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Title
A wearable electrochemical biosensor for the monitoring of metabolites and nutrients.
Permalink
https://escholarship.org/uc/item/2r8949kg
Journal
Nature biomedical engineering, 6(11)
ISSN
2157-846X
Authors
Wang, Minqiang
Yang, Yiran
Min, Jihong
et al.
Publication Date
2022-11-01
DOI
10.1038/s41551-022-00916-z
Peer reviewed
eScholarship.org
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University of California
A Wearable Electrochemical Biosensor for the Monitoring of Metabolites and
Nutrients
Minqiang Wang
1,&
, Yiran Yang
1,&
, Jihong Min
1,&
, Yu Song
1
, Jiaobing Tu
1
, Daniel Mukasa
2
, Cui Ye
1
,
Changhao Xu
1
, Nicole Heflin
3
, Jeannine S. McCune
4
, Tzung K. Hsiai
5
, Zhaoping Li
6
,
and
Wei Gao
1
*
1
Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and
Applied Science, California Institute of Technology; Pasadena, California, 91125, USA.
2
Department of Applied Physics and Materials Science, Division of Engineering and Applied
Science, California Institute of Technology; Pasadena, California, 91125, USA.
3
Department of Electrical Engineering, Division of Engineering and Applied Science, California
Institute of Technology; Pasadena, California, 91125, USA.
4
Department of Hematologic Malignancy Translational Sciences, Beckman Research Institute at
City of Hope; Duarte, CA, 91010, USA.
5
Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles;
California, 90095, USA.
6
Division of Clinical Nutrition, David Geffen School of Medicine, University of California, Los
Angeles; California, 90095, USA.
&
These authors contributed equally
*Corresponding authors,
weigao@caltech.edu
Wearable non-invasive biosensors for the continuous monitoring of metabolites in
sweat can detect a few analytes at sufficiently high concentrations, typically during
vigorous exercise so as to generate sufficient quantity of the biofluid. Here, we
report the design and performance of a wearable electrochemical biosensor for the
continuous analysis, in sweat during physical exercise and at rest, of trace levels of
multiple metabolites and nutrients, including all essential amino acids and vitamins.
The biosensor consists of graphene electrodes that can be repeatedly regenerated
in situ, functionalized with metabolite-specific antibody-like molecularly imprinted
polymers and redox-active reporter nanoparticles, and integrated with modules for
iontophoresis-based sweat induction, microfluidic sweat sampling, signal processing
and calibration, and wireless communication. In volunteers, the biosensor enabled
the real-time monitoring of the intake of amino acids and their levels during
physical exercise, as well as the assessment of the risk of metabolic syndrome (by
correlating amino acid levels in serum and sweat). The monitoring of metabolites for
the early identification of abnormal health conditions could facilitate applications in
precision nutrition.
One-sentence editorial summary (to appear right below the title of your Article on the journal's
website):
A wearable electrochemical biosensor can continuously detect, in sweat during
physical exercise and at rest, trace levels of multiple metabolites and nutrients,
including all essential amino acids and vitamins.
Circulating nutrients are essential indicators for overall health and body function
1
. Amino acids
(AAs), sourced from dietary intake, gut microbiota synthesis, and influenced by personal
lifestyles, are important biomarkers for a number of health conditions (
Fig. 1a
)
2
. Elevated
branched-chain amino acids (BCAAs) including leucine (Leu), isoleucine (Ile), and valine (Val),
are associated with obesity, insulin resistance, and the future risk of type 2 diabetes mellitus
(T2DM), cardiovascular diseases (CVDs), and pancreatic cancer
3–5
. Deficiencies in AAs (e.g.,
arginine and cysteine) could hamper the immune system by reducing immune-cell activation
6
.
Tryptophan (Trp), tyrosine (Tyr) and phenylalanine (Phe) are precursors of serotonin and
catecholamine neurotransmitters (dopamine, norepinephrine, and epinephrine), respectively,
and play an important role in the function of complex neural systems and mental health
7,8
. A
number of metabolic fingerprints (including Leu, Phe, and vitamin D) are linked to COVID-19
severity
9,10
; health disparities in nutrition also correlate well with the alarming racial and ethnic
disparities that are worsened by COVID-19 vulnerability and mortality
11
;
moreover, organ and
tissue dysfunction induced by SARS-CoV-2 could result in an increased incidence of
cardiometabolic diseases
12
.
Metabolic profiling and monitoring are a key approach to enabling precision nutrition and
precision medicine
13
. Current gold standards in medical evaluation and metabolic testing
heavily rely on blood analyses that are invasive and episodic, often requiring physical visits to
medical facilities, labor-intensive sample processing and storage, and delicate instrumentation
(e.g., gas chromatography-mass spectrometry (GC-MS))
14
. As the current COVID-19 pandemic
remains uncontrolled around the world, there is a pressing need for developing wearable and
telemedicine sensors to monitor an individual’s health state and to enable timely intervention
under home- and community-based settings
15–23
; it is also increasingly important to monitor a
person’s long-term cardiometabolic and nutritional health status after recovery from severe
COVID-19 infection using wearables to capture early signs of potential endocrinological
complications such as T2DM
12
.
Sweat is an important body fluid containing a wealth of chemicals reflective of nutritional and
metabolic conditions
24–27
. The progression from blood analyses to wearable sweat analyses
could provide great potential for non-invasive, continuous monitoring of physiological
biomarkers critical to human health
28-38
. However, currently reported wearable electrochemical
sensors primarily focus on a limited number of analytes including electrolytes, glucose, and
lactate, due to the lack of a suitable continuous monitoring strategy beyond ion-selective and
enzymatic electrodes or direct oxidation of electroactive molecules
25–27,
34-40
. Thus, most
clinically relevant nutrients and metabolites in sweat are rarely explored and undetectable by
existing wearable sensing technologies. Moreover, current wearable biosensors usually require
vigorous exercise to access sweat; although a few recent reports use pilocarpine gel-based
iontophoresis for sedentary sweat sampling
22,30,36
, this approach suffers from short sweat
periods and low sensing accuracy due to the mixing of sweat and gel fluid and the lack of
dynamic sweat sampling.
