ArticlePDF AvailableLiterature Review

Working Up a Good Sweat – The Challenges of Standardising Sweat Collection for Metabolomics Analysis

Authors:
  • Extreme Wellness Institute

Abstract

Introduction: Human sweat is a complex biofluid of interest to diverse scientific fields. Metabolomics analysis of sweat promises to improve screening, diagnosis and self-monitoring of numerous conditions through new applications and greater personalisation of medical interventions. Before these applications can be fully developed, existing methods for the collection, handling, processing and storage of human sweat need to be revised. This review presents a cross-disciplinary overview of the origins, composition, physical characteristics and functional roles of human sweat, and explores the factors involved in standardising sweat collection for metabolomics analysis. Methods: A literature review of human sweat analysis over the past 10 years (2006-2016) was performed to identify studies with metabolomics or similarly applicable 'omics' analysis. These studies were reviewed with attention to sweat induction and sampling techniques, timing of sweat collection, sweat storage conditions, laboratory derivation, processing and analytical platforms. Results: Comparative analysis of 20 studies revealed numerous factors that can significantly impact the validity, reliability and reproducibility of sweat analysis including: anatomical site of sweat sampling, skin integrity and preparation; temperature and humidity at the sweat collection sites; timing and nature of sweat collection; metabolic quenching; transport and storage; qualitative and quantitative measurements of the skin microbiota at sweat collection sites; and individual variables such as diet, emotional state, metabolic conditions, pharmaceutical, recreational drug and supplement use. Conclusion: Further development of standard operating protocols for human sweat collection can open the way for sweat metabolomics to significantly add to our understanding of human physiology in health and disease.
Sweat Collection for Metabolomics AnalysisHussain JN et al.
Clin Biochem Rev 38 (1) 2017 1312 Clin Biochem Rev 38 (1) 2017
Review Article
Working Up a Good Sweat – The Challenges of Standardising Sweat
Collection for Metabolomics Analysis
*Joy N Hussain,1 Nitin Mantri,2 Marc M Cohen1
1School of Health and Biomedical Sciences, RMIT University, Bundoora, Vic. 3083; 2Health Innovations Research Institute,
School of Applied Sciences, RMIT University, Bundoora, Vic. 3083, Australia.
*For correspondence: Dr Joy Hussain, joyhussain9@gmail.com
Abstract
Introduction
Human sweat is a complex biouid of interest to diverse scientic elds. Metabolomics analysis of sweat promises to improve
screening, diagnosis and self-monitoring of numerous conditions through new applications and greater personalisation of medical
interventions. Before these applications can be fully developed, existing methods for the collection, handling, processing and
storage of human sweat need to be revised. This review presents a cross-disciplinary overview of the origins, composition,
physical characteristics and functional roles of human sweat, and explores the factors involved in standardising sweat collection
for metabolomics analysis.
Methods
A literature review of human sweat analysis over the past 10 years (2006–2016) was performed to identify studies with metabolomics
or similarly applicable ‘omics’ analysis. These studies were reviewed with attention to sweat induction and sampling techniques,
timing of sweat collection, sweat storage conditions, laboratory derivation, processing and analytical platforms.
Results
Comparative analysis of 20 studies revealed numerous factors that can signicantly impact the validity, reliability and
reproducibility of sweat analysis including: anatomical site of sweat sampling, skin integrity and preparation; temperature
and humidity at the sweat collection sites; timing and nature of sweat collection; metabolic quenching; transport and storage;
qualitative and quantitative measurements of the skin microbiota at sweat collection sites; and individual variables such as diet,
emotional state, metabolic conditions, pharmaceutical, recreational drug and supplement use.
Conclusion
Further development of standard operating protocols for human sweat collection can open the way for sweat metabolomics to
signicantly add to our understanding of human physiology in health and disease.
Introduction
Human sweat is a biological uid (biouid) that is generating
increasing interest across a diverse set of elds including
dermatology, paediatrics, toxicology, analytical chemistry,
forensic pathology, psychiatry, illicit drug testing and infectious
diseases. Currently sweat is primarily used in clinical medicine
for chloride sweat testing which is used in the diagnosis
of cystic brosis (CF). Additionally, some centres around
the world use a sweat patch for monitoring drugs of abuse,
while others have developed an indicator test (Neuropad) to
detect peripheral neuropathy in the foot sweat of diabetics.1-3
Aside from these applications, the use of sweat in medical
practice is limited in part due to challenges involved with
sweat collection and the range and reproducibility of testing.
This is likely to change as advances in analytical technology
methods within metabolomics and other related ‘omics elds
allow more complex physiological information to be derived
from smaller amounts of sweat with less arduous processing.
