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NCT Number: NCT06980701

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

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.

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Key information

Conditions

Gum Disease Diabetes Mellitus Endocrine System Diseases Gingival Diseases Glucose Metabolism Disorders Metabolic Diseases Mouth Diseases Nutritional and Metabolic Diseases Periodontal Diseases PreDiabetes Prediabetic State Stomatognathic Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Prince Philip Dental Hospital

Hong Kong, Sai Ying Pun, 999077

Location status: Recruiting

Location contact

Walter Lam, Prof.

CONTACT

[email protected]

+852 28590306

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

AI OHI group

Behavioral

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

Dental professionals OHI group

Behavioral

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

Primary outcomes

  1. Gum inflammation at baseline

    Time frame: 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)

  2. Gingival health at baseline

    Time frame: 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)

  3. Oral hygiene status at baseline

    Time frame: 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)

  4. HbA1c level at baseline

    Time frame: 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)

  5. FPG level at baseline

    Time frame: 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)

  6. 2-h PG during 75-g OGTT level at baseline

    Time frame: 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)

  7. Gum inflammation at 3-month

    Time frame: 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

  8. Gingival health at 3-month

    Time frame: 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

  9. Oral hygiene status at 3-month

    Time frame: 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

  10. HbA1c level at 3-month

    Time frame: 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)

  11. FPG level at 3-month

    Time frame: 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)

  12. 2-h PG during 75-g OGTT level at 3-month

    Time frame: 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)

  13. Gum inflammation at 9-month

    Time frame: 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

  14. Gingival health at 9-month

    Time frame: 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

  15. Oral hygiene status at 9-month

    Time frame: 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

  16. HbA1c level at 9-month

    Time frame: 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)

  17. FPG level at 9-month

    Time frame: 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)

  18. 2-h PG during 75-g OGTT level at 9-month

    Time frame: 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)

Secondary outcomes

  1. C-reactive protein level at baseline, 3-month and 9-month follow-ups

    Time frame: 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

  2. IL6 level at baseline, 3-month and 9-month follow-ups

    Time frame: 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

  3. IL8 level at baseline, 3-month and 9-month follow-ups

    Time frame: 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

  4. Body weight at baseline, 3-month and 9-month follow-ups

    Time frame: 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

  5. Percentage body fat at baseline, 3-month and 9-month follow-ups

    Time frame: 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

  6. Shannon diversity index of oral and gut microbiota at baseline, 3-month and 9-month follow-ups

    Time frame: 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.

  7. Concentration of short chain fatty acid in stool samples at baseline, 3-month and 9-month follow-ups

    Time frame: 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

  8. 3-day food record at baseline, 3-month and 9-month follow-ups

    Time frame: 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

  9. Chinese version of Chrono-nutrition Profile Questionnaire at baseline, 3-month and 9-month follow-ups

    Time frame: 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.

  10. Chinese version of Munich Chronotype Questionnaire at baseline, 3-month and 9-month follow-ups

    Time frame: 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

  11. Chinese version of international physical activity questionnaire short form at baseline, 3-month and 9-month follow-ups

    Time frame: 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

Study contacts

Contact information is provided by the study sponsor or research team.

Mandy M Ho, Prof.

CONTACT

[email protected]

(852) 39176973

Walter Y.H. Lam, Prof.

CONTACT

[email protected]

(852) 28590306

Sponsors and collaborators

Lead sponsor

The University of Hong Kong

Other

Registry information

Official study title

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

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
May 20, 2025
Registry last updated
Dec 3, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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