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Record W4403027020 · doi:10.1172/jci.insight.174007

Targeted Bmal1 restoration in muscle prolongs lifespan with systemic health effects in aging model

2024· article· en· W4403027020 on OpenAlexaff
Miguel A. Gutierrez‐Monreal, Christopher A. Wolff, Eduardo E Rijos, Mark Viggars, Collin M. Douglas, Vishwajeeth Pagala, Junmin Peng, Liam C. Hunt, Haocheng Ding, Zhiguang Huo, Fabio Demontis, Karyn A. Esser

Bibliographic record

VenueJCI Insight · 2024
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsInstitute of Aging
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute on AgingNational Institutes of Health
KeywordsMedicineGerontologyNeurosciencePsychology

Abstract

fetched live from OpenAlex

Disruption of the circadian clock in skeletal muscle worsens local and systemic health, leading to decreased muscle strength, metabolic dysfunction, and aging-like phenotypes. Whole-body knockout mice that lack Bmal1, a key component of the molecular clock, display premature aging. Here, by using adeno-associated viruses, we rescued Bmal1 expression specifically in the skeletal muscle fibers of Bmal1-KO mice and found that this engaged the circadian clock and clock output gene expression, contributing to extended lifespan. Time course phenotypic analyses found that muscle strength, mobility, and glucose tolerance were improved with no effects on muscle mass or fiber size or type. A multiomics approach at 2 ages further determined that restored muscle Bmal1 improved glucose handling pathways while concomitantly reducing lipid and protein metabolic pathways. The improved glucose tolerance and metabolic flexibility resulted in the systemic reduction of inflammatory signatures across peripheral tissues, including liver, lung, and white adipose fat. Together, these findings highlight the critical role of muscle Bmal1 and downstream target genes for skeletal muscle homeostasis with considerable implications for systemic health.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations25
Published2024
Admission routes1
Has abstractyes

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