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Record W4387380762 · doi:10.1210/jendso/bvad114.1772

OR33-04 Mechanistic Dissection Of Circadian-Specific Glucocorticoid Effects On Exercise Tolerance And Muscle Glucose Utilization

2023· article· en· W4387380762 on OpenAlexfundno aff
Ashok Daniel Prabakaran, Michelle Wintzinger, Kevin Piczer, Karen Miz, A. Walton, Hima Bindu Durumutla, Mattia Quattrocelli

Bibliographic record

VenueJournal of the Endocrine Society · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsEndocrinologyInternal medicineGlucocorticoidCircadian rhythmDosingLean body massAdiponectinMedicineBiologyInsulin resistanceInsulinBody weight

Abstract

fetched live from OpenAlex

Abstract Disclosure: A. Prabakaran: None. M. Wintzinger: None. K. Piczer: None. K. Miz: None. A. Walton: None. H. Durumutla: None. M. Quattrocelli: None. Glucocorticoids (GCs) are circadian hormones regulating metabolism by activating the glucocorticoid receptor (GR) as pleiotropic transcription factor. Timing of exogenous GC drug regimens is emerging as a key determinant of their metabolic effects. This was shown by the surprising finding that once-weekly GC intermittence reverses many of the pro-obesogenic side effects of once-daily GC dosing in mice and humans. However, despite the intrinsic circadian nature of this signaling, the impact of time-of-day of exogenous GC dosing, i.e. chrono-pharmacology, remains remarkably unknown for metabolic physiology and exercise tolerance. Relevance of this question for humans is supported by the initial findings of increased lean mass and motor function with circadian-restricted prednisone intermittence in a recent pilot clinical trial in patients with genetic myopathies. However, mechanisms to support significance of these effects for metabolic diseases and aging remain unknown. We studied 12-week-long intermittent regimens of the exogenous GC prednisone in mice in conditions of diet-induced obesity or advanced aging, comparing dosing at light-phase start (ZT0) versus dark-phase start (ZT12). We found that, compared to dark-phase, light-phase dosing enhanced the regimen-driven increase in body-wide lean mass, muscle mass and muscle function in both obese and aged mice. Intriguingly, experiments with adiponectin-knockout mice showed that these effects were dependent on adiponectin, which promotes muscle insulin sensitivity. We then used tissue-specific inducible knockout models to dissect non-muscle versus muscle-autonomous effects of GC chrono-pharmacology. We found that the GC effects on total and high-molecular-weight adiponectin upregulation were dependent on regimen-specific engagement of the adipocyte-specific GR on the adiponectin gene promoter. Complementary to adipose-driven adiponectin production, we found that adiponectin receptor AdipoR1 upregulation in muscle depended on circadian-specific engagement of the myocyte-specific GR on the AdipoR1 promoter. Consistent with the adiponectin action on muscle insulin sensitivity, ablation of myocyte-specific GR blocked the regimen-driven effects on insulin-sensitive muscle glucose uptake and muscle mass. Furthermore, we found that light-phase GC dosing engaged the muscle GR for a non-canonical interaction with the clock factor BMAL1 to upregulate the mitochondrial regulator PGC1alpha. Indeed, we found that the regimen-driven increase in glucose oxidation and amino acid biogenesis from TCA cycle intermediates in muscle was dependent on the myocyte-specific PGC1alpha. In summary, we present here novel circadian and molecular mechanisms reconverting glucocorticoid drugs from deleterious to re-energizing agents for potential chrono-treatment of metabolic conditions and aging. Presentation: Sunday, June 18, 2023

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 score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.002

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 head0.012
GPT teacher head0.252
Teacher spread0.239 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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