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Related Concept Videos

Teeth01:15

Teeth

549
The formation of teeth, also known as odontogenesis, is a complex process that begins in utero, around the sixth week of embryonic development. There are three stages to this process: the bud stage, the cap stage, and the bell stage.
In the bud stage, the tooth germ (an aggregation of cells) starts to form in the developing jawbone. During the cap stage, the tooth germ differentiates into enamel organ, dental papilla, and dental sac, which will later develop into the tooth's enamel, dentin...
549

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Related Experiment Video

Updated: Aug 10, 2025

Robust Ligature-Induced Model of Murine Periodontitis for the Evaluation of Oral Neutrophils
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Identifying Factors Associated with Periodontal Disease Using Machine Learning.

Hussam M Alqahtani1,2,3, Siran M Koroukian1, Kurt Stange1,4

  • 1Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA.

Journal of International Society of Preventive & Community Dentistry
|February 13, 2023
PubMed
Summary

Sociodemographic and behavioral factors, not chronic conditions, are most strongly linked to periodontal disease (PD) presence and severity. Understanding these factors is key to identifying individuals at risk for PD.

Keywords:
Machine learningperiodontal medicineperiodontal-systemic disease interactionsperiodontitisrisk factor(s)

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Area of Science:

  • Oral health research
  • Chronic disease epidemiology

Background:

  • Periodontal disease (PD) affects a significant portion of the adult population.
  • Identifying risk factors for PD is crucial for public health interventions.

Purpose of the Study:

  • To identify combinations of chronic conditions associated with periodontal disease (PD) presence and severity.
  • To analyze demographic and behavioral factors influencing PD in a US adult population.

Main Methods:

  • Cross-sectional study using 2013-2014 National Health and Nutrition Examination Survey data (n=4555).
  • Periodontal disease assessed via clinical attachment loss.
  • Classification and regression tree, and random forest analyses employed.

Main Results:

  • Prevalence of PD was 77% in the study population.
  • Age and education level were the strongest predictors for PD presence and severity.
  • Hypertension and diabetes were associated with PD when considering only chronic conditions, but sociodemographic and behavioral factors were more significant overall.

Conclusions:

  • Sociodemographic and behavioral factors are more strongly associated with PD than chronic conditions.
  • Identifying individuals vulnerable to PD requires accounting for the interplay of these factors.