This is leading to a greater understanding of the physiology of
human sweating and the skin’s excretory pathways in relation
to metabolites, pathogens, and xenobiotics.4 Incorporation
of Bluetooth capabilities with some of the newer wearable
sweat electrolyte and metabolite detecting systems reects
even wider trends in applications to enhance personalised
analysis.5-7
Sweat Collection for Metabolomics AnalysisHussain JN et al.
Clin Biochem Rev 38 (1) 2017 1514 Clin Biochem Rev 38 (1) 2017
Each type of human biouid or tissue sample has its own
signature metabolome, but most of what is known about the
human metabolome is based upon ndings in the ‘serum/blood
metabolome’ and the ‘urine metabolome’. Further study and
standardised procedures are now required to characterise the
‘sweat metabolome’ and how it ts into the bigger picture of
the human metabolome, and whether the case exists for wider
application of sweat metabolomic testing.
When applying a metabolomics approach to analysing human
sweat, a number of variables need to be examined within the
context of the origins, composition, physical characteristics
and functional roles of sweat. These variables include:
sweat induction and sampling techniques, timing of sweat
collections, sweat storage conditions, and laboratory aspects
such as metabolic quenching, extraction, concentration,
fractionation, separation and other processing methods
applicable to sweat. Exploring these variables within the
framework of newer laboratory analytical platforms that
optimise qualitative and quantitative detection of sweat
metabolites will pave the way forward to make more rigorous
and meaningful comparisons of sweat metabolomics studies.8
Standardising the collection, handling, processing and storage
of sweat for further metabolomics analysis is vital to this
endeavour and working out the further steps necessary to
achieve this standardisation is the focus of this review.
Background – Metabolomics
Metabolomics is the multidisciplinary science involving
the measurement and analysis of low molecular weight
metabolites such as electrolytes, sugars, lipids and other
compounds that exist in a selected biouid, cell, tissue or
organism under a given set of physiological conditions. It has
its roots in the works of many biochemists who pioneered
the discovery and detection of various vitamins in the 1940s
and progressed the concepts of ‘metabolic variance’ and
‘biochemical individuality’.9-12
The exact number of unique metabolites in the human
metabolome has yet to be rmly established, but it is
generally thought that there is a lower number of metabolites
in the human metabolome (>3,500) compared with the total
number of genes (>30,000 in genome), RNAs/transcription
factors (>30,000 in transcriptome) and proteins (>100,000 in
proteome).13 Small changes in the transcriptome may translate
into more amplied changes in metabolites.14 With presumed
fewer total metabolites to analyse and a potentially more
amplied signal to be detected, the power and potential of
metabolomics to pick up minute but signicant health-related
changes holds promise.
As with all newly emerging elds, within metabolomics
there is multiplicity and various expansions of terminology.
Although metabolomics and metabonomics are often used
interchangeably in the literature, metabonomics technically
refers to the study of the interactions of metabolites over
a timeframe in a complex system.15 Fluxomics refers to an
extension of metabolomics, in which metabolomics is applied
at various experimental time points generating kinetic data
which can then be used to study metabolic pathway uxes.16
Exposomics, another extension of metabolomics, refers to
identifying metabolites linked to environmental risk factors
for disease.17 Metabolites can be classied into two categories:
endogenous metabolites (synthesised and utilised within a
biological system) and exogenous metabolites (imported
from outside the biological system into the cell, such as drugs,
xenobiotics and nutrients).13,16 The Human Metabolome
Project (HMP) led by Dr David Wishart of the University
of Alberta in Canada published a rst draft of the human
metabolome in 2007 which consisted of 2180 metabolites,
1200 drugs and 3500 food components.18 A growing list of
ndings additional to the HMP is being compiled and veried
on the Human Metabolome Database – a freely accessible
and continually updated web resource (http://www.hmdb.
