Improving Oral and Systemic Health in Individuals With Prediabetes Through Personalized Oral Hygiene Advice Provided by Dentists or by AI

Improving Oral and Systemic Health in Individuals With Prediabetes Through Personalized Oral Hygiene Advice Provided by Dentists or by Artificial Intelligence: A Randomized Clinical Trial

Prediabetes is an intermediate stage before the development of diabetes, characterized by elevated blood glucose levels but lower than the diagnostic criteria of diabetes and is associated with multiple long-term complications. This systemic disease is mutually linked to inflammatory gum diseases through circulating inflammatory mediators. Controlling inflammatory gum diseases improves blood glucose levels and reduces long-term complications. While maintaining good oral hygiene through home care is essential for managing inflammatory gum diseases, close supervision of patients' home care is labor-intensive and expensive. Artificial Intelligence (AI) has been used to provide personalized advice on the adequacy of patients' home care (oral hygiene). The investigators hypothesize that the use of AI can improve home care, thereby enhancing both gum health and systemic health, similar to human dental professionals.

Study Overview

• Status: Recruiting
• Conditions: Prediabetes; Gum Disease
• Intervention / Treatment: Behavioral: AI OHI group; Behavioral: Dental professionals OHI group

Detailed Description

Prediabetes is an intermediate stage before the development of diabetes, characterized by elevated blood glucose levels but lower than the diagnostic criteria of diabetes and is associated with multiple long-term complications. This systemic disease is mutually linked to inflammatory gum diseases through circulating inflammatory mediators. The relation between oral health and prediabetes management has long been under-appreciated. People with prediabetes have a 2-3-fold greater risk for periodontitis compared to people without prediabetes. The progression and severity of periodontitis are also greater in prediabetic patients. According to the National Health and Nutrition Examination Survey, the severity of periodontitis is positively associated with the risk as well as the prevalence of prediabetes. A growing body of data indicates that oral inflammation has an impact on general diseases. Controlling inflammatory gum diseases improves blood glucose levels and reduces long-term complications. While maintaining good oral hygiene through home care is essential for managing inflammatory gum diseases, close supervision of patients' home care is labor-intensive and expensive.

Nowadays, artificial intelligence (AI) can readily assist in the self-detection of diseases, including gum disease, allowing older adults to identify diseases early and prevent further complications. The use of AI-based mHealth has become increasingly effective in promoting periodontal health by adopting simple, AI-driven self-tests using smartphones. Another systematic review done by investigators' team found that AI-based mHealth for oral hygiene and gum disease monitoring showed clinical effectiveness across different clinical scenarios. The investigators' team has already launched an AI system for the detection of gum disease using smartphone intraoral photography, in which the system can detect colour changes of gum inflammation in specific sites in intraoral photography and diagnose as three simple situations (severe, mild and no inflammation). The AI system have high sensitivity 92% to identify disease from sites that have gingivitis, and high specificity 94% to identify healthy tissue from sites that have no gingivitis using professional intraoral photography. Moreover, the investigators have tested that the accuracy of colour captured by a smartphone is comparable to that captured by a professional single-lens reflective camera. The investigators' team already have applied the AI-powered smartphone photography among 38 older adults in 5 day-care centres of Hong Kong to test participants' gum health. The result is promising with accuracy of 96% sensitivity and 82% specificity. The present study will apply AI technology on disease detection and giving personalized oral health instruction (OHI) closely to the patients to maintain periodontal health and consequently prediabetic control.

In this study, the hypothesis is that the use of AI can improve home care, thereby enhancing both gum health and systemic health, similar to human dental professionals.

