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The Impact of Nutritional Supplementation on Sweat Metabolomic Content: A Proof-of-Concept Study

Sweat is emerging as a prominent biosource for real-time human performance monitoring applications. Although promising, sources of variability must be identified to truly utilize sweat for biomarker applications. In this proof-of-concept study, a targeted metabolomics method was applied to sweat col...

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Detalles Bibliográficos
Autores principales: Harshman, Sean W., Browder, Andrew B., Davidson, Christina N., Pitsch, Rhonda L., Strayer, Kraig E., Schaeublin, Nicole M., Phelps, Mandy S., O'Connor, Maegan L., Mackowski, Nicholas S., Barrett, Kristyn N., Eckerle, Jason J., Strang, Adam J., Martin, Jennifer A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8138560/
https://www.ncbi.nlm.nih.gov/pubmed/34026725
http://dx.doi.org/10.3389/fchem.2021.659583
Descripción
Sumario:Sweat is emerging as a prominent biosource for real-time human performance monitoring applications. Although promising, sources of variability must be identified to truly utilize sweat for biomarker applications. In this proof-of-concept study, a targeted metabolomics method was applied to sweat collected from the forearms of participants in a 12-week exercise program who ingested either low or high nutritional supplementation twice daily. The data establish the use of dried powder mass as a method for metabolomic data normalization from sweat samples. Additionally, the results support the hypothesis that ingestion of regular nutritional supplementation semi-quantitatively impact the sweat metabolome. For example, a receiver operating characteristic (ROC) curve of relative normalized metabolite quantities show an area under the curve of 0.82 suggesting the sweat metabolome can moderately predict if an individual is taking nutritional supplementation. Finally, a significant correlation between physical performance and the sweat metabolome are established. For instance, the data illustrate that by utilizing multiple linear regression modeling approaches, sweat metabolite quantities can predict VO(2) max (p = 0.0346), peak lower body Windage (p = 0.0112), and abdominal circumference (p = 0.0425). The results illustrate the need to account for dietary nutrition in biomarker discovery applications involving sweat as a biosource.