The Triangle of Nonalcoholic Fatty Liver Disease, Metabolic Dysfunction, and Periodontitis: Analysis of the Dental, Oral, Medical and Epidemiological (DOME) Records-Based Nationwide Research

Association between serum antibodies to oral microorganisms and nonalcoholic fatty liver disease in adults

BACKGROUND

Alterations in the bacteria, such as the periodontal bacteria, might be considered potential risk factors for nonalcoholic fatty liver disease (NAFLD). Most studies analyzing this association have focused mainly on a specific periodontal bacteria (Porphyromonas gingivalis) and have involved relatively small study populations (tens or hundreds of individuals). To address this gap, a sizable, nationally representative adult population was utilized to investigate the association between the incidence of NAFLD and high serum IgG antibodies for 19 periodontal bacteria.

METHODS

To explore this association, data from the Third National Health and Nutrition Examination Survey (NHANES III)-which provides a cross-sectional representation of the noninstitutionalized U.S. population, encompassing 33,994 individuals-were analyzed. Participants aged 40 years and above with data on NAFLD-determined by the gold standard of ultrasound examination (USON)-as well as comprehensive records of serum IgG antibodies against periodontal bacteria, were included, resulting in a final analysis subset of 6,330 individuals.

RESULTS

Using a cluster analysis based on the Socransky classification scheme for oral microorganisms, antibody titers for the 19 bacteria were grouped into four clusters-Red-Green, Orange-Blue, Yellow-Orange, and Orange-Red. When these clusters, as well as individual antibody relationships with NAFLD, were examined, the adjusted odds ratios (ORs) ranged from 0.958 [95% confidence interval (CI): 0.916, 1.003] to 1.021 [95% CI: 0.987, 1.055]. This indicated that no statistically significant associations were found (P > 0.05), underscoring the absence of a meaningful link.

CONCLUSIONS

In summary, it was discovered that there is currently no evidence to correlate serum antibodies to periodontal pathogens with NAFLD in the nationally representative NHANES III.

CLINICAL TRIAL NUMBER

Not applicable.

Statistical Methods and Machine Learning Algorithms for Investigating Metabolic Syndrome in Temporomandibular Disorders: A Nationwide Study

The objective of this study was to analyze the associations between temporomandibular disorders (TMDs) and metabolic syndrome (MetS) components, consequences, and related conditions. This research analyzed data from the Dental, Oral, Medical Epidemiological (DOME) records-based study which integrated comprehensive socio-demographic, medical, and dental databases from a nationwide sample of dental attendees aged 18-50 years at military dental clinics for 1 year. Statistical and machine learning models were performed with TMDs as the dependent variable. The independent variables included age, sex, smoking, each of the MetS components, and consequences and related conditions, including hypertension, hyperlipidemia, diabetes, impaired glucose tolerance (IGT), obesity, cardiac disease, obstructive sleep apnea (OSA), nonalcoholic fatty liver disease (NAFLD), transient ischemic attack (TIA), stroke, deep venous thrombosis (DVT), and anemia. The study included 132,529 subjects, of which 1899 (1.43%) had been diagnosed with TMDs. The following parameters retained a statistically significant positive association with TMDs in the multivariable binary logistic regression analysis: female sex [OR = 2.65 (2.41-2.93)], anemia [OR = 1.69 (1.48-1.93)], and age [OR = 1.07 (1.06-1.08)]. Features importance generated by the XGBoost machine learning algorithm ranked the significance of the features with TMDs (the target variable) as follows: sex was ranked first followed by age (second), anemia (third), hypertension (fourth), and smoking (fifth). Metabolic morbidity and anemia should be included in the systemic evaluation of TMD patients.

Periodontitis and Metabolic Syndrome: Statistical and Machine Learning Analytics of a Nationwide Study

