Factors Associated with Specific Patterns of Tooth Decay 33

Dental caries affects most adults worldwide; however, the risk factors for dental caries do not necessarily exert their effects uniformly across all tooth surfaces. Instead, the actions of some risk factors may be limited to a subset of teeth/surfaces. Therefore, we used hierarchical clustering on tooth surface-level caries data for 1,068 Appalachian adults (ages 18-75 yrs) to group surfaces based on co-occurrence of caries. Our cluster analysis yielded evidence of 5 distinct groups of tooth surfaces that differ with respect to caries: (C1) pit and fissure molar surfaces, (C2) mandibular anterior surfaces, (C3) posterior nonpit and fissure surfaces, (C4) maxillary anterior surfaces, and (C5) mid-dentition surfaces. These clusters were replicated in a national dataset (NHANES 1999-2000, N = 3,123). We created new caries outcomes defined as the number of carious tooth surfaces within each cluster. We show that some cluster-based caries outcomes are heritable (i.e., under genetic regulation; p < 0.05), whereas others are not. Likewise, we demonstrate the association between some cluster-based caries outcomes and potential risk factors such as age, sex, educational attainment, and toothbrushing habits. Together, these results suggest that the permanent dentition can be subdivided into groups of tooth surfaces that are useful for understanding the factors influencing cariogenesis. Abbreviations: COHRA, Center for Oral Health in Appalachia, the principal study sample; C1-5, clusters 1-5, groups of similarly behaving tooth surfaces identified through hierarchical clustering; DMFS index, decayed, missing, or filled surfaces, a traditional caries measure representing the number of affected surfaces across the entire dentition; DMFS1-5, partial DMFS indices representing the number of affected surfaces within a hierarchical cluster; and NHANES, National Health and Nutrition Examination Survey, the secondary study sample. KEY WOrDs: dental caries, permanent dentition, white spots, hierarchical clustering, cluster analysis, heritability. IntrODuctIOn Dental caries, which affects the great majority of adolescents and adults throughout the world, is a multi-factorial disease caused by the effects of numerous environmental, behavioral, and genetic factors. Many risk factors have been identified, such as: host genetics (Horowitz et al., 1958); environmental exposures, including fluoride, cariogenic bacteria, and pH-altering agents; behavioral factors, including diet and oral hygiene; characteristics of the dentition, including enamel composition and positions and morphology of teeth; characteristics of the oral environment, including saliva composition, flow rate, and pH buffering capacity; and demographic factors, including age, sex, race, ethnicity, socio-economic status, and access to oral health care (Hunter, 1988). This complexity is further compounded by the disease phenotype, which may manifest as innumerable combinations of caries lesions across tooth surfaces of the full dentition. Because caries risk factors may exert differential effects across tooth surfaces of the permanent dentition, measurable caries experience may be modeled as the cumulative result of multiple superimposed patterns of decay due to the various risk factors (Batchelor and Sheiham, 2004; Shaffer et al., 2012a). In epidemiological studies, however, caries experience is typically reduced to a single measure of decay, such as the widely used DMFT/S index (calculated as the sum of decayed, missing due to decay, or filled/restored teeth/ surfaces). Such global measures of caries experience ignore the fact that categories of tooth surfaces exhibit differences in susceptibility to decay and are differentially affected by risk factors. Because they ignore the patterns of dental caries across the dentition, global measures of decay such as DMFT/S index may be limited in their ability to identify caries risk factors that exert their effects on specific categories of tooth surfaces. Indeed, previous studies have demonstrated that modeling the patterns of tooth decay is beneficial for epidemiological (Psoter et al., 2003, 2009) and genetic studies (Shaffer et al., 2012a, 2012b). We used hierarchical clustering analysis to group tooth surfaces into categories based on co-occurrence of caries lesions. We then generated novel caries outcomes reflecting these tooth-surface categories, and explored their utility for studying caries etiology. clustering tooth surfaces into biologically Informative caries Outcomes by guest on January 24, 2013 For personal use only. No other uses without permission. jdr.sagepub. com Downloaded from © 2013 International & American Associations for Dental Research J Dent Res 92(1) 2013 Factors Associated with Specific Patterns of Tooth Decay 33