ArticleFrontiers in endocrinology2023
Dysbiosis signatures of gut microbiota and the progression of type 2 diabetes: a machine learning approach in a Mexican cohort.
Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 3 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- STROBE-causal machine learning for the human microbiome: systematic review on methodological innovations and validation frameworks.Frontiers in microbiology · 2026Pooled it
- Multi-Omics Analyses Reveal Relationships Between Gut Microbiota and Frailty.Brain and behavior · 2025Pooled it
- Beyond just correlation: causal machine learning for the microbiome, from prediction to health policy with econometric tools.Frontiers in microbiology · 2025Pooled it
- Improvement of glucose homeostasis during leptin treatment does not alter the intestinal microbiome in male diabetic UC Davis type-2 diabetes mellitus rats.American journal of physiology. Gastrointestinal and liver physiology · 2026Article
- Oral-gut microbiome dysbiosis in obese smokers reveals compartment-specific shifts.AMB Express · 2026Article
- Gut Microbiome Signatures Distinguish Susceptibility from Disease Development in Type 2 Diabetes.International journal of molecular sciences · 2026Article
- Digital modeling of metformin and diet interactions on gut-microbiota metabolism in prediabetic patients.Computational and structural biotechnology journal · 2026Article
- Modulating the Gut Microbiome in Type 2 Diabetes: Nutritional and Therapeutic Strategies.Nutrients · 2025Review
- Prospective association between the gut microbiota and incident pneumonia: a cohort study of 6419 individuals.Respiratory research · 2025Article
- Gut Microbiota and Metabolites: Biomarkers and Therapeutic Targets for Diabetes Mellitus and Its Complications.Nutrients · 2025Review
- Artificial Intelligence Enabled Lifestyle Medicine in Diabetes Care: A Narrative Review.American journal of lifestyle medicine · 2025Review
- The role of the gut microbiota and its metabolites: a new predictor in diabetes and its complications.European journal of medical research · 2025Review
- Applications of Artificial Intelligence and Machine Learning in Prediabetes: A Scoping Review.Journal of diabetes science and technology · 2025Review
- Probiotics as Antioxidant Strategy for Managing Diabetes Mellitus and Its Complications.Antioxidants (Basel, Switzerland) · 2025Review
- Influence of menstrual cycle and oral contraception on taxonomic composition and gas production in the gut microbiome.Journal of medical microbiology · 2025Article
- Effect of docosahexaenoic acid as an anti-inflammatory for Caco-2 cells and modulating agent for gut microbiota in children with obesity (the DAMOCLE study).Journal of endocrinological investigation · 2025Article
- Understanding Patterns of the Gut Microbiome May Contribute to the Early Detection and Prevention of Type 2 Diabetes Mellitus: A Systematic Review.Microorganisms · 2025Review
- Impact of Ketogenic and Mediterranean Diets on Gut Microbiota Profile and Clinical Outcomes in Drug-Naïve Patients with Diabesity: A 12-Month Pilot Study.Metabolites · 2025Article
- Multi-omics approaches for biomarker discovery and precision diagnosis of prediabetes.Frontiers in endocrinology · 2025Review
- The emerging role of probiotics in the management and treatment of diabetic foot ulcer: a comprehensive review.AIMS microbiology · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The gut microbiota (GM) dysbiosis is one of the causal factors for the progression of different chronic metabolic diseases, including type 2 diabetes mellitus (T2D). Understanding the basis that laid this association may lead to developing new therapeutic strategies for preventing and treating T2D, such as probiotics, prebiotics, and fecal microbiota transplants. It may also help identify potential early detection biomarkers and develop personalized interventions based on an individual's gut microbiota profile. Here, we explore how supervised Machine Learning (ML) methods help to distinguish taxa for individuals with prediabetes (prediabetes) or T2D. Methods: To this aim, we analyzed the GM profile (16s rRNA gene sequencing) in a cohort of 410 Mexican naïve patients stratified into normoglycemic, prediabetes, and T2D individuals. Then, we compared six different ML algorithms and found that Random Forest had the highest predictive performance in classifying T2D and prediabetes patients versus controls. Results: We identified a set of taxa for predicting patients with T2D compared to normoglycemic individuals, including Discussion: These findings allow us to postulate that GM is a distinctive signature in prediabetes and T2D patients during the development and progression of the disease. Our study highlights the role of GM and opens a window toward the rational design of new preventive and personalized strategies against the control of this disease.
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