Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 8 1 2022 03 15 61 76 Maryam Ghodsi Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran Bagher Larijani Endocrinology and Metabolism Research Center (EMRC), Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran Shahin Roshani Non-Communicable Diseases Research Center (NCDRC), Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran Mahsa Mohammad Amoli Metabolic Disorders Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran Farideh Razi Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran Abbas Ali Keshtkar Department of Health Sciences Education Development, School of Public Health, Tehran University of Medical Sciences (TUMS), Tehran, Iran Patricia Khashayar Center for microsystem technology, Imec and Ghent University, Zwijnaarde, Ghent, Belgium ANDOsteoporosis Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran Fariba Zarrabi Thrombosis Hemostasis Research Center, Tehran University of Medical Sciences, Tehran, Iran AND Department of Biology, College of Basic Science, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran Mohammad Reza Mohajeri-Tehrani Endocrinology and Metabolism Research Center (EMRC), Endocrinology and Metabolism Clinical Sciences Institute , Tehran University of Medical Sciences (TUMS), Tehran, Iran 2021 08 04 2022 02 01 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. https://jbe.tums.ac.ir/index.php/jbe/article/view/685 https://jbe.tums.ac.ir/index.php/jbe/article/download/685/357