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The aim of the present study was to construct a biological age score reflecting one’s physiologic capability and aging condition with respect to tooth loss over 10 y. From the follow-up to the population-based Study of Health in Pomerania (i.e., SHIP-2), 2,049 participants were studied for their baseline biomarker measures 10 y before (i.e., in SHIP-0). Metabolic and periodontal data were regressed onto chronological age to construct a score designated as “biological age.” For either sex separately, the impact of this individualized score was used to predict tooth loss in the follow-up cohort in comparison with each participant’s chronological age. Outcome data after 10 y with respect to tooth loss, periodontitis, obesity, and inflammation were shown to be better for biologically younger subjects than as expected by their chronological age, whereas for the older subjects, data were worse. Especially for tooth loss, a striking increase was observed in subjects whose biological age at baseline appeared to be higher than their chronological age. Biological age produced significantly better tooth loss predictions than chronological age (P < 0.001). Areas under receiver operating characteristic curves for tooth loss of ≥3 teeth in men during follow-up were 0.811 and 0.745 for biological and chronological age, respectively. For women, these figures were 0.788 and 0.724. For total tooth loss, areas under the curve were 0.890 and 0.749 in men and 0.872 and 0.752 in women. Biological age combines various measures into a single score and allows identifying individuals at increased risk of tooth loss.
Evidence is limited regarding whether periodontal treatment improves hemoglobin A1c (HbA1c) among people with prediabetes and periodontal disease, and it is unknown whether improvement of metabolic status persists >3 mo. In an exploratory post hoc analysis of the multicenter randomized controlled trial “Antibiotika und Parodontitis” (Antibiotics and Periodontitis)—a prospective, stratified, double-blind study—we assessed whether nonsurgical periodontal treatment with or without an adjunctive systemic antibiotic treatment affects HbA1c and high-sensitivity C-reactive protein (hsCRP) levels among periodontitis patients with normal HbA1c (≤5.7%, n = 218), prediabetes (5.7% < HbA1c < 6.5%, n = 101), or unknown diabetes (HbA1c ≥ 6.5%, n = 8) over a period of 27.5 mo. Nonsurgical periodontal treatment reduced mean pocket probing depth by >1 mm in both groups. In the normal HbA1c group, HbA1c values remained unchanged at 5.0% (95% CI, 4.9% to 6.1%) during the observation period. Among periodontitis patients with prediabetes, HbA1c decreased from 5.9% (95% CI, 5.9% to 6.0%) to 5.4% (95% CI, 5.3% to 5.5%) at 15.5 mo and increased to 5.6% (95% CI, 5.4% to 5.7%) after 27.5 mo. At 27.5 mo, 46% of periodontitis patients with prediabetes had normal HbA1c levels, whereas 47.9% remained unchanged and 6.3% progressed to diabetes. Median hsCRP values were reduced in the normal HbA1c and prediabetes groups from 1.2 and 1.4 mg/L to 0.7 and 0.7 mg/L, respectively. Nonsurgical periodontal treatment may improve blood glucose values among periodontitis patients with prediabetes (ClinicalTrials.gov NCT00707369).
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.
The long-term effectiveness of powered toothbrushes (PTBs) and interdental cleaning aids (IDAs) on a population level is unproven. We evaluated to what extent changes in PTB and IDA use may explain changes in periodontitis, caries, and tooth loss over the course of 17 y using data for adults (35 to 44 y) and seniors (65 to 74 y) from 3 independent cross-sectional surveys of the German Oral Health Studies (DMS). Oaxaca decomposition analyses assessed to what extent changes in mean probing depth (PD), number of caries-free surfaces, and number of teeth between 1) DMS III and DMS V and 2) DMS IV and DMS V could be explained by changes in PTB and IDA use. Between DMS III and V, PTB (adults: 33.5%; seniors: 28.5%) and IDA use (adults: 32.5%; seniors: 41.4%) increased along with an increase in mean PD, number of caries-free surfaces, and number of teeth. Among adults, IDA use contributed toward increased number of teeth between DMS III and V as well as DMS IV and V. In general, the estimates for adults were of lower magnitude. Among seniors between DMS III and V, PTB and IDA use explained a significant amount of explained change in the number of caries-free surfaces (1.72 and 5.80 out of 8.44, respectively) and the number of teeth (0.49 and 1.25 out of 2.19, respectively). Between DMS IV and V, PTB and IDA use contributed most of the explained change in caries-free surfaces (0.85 and 1.61 out of 2.72, respectively) and the number of teeth (0.25 and 0.46 out of 0.94, respectively) among seniors. In contrast to reported results from short-term clinical studies, in the long run, both PTB and IDA use contributed to increased number of caries-free healthy surfaces and teeth in both adults and seniors.
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.