Here we present a universal wearable biosensing strategy based on a judicious combination of
the mass-producible laser-engraved graphene (LEG), electrochemically synthesized redox-
active nanoreporters (RARs), molecularly imprinted polymer (MIP)-based ‘artificial antibodies’,
as well as unique
in situ
regeneration and calibration technologies (
Fig. 1b
). Unlike bioaffinity
sensors based on antibodies or classic MIPs which are generally one-time use and require
multiple washing steps in order to transduce the bioaffinity interactions in standard ionic
solutions
41,42
, this approach enables the demonstration of sensitive, selective, and continuous
monitoring of a wide range of trace-level biomarkers in biofluids including all nine essential AAs
as well as vitamins, metabolites, and lipids commonly found in human sweat (
Supplementary
Table 1
). Seamless integration of this unique approach with
in situ
signal processing and
wireless communication leads to a powerful wearable sweat sensing technology ‘NutriTrek’
that is able to perform personalized and non-invasive metabolic and nutritional monitoring
toward timely intervention (
Fig. 1b
). The incorporation of the carbachol iontophoresis-based
sweat induction and efficient microfluidic-based surrounding sweat sampling enables prolonged
autonomous and continuous molecular analysis with high temporal resolution and accuracy
across activities, during physical exercise and at rest. Using five essential or conditionally
essential AAs (i.e., Trp, Try, and three BCAAs (Leu, Ile, Val)) as exemplar nutrients, we
corroborated the system in several human trials by enrolling both healthy subjects and patients
toward personalized monitoring of central fatigue, standard dietary intakes, nutrition status,
metabolic syndrome risks, and COVID-19 severity.
Results
Design and overview of the autonomous wearable biosensor technology
The flexible and disposable sensor patch consists of two carbachol-loaded iontophoresis
electrodes, a multi-inlet microfluidic module, a multiplexed MIP nutrient sensor array, a
temperature sensor, and an electrolyte sensor (
Fig. 1c–f
and
Supplementary Fig. 1
). All
flexible electrode and sensor designs are based on the LEG which has large surface area,
excellent electrochemical properties, and can be produced at a large scale directly on a
polyimide substrate
via
CO
2
laser engraving (
Supplementary Fig. 2
). The sensor patch can
be easily attached to skin with conformal contact and interfaces with a miniaturized electronic
module for on-demand iontophoresis control,
in situ
signal processing and wireless
communication with the user interfaces through Bluetooth (
Fig. 1g
and
Supplementary Figs.
3 and 4
). A custom mobile app ‘NutriTrek’ was developed to process, display, and store the
dynamic metabolic information monitored by the wearable sensors (
Fig. 1h
and
Supplementary Video S1
). The wearable system was also integrated into a smartwatch with
an electronic paper display (
Fig. 1i
and
Supplementary Fig. 5
).
Biosensor design and evaluation for universal metabolic and nutritional analysis
Universal detection of AAs and other metabolites/nutrients with high sensitivity and selectivity
was achieved through careful design of the selective binding MIP layer on the LEG. MIPs are
chemically synthesized receptors formed by polymerizing functional monomer(s) with template
molecules. Although MIP technology has been proposed for sensing, separation and
diagnosis
42,43
, it has not yet been demonstrated for continuous wearable sensing as classic MIP
sensors require washing steps for sensor regeneration and the detection is generally performed
in standard buffer or redox solutions. In our case, the functional monomer (e.g., pyrrole) and
crosslinker (e.g., 3-Aminophenylboronic acid) initially form a complex with the target molecule;
following polymerization, their functional groups are embedded in the polymeric structure on
the LEG; subsequent extraction of the target molecules reveals binding sites on the LEG-MIP
electrode that are complementary in size, shape, and charge to the target analyte
(
Supplementary Fig. 6
). Two detection strategies – direct and indirect – are designed based
on the electrochemical properties of the target molecules (
Fig. 2
). Optimizations and
characterizations of the LEG-MIP sensors are detailed in
Supplementary Note 1
and
Supplementary Figs. 7–13
.
For electroactive molecules in sweat, the oxidation of bound target molecules in the MIP
template can be directly measured by differential pulse voltammetry (DPV) in which the peak
current height correlates to analyte concentration (
Fig. 2a
). Considering that multiple
electroactive molecules can be oxidized at similar potentials, this LEG-MIP approach addresses
both sensitivity and selectivity issues. For example, Tyr and Trp, two AAs with close redox
potentials (~0.7 V), could be detected selectively with this strategy (
Fig. 2b,c
and
Supplementary Fig. 14
). Linear relationships between peak height current densities and
target concentrations with sensitivities of 0.63 μA μM
−1
cm
−2
and 0.71 μA μM
−1
cm
−2
respectively
for the LEG-MIP Tyr and Trp sensors were observed (
Supplementary Fig. 15
). It is worth
noting that choices of monomer/crosslinker/template ratios and incubation periods have
substantial influences on sensor response while sample volume does not (
Supplementary
Fig. 10
). The Tyr and Trp sensors can be readily and repeatably regenerated
in situ
without
any washing step with a high-voltage amperometry (IT) that oxidizes the bound targets at their
redox potentials (
Fig. 2d
).
As the majority of metabolites and nutrients (e.g., BCAAs) are non-electroactive and cannot
easily be oxidized under operational conditions, we herein utilize an indirect detection
approach involving an RAR layer sandwiched between the LEG and MIP layers to enable rapid
quantitation (
Fig. 2e
).
The selective adsorption of the target molecules onto the imprinted
polymeric layer decreases the exposure of the
RAR
to the sample matrix.
Controlled-potential
voltammetric techniques such as DPV or linear sweeping voltammetry (LSV) can be applied to
measure the RAR’s oxidization or reduction peak, where the decrease in peak height current
density corresponds to an increase in analyte levels. For example, using Prussian Blue
nanoparticles (PBNPs) as the RAR (
Supplementary Fig. 11
), we developed a MIP-LEG Leu
sensor with a log-linear relationship between the peak height decrease and Leu concentration
and a sensitivity of 702 nA mm
-2
per decade of concentration (
Fig. 2f
). We established this
approach to quantify the physiologically relevant range of all nine essential AAs (i.e., Leu, Ile,
Val, Trp, Phe, histidine (His), lysine (Lys), methionine (Met), and threonine (Thr)) (
Fig. 2g
and
Supplementary Fig. 16
) as well as a number of vitamins, metabolites, and lipids (vitamins B
6
,
C, D
3
, and E, glucose, uric acid, creatine, creatinine, and cholesterol) (
Fig. 2h
and
Supplementary Fig. 17
). In addition to these nutrients and metabolites, this approach can be
easily reconfigured to enable the monitoring of a broad spectrum of biomarkers ranging from
hormones (e.g., cortisol) to drugs (e.g., immunosuppressive drug mycophenolic acid)
(
Supplementary Fig. 18
and
Supplementary Tables 2 and 3
). Most of these targets are
undetectable continuously by any existing wearable technology. Considering that a total level
of multiple nutrients (e.g., total BCAAs) is often an important health indicator, a multi-template
MIP approach can be used to enable accurate and sensitive detection of the total concentration
of multiple targets with a single sensor (
Fig. 2i,j
). These indirect LEG-RAR-MIP sensors can be
regenerated
in situ
upon constant potential applied to the working electrode repels the bound
target molecules from the MIP layer with prolonged re-usability (
Fig. 2k
).
