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Role of Peptides in Skeletal Muscle Wasting: A Scoping Review
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ABSTRACT
Background
Systemic muscle wasting is a prevalent condition that predicts adverse health outcomes in aging and disease. Despite its clinical relevance, the development of predictive biomarkers and effective pharmacological therapies remains limited. Peptides have recently gained attention for their diverse bioactive functions, positioning them as promising biomarkers and therapeutic agents for muscle wasting.
Methods
This scoping review systematically identifies studies examining the direct association between well‐defined peptides and clinical components of muscle wasting: muscle mass, strength and physical performance. The review follows the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis for Scoping Reviews (PRISMA‐ScR) guidelines. A comprehensive search of Embase, PubMed and Web of Science was conducted up to 31 October 2024, focusing on original human or animal studies. Studies involving congenital or inherited muscle disorders, inflammatory myopathies and neurodegenerative diseases, such as Parkinson's disease, were excluded. A snowball approach was used to synthesize the presumed cellular pathways of identified peptides.
Results
A total of 126 studies were included: 71 (56.3%) focused on a single muscle wasting component (48 on mass, 16 on strength and 7 on performance), 31 (24.6%) examined two, 16 (12.7%) analysed all three separately, and 8 (6.3%) assessed sarcopenia as a categorical variable.
Eighty‐seven distinct peptides linked to muscle wasting were identified, ranging from collagen tripeptide (3 amino acids) to insulin (51 amino acids). The most studied peptides are ghrelin (14.3%), brain natriuretic peptide (BNP, 11.1%), C‐peptide (11.1%), insulin (10.3%) and Szeto‐Schiller 31 (SS‐31, 6.3%). Most (62.1%) influence one or more of four key muscle homeostasis pathways (PI3K/Akt/mTOR, ActR/SMAD, IKK/NF‐κB and AMPK/PGC1α), which regulate atrophy (via FOXO, NF‐κB, SMAD2/3, glucocorticoid receptor and GSK‐3β) and hypertrophy (via androgen receptors, PGC‐1α and S6K). Flaws in study design and reporting were prevalent, hindering clinical translation. Sex bias was evident, with females comprising 23.9% of participants in human interventional studies and only 9.1% and 12.4% of mice and rats in rodent studies, respectively. Clinical, pre‐analytical and analytical reporting gaps were common: 56.6% documented diurnal timing, food intake and activity around peptide collection; none specified storage‐to‐analysis duration; and only 11.5% reported detection limits for peptide measurements.
Conclusion
This scoping review highlights the potential of peptides as biomarkers and intervention targets for muscle wasting. It connects the cellular receptors and signaling pathways linking peptides with skeletal muscle wasting. Improving clinical translation requires addressing study design limitations, incorporating more representative study populations and adhering to standardized reporting guidelines. The application of machine learning can support the identification of novel bioactive peptides.
Title: Role of Peptides in Skeletal Muscle Wasting: A Scoping Review
Description:
ABSTRACT
Background
Systemic muscle wasting is a prevalent condition that predicts adverse health outcomes in aging and disease.
Despite its clinical relevance, the development of predictive biomarkers and effective pharmacological therapies remains limited.
Peptides have recently gained attention for their diverse bioactive functions, positioning them as promising biomarkers and therapeutic agents for muscle wasting.
Methods
This scoping review systematically identifies studies examining the direct association between well‐defined peptides and clinical components of muscle wasting: muscle mass, strength and physical performance.
The review follows the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis for Scoping Reviews (PRISMA‐ScR) guidelines.
A comprehensive search of Embase, PubMed and Web of Science was conducted up to 31 October 2024, focusing on original human or animal studies.
Studies involving congenital or inherited muscle disorders, inflammatory myopathies and neurodegenerative diseases, such as Parkinson's disease, were excluded.
A snowball approach was used to synthesize the presumed cellular pathways of identified peptides.
Results
A total of 126 studies were included: 71 (56.
3%) focused on a single muscle wasting component (48 on mass, 16 on strength and 7 on performance), 31 (24.
6%) examined two, 16 (12.
7%) analysed all three separately, and 8 (6.
3%) assessed sarcopenia as a categorical variable.
Eighty‐seven distinct peptides linked to muscle wasting were identified, ranging from collagen tripeptide (3 amino acids) to insulin (51 amino acids).
The most studied peptides are ghrelin (14.
3%), brain natriuretic peptide (BNP, 11.
1%), C‐peptide (11.
1%), insulin (10.
3%) and Szeto‐Schiller 31 (SS‐31, 6.
3%).
Most (62.
1%) influence one or more of four key muscle homeostasis pathways (PI3K/Akt/mTOR, ActR/SMAD, IKK/NF‐κB and AMPK/PGC1α), which regulate atrophy (via FOXO, NF‐κB, SMAD2/3, glucocorticoid receptor and GSK‐3β) and hypertrophy (via androgen receptors, PGC‐1α and S6K).
Flaws in study design and reporting were prevalent, hindering clinical translation.
Sex bias was evident, with females comprising 23.
9% of participants in human interventional studies and only 9.
1% and 12.
4% of mice and rats in rodent studies, respectively.
Clinical, pre‐analytical and analytical reporting gaps were common: 56.
6% documented diurnal timing, food intake and activity around peptide collection; none specified storage‐to‐analysis duration; and only 11.
5% reported detection limits for peptide measurements.
Conclusion
This scoping review highlights the potential of peptides as biomarkers and intervention targets for muscle wasting.
It connects the cellular receptors and signaling pathways linking peptides with skeletal muscle wasting.
Improving clinical translation requires addressing study design limitations, incorporating more representative study populations and adhering to standardized reporting guidelines.
The application of machine learning can support the identification of novel bioactive peptides.
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