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INTRODUCTION: Dietary factors may play an important role in periodontal health. However, current evidence from observational studies remains inconclusive. OBJECTIVE: This study aimed to investigate the causal relationships between dietary exposures and periodontal disease risks using Mendelian randomization analysis. METHODS: Large-scale genome-wide association study summary statistics for 20 dietary factors were obtained from the MRC-IEU consortium. Multivariable and univariable 2-sample Mendelian randomization analyses were performed to assess the causal effects of each dietary exposure on 6 periodontal outcomes, including gingivitis and periodontitis. RESULTS: Genetically predicted higher dried fruit intake was significantly associated with reduced risks of acute gingivitis (odds ratio [OR]: 0.02; 95% confidence interval [CI]: 0.00-0.42; P = 0.01) and bleeding gums (OR: 0.96; 95% CI: 0.93-0.99; P = 0.01). Higher fresh fruit and water intake showed protective effects against chronic gingivitis (OR: 0.18; 95% CI: 0.04-0.91; P = 0.04 and OR: 0.15; 95% CI: 0.04-0.53; P = 0.00) and bleeding gums (OR: 0.95; 95% CI: 0.92-0.981; P = 0.00 and OR: 0.98; 95% CI: 0.96-0.99; P = 0.02). Alcohol intake frequency and processed meat intake were risk factors for bleeding gums (OR: 1.01; 95% CI: 1.00-1.02; P = 0.01 and OR: 1.05; 95% CI: 1.01-1.08; P = 0.00) and painful gums (OR: 1.01; 95% CI: 1.00-1.01; P = 0.00 and OR: 1.02; 95% CI: 1.01-1.03; P = 0.00). Most of the causal relationships between genetic predisposition to the specified dietary factors and periodontal diseases remained statistically significant (P < 0.05) after adjusting for genetic risks associated with dentures, smoking, and type 2 diabetes in multivariable Mendelian randomization models. CONCLUSIONS: The findings suggest potential protective effects of higher fruit and water intake against gingivitis and other periodontal problems, while alcohol and processed meat intake may increase the risks of periodontal disease. Our study provides preliminary causal evidence on the effects of diet on periodontal health and could inform prevention strategies targeting dietary habits to improve oral health. KNOWLEDGE TRANSFER STATEMENT: This study suggests that fruit and water intake may protect against periodontal disease, while alcohol and processed meats increase risk, informing dietary guidelines to improve oral health.
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OBJECTIVE: End-stage renal failure has profound changes in human gene expressions, but the molecular causation of these pleomorphic effects termed uremia is poorly understood. The purpose of this study was to explore key genes in uremia by comparing classification performance of five kinds of significant genes based on the support vector machines (SVM) model. MATERIALS AND METHODS: The five kinds of genes were differentially expressed genes (DEGs), differential pathway genes (DPGs), common differential genes between DEGs and DPGs (CDGs), hub genes (HUGs) and common genes of hub genes and DEGs (CHDGs). In detailed, DEGs were detected by linear models for microarray data (Limma) package. Attract method was utilized to capture DPGs from differential pathways. HUGs were determined according to topological centrality analysis of mutual information network (MIN). Subsequently, SVM model was implemented to assess the classification performance of DEGs, DPGs, CDGs, HUGs and CHDGs, depending on its induces the area under the receiver operating characteristics curve (AUC), true negative rate (TNR), true positive rate (TPR) and the Matthews coefficient correlation classification (MCC). RESULTS: A total of 166 DEGs, 597 DPGs, 13 CDGs, 29 HUGs and 10 CHDGs were obtained in uremia. By assessing the SVM model classification analysis, CHDGs had the best performance of all with AUC = 0.99, TNR = 1.00, TPR = 0.97 and MCC = 0.95. Hence, we considered the CHDGs as key genes in uremia. CONCLUSIONS: Key genes concluded in this investigation might provide vital insights into uremia progression and new therapies.
