Praveen Sharma

Aim: The relationship between oral and systemic inflammation has profound implications for understanding the broader health impacts of periodontitis. The aim of this study was to (a) explore the association between periodontal inflammation and markers of systemic inflammation and metabolic health, and (b) preliminarily assess periodontal status based on systemic health indicators using machine learning techniques.Methods: Data from a cross‐sectional cohort (N = 667) were modelled (simple/multiple linear, fractional polynomial, logistic and random forest regression) to examine the association between systemic and periodontal measures. Three classifiers—random forest (RF), support vector machine (SVM) and gradient boosting (GB)—were used using periodontal inflamed surface area (PISA) and demographic and anthropometric variables (age, gender, ethnicity, body mass index [BMI] and smoking habits) as inputs to predict systemic inflammation (defined using serum C‐reactive protein [CRP] levels). The best performing classification models (evaluated using area under the curve, AUC analyses) were validated using a second nationally representative dataset from the National Health and Nutrition Examination Surveys (NHANES) 2001–2002 and 2003–2004 combined datasets (N = 2288). Next, RF, SVM and GB were employed incorporating a set of systemic parameters (including serum CRP and lipid profiles) to predict the diagnosis of periodontitis. The best performing classification models were then validated using the NHANES 2009–2010 (N = 664) dataset.Results: A nonlinear trend of CRP levels and PISA was confirmed by fractional polynomial regression (p = 0.008). Further, multiple linear regression analyses (adjusted for age, gender, ethnicity, BMI and smoking habits) confirmed a statistically significant relationship between log‐transformed CRP levels and PISA (p < 0.0001). Logistic regression confirmed a relationship between PISA and low‐density lipoprotein (LDL) in both crude and adjusted models. Among the classification models, SVM showed the highest performance in distinguishing CRP < 2 mg/L from CRP ≥ 2 mg/L (AUC = 0.71). The SVM model was successfully replicated in the NHANES 2001–2002 and 2003–2004 waves (AUC = 0.74). Prediction of periodontitis status (case vs. control) based on systemic indicators using the SVM model achieved the best performance with a mean AUC of 0.82. This was partially confirmed after external validation using the 2009–2010 NHANES dataset (AUC of 0.72).Conclusion: This study confirmed a consistent relationship between measures of cumulative periodontal inflammation and systemic inflammation through machine learning models. A predictive model incorporating systemic health parameters helped in identifying a case with periodontitis. Both models have potential for use in primary healthcare settings, including screening programmes, as providing confirmation of the bidirectional link between periodontitis and systemic health.

Background: Peri‐implantitis is an oral inflammatory disease with increased incidence across the population as linked to the wide use of dental implants to replace missing teeth. While the recent World Workshop Classifications provided a framework for clinicians to diagnose and assess the severity of peri‐implantitis, an objective assessment of the mucosal inflammatory burden around the dental implant is still lacking. Based on the periodontal epithelium surface area (PESA) and periodontal inflamed surface area (PISA) scores previously reported, a study was conducted to explore a similar approach for peri‐implant inflammatory surface quantification. The aim of this study was to develop two novel scores of peri‐implant mucosal inflammation and their clinical application to help with the quantification of the dental‐implant inflamed surface area (DISA).Methods: Formulas were created to quantify the dental‐implant surface area (DESA), and then DESA of both tapered and cylinder implants was compared based on a dataset that included probing pocket depth, mucosal recession, and implant morphology parameters. The DISA was subsequently calculated using the epithelial/connective tissue areas multiplied by the proportion of bleeding on probing sites around the implant. The new scores were applied and validated using clinical cases of peri‐implantitis.Results: Firstly, a total of 10,000 dental implants were included in a simulation study to evaluate the performance of the new scores. Under‐estimation of the real surface areas around dental implants was less than 2% when using the DESA score for cylinder and tapered dental implants (universal formula is presented). The DISA score was created in the simulations and then applied to 21 participants suffering from peri‐implantitis. DESA scores ranged from 54.24 to 400.29 mm², and the DISA scores ranged from 36.76 to 400.29 mm².Conclusions: Two novel scores (DESA and DISA) to estimate the peri‐implant surface area in health and disease were proposed and applied to clinical cases. The inflammatory surface caused by peri‐implantitis could be quantified by DISA with good precision. Future steps could include microbiome assessments and investigation of the association of these scores with the host response and general health status of patients with peri‐implantitis.Plain language summary: Peri‐implantitis is a common gum disease surrounding dental implants. Although recent guidelines help diagnose it, there's still no clear way to measure the inflammation surface area around implants. We developed formulas to calculate the dental‐implant surface area (DESA) and compared them for different implant shapes using data on pocket depth, gum recession, and implant type. We then calculated the dental‐implant inflamed surface area (DISA) by multiplying the tissue area around the implant by the proportion of bleeding sites. In our study, we included data from 10,000 implants to test the new scores. The DESA score was very accurate, underestimating the real surface area by < 2%. We then used the DISA score on 21 patients with peri‐implantitis. The DESA scores ranged from 54.24 to 400.29 mm², while the DISA scores ranged from 36.76 to 400.29 mm². In conclusion, we introduced two new scores (DESA and DISA) to measure the inflammation caused by peri‐implantitis. These scores could help in better diagnosing and treating peri‐implantitis, with future research exploring their link to overall health.

