabetic patient records were analysed (mean age 54.2 ± 10.5 years; mean HbA1c 8.1% ± 1.8%). HbA1c significantly correlated (p < 0.001) with BOP% (r = 0.35), percentage of sites with PD ≥ 5 mm (r = 0.38), and number of missing teeth (r = 0.30). A multivariable regression model including these periodontal markers, age, and smoking status explained 27.5% of HbA1c variance (Adjusted R2 = 0.275, p < 0.001) in the training set (n = 245). In the testing set (n = 105), the model showed an R2 of 0.26 and a Mean Absolute Error (MAE) of 1.02% HbA1c. This 10-year retrospective analysis demonstrates that specific periodontal markers (percentage of sites with PD ≥ 5 mm: r = 0.38, BOP%: r = 0.35, missing teeth: r = 0.30) are significantly associated with HbA1c levels in diabetic patients. The developed model, while not a substitute for direct HbA1c measurement, offers a validated, non-invasive, and cost-effective chairside tool for risk-stratifying patients with diabetes, potentially enhancing early identification of those with poor glycemic control and facilitating timely medical referrals.

Periodontal markers predict glycemic control in diabetic patients a ten year retrospective analysis

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2025年10月17日

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Salah M. Ibrahim,
Munir Nasr Hamed,
Ansam Mahdi Khalel

摘要

The synergistic and detrimental interplay between diabetes mellitus and periodontitis poses a significant health burden. This study aimed to rigorously evaluate the association between routinely documented clinical periodontal parameters and glycemic control (as measured by HbA1c) in a cohort of diabetic patients attending Al-Kufa University College of Dentistry over a decade. A further objective was to develop and internally validate a multivariable predictive model for estimating HbA1c based on these readily accessible markers. A retrospective cohort study design was employed, analyzing patient records from January 2014 to December 2023. Diabetic patients were identified through documented clinical diagnosis in medical records, not solely based on HbA1c values. The inclusion criteria targeted diabetic patients (Type 1 or 2) aged 18 years or older with at least one comprehensive periodontal examination and a corresponding HbA1c value (within ± 3 months). Both periodontal parameters and HbA1c values were extracted from records at the same time point within this timeframe. Data on demographics, smoking status, HbA1c levels, and detailed periodontal parameters were meticulously extracted. Statistical analyses included descriptive statistics, Pearson correlation coefficients, and multivariable linear regression, considering alternative variable selection methods to construct the HbA1c predictive model. The dataset was split for internal validation (70% training, 30% testing). Out of 987 screened records, 350 diabetic patient records were analysed (mean age 54.2 ± 10.5 years; mean HbA1c 8.1% ± 1.8%). HbA1c significantly correlated (p < 0.001) with BOP% (r = 0.35), percentage of sites with PD ≥ 5 mm (r = 0.38), and number of missing teeth (r = 0.30). A multivariable regression model including these periodontal markers, age, and smoking status explained 27.5% of HbA1c variance (Adjusted R² = 0.275, p < 0.001) in the training set (n = 245). In the testing set (n = 105), the model showed an R² of 0.26 and a Mean Absolute Error (MAE) of 1.02% HbA1c. This 10-year retrospective analysis demonstrates that specific periodontal markers (percentage of sites with PD ≥ 5 mm: r = 0.38, BOP%: r = 0.35, missing teeth: r = 0.30) are significantly associated with HbA1c levels in diabetic patients. The developed model, while not a substitute for direct HbA1c measurement, offers a validated, non-invasive, and cost-effective chairside tool for risk-stratifying patients with diabetes, potentially enhancing early identification of those with poor glycemic control and facilitating timely medical referrals.