Objectives: Periodontitis (PD) patients frequently suffer from comorbidities, necessitating increased attention to disease management and monitoring. The aim of this study is to describe the prevalence and patterns of comorbidities among patients with PD in a private periodontal referral practice. Methods: This study involved 3171 adults with PD. Data on demographics, lifestyle, number of teeth, pockets of size ≥ 6 mm, bleeding on probing, periodontal inflammatory surface area, and comorbidities were extracted from electronic patient records. Descriptive and statistical analyses, including t-tests, chi-square tests, cluster analysis, binomial logistic regression analysis, and hypergraph network analysis, were performed. Results: Among this PD population, 47% had a comorbidity, and 20% had multimorbidity (≥2 diseases). Based on the disease patterns, two distinct clusters emerged: Cluster 1 was dominated by respiratory tract conditions (asthma, lung disease, and allergic rhinitis), allergies, and hypothyroidism, while Cluster 2 primarily included cardiometabolic diseases (angina pectoris, hypertension, diabetes mellitus (DM), and hyperthyroidism). The hypergraph network analysis for those with multimorbidity identified two main groups: (i) pulmonary conditions (lung disease, asthma, allergic rhinitis, and allergies) and (ii) cardiometabolic disorders (hypertension, myocardial infarction, cerebrovascular disease, and DM). Hypertension, allergies, and allergic rhinitis showed high centrality, serving as central nodes frequently co-occurring with other diseases. Conclusions: Nearly half of the PD patients in a private periodontal referral practice were found to have comorbidities, primarily clustering into cardiometabolic and respiratory tract diseases. These findings, based on real-world data, should encourage dental professionals to integrate systemic conditions into their care strategies. They could also guide policymakers and practitioners in developing evidence-based approaches to mitigate the reciprocal negative effects of PD and comorbidities.

Purpose: To investigate the effect of vitamin C supplementation with or without flavonoids on periodontal conditions, and microbial and systemic variables before and after non-surgical periodontal treatment (NSPT). Materials and Methods: A case series derived from a randomized controlled trial was conducted to explore the effects of daily vitamin C supplementation, with or without flavonoids, on periodontal conditions. The study population was recruited from patients with periodontitis who had been referred to the Department of Periodontology at the Academic Centre for Dentistry Amsterdam (ACTA). The study consisted of a 2-month observation of untreated periodontitis followed by a 3-month period after NSPT. Descriptive statistics, correlation and clustering analyses, and dimensionality reduction methods were used to evaluate the interventions’ impact. Results: Due to COVID-19, the study was prematurely terminated and reported findings from 13 patients. Results indicate a correlation between higher plasma vitamin C levels and reduced gingival inflammation, suggesting benefits for untreated periodontal conditions. Clustering analysis showed no differences based on supplementation type, indicating it did not affect outcomes, and microbiological data had limited effects. Principal Component Analysis visualized clusters and illustrated no distinct groups corresponding to supplementation types. Violin plots highlighted variability, with one cluster comprising individuals with more severe periodontal conditions. Conclusions: Higher plasma vitamin C levels were associated with lower gingival inflammation. However, daily vitamin C supplementation, with or without flavonoids, did not show additional benefits on periodontal conditions before or after treatment. Clustering suggests that periodontal severity, rather than supplementation, influenced patient profiles. The study’s small sample size limits the generalizability of the findings.

Epidemiological cohorts often collect self-reported oral health (SROH) questionnaires but lack clinical periodontal measurements. We developed a selective, explainable machine learning (ML) pipeline that can assign labels for severe periodontitis (SP) or no periodontitis (NP). Three datasets (n = 498) with SROH questionnaires, demographics, and Community Periodontal Index of Treatment Needs (CPITN) scores were used to derive NP, moderate periodontitis (MP), and SP categories. MP cases were excluded from model development. After cleaning and feature engineering, non-similar label duplicates were removed. A CatBoost model (Separator-A) was trained with tenfold cross-validation; NP/SP predictions were retained when probability ≥ 0.85. From these outputs and domain rules, a rule-consistent subset was created to train a second model (Separator-Z). Performance was evaluated on internal test and hold-out inference sets. Next, the pipeline was applied to MP cases. The pipeline achieved complete separation across all evaluation sets within the retained high-confidence subset, which represented 4.31% of eligible NP/SP cases, while no MP cases were misclassified as NP or SP. Thus, a two-stage, explainable ML pipeline can selectively identify SP and NP from SROH questionnaire data, supporting case-control selection in cohorts without clinical periodontal examinations, though validation is warranted to confirm generalizability.

