Department of Dentistry, Puzi Hospital, Ministry of Health and Welfare, Chiayi, Taiwan.
Research Center for Humanities and Social Sciences, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan.
BMC Public Health. 2023 Oct 9;23(1):1952. doi: 10.1186/s12889-023-16897-w.
Oral health could influence cognitive function by stimulating brain activity and blood flow. The quantified oral status from oral inflammation, frailty and masticatory performance were rarely applied to the cognitive function screening. We aimed to adopt non-invasive digital biomarkers to quantify oral health and employ machine learning algorithms to detect cognitive decline in the community.
We conducted a prospective case-control study to recruit 196 participants between 50 and 80 years old from Puzi Hospital (Chiayi County, Taiwan) between December 01, 2021, and December 31, 2022, including 163 with normal cognitive function and 33 with cognitive decline. Demographics, daily interactions, electronically stored medical records, masticatory ability, plaque index, oral diadochokinesis (ODK), periodontal status, and digital oral health indicators were collected. Cognitive function was classified, and confirmed mild cognitive impairment diagnoses were used for sensitivity analysis.
The cognitive decline group significantly differed in ODK rate (P = 0.003) and acidity from SILL-Ha (P = 0.04). Younger age, increased social interactions, fewer cariogenic bacteria, high leukocytes, and high buffering capacity led to lower risk of cognitive decline. Patients with slow ODK, high plaque index, variance of hue (VOH) from bicolor chewing gum, and acidity had increased risk of cognitive decline. The prediction model area under the curve was 0.86 and was 0.99 for the sensitivity analysis.
A digital oral health biomarker approach is feasible for tracing cognitive function. When maintaining oral hygiene and oral health, cognitive status can be assessed simultaneously and early monitoring of cognitive status can prevent disease burden in the future.
口腔健康通过刺激大脑活动和血流可能影响认知功能。口腔炎症、虚弱和咀嚼性能的量化口腔状况很少应用于认知功能筛查。我们旨在采用非侵入性数字生物标志物来量化口腔健康，并运用机器学习算法在社区中检测认知能力下降。
我们进行了一项前瞻性病例对照研究，从 2021 年 12 月 1 日至 2022 年 12 月 31 日期间从台湾嘉义县朴子医院招募了 196 名年龄在 50 至 80 岁之间的参与者，包括 163 名认知功能正常和 33 名认知能力下降的参与者。收集了人口统计学资料、日常互动、电子存储的医疗记录、咀嚼能力、牙菌斑指数、口腔交替运动速度（ODK）、牙周状况以及数字口腔健康指标。对认知功能进行了分类，并对确诊的轻度认知障碍诊断进行了敏感性分析。
认知能力下降组的 ODK 率（P=0.003）和 SILL-Ha 的酸度（P=0.04）差异有统计学意义。年龄较小、社交互动增加、致龋菌较少、白细胞增多和缓冲能力增强，认知能力下降的风险较低。ODK 速度较慢、牙菌斑指数较高、双色口香糖的 VOH 差异较大以及酸度较高的患者认知能力下降的风险增加。预测模型的曲线下面积为 0.86，敏感性分析的曲线下面积为 0.99。
数字口腔健康生物标志物方法可用于追踪认知功能。在保持口腔卫生和口腔健康的同时，可以同时评估认知状态，并可以进行早期监测，以防止未来的疾病负担。
