Investigation of cortisol dynamics in human sweat using a graphene-based wireless mHealth system

Abstract

Understanding and assessing endocrine response to stress is crucial to human performance analysis, stress-related disorder diagnosis, and mental health monitoring. Current approaches for stress monitoring are largely based on questionnaires, which could be very subjective. To avoid stress-inducing blood sampling and to realize continuous, non-invasive, and real-time stress analysis at the molecular levels, we investigate the dynamics of a stress hormone, cortisol, in human sweat using an integrated wireless sensing device. Highly sensitive, selective, and efficient cortisol sensing is enabled by a flexible sensor array that exploits the exceptional performance of laser-induced graphene for electrochemical sensing. Herein, we report the first cortisol diurnal cycle and the dynamic stress response profile constructed from human sweat. Our pilot study demonstrates a strong empirical correlation between serum and sweat cortisol, revealing exciting opportunities offered by sweat analysis toward non-invasive dynamic stress monitoring via wearable and portable sensing platforms.

Keywords: cortisol; flexible sensors; graphene; mHealth; stress hormone; stress response; sweat.

Figure 1.. An integrated wireless graphene-based sweat stress sensing system (GS⁴) for dynamic and non-invasive stress hormone analysis.

(A) Schematic illustration of the origin of cortisol in sweat and saliva and the use of the GS⁴ to track the circulating cortisol level. CRH, corticotrophin-releasing hormone; ACTH, adrenocorticotropic hormone. (B and C) Conceptual illustration of cortisol dynamics regulated by circadian rhythm (B) and triggered by physiological and psychological stress (C). (D) Illustration of the laser engraving process of a graphene platform. (E) Graphene sensor arrays mass-produced on a polyimide (PI) substrate. (F) Image of a disposable flexible graphene sensor array. (G) Transmission electron microscopy (TEM) image of the graphene electrode surface.

Figure 2.. Characterization and validation of the electrochemical sensor for non-invasive cortisol analyses.

(A and B) Schematic of the electrochemical detection of cortisol in human sweat (A) and representation of the affinity-based electrochemical cortisol sensor construction and sensing strategy (B). HRP, horseradish peroxidase; HQ, hydroquinone; PPA, pyrrole propionic acid; BSA, bovine serum albumin; mAb, monoclonal antibody. (C) Scanning electron microscopy (SEM) images of the graphene electrode surface before and after PPA polymerization. (D and E) Raman spectra (D) and X-ray photoelectron spectra (E) of bare graphene electrode, and graphene electrodes modified with PPA (pPPA) and capture antibody (CAb). (F) Nyquist plots of a graphene electrode in a 0.01 M PBS solution containing 2.0 mM of K₄Fe(CN)₆/K₃Fe(CN)₆ (1:1) after each surface modification step: bare graphene, electropolymerization of PPA (pPPA), capture antibody immobilization (CAb), blocking with BSA and incubation with enzyme-tagged cortisol (cortisol-HRP). (G) Amperometric signals of the flexible graphene-based biosensors for 0.0–10.0 ng/mL cortisol in 0.01 M PBST, pH 7.4. (H) Sensor performance of laser-induced graphene electrode (LGE) vs. screen printed carbon electrode (SPCEs) and glassy carbon electrodes (GCEs). Current densities were obtained from 0.0, 1.0, and 5.0 ng/mL cortisol solutions. Data are presented as mean ± standard deviation (SD) (n = 3). (I) Full sigmoidal calibration curves constructed for cortisol in buffer, sweat and saliva. The sweat and saliva samples were collected from a healthy subject. Data are presented as mean ± SD (n = 3). (J) Amperometric responses and percentage competition observed for 0.0 and 5.0 ng/mL cortisol with 30-second, 1-, 5-, 15-, and 60-minute incubation. Data are presented as mean ± SD (n = 3). (K) Validation of the flexible graphene-based biosensors toward cortisol monitoring in real samples with enzyme-linked immunosorbent assay (ELISA).

Figure 3.. System integration and validation of the GS⁴ toward personalized on-body use.

(A) Design of the flexible microfluidic three-working electrode (3WE) sensor array for cortisol detection and photograph of the printed circuit board (PCB) with the graphene sensor patch for signal processing and wireless communication. WE, working electrode; CE, counter electrode; RE, reference electrode. (B) Block diagram of the GS⁴. MCU, microcontroller unit; LPF, low pass filter; DAC, digital-to-analog converter; ADC, analog-to-digital converter. (C) Sensor readings obtained wirelessly with the GS⁴. Data from inset are presented as mean ± SD (n = 3). (D) Comparison of average signals and standard deviations obtained with 1, 2, and 3 working electrodes. Data are presented as mean ± SD (n = 8). (E) The flexible microfluidic graphene sensor array on the skin and under mechanical deformations. (F) The responses of the sensor arrays with cortisol recognition under mechanical deformation (with radii of bending curvatures of 2.3 and 3.8 cm in 1.0, 5.0, and 10.0 ng/mL cortisol).

Figure 4.. Investigation of the circadian rhythm of sweat stress hormone using the GS⁴.

(A) Conceptual illustration of the light/dark-cycle regulated cortisol circadian rhythm and the transport of circulating cortisol to sweat. (B) Circadian rhythm of sweat cortisol constructed for a healthy subject in a period of 6 days. Sweat was sampled and analyzed in the morning (AM) and in the afternoon (PM) each day. (C-F) Cortisol levels found in serum, saliva and sweat sampled in the AM and in the PM from four healthy subjects. (G) Correlation of serum cortisol to sweat cortisol. The correlation coefficient r was acquired through Pearson’s correlation analysis (eight subjects, n = 4 for each subject, p < 0.001). (H) Correlation of salivary cortisol to sweat cortisol. The correlation coefficient r was acquired through Pearson’s correlation analysis (eight subjects, n = 4 for each subject, p < 0.001).

Figure 5.. Dynamic monitoring of stress response using the GS⁴.

(A) Conceptual illustration of stress response monitoring by tracking of a subject’s cortisol level with data wirelessly transmitted to a cell phone via Bluetooth. Physical exercise is utilized as a stressor. (B) Cortisol monitoring from three physically untrained subjects (B1-B3) and one trained subject (B4) in a constant load cycling exercise. (C) Cortisol levels in serum sampled and analyzed before and after the cycling exercise for four subjects. (D) Illustration of stress response in relation to the timeframe of cold pressor test (CPT) performed. (E) Cortisol monitoring from four subjects (C1-C4) undergoing CPT. Dynamic cortisol response was evaluated with iontophoresis sweat from forearm sampled and analyzed at 10-minute intervals. (F) Cortisol levels in serum sampled before, 8 minutes after, and at the end of the CPT experiment.