ArticleJournal of translational medicine2024
Interactions between the gut microbiome and DNA methylation patterns in blood and visceral adipose tissue in subjects with different metabolic characteristics.
Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- The Gut-Brain Axis in Metabolic Syndrome: Emerging Mechanisms and Perspectives in Personalized Medicine.International journal of molecular sciences · 2026Review
- Targeting the Gut Microbiota in Pediatric Obesity: A Paradigm Shift in Prevention and Treatment? A Comprehensive Review.Nutrients · 2025Review
- Microbiota-driven epigenetic modifications in gastrointestinal cancer: Implications for pathogenesis and therapeutic strategies.World journal of microbiology & biotechnology · 2025Review
- Epigenomic mechanisms of dietary prescriptions for obesity therapy.Epigenomics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundThe gut microbiome has been reported to induce epigenetic modifications in the host, which may be involved in the pathophysiology of metabolic diseases.
objectiveTo evaluate the potential interactions between the gut microbiome and DNA methylome in subjects with different metabolic characteristics.
methodsSixty-four participants with different metabolic characteristics (i.e., participants without obesity -healthy controls-, and participants with obesity and normal insulin sensitivity/insulin resistance/ type 2 diabetes-T2DM-) were included in this study. A machine learning approach was performed in order to identify distinctive patterns in three omics (gut microbiome, blood DNA methylome, and visceral adipose tissue-VAT- DNA methylome) according to the different study groups.
resultsRobust distinctive distribution patterns of the three different omics were found between healthy controls and patients with obesity; participants with and without T2DM, and patients with obesity with and without insulin resistance. Importantly, strong correlations between the gut microbiome (including Odoribacteriaceae and Christensenllaceae families) and both blood and VAT DNA methylome were found. Moreover, in the entire study population, three main bacterial genera (Sutterella, Collinsella and Eubacterium) were related to the epigenetic regulation of different genes involved in distinct processes related to cellular metabolism and metabolic diseases, including small ubiquitin-related modifier (SUMO) transferase activity or lipid binding.
conclusionWe show that distinctive interactions between the gut microbiome and DNA methylome may occur in subjects with different metabolic characteristics. Further research is needed to elucidate the potential role of these interactions in the pathophysiology of obesity and related comorbidities.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.