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Record W3114620049 · doi:10.3803/enm.2020.405

Sarcopenia and Muscle Aging: A Brief Overview

2020· review· en· W3114620049 on OpenAlexafffund
Tam Dao, Alex E. Green, Yun-A Kim, Sung‐Jin Bae, Ki‐Tae Ha, Karim Gariani, Mi-ra Lee, Keir J. Menzies, Dongryeol Ryu

Bibliographic record

VenueEndocrinology and Metabolism · 2020
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaCanadian Institutes of Health ResearchNational Research FoundationUniversity of Ottawa
KeywordsSarcopeniaMedicinePhysical medicine and rehabilitationGerontologyInternal medicine

Abstract

fetched live from OpenAlex

The world is facing the new challenges of an aging population, and understanding the process of aging has therefore become one of the most important global concerns. Sarcopenia is a condition which is defined by the gradual loss of skeletal muscle mass and function with age. In research and clinical practice, sarcopenia is recognized as a component of geriatric disease and is a current target for drug development. In this review we define this condition and provide an overview of current therapeutic approaches. We further highlight recent findings that describe key pathophysiological phenotypes of this condition, including alterations in muscle fiber types, mitochondrial function, nicotinamide adenine dinucleotide (NAD+) metabolism, myokines, and gut microbiota, in aged muscle compared to young muscle or healthy aged muscle. The last part of this review examines new therapeutic avenues for promising treatment targets. There is still no accepted therapy for sarcopenia in humans. Here we provide a brief review of the current state of research derived from various mouse models or human samples that provide novel routes for the development of effective therapeutics to maintain muscle health during aging.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.110
GPT teacher head0.402
Teacher spread0.292 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations219
Published2020
Admission routes2
Has abstractyes

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