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Development and Internal Validation of an Exploratory Nomogram for Cerebral Small Vessel Disease Burden Using
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This study explored if periodontal disease predicts cerebral small vessel disease (CSVD) burden. While tooth loss showed a link, severe periodontitis and tooth loss were not independent predictors in multivariable analysis.
Area of Science:
- Neurology
- Periodontology
- Medical Imaging
Background:
- Cerebral small vessel disease (CSVD) is a significant factor in stroke and cognitive decline.
- The relationship between periodontal disease and CSVD burden requires further investigation.
Purpose of the Study:
- To develop and validate a clinical nomogram for estimating high CSVD burden using periodontal parameters.
- To assess the association between periodontal disease and CSVD.
Main Methods:
- Magnetic resonance imaging (MRI) was used to assess CSVD burden in 234 individuals.
- Periodontitis severity and tooth count were recorded.
- LASSO regression and multivariable logistic regression were employed to construct and analyze the nomogram.
Main Results:
- A linear dose-response relationship was observed between tooth loss and high CSVD burden.
- Advanced age and hypertension were independent predictors of high CSVD burden.
- Severe periodontitis and severe tooth loss were not independent predictors after multivariable adjustment.
Conclusions:
- The exploratory nomogram showed modest discrimination and good calibration for CSVD risk stratification.
- While periodontal disease showed initial associations, it was not an independent predictor of high CSVD burden.
- Further external validation is needed before clinical application of the nomogram.
Abstract:
Cerebral small vessel disease (CSVD) is a major cause of stroke and cognitive decline, but the contribution of periodontal disease to the overall CSVD burden remains unclear. We aimed to develop and internally validate an exploratory clinical nomogram incorporating periodontal parameters to estimate a high CSVD burden. A total of 234 individuals underwent magnetic resonance imaging (MRI) for assessment of total CSVD burden (score 0-4). Periodontitis severity and retained tooth count were recorded. Optimal predictors selected using the least absolute shrinkage and selection operator (LASSO) regression were entered into multivariable logistic regression to construct the nomogram, which was evaluated for discrimination, calibration, and clinical utility. Restricted cubic spline analysis demonstrated a linear dose-response relationship between tooth loss and high CSVD burden (P for non-linearity = 0.332). Multivariable analysis identified advanced age and hypertension as independent prognostic factors; however, the associations of severe periodontitis (P = 0.580) and severe tooth loss (P = 0.112) were attenuated and were not independently associated with high CSVD burden after adjustment. The nomogram demonstrated modest discrimination (area under the curve = 0.675) and favorable bootstrap-validated calibration (mean absolute error = 0.038). Decision curve analysis suggested potential clinical utility across selected risk thresholds, although interpretation remains exploratory because of the absence of external validation. Although severe periodontitis and tooth loss showed univariable associations with high CSVD burden, they were not independent predictors after multivariable adjustment. This exploratory nomogram provides a preliminary visualization framework for individualized risk stratification and warrants further external validation before clinical implementation.