• Study Type: Interventional
• Enrollment (Estimated): 148
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Walter Y.H. Lam, Prof.; Phone Number: (852) 28590306; Email: retlaw@hku.hk
• Study Contact Backup: Name: Mandy M Ho, Prof.; Phone Number: (852) 39176973; Email: mandyho1@hku.hk

Study Locations

• Hong Kong
  • Sai Ying Pun
    • Hong Kong, Sai Ying Pun, Hong Kong, 999077
      • Recruiting
      • Prince Philip Dental Hospital
      • Contact: Walter Lam, Prof.; Phone Number: +852 28590306; Email: retlaw@hku.hk

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• - Subjects who are >18 years of age and able to give informed consent.
• - Subjects who are mentally and cognitively healthy.
• - Subjects who have at least 6 anterior maxillary or mandibular natural teeth including incisors and canine.
• - Subjects who are with prediabetic state with impaired HbA1c 5.7% to 6.4%, impaired fasting plasma glucose level 5.6mmol/L to 6.9 mmol/L and/or impaired plasma glucose level after 2h OGTT 7.8 mmol/L to 11.0 mmol/L.
• - Subjects who can speak, read, or understand Cantonese to complete the satisfaction questionnaire.
• - Subjects who can practice oral hygiene procedure (regular tooth brushing and interdental cleansing/flossing/brushing) daily on their own independently.

Exclusion Criteria:

• - Subjects who have less than 6 anterior maxillary or mandibular natural teeth with or without dental prostheses in those area.
• - Subjects who are with a current diagnosis or clinical history of T2DM.
• - Subjects who have mental illness, or similar problems that unable to complete the satisfaction questionnaire.
• - Subjects who cannot perform oral hygiene procedure (regular tooth brushing and interdental cleansing/flossing/brushing) by any condition of oral cavity such as tumor or maxillomandibular fixation.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Treatment
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Triple

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: AI OHI group; Personalized oral hygiene instructions provided by AI on the adequacy of patients' home care.	Behavioral: AI OHI group; The participants will receive personalized OHI such as toothbrush and interdental cleaning to specific areas provided by AI. An mHealth system will be used to detect intraoral photograph of anterior teeth and analysis of the photograph and label the gum condition as Healthy (green)/questionable (yellow)/diseased (red) within 2 minutes by AI. Then specific OHI to each particular site would be provided by AI according to tested results on the photograph; Other Names: AI
Active Comparator: Dental professionals OHI group; Personalized oral hygiene instructions provided by a dental professional on the adequacy of patients' home care	Behavioral: Dental professionals OHI group; ll participants will receive personalized OHI by dental professionals. This instruction includes brushing and interdental cleaning in each particular dental site. If they have any personal concern or unclear points regarding oral hygiene practice, they can ask.; Other Names: conventional OHI

