Host-Variable-Embedding Augmented Microbiome-Based Simultaneous Detection of Multiple Diseases by Deep Learning

Abstract

Microbiome has emerged as a promising indicator or predictor of human diseases. However, previous studies have typically labeled each specimen as either healthy or with a specific disease, ignoring prevalence of complications or comorbidities in actual cohorts, which may confound the microbial–disease associations. For instance, a patient may suffer from multiple diseases, making it challenging to detect their health status accurately. Furthermore, host phenotypes like physiological characteristics and lifestyles can alter microbiome structure, but such information has not yet been fully utilized in data models. To address these issues, a highly explainable deep learning (DL) method called Meta-Spec is proposed. Using a deep neural network (DNN)-based approach, it encodes and embeds refined host variables with microbiome features, enabling the detection of multiple diseases simultaneously. Experiments show that Meta-Spec outperforms regular machine learning (ML) strategies for multilabel disease screening in several cohorts. More importantly, Meta-Spec successfully detects comorbidities that are often missed by other approaches. In addition, for its high interpretability, Meta-Spec captures key factors that shape disease patterns from host variables and microbes. Hence, these efforts improve the feasibility and sensitivity of microbiome-based disease screening in practical scenarios, representing a significant step toward personalized medicine and better health outcomes.