The LEG-MIP sensors show stable responses during repeatable use: The PBNPs-based RAR
showed stable redox signals throughout 60 repetitive cyclic voltammetry (CV) scans (
Fig. 2l
and
Supplementary Fig. 11
); minimal output changes were observed throughout a 42-day
storage period (
Supplementary Fig. 19a,b
); the sensors also showed no substantial relative
signal shift when used continuously over 5 days (
Supplementary Fig. 19c
). Compared to
traditional MIP preparation processes, the electrodeposited MIP layer on the mass-producible
LEG leads to high reproducibility in both selectivity, sensitivity, and device to device
consistency (
Supplementary Figs. 20
and
21
). The choice of LEG as the MIP deposition
substrate also showed advantages in sensor sensitivity as compared to classic electrodes such
as glassy carbon electrode, printed carbon electrode, and Au electrode (
Supplementary Fig.
22
). Other RARs such as the anthraquinone-2-carboxylic acid (AQCA) can also be used for
indirect AA sensing with stable performance (negatively scanned DPV was used here to monitor
AQCA reduction) (
Fig. 2m
and
Supplementary Fig. 23
). As illustrated in
Fig. 2n
, the LEG-
AQCA-MIP sensors could be directly regenerated in a raw human sweat sample, resolving a
main bottleneck of wearable biosensing. The MIP-LEG AA sensors have excellent selectivity for
other analytes in sweat (including AAs with similar structures) at physiologically relevant
concentrations (
Fig. 2o
,
Supplementary Fig. 24
, and
Supplementary Table 3
). The LEG-
MIP technology showed a comparable sensitivity with the current gold standard laboratory-
based GC-MS
44
(
Supplementary Fig. 25
); the sensor measurements in raw human sweat
samples have been validated against GC-MS (
Fig. 2p
,
Supplementary Figs. 26
and
27
).
Wearable system design for autonomous sweat induction, sampling, analysis, and
calibration
To enable on-body continuous metabolic and nutritional monitoring, the flexible sensor patch
was designed to comprise of an iontophoresis module for localized on-demand sweat induction,
a multi-inlet microfluidic module for efficient sweat sampling, a multiplex LEG-MIP sweat
nutrient sensor array for continuous AA analysis, and LEG-based temperature and electrolyte
sensors for real-time AA sensor calibration (
Fig. 3a
). Unlike classic bioaffinity sensor’s
detection in buffer or redox solutions,
in situ
sweat analysis poses more challenges due to
complex and interpersonally varied sweat composition and demands technological innovations
for accurate on-body sensing. For example, for direct LEG-MIP Trp sensing, a DPV scan in sweat
even before target/MIP recognition could lead to an oxidation peak as a small amount of
electroactive molecules (e.g., Trp and Tyr) can be oxidized on the surface of MIP layer; after
recognition and binding of Trp into the MIP cavities, a substantially higher current peak height
can be obtained; measuring difference of the two peak heights allows more accurate bound Trp
measurement directly in sweat with high selectivity (
Fig. 3b–d
). The influence of temperature
and ionic strength on the AA sensors can be calibrated in real time based on the readings from
an LEG-based strain-resistive temperature sensor and an ion-selective Na
+
sensor (
Fig. 3e
,
Supplementary Fig. 28
). Considering that sweat rate during exercise was reported to have
influence on certain biomarker levels; we could use sweat Na
+
level (which showed a linear
correlation with sweat rate) to further calibrate the nutrient levels for personalized analysis.
This unique transduction strategy involving both the two-step DPV scans and the temperature/
electrolyte calibrations allows us to obtain accurate reading continuously in sweat during on-
body use (
Supplementary Fig. 29
).
In order to make this wearable technology broadly applicable, particularly for sedentary
individuals, we utilize here a custom-designed iontophoresis module consisting of the LEG
anode and cathode coupled with hydrogels containing muscarinic agent carbachol (carbagel)
for sustainable sweat extraction. Carbachol was selected from various muscarinic agents as it
allows the most efficient, repeatable, and long-lasting sweat secretion from the surrounding
sweat gland thanks to its additional nicotinic effects
45
(
Fig. 3f–h
,
Supplementary Fig. 30
,
and
Supplementary Note 2
). In contrast, the classic sweat inducing agent – pilocarpine –
used by the standard sweat test and previously reported wearable systems
22,30,36
offers only a
short period of sweat and very limited sweat rate from the neighboring sweat glands (
Fig. 3f–
h
). Furthermore, sampling the mixture of the leaked sweat underneath the pilocarpine gel and
the gel fluid could result in substantial wearable sensor errors and fail to provide real-time
information due to the absence of efficient sweat refreshing. A very small current (50–100
μA
)
is used for our iontophoresis module, as compared to commonly used 1–1.5 mA
22,30,36
, greatly
reducing the risks of skin irritation. To maximize the efficiency of low-volume sweat sampling
and improve the temporal resolution of wearable sensing, a compact and flexible microfluidic
module was carefully designed to isolate sweat sampling areas from iontophoresis gels.
Numerical simulations were performed to optimize the geometric design of the microfluidic
module, including inlet numbers, angle span, orientation, and flow direction with respect to the
reservoir geometry (
Fig. 3i
,
Supplementary Note 3
,
Supplementary Figs. 31
and
32
,
Supplementary Video 2,
and
Supplementary Table 4
). With the optimized design for
sweat induction and sampling, sweat can be conveniently induced locally and readily sampled
with the multi-inlet microfluidics over a prolonged period (
Fig. 3g,j
,
Supplementary Fig. 33
,
and
Supplementary Video 3
). At the physiological sweat rates ranging from 0.15 μL min
−1
to
3 μL min
−1
, our wearable sensor patch could provide reliable and accurate analysis of the
dynamic changes of the AA levels (
Supplementary Figs. 34 and 35
).
Evaluation of the wearable system for dynamic physiological and nutritional
monitoring
Evaluation of the wearable system was conducted first
via
sensing of sweat Trp and Tyr in
human subjects during a constant-load cycling exercise trial (
Fig. 4a–d
and
Supplementary
Fig. 36
). The DPV data from the sensors were wirelessly transmitted along with temperature
and Na
+
sensor readings to the mobile app that automatically extracted the oxidation peaks
using a custom developed iterative baseline correction algorithm (
Fig. 4e
and
Supplementary Fig. 37
) and performed calibration for the accurate quantification of sweat
Tyr and Trp. Considering that AAs (e.g., Try and BCAAs) play a crucial role in central fatigue
during physical exercise
46
, a flexible Trp and BCAA sensor array was used to monitor the AA
dynamics during vigorous exercise (
Fig. 4f–j
and
Supplementary Fig. 38
). Both Trp and
BCAA levels decreased during the exercise due to the serotonin synthesis and BCAA ingestion,
respectively. The increased sweat Trp/BCAA ratio was observed which could potentially serve
as an indicator for central fatigue, in agreement with a previous report on its plasma
counterpart
46
.
