Thiamine-reduced Fatigue in Quiescent Inflammatory Bowel Disease is Linked to Faecalibacterium Prausnitzii Abundance
GASTRO HEP ADVANCES(2025)
Tech Univ Denmark | Aarhus Univ Hosp | Dept Hepatol & Gastroenterol
Abstract
BACKGROUND AND AIMS: Chronic fatigue is common in patients with inflammatory bowel disease (IBD). The gut micro- biota, specifically, microbial diversity and butyrate-producing bacteria have been linked to the fatigue pathogenesis. High-dose oral thiamine reduces fatigue, potentially through gut microbiota modification. In this study, we investigated how the gut micro- biota influences the efficacy of high-dose thiamine in alleviating chronic fatigue in quiescent IBD (qIBD). METHODS: We analyzed the microbiota and short-chain fatty acids concentrations in fecal samples from patients with qIBD, with (n- 40) or without (n- 20) chronic fatigue. The 40 patients with qIBD and fatigue were included in a randomized, placebo-controlled, crossover trial to assess a 4-week high-oral-dose thiamine regimen. RESULTS: Butyrate and butyrate-producing bacteria were similar in patients with and without fatigue and did not change with high-dose thiamine treatment. Notably, Faecalibacterium prausnitzii was more abundant in thiamine responders compared with nonresponders both pretreatment (P- .019) and post-treatment (P- .038). The relative abundances of Faecalibacterium prausnitzii and Roseburia hominis, both pretreatment and post-treatment, inversely correlated with IBD fatigue score changes for patients with chronic fatigue (PRE; R-- 0.48, P- .004, and R-- 0.40, P- .018; POST; R-- 0.42, P- .012, and R-- 0.40, P- .017) respectively. CONCLUSION: Faecalibacterium prausnitzii and Roseburia hominis may serve as markers for response to high-dose thiamine in alleviating chronic fatigue in patients with qIBD. The mechanistic role of gut bacteria and butyrate in patients with chronic fatigue and qIBD should be further explored.
MoreTranslated text
Key words
Gut Microbiota,Chronic Fatigue,Crohn’s Disease,Colitis,Ulcerative,Thiamine
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper