Sarcopenia: recent advances for detection, progression, and metabolic alterations along with therapeutic targets
Bibliographic record
Abstract
Sarcopenia, a disorder marked by muscle loss and dysfunction, is a global health concern, particularly in aging populations. Sarcopenia is intricately related to various health conditions, including obesity, dysphagia, and frailty, which underscores the complexity. Despite recent advances in metabolomics and other omics data for early detection and treatment, the precise characterization and diagnosis of sarcopenia remains challenging. In the present review we provide an overview of the complex metabolic mechanisms that underlie sarcopenia, with particular emphasis on protein, lipid, carbohydrate, and bone metabolism. The review highlights the importance of leucine and other amino acids in promoting muscle protein synthesis and clarifies the critical role played by amino acid metabolism in preserving muscular health. In addition, the review provides insights regarding lipid metabolism on sarcopenia, with an emphasis on the effects of inflammation and insulin resistance. The development of sarcopenia is largely influenced by insulin resistance, especially with regard to glucose metabolism. Overall, the review emphasizes the complex relationship between bone and muscle health by highlighting the interaction between sarcopenia and bone metabolism. Furthermore, the review outlines various therapeutic approaches and potential biomarkers for diagnosing sarcopenia. These include pharmacological strategies such as hormone replacement therapy and anabolic steroids as well as lifestyle modifications such as exercise, nutrition, and dietary changes.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".