ArticleJournal of advanced research2024
Machine learning approach reveals microbiome, metabolome, and lipidome profiles in type 1 diabetes.
Article in Journal of advanced research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Review
- Comparative study on the predictive value of PNI and GNRI for all-cause mortality rates of elderly patients in cardiac care unit and cardiovascular intensive care unit.European journal of clinical nutrition · 2026Observational
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Lipidomic signatures associated with cognitive impairment in type 1 diabetes mellitus: a pilot study integrating clinical serum and mouse hippocampus.Journal of translational medicine · 2026Article
- Identifying gut microbiome signatures of type 1 diabetes using machine learning and evolutionary feature selection.BMC microbiology · 2026Article
- Association Between Gut Microbiota and HIV Infection Risk: Insights from Mendelian Randomization and 16S rRNA Amplicon Sequencing.Microorganisms · 2026Article
- Oral GAD65-Microorganisms · 2026Article
- Childhood type 1 diabetes mellitus and gut microbiota: from microbiome characteristics to prevention and treatment strategies.Frontiers in cellular and infection microbiology · 2026Review
- Microbial dysbiosis and predictive neuroactive functional profiles in paediatric neurogenic bladder: an exploratory machine learning-based approach.Frontiers in cellular and infection microbiology · 2026Article
- Artificial intelligence-driven therapeutics for disease modification in type 1 diabetes: a digital public health and clinical translation framework.Frontiers in public health · 2026Review
- Machine Learning-Based Identification of Serum Metabolic Signatures in Adult Patients with Type 1 Diabetes.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- The Gut Microbiota: Emerging Evidence in Autoimmune and Inflammatory Diseases.Research (Washington, D.C.) · 2026Review
- Nanomedicines Against Mitochondrial Dysfunction-Induced Metabolic Diseases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Gut Microbiota and Metabolites: Biomarkers and Therapeutic Targets for Diabetes Mellitus and Its Complications.Nutrients · 2025Review
- Metabolome-wide Mendelian randomisation reveals causal links between circulating metabolites and type 1 diabetes.EBioMedicine · 2025Article
- Association of the lipidome with type 1 diabetes and the mediated effect of metabolites: A Mendelian randomization study.Medicine · 2025Article
- Causal effects of metabolites on malignant neoplasm of bone and articular cartilage: a mendelian randomization study.Frontiers in genetics · 2025Article
- Unravelling the Role of Gut and Oral Microbiota in the Pediatric Population with Type 1 Diabetes Mellitus.International journal of molecular sciences · 2024Review
- Gut microbiota, serum metabolites, and lipids related to blood glucose control and type 1 diabetes.Journal of diabetes · 2024Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionType 1 diabetes (T1D) is a complex disorder influenced by genetic and environmental factors. The gut microbiome, the serum metabolome, and the serum lipidome have been identified as key environmental factors contributing to the pathophysiological mechanisms of T1D.
objectivesWe aimed to explore the gut microbiota, serum metabolite, and serum lipid signatures in T1D patients by machine learning.
methodsWe evaluated 137 individuals in a cross-sectional cohort involving 38 T1D patients, 38 healthy controls, and 61 T1D patients for validation. We characterized gut microbiome, serum metabolite, and serum lipid profiles with machine learning approaches (logistic regression, support vector machine, Gaussian naive Bayes, and random forest).
resultsThe machine learning approaches using the microbiota composition did not accurately diagnose T1D (model accuracy = 0.7555), while the accuracy of the model using the metabolite composition was 0.9333. Based on the metabolite composition, 3-hydroxybutyric acid and 9-oxo-ode (area under curve = 0.70 and 0.67, respectively, both increased in T1D) were meaningful overlap metabolites screened by multiple bioinformatics methods. We confirmed the biological relevance of the microbiome, metabolome, and lipidome features in the validation group.
conclusionBy using machine learning algorithms and multi-omics, we demonstrated that T1D patients are associated with altered microbiota, metabolite, and lipidomic signatures or functions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.