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Record W3166941069 · doi:10.1016/j.molmet.2021.101270

Exercise of high intensity ameliorates hepatic inflammation and the progression of NASH

2021· article· en· W3166941069 on OpenAlexaff
Gavin Fredrickson, Fanta Barrow, Katrina Dietsche, Preethy Parthiban, Saad Khan, Sacha Robert, Maya Demirchian, Hailey Rhoades, Haiguang Wang, Oyedele Adeyi, Xavier S. Revelo

Bibliographic record

VenueMolecular Metabolism · 2021
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesAmerican Association of Immunologists
KeywordsInflammationMedicineNonalcoholic steatohepatitisInternal medicineFatty liverNonalcoholic fatty liver diseaseDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: Non-alcoholic fatty liver disease (NAFLD) covers a wide spectrum of liver pathology ranging from simple fatty liver to non-alcoholic steatohepatitis (NASH). Notably, immune cell-driven inflammation is a key mechanism in the transition from fatty liver to the more serious NASH. Although exercise training is effective in ameliorating obesity-related diseases, the underlying mechanisms of the beneficial effects of exercise remain unclear. It is unknown whether there is an optimal modality and intensity of exercise to treat NAFLD. The objective of this study was to determine whether high-intensity interval training (HIIT) or moderate-intensity continuous training (MIT) is more effective at ameliorating the progression of NASH. METHODS: Wild-type mice were fed a high-fat, high-carbohydrate (HFHC) diet for 6 weeks and left sedentary (SED) or assigned to either an MIT or HIIT regimen using treadmill running for an additional 16 weeks. MIT and HIIT groups were pair-fed to ensure that energy intake was similar between the exercise cohorts. To determine changes in whole-body metabolism, we performed insulin and glucose tolerance tests, indirect calorimetry, and magnetic resonance imaging. NASH progression was determined by triglyceride accumulation, expression of inflammatory genes, and histological assessment of fibrosis. Immune cell populations in the liver were characterized by cytometry by time-of-flight mass spectrometry, and progenitor populations within the bone marrow were assessed by flow cytometry. Finally, we analyzed the transcriptional profile of the liver by bulk RNA sequencing. RESULTS: Compared with SED mice, both HIIT and MIT suppressed weight gain, improved whole-body metabolic parameters, and ameliorated the progression of NASH by reducing hepatic triglyceride levels, inflammation, and fibrosis. However, HIIT was superior to MIT at reducing adiposity, improving whole-body glucose tolerance, and ameliorating liver steatosis, inflammation, and fibrosis, without any changes in body weight. Improved NASH progression in HIIT mice was accompanied by a substantial decrease in the frequency of pro-inflammatory infiltrating, monocyte-derived macrophages in the liver and reduced myeloid progenitor populations in the bone marrow. Notably, an acute bout of MIT or HIIT exercise had no effect on the intrahepatic and splenic immune cell populations. In addition, bulk mRNA sequencing of the entire liver tissue showed a pattern of gene expression confirming that HIIT was more effective than MIT in improving liver inflammation and lipid biosynthesis. CONCLUSIONS: Our data suggest that exercise lessens hepatic inflammation during NASH by reducing the accumulation of hepatic monocyte-derived inflammatory macrophages and bone marrow precursor cells. Our findings also indicate that HIIT is superior to MIT in ameliorating the disease in a dietary mouse model of NASH.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.240
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations83
Published2021
Admission routes1
Has abstractyes

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