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Gum inflammation at baseline	Gum inflammation will be evaluated using BPE score (from 0 to 4), which will be examined by a blinded assessor (a calibrated dentist who blinded to participants' group)	baseline
Gingival health at baseline	Gingival health will be assessed by gingival index (Löe H 1967), with a scale from 0 to 3, which will be examined by a blinded assessor (a calibrated dentist who blinded to participants' group)	baseline
Oral hygiene status at baseline	Oral hygiene status will be assessed by plaque index (Silness and Loe, 1965), with a scale from 0 to 3, which will be examined by a blinded assessor (a calibrated dentist who blinded to participants' group)	baseline
HbA1c level at baseline	Glycaemic level at baseline is evaluated by HbA1c level (glycated haemoglobin that measures glycaemic control over the past 2-3 months). Prediabetes is defined as HbA1c of 5.7-6.4% (39-47 mmol/mol)	baseline
FPG level at baseline	Glycaemic level at baseline is evaluated by FPG (fasting plasma glucose level that measures the blood sugar levels). Prediabetes is defined as 100-125 mg/dL (5.6-6.9 mmol/L)	baseline
2-h PG during 75-g OGTT level at baseline	Glycaemic level at baseline is evaluated by 2-h PG during 75-g OGTT (plasma glucose level after 2-hour 75-g oral glucose tolerance test). Prediabetes is defined as 2-h PG during 75-g OGTT of 140-199 mg/dL (7.8-11.0 mmol/L)	baseline
Gum inflammation at 3-month	Gum inflammation will be evaluated using BPE score (from 0 to 4), which will be examined by a blinded assessor (a calibrated dentist who blinded to participants' group) at 3-month follow up	3-month
Gingival health at 3-month	Gingival health will be assessed by gingival index (Löe H 1967), with a scale from 0 to 3, which will be examined by a blinded assessor (a calibrated dentist who blinded to participants' group) at 3-month follow-up	3-month
Oral hygiene status at 3-month	Oral hygiene status will be assessed by plaque index (Silness and Loe, 1965), with a scale from 0 to 3, which will be examined by a blinded assessor (a calibrated dentist who blinded to participants' group) at 3-month follow-up	3-month
HbA1c level at 3-month	Glycaemic level at 3-month follow-up is evaluated by HbA1c level (glycated haemoglobin that measures glycaemic control over the past 2-3 months). Prediabetes is defined as HbA1c of 5.7-6.4% (39-47 mmol/mol)	3-month
FPG level at 3-month	Glycaemic level at 3-month follow-up is evaluated by FPG (fasting plasma glucose level that measures the blood sugar levels). Prediabetes is defined as 100-125 mg/dL (5.6-6.9 mmol/L)	3-month
2-h PG during 75-g OGTT level at 3-month	Glycaemic level at 3-month follow-up is evaluated by 2-h PG during 75-g OGTT (plasma glucose level after 2-hour 75-g oral glucose tolerance test). Prediabetes is defined as 2-h PG during 75-g OGTT of 140-199 mg/dL (7.8-11.0 mmol/L)	3-month
Gum inflammation at 9-month	Gum inflammation will be evaluated using BPE score (from 0 to 4), which will be examined by a blinded assessor (a calibrated dentist who blinded to participants' group) at 9-month follow up	9-month
Gingival health at 9-month	Gingival health will be assessed by gingival index (Löe H 1967), with a scale from 0 to 3, which will be examined by a blinded assessor (a calibrated dentist who blinded to participants' group) at 9-month follow-up	9-month
Oral hygiene status at 9-month	Oral hygiene status will be assessed by plaque index (Silness and Loe, 1965), with a scale from 0 to 3, which will be examined by a blinded assessor (a calibrated dentist who blinded to participants' group) at 9-month follow-up	9-month
HbA1c level at 9-month	Glycaemic level at 9-month follow-up is evaluated by HbA1c level (glycated haemoglobin that measures glycaemic control over the past 2-3 months). Prediabetes is defined as HbA1c of 5.7-6.4% (39-47 mmol/mol)	9-month
FPG level at 9-month	Glycaemic level at 9-month follow-up is evaluated by FPG (fasting plasma glucose level that measures the blood sugar levels). Prediabetes is defined as 100-125 mg/dL (5.6-6.9 mmol/L)	9-month
2-h PG during 75-g OGTT level at 9-month	Glycaemic level at 9-month follow-up is evaluated by 2-h PG during 75-g OGTT (plasma glucose level after 2-hour 75-g oral glucose tolerance test). Prediabetes is defined as 2-h PG during 75-g OGTT of 140-199 mg/dL (7.8-11.0 mmol/L)	9-month

