Background: Recent interests in continuous biomonitoring and the surge of wearable biotechnology demand a better understanding of sweat as a noninvasive biomarker resource. The ability to use sweat as a biofluid provides the opportunity for noninvasive early and continuous diagnostics. This thesis serves to help fill the existing knowledge gap in sweat biomarker discovery and applications.

Experimental Design: In part one of this study, exercise-induced eccrine sweat was collected from 50 healthy individuals and analyzed using mass spectrometry, protein microarrays, and quantitative ELISAs to identify a broad range of proteins, antibody isotypes, and cytokines in sweat. In part two of this study, cortisol and melatonin levels were analyzed in exercise-induced sweat and plasma samples collected from 22 individuals.

Results: 220 unique proteins were identified by shotgun analysis in pooled sweat samples. Detectable antibody isotypes include IgA (100% positive; median 1230 ± 28 700 pg/mL), IgD (18%; 22.0 ± 119 pg/mL), IgG1 (96%;1640 ± 6750 pg/mL), IgG2 (37%; 292 ± 6810 pg/mL), IgG3 (71%;74.0 ± 119 pg/mL), IgG4 (69%; 43.0 ± 42.0 pg/mL), and IgM (41%;69.0 ± 1630 pg/mL). Of 42 cytokines, three were readily detected in all sweat samples (p<0.01). The median concentration for interleukin-1α was 352 ± 521 pg/mL, epidermal growth factor was 86.5 ± 147 pg/mL, and angiogenin was 38.3 ± 96.3 pg/mL. Multiple other cytokines were detected at lower levels. The median and standard deviation of cortisol was determined to be 4.17 ± 11.1 ng/mL in sweat and 76.4 ± 28.8 ng/mL in plasma. The correlation between sweat and plasma cortisol levels had an R-squared value of 0.0802 (excluding the 2 highest sweat cortisol levels). The median and standard deviation of melatonin was determined to be 73.1 ± 198 pg/mL in sweat and 194 ± 93.4 pg/mL in plasma. Similar to cortisol, the correlation between sweat and plasma melatonin had an R-squared value of 0.117.

Conclusion: These studies suggest that sweat holds more proteomic and hormonal biomarkers than previously thought and may eventually serve as a noninvasive biomarker resource. These studies also highlight many of the challenges associated with monitoring sweat content including differences between collection devices and hydration, evaporation losses, and sweat rate.