The wearable iontophoresis-integrated patch enables daily continuous AA monitoring at rest
beyond the physical exercise. As illustrated in
Fig. 4k–o
and
Supplementary Figs. 39–42
,
rising Trp and Tyr levels in sweat were observed from all four subjects after Trp and Tyr
supplement intake while the readings from the sensors remained stable during the studies
without intake. Such capability opens the door for personalized nutritional monitoring and
management through personalized sensor-guided dietary intervention. It should be noted that
our pilot study showed that sweat nutrient and electrolyte levels were independent of sweat
rate changes during the carbachol-based iontophoresis-induced sweat (
Supplementary Fig.
43
).
Personalized monitoring of metabolic syndrome risk factors using the wireless
biosensors
Metabolic syndrome, characterized by abdominal obesity and insulin resistance, is now on the
rise as the leading cause of morbidity and mortality, affecting more than a third of all U.S.
adults
47
. Elevated circulating BCAAs levels are predictive of insulin-resistant obesity, metabolic
syndrome, and linked to CVDs and T2DM (
Fig. 5a
and
Supplementary Note 4
)
3,4
, which could
lead to potential complications of severe COVID-19
12
. Recent studies have shown the potential
use of BCAAs supplementation as dietary intervention to ameliorate insulin resistance
48
.
Monitoring changes in essential nutrient levels provides a highly sensitive early detection of
metabolic syndrome risks, enabling effective personalized dietary intervention (
Fig. 5b
). To
explore the use of sweat BCAAs as a non-invasive risk factor of metabolic syndrome, we
performed a pilot study to investigate the correlations between serum and sweat BCAAs
involving three groups of subjects: normal weight (I, n=10), overweight/obesity (II, n=7), and
obesity with T2DM (III, n=3) (
Fig. 5c,d
). Positive Pearson correlation coefficients of 0.66
(n=65) and 0.69 (n=65) were observed between sweat and serum levels (all analyzed by the
sensors) of Leu and total BCAA, respectively (
Fig. 5c
). Compared to healthy participants in
Group I, substantially elevated sweat and serum Leu levels (analyzed by the sensors) were
observed in Group II and III (
Fig. 5d
), consistent with previous reports that higher circulating
BCAA levels were identified in individuals with obesity and T2DM
3
. Considering the well-
established role of BCAAs on insulin production and inhibition of glycogenolysis, we also
investigated the postprandial response of sweat Leu/BCAAs and blood glucose/insulin after
BCAA supplement and dietary intake on healthy subjects (
Fig. 5e,f
). All biomarkers remained
stable during fasting period; protein diet intake resulted in the increase of both blood glucose
and insulin while BCAA intake only led to a rapid insulin increase. In both studies, sweat Leu
and BCAAs increased first in the 30–60 min and then decreased. For subjects with different
metabolic conditions, Leu levels in iontophoretic sweat after BCAA vary differently: although
substantial increase in sweat Leu levels were observed in all cases, healthy subjects showed a
drastic percentage fluctuation and individuals with obesity/T2DM showed blunted fluctuation
that may indicate the different metabolic stage of BCAA in those individuals (
Fig. 5g
).
Considering that circulating elevated Leu has been reported as a key metabolic fingerprint for
the COVID-19 severity, we also evaluated our biosensors for analyzing the samples from
patients with COVID-19 and healthy individuals; substantially elevated Leu levels were
identified in from COVID-19 positive samples as compared to the negative ones (415.6 ± 133.7
μM
vs
. 151.5 ± 36.0 μM), indicating the great potential of our biosensors for at-home COVID-19
monitoring and management (
Fig. 5h
).
Discussion
Circulating metabolic biomarkers, such as amino acids and vitamins, have been associated
with various health conditions, such as diabetes and cardiovascular diseases. Metabolic
profiling using wearable sensors has become increasingly crucial in precision nutrition and
precision medicine, especially in the era of COVID-19 pandemic, as it provides not only insights
into COVID-19 severity but also guidance to stay metabolically healthy to minimize the risk of
potential COVID-19 infections. As the pandemic remains rampant in the world and the regular
medical services could be in shortage, it is of urgent need to develop and apply wearable
sensors that can monitor health conditions via metabolic profiling so that at-home diagnosis
and timely intervention via telemedicine could be achieved. However, current wearable
electrochemical sensors are limited to a narrow range of detection targets due to lack of
continuous sensing strategies beyond ion-selective and enzymatic electrodes. Though various
bio-affinity based sensors have been developed to detect a broader spectrum of targets using
antibodies or MIPs, they generally require multiple washing steps or provide only one-time use;
these limitations have hampered their useability in wearable devices. Moreover, the majority of
wearable biosensors rely on vigorous exercise to access sweat and are not suitable for daily
continuous use.
By integrating the mass-producible LEG, electrochemically synthesized RARs, and ‘artificial
antibodies’, we have demonstrated a powerful universal wearable biosensing strategy that can
achieve selective detection of a broad range of biomarkers (including all essential AAs,
vitamins, metabolites, lipids, hormones and drugs) and reliable
in situ
regeneration.
Furthermore, to enable continuous and on-demand metabolic and nutritional monitoring across
the activities, we have integrated the LEG-MIP sensor array and iontophoresis-based sweat
induction into a wireless wearable technology, with optimized multi-inlet microfluidic
sudomotor axon reflex sweat sampling,
in situ
signal processing, calibration, and wireless
communication. Using this telemedicine technology, we have demonstrated the wearable and
continuous monitoring of postprandial AA responses to identify risks for metabolic syndrome.
The high correlation between sweat and serum BCAAs shows great promise of this technology
towards metabolic syndrome risk monitoring. The substantial difference in Leu between COVID-
19 positive and negative blood samples indicates the potential of using this technology for at-
home COVID-19 management. We envision that this wearable technology could play a crucial
role in the realization of precision nutrition through continuous monitoring of circulating
biomarkers and enabling personalized nutritional intervention. This technology could also be
reconfigured to continuously monitor a variety of other biomarkers toward a wide range of
personalized preventive, diagnostic, and therapeutic applications.
Methods
Materials and reagents.
