An application of CART algorithms for detection of an association between VDR polymorphisms and reduced bone density in individuals with type 2 diabetes: a population-based cross-sectional study

Abstract

Background. An important part of preventing major common diseases is identifying genetic factors that contribute to their occurrence. For the first time in our knowledge, we investigated the association between polymorphisms of five vitamin D receptor (VDR) genes (ApaI, BsmI, FokI, EcoRV, and TaqI) and low bone density/osteopenia/osteoporosis in individuals with type 2 diabetes using classification and regression tree (CART) algorithms. Methods. Data from 158 participants with T2D were used to develop the CART analysis. The binary output variable was "bone state" with low or normal values. Age and BMI (continuous variables), vitamin D deficiency (yes/no), and gender (binary variables), as well as polymorphisms of the five VDR genes (categorical variables) all played a role in the explanatory model. A 10-fold cross-validation process was used for model validation. Results. Participants were divided into three groups based on their sex. In all groups, age was the major factor predicting the low state in the final obtained tree model. The second most significant predictor in each model was BMI in both sexes (accuracy:75.32% and, AUC:0.748), EcoRV polymorphism in women (accuracy:78.79 %, AUC: 0.794), and TaqI polymorphism in men (accuracy:71.19%, AUC: 0.651). Conclusion Model validation of the final tree models demonstrated that the use of CART algorithms could be a valuable technique for identifying individuals with T2D who are at risk for early-onset osteoporosis based on their polymorphism of the studied VDR genes. Our recommendation is to conduct more population-based studies. We hope this study will serve as a basis for future research.

Keywords

• classification and regression tree (CART)
• Osteoporosis
• Osteopenia
• low bone density
• type 2 diabetes
• Vitamin D receptor (VDR)