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Updated: May 7, 2026

Inducing Apical Periodontitis in Mice
Published on: August 6, 2019
Association of OPG, TNF-α, and IL-1B Gene Variants With Periodontitis in a South African Population
Salma Kabbashi1, Ndonwi Elvis Ngwa2, Haly Holmes3
1Department of Craniofacial Biology, Pathology, & Radiology, Faculty of Dentistry, University of the Western Cape, Cape Town, South Africa.
Aim:
This study aimed to investigate candidate single-nucleotide polymorphisms linked to periodontitis susceptibility in a Western Cape cohort, providing insights into population-specific host genetic factors.
Materials And Methods:
This observational case-control study recruited a total of 150 South African participants. Saliva samples were genotyped using the OpenArray QuantStudio 12K Flex qPCR System. Genotype and allele frequencies were assessed for Hardy-Weinberg equilibrium and tested for associations with clinical variables and disease status using Chi-square tests and logistic regression adjusted for age, gender, and smoking.
Results:
A total of 24 SNPs were analysed. Several genotypes showed suggestive associations with periodontitis risk prior to multiple testing correction. The OPG + 1181 CG, RANKL RL2 AG, and IL-17A + 197 GG genotypes were linked to higher plaque score. The TNF-α -238 GG genotype was associated with lower bleeding score and reduced disease risk (OR = 0.157, 95% CI: 0.039-0.638, P = .010), while IL-1B -511 GG genotype corresponded with a reduced disease risk (adjusted OR = 0.216, 95% CI: 0.054-0.867, P = .031). Conversely, OPG + 1181 CC was related to increased disease risk under multiple models (adjusted OR = 20.42, 95% CI: 1.95-213.9, P = .012). These associations lost significance after correction for multiple testing. Ethnicity-based subgroup analysis revealed differences in genotype distribution, while smoking status showed no effect.
Conclusion:
Genetic variants may influence periodontitis susceptibility, underscoring the importance of population-specific risk profiling and the need for replication in larger cohorts to support targeted diagnostics in resource-limited settings.
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