Uric acid, L-tyrosine, silver nitrate, iron chloride (III), dopamine
hydrochloride, choline chloride, creatinine, pantothenic acid calcium salt, citrulline, pyridoxine,
and lactic acid were purchased from Alfa Aesar. Sodium thiosulfate pentahydrate, sodium
bisulfite, tryptophan, leucine, alanine, isoleucine, methionine, valine, lysine, thiamine
hydrochloride, serine, sulfuric acid, hydrochloric acid, anthraquinone-2-carboxylic acid (AQCA),
3-Aminophenylboronic acid (APBA), aniline, o-phenylenediamine (o-PD), methylene blue (MB),
thionine, 2-(N-morpholino)ethanesulfonic acid hydrate (MES), ethanolamine, N-(3-dimethyl-
aminopropyl)-N’-ethylcarbodiimide (EDC), N-hydroxysulfosuccinimide sodium salt (sulfo-NHS),
bovine serum albumin (BSA), tris(hydroxymethyl)aminomethane hydrochloride (Tris-HCl),
streptavidin-peroxidase conjugate (strep-POD, Roche), and hydroquinone (HQ) were purchased
from Sigma Aldrich. Carboxylic acid-modified-magnetic beads (Dynabeads®, M-270) were
obtained from Invitrogen. Potassium ferricyanide (III), and potassium ferrocyanide (IV) was
purchased from Acros Organics. Acetic acid, methanol, sodium acetate, sodium chloride,
sodium dihydrogen phosphate, potassium chloride, potassium hydrogen phosphate, urea, L-
ascorbic acid and dextrose (D-glucose) anhydrous, glycine, arginine, inositol, ornithine, aspartic
acid, threonine, histidine, riboflavin, creatine, phenylalanine, nicotinic acid, folic acid, glutamic
acid, and hydrogen peroxide (30% (w/v)) were purchased from Thermo Fisher Scientific. Insulin
capture antibody and biotinylated detector antibody were purchased from R&D systems
(Human/Canine/Porcine Insulin DuoSet ELISA). Screen printed carbon electrodes (SPCE) and
magnetic holder were purchased from Metrohm DropSens. Medical adhesives were purchased
from 3M and Adhesives Research. Polyimide (PI) films (75 μm thick) were purchased from
DuPont. PET films (12 μm thick) were purchased from McMaster-Carr.
Fabrication and preparation of the LEG sensors.
The LEG electrodes were fabricated on a
polyimide film with a thickness of 75 μm (DuPont) with a 50 W CO
2
laser cutter (Universal Laser
System). When engraving the PI with a CO
2
laser cutter, the absorbed laser energy is converted
to local heat and thus leads to a high localized temperature (>2500 °C), chemical bonds in the
PI network are broken and thermal reorganization of the carbon atoms occurs, resulting in
sheets of graphene structures.
The optimized parameters for the graphene electrodes and
electronic connections were power 8%, speed 15%, points per inch (PPI) 1000 in raster mode
with 3-time scan. For the active sensing area of the temperature sensor, the optimized
parameters were power 3%, speed 18%, PPI 1000 in vector mode with 1-time scan. To prepare
the reference electrode, Ag was first modified on the corresponding graphene electrode by
multi-current electrodeposition with electrochemical workstation (CHI 832D) at -0.01 mA for
150 s, -0.02 mA for 50 s, -0.05 mA for 50 s, -0.08mA for 50 s, and -0.1 mA for 350 s using a
plating solution containing 0.25 M silver nitrate, 0.75 M sodium thiosulfate and 0.5 M sodium
bisulfite. 0.1 M FeCl
3
solution was further dropped on the Ag surface for 30 s to obtain the
Ag/AgCl electrode, and then 3 μL PVB reference cocktail prepared by dissolving 79.1 mg of
PVB, 50 mg of NaCl in 1 mL of methanol was dropped on the Ag/AgCl electrode and dried
overnight. The Na
+
selective electrode was prepared as follows: 0.6 μL of Na
+
selective
membrane cocktail prepared by dissolving 1 mg of Na ionophore X, 0.55 mg Na-TFPB, 33 mg
PVC and 65.45 mg DOS into 660 μL of THF was drop-casted onto the graphene electrode and
dried overnight. To obtain the desired stable Na
+
sensing performance for long-term continuous
measurements, the obtained Na
+
sensor was conditioned overnight in 100 mM NaCl.
The fabrication process of the LEG-MIPs sensor array is illustrated in
Supplementary Fig. 6
.
All the MIP layers are synthesized by electro-polymerization. The polymerization solution was
prepared by dissolving 5 mM template (e.g., target amino acid), 12.5 mM aminophenylboronic
acid (APBA) and 37.5 mM pyrrole into 0.01 M phosphate buffer saline (PBS) (pH=6.5). For multi-
MIP BCAA sensor, 5 mM of each target (i.e., Leu, Ile, and Val) was used. Prior to MIP deposition,
the LEG was activated in 0.5 M H
2
SO
4
with CV scans for 60 segments (-1.2–1 V with a scan rate
of 500 mV s
-1
). For the direct-detection LEG-MIP sensors, the target imprinted polymer was
electrochemically synthesized on the LEG electrode with CV deposition (0–1 V for 10 cycles, 50
mV s
-1
) using the prepared polymerization solution. The target molecules were extracted by
soaking the electrode into an acetic acid/methanol mixture (7:3 v/v) for 1 hour. Subsequently,
the resulting electrode was immersed into 0.01 M phosphate buffer saline (pH=6.5) for
repetitive CV scans (0.4–1 V with a scan rate of 50 mV s
-1
) until a stable response was obtained.
For LEG-non-imprinted polymer (NIP), the electrode was prepared following the same
procedure as LEG-MIP except that there was no template added in the polymerization solution.
For the indirect-detection MIP sensors, electrochemically synthesized redox-active
nanoreporters (RARs) (e.g., Prussian Blue nanoparticles (PBNPs) or
anthraquinone-2-carboxylic
acid (AQCA
)) was first modified on the LEG electrode. The PBNPs RAR on the LEG was prepared
with cyclic voltammetry (20 cycles) (-0.2 to 0.6 V with a scan rate of 50 mV s
-1
) in an aqueous
solution containing 3 mM FeCl
3
, 3 mM K
3
Fe(CN)
6
, 0.1 M HCl and 0.1 M KCl. A PBNP layer with
appropriate redox signal is necessary to produce a good sensitivity for the final MIP sensors; to
achieve this stable and suitable redox signal, the LEG-electrode was rinsed with distilled water
after the initial PB deposition and the PB electrodeposition step was repeated for two more
times until a stable 70
μ
A LSV peak in 0.1 M KCl solution was achieved. Subsequently, the LEG-
PB was rinsed with distilled water and immersed into a solution containing 0.1 M HCl and 0.1 M
KCl for repetitive CV scans (-0.2–0.6 V with a scan rate of 50 mV s
-1
) until a stable response was
obtained.
