Evidence map›Paper›PMID 42464764›Full record

ArticleThe Journal of physiology2026

Multifactorial nature of anabolic resistance in ageing skeletal muscle: A systems modelling study.

Taylor J McColl, Daniel R Moore, Eldon Emberly, David D Church, David C Clarke

Abstract read
In one paragraph

Article in The Journal of physiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Taylor J McCollDepartment of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada.ORCID https://orcid.org/0009-0006-3593-207X
Daniel R MooreFaculty of Kinesiology and Physical Education, University of Toronto, Toronto, Ontario, Canada.ORCID https://orcid.org/0000-0001-5865-4398
Eldon EmberlyDepartment of Physics, Simon Fraser University, Burnaby, British Columbia, Canada.
David D ChurchDepartment of Geriatrics, Donald W. Reynolds Institute of Aging, Center for Translational Research in Aging and Longevity, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
David C ClarkeDepartment of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada.ORCID https://orcid.org/0000-0002-1520-5426

Funding

Natural Sciences and Engineering Research Council of Canada (NSERC) Collaborative Research and Training Experience scholarshipNSERC Discovery Grant RGPIN 06004-2014
6 · The paper itself

Abstract

Sarcopenia, the age-related loss of skeletal muscle mass and function, is primarily caused by anabolic resistance, which is the blunted stimulation of muscle protein synthesis (MPS) and impaired suppression of muscle protein breakdown following anabolic stimuli such as feeding. Multiple age-related impairments have been implicated, but none alone explains reduced MPS in older adults, suggesting that anabolic resistance arises from interacting dysregulated processes. Studying these interactions experimentally is challenging, motivating systems approaches. Here, we applied a mechanistic, multiscale kinetic model of leucine-mediated signalling and protein metabolism in human skeletal muscle to study anabolic resistance mechanisms. Parameter values were estimated from published data. Global sensitivity analysis identified key controllers of MPS and net protein balance (NB). Virtual population simulations of MPS responses to amino acid feeding were classified as anabolic sensitive or anabolic resistant. We quantified individual and combined age-related impairments and simulated therapeutic interventions to restore anabolic responsiveness. Sensitivity analyses revealed that intracellular signalling processes controlling MPS dominate NB. Feeding simulations indicated that dysregulation of these processes distinguished anabolic-sensitive from anabolic-resistant phenotypes. No single dysregulated mechanism reproduced age-related reductions in MPS. Instead, anabolic resistance emerged only when multiple impairments operated together. Restoring MPS from multifactorial dysregulation required co-ordinated, multitarget therapeutic strategies. These findings demonstrate that anabolic resistance arises from multiple interacting dysregulations in nutrient sensing and signalling. By quantifying their contributions in a systems framework, this work advances mechanistic understanding of sarcopenia and supports the design of combined interventions to restore muscle protein metabolism in older adults. KEY POINTS: Anabolic resistance is a key contributor to sarcopenia, but no single age-related physiological impairment fully explains the reduced muscle protein synthesis response observed in older adults. Systems modelling identified intracellular signalling processes that directly control muscle protein synthesis as dominant drivers of net protein balance, distinguishing anabolic-sensitive from anabolic-resistant phenotypes. Simulations reveal that anabolic resistance emerges from the combined effects of multiple dysregulated mechanisms rather than from single impairments acting independently. Clinically, interventions targeting single mechanisms are unlikely to fully restore muscle anabolism in older adults, particularly when multiple impairments coexist. Simulations predict combinations of age-related impairments that are most important to address to restore anabolic responsiveness, thereby informing multimodal therapeutic strategies for sarcopenia.

Indexed as

AgingModels, BiologicalMuscle ProteinsMuscle, SkeletalAgedHumansLeucineSarcopeniaSignal TransductionLeucineMuscle Proteinsanabolic resistancecomputational biologymathematical modelprotein synthesissarcopeniaskeletal muscle

Identifiers

PMID42464764
PMCPMC13423136

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.