Although a potential link between periodontitis and cardiorespiratory fitness might provide a reasonable explanation for effects of tooth-related alterations seen on cardiometabolic diseases, evidence is currently limited. Thus, we investigated the association between clinically assessed periodontitis and cardiopulmonary exercise testing (CPET). Data from 2 independent cross-sectional population-based studies (5-y follow-up of the Study of Health in Pomerania [SHIP-1; N = 1,639] and SHIP-Trend-0 [N = 2,439]) were analyzed. Participants received a half-mouth periodontal examination, and teeth were counted. CPET was based on symptom limited-exercise tests on a bicycle ergometer. Associations of periodontitis parameters with CPET parameters were analyzed by confounder-adjusted multivariable linear regression. In the total sample, mean pocket probing depth (PPD), mean clinical attachment levels, and number of teeth were consistently associated with peak oxygen uptake (peakVO2) and exercise duration in both studies, even after restriction to cardiorespiratory healthy participants. Statistically significant associations with oxygen uptake at anaerobic threshold (VO2@AT), slope of the efficiency of ventilation in removing carbon dioxide, and peak oxygen pulse (VÉ/VCO2 slope) occurred. Further, interactions with age were identified, such that mainly older individuals with higher levels of periodontal disease severity were associated with lower peakVO2. Restricted to never smokers, associations with mean clinical attachment levels and the number of teeth mostly diminished, while associations of mean PPD with peakVO2, VO2@AT, VÉ/VCO2 slope, and exercise duration in SHIP-1 and SHIP-Trend-0 were confirmed. In SHIP-1, mean peakVO2 was 1,895 mL/min in participants with a mean PPD of 1.6 mm and 1,809 mL/min in participants with a mean PPD of 3.7 mm. To conclude, only mean PPD reflecting current disease severity was consistently linked to cardiorespiratory fitness in 2 cross-sectional samples of the general population. If confirmed in well-designed large-scale longitudinal studies, the association between periodontitis and cardiorespiratory fitness might provide a biologically plausible mechanism linking periodontitis with cardiometabolic diseases.
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.
For the goal of individualized medicine, it is critical to have clinical phenotypes at hand which represent the individual pathophysiology. However, for most of the utilized phenotypes, two individuals with the same phenotype assignment may differ strongly in their underlying biological traits. In this paper, we propose a definition for individualization and a corresponding statistical operationalization, delivering thereby a statistical framework in which the usefulness of a variable in the meaningful differentiation of individuals with the same phenotype can be assessed. Based on this framework, we develop a statistical workflow to derive individualized phenotypes, demonstrating that under specific statistical constraints the prediction error of prediction scores contains information about hidden biological traits not represented in the modeled phenotype of interest, allowing thereby internal differentiation of individuals with the same assigned phenotypic manifestation. We applied our procedure to data of the population-based Study of Health in Pomerania to construct a refined definition of obesity, demonstrating the utility of the definition in prospective survival analyses. Summarizing, we propose a framework for the individualization of phenotypes aiding personalized medicine by shifting the focus in the assessment of prediction models from the model fit to the informational content of the prediction error.
Periodontitis is one of the most prevalent oral diseases worldwide and is caused by multifactorial interactions between host and oral bacteria. Altered cellular metabolism of host and microbes releases a number of intermediary end products known as metabolites. There is an increasing interest in identifying metabolites from oral fluids such as saliva to widen the understanding of the complex pathogenesis of periodontitis. It is believed that some metabolites might serve as indicators toward early detection and screening of periodontitis and perhaps even for monitoring its prognosis in the future. Because contemporary periodontal screening methods are deficient, there is an urgent need for novel approaches in periodontal screening procedures. To this end, we associated oral parameters (clinical attachment level, periodontal probing depth, supragingival plaque, supragingival calculus, number of missing teeth, and removable denture) with a large set of salivary metabolites (n = 284) obtained by mass spectrometry among a subsample (n = 909) of nondiabetic participants from the Study of Health in Pomerania (SHIP-Trend-0). Linear regression analyses were performed in age-stratified groups and adjusted for potential confounders. A multifaceted image of associated metabolites (n = 107) was revealed with considerable differences according to age groups. In the young (20 to 39 y) and middle-aged (40 to 59 y) groups, metabolites were predominantly associated with periodontal variables, whereas among the older subjects (≥60 y), tooth loss was strongly associated with metabolite levels. Metabolites associated with periodontal variables were clearly linked to tissue destruction, host defense mechanisms, and bacterial metabolism. Across all age groups, the bacterial metabolite phenylacetate was significantly associated with periodontal variables. Our results revealed alterations of the salivary metabolome in association with age and oral health status. Among our comprehensive panel of metabolites, periodontitis was significantly associated with the bacterial metabolite phenylacetate, a promising substance for further biomarker research.