This study aimed to analyze the associations between periodontitis and metabolic syndrome (MetS) components and related conditions while controlling for sociodemographics, health behaviors, and caries levels among young and middle-aged adults. We analyzed data from the Dental, Oral, and Medical Epidemiological (DOME) record-based cross-sectional study that combines comprehensive sociodemographic, medical, and dental databases of a nationally representative sample of military personnel. The research consisted of 57,496 records of patients, and the prevalence of periodontitis was 9.79% (5630/57,496). The following parameters retained a significant positive association with subsequent periodontitis multivariate analysis (from the highest to the lowest OR (odds ratio)): brushing teeth (OR = 2.985 (2.739-3.257)), obstructive sleep apnea (OSA) (OR = 2.188 (1.545-3.105)), cariogenic diet consumption (OR = 1.652 (1.536-1.776)), non-alcoholic fatty liver disease (NAFLD) (OR = 1.483 (1.171-1.879)), smoking (OR = 1.176 (1.047-1.322)), and age (OR = 1.040 (1.035-1.046)). The following parameters retained a significant negative association (protective effect) with periodontitis in the multivariate analysis (from the highest to the lowest OR): the mean number of decayed teeth (OR = 0.980 (0.970-0.991)); North America as the birth country compared to native Israelis (OR = 0.775 (0.608-0.988)); urban non-Jewish (OR = 0.442 (0.280-0.698)); and urban Jewish (OR = 0.395 (0.251-0.620)) compared to the rural locality of residence. Feature importance analysis using the eXtreme Gradient Boosting (XGBoost) machine learning algorithm with periodontitis as the target variable ranked obesity, OSA, and NAFLD as the most important systemic conditions in the model. We identified a profile of the "patient vulnerable to periodontitis" characterized by older age, rural residency, smoking, brushing teeth, cariogenic diet, comorbidities of obesity, OSA and NAFLD, and fewer untreated decayed teeth. North American-born individuals had a lower prevalence of periodontitis than native Israelis. This study emphasizes the holistic view of the MetS cluster and explores less-investigated MetS-related conditions in the context of periodontitis. A comprehensive assessment of disease risk factors is crucial to target high-risk populations for periodontitis and MetS.

The Presence of Periodontitis Exacerbates Non-Alcoholic Fatty Liver Disease via Sphingolipid Metabolism-Associated Insulin Resistance and Hepatic Inflammation in Mice with Metabolic Syndrome

Clinical studies have shown that periodontitis is associated with non-alcoholic fatty liver disease (NAFLD). However, it remains unclear if periodontitis contributes to the progression of NAFLD. In this study, we generated a mouse model with high-fat diet (HFD)-induced metabolic syndrome (MetS) and NAFLD and oral P. gingivalis inoculation-induced periodontitis. Results showed that the presence of periodontitis increased insulin resistance and hepatic inflammation and exacerbated the progression of NAFLD. To determine the role of sphingolipid metabolism in the association between NAFLD and periodontitis, we also treated mice with imipramine, an inhibitor of acid sphingomyelinase (ASMase), and demonstrated that imipramine treatment significantly alleviated insulin resistance and hepatic inflammation, and improved NAFLD. Studies performed in vitro showed that lipopolysaccharide (LPS) and palmitic acid (PA), a major saturated fatty acid associated with MetS and NAFLD, synergistically increased the production of ceramide, a bioactive sphingolipid involved in NAFLD progression in macrophages but imipramine effectively reversed the ceramide production stimulated by LPS and PA. Taken together, this study showed for the first time that the presence of periodontitis contributed to the progression of NAFLD, likely due to alterations in sphingolipid metabolism that led to exacerbated insulin resistance and hepatic inflammation. This study also showed that targeting ASMase with imipramine improves NAFLD by reducing insulin resistance and hepatic inflammation.

Obstructive Sleep Apnea, Metabolic Dysfunction, and Periodontitis-Machine Learning and Statistical Analyses of the Dental, Oral, Medical Epidemiological (DOME) Big Data Study

This study aimed to analyze the associations of obstructive sleep apnea (OSA) with dental parameters while controlling for socio-demographics, health-related habits, and each of the diseases comprising metabolic syndrome (MetS), its consequences, and related conditions. We analyzed data from the dental, oral, and medical epidemiological (DOME) cross-sectional records-based study that combines comprehensive socio-demographic, medical, and dental databases of a nationally representative sample of military personnel for one year. Analysis included statistical and machine learning models. The study included 132,529 subjects; of these, 318 (0.2%) were diagnosed with OSA. The following parameters maintained a statistically significant positive association with OSA in the multivariate binary logistic regression analysis (descending order from highest to lowest OR): obesity (OR = 3.104 (2.178-4.422)), male sex (OR = 2.41 (1.25-4.63)), periodontal disease (OR = 2.01 (1.38-2.91)), smoking (OR = 1.45 (1.05-1.99)), and age (OR = 1.143 (1.119-1.168)). Features importance generated by the XGBoost machine learning algorithm were age, obesity, and male sex (located on places 1-3), which are well-known risk factors of OSA, as well as periodontal disease (fourth place) and delivered dental fillings (fifth place). The Area Under Curve (AUC) of the model was 0.868 and the accuracy was 0.92. Altogether, the findings supported the main hypothesis of the study, which was that OSA is linked to dental morbidity, in particular to periodontitis. The findings highlight the need for dental evaluation as part of the workup of OSA patients and emphasizes the need for dental and general medical authorities to collaborate by exchanging knowledge about dental and systemic morbidities and their associations. The study also highlights the necessity for a comprehensive holistic risk management strategy that takes systemic and dental diseases into account.