Yan Lingjun, Liu Zilin, Xie Bingqin, Huang Yu, Wu Yuxuan, He Baochang, Li Yanfen, Luo Lan, Yan Fuhua, Chen Fa
Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fuzhou, China.
J Periodontal Res. 2025 Jul 10. doi: 10.1111/jre.70001.
Mendelian randomization is a more appropriate tool for causal inference, as the main suspected risk factors for periodontitis are difficult to test by randomized controlled trials due to ethical or feasibility issues. This study aimed to evaluate potential causal relationships between 50 known and suspected factors and periodontitis risk by a two-sample Mendelian randomization study and meta-analysis.
By utilizing the databases of the Gene-Lifestyle Interactions at Dental Endpoints (GLIDE) consortium and the Finnish Genetics (FinnGen) consortium, 25 obesity-related indicators (BMI, birth weight, weight, height, waist-hip ratio, waist circumference, hip circumference, 18 body fat percentage or fat-free mass factors), eight hormone-related indicators (estradiol levels, total testosterone levels, sex hormone-binding globulin, age at menarche, age at menopause, three bone mineral density factors), five lifestyle factors (smoking, alcohol drinking, sleep duration, morning/evening chronotype, years of schooling), three dietary factors (coffee, tea, fruit), six blood biomarkers (fasting glucose, high-density lipoprotein cholesterol [HDL cholesterol], low-density lipoprotein cholesterol [LDL cholesterol], total cholesterol, serum 25-hydroxyvitamin D levels, hemoglobin A1c [HbA1c]), and three diseases (hypertension, type 2 diabetes, COVID-19). The odds ratios (ORs) and 95% confidence intervals (CIs) associated with the risk of periodontitis were estimated for each trait using the inverse variance weighting (IVW) method. A meta-analysis was conducted to analyze the causal associations from these databases.
Among the 50 potential risk factors, the IVW analyses revealed significant associations with the risk of periodontitis for 22 and two traits (FDR-corrected p < 0.05) in the GLIDE database as well as the FinnGen database, respectively. The meta-analyses revealed that 23 traits maintained statistically significant associations with periodontitis risk. Noteworthy associations included 20 obesity-related indicators with ORs ranging from 1.11 to 1.25, smoking (OR = 1.74), and hemoglobin A1c (OR = 1.07), which were associated with an increased risk of periodontitis. Conversely, increased years of education (OR = 0.81) were identified as potential mitigators of periodontitis risk. The sensitivity analyses utilizing five additional methods further bolstered the robustness of these findings.
This comprehensive study provides evidence for the potential causal association of several modifiable risk factors with periodontitis, highlighting the importance of addressing these factors in preventive strategies for periodontal health.
孟德尔随机化是一种更适合因果推断的工具，因为由于伦理或可行性问题，牙周炎的主要可疑危险因素难以通过随机对照试验进行检验。本研究旨在通过双样本孟德尔随机化研究和荟萃分析评估50个已知和可疑因素与牙周炎风险之间的潜在因果关系。
利用牙齿终点基因-生活方式相互作用（GLIDE）联盟和芬兰遗传学（FinnGen）联盟的数据库，纳入25个肥胖相关指标（体重指数、出生体重、体重、身高、腰臀比、腰围、臀围、18个体脂百分比或去脂体重因素）、8个激素相关指标（雌二醇水平、总睾酮水平、性激素结合球蛋白、初潮年龄、绝经年龄、3个骨密度因素）、5个生活方式因素（吸烟、饮酒、睡眠时间、晨/夜型、受教育年限）、3个饮食因素（咖啡、茶、水果）、6个血液生物标志物（空腹血糖、高密度脂蛋白胆固醇[HDL胆固醇]、低密度脂蛋白胆固醇[LDL胆固醇]、总胆固醇、血清25-羟基维生素D水平、糖化血红蛋白[HbA1c]）以及3种疾病（高血压、2型糖尿病、COVID-19）。使用逆方差加权（IVW）方法估计每个性状与牙周炎风险相关的比值比（OR）和95%置信区间（CI）。进行荟萃分析以分析来自这些数据库的因果关联。
在50个潜在危险因素中，IVW分析分别在GLIDE数据库和FinnGen数据库中显示，有22个和2个性状（FDR校正p<0.05）与牙周炎风险存在显著关联。荟萃分析显示，23个性状与牙周炎风险保持统计学上的显著关联。值得注意的关联包括20个肥胖相关指标，OR范围为1.11至1.25、吸烟（OR=1.74）和糖化血红蛋白（OR=1.07），它们与牙周炎风险增加相关。相反，受教育年限增加（OR=0.81）被确定为牙周炎风险的潜在缓解因素。使用另外五种方法进行的敏感性分析进一步支持了这些发现的稳健性。
这项综合性研究为几种可改变的危险因素与牙周炎之间的潜在因果关联提供了证据，突出了在牙周健康预防策略中解决这些因素的重要性。
