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Objectives: Medical professionals should advise their patients to visit a dentist if necessary. Due to the lack of time and knowledge, screening for periodontitis is often not done. To alleviate this problem, a screening model for total (own teeth/gum health, gum treatment, loose teeth, mouthwash use, and age)/severe periodontitis (gum treatment, loose teeth, tooth appearance, mouthwash use, age, and sex) in a medical care setting was developed in the Academic Center of Dentistry Amsterdam (ACTA) [1]. The purpose of the present study was to externally validate this tool in an outpatient medical setting. Materials and methods: Patients were requited in an outpatient medical setting as the validation cohort. The self-reported oral health questionnaire was conducted, demographic data were collected, and periodontal examination was performed. Algorithm discrimination was expressed as the area under the receiver operating characteristic curve (AUROCC). Sensitivity, specificity, and positive and negative predictive values were calculated. Calibration plots were made. Results: For predicting total periodontitis, the AUROCC was 0.59 with a sensitivity of 49% and specificity of 68%. The PPV was 57% and the NPV scored 55%. For predicting severe periodontitis, the AUROCC was 0.73 with a sensitivity of 71% and specificity of 63%. The PPV was 39% and the NPV 87%. Conclusions: The performance of the algorithm for severe periodontitis is found to be sufficient in the current medical study population. Further external validation of periodontitis algorithms in non-dental school populations is recommended. Clinical relevance: Because general physicians are obligated to screen patients for periodontitis, it is our general goal that they can use a prediction model in medical settings without an oral examination. [ABSTRACT FROM AUTHOR]

This correction notice addresses an error in a published article titled "External validation of a rapid, non-invasive tool for periodontitis screening in a medical care setting." The authors discovered that the number of patients with severe periodontitis was incorrectly reported as 38, when the correct number should be 24. This mistake occurred due to the conversion from the Dutch Periodontal Screening Index (DPSI) to the Community Periodontal Index of Treatment Needs (CPITN). The corrected results will be presented, but the discussion, conclusions, and implications of the study remain unchanged. The authors apologize for any inconvenience caused. [Extracted from the article]

Periodontal diseases are complex inflammatory diseases and affect up to 20% of the worldwide population. An unbalanced reaction of the immune system toward microbial pathogens is considered as the key factor in the development of periodontitis. Defensins have a strong antimicrobial function and are important contributors of the immune system toward maintaining health. Here, we present the first systematic association study of DEFB1. Using a haplotype-tagging single nucleotide polymorphism (SNP) approach, including described promoter SNPs of DEFB1, we investigated the associations of the selected variants in a large population (N=1337 cases and 2887 ethnically matched controls). The 3′ untranslated region SNP, rs1047031, showed the most significant association signal for homozygous carriers of the rare A allele (P=0.002) with an increased genetic risk of 1.3 (95% confidence interval: 1.11–1.57). The association was consistent with the specific periodontitis forms: chronic periodontitis (odds ratio=2.2 (95% confidence interval: 1.16–4.35), P=0.02), and aggressive periodontitis (odds ratio=1.3 (95% confidence interval 1.04–1.68), P=0.02). Sequencing of regulatory and exonic regions of DEFB1 identified no other associated variant, pointing toward rs1047031 as likely being the causative variant. Prediction of microRNA targets identified a potential microRNA-binding site at the position of rs1047031. [ABSTRACT FROM AUTHOR]