Clinical Research Directory

Salivary Flow, pH, and Buffering Capacity in Fixed and Clear Aligner Orthodontic Treatment

Study Design: A randomized controlled trial with two parallel arms and an allocation ratio of 1:1. Setting: The study will be conducted in the orthodontic department of Riyadh Elm University hospitals in Riyadh City, Saudi Arabia. Participants: Patients undergoing fixed or clear aligner orthodontic treatment referred to REU dental hospital will be randomly allocated to either the clear aligner group or the fixed orthodontic appliance group. Intervention: Prior to orthodontic treatment, all patients will receive phase I periodontal therapy and oral hygiene instructions. Fixed orthodontic appliances will be bonded using metallic brackets with a 0.022-inch slot and 0.014-inch NiTi archwires. Clear aligner patients will receive Invisalign® treatment. Outcomes and Saliva Collection: Salivary samples will be collected using the spitting method at baseline (T0) and follow-up time points according to the study protocol. Salivary flow rate, pH, and buffering capacity will be assessed as described in the proposal. Randomization and Blinding: Randomization will be performed using a random number generator with allocation concealment via opaque envelopes. The investigators involved in outcome assessment and data analysis will be blinded. Ethical Considerations: The study will be submitted to the Institutional Review Board at Riyadh Elm University and conducted in accordance with IRB policies.

Discovery and Validation of Periodontitis Biomarkers

Periodontitis is a major public health issue in China: it is responsible for loss of masticatory function in 60 million older adults, and 400-500 million adults are on the same disease trajectory. In addition, gingivitis and early-stage periodontitis are highly prevalent in all age groups. The Lancet 2021 burden of disease study provides worrying projections for China's oral health, with a 47.8% increase in advanced-stage periodontitis and a 217% increase in edentulism by the year 2050. The numbers are not manageable by the Chinese health system unless a series of coordinated actions are implemented: i) health education promoting oral hygiene in school and the workplace; ii) effective AI-based self-detection strategies and accurate identification of high-risk subjects; iii) efficient treatment modalities; and iv) reorganization of the health system. We have developed, patented, and validated a self-detection AI-based screening test for the general population through an app. It is based on a few validated questions and the performance of a lateral flow immunoassay to detect activated matrix metalloproteinase 8 (aMMP8). The algorithm enables accurate self-detection of severe periodontitis. The system, however, cannot identify subjects without clinically evident periodontitis (subjects who present with superficial inflammation consistent with gingivitis and incipient periodontitis) who will develop the disease, which, therefore, should be the target of early interventions. This limitation is due to insufficient knowledge of the process that turns superficial inflammation (gingivitis) into periodontitis. This limitation is apparent in the recently published NIH-sponsored American diagnostic trial results to detect periodontitis onset biomarkers (and progression). In their study, Teles et al. (2024) show that almost 24% of gingivitis subjects progress to periodontitis over a 12-month period but failed to identify salivary or serum biomarkers. Similarly, our recently completed study (Li et al. in preparation) did not identify highly accurate biomarkers for disease onset and progression. Importantly, the American and our study have tested putative biomarkers identified based on the current crude knowledge of the disease process. Gaps in fundamental knowledge are now apparent and limit our ability to detect periodontitis early. In addition, the current crude differential diagnosis based on clinical examination with a periodontal probe with millimeter markings cannot accurately differentiate gingivitis from early-stage periodontitis, complicating the ground truth definition (gold standard). In the current study, we propose implementing a multi-omics approach to test the ability to discriminate a mixed population of clinically undifferentiable gingivitis and stage I periodontitis into two or more clusters. In this biomarker discovery phase, we plan to use multiple state-of-the-art methods: i) laser scanning microdissection proteomics of tissue biopsies, ii) conventional salivary proteomics, iii) tissue biopsy transcriptomics, and iv) shotgun microbiome analysis. The methods will be applied in an agnostic approach to test the following hypotheses: 1. It is possible to identify two or more clusters of subjects from a mixed population of gingivitis and stage I periodontitis subjects. 2. The clusters differ based on host-derived biomarkers and/or microbiome factors and the risk of progression to periodontitis. 3. The biomarker pathways and microbial virulence factors among subjects identified according to the different approaches used to explore disease biology are generally consistent. 4. It is possible to identify a limited set of biomarkers that can be used to predict periodontitis onset and thus target early interventions for this high-risk population.

