Abstract
Dental caries is a multifactorial disease that can be caused by interactions between genetic and environmental risk factors. Despite the availability of caries risk assessment tools, caries risk prediction models incorporating new factors, such as human genetic markers, have not yet been reported. The aim of this study was to construct a new model for caries risk prediction in teenagers, based on environmental and genetic factors, using a machine learning algorithm. We performed a prospective longitudinal study of 1,055 teenagers (710 teenagers for cohort 1 and 345 teenagers for cohort 2) aged 13 years, of whom 953 (633 teenagers for cohort 1 and 320 teenagers for cohort 2) were followed for 21 months. All participants completed an oral health questionnaire, an oral examination, biological (salivary and cariostate) tests, and single nucleotide polymorphism sequencing analysis. We constructed a caries risk prediction model based on these data using a random forest with an AUC of 0.78 in cohort 1 (training cohort). We further verified the discrimination and calibration abilities of this caries risk prediction model using cohort 2. The AUC of the caries risk prediction model in cohort 2 (testing cohort) was 0.73, indicating high discrimination ability. Risk stratification revealed that our caries risk prediction model could accurately identify individuals at high and very high caries risk but underestimated risks for individuals at low and very low caries risk. Thus, our caries risk prediction model has the potential for use as a powerful community-level tool to identify individuals at high caries risk.
Highlights
Permanent teeth caries was the most common chronic disease worldwide in 2016
The results showed that the prediction performance of the caries risk prediction models (CRPMs) constructed using Random Forest was stable
We further verified the accuracy of this prediction model using another independent cohort, and the results demonstrated that this CRPM could effectively identify high caries-risk individuals
Summary
A previous study reported that the global cost of dental diseases exceeded 540 billion dollars in 2015 and resulted in major health and financial burdens (Righolt et al, 2018). There is an urgent need for effective caries control. Accumulating evidence has shown a skewed distribution of caries; the majority of the disease was suffered by the minority teenagers in the population (Kaste et al, 1996). The conference of National Institutes of Health Consensus Development Conference Statement (2001) concluded that a focus on high-risk individuals was required for the prevention and control of dental caries (2001). Since caries is largely preventable, risk prediction models for early and accurate identification of teenagers at high risk of caries would be useful tools for designing more cost-effective caries control measures
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