To prepare the AQCA RAR on the LEG, the LEG electrode was first incubated in 50 μL
PBS (pH=6.5) with 5 mM AQCA at 4 ºC overnight. S
ubsequently, the LEG-AQCA was rinsed with
distilled water and immersed into a phosphate buffer solution for repetitive CV scans (-0.8–0 V
with a scan rate of 50 mV s
-1
) until a stable response was obtained. For the indirect-detection
LEG-PB-MIP sensors, an additional PB activation process was conducted right after the template
extraction (IT scan at 1 V in 0.5 M HCl for 600 s), followed by an LEG-PB-MIP sensor stabilization
process in 0.1 M KCl (CV scans at -0.2–0.6 V with a scan rate of 50 mV s
-1
). It should be noted
that for the LEG-AQCA-MIP sensor, only 3 CV cycles polymerization was used to prepare the MIP
layer, and the sensor was stabilized in 0.01 M phosphate buffer saline (PBS) (pH=6.5) (CV
scans at -0.8–0 V with a scan rate of 50 mV s
-1
).
The morphology of materials was characterized by scanning electron microscopy (SEM, Nova
Nano SEM 450) and transmission electron microscope (TEM, Talos S-FEG FEI, USA).
The Raman
spectrum of the electrodes with different modification were recorded using a 532.8 nm laser
with an inVia Reflex (Renishaw). Fourier transform infrared (FT-IR) spectra were measured
using IR spectrometer (Nicolet 6700).
Characterization of the LEG sensor performance.
A set of electrochemical sensors were
characterized in solutions of target analytes. All the
in vitro
sensor characterizations were
performed through CHI 832D. The response of the Na
+
sensor
was characterized with open
circuit potential measurements in the solutions containing varied Na
+
levels.
DPV analysis was
performed for all the direct-detection LEG-MIP sensor characterizations in 0.01 M PBS (pH 6.5)
or in raw sweat. DPV conditions: range, 0.4–1 V; incremental potential, 0.01 V; pulse amplitude,
0.05 V; pulse width, 0.05 s; pulse period, 0.5 s; and sensitivity, 1 × 10
−5
A V
-1
. For
in vitro
indirect-detection of the target molecules based on the LEG-PB-MIP sensors, LSV analysis (0.4–
0 V) was performed in 0.1 M KCl. The LSV conditions: range, 0.4–0 V; scan rate, 0.005 V s
-1
;
sample interval, 0.001 V; quiet time, 2 s, and sensitivity, 1 × 10
−4
A V
-1
. For
in vitro
indirect-
detection of the target molecules based on the LEG-AQCA-MIP sensors, negative DPV analysis
(0–-0.8 V) was performed in 0.01 M PBS. The negative DPV conditions: 0–-0.8 V; incremental
potential, 0.01 V; pulse amplitude, 0.05 V; pulse width, 0.05 s; pulse period, 0.5 s; and
sensitivity, 1 × 10
−5
A V
-1
. For
in situ
sweat analyte measurement, background and signal
curves were recorded before and after incubation; the signal current was obtained as the
difference of the peak amplitudes between the post-incubation signal and the background
current curves (
Fig. 3b–d
and
Supplementary Fig. 29
). The temperature sensor
characterization was carried out on a ceramic hot plate (Thermo Fisher Scientific)
(
Supplementary Fig. 28
). The sensor response was recorded using a parameter analyzer
(Keithley 4200A-SCS) and compared with the readings from an infrared thermometer
(LASERGRIP 800; Etekcity).
To evaluate the performance of the various electrode substrates for MIP-based AA sensing,
LEG, printed carbon electrode (PCE), Au electrode (AuE), and glassy carbon electrode (GCE)
were chosen. The GCEs were purchased from CH Instruments. The PCEs were printed on the PI
substrate using a Dimatix Materials Printer DMP-2850 (Fujifilm, Minato, Japan) with a
commercial carbon ink from NovaCentrix. The AuEs were fabricated via E-beam evaporation:
20 nm of Cr and 100 nm of Au were deposited onto an O
2
-plasma pretreated PET substrate. MIP
films were prepared with CV deposition (0–1 V for 10 cycles, 50 mV s
-1
).
Fabrication and characterization of microfluidic channels.
The microfluidic module was
fabricated using a 50 W CO
2
laser cutter (Universal Laser System) (
Supplementary Fig. 1
).
Briefly, layers of double-sided and single-sided medical adhesives (3M) were patterned with
channels, inlets, the iontophoresis gel outlines and reservoirs. For all microfluidic layers, the
iontophoresis gel outlines were patterned to enable the current flow from the top polyimide
electrode layer. The bottom layer, which is the double-sided adhesive layer in contact with the
skin (accumulation layer), was patterned with a sweat accumulation well (3M 468MP, laser
parameter: power 60%, speed 90%, PPI 1000). The second layer (the inlets layer), in contact
with the accumulation layer, was patterned with the multiple inlets (12 μm thick PET, laser
parameter: power 20%, speed 100%, PPI 1000). The third layer (channel layer), in contact with
the inlets layer, was patterned with microfluidic channels (Adhesives Research 93049, laser
parameter: power 45%, speed 100%, PPI 1000). The fourth layer (reservoir layer), sandwiched
between the channel layer and the electrode polyimide layer, was patterned with the reservoir
and the outlet (3M 468MP, laser parameter: power 60%, speed 90%, PPI 1000). The reservoir is
an ellipse with a 5.442 mm major axis and a 4.253 mm minor axis to fully enclose the active
sensing area. The thickness of the channel layer is ~0.1 mm (Adhesives Research 93049) and
the thickness of the reservoir layer is 0.13 mm (3M 468MP). The reservoir area is 18.17 mm
2
,
and thus the reservoir volume can be calculated as the area multiplied by the thickness of the
reservoir layer (0.13 mm) which totals 2.36 μL.
Fabrication of agonist agent hydrogels.
Hydrogels containing muscarinic agent carbachol
was prepared as follows: Briefly, for anode gel, agarose (3% w/w) was added into deionized
water and then heated to 250 °C under constant stirring. After the mixture was fully boiled and
became homogenous without agarose grains, the mixture was cooled down to 165 °C and 1%
carbachol was added to the above mixture. Subsequently, the cooled mixture was slowly
poured into pre-made cylindrical molds or into assembled microfluidic patch and solidified for
10 min at 4 °C. The cathode gel was prepared similarly except that NaCl (1% w/w) was used
instead of carbachol.
Signal conditioning, processing and wireless transmission for the wearable sensor.
The block diagram of the electronic system (
Fig. 1g
and
Supplementary Fig. 4
) represents
both the wearable electronic patch and the smart watch that can (i) induce sweat
via
iontophoresis and (ii) monitor sweat via electrochemical methods. The sweat induction and the
sweat sensing procedures are initiated and controlled by the microcontroller (STM32L432KC,
STMicroelectronics) when it receives a user command from the Bluetooth module over
Universal Asynchronous Receiver/Transmitter (UART) communication.
