B-220 A Novel Urinary Biomarker Panel for Detecting Sarcopenia
Notice bibliographique
Résumé
Abstract Background Sarcopenia, characterized by the progressive loss of muscle mass and strength, significantly increases the risk of mobility impairments, frailty, and injury in aging populations. As a result, seniors face a heightened likelihood of falls, hospitalizations, and reduced independence, severely impacting their quality of life and longevity. Despite its serious implications, sarcopenia remains challenging to diagnose effectively. Current methods, such as dual-energy x-ray absorptiometry (DEXA) and physical performance tests, are often inaccessible due to their cost and specialized nature, limiting the ability to screen and detect sarcopenia in its early stages. Early detection of sarcopenia is crucial for implementing preventative measures to slow down or reverse muscle deterioration. However, there is a clear lack of affordable, scalable, and clinically viable diagnostic tools to achieve this. Addressing this gap could significantly improve outcomes for older adults by enabling earlier intervention strategies. Methods Adults aged 50 to 70 years (n=60) underwent physical assessments, including the Short Physical Performance Battery (SPPB), DEXA scans for muscle mass evaluation, and the International Physical Activity Questionnaire (IPAQ). Urine samples were collected in a fasted state, processed, and stored at -80°C until analysis. Metabolomic profiling was performed using liquid chromatography-mass spectrometry (LC-MS) to quantify five key urinary metabolites: glutamate, xanthine, taurine, succinate, and carnitine. These biomarkers were analyzed for correlations with DEXA and physical performance measures. Statistical methods included principal component analysis (PCA) to explore metabolic patterns and receiver operating characteristic (ROC) curve analysis to evaluate the predictive accuracy of individual metabolites and the combined biomarker panel for sarcopenia diagnosis. Results PCA revealed distinct metabolic profiles between sarcopenia and non-sarcopenia individuals, with clear clustering based on activity levels (IPAQ) and sarcopenic status. Individual urinary metabolites exhibited modest predictive power (area under the ROC curve [AUC]: 0.52–0.65), whereas the combined biomarker panel demonstrated significantly improved diagnostic performance. The panel yielded an AUC of 0.91 when compared to DXA-based classifications, indicating excellent discrimination between sarcopenic and non-sarcopenic individuals, and an AUC of 0.89 relative to physical performance metrics. Notably, combining DXA and physical assessments resulted in a slightly lower AUC (0.82), suggesting the urinary biomarker panel may provide more consistent diagnostic accuracy, especially in borderline cases. Conclusion The identified urinary biomarker panel presents a practical, non-invasive, and cost-effective tool for routine sarcopenia screening and ongoing muscle health monitoring in aging populations. By providing strong predictive value comparable to, and in some cases surpassing, established diagnostic methods such as DEXA and physical performance tests, this panel enables earlier detection and supports personalized intervention strategies for muscle-wasting conditions. The robust correlation between the biomarker panel and traditional diagnostic approaches validates its potential to predict sarcopenia, improve patient outcomes, and reduce the healthcare burdens associated with age-related muscle decline. Integration of this tool into standard clinical practice could facilitate proactive sarcopenia management, offering a scalable solution for primary care settings and improving the quality of life for at-risk individuals.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».