A VAE-based methodology for deep enterotyping and Parkinson's disease diagnosis

A VAE-based methodology for deep enterotyping and Parkinson’s disease diagnosis | medRxiv Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search A VAE-based methodology for deep enterotyping and Parkinson’s disease diagnosis Yuting Qiao , Zhanshan (Sam) Ma doi: https://doi.org/10.64898/2026.03.17.26348604 Yuting Qiao 1 Computational Biology and Medical Ecology Lab Kunming Institute of Zoology Chinese Academy of Sciences Kunming , Yunnan, 650201, China 2 Kunming College of Life Science University of the Chinese Academy of Sciences Kunming , Yunnan, 650201, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zhanshan (Sam) Ma 1 Computational Biology and Medical Ecology Lab Kunming Institute of Zoology Chinese Academy of Sciences Kunming , Yunnan, 650201, China 2 Kunming College of Life Science University of the Chinese Academy of Sciences Kunming , Yunnan, 650201, China 3 Microbiome Medicine and Advanced AI Lab Kunming , Yunnan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: ma{at}vandals.uidaho.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Gut microbiome studies in Parkinson’s disease (PD) are challenged by high dimensionality, sparsity, compositionality, and substantial between-cohort heterogeneity, all of which complicate robust community typing and disease-status classification. Here, we developed a variational autoencoder (VAE)-based methodology for deep enterotyping and PD diagnosis prediction ( i.e ., predicting diseased vs. control status) using a harmonized multi-cohort gut microbiome compendium comprising 1,957 16S rRNA samples from six PD case–control cohorts and an independent shotgun metagenomic validation cohort of 725 samples. Compared with conventional enterotyping approaches such as partitioning around medoids (PAM) and Dirichlet multinomial mixture (DMM) modelling, the VAE-derived latent space supported a clearer and more reproducible three-cluster solution. These three enterotype-like community states were biologically interpretable and were annotated as Enterococcus-type, Bacteroides-type, and Ruminococcus-type configurations. The same broad three-enterotype structure was independently recapitulated in the metagenomic dataset, supporting cross-platform robustness. Across the three inferred types, the proportion of PD samples was similar, and both the primary generalized linear mixed-effects model and sensitivity model showed that enterotype assignment was not a significant differentiating factor for PD status and that the lack of association was not dependent on a single modelling strategy. In the supervised branch, VAE-derived representations supported PD case–control classification while also providing a shared latent representation for clustering, enterotype transfer, and downstream interpretation. Collectively, these findings show that deep representation learning can improve the resolution, reproducibility, and interpretability of enterotype inference in heterogeneous microbiome datasets, and provide a practical methodology for organizing broad community structure in PD. In this setting, the main advantage of the VAE method lies in its ability to link unsupervised community typing with supervised prediction through a shared latent representation, even when broad community types do not function as stand-alone disease biomarkers. Competing Interest Statement The authors have declared no competing interest. Funding Statement n/a Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability Data availability All raw sequencing data analyzed in this study are publicly available from the National Center for Biotechnology Information (NCBI) under the following BioProject accessions: PRJDB8639, PRJEB27564, PRJEB30615, PRJNA381395, PRJNA601994, PRJNA742875, and PRJNA834801. Dataset-level metadata were curated from the corresponding original publications. The processed genus-level abundance tables generated in this study are available from the corresponding author upon reasonable request. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Back to top Previous Next Posted March 19, 2026. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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