Sweat induction:
Programmable iontophoretic current is generated by a voltage controlled
current source that consists of a unity-gain difference amplifier (AD8276, Analog Devices) and
a boost transistor (BC846, ON Semiconductor). The circuit is supplied by the output of a boost
converter (LMR64010) that boosts the 3.7 V battery voltage to 36 V. The microcontroller
controls the digital to analog converter (DAC) (DAC8552, Texas Instruments) over serial
peripheral interface (SPI) to set the control voltage of the current source. The current source
output is checked by a comparator (TS391, STMicroelectronics) and the microcontroller is
interrupted through its general-purpose input/output (GPIO) pin at output failure. The
protection circuit consists of a current limiter (MMBF5457, ON Semiconductor) and analog
switches (MAX4715, Maxim Integrated; ADG5401, Analog Devices). The microcontroller’s GPIO
is also used to enable or disable the iontophoresis circuit. For the optimized design, a 100-μA
current (~2.6 μA mm
-2
) was applied for on-body iontophoresis sweat induction using the
flexible microfluidic patch.
Power analysis:
When powered at 3.3 V, the electronic system consumes ~28 mA during an
active electrochemical measurement and ~61 mA during iontophoresis. The microcontroller
and Bluetooth module each consume ~12 mA; the sensor interface consumes ~4 mA; the
boost converter and iontophoresis module consumes ~33 mA, and the display module
consumes an additional ~8 mA when refreshing its screen.
Sweat sensing:
The sweat sensing circuitry can perform two channel simultaneous DPV, as well
as potentiometric and temperature measurements. A bipotentiostat circuit is constructed by a
control amplifier (AD8605) and two transimpedance amplifiers (AD8606). A series voltage
reference (ISL60002, Renesas Electronics) and a DAC (DAC8552, Texas Instruments) is used to
generate a dynamic potential bias across the reference and working electrodes. In
instrumentation amplifier (INA333, Texas Instruments) is used for potentiometric
measurements; and a voltage divider is used for the resistive temperature sensor. All analog
voltage signals are acquired by the microcontroller’s built-in analog-to-digital converter (ADC)
channels, processed, then transmitted over Bluetooth to a user device.
Custom mobile application design.
The custom mobile application was developed with the
cross-platform Flutter framework. The mobile application can wirelessly communicate with the
wearable devices via Bluetooth to send commands, and to acquire, process, and visualize the
sweat biomarker levels. The application establishes a secure Bluetooth connection to the
wearable sensor. The home page plots the user’s historical biomarker levels, and highlights the
most recently measured analyte concentrations. When a sweat biomarker measurement is
prompted, the user can switch over to the measurement page that plots the sweat sensors’
voltammograms in real time. Following the voltammetric measurement, the app extracts the
voltammograms’ peak currents using a custom baseline correction algorithm, then converts
the peak currents to corresponding biomarker concentrations. This measurement data is added
to the list of historic analyte levels in the home page.
Refreshing time analysis and simulations.
The refreshing time analyses were performed
using numerical simulations (COMSOL).
Three-dimensional models of different microfluidic
designs with same dimensions of the actual device were created in Rhinoceros and imported
into COMSOL Multiphysics. The mass transport process was simulated by numerically solving
the Stokes equation for an incompressible flow coupled with convection-diffusion equation (see
Supplementary Note 3
).
Human subject recruitment.
The validation and evaluation of the sweat sensor were
performed using human subjects in compliance with all the ethical regulations under protocols
(ID 19-0892 and 21-1079) that were approved by the Institutional Review Board (IRB) at
California Institute of Technology. The participating subjects (age over 18 years) were recruited
from the California Institute of Technology campus and the neighboring communities through
advertisement. All subjects gave written informed consent before study participation. For
wearable sensor evaluation, healthy subjects with a BMI of 18.5 to 24.9 kg m
-2
with fasting
serum glucose <100 mg dL
-1
were recruited. For the BCAA study, inclusion criteria include:
Group I, normal weight individuals with a body mass index (BMI) of 18.5 to 24.9 kg m
-2
with
fasting serum glucose <100 mg dL
-1
(Healthy); Group II, overweight/obese individuals with a
BMI of 25 to 35 kg m
-2
and fasting serum glucose <6 mg dL
-1
(Overweight/Obesity); Group III,
obese individuals with a BMI of 25 to 35 kg m
-2
and fasting serum glucose >= 126 mg dL
-1
(Obesity & T2DM). COVID-19 positive and COVID-19 negative serum samples were purchased
from RayBiotech, Inc.
Gas chromatography–mass spectrometry (GC-MS) analysis for sensor validation.
GC-
MS analysis of the amino acids in sweat and serum samples were performed using EZ:Faast kit
from Phenomenex which provides sample preparation, derivatization and GC-MS analysis of
free amino acids. A Varian Saturn 2000 was used for the GC-MS runs. 1 μL of prepared sample
solution was injected for GC in Helium carrier gas at 1.0 mL min
-1
constant flow with a pulse
pressure of 20 pounds per square inch (psi) for 0.2 min, with the oven programmed from 110
°C to 320 °C at 32 °C min
-1
. The mass chromatography was set with source at 240 °C, quad at
180 °C, and auxiliary at 310 °C with a scan range from 45−450 m z
-1
at a sampling rate of 3.5
scans s
-1
. Selected ion monitoring was used, which records the ion current at selected masses
that are characteristic of the certain amino acid in an expected retention time
49
. For example,
after the derivatization of the EZ:Faast kit, Trp has a characteristic mass at 130 with a
retention time at around 5.1 min, and peak height is recorded for Trp measurements at ion
number 130 and at 5.1 min from the raw data spectrum. The internal standard (norvaline) was
added during the sample derivatization process to account for potential evaporation-induced
increase in peak detection; the internal standard (IS) norvaline peak height is recorded at its
ion number 158 at 1.65 min (
Supplementary Fig. 26
). The Trp peak height recorded from
raw data spectrum was calibrated with respect to the internal standard in the same run:
normalized Trp peak height = Trp peak height/IS peak height. With normalized peak heights of
different levels of Trp standards, calibration plots were constructed. For other samples, the
normalized peak height of Trp was used to calculate the concentration.
Integrated system validation in human subjects.
