The Patterning Cascade Model and Carabelli's trait expression in metameres of the mixed human dentition: exploring a morphogenetic model.

OBJECTIVES: The Patterning Cascade Model (PCM) provides an evolutionary developmental framework for exploring diversity in tooth crown form. According to the model, proximity of secondary enamel knots and tooth germ size track underlying developmental processes that dictate ultimate crown morphology (i.e., cusp number, accessory cusp presence/size). Previous research has shown the model to successfully predict variation in Carabelli's trait expression between antimeric and metameric pairs of human permanent molars. In this study, we quantify Carabelli's trait expression for metameres of the mixed dentition (dm2 and M1) and assess the PCM's potential for explaining differences in expression between the two elements.

MATERIALS AND METHODS: Crown dimensions, intercusp distances, and Carabelli's trait expression were collected from 49 subadults possessing observable dm2/M1 pairs. Wilcoxon signed-rank tests and paired t-tests were performed to assess whether metameres differ significantly in morphometric variables. We explored the relationships between relative intercusp distances (RICDs) and Carabelli's trait expression using proportional odds logistic regression.

RESULTS: Intra-individual dm2/M1 pairs differed significantly in Carabelli's trait expression (p = 0.01), with dm2 exhibiting higher grades of expression more commonly despite its smaller crown size. Paired molars differed in only one statistically significant RICD: metacone-hypocone (p < 0.01). Most RICDs shared the predicted negative relationship with Carabelli's trait expression, but this relationship was only statistically significant for three RICDs in the dm2 (mean, protocone-paracone, metacone-hypocone).

CONCLUSIONS: We found mixed support for the PCM's ability to explain differences in Carabelli's trait expression between metameres of the mixed molar row. Results suggest that protocone-paracone enamel knot spacing has the greatest influence on Carabelli's trait expression. Lack of statistical significance for many of the relationships explored may reflect limitations related to sample composition and sample size.