Periodontitis is a ubiquitous chronic inflammatory disease affecting the supporting tissues of the teeth and is a major cause of multiple tooth loss. Despite being preventable, periodontitis and dental caries are responsible for more years lost to disability than any other human condition. The most severe form of periodontitis affects 1 billion individuals, and its prevalence is increasing globally. Periodontitis arises from a dysregulated and hyperactive inflammatory response to dysbiosis in the periodontal microbiome. This response has systemic effects associated with premature mortality and elevated risk of several systemic non-communicable diseases (NCDs), including atheromatous cardiovascular disease, type 2 diabetes and chronic kidney disease (CKD). This risk association between periodontitis and NCDs is independent of their shared common risk factors, suggesting that periodontitis is a non-traditional risk factor for NCDs such as CKD. As periodontitis progresses, the immune cells and mediators underpinning its pathophysiology leak into the systemic circulation through the ulcerated oral mucosal lining, inducing in a systemic inflammatory profile that closely mirrors that observed in patients with CKD. The relationship between periodontitis and CKD seems to be bi-directional, but large-scale intervention studies are required to clarify causality and could lead to new care pathways for managing each condition as an exposure for the other.

Aim: To explore the associations between periodontal health and patient-reported outcomes (PROs), accounting for changes over time, in a large, non-specialist dental practice patient cohort.Materials and Methods: This longitudinal study used data from 13,162 dentate patients, collected by 162 dentists at routine appointments between May 2013 and April 2020, in 238 non-specialist dental practices across the United Kingdom. Dentists collected data, as part of routine clinical care, on periodontal probing pocket depths, alveolar bone loss, bleeding on probing, as well as a range of covariates. Patients inputted data on outcomes (oral pain/discomfort, dietary restrictions, and dental appearance). Mixed-effects logistic regression analysis was used to investigate the associations between periodontal health and PROs. Models accounted for clustering at the patient and dentist level and were adjusted for time and variables which were thought to confound these associations.Results: The odds of all PROs tended to increase with worsening periodontal parameters. For example, the odds of reporting pain in the worst periodontal health category were 1.99 (95% confidence interval: 1.57-2.53) times higher than in the best periodontal health category.Conclusions: This study confirms, using a large longitudinal dataset from a unique non-specialist setting, the associations between poorer periodontal health and poorer PROs.

Point of care testing (POCT) devices have been developed to facilitate immediate results with the potential to aid screening for new disease and enable patients to self-monitor their disease. Non-communicable diseases (NCDs) are the major cause of mortality globally and are increasing in prevalence as the population ages. Allied health care professionals (AHPs) are skilled in undertaking risk assessment and delivering preventative advice, providing opportunities to access large proportions of the population who may not visit their doctor, within non-traditional community settings. There is evidence of high levels of support from public, patients and health professionals for engaging AHPs in risk-targeted early case detection of certain NCDs. Thus, POCT devices offer a potential alternative to traditional venous blood collection, as novel care pathways for increasing early case detection and access to preventative care. The objectives of this study were to: (i) determine the concordance of the specific POCT devices with laboratory-based standard assays employed within clinical biochemistry laboratories. (ii) compare the sampling experience of both methods via patient-reported experiences. A prospective, two-centre study was undertaken involving 158 participants who provided informed consent. Venous blood was collected for traditional assays of HbA1c, creatinine/ estimated Glomerular-Filtration-Rate (eGFR) and vitamin-D. Capillary blood was collected by finger prick test and also assayed for the same biochemical indices (Nova StatSensor (creatinine/eGFR); Siemens DCA-Vantage (HbA1C); CityAssays (vitamin-D)). All users were provided with device training. Participants reported any discomfort experienced by each simultaneously applied method (randomised in order) via a 100 mm Visual-Analogue-Scale. Results for each POCT device and the laboratory standard were analysed by Bland-Altman plots to determine assay concordance. POCT devices demonstrated good concordance with laboratory testing, with at least 95% of all samples being within two standard deviations, for each of the devices tested. The majority of participants reported less discomfort with POCT than venepuncture, with the average reported discomfort being 17/100 mm less for POCT compared to venous blood sample collection on the visual analogue scale. The POCT devices demonstrated acceptable concordance with laboratory-based assays, and patients reported lower levels of discomfort compared to traditional means of blood collection. This study demonstrates the potential of using these devices as acceptable methods for opportunistic testing of “at-risk” individuals within non-traditional community care settings.