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
C-reactive protein level at baseline, 3-month and 9-month follow-ups	Inflammatory markers, i.e., C-reactive protein from serum, saliva and plaque samples (pg/mL) will be assessed at baseline, 3-month and 9-month follow-ups	at baseline, 3-month and 9-month follow-ups
IL6 level at baseline, 3-month and 9-month follow-ups	Inflammatory markers, i.e., IL6 from serum, saliva and plaque samples (pg/mL) will be assessed at baseline, 3-month and 9-month follow-ups	at baseline, 3-month and 9-month follow-ups
IL8 level at baseline, 3-month and 9-month follow-ups	Inflammatory markers, i.e., IL8 from serum, saliva and plaque samples (pg/mL) will be assessed at baseline, 3-month and 9-month follow-ups	at baseline, 3-month and 9-month follow-ups
Body weight at baseline, 3-month and 9-month follow-ups	Body weight (measured with standard procedures, in kilograms) will be assessed at baseline, 3-month and 9-month follow-ups	at baseline, 3-month and 9-month follow-ups
Percentage body fat at baseline, 3-month and 9-month follow-ups	Percentage body fat (using bioelectrical impedance analysis, %) will be assessed at baseline, 3-month and 9-month follow-ups	at baseline, 3-month and 9-month follow-ups
Shannon diversity index of oral and gut microbiota at baseline, 3-month and 9-month follow-ups	Oral and gut microbiota will be analyzed from stool, plaque and salivary sample. The diversity of the oral and gut microbiota samples will be measured by the Shannon diversity index.	at baseline, 3-month and 9-month follow-ups
Concentration of short chain fatty acid in stool samples at baseline, 3-month and 9-month follow-ups	Fecal metabolites will be measured through the concentration of short chain fatty acid in stool samples	at baseline, 3-month and 9-month follow-ups
3-day food record at baseline, 3-month and 9-month follow-ups	Dietary intake will be assessed using a 3-day food record to determine meal patterns, including macronutrient intake, energy intake, and meal timing	at baseline, 3-month and 9-month follow-ups
Chinese version of Chrono-nutrition Profile Questionnaire at baseline, 3-month and 9-month follow-ups	Chrono-nutrition behaviors will be evaluated using the Chinese version of the Chrono-nutrition Profile Questionnaire (CP-Q), which includes six distinct aspects: breakfast skipping, timing of the largest meal, evening eating habits, evening latency, nighttime eating behaviors, and eating window.	at baseline, 3-month and 9-month follow-ups
Chinese version of Munich Chronotype Questionnaire at baseline, 3-month and 9-month follow-ups	Chronotype will be assessed by Chinese version of Munich Chronotype Questionnaire (MCTQ). The computed variables of workdays and work-free days from this questionnaire include sleep onset (SOw, SOf, hh:mm), local time of getting out of bed (GUw, GUf, hh:mm), sleep duration (SDw, SDf, hh:mm), total time in bed (TBTw, TBTf, hh:mm), mid-sleep (MSW, MSF, hh:mm). The computation of the variables include: Average weekly sleep duration (hh:mm) =(SDw x WD + SDf x FD)/7, Chronotype (hh:mm) =If SDf ≤ SDw: MSF; If SDf > SDw: MSF - (SDf - SDweek)/2, Weekly sleep loss (hh:mm) = If SDweek > SDw: (SDweek - SDw) x WD; If SDweek ≤ SDw: (SDweek - SDf) x FD, Relative social jetlag (hh:mm) = MSF - MSW, Absolute social jetlag (hh:mm) = | MSF - MSW |, Average weekly light exposure (hh:mm) = (LEw x WD + LEf x FD)/7	at baseline, 3-month and 9-month follow-ups
Chinese version of international physical activity questionnaire short form at baseline, 3-month and 9-month follow-ups	Physical activity (PA) levels will be evaluated by a Chinese version of international physical activity questionnaire short form (IPAQ). Total PA (min/wk) = 2x time spent on vigorous + moderate + walking, MET (Metabolic Equivalent of Task, min/wk) = 8 x vigorous +4x moderate + 3.3 x walking	at baseline, 3-month and 9-month follow-ups

Collaborators and Investigators

• Sponsor: The University of Hong Kong
• Investigators: Principal Investigator: Walter Y.H. Lam, Prof., The University of Hong Kong

Study record dates

Study Major Dates

• Study Start (Actual): April 12, 2025
• Primary Completion (Estimated): April 18, 2026
• Study Completion (Estimated): April 18, 2026

Study Registration Dates

• First Submitted: May 1, 2025
• First Submitted That Met QC Criteria: May 11, 2025
• First Posted (Actual): May 20, 2025

Study Record Updates

• Last Update Posted (Estimated): December 3, 2025
• Last Update Submitted That Met QC Criteria: November 25, 2025
• Last Verified: May 1, 2025

More Information

Terms related to this study

• Keywords: prediabetes; oral health; oral hygiene; gum disease
• Additional Relevant MeSH Terms: Endocrine System Diseases; Periodontal Diseases; Mouth Diseases; Stomatognathic Diseases; Metabolic Diseases; Glucose Metabolism Disorders; Diabetes Mellitus; Nutritional and Metabolic Diseases; Prediabetic State; Gingival Diseases
• Other Study ID Numbers: UW 24-658

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No