Using gut microbiome metagenomic hypervariable features for diabetes screening and typing through supervised machine learning.

ABSTRACT: Diabetes mellitus is a complex metabolic disorder and one of the fastest-growing global public health concerns. The gut microbiota is implicated in the pathophysiology of various diseases, including diabetes. This study utilized 16S rRNA metagenomic data from a volunteer citizen science initiative to investigate microbial markers associated with diabetes status (positive or negative) and type (type 1 or type 2 diabetes mellitus) using supervised machine learning (ML) models. The diversity of the microbiome varied according to diabetes status and type. Differential microbial signatures between diabetes types and negative group revealed an increased presence of Brucellaceae, Ruminococcaceae, Clostridiaceae, Micrococcaceae, Barnesiellaceae and Fusobacteri

SUBMITTER: Chavarria X 

PROVIDER: S-EPMC11893737 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature