Current data mining and statistical methods to extract patterns and relationships in microbiomic data are often based on several assumptions such as Euclidean, linear, continuous and metric space which may not be the true space of microbiomic data. For example, the microbial profiles (functional and taxonomic classifications) are often correlated in a hierarchical style. These assumptions prevent discovering the true relationships in microbiomic data analysis. Thus, it is urgent to develop new computational methods to overcome these assumptions and consider the microbiomic data properties in the analysis procedure. In this study, we will propose novel variable selection method based on manifold-constrained regularization (McRe). Considering the nonlinear and correlation structure of data, McRe get improved results in simulation data. The method is also applied to a microbiomic dataset.