ArticleJournal of clinical medicine2022
Associations between Periodontitis and COPD: An Artificial Intelligence-Based Analysis of NHANES III.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 23 citations in OpenAlex.
- Characteristics of Respiratory Microbiome in COPD-A Literature Review.Advances in respiratory medicine · 2026Review
- Machine Learning Unveils Dietary Antioxidants as Influential Factors for Diabetes-Cancer Comorbidity: Insights From National Health and Nutrition Examination Survey.Food science & nutrition · 2026Article
- Comparative evaluation of composite inflammatory indices for predicting prostate cancer risk and mortality: a NHANES-based study with external validation.Frontiers in oncology · 2026Article
- Artificial Intelligence Applications in Chronic Obstructive Pulmonary Disease: A Global Scoping Review of Diagnostic, Symptom-Based, and Outcome Prediction Approaches.Biomedicines · 2025Review
- The association between Planetary Health Diet Index (PHDI) and chronic obstructive pulmonary disease (COPD): the mediating role of dietary inflammatory index (DII).BMC pulmonary medicine · 2025Article
- Validity of the self-reported number of teeth in independent older people in Japan.BMC geriatrics · 2024Article
- Environmental exposures, the oral-lung axis and respiratory health: The airway microbiome goes on stage for the personalized management of human lung function.Microbial biotechnology · 2024Review
- Article
- Association Between Dietary Fiber Intake and Prevalence of Chronic Obstructive Pulmonary Disease in a Middle-Aged and Elderly Population: a Study Based on the National Health and Nutrition Examination Survey Database.Chronic obstructive pulmonary diseases (Miami, Fla.) · 2024Article
- Sputum microbiota and inflammatory subtypes in asthma, COPD, and its overlap.The journal of allergy and clinical immunology. Global · 2024Article
- Climbing the longevity pyramid: overview of evidence-driven healthcare prevention strategies for human longevity.Frontiers in aging · 2024Review
- Low-carbohydrate diet score and chronic obstructive pulmonary disease: a machine learning analysis of NHANES data.Frontiers in nutrition · 2024Article
- Association between periodontal disease and chronic obstructive pulmonary disease.The Japanese dental science review · 2023Review
- Article
- Review
- Research on the Association Between Periodontitis and COPD.International journal of chronic obstructive pulmonary disease · 2023Review
- The association between sex hormones and periodontitis among American adults: A cross-sectional study.Frontiers in endocrinology · 2023Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A number of cross-sectional epidemiological studies suggest that poor oral health is associated with respiratory diseases. However, the number of cases within the studies was limited, and the studies had different measurement conditions. By analyzing data from the National Health and Nutrition Examination Survey III (NHANES III), this study aimed to investigate possible associations between chronic obstructive pulmonary disease (COPD) and periodontitis in the general population. COPD was diagnosed in cases where FEV (1)/FVC ratio was below 70% (non-COPD versus COPD; binary classification task). We used unsupervised learning utilizing k-means clustering to identify clusters in the data. COPD classes were predicted with logistic regression, a random forest classifier, a stochastic gradient descent (SGD) classifier, k-nearest neighbors, a decision tree classifier, Gaussian naive Bayes (GaussianNB), support vector machines (SVM), a custom-made convolutional neural network (CNN), a multilayer perceptron artificial neural network (MLP), and a radial basis function neural network (RBNN) in Python. We calculated the accuracy of the prediction and the area under the curve (AUC). The most important predictors were determined using feature importance analysis. Results: Overall, 15,868 participants and 19 feature variables were included. Based on k-means clustering, the data were separated into two clusters that identified two risk characteristic groups of patients. The algorithms reached AUCs between 0.608 (DTC) and 0.953% (CNN) for the classification of COPD classes. Feature importance analysis of deep learning algorithms indicated that age and mean attachment loss were the most important features in predicting COPD. Conclusions: Data analysis of a large population showed that machine learning and deep learning algorithms could predict COPD cases based on demographics and oral health feature variables. This study indicates that periodontitis might be an important predictor of COPD. Further prospective studies examining the association between periodontitis and COPD are warranted to validate the present results.
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