The gut microbiome and metabolome interact to influence human health, but the relationship between these two components is often oversimplified. This study proposes a novel approach, LOCATE (Latent Variables Of miCrobiome And meTabolites rLations), to predict metabolite concentrations from microbial taxa frequencies and to produce a latent representation of the interaction. LOCATE outperforms existing tools in predicting metabolite concentrations and host conditions. The latent representation, which is strongly correlated with host demographics, significantly improves the prediction accuracy of host conditions compared to using either the microbiome or metabolome alone. The study highlights the importance of considering the complex, bidirectional interactions between the microbiome and metabolome, and suggests that a latent representation of these interactions can better predict host conditions than either component alone.The gut microbiome and metabolome interact to influence human health, but the relationship between these two components is often oversimplified. This study proposes a novel approach, LOCATE (Latent Variables Of miCrobiome And meTabolites rLations), to predict metabolite concentrations from microbial taxa frequencies and to produce a latent representation of the interaction. LOCATE outperforms existing tools in predicting metabolite concentrations and host conditions. The latent representation, which is strongly correlated with host demographics, significantly improves the prediction accuracy of host conditions compared to using either the microbiome or metabolome alone. The study highlights the importance of considering the complex, bidirectional interactions between the microbiome and metabolome, and suggests that a latent representation of these interactions can better predict host conditions than either component alone.