Improving Oral and Systemic Health in Individuals With Prediabetes Through Personalized Oral Hygiene Advice Provided by Dentists or by AI
- Conditions
- Gum DiseasePrediabetes
- Registration Number
- NCT06980701
- Lead Sponsor
- The University of Hong Kong
- Brief Summary
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.
- 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.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 148
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- Subjects who are >18 years of age and able to give informed consent.
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- Subjects who are mentally and cognitively healthy.
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- Subjects who have at least 6 anterior maxillary or mandibular natural teeth including incisors and canine.
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- 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.
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- Subjects who can speak, read, or understand Cantonese to complete the satisfaction questionnaire.
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- Subjects who can practice oral hygiene procedure (regular tooth brushing and interdental cleansing/flossing/brushing) daily on their own independently.
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- Subjects who have less than 6 anterior maxillary or mandibular natural teeth with or without dental prostheses in those area.
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- Subjects who are with a current diagnosis or clinical history of T2DM.
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- Subjects who have mental illness, or similar problems that unable to complete the satisfaction questionnaire.
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- 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 & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Primary Outcome Measures
Name Time Method HbA1c level at baseline 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)
FPG level at 3-month 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)
Gum inflammation at baseline 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)
Gum inflammation at 3-month 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
Gingival health at baseline 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)
2-h PG during 75-g OGTT level at baseline 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)
Oral hygiene status at baseline 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)
FPG level at baseline 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)
Gingival health at 3-month 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
Oral hygiene status at 3-month 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
HbA1c level at 3-month 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)
2-h PG during 75-g OGTT level at 3-month 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)
- Secondary Outcome Measures
Name Time Method IL6 level at baseline and 3 months at baseline and 3 month Inflammatory markers, i.e., IL6 from serum, saliva and plaque samples (pg/mL) will be assessed at baseline and 3-month follow-up
IL8 level at baseline and 3 months at baseline and 3 month Inflammatory markers, i.e., IL8 from serum, saliva and plaque samples (pg/mL) will be assessed at baseline and 3-month follow-up
Body weight at baseline and 3 month at baseline and 3 month Body weight (measured with standard procedures, in kilograms) will be assessed at baseline and 3 month
Percentage body fat at baseline and 3 month at baseline and 3 month Percentage body fat (using bioelectrical impedance analysis, %) will be assessed at baseline and 3 month
Shannon diversity index of oral and gut microbiota at baseline and 3 months at baseline and 3 months 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.
Concentration of short chain fatty acid in stool samples at baseline and 3 months at baseline and 3 months Fecal metabolites will be measured through the concentration of short chain fatty acid in stool samples
3-day food record at baseline and 3 months at baseline and 3 months Dietary intake will be assessed using a 3-day food record to determine meal patterns, including macronutrient intake, energy intake, and meal timing
C-reactive protein level at baseline and 3 months at baseline and 3 month Inflammatory markers, i.e., C-reactive protein from serum, saliva and plaque samples (pg/mL) will be assessed at baseline and 3-month follow-up
Chinese version of Chrono-nutrition Profile Questionnaire at baseline and 3 months at baseline and 3 months 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.
Chinese version of Munich Chronotype Questionnaire at baseline and 3 months at baseline and 3 months 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
Chinese version of international physical activity questionnaire short form at baseline and 3 months at baseline and 3 months 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
Trial Locations
- Locations (1)
Prince Philip Dental Hospital
🇭🇰Hong Kong, Sai Ying Pun, Hong Kong