Gut Microbiota and Metabolomic Changes In Type 2 Diabetes Mellitus: Insights From 16S rDNA Sequencing and Bioinformatics

Summary

This study identifies reduced gut microbiota diversity and altered metabolic pathways in type 2 diabetes (T2DM), highlighting increased proteobacteria and decreased beneficial taxa, suggesting that microbial dysbiosis is associated with T2DM and may contribute to its pathophysiology.

Abstract

The global rise in type 2 diabetes mellitus (T2DM) underscores the need to better understand its underlying biological mechanisms, particularly those involving host–microbiome interactions. This study aimed to characterize gut microbial diversity, taxonomic composition, and predicted metabolic pathways in newly diagnosed T2DM patients compared with the non-diabetic matched (NM) group. Fresh stool samples were analyzed using 16S rDNA sequencing. Alpha diversity (Chao, ACE, Shannon, and Simpson indices) and beta diversity were calculated to assess microbial community structure. Taxonomic differences were evaluated using Wilcoxon rank-sum tests and linear discriminant analysis effect size (LEfSe). Functional pathway prediction was performed using phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt2) based on KEGG and MetaCyc annotations. Mendelian randomization (MR) analysis, including inverse-variance weighting, MR-Egger, and weighted median methods, was applied to assess genetically predicted associations between microbial taxa and T2DM. Results showed reduced microbial richness, as reflected by lower Chao and ACE indices, and altered diversity structure, as reflected by lower Shannon and higher Simpson indices, in T2DM patients, accompanied by significant compositional shifts. Increased relative abundance of Proteobacteria and decreased abundance of beneficial taxa such as Lachnospiraceae and Blautia were observed. Functional prediction indicated reduced abundance of pathways related to the non-oxidative pentose phosphate pathway, isobutanol biosynthesis, and L-isoleucine biosynthesis. MR analysis provided complementary evidence supporting associations between specific microbial taxa and T2DM susceptibility. In conclusion, T2DM is associated with reduced microbial richness, altered diversity structure, and distinct taxonomic and functional changes. These findings highlight the relevance of gut microbiota in T2DM and support the potential utility of microbiome-based biomarkers and therapeutic strategies. Further studies are required to validate these findings and clarify underlying mechanisms.

Introduction

Type 2 diabetes mellitus (T2DM) has become one of the fastest-growing chronic diseases worldwide, driven by rapid urbanization, Westernized dietary patterns, and increasingly sedentary lifestyles^1,2. It is a major contributor to global morbidity and mortality and imposes a substantial public health burden³. Although genetic susceptibility plays a role, the rapid rise in T2DM incidence highlights the importance of environmental and metabolic determinants⁴. Identifying modifiable biological mechanisms is therefore essential for improving early detection and prevent....

Protocol

All procedures involving human participants were reviewed and approved by the Ethics Committee of Chongqing Chenjiaqiao Hospital (approval No. 20240131). Written informed consent was obtained from all participants before sample collection. All stool samples and participant information were handled using de-identified study codes, as listed in the Table of Materials.

Participant screening and enrollment

Newly diagnosed patients with T2DM who visited Chongqing Chenjiaqiao Hospital between June 2023 and December 2023 were screened for eligibility. Participants were enrolled in the D....

Results

Baseline demographic and clinical characteristics of the initially screened clinical cohort are presented in Table 1. A representative subgroup consisting of 12 newly diagnosed T2DM and 12 NM participants was subsequently selected for 16S rDNA sequencing and downstream microbiome analyses.

Sequencing depth evaluation

The Shannon index was calculated, and the rarefaction curve was plotted to assess the sequencing depth. A total of 1,.......

Discussion

In recent years, the human microbiome, particularly the gut microbiota, has emerged as a complex and influential factor in host metabolism, immune regulation, and disease development. In a healthy host, the gut microbiota contributes to immune homeostasis and intestinal barrier integrity¹¹. Dysbiosis has been associated with a range of pathological conditions, including metabolic disorders, autoimmune diseases, and malignancies^{12,13,<.......}

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
16S rRNA Primers (V3–V4)	Sangon Biotech	Custom	Universal primers for bacterial 16S region
80 °C Freezer	Haier Biomedical	DW-86L626	Long-term sample storage
Agarose Gel System (Sub-Cell GT)	Bio-Rad	170-4401	PCR product validation
AMPure XP Magnetic Beads	Beckman Coulter	A63880	DNA purification and size selection
Bead-beating Tubes (Lysing Matrix E)	MP Biomedicals	116913050	Mechanical lysis of microbial cells
Bioinformatics Workstation	Dell	Precision 7920	Data analysis using QIIME2 and R
DADA2 pipeline	Bioconductor	Version 1.28	Sequence denoising and ASV generation; https://benjjneb.github.io/dada2
Deblur	QIIME2	Version 2023.5	Sequence denoising and error correction; https://docs.qiime2.org
European Genome-Phenome Archive	EMBL-EBI	EGAS00001005020	Public metagenomic dataset repository; https://ega-archive.org
GWAS Catalog	NHGRI-EBI	Accessions GCST90032172–GCST90032644	Public genome-wide association summary statistics database; https://www.ebi.ac.uk/gwas
Illumina MiSeq Sequencing System	Illumina	SY-410-1003	High-throughput sequencing platform
KEGG database	Kyoto Encyclopedia of Genes and Genomes	Release 107	Functional pathway annotation database; https://www.genome.jp/kegg
MetaCyc database	SRI International	Version 27.1	Metabolic pathway database; https://metacyc.org
Microcentrifuge	Eppendorf	5424R	Cooling centrifuge for sample preparation
Nextera XT Library Prep Kit	Illumina	20018705	Library prep for sequencing
PCR Master Mix (2x PrimeSTAR Max Premix)	Takara Bio	RR350A	For high-fidelity PCR
PCR Thermocycler	Bio-Rad	T100	PCR amplification instrument
PICRUSt2	PICRUSt2 Development Team	Version 2.5.2	Functional pathway prediction from 16S rDNA sequencing data; https://github.com/picrust/picrust2
QIAamp Fast DNA Stool Mini Kit	QIAGEN	51504	DNA extraction from stool samples
QIAquick Gel Extraction Kit	QIAGEN	28704	DNA purification from agarose gel
Qubit Fluorometer	Thermo Fisher Scientific	Q33226	DNA quantification
R software	R Foundation for Statistical Computing	Version 4.3.1	Statistical analysis and Mendelian randomization analysis; https://www.r-project.org
SILVA 138 database	SILVA ribosomal RNA database project	Release 138	Reference database for taxonomic annotation; https://www.arb-silva.de
Stool Collection Tube	Sarstedt	76.9923.001	Sterile stool specimen collection tube
Stool DNA Kit	Omega	D4015-02	DNA extraction kit
TwoSampleMR package	MR-Base	Version 0.5.7	Mendelian randomization analysis in R; https://mrcieu.github.io/TwoSampleMR