Association between periodontitis and mortality of patients with cardiovascular diseases: A cohort study based on NHANES

Chen, Fangman; Song, Yansong; Li, Weiqi; Xu, Hao; Dan, Hongxia; Chen, Qianming

doi:10.1002/jper.23-0276

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…Multiple research studies have provided evidence indicating a correlation between periodontitis and an elevated likelihood of developing cardiovascular disease (12)(13)(14)(15). Periodontitis also has an impact on the outcome of individuals with cardiovascular disease and raises the likelihood of mortality (16). This association may be explained by inflammatory mediators since periodontitis can alter the amounts of inflammatory markers in the blood (17).…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk in patients with periodontitis: a population-based study

Wang,

Ni,

Xiao

et al. 2023

Front. Cardiovasc. Med.

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BackgroundThe association between periodontitis and cardiovascular disease is increasingly recognized. In this research, a prediction model utilizing machine learning (ML) was created and verified to evaluate the likelihood of coronary heart disease in individuals affected by periodontitis.MethodsWe conducted a comprehensive analysis of data obtained from the National Health and Nutrition Examination Survey (NHANES) database, encompassing the period between 2009 and 2014.This dataset comprised detailed information on a total of 3,245 individuals who had received a confirmed diagnosis of periodontitis. Subsequently, the dataset was randomly partitioned into a training set and a validation set at a ratio of 6:4. As part of this study, we conducted weighted logistic regression analyses, both univariate and multivariate, to identify risk factors that are independent predictors for coronary heart disease in individuals who have periodontitis. Five different machine learning algorithms, namely Logistic Regression (LR), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Classification and Regression Tree (CART), were utilized to develop the model on the training set. The evaluation of the prediction models’ performance was conducted on both the training set and validation set, utilizing metrics including AUC (Area under the receiver operating characteristic curve), Brier score, calibration plot, and decision curve analysis (DCA). Additionally, a graphical representation called a nomogram was created using logistic regression to visually depict the predictive model.ResultsThe factors that were found to independently contribute to the risk, as determined by both univariate and multivariate logistic regression analyses, encompassed age, race, presence of myocardial infarction, chest pain status, utilization of lipid-lowering medications, levels of serum uric acid and serum creatinine. Among the five evaluated machine learning models, the KNN model exhibited exceptional accuracy, achieving an AUC value of 0.977. The calibration plot and brier score illustrated the model's ability to accurately estimate probabilities. Furthermore, the model's clinical applicability was confirmed by DCA.ConclusionOur research showcases the effectiveness of machine learning algorithms in forecasting the likelihood of coronary heart disease in individuals with periodontitis, thereby aiding healthcare professionals in tailoring treatment plans and making well-informed clinical decisions.

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Section: Introductionmentioning

confidence: 99%

Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk in patients with periodontitis: a population-based study

Wang,

Ni,

Xiao

et al. 2023

Front. Cardiovasc. Med.

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Red blood cell folate level is associated with periodontitis in American adults: results from the NHANES 2009–2014

Liu,

Luo,

Jiao

et al. 2024

BMC Oral Health

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Background Red blood cell (RBC) folate is an indicator of long-term folate nutrition. Whether there is an association between RBC folate and periodontitis is unclear. This study aimed to use the NHANES database to determine whether RBC folate is associated with moderate/severe periodontitis. Methods A cross-sectional analysis of 10,151 participants in the NHANES database from 2009 to 2014 was performed. Multivariate logistic regression was used to analyze the independent relationship between RBC folate and moderate/severe periodontitis. The generalized additive model (GAM), restricted cubic splines (RCS), smooth curve fitting, and threshold effect analysis were used to explore the dose–response relationship and the potential nonlinear relationship between RBC folate and periodontitis. Finally, subgroup analysis and interaction tests were performed to determine the effect of covariates on the relationship between RBC folate and moderate/severe periodontitis. Results After adjusting for all confounders, there was a negative association between RBC folate concentration and moderate/severe periodontitis. The lowest fraction Q1 (< 360 ng/mL) of RBC folate concentration was used as the reference group, multivariable-adjusted ORs and 95% CIs of the second (360-463 ng/mL), third (464-569 ng/mL), fourth (570-732 ng/mL), and the highest quintile (> 733 ng/mL) categories were 0.88 (0.77, 1.01), 0.83 (0.72, 0.96), 0.77 (0.67, 0.90), 0.65 (0.56, 0.77) respectively. Additionally, a threshold nonlinear association was found between RBC folate (ng/mL) log2 transformation and moderate/severe periodontitis. Conclusion This cross-sectional study revealed a negative relationship between RBC folate and moderate/severe periodontitis within a certain threshold range. Dentists and policymakers should pay closer attention to oral hygiene and health care for people with low or high RBC folate levels. Further causal and longitudinal research mechanisms are needed to validate our findings.

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Associations between trans fatty acids and systemic immune-inflammation index: a cross-sectional study

Zhu,

Hu,

Dai

et al. 2024

Lipids Health Dis

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Background Previous studies have demonstrated that trans fatty acids (TFAs) intake was linked to an increased risk of chronic diseases. As a novel systemic inflammatory biomarker, the clinical value and efficacy of the systemic immune-inflammation index (SII) have been widely explored. However, the association between TFAs and SII is still unclear. Therefore, the study aims to investigate the connection between TFAs and SII in US adults. Methods The study retrieved data from the National Health and Nutrition Examination Survey (NHANES) for the years 1999–2000 and 2009–2010. Following the exclusion of ineligible participants, the study encompassed a total of 3047 individuals. The research employed a multivariate linear regression model to investigate the connection between circulating TFAs and SII. Furthermore, the restricted cubic spline (RCS) model was utilized to evaluate the potential nonlinear association. Subgroup analysis was also conducted to investigate the latent interactive factors. Results In this investigation, participants exhibited a mean age of 47.40 years, with 53.91% of them being female. Utilizing a multivariate linear regression model, the independent positive associations between the log2-transformed palmitelaidic acid, the log2 transformed-vaccenic acid, the log2-transformed elaidic acid, the log2-transformed linolelaidic acid, and the log2-transformed-total sum of TFAs with the SII (all P < 0.05) were noted. In the RCS analysis, no nonlinear relationship was observed between the log2-transformed palmitelaidic acid, the log2 transformed-vaccenic acid, the log2-transformed elaidic acid, the log2-transformed linolelaidic acid, the log2-transformed-total sum of TFAs and the SII (all P for nonlinear > 0.05). For the stratified analysis, the relationship between the circulating TFAs and the SII differed by the obesity status and the smoking status. Conclusions A positive association was investigated between three types of TFA, the sum of TFAs, and the SII in the US population. Additional rigorously designed studies are needed to verify the results and explore the potential mechanism.

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Association between periodontitis and mortality of patients with cardiovascular diseases: A cohort study based on NHANES

Cited by 4 publications

References 39 publications

Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk in patients with periodontitis: a population-based study

Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk in patients with periodontitis: a population-based study

Red blood cell folate level is associated with periodontitis in American adults: results from the NHANES 2009–2014

Associations between trans fatty acids and systemic immune-inflammation index: a cross-sectional study

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