Investigating Tendon Fascicle Structure–Function Relationships in a Transgenic-Age Mouse Model Using Multiple Regression Models

Abstract

Proper replacement or repair of damaged tendons or ligaments requires functionally engineered tissue that mimics their native mechanical properties. While tendon structure–function relationships are generally assumed, there exists little quantitative evidence of the roles of distinct tendon components in tendon function. Previous work has used linear correlations to assess the independent, univariate effects of one structural or one biochemical variable on mechanics. The current study's objective was to simultaneously and rigorously evaluate the relative contributions of seven different structural and compositional variables in predicting tissue mechanical properties through the use of multiple regression statistical models. Structural, biochemical, and mechanical analysis were all performed on tail tendon fascicles from different groups of transgenic mice, which provide a reproducible, noninvasive, in vivo model of changes in tendon structure and composition. Interestingly, glycosaminoglycan (GAG) content was observed to be the strongest predictor of mechanical properties. GAG content was also well correlated with collagen content and mean collagen fibril diameter. Collagen fibril area fraction was a significant predictor only of material properties. Therefore, in a large multivariate model, GAG content was the largest predictor of mechanical properties, perhaps both through direct influence and indirectly through its correlation with collagen content and fibril structure.