Microbiome and NAFLD: potential influence of aerobic fitness and lifestyle modification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nonalcoholic fatty liver disease (NAFLD) is a chronic liver disease with prevalence rates that are on the rise in the US and worldwide. NAFLD encompasses a spectrum of liver pathologies including simple steatosis to nonalcoholic steatohepatitis (NASH) with inflammation and fibrosis. The gut microbiome has emerged as a potential therapeutic target in combating metabolic diseases including obesity, Type 2 diabetes, and NAFLD/NASH. Diet-induced obesity/Western style diet feeding causes severe microbial dysbiosis initiating a microbiome signature that promotes metabolite production that directly impacts hepatic metabolism. Changes in lifestyle (i.e., diet, exercise, and aerobic fitness) improve NAFLD outcomes and can significantly influence the microbiome. However, directly linking lifestyle-induced remodeling of the microbiome to NAFLD pathogenesis is not well understood. Understanding the reshaping of the microbiome and the metabolites produced and their subsequent actions on hepatic metabolism are vital in understanding the gut-liver axis. In this review, we 1) discuss microbiome-derived metabolites that significantly contribute to the gut-liver axis and are directly linked to NAFLD/NASH and 2) present evidence on lifestyle modifications reshaping the microbiome and the potential therapeutic aspects in combating the disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it