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Voluntary wheel running and lithium supplementation promotes fatigue resistance, fat oxidation, and improves insulin tolerance in D2 mdx mice

2023· article· en· W4378674670 on OpenAlexaff
Bianca M. Marcella, Briana L. Hockey, Luc Wasilewicz, Sophie I. Hamstra, Mia S. Geromella, Rebecca E. K. MacPherson, Val A. Fajardo

Bibliographic record

VenuePhysiology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsBrock University
Fundersnot available
KeywordsInsulin resistanceInternal medicineEndocrinologymdx mouseDuchenne muscular dystrophyType 2 diabetesMedicineInsulinWastingDiabetes mellitusDystrophin

Abstract

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BACKGROUND: Duchenne muscular dystrophy (DMD) is an X-linked disorder characterized by progressive muscle wasting and premature death. Many DMD patients will also exhibit signs of metabolic dysfunction such as insulin resistance that predisposes them to type 2 diabetes and worsened cardiovascular disease. Most treatments for DMD aim to lower muscle inflammation and improve strength, however, none have addressed the complication of insulin resistance. The current standard of treatment is corticosteroids, which prolongs ambulation, however, chronic use increases susceptibility to obesity, insulin resistance, and type 2 diabetes. It is well-established that regular aerobic exercise enhances insulin sensitivity. Further, we have recently found that inhibiting glycogen synthase kinase 3 with lithium (Li), a known insulin mimetic, improves force production and fatigue resistance in wildtype (WT) mice. Here, we tested whether voluntary wheel running (VWR) and Li supplementation, together, could improve whole-body fatigue, metabolism, and insulin sensitivity in the DBA/2J (D2) mdx mouse model of DMD. METHODS: 5-week old male D2 WT and mdx mice were separated into four groups: WT, mdx sedentary (SED), mdx VWR, and mdx Li+VWR (n=11/group). The mdx VWR and mdx Li+VWR mice had unlimited access to a cagewheel, and mdx Li+VWR mice were given a low dose of Li (50 mg/kg/day) via their drinking water throughout the study. A treadmill time-to-fatigue test was performed to assess whole-body fatigue. Mice were housed for 48-hours in metabolic cages to measure energy expenditure. Glucose handling was measured using glucose and insulin tolerance tests. RESULTS: As expected, WT mice had ~2-fold greater total cage activity compared to all mdx groups. When examining cagewheel distance, we found that mdx Li+VWR mice ran half the total distance in kilometres than mdx VWR mice (39.1 ± 9.6 km vs. 74.8 ± 13.6 km). Despite this, VWR with and without Li improved whole-body fatigue as only mdx SED mice (22.8 ± 0.9 min) had a shorter time to exhaustion than WT mice (32.6 ± 0.9 min). All mdx groups had higher energy expenditure than WT mice, suggestive of a hypermetabolic phenotype. When examining the respiratory exchange ratio (RER) among mdx mice, mdxLi+VWR (0.87) mice had a lower RER than mdx SED mice (0.91), indicative of enhanced fat oxidation. Glucose tolerance was equally impaired among mdx groups compared to WT mice. However, insulin tolerance was notably improved in mdx VWR and mdx Li+VWR mice, with a greater improvement observed in the mdx Li+VWR mice. CONCLUSION: Despite running less than mdx VWR mice, mdx Li+VWR mice had enhanced fatigue resistance, fat utilization, and insulin sensitivity, suggesting Li may be a viable treatment to improve muscle and metabolic function in mdx mice. CIHR CRC (Tier II) to VAF This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

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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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.261
Teacher spread0.250 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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