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Record W4301600585 · doi:10.1038/s41467-022-33008-2

Deletion of skeletal muscle Akt1/2 causes osteosarcopenia and reduces lifespan in mice

2022· article· en· W4301600585 on OpenAlexfundno aff
Takayoshi Sasako, T. Umehara, Kotaro Soeda, Kazuma Kaneko, Miho Suzuki, Naoki Kobayashi, Yukiko Okazaki, Miwa Tamura‐Nakano, Tomoki Chiba, Domenico Accili, C. Ronald Kahn, Tetsuo Noda, Hiroshi Asahara, Toshimasa Yamauchi, Takashi Kadowaki, Kohjiro Ueki

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
FundersDaiichi Sankyo EuropeNational Institute of Diabetes and Digestive and Kidney DiseasesYork UniversityUniversity of TokyoPfizerDaiichi-SankyoKowa CompanyMinistry of Education, Culture, Sports, Science and Technology
KeywordsSarcopeniaSkeletal muscleAKT1MedicineInternal medicineGerontologyEndocrinologyBiologyBioinformaticsCell biologyPhosphorylation

Abstract

fetched live from OpenAlex

Aging is considered to be accelerated by insulin signaling in lower organisms, but it remained unclear whether this could hold true for mammals. Here we show that mice with skeletal muscle-specific double knockout of Akt1/2, key downstream molecules of insulin signaling, serve as a model of premature sarcopenia with insulin resistance. The knockout mice exhibit a progressive reduction in skeletal muscle mass, impairment of motor function and systemic insulin sensitivity. They also show osteopenia, and reduced lifespan largely due to death from debilitation on normal chow and death from tumor on high-fat diet. These phenotypes are almost reversed by additional knocking out of Foxo1/4, but only partially by additional knocking out of Tsc2 to activate the mTOR pathway. Overall, our data suggest that, unlike in lower organisms, suppression of Akt activity in skeletal muscle of mammals associated with insulin resistance and aging could accelerate osteosarcopenia and consequently reduce lifespan.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.364
Teacher spread0.325 · 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 designObservational
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

Citations73
Published2022
Admission routes1
Has abstractyes

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