Aim: To investigate the sources of periodontitis misclassification under partial‐mouth protocols (PMPs) and to explore possible approaches to enhancing protocol validity.Materials and Methods: Using data from 10,680 adults with 244,999 teeth from the National Health and Nutrition Examination Survey, we compared tooth‐, site‐, and quadrant‐specific periodontal parameters and case identification under full‐mouth protocols and PMPs. Separately, we utilized population measures of tooth‐specific periodontal severity to generate PMPs with tooth selection based on the population ranking of clinical severity and assessed the sensitivity of case identification.Results: Symmetry of clinical severity was generally confirmed, with the exception of lingual inter‐proximal sites, which yielded greater sensitivity in identifying periodontitis compared to buccal sites due to more severe pocketing and attachment loss on average. Misclassification of severe periodontitis occurred more frequently under commonly implemented PMPs compared to ranking‐based selection of teeth, which yielded sensitivity estimates of 70.1%–79.4% with the selection of 8 teeth and reached 90% with the selection of only 14 teeth.Conclusions: Clinical symmetry and sources of periodontitis misclassification were confirmed. The proposed selection of teeth based on population rankings of clinical severity yielded optimal sensitivity estimates for the detection of severe periodontitis and may present a favourable alternative to current options.

Aims: Patients with chronic kidney disease (CKD) are also susceptible to periodontitis. The causal link between periodontitis and CKD may be mediated via systemic inflammation/oxidative stress. Using structural equation modelling (SEM), this cross‐sectional study aimed to explore the causal relationship between periodontal inflammation (PI) and renal function.Materials and methods: Baseline data on 770 patients with stage 3–5 (pre‐dialysis) CKD from an ongoing cohort study were used. Detailed, bioclinical data on PI and renal function, as well as potential confounders and mediators of the relationship between the two, were collected. SEMs of increasing complexity were created to test the causal assumption that PI affects renal function and vice versa.Results: Structural equation modelling confirmed the assumption that PI and renal function are causally linked, mediated by systemic oxidative stress. The magnitude of this effect was such that a 10% increase in PI resulted in a 3.0% decrease in renal function and a 10% decrease in renal function resulted in a 25% increase in PI.Conclusions: Periodontal inflammation represents an occult source of oxidative stress in patients with CKD. Further clinical studies are needed to confirm whether periodontal therapy, as a non‐pharmacological approach to reducing systemic inflammatory/oxidative stress burden, can improve outcomes in CKD.

Background: Type 2 diabetes mellitus (T2DM) is a growing global health burden and is expected to affect more than 590 million people by the year 2035. Evidence exists to demonstrate that dental settings have been used for risk assessment and identification of individuals who may be at high risk for T2DM or who may already unknowingly have the condition. Objective: This protocol aims to outline the methodology that will be undertaken to synthesize the literature relating to the use of primary care (nonhospital-based) dental services for the identification of undiagnosed T2DM or prediabetes-often termed nondiabetic hyperglycemia-in adult patients. Methods: This paper outlines the protocol that will be followed to conduct a systematic review and meta-analysis of the available literature. The protocol outlines the aims, objectives, search strategy, data extraction and data management methods, as well as the statistical analysis plan. The Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols guidelines were followed in developing the protocol as were elements of the Cochrane handbook. Results: We expect the systematic review to be completed within 18 months of publication of this protocol and expect to see a high degree of heterogeneity in the existing literature. Conclusions: This review is of importance as it will synthesize the existing evidence base and inform future studies in the field. Following the publication of the protocol, the review will be registered on Prospective Register of Systematic Reviews. Following the completion of the review, results will be published in a suitable peer-reviewed journal.