Microbial network inference for longitudinal microbiome studies with LUPINE DOI Creative Commons
Saritha Kodikara, Kim‐Anh Lê Cao

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 10, 2024

Abstract The microbiome is a complex ecosystem of interdependent taxa that has traditionally been studied through cross-sectional studies. However, longitudinal studies are becoming increasingly popular. These enable researchers to infer associations towards the understanding coexistence, competition, and collaboration between microbes across time. Traditional metrics for association analysis, such as correlation, limited due data characteristics (sparse, compositional, multivariate). Several network inference methods have proposed, but largely unexplored in setting. We introduce LUPINE (LongitUdinal modelling with Partial least squares regression NEtwork inference), novel approach leverages on conditional independence low-dimensional representation. This method specifically designed handle scenarios small sample sizes number time points. first its kind microbial networks time, while considering information from all past points thus able capture dynamic interactions evolve over validate variant, single (for point analysis) simulated four case studies, where we highlight LUPINE’s ability identify relevant each study context, different experimental designs (mouse human or without interventions, short long courses). propose compare inferred detect changes groups response external disturbances. simple yet innovative methodology suitable for, not to, analysing data. R code publicly available readers interested applying these new their

Language: Английский

The central role of the gut microbiota in the pathophysiology and management of type 2 diabetes DOI

Daniel P Baars,

Marcos F. Fondevila, Abraham S. Meijnikman

et al.

Cell Host & Microbe, Journal Year: 2024, Volume and Issue: 32(8), P. 1280 - 1300

Published: Aug. 1, 2024

Language: Английский

Citations

14
Associations of Dietary Live Microbes Intake and Prevalence of Prediabetes in US Adults: A Cross-Sectional Analysis DOI Creative Commons
Xiaoxu Ge,

Juan Du,

Jiajia Wang

et al.

Journal of Multidisciplinary Healthcare, Journal Year: 2025, Volume and Issue: Volume 18, P. 1135 - 1145

Published: Feb. 1, 2025

A higher dietary intake of live microbes has been shown to be associated with a range health benefits. We aimed elucidate the associations between and risk prediabetes. Adult participants from 1999-2018 US National Health Nutrition Examination Survey were included categorized into low, medium, high microbe groups based on Sanders classification system. Associations consumption prevalence prediabetes explored using univariate multivariate logistic regression, stratified analysis, sensitivity analysis. Among 28201 (mean age 45.83 years, 48.40% men, 32.78% prediabetes) included, 9761 (31.80%), 12,076 (41.42%) 6364 (26.78%) classified groups, respectively. After adjusting for all potential covariates, odds ratios 95% confidence intervals medium 0.868 (0.803-0.937) 0.891 (0.807-0.983), respectively (P trend = 0.017), low group as reference. This association is robust not affected by participant's age, sex, race, poverty-income ratio, education level, hypertension status estimated glomerular filtration rate. was found cross-sectionally linked lower in adults.

Language: Английский

Citations

1
Understanding Patterns of the Gut Microbiome May Contribute to the Early Detection and Prevention of Type 2 Diabetes Mellitus: A Systematic Review DOI Creative Commons
Natalia G. Bednarska, Asta K. Håberg

Microorganisms, Journal Year: 2025, Volume and Issue: 13(1), P. 134 - 134

Published: Jan. 10, 2025

The rising burden of type 2 diabetes mellitus (T2DM) is a growing global public health problem, particularly prominent in developing countries. early detection T2DM and prediabetes vital for reversing the outcome disease, allowing intervention. In past decade, various microbiome-metabolome studies have attempted to address question whether there are any common microbial patterns that indicate either prediabetic or diabetic gut signatures. Because current high methodological heterogeneity risk bias, we selected adhered similar design methodology. We performed systematic review assess if were changes microbiome belonging diabetic, healthy individuals. cross-sectional presented here collectively covered population 65,754 people, with 1800 2TD group, 2770 group 61,184 control group. overall diversity scores lower T2D cohorts 86% analyzed studies. Re-programming potentially one safest long-lasting ways eliminate its stages. differences abundance certain species could serve as an warning dysbiotic environment be easily modified before onset disease by lifestyle, taking probiotics, introducing diet modifications stimulating vagal nerve. This shows how metagenomic will continue identify novel therapeutic targets (probiotics, prebiotics elimination from flora). work clearly intervention studies, according standard operating protocols using predefined analytic framework (e.g., STORMS), combined other broader conclusions collating all cohort efforts eliminating effect-size statistical insufficiency single study.

