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Objectives Dental caries of the permanent dentition is a multifactorial disease resulting from the complex interplay of endogenous and environmental risk factors. The disease is not easily quantitated due to the innumerable possible combinations of carious lesions across individual tooth surfaces of the permanent dentition. Global measures of decay, such as the DMFS index (which was developed for surveillance applications), may not be optimal for studying the epidemiology of dental caries because they ignore the distinct patterns of decay across the dentition. We hypothesize that specific risk factors may manifest their effects on specific tooth surfaces leading to patterns of decay that can be identified and studied. In this study, we utilized two statistical methods of extracting patterns of decay from surface-level caries data to create novel phenotypes with which to study the risk factors affecting dental caries. Methods Intra-oral dental examinations were performed on 1068 participants aged 18-75 years to assess dental caries. The 128 tooth surfaces of the permanent dentition were scored as carious or not and used as input for principal components analysis ( PCA) and factor analysis ( FA), two methods of identifying underlying patterns without a priori knowledge of the patterns. Demographic (age, sex, birth year, race/ethnicity, and educational attainment), anthropometric (height, body mass index, waist circumference), endogenous (saliva flow), and environmental (tooth brushing frequency, home water source, and home water fluoride) risk factors were tested for association with the caries patterns identified by PCA and FA, as well as DMFS, for comparison. The ten strongest patterns (i.e. those that explain the most variation in the data set) extracted by PCA and FA were considered. Results The three strongest patterns identified by PCA reflected (i) global extent of decay (i.e. comparable to DMFS index), (ii) pit and fissure surface caries and (iii) smooth surface caries, respectively. The two strongest patterns identified by FA corresponded to (i) pit and fissure surface caries and (ii) maxillary incisor caries. Age and birth year were significantly associated with several patterns of decay, including global decay/ DMFS index. Sex, race, educational attainment, and tooth brushing were each associated with specific patterns of decay, but not with global decay/ DMFS index. Conclusions Taken together, these results support the notion that caries experience is separable into patterns attributable to distinct risk factors. This study demonstrates the utility of such novel caries patterns as new outcomes for exploring the complex, multifactorial nature of dental caries. [ABSTRACT FROM AUTHOR]

Background: Dental caries is the result of a complex interplay among environmental, behavioral, and genetic factors, with distinct patterns of decay likely due to specific etiologies. Therefore, global measures of decay, such as the DMFS index, may not be optimal for identifying risk factors that manifest as specific decay patterns, especially if the risk factors such as genetic susceptibility loci have small individual effects. We used two methods to extract patterns of decay from surface-level caries data in order to generate novel phenotypes with which to explore the genetic regulation of caries., Methods: The 128 tooth surfaces of the permanent dentition were scored as carious or not by intra-oral examination for 1,068 participants aged 18 to 75 years from 664 biological families. Principal components analysis (PCA) and factor analysis (FA), two methods of identifying underlying patterns without a priori surface classifications, were applied to our data., Results: The three strongest caries patterns identified by PCA recaptured variation represented by DMFS index (correlation, r = 0.97), pit and fissure surface caries (r = 0.95), and smooth surface caries (r = 0.89). However, together, these three patterns explained only 37% of the variability in the data, indicating that a priori caries measures are insufficient for fully quantifying caries variation. In comparison, the first pattern identified by FA was strongly correlated with pit and fissure surface caries (r = 0.81), but other identified patterns, including a second pattern representing caries of the maxillary incisors, were not representative of any previously defined caries indices. Some patterns identified by PCA and FA were heritable (h(2) = 30-65%, p = 0.043-0.006), whereas other patterns were not, indicating both genetic and non-genetic etiologies of individual decay patterns., Conclusions: This study demonstrates the use of decay patterns as novel phenotypes to assist in understanding the multifactorial nature of dental caries.