Correlation and mechanism between chronic kidney disease and periodontitis based on cross-sectional data

Abstract

Objective: To explore the correlation between periodontitis (PD) and chronic kidney disease (CKD) in adults, as well as the potential mechanisms involved. Methods: Data on PD and CKD from the National Health and Nutrition Examination Survey (NHANES) database between 1999 and 2014 were downloaded. Weighted univariate and multivariate logistic regression analyses were conducted to investigate the risk factors associated with PD and CKD, considering demographic and clinical indicators. Using publicly available genome-wide association study (GWAS) summary datasets for CKD and PD as outcome variables, as well as 731 immune cell phenotypes and 91 inflammatory proteins as exposure factors from the OPEN GWAS database, a two-sample Mendelian randomization (TSMR) analysis was performed using the inverse-variance weighted (IVW) method. Results: Seven demographic indicators including gender, age, race, education level, marital status, income, and health are related to the incidence of CKD and PD. Among them, the elderly (≥60 years old), poverty (poverty-income ratio <1.3), divorce or widowhood, and male ratio in the comorbidity group of CKD and PD [67.12% (833/1 241), 36.83% (457/1 241), 34.41% (427/1 241), and 57.78% (717/1 241) respectively] were significantly higher than those in the control group [23.71% (4 179/17 623), 29.17% (5 141/17 623), 18.16% (3 200/17 623), and 48.73% (8 587/17 623) respectively] (all P<0.001). Those with high educational level (university and above) and self-rated excellent health accounted for a relatively small proportion in the comorbidity group [14.10% (175/1 241) and 8.22% (102/1 241) respectively]. The prevalence of PD increased among individuals with abnormal renal function indices, including glomerular filtration rate, urine protein/creatinine ratio, serum creatinine, serum uric acid, and blood urea nitrogen. Univariate logistic regression analysis showed a positive correlation between the incidence of PD and CKD (OR=2.14, 95%CI: 1.90-2.42, P<0.001). Multivariate logistic regression analysis also indicated that PD and CKD were potential risk factors for each other (PD for CKD: OR=1.22, 95%CI: 1.07-1.40, P=0.004; CKD for PD: OR=1.19, 95%CI: 1.04-1.37, P=0.012). Furthermore, after adjusting the model based on demographic indicators, there was still a significant correlation between PD and CKD (P=0.010). Mechanistically, the results of the TSMR analysis support the existence of a common risk factor mediated by immune cells between CKD and PD, namely the expression of CD64 on multiple innate immune cells mediates the occurrence of CKD and PD. The absolute count of CD64+ monocytes is associated with an increased risk for both CKD (HR=1.11) and PD (HR=1.07), while same tendency showed in the absolute count of CD64+ neutrophils for CKD (HR=1.22) and PD (HR=1.23). Conclusions: There is a positive correlation between CKD and PD, particularly moderate to severe PD, and the shared pathogenesis involves CD64+ monocytes in the circulatory system. Targeted interventions focusing on CD64 molecules or monocyte subsets may be beneficial.