Periodontal disease as a model to study chronic inflammation in aging

Periodontal disease is a chronic inflammatory condition that results in the destruction of the teeth supporting tissues, eventually leading to the loss of teeth and reduced quality of life. In severe cases, periodontal disease can limit proper nutritional intake, cause acute pain and infection, and cause a withdrawal from social situations due to esthetic and phonetic concerns. Similar to other chronic inflammatory conditions, periodontal disease increases in prevalence with age. Research into what drives periodontal disease pathogenesis in older adults is contributing to our general understanding of age-related chronic inflammation. This review will present periodontal disease as an age-related chronic inflammatory disease and as an effective geroscience model to study mechanisms of age-related inflammatory dysregulation. The current understanding of the cellular and molecular mechanisms that drive inflammatory dysregulation as a function of age will be discussed with a focus on the major pathogenic immune cells in periodontal disease, which include neutrophils, macrophages, and T cells. Research in the aging biology field has shown that the age-related changes in these immune cells result in the cells becoming less effective in the clearance of microbial pathogens, expansion of pathogenic subpopulations, or an increase in pro-inflammatory cytokine secretions. Such changes can be pathogenic and contribute to inflammatory dysregulation that is associated with a myriad of age-related disease including periodontal disease. An improved understanding is needed to develop better interventions that target the molecules or pathways that are perturbed with age in order to improve treatment of chronic inflammatory conditions, including periodontal disease, in older adult populations.

Causal effects of denture wearing on epigenetic age acceleration and the mediating pathways: a mendelian randomization study

BACKGROUND

The epigenetic-age acceleration (EAA) represents the difference between chronological age and epigenetic age, reflecting accelerated biological aging. Observational studies suggested that oral disorders may impact DNA methylation patterns and aging, but their causal relationship remains largely unexplored. This study aimed to investigate potential causal associations between dental traits and EAA, as well as to identify possible mediators.

METHODS

Using summary statistics of genome-wide association studies of predominantly European ancestry, we conducted univariable and multivariable Mendelian randomization (MR) to estimate the overall and independent effects of ten dental traits (dentures, bleeding gums, painful gums, loose teeth, toothache, ulcers, periodontitis, number of teeth, and two measures of caries) on four EAA subtypes (GrimAge acceleration [GrimAA], PhenoAge acceleration [PhenoAA], HannumAge acceleration [HannumAA] and intrinsic EAA [IEAA]), and used two-step Mendelian randomization to evaluate twelve potential mediators of the associations. Comprehensive sensitivity analyses were used to verity the robustness, heterogeneity, and pleiotropy.

RESULTS

Univariable inverse variance weighted MR analyses revealed a causal effect of dentures on greater GrimAA (β: 2.47, 95% CI: 0.93-4.01, p = 0.002), PhenoAA (β: 3.00, 95% CI: 1.15-4.85, p = 0.001), and HannumAA (β: 1.96, 95% CI: 0.58-3.33, p = 0.005). In multivariable MR, the associations remained significant after adjusting for periodontitis, caries, number of teeth and bleeding gums. Three out of 12 aging risk factors were identified as mediators of the association between dentures and EAA, including body mass index, body fat percentage, and waist circumference. No evidence for reverse causality and pleiotropy were detected (p > 0.05).

CONCLUSIONS

Our findings supported the causal effects of genetic liability for denture wearing on epigenetic aging, with partial mediation by obesity. More attention should be paid to the obesity-monitoring and management for slowing EAA among denture wearers.

Association between biological aging and periodontitis using NHANES 2009-2014 and mendelian randomization

Aging is a recognized risk factor for periodontitis, while biological aging could provide more accurate insights into an individual's functional status. This study aimed to investigate the potential association between biological aging and periodontitis. Epidemiological data from 9803 participants in the 2009-2014 National Health and Nutrition Examination Survey were analyzed at a cross-sectional level to assess this link. Three biological ages [Klemera-Doubal method (KDM), PhenoAge, and homeostatic dysregulation (HD)] and two measures of accelerated biological aging (BioAgeAccel and PhenoAgeAccel) were set as primary exposure and were calculated. Logistic regression and restricted cubic spline regression were employed to examine the relationship between biological aging and periodontitis. Additionally, Mendelian randomization analysis was conducted to explore the causal connection between accelerated biological aging and periodontitis. After adjusting for age, gender, race, educational level, marital status, ratio of family income, and disease conditions, this study, found a significant association between subjects with older higher biological ages, accelerated biological aging, and periodontitis. Specifically, for a per year increase in the three biological ages (HD, KDM, and PhenoAge), the risk of periodontitis increases by 15%, 3%, and 4% respectively. Individuals who had positive BioAgeAccel or PhenoAgeAccel were 20% or 37% more likely to develop periodontitis compared with those who had negative BioAgeAccel or PhenoAgeAccel. Furthermore, a significant non-linear positive relationship was observed between the three biological ages, accelerated biological aging, and periodontitis. However, the Mendelian randomization analysis indicated no causal effect of accelerated biological aging on periodontitis. Our findings suggest that biological aging may contribute to the risk of periodontitis, highlighting the potential utility of preventive strategies targeting aging-related pathways in reducing periodontitis risk among older adults.

Dietary inflammatory potential and biological aging among US adults: a population-based study

OBJECTIVES

The rate of biological aging is influenced by various factors such as genetics, environment, and diet. The dietary inflammatory index (DII) is strongly associated with various chronic diseases. The aim of this study was to investigate the association between DII and biological aging in US adults using quantitative indicators.