AI-Assisted Saliva Diagnostics Using an Electrochemical Sensor Platform for Periodontitis Detection (SALIENCE)

This observational study aims to develop and validate a novel, AI-assisted electrochemical sensor platform for saliva-based diagnostics in periodontitis. Periodontitis is a chronic inflammatory disease affecting the gums and supporting tissues of the teeth. Despite its high global prevalence, early diagnosis remains challenging because the disease often progresses silently until irreversible damage has occurred. Saliva offers a promising, non-invasive diagnostic medium that reflects both oral and systemic health. However, its biological complexity and variability have limited its clinical use. This project addresses these challenges by combining advanced electrochemical sensing with artificial intelligence (AI) and synthetic data generation to improve diagnostic precision and reliability. The study involves the collection of saliva samples from adult participants with diagnosed periodontitis and from healthy controls. The samples will be analyzed using a modular sensor platform equipped with multiple electrodes that detect electrochemical signals from a wide range of salivary biomarkers. The sensor data will then be processed using machine learning models trained on both real and synthetic data to classify disease states. The main goals are to: Evaluate the performance of the electrochemical sensor array for saliva analysis. Develop and validate AI-based algorithms for detecting and differentiating between healthy and diseased samples. Generate feasibility data supporting future clinical implementation of saliva-based diagnostics for periodontitis. This interdisciplinary project combines expertise in clinical dentistry, biomedical engineering, and computer science. It is conducted in collaboration between Linköping University and Malmö University, with patient sampling carried out at an affiliated dental clinic. The study is expected to result in a working proof-of-concept device that enables real-time, non-invasive detection of periodontitis at the point of care. By enabling earlier diagnosis and more personalized treatment, this technology may transform periodontal care and serve as a foundation for future saliva-based diagnostics targeting other oral and systemic diseases.

Influence of Timing of Implant Placement on Early Healing Molecular Events

Dental implants have been on the market for several years and they are routinely used to replace single/multiple missing teeth with a high success rate. However, there is still a limited number of studies comparing the influence of timing of implant placement on wound healing. In addition, there is no data available on the signaling pathways and the expression of healing biomarkers involved in the early stages of osseointegration after immediate implant placement (IP) or delayed implant placement (DP). The primary objective of this study is to describe changes in the expression of inflammatory, angiogenesis and osseous biomarkers of saliva at 1, 3, 7, 15 and 30 days and of PICF at 3, 7, 15 and 30 days after immediate implant placement (IP) compared with delayed placement (DP).

Application of Salivary Biomarkers in Risk Assessment for Oral Diseases in Children With Type 1 Diabetes

This research contributes to a deeper understanding of the etiopathogenesis of periodontal and other oral diseases in children with T1D. By analyzing the composition of the salivary microbiome and detecting pathogenic and opportunistic microorganisms, the study aims to develop targeted preventive strategies. The findings could lead to personalized preventive programs, improving early diagnosis and oral health management in this vulnerable population.This study hypothesizes that children with Type 1 Diabetes (T1D) will exhibit significantly different oral health parameters compared to healthy peers. Specifically: 1. Higher KEP and KEPS index values (Klein-Palmer system) indicating increased caries incidence. 2. Higher Silness and Loe plaque index and Loe and Silness gingival index, suggesting greater plaque accumulation and gingival inflammation. 3. Lower salivary buffering capacity and pH, potentially contributing to an increased risk of oral diseases. 4. A distinct microbial profile, with a greater presence of pathogenic and opportunistic bacteria. 5. A significantly higher Candida albicans count in the saliva. These findings could provide insights into the oral health challenges faced by children with T1D and guide preventive strategies. This study explores how saliva can help assess the risk of dental and gum problems in children with Type 1 Diabetes (T1D). Researchers will analyze saliva samples to identify specific markers that may indicate a higher chance of cavities, gum disease, and oral infections. The goal is to develop early detection and prevention methods to improve oral health care for children with T1D. The study will include 112 children aged 6 to 18. Half of them have Type 1 Diabetes, while the other half are healthy children of the same age and gender for comparison. All participants will be selected from the Clinic for Dentistry of Vojvodina, ensuring they are not currently sick and have not taken antibiotics in the past month. Children with other serious health conditions, fixed braces, or difficulty cooperating will not be included. Researchers will examine different factors that could affect oral health in children with T1D, including saliva acidity (pH), its ability to neutralize acids, the presence of bacteria and fungi, and the condition of teeth and gums. They expect that children with T1D will have: 1. More cavities compared to healthy children. 2. More plaque buildup on teeth and greater gum inflammation. 3. Lower saliva protection against acids, increasing the risk of dental problems. 4. A different mix of bacteria in the mouth, with more potentially harmful microbes. 5. Higher levels of the fungus Candida albicans in saliva. The findings from this study may help better understand oral health challenges in children with T1D and lead to improved prevention and treatment strategies.

Comparison of Salivary Procalcitonin (PCT) Levels and Serum PCT Levels

Recently, it has been seen that investigations from saliva samples could be an alternative to those from blood samples. Saliva collection is a simple, non-invasive, cost-effective, and relatively easy method, making it potentially suitable as a new diagnostic tool in pediatric patients. In the current literature, elevated levels of saliva CRP, TNF-α, IL-6, and IFN-γ have been reported in inflammatory conditions. However, while there are animal studies suggesting the use of saliva PCT levels for focal diseases such as gingival inflammation and periodontitis and as a potential tool for non-invasive detection of sepsis, there is no human study regarding its use in systemic infections. The aim of this study is to evaluate the correlation between serum PCT levels and saliva PCT levels in children suspected of SBE and to determine the diagnostic value of saliva PCT.