Muscle Biomarkers as Molecular Signatures for Early Detection and Monitoring of Muscle Health in Aging
Bibliographic record
Abstract
Maintaining muscle health is essential for preserving mobility, independence, and quality of life with age. As muscle mass and function decline, the risk of frailty, chronic disease, and disability increases. Sarcopenia, characterized by the progressive loss of muscle mass, strength, and function, is a major contributor to these adverse outcomes in older adults. Early identification and monitoring of sarcopenia are critical for timely intervention to prevent irreversible decline. Muscle biomarkers offer a promising approach for detecting muscle deterioration and guiding treatment strategies. This review explores key biomarkers—including insulin-like growth factor 1 (IGF-1), myostatin, interleukin-6 (IL-6), irisin, interleukin 15 (IL-15), and procollagen type III N-terminal propeptide (P3NP)—that reflect underlying processes such as muscle anabolism, inflammation, metabolism, and remodeling. Alterations in these markers are associated with muscle health status. Furthermore, hormonal status, biological sex, and nutritional factors all modulate biomarker levels, emphasizing the need for personalized assessments. Integrating biomarker analysis into clinical practice has the potential to enhance early diagnosis, inform personalized interventions, and ultimately promote healthy aging by maintaining muscle function and reducing disability risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".