METHODS

Based on data from the National Health and Nutrition Examination Survey (NHANES) 1999-2018, weighted multiple linear regression models, generalized weighted models, and smoothed fitted curves were used to investigate the linear and nonlinear relationships of DII with four biological markers of aging (biological age, phenotypic age, telomere length, and serum klotho concentration).

RESULTS

A total of 35,575 adult participants with complete data were included in the study. After adjusting for all confounders, significant positive correlations were found between DII with biological age [0.070 (0.045, 0.095)] and phenotypic age [0.421 (0.371, 0.471)], with an increase of 0.07 and 0.42 years in biological age and phenotypic age, respectively, for each increase in DII score. The negative correlations between DII with telomere length [ - 0.005 ( - 0.008, - 0.002)] and klotho [ - 3.874 ( - 7.409, - 0.338)] were significant only in partially adjusted models and differed across subgroups.

CONCLUSIONS

In the current study, higher DII scores (greater pro-inflammatory dietary potential) were associated with biological aging. These findings may contribute to the development of aging prevention strategies through dietary interventions.

Predicting the Risk of Dental Implant Loss Using Deep Learning

AIM

To investigate the feasibility of predicting dental implant loss risk with deep learning (DL) based on preoperative cone-beam computed tomography.

MATERIALS AND METHODS

Six hundred and three patients who underwent implant surgery (279 high-risk patients who did and 324 low-risk patients who did not experience implant loss within 5 years) from January 2012 to January 2020 were enrolled. Three models, a logistic regression clinical model (CM) based on clinical features, a DL model based on radiography features, and an integrated model (IM) developed by combining CM with DL, were developed to predict the 5-year implant loss risk. The area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. Time to implant loss was considered for both groups, and Kaplan-Meier curves were created and compared by the log-rank test.

RESULTS

The IM exhibited the best performance in predicting implant loss risk [AUC = 0.90, 95% confidence interval (CI) 0.84-0.95], followed by the DL model (AUC = 0.87, 95% CI 0.80-0.92) and the CM (AUC = 0.72, 95% CI 0.63-0.79).

CONCLUSION

Our study offers preliminary evidence that both the DL model and IM performed well in predicting implant fate within 5 years and thus may greatly facilitate implant practitioners in assessing preoperative risks.

Periodontitis and Accelerated Biological Aging: A Geroscience Approach

As the whole world is epidemically aging, the burden of periodontitis and tooth loss is becoming a major health concern. Growing meta-epidemiological data implicate chronic systemic inflammation/infection due to periodontitis as an independent risk factor for aging-related diseases and mortality. However, because people age differently, chronological age is not a reliable marker of an individual's functional status. Recent advances in geroscience have shown that various biomarker signatures of biological aging are longitudinally associated with declined physical function, morbidity, and mortality due to major age-related diseases, including periodontitis. Here, we emphasize novel research developments bidirectionally linking periodontitis to accelerated biological aging. Using a composite biomarker age estimator, a striking increase in periodontitis and tooth loss was observed in subjects whose biological age at baseline was higher than their chronological age. Moreover, significantly shortened telomeres were encountered in populations affected by severe periodontitis. Second, we elucidate the cellular and molecular pillars of the aging process at the periodontal level. Accumulating evidence suggests that cellular senescence, stem cell exhaustion, and immunoaging are hallmarks of biological aging implicated in the impairment of periodontal homeostasis and the pathophysiology of periodontitis. Indeed, persistent bacteria-derived lipopolysaccharide stimulation influences cellular senescence in osteocytes, driving alveolar bone resorption. Moreover, inflammaging status induced by chronic hyperglycemia elevates the burden of senescent cells in gingival tissues, impairing their barrier function. Lastly, we reviewed a recent breakthrough in senotherapy to directly target the mechanisms of aging at the periodontal level. Physical exercise and intermittent fasting, together with natural compounds, senolytic drugs, and cell therapy, are increasingly being evaluated to rejuvenate the oral cavity. Following these innovations in geroscience, further advancements could provide oral clinicians the chance to intercept biological aging when still "subclinical" and set interventions for halting or delaying the trajectory toward aging-related diseases while patients are still chronologically young.

Individual predisposition and the intricate interplay between systemic biomarkers and periodontal risk in a general population

BACKGROUND

Increasing age is associated with systemic diseases as well as with periodontal diseases. We wondered whether a biological age score constructed exclusively from systemic biomarkers would reflect periodontal risk factors at baseline and tooth loss as well as periodontal outcome during 10 years follow-up.

METHODS

From the Study of Health in Pomerania (SHIP) 2256 participants (1072 male, 1184 female) were studied for the relationship of the systemic biomarkers glycated hemoglobin (HbA1c), low density lipoprotein cholesterol (LDL), fibrinogen, white blood cell count, blood pressure, and waist circumference to their age. Construction of a biological age (BA) score allowed its comparison with the participants' actual chronological age (CA) and their predisposition to periodontal disease.

RESULTS

Though nearly identical in CA, participants appearing younger than their true age had a significantly reduced burden of periodontal risk factors. If BA > CA, then risk factors were more frequent including smoking, oral hygiene, dental visits, education, and income. After 10 years, in participants with identical CA, tooth loss followed their BA calculated at baseline, that is, with BA > CA fewer teeth were preserved. Similarly, periodontal measures varied according to BA; sex differences were obvious. Most significant were BA-related differences in inflammatory and anthropometry parameters.

CONCLUSIONS

The results support the assumption that risk profiles aggregated in BA constitute a characteristic susceptibility pattern unique to each individual, common to both systemic and periodontal diseases. Although BA was constructed exclusively from systemic measures at baseline, BA reflects the oral conditions at follow-up.