Risk of coronary heart disease in patients with periodontitis among the middled-aged and elderly in China: a cohort study

Gao, Kaikai; Wu, Zhiyuan; Liu, Yue; Tao, Lixin; Luo, Yanxia; Yang, Xinghua; Zhang, Jingbo; Guo, Xiuhua; Wang, Songlin

doi:10.1186/s12903-021-01951-z

Cited by 5 publications

(4 citation statements)

References 45 publications

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“…Thus, the increased periodontal and cardiovascular destruction with age may be attributed to increased time of exposure to the local factors. This finding was similar to a study by Gao et al., where the elderly Chinese population with periodontitis was found to be at a higher risk for coronary heart disease 25 …”

Section: Discussionsupporting

confidence: 92%

“…This finding was similar to a study by Gao et al, where the elderly Chinese population with periodontitis was found to be at a higher risk for coronary heart disease. 25 When cardiac parameters were compared, SBP, DBP, and RBS were statistically significant between the cardiac and noncardiac groups, which clearly supports the fact that hypertension and diabetes are known risk factors for CVD. Similarly, significantly higher LDL and lower HDL in cardiac patients proves that cholesterol is also a notable risk factor.…”

Section: Discussionsupporting

confidence: 54%

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Evidence linking the association of periodontal viruses and pentraxins in periodontitis patients with coronary artery disease

Ilango

Mahendra

et al. 2023

Journal of Periodontology

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BackgroundAlthough there is ample evidence in the literature supporting a significant positive association between key periodontal pathogens and established inflammatory markers of periodontitis and coronary artery disease (CAD), their exact role remain unclear. Especially, the role of viruses in the etiology and specific biomarkers have not been validated. Thus, the current study aims to evaluate the role of periodontal viruses such as Epstein–Barr virus (EBV), cytomegalovirus (CMV), and herpes simplex virus (HSV), as well as the inflammatory marker pentraxin‐3 (PTX3), and to analyze their association with CAD.MethodsThe study included 240 patients divided into four groups of 60 patients each: nonperiodontitis + noncardiac (NP+NC) group, periodontitis + noncardiac patients (P+NC) group, nonperiodontitis + cardiac patients (NP+C) group, and periodontitis + cardiac (P+C) group. The cardiac surgery group (C‐S) was a subgroup of NP+C and P+C. It consisted of 60 patients from the abovementioned two cardiac groups in whom coronary artery bypass graft (CABG) was indicated. Demographic variables, cardiac parameters, and periodontal parameters were recorded. The viruses (EBV, CMV, and HSV) and the inflammatory marker PTX3 were evaluated in the subgingival plaque samples of all the four groups and atheromatous plaque samples of the C‐S using reverse transcriptase‐polymerase chain reaction (RT‐PCR) and quantitative polymerase chain reaction (qPCR), respectively, and were compared between the groups. The results were obtained and statistically analyzed.ResultsThe demographic variables did not differ significantly between the groups, except for age. Systolic blood pressure, diastolic blood pressure, low‐density lipoprotein, and random blood sugar were significantly higher in NP+C and P+C, whereas high‐density lipoprotein was significantly lower (p ≤ 0.05) in the same. Plaque index (PI), probing pocket depth (PPD), and clinical attachment level (CAL) were significantly higher (p ≤ 0.05) in P+NC and P+C. PTX were significantly elevated in P+C among the four groups. On evaluating the subgingival plaque samples, EBV and CMV were significantly higher in the two periodontitis groups P+NC and P+C (p = 0.000). HSV was significantly higher in the two cardiac groups (NP+C and P+C) (p ≤ 0.05). Cardiac EBV and CMV were significantly elevated in the P+C group with a p value of 0.004 and 0.033, respectively. Cardiac HSV was found in the NP+C group with statistical insignificance (p = 0.410) between the groups. On correlation, oral PTX were significantly associated with bleeding index (BI), PPD, and CAL (p = 0.000). Similarly, cardiac PTX showed significant association with PI, BI, PPD, and CAL (p = 0.000). Oral and cardiac PTX also showed significant correlation with each other. Multiple logistic regression analysis revealed a significant association between CAL and oral EBV (p ≤ 0.05). Similarly, cardiac EBV showed a significant association with CAL and oral EBV (p ≤ 0.05). Multiple logistic regression analysis also revealed that both cardiac and oral PTX showed a significant association only with oral EBV, CMV, and HSV.ConclusionThe results of the current study suggest that the clinical severity of periodontitis (CAL), etiology of periodontitis (EBV and CMV), and inflammatory marker of periodontitis (PTX3) were found to be significantly elevated in CAD. These findings suggests that periodontal diseases may be a risk factor that could influence the progression of CAD.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 54%