Language: Английский

Citations

0
Microbial network inference for longitudinal microbiome studies with LUPINE DOI Creative Commons
Saritha Kodikara, Kim‐Anh Lê Cao

Microbiome, Journal Year: 2025, Volume and Issue: 13(1)

Published: March 3, 2025

Abstract Background The microbiome is a complex ecosystem of interdependent taxa that has traditionally been studied through cross-sectional studies. However, longitudinal studies are becoming increasingly popular. These enable researchers to infer associations towards the understanding coexistence, competition, and collaboration between microbes across time. Traditional metrics for association analysis, such as correlation, limited due data characteristics (sparse, compositional, multivariate). Several network inference methods have proposed, but largely unexplored in setting. Results We introduce LUPINE (LongitUdinal modelling with Partial least squares regression NEtwork inference), novel approach leverages on conditional independence low-dimensional representation. This method specifically designed handle scenarios small sample sizes number time points. first its kind microbial networks time, while considering information from all past points thus able capture dynamic interactions evolve over validate variant, LUPINE_single (for single point analysis) simulated four case studies, where we highlight LUPINE’s ability identify relevant each study context, different experimental designs (mouse human or without interventions, short long courses). To detect changes groups response external disturbances, used compare inferred networks. Conclusions simple yet innovative methodology suitable for, not to, analysing data. R code publicly available readers interested applying these new their

Language: Английский

Citations

0
A novel approach to finding the compositional differences and biomarkers in gut microbiota in type 2 diabetic patients via meta-analysis, data-mining, and multivariate analysis DOI
Faezeh Ebrahimi, Hadi Maleki, Mansour Ebrahimi

et al.

Endocrinología Diabetes y Nutrición, Journal Year: 2025, Volume and Issue: unknown, P. 501561 - 501561

Published: March 1, 2025

Language: Английский

Citations

0
Gut microbiota and its metabolites regulate insulin resistance: traditional Chinese medicine insights for T2DM DOI Creative Commons
Jing Liu, Fuxing Li, Le Yang

et al.

Frontiers in Microbiology, Journal Year: 2025, Volume and Issue: 16

Published: March 19, 2025

The gut microbiota is closely associated with the onset and development of type 2 diabetes mellitus (T2DM), characterized by insulin resistance (IR) chronic low-grade inflammation. However, despite widespread use first-line antidiabetic drugs, IR in its complications continue to rise. metabolic products may promote T2DM exacerbating IR. Therefore, regulating has become a promising therapeutic strategy, particular attention given probiotics, prebiotics, synbiotics, fecal transplantation. This review first examines relationship between T2DM, summarizing research progress microbiota-based therapies modulating We then delve into how microbiota-related contribute Finally, we summarize findings on role traditional Chinese medicine improve In conclusion, play crucial pathophysiological process IR, offering new insights potential strategies for T2DM.

Language: Английский

Citations

0
Microbial network inference for longitudinal microbiome studies with LUPINE DOI Creative Commons
Saritha Kodikara, Kim‐Anh Lê Cao

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 10, 2024

Abstract The microbiome is a complex ecosystem of interdependent taxa that has traditionally been studied through cross-sectional studies. However, longitudinal studies are becoming increasingly popular. These enable researchers to infer associations towards the understanding coexistence, competition, and collaboration between microbes across time. Traditional metrics for association analysis, such as correlation, limited due data characteristics (sparse, compositional, multivariate). Several network inference methods have proposed, but largely unexplored in setting. We introduce LUPINE (LongitUdinal modelling with Partial least squares regression NEtwork inference), novel approach leverages on conditional independence low-dimensional representation. This method specifically designed handle scenarios small sample sizes number time points. first its kind microbial networks time, while considering information from all past points thus able capture dynamic interactions evolve over validate variant, single (for point analysis) simulated four case studies, where we highlight LUPINE’s ability identify relevant each study context, different experimental designs (mouse human or without interventions, short long courses). propose compare inferred detect changes groups response external disturbances. simple yet innovative methodology suitable for, not to, analysing data. R code publicly available readers interested applying these new their

Language: Английский

Citations

0