Quantification of Amino Acids in Sweat Samples with HPLC

A joint study by the Graduate School of Pharmaceutical Sciences of the University of Tokyo (Japan) and Pico Device (Nagoya, Japan) investigated the significance of quantifying amino acids in minute sweat volumes using high performance liquid chromatography (HPLC) with fluorescence detection, offering a non-invasive and efficient way to screen for diseases by detecting amino acids in sweat, even with minimal sweat volumes. The approach, outlined in a recent article in the journal Heliyon (1), could also be used to analyze other biomolecules in sweat, expanding its potential applications.

The measurement of metabolites in biological samples other than blood has gained increasing significance due to the rising demand for non-invasive analytical methods, with sweat, saliva, and tears emerging as particularly relevant sources for such analyses (2), with sweat especially notable as a biofluid allowing for non-invasive, continuous, and real-time health monitoring (3–6). Primarily produced by specific bodily glands to regulate body temperature, hydrate the skin, and provide protection, sweat, while composed of about 99 % water, also contains essential ions like chloride, sodium, and potassium, along with smaller amounts of calcium, magnesium, lactate, and other minor electrolytes (7,8). Sweat also contains trace amounts of various substances which can offer valuable insights into the status of a person's health, thus making its analysis crucial in non-invasive health monitoring. Continuous monitoring of sweat metabolites can reveal real-time information on changes in the physiological and metabolic state of the body, making it a promising approach for personalized medicine and disease management(9,10).

Sweat samples of four healthy volunteers (male, 21–24 years old) from whom informed consent was obtained were collected in a temperature-controlled room set to 26 °C, although ambient humidity was not regulated. Sweat production was monitored at 1-min intervals for 5 min, and the average of these readings was used as the representative sweat volume. The use of this advanced perspiration meter ensures precise and accurate measurements for the analysis of bilateral palm perspiration. The HPLC system utilized was comprised of a PU-2080 pump, an AS-950 autosampler, an Inertsil ODS-4V column in a CO-965 column oven, and a FP-2020 fluorescence detector. Samples containing 0.01, 0.05, 1, 5, or 10 μM of each amino acid were injected into the HPLC system after derivatization. Calibration curves were generated by plotting the ratio of the peak area of each sample to that of the internal standard (ε-amino-n-caproic acid) against the concentration of these compounds (1).

The researchers were able to quantify the amino acid concentrations in human sweat samples, successfully identifying fourteen amino acids with robust validation data. The authors of the paper believe that their technique has the potential for non-invasive and straightforward disease screening, due to allowing for analysis without need for excessive sweat samples, such as an amount generated through exercise. Additionally, they imagine that the technique has potential applications in the detection of other biomolecules in sweat besides amino acids.

Hand perspiration clean up with tissue. © luengchopan - stock.adobe.com

References

1. Tsunoda, M.;Tsuda, T. Quantification of Amino Acids in Small Volumes of Palm Sweat Samples. Heliyon 2024, 10 (17), e36286. DOI: 10.1016/j.heliyon.2024.e36286

2. Nair, R. R.; An, J. M.; Kim, J.; Kim, D. Recent Progress in Fluorescent Molecular Systems for the Detection of Disease-Related Biomarkers in Biofluids. Coord. Chem. Rev. 2023, 494, 215336. DOI: 10.1016/j.ccr.2023.215336

3. Davis, N.; Heikenfeld, J.; Milla, C.; Javey, A. The Challenges and Promise of Sweat Sensing. Nat. Biotech. 2024, 42, 860–871. DOI: 10.1038/s41587-023-02059-1

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5. Yang, D. S.; Ghaffari, R.; Rogers, J. A. Sweat as a Diagnostic Biofluid. Science 2023, 379 (6634), 760–761. DOI: 10.1126/science.abq5916

6. Brasier, Noé et al.Next-Generation Digital Biomarkers: Continuous Molecular Health Monitoring Using Wearable Devices. Trends Biotechnol. 2024, 42 (3), 255–257. DOI: 10.1016/j.tibtech.2023.12.001

7. Hussain, J. N.; Mantri, N.; Cohen, M. M. Working up a Good Sweat–The Challenges of Standardising Sweat Collection for Metabolomics Analysis. Clin. Biochem. Rev. 2017, 38 (1), 13–34.

8. Baker, L. B.; Wolfe, A. S. Physiological Mechanisms Determining Eccrine Sweat Composition. Eur. J. Appl. Physiol. 2020, 120, 719–752. DOI: 10.1007/s00421-020-04323-7

9. Tsunoda, M.; Hirayama, M.; Tsuda, T.; Ohno, K. Noninvasive Monitoring of Plasma L-dopa Concentrations Using Sweat Samples in Parkinson's Disease. Clin. Chim. Acta 2015, 442, 52–55.DOI: 10.1016/j.cca.2014.12.032

10. Hirayama, M.; Tsunoda, M.; Yamamoto, M.; Tsuda, T.; Ohno, K. Serum Tyrosine-to-Phenylalanine Ratio is Low in Parkinson’s Disease. J. Parkinson's Dis. 2016, 6 (2), 423–431. DOI: 10.3233/JPD-150736