Evidence linking the association of periodontal viruses and pentraxins in periodontitis patients with coronary artery disease

Ilango

Mahendra

et al. 2023

Journal of Periodontology

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“…Periodontitis contributes to the development of systemic diseases, including coronary heart disease and diabetes [ 1 , 2 ]. A prospective cohort study showed that during follow-up from 2014 to 2019, the risk of coronary heart disease increased by 37% in participants with periodontitis compared to those without periodontitis at baseline [ 3 ]. It was reported that individuals with type 2 diabetes had the potential to improve glycemic control and C-reactive protein concentrations by periodontal intervention [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Periodontitis is associated with stroke

Zheng,

Li,

Zhen

et al. 2023

J Transl Med

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Background Periodontitis is considered as a risk factor for cardiovascular diseases and atherosclerosis. However, the relationship between periodontitis and stroke is rarely studied. Therefore, we aimed to explore the relationship between periodontitis and stroke. Methods Statistical analysis was performed using the complex sampling design. We analyzed data on 6,460 participants, representing 92,856,028 American citizens aged 30 years or older, who had valid data on periodontitis and stroke from the National Health and Nutrition Examination Survey 2009–2014. We used clinical attachment level and probing pocket depth precisely to determine periodontitis and it is the first time to use such a precise method for exploring the relationship between periodontitis and stroke. Results 39.9% of participants had periodontitis and 2.1% of participants had a record of stroke diagnosis. Stroke was associated with severity levels of periodontitis (p for trend = 0.018). The odds ratio for stroke was significantly elevated in the severe periodontitis and moderate periodontitis participants compared to participants without periodontitis (OR for severe periodontitis: 2.55, 95% CI 1.25–5.21; OR for moderate periodontitis: 1.71, 95% CI 1.17–2.50). After adjusting for race/ethnicity and sex, the association remained significant (p for trend = 0.009). After further adjusting for BMI, hypercholesterolemia, diabetes, alcohol consumption and physical activity, the association still existed (p for trend = 0.027). The association was significant consistently after further adjusting for age (p for trend = 0.033). Conclusions In this nationally representative study, we found an association between periodontitis and stroke. The risk of stroke in participants with severe periodontitis and moderate periodontitis was 2.55 times and 1.71times as high as those without periodontitis. Dental health management may be of benefit to stroke prevention.

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“…The bacteremia and systemic inflammatory state associated with periodontitis plays a significant role in the development of vascular endothelial lesions and the enhancement of inflammatory processes in the vascular wall ( 18 ). Through the induction of a systemic inflammatory response and autoimmune illness, a chronic infection brought on by periodontitis progresses to atherosclerosis and, ultimately, coronary heart disease ( 19 ).…”

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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Risk of coronary heart disease in patients with periodontitis among the middled-aged and elderly in China: a cohort study

Cited by 5 publications

References 45 publications

Evidence linking the association of periodontal viruses and pentraxins in periodontitis patients with coronary artery disease

Evidence linking the association of periodontal viruses and pentraxins in periodontitis patients with coronary artery disease

Periodontitis is associated with stroke

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

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