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Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
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Rate normalization for sweat metabolomics biomarker discovery.

Sean W Harshman1, Kraig E Strayer1, Christina N Davidson2

  • 1UES Inc., Air Force Research Laboratory, 711th Human Performance Wing/RHBBF, 2510 Fifth Street, Area B, Building 840, Wright- Patterson AFB, OH, 45433, USA.

Talanta
|December 11, 2020
PubMed
Summary

Accurate sweat rate measurement is crucial for normalizing sweat analytes, enabling reliable human performance monitoring. This normalization reduces data variability and links sweat biomarkers to performance metrics.

Keywords:
Global metabolomicsIon chromatographyLiquid chromatography-mass spectrometrySweatSweat rate

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Area of Science:

  • Exercise physiology
  • Biomarker discovery
  • Human performance analytics

Background:

  • Real-time exercise performance feedback is increasingly demanded.
  • Excreted sweat is a promising biosource for continuous human performance assessment.
  • Accurate normalization of sweat analyte concentrations is essential for reliable individual and day-to-day comparisons.

Purpose of the Study:

  • To highlight the use of localized sweat rate for normalizing ion and global metabolomic data.
  • To demonstrate the variability in sweat rate among individuals and between body locations.
  • To establish the potential of sweat as a biosource for performance monitoring through biomarker discovery.

Main Methods:

  • Localized sweat rate determination during two distinct exercise protocols.
  • Sweat ion conductivity analysis.
  • Global metabolomic analysis of sweat.
  • Principal component analysis for data variation assessment.
  • Correlation analysis between normalized sweat metabolomic features and performance metrics.

Main Results:

  • Significant variability in sweat rate was observed among individuals and between contralateral forearms.
  • Sweat rate normalization reduced variability in ion and global metabolomic data, explaining 77.8% and 72.7% of the variation, respectively.
  • Normalized sweat metabolomic features showed significant correlations with measured performance metrics (ρ ≥ 0.7, ρ ≤ -0.7).

Conclusions:

  • Accurate localized sweat rate determination is critical for normalizing sweat analyte data.
  • Normalized sweat data supports the use of sweat as a biosource for biomarker discovery and human performance prediction.
  • This approach enhances the reliability of using sweat for continuous performance monitoring.