Sweat is a biofluid with several attractive attributes. However, investigation into sweat for biomarker discovery applications is still in its infancy. To add support for the use of sweat as a non-invasive media for human performance monitoring, volunteer participants were subjected to a physical exertion model using a treadmill. Following exercise, sweat was collected, aliquotted, and analyzed for metabolite and protein content via high-resolution mass spectrometry. Overall, the proteomic analysis illustrates significant enrichment steps will be required for proteomic biomarker discovery from single sweat samples as protein abundance is low in this medium. Furthermore, the results indicate a potential for protein degradation, or a large number of low molecular weight protein/peptides, in these samples. Metabolomic analysis shows a strong correlation in the overall abundance among sweat metabolites. Finally, hierarchical clustering of participant metabolite abundances show trends emerging, although no significant trends were observed (alpha = 0.8, lambda = 1 standard error via cross validation). However, these data suggest with a greater number of biological replicates, stronger, statistically significant results, can be obtained. Collectively, this study represents the first to simultaneously use both proteomic and metabolomic analysis to investigate sweat. These data highlight several pitfalls of sweat analysis for biomarker discovery applications.
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 VO2 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.
To investigate how the CNS copes with load uncertainty in catching, anticipatory postural adjustments (APAs) in one-handed catching of balls of known and unknown weights were compared. Twenty-nine (n = 29) men (mean age = 21.1 years) participated, all of whom had engaged in a sport activity requiring hand-eye coordination. Participants' muscle activity in the biceps brachii, triceps brachii, wrist flexor group, and bilateral erector spinae at L4-5 was recorded using electromyography (EMG) while they caught visually identical balls of four different weights (0.5, 1.33, 2.17, and 3.0 kg). EMG integrals were computed for the 1 s prior to ball drop (pre-drop period), and the interval between ball drop and catch (drop period). Uncertainty about ball weight had no effect on APA activity during the pre-drop period. During the drop period, however, load uncertainty did influence APA activity in the biceps brachii, triceps brachii, and the wrist flexor muscles (i.e., the effect of ball weight on APA magnitude depended on the presence or absence of load knowledge). In the known ball weight condition, participants exhibit greater APA magnitude with increases in ball weight. In contrast, under the unknown ball weight condition, APA magnitude was relatively consistent across ball weights and at a level similar to the APA magnitude for an intermediate weight (i.e., the second heaviest ball of four) in the known weight condition. In catching balls of unknown weights, the CNS appears to scale APA magnitude to afford the greatest chance of catching the ball, regardless of the weight.
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