Poliklinik für Kieferorthopädie, Präventive Zahnmedizin und Kinderzahnheilkunde
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Genetic risk factors play important roles in the etiology of oral, dental, and craniofacial diseases. Identifying the relevant risk loci and understanding their molecular biology could highlight new prevention and management avenues. Our current understanding of oral health genomics suggests that dental caries and periodontitis are polygenic diseases, and very large sample sizes and informative phenotypic measures are required to discover signals and adequately map associations across the human genome. In this article, we introduce the second wave of the Gene-Lifestyle Interactions and Dental Endpoints consortium (GLIDE2) and discuss relevant data analytics challenges, opportunities, and applications. In this phase, the consortium comprises a diverse, multiethnic sample of over 700,000 participants from 21 studies contributing clinical data on dental caries experience and periodontitis. We outline the methodological challenges of combining data from heterogeneous populations, as well as the data reduction problem in resolving detailed clinical examination records into tractable phenotypes, and describe a strategy that addresses this. Specifically, we propose a 3-tiered phenotyping approach aimed at leveraging both the large sample size in the consortium and the detailed clinical information available in some studies, wherein binary, severity-encompassing, and “precision,” data-driven clinical traits are employed. As an illustration of the use of data-driven traits across multiple cohorts, we present an application of dental caries experience data harmonization in 8 participating studies (N = 55,143) using previously developed permanent dentition tooth surface–level dental caries pattern traits. We demonstrate that these clinical patterns are transferable across multiple cohorts, have similar relative contributions within each study, and thus are prime targets for genetic interrogation in the expanded and diverse multiethnic sample of GLIDE2. We anticipate that results from GLIDE2 will decisively advance the knowledge base of mechanisms at play in oral, dental, and craniofacial health and disease and further catalyze international collaboration and data and resource sharing in genomics research.
The aims of this study were to 1) determine if continuous eruption occurs in the maxillary teeth, 2) assess the magnitude of the continuous eruption, and 3) evaluate the effects of continuous eruption on the different periodontal parameters by using data from the population-based cohort of the Study of Health in Pomerania (SHIP). The jaw casts of 140 participants from the baseline (SHIP-0) and 16-y follow-up (SHIP-3) were digitized as 3-dimensional models. Robust reference points were set to match the tooth eruption stage at SHIP-0 and SHIP-3. Reference points were set on the occlusal surface of the contralateral premolar and molar teeth, the palatal fossa of an incisor, and the rugae of the hard palate. Reference points were combined to represent 3 virtual occlusal planes. Continuous eruption was measured as the mean height difference between the 3 planes and rugae fix points at SHIP-0 and SHIP-3. Probing depth, clinical attachment levels, gingiva above the cementoenamel junction (gingival height), and number of missing teeth were clinically assessed in the maxilla. Changes in periodontal variables were regressed onto changes in continuous eruption after adjustment for age, sex, number of filled teeth, and education or tooth wear. Continuous tooth eruption >1 mm over the 16 y was found in 4 of 140 adults and averaged to 0.33 mm, equaling 0.021 mm/y. In the total sample, an increase in continuous eruption was significantly associated with decreases in mean gingival height (B = −0.34; 95% CI, −0.65 to −0.03). In a subsample of participants without tooth loss, continuous eruption was negatively associated with PD. This study confirmed that continuous eruption is clearly detectable and may contribute to lower gingival heights in the maxilla.
Prediction models learn patterns from available data (training) and are then validated on new data (testing). Prediction modeling is increasingly common in dental research. We aimed to evaluate how different model development and validation steps affect the predictive performance of tooth loss prediction models of patients with periodontitis. Two independent cohorts (627 patients, 11,651 teeth) were followed over a mean ± SD 18.2 ± 5.6 y (Kiel cohort) and 6.6 ± 2.9 y (Greifswald cohort). Tooth loss and 10 patient- and tooth-level predictors were recorded. The impact of different model development and validation steps was evaluated: 1) model complexity (logistic regression, recursive partitioning, random forest, extreme gradient boosting), 2) sample size (full data set or 10%, 25%, or 75% of cases dropped at random), 3) prediction periods (maximum 10, 15, or 20 y or uncensored), and 4) validation schemes (internal or external by centers/time). Tooth loss was generally a rare event (880 teeth were lost). All models showed limited sensitivity but high specificity. Patients’ age and tooth loss at baseline as well as probing pocket depths showed high variable importance. More complex models (random forest, extreme gradient boosting) had no consistent advantages over simpler ones (logistic regression, recursive partitioning). Internal validation (in sample) overestimated the predictive power (area under the curve up to 0.90), while external validation (out of sample) found lower areas under the curve (range 0.62 to 0.82). Reducing the sample size decreased the predictive power, particularly for more complex models. Censoring the prediction period had only limited impact. When the model was trained in one period and tested in another, model outcomes were similar to the base case, indicating temporal validation as a valid option. No model showed higher accuracy than the no-information rate. In conclusion, none of the developed models would be useful in a clinical setting, despite high accuracy. During modeling, rigorous development and external validation should be applied and reported accordingly.