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

Abstract

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.

The Metabolic Signature of Cardiorespiratory Fitness: A Systematic Review

“…Six studies included healthy participants free of any diseases [ 46 , 48 , 50 , 53 – 55 ], of which one enrolled elite long-distance runners [ 50 ] and one enrolled amateur marathon runners [ 54 ]. In 14 studies, a subfraction or even all participants were overweight or obese [ 36 – 45 , 47 , 49 , 51 , 52 ].…”

“…Ten studies investigated plasma samples [ 34 , 36 , 40 , 41 , 44 , 45 , 47 , 50 , 51 , 53 ], eight analyzed serum samples [ 35 , 37 – 39 , 48 , 49 , 52 , 54 ], two investigated urine samples [ 43 , 46 ], one analyzed skeletal muscle [ 42 ], and one investigated sweat [ 55 ]. Twelve studies applied a targeted approach [ 34 – 38 , 42 , 44 , 46 , 47 , 49 , 51 , 52 ], and eight studies applied an untargeted approach [ 39 , 40 , 43 , 48 , 50 , 53 – 55 ].…”

“…Ten studies investigated plasma samples [ 34 , 36 , 40 , 41 , 44 , 45 , 47 , 50 , 51 , 53 ], eight analyzed serum samples [ 35 , 37 – 39 , 48 , 49 , 52 , 54 ], two investigated urine samples [ 43 , 46 ], one analyzed skeletal muscle [ 42 ], and one investigated sweat [ 55 ]. Twelve studies applied a targeted approach [ 34 – 38 , 42 , 44 , 46 , 47 , 49 , 51 , 52 ], and eight studies applied an untargeted approach [ 39 , 40 , 43 , 48 , 50 , 53 – 55 ]. Depending on the nature of metabolites, one study used a targeted or untargeted approach [ 41 ], whereas another used a targeted or semi-targeted approach [ 45 ].…”

“…Included studies were published between 2012 and 2021 (Table 1 and ESM). Fifteen studies included both female and male participants [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48], and seven studies investigated male participants only [49][50][51][52][53][54][55]. One study examined adolescents [38], and the remaining 21 studies enrolled only adults (defined as participants aged > 18 years).…”

Probabilistic quotient’s work & pharmacokinetics’ contribution: countering size effect in metabolic time series measurements

“…One advantage of this method is to not only identify already illegal substances but their metabolic degradation products as well, thereby allowing to distinguish between drug consumption and mere contact [1]. Another application of sweat metabolomics is in diagnostics for personalized medicine, where the focus is put on discerning metabolic states of the body and trying to optimize nutrition and treatment based upon information of biomarkers in sweat [5][6][7].…”