School of Dentistry, University of Leeds, Leeds, UK.
Oral Biology, School of Dentistry, University of Leeds, Leeds, UK.
J Dent Res. 2022 Oct;101(11):1335-1342. doi: 10.1177/00220345221098910. Epub 2022 Jun 9.
This study aimed to identify systemic multimorbidity clusters in people with periodontitis via a novel artificial intelligence-based network analysis and to explore the effect of associated factors. This study utilized cross-sectional data of 3,736 participants across 3 cycles of the National Health and Nutrition Examination Survey (2009 to 2014). Periodontal examination was carried out by trained dentists for participants aged ≥30 y. The extent of periodontitis was represented by the proportion of sites with clinical attachment loss (CAL)≥ 3 mm, split into 4 equal quartiles. A range of systemic diseases reported during the survey were also extracted. Hypergraph network analysis with eigenvector centralities was applied to identify systemic multimorbidity clusters and single-disease influence in the overall population and when stratified by CAL quartile. Individual factors that could affect the systemic multimorbidity clusters were also explored by CAL quartile. In the study population, the top 3 prevalent diseases were hypertension (63.9%), arthritis (47.6%), and obesity (45.9%). A total of 106 unique systemic multimorbidity clusters were identified across the study population. Hypertension was the most centralized disease in the overall population (centrality [C]: 0.50), followed closely by arthritis (C: 0.45) and obesity (C: 0.42). Diabetes had higher centrality in the highest CAL quartile (C: 0.31) than the lowest (C: 0.26). "Hypertension, obesity" was the largest weighted multimorbidity cluster across CAL quartiles. This study has revealed a range of common systemic multimorbidity clusters in people with periodontitis. People with periodontitis are more likely to present with hypertension and obesity together, and diabetes is more influential to multimorbidity clusters in people with severe periodontitis. Factors such as ethnicity, deprivation, and smoking status may also influence the pattern of multimorbidity clusters.
本研究旨在通过一种新的基于人工智能的网络分析来确定牙周炎患者的系统性多病种聚类，并探讨相关因素的影响。本研究利用了美国国家健康和营养检查调查（2009 年至 2014 年）三个周期的 3736 名参与者的横断面数据。对年龄≥30 岁的参与者进行了由经过培训的牙医进行的牙周检查。牙周炎的严重程度由临床附着丧失（CAL）≥3mm 的位点比例表示，分为 4 个相等的四分位数。还提取了调查期间报告的一系列系统性疾病。使用特征向量中心度的超图网络分析来确定总体人群和按 CAL 四分位数分层时的系统性多病种聚类和单病种影响。还通过 CAL 四分位数探索了可能影响系统性多病种聚类的个体因素。在研究人群中，前三种常见疾病是高血压（63.9%）、关节炎（47.6%）和肥胖症（45.9%）。在整个研究人群中总共确定了 106 个独特的系统性多病种聚类。高血压是总体人群中最集中的疾病（中心度 [C]：0.50），紧随其后的是关节炎（C：0.45）和肥胖症（C：0.42）。在最高 CAL 四分位数（C：0.31）中，糖尿病的中心度高于最低 CAL 四分位数（C：0.26）。“高血压、肥胖症”是跨越 CAL 四分位数的最大加权多病种聚类。本研究揭示了牙周炎患者中一系列常见的系统性多病种聚类。牙周炎患者更有可能同时出现高血压和肥胖症，而糖尿病对严重牙周炎患者的多病种聚类的影响更大。种族、贫困和吸烟状况等因素也可能影响多病种聚类的模式。
