Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Metabarcoding detection of Vibrio species in black-tailed gulls reveals seabirds as overlooked sentinels of coastal zoonotic risk in South Korea.

Environmental research·2026

Same author

High-throughput microbiome profiling and co-occurrence with antibiotic-resistance genes in Lucilia sericata.

FEMS microbiology letters·2026

Same author

Topic Modeling of Nursing Documentation in Hemodialysis Units: A Mixed-Methods Study of Nursing Surveillance Activities.

Journal of nursing management·2026

Same author

Corrigendum to "Relationship of parental phthalate exposure with fetal growth and placental development at birth" [Reprod. Toxicol. 137 (2025) 109025].

Reproductive toxicology (Elmsford, N.Y.)·2026

Same author

Comparative Analysis of Gut Eukaryotic Communities in Three Laboratory-Reared Cockroach Species Using Metabarcoding.

The Journal of eukaryotic microbiology·2026

Same author

Nematode in Cockroaches: A Potential Allergen Source.

Allergy·2026

Same journal

Culture-enriched metagenomic sequencing reveals within-patient diversity and transmission of vancomycin-resistant <i>Enterococcus faecium</i>.

Microbial genomics·2026

Same journal

New isolates from the 1970s to early 2000s provide insights into the evolution of <i>Acinetobacter baumannii</i> international clone 2 and its resistome.

Microbial genomics·2026

Same journal

Comprehensive identification of sequence types belonging to <i>Acinetobacter baumannii</i> clonal complexes.

Microbial genomics·2026

Same journal

Genomic outbreak investigation of biosafety-level-3 pathogens using nanopore sequencing.

Microbial genomics·2026

Same journal

Genome mining and phylogenomics reveal diverse biosynthetic gene clusters in desert Actinobacteria.

Microbial genomics·2026

Same journal

Genomic insights into the resistome, mobilome and functional adaptation of <i>Achromobacter xylosoxidans</i> across clinical and environmental contexts.

Microbial genomics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 23, 2025

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19

Published on: July 5, 2022

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

Xavier Chavarria¹, Hyun Seo Park^1,2, Singeun Oh¹

¹Department of Tropical Medicine, Institute of Tropical Medicine, Arthropods of Medical Importance Resource Bank, Yonsei University College of Medicine, Yonsei-ro 50-1, Seodaemun-gu, Seoul 03722, Republic of Korea.

Microbial Genomics

|March 10, 2025

Summary

This summary is machine-generated.

Machine learning models identified gut bacteria linked to diabetes. Specific microbial profiles can help screen for type 1 and type 2 diabetes, potentially aiding early diagnosis.

Keywords:

diabetes mellitus gut microbiome metabarcoding microbial markers random forest supervised machine learning

More Related Videos

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Related Experiment Videos

Last Updated: May 23, 2025

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19

Published on: July 5, 2022

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Area of Science:

Microbiome research
Computational biology
Metagenomics

Background:

Diabetes mellitus is a growing global health concern.
The gut microbiota plays a role in various diseases, including diabetes.
Identifying microbial markers could aid in diabetes diagnosis and management.

Purpose of the Study:

To investigate microbial markers associated with diabetes status and type using machine learning.
To develop and evaluate machine learning models for screening and typing diabetes based on gut microbiome profiles.

Main Methods:

Utilized 16S rRNA metagenomic data from citizen science participants.
Applied supervised machine learning algorithms including decision tree, elastic net, random forest, and support vector machine.
Trained models on data from individuals with type 1 diabetes, type 2 diabetes, and healthy controls.

Main Results:

Microbiome diversity varied significantly with diabetes status and type.
Differential microbial signatures were identified for type 1 and type 2 diabetes.
Random Forest models showed promising performance (AUC 0.76-0.77) for diabetes screening, with improved sensitivity when using 500 features.

Conclusions:

Machine learning models can identify gut microbial profiles associated with diabetes.
These findings suggest the potential for early diabetes diagnosis using gut microbiome data.
Further model refinement is needed to improve sensitivity and accuracy for all diabetes types.