Metabolomics analysis of human sweat collected after moderate exercise
Abstract
Sweat is a promising biofluid scarcely used in clinical analysis despite its non-invasive sampling. A more frequent clinical use of sweat requires to know its whole composition, especially concerning to non-polar compounds, and the development of analytical strategies for its characterization. The aim of the present study was to compare different sample preparation strategies to maximize the detection of metabolites in sweat from humans collected after practicing moderate exercise. Special emphasis was put on non-polar compounds as they have received scant attention in previous studies dealing with this biofluid. Sample preparation by liquid-liquid extraction (LLE) using extractants with different polarity index was compared to deproteination. Then, derivatization by methoxymation with subsequent silylation was compared to direct analysis of sweat extracts to check the influence of derivatization on the subsequent determination of volatile organic compounds (VOCs). 135 compounds were tentatively identified by combining spectral and retention time information after analysis by gas chromatography coupled to mass spectrometry in high resolution mode (GC-TOF/MS). Lipids, VOCs, benzenoids and other interesting metabolites such as alkaloids and ethanolamines were identified. Among the tested protocols, methyoxiamination plus silylation after LLE with dichloromethane was the best option to obtain a representative snapshot of sweat metabolome collected from different body parts after moderate exercise. Passive and active sweat pools from a cohort of volunteers (n = 6) were compared to detect compositional differences which can be explained by the sampling process and sweating induction. As most of the identified compounds are metabolites involved in key biochemical pathways, this study opens new opportunities to extend the applicability of human sweat as a source of metabolite biomarkers of pathologies or specific processes such as dehydration or nutritional unbalance.
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Most amino acid, alcohol, carbohydrate, and fatty acid concentrations have been higher with exercise than pharmacologically induced sweating.
“Most amino acid (Souza et al. 2018), alcohol, carbohydrate, and fatty acid (Delgado-Povedano et al. 2018) concentrations have been higher with exercise than pharmacologically induced sweating.”
From Physiological mechanisms determining eccrine sweat composition (2020) · cites this paper as Delgado-Povedano et al. 2018
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- Authors
- Delgado-Povedano, M. M., Calderón-Santiago, M., Luque de Castro, M. D., & Priego-Capote, F.
- Affiliations
- Instituto de Salud Carlos III (ES)
- Venue
- Talanta
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 177
- Pages
- 47-65
- ISSN
- 0039-9140 (ISSN-L)
- OpenAlex
- W2756438808
- Cited by
- 68
- References
- 28
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 4 · 2023: 9 · 2022: 10 · 2021: 10 · 2020: 8 · 2019: 6 · 2018: 4
- FWCI
- 2.01
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Talanta (publishedVersion)
- PubMed (publishedVersion)
- Funders
- MINECO (CTQ2015-68813-R)
- Junta de Andalucía and FEDER program (FQM-1602)
- MECD (FPU14/03068)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Chemical Sensor Technologies
- Physical Sciences › Chemistry › Analytical Chemistry › Analytical chemistry methods development
- Keywords
- human sweat, moderate-intensity exercise, nonpolar molecules, sample preparation, liquid-liquid extraction, silylation, volatile organic compounds, benzenoids, monoethanolamine, non-invasive biomarkers, dehydration
- MeSH terms
- Humans, Solvents, Sweat, Exercise, Metabolomics, Liquid-Liquid Extraction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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