Background: Oral fluids may provide an alternative to traditional invasive sampling methods for screening and monitoring chronic conditions, such as Type 2 diabetes mellitus (T2DM), which is commonly assessed using blood HbA1c levels. We aimed to identify putative proteomic signatures in oral rinse samples to differentiate individuals with low versus high HbA1c levels using a machine learning (ML) approach. Methods: Participants were selected from an existing population cohort (The Maastricht Study), and oral rinse samples were derived from the associated Biobank. A two-threshold ML approach used the population median threshold (37 mmol/mol), followed by the clinical cutoff for elevated HbA1c (48 mmol/mol), the diagnostic threshold for diabetes, to stratify participants into two HbA1c-based groups. Proteomic analysis of the oral rinse samples was performed using the Proximity Extension Assay (Olink ^® ) targeting 92 cardiometabolic proteins. The ML approach employed repeated nested cross-validation with Boruta feature selection and evaluated Logistic Regression, Random Forest, and XGBoost. SHapley Additive exPlanations (SHAP) were used to quantify feature importance and interpret the contribution and directionality of individual proteins in the model predictions. Post hoc analyses evaluated the levels of identified proteins across HbA1c and T2DM categories and assessed potential confounding factors, including periodontitis, an oral inflammatory disease. Results: Among the 176 participants, no discriminatory proteins were identified at the population median threshold. Using the clinical cutoff, Logistic Regression achieved the highest performance (ROC AUC: 0.78 ± 0.09; 95% CI: [0.74, 0.82]). REG1A (Regenerating Islet-Derived Protein 1 Alpha), also known as Pancreatic Stone Protein, emerged as the sole oral biomarker for elevated blood HbA1c. In the post hoc multivariable analysis, after adjusting for potential confounders, REG1A remained significantly associated with elevated HbA1c, and REG1A levels showed significant positive trends across the clinically relevant HbA1c and T2DM categories (normal, prediabetes, and diabetes). Conclusions: REG1A, a protein linked to pancreatic β-cell regeneration, emerged as an oral biomarker for elevated HbA1c levels. This finding suggests that oral rinse proteomics may offer a simple, non-invasive tool for screening and monitoring elevated HbA1c levels, with potential applicability to point-of-care testing and large epidemiological studies. However, validation in larger cohorts is warranted.

This study evaluated associations between protein expression and postoperative healing scores following three periodontal regenerative procedures (Open Flap Debridement–OFD, Enamel Matrix Derivative–EMD, Advanced Platelet Rich Fibrin–A-PRF +) in molar degree II furcations over six months. Associations between baseline systemic biochemical parameters and six-month clinical outcomes were also assessed. Eighteen patients were randomly assigned to control (OFD), EMD, or A-PRF + groups. Baseline plasma biomarkers were modeled on probing depth (PD), clinical attachment level (CAL), and alveolar bone level (ABL) at six months. Gingival crevicular fluid (GCF) samples were analyzed using the Olink® Target 96 Immuno-Oncology panel at day 3, weeks 1, 2, 6, and months 3 and 6. Multivariable linear regressions identified proteins associated with the modified Early Wound Healing Index (mEHI) per treatment. Seventeen patients completed follow-up. HDL- and LDL-cholesterol and creatinine were negatively associated with PD, CAL, and ABL improvements (p < 0.05). At early timepoints, CASP-8, IL-8, and LAP TGF-β1 showed stronger associations with mEHI in A-PRF + than OFD or EMD, while ARG-1 and TRAIL demonstrated treatment-specific patterns over time. No differences were observed at month 6. High baseline cholesterol and creatinine may impair periodontal regeneration. Distinct proteomic signatures suggest differential biological pathways underlying healing across OFD, EMD, and A-PRF + procedures.

This pilot study investigated whether candidate protein signatures from oral rinse samples can distinguish patients with severe periodontitis (stage III/IV) and its subtypes, generalized and localized periodontitis, from non-periodontitis controls. Participants rinsed with phosphate-buffered saline, and samples were analyzed using a Proximity Extension Assay targeting 92 inflammatory and 92 immuno-oncology proteins. A machine learning approach using repeated nested cross-validation and SHAP was implemented to identify protein signatures. The study included 38 patients (18 with localized periodontitis and 20 with generalized periodontitis) and 16 controls. After data preprocessing, 54 samples and 141 proteins were retained. Proteins Gal-1, HGF, TNFSF14, CD27, and ARG1 distinguished periodontitis from controls (ROC-AUC = 0.85, 95% CI 0.82, 0.87). For generalized periodontitis, we found a protein signature including TNFSF14, Gal-1, STAMBP, MUC-16, S100A12, HGF, CASP-8, CD27, LAP TGF-β1, TNFRSF9, and uPA (ROC-AUC = 0.92, 95% CI 0.90, 0.94). For localized periodontitis, we identified ARG1 (ROC-AUC = 0.72, 95% CI 0.68, 0.76). No proteomic signature distinguishing generalized periodontitis from localized periodontitis was identified. This pilot study indicated that oral rinses are suitable for proteomic profiling, and there was a putative protein signature that could differentiate periodontitis, generalized periodontitis, and localized periodontitis from controls. These findings warrant validation in larger independent cohorts, including a clearly defined gingivitis group, before real-world non-invasive screening applications can be considered.