System evaluation during exercise:
To validate the wearable sensor system, we conducted
constant-load cycling exercise on healthy subjects. The subjects reported to the lab after
fasting overnight and were given a standardized protein drink (Fairlife, Core Power Elite). The
subjects’ foreheads and necks were cleaned with alcohol swabs and gauze before the sensor
patches were placed on the body. A stationary exercise bike (Kettler Axos Cycle M-LA) was
used for cycling trials. The subjects cycled at 60 rpm for 60 min or until fatigue. During the on-
body trial, the data from the sensor patches were wirelessly sent to the user interface via
Bluetooth. When the subjects started biking, the sensor system continuously acquired and
transmitted temperature and sodium sensor data. Every minute, the electronic system initiated
a transient voltage bias between the reference and working electrodes. When the bias
triggered a current above an experimentally determined threshold, the system would start a
CV cleaning cycle and then the first DPV scan as the initial background without target
incubation. The DPV scan was repeated 7 min later as the post-incubation curve. Between the
two scans, sodium and temperature sensor data were continuously recorded. Right after the
post-incubation DPV, another cycle started with a
n
IT cleaning/regeneration step, followed by
an initial background DPV scan. The collected temperature, sodium, and DPV data were
wirelessly transmitted to a user device via Bluetooth in real-time, where the molecular data
was extracted, calibrated, and converted to concentration levels.
Sweat samples were
collected periodically from the subjects during the studies using centrifuge tubes. The sweat
samples were then frozen at −20 °C for further testing and validation via electrochemical test
with the biosensors and GC-MS analysis.
System evaluation with Tyr/Trp supplement intake:
The subjects reported to the lab after
fasting overnight. The subjects’ arms were cleaned with alcohol swabs and gauze before the
sensor patches were placed on the body. The subjects were provided Tyr and Trp supplement
(1 g each) for the intake study. In contrast, the control study was performed on the subjects
without any supplementary intake. A 5-min iontophoresis was applied on the subjects. The
sensor data recording process was the same as exercise-based human trials.
Sensor evaluation with BCAA diet challenge
: For the BCAA studies, the subjects were asked to
consume 5 g BCAAs (2:1:1=Leu:Ile:Val) or a standardized snack including a protein drink
(Fairlife, Core Power Elite) and a CLIF energy bar. An iontophoresis session was implemented
with carbachol gels for sweat induction. Over entire study period, the subject’s sweat was
sampled periodically and analyzed by the sensor patch. Blood glucose level was recorded every
15 min with a commercial Care Touch Blood Glucose Meter.
Fresh capillary blood samples were
collected using a finger-prick approach during the human studies. After cleaning the fingertip
with alcohol wipe and allowing it to air dry, the skin was punctured with a CareTouch lancing
device. Samples were collected with centrifuge tubes after wiping off the first drop of blood
with gauze. After the 90-min standardized clotting procedure finished, serum was separated by
centrifuging at 6,000 rpm for 15 min, and instantly stored at −20 °C for analysis with GC-MS,
the LEG-MIP sensors, and the custom insulin assay.
Blood insulin analysis
: For the BCAA diet challenge study, the collected serum samples were
analyzed using a custom insulin sandwich immunoassay. The magnetic beads (MB) were
modified based on a previous publication
50
. Briefly, 3 μL MBs were activated with 50 mg mL
−1
EDC/sulfo-NHS in MES buffer (25 mM, pH 5) for 35 minutes followed by capture antibody
immobilization (25 μg mL
−1
in MES buffer) for 15 minutes. After deactivation with 1 M
ethanolamine in phosphate buffer (0.1 M, pH 8), MBs were incubated in 25 μL standards
prepared in 1% BSA or serum samples diluted 5 times in 1% BSA for 15 min. From here, the
beads were rinsed with 1% BSA twice after each binding step. Next, the MBs were incubated in
25 μL of biotin-detector Ab (1.0 μg mL
-1
) in 1% BSA for 30 min followed by 15 min in Strep-POD
(2500X) prepared in 1% BSA. The amperometric detection was carried out by applying a
constant potential of -0.2 V to MBs resuspended in 45 μL 1 mM HQ, 5 μL 5 mM H
2
O
2
was
pipetted onto the SPCE when background current stabilized.
Data availability
The data that support the plots within this paper and other findings of this study are available
from the corresponding author upon request.
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Acknowledgements
This project was supported by the National Institutes of Health grant R01HL155815, Office of
Naval Research grants N00014-21-1-2483 and N00014-21-1-2845, the Translational Research
Institute for Space Health through NASA NNX16AO69A, NASA Cooperative Agreement
80NSSC20M0167, High Impact Pilot Research Award T31IP1666 and grant R01RG3746 from the
Tobacco-Related Disease Research Program, Caltech-City of Hope Biomedical Initiative Pilot
Grant, and the Rothenberg Innovation Initiative Program at California Institute of Technology.
J.T. was supported by the National Science Scholarship (NSS) from the Agency of Science
Technology and Research (A*STAR) Singapore. We gratefully acknowledge critical support and
infrastructure provided for this work by the Kavli Nanoscience Institute at Caltech. This project
benefited from the use of instrumentation made available by the Caltech Environmental
Analysis Center and we gratefully acknowledge support on GC-MS from Dr. Nathan Dalleska.
We also gratefully acknowledge Zi Wang for the contribution on mobile app development, Dr.
Rebeca M. Torrente-Rodríguez for the insulin assay optimization, and Dr. Shujuan Bao for the
valuable inputs.
Author contributions
W.G., M.W., Y.Y., and J.M. initiated the concept and designed the studies; W.G. supervised the
work; M.W., Y.Y., and J.M. led the experiments and collected the overall data; Y.S., J.T., D.M.,
C.Y., and C.X. contributed to sensor characterization, validation, and sample analysis; N.H.
contributed to the signal processing and app development. W.G., M.W., Y.Y., and J.M. co-wrote
the paper. All authors contributed the data analysis and provided the feedback on the
manuscript.
Competing interests
The authors declare no competing interests.
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Figure captions
Fig. 1
|
Schematics and images of the wearable biosensor ‘NutriTrek’. a,
Circulating
nutrients such as amino acids are associated with various physiological and metabolic
conditions.
b,
Schematic of the wearable ‘NutriTrek’ that enables metabolic monitoring through
a synergistic fusion of laser-engraved graphene, redox-active nanoreporters, and artificial.
c,d,
Schematic (
c
) and layer assembly (
d
) of the microfluidic ‘NutriTrek’ patch for sweat induction,
sampling, and biosensing. T, temperature; PI, polyimide.
e,f,
Images of a flexible sensor patch
(
e
) and a skin-interfaced wearable system (
f
). Scale bars, 5 mm (
e
) and 2 cm (
f
).
g,
Block
diagram of electronic system of ‘NutriTrek’. The modules outlined in red dashes are included in
the smartwatch version. ADC, analog-to-digital converter; DAC, digital-to-analog converter;
CPU, central processing unit; GPIO, general-purpose input/output; POT, potentiometry; In-Amp,
instrumentation amplifier; MCU, microcontroller; SPI, serial peripheral interface; TIA,
transimpedance amplifier; UART, universal asynchronous receiver-transmitter; IP,
iontophoresis; CE, counter electrode; RE, reference electrode; WE, working electrode; DPV,