ca/).11,13,19 Not all known human metabolites can be found
in any given biouid because different biouids serve
different functions and play different metabolic roles. As of
November 2016, the HMP had identied and/or quantied
over 3848 metabolites: 440 metabolites in cerebrospinal uid,
1233 metabolites in saliva, 2287 metabolites in blood, 1746
metabolites in urine, 695 metabolites is faeces and over 172
metabolites in other tissues and biouids including sweat.19
The methodology of metabolomics can be divided into
different conceptual approaches such as targeted analysis,
global metabolite proling, metabolomics and metabolic
ngerprinting/ metabolic footprinting.20 A targeted
metabolomics approach involves a targeted search and
quantitative analysis of a set number of known metabolites or
substances that play a particular role, much like a typical clinical
laboratory test. Global metabolite proling is untargeted
and comprises an analysis of all measured metabolites or
substances, including those known and unknown, which
make up a metabolic prole of the total complement of
metabolites in a particular sample.13,20,21 Metabolomics
utilises complementary analytical methodologies such as
liquid chromatography-dual mass spectrometry (LC-MS/
MS), gas chromatography-mass spectrometry (GC-MS),
and nuclear magnetic resonance (NMR) spectroscopy
in a coordinated attempt at global metabolite proling.20
Metabolic ngerprinting refers to the metabolic ‘signature’ or
mass prole of the biouid or tissue sample of interest which
is then compared in a large sample population to screen for
differences between samples. When signals or signicant
differences can be detected, the metabolites are then identied
and the biological relevance of these particular metabolites
can be more easily elucidated. Metabolic footprinting is
analogous to metabolic ngerprinting except the differences
detected involve focus on extracellular metabolites.15
Sweat Origins, Components and Functions
Sweat Denitions
Whole body sweat is a complex mixture of cumulative
secretions from millions (1.6–5 million) of eccrine, apocrine,
apoeccrine and sebaceous glands as well as bacteria, yeast,
fungi, other microbiota and cellular debris that reside in
and on the largest organ of the human body – the skin.22
These microscopic glands dwell largely in the dermis and
hypodermis layers with secretory canals through which sweat
ows onto the skin surface and into hair follicles. Dening
sweat precisely is complicated by confusing nomenclature
across different disciplines in the scientic literature and
a lack of a biological systems-based approach to studying
sweat. Sweat collected from the skin surface in experimental
studies, especially older studies, is often referred to as
‘eccrine’ sweat because eccrine sweat glands are the most
numerous and ubiquitous glands in the skin, however many
sweat samples also contain potentially trace amounts of
apocrine, apoeccrine and sebaceous gland secretions (called
sebum), depending upon the body site of sampling. This
imprecision of nomenclature is the case in some toxicology
literature dealing with sweat patch testing of illicit drugs and
physiology literature studying electrolyte changes in exercise.
However, in the dermatology and cosmetic literature, ‘sebum’
can gure in addition to ‘sweat’ with more emphasis on the
underlying structures within the skin. The converse can also
be true with studies focused on collection of sebum. The
term ‘residual skin surface components’ (RSSC) is another
synonym of ‘sweat’ as it comprises potential sweat glandular
secretions and cellular debris (from stratum corneum –
outermost epidermal skin layer).23
Mindful of the semantics surrounding sweat, it is useful
to revisit the anatomy, histology and secretions of the four
known gland types that can contribute to sweat. This sets the
stage for better understanding and targeting of future studies
to fully characterise the sweat metabolome.
Eccrine Sweat Glands and Secretions
Eccrine sweat glands exist at birth and can be located all over
the body’s skin except on lips, on the nail bed and on some
elds of the genitalia (e.g. glans penis). They can average
100–200/cm2 body surface area, with higher densities (600–
700/cm2) on palms and soles, and at luminal diameters of
20–60 μm at skin openings.24,25 Eccrine glands consist of
single tubules ranging 4–8 mm in length that are generally
divided into 3 parts: (i) deep, coiled secretory portion in deep
dermis layers; (ii) upper dermal portion with straight and
coiled parts; and (iii) intra-epidermal part often referred to as
the acrosyringium. The dermal portion, or dermal duct, has
epithelial cells connected at numerous sites by desmosomes
and intercellular junctions that are believed to constitute a
barrier between the luminal and extracellular compartments.
The inner luminal cells contain various tonolaments while
the outer basal cells are surrounded by collagenous and
brocyte-rich sheathes.22,26
Eccrine sweat glands are classed as merocrine glands (Figure
1). Eccrine sweat gland secretions are released from cells as
an aqueous uid, without disintegration of cells, containing
various electrolytes, elements, ions, amino acids, proteins
and other known and unknown small molecules as outlined
in Figure 2.27,4 Composition varies with many factors: rate
of sweat production, transit time through the excretory
duct, aldosterone activity, physical training, psychological
states and acclimatisation to environmental temperatures.26
These give hints to underlying functions that have not yet
been fully determined. There also exists debate whether
secretory eccrine sweat is perhaps an isotonic ultraltrate of
plasma since sweat contains many of the same solutes found
in plasma, but at much lower concentrations.28 However,
based on a recent proteomics study of pooled sweat samples
collected from schizophrenic and control subjects, only 6
of 185 unique proteins identied in sweat were reported in
serum. The authors therefore argue that sweat is not merely
a plasma transudate and future metabolomics studies are
required to shed more light on this topic.29
Apocrine and Apoeccrine Sweat Glands and Secretions
Apocrine sweat glands also exist at birth but do not become
active until the androgenic stimulation of puberty.26 They are
conned to hairy body areas (i.e. axilla, mammary areola,
peri-umbilicus, perineal and genital areas) since they open and
secrete into adjacent hair ducts (e.g. apopilosebaceous ducts)
before secretions reach the skin surface. They are generally
larger than eccrine sweat glands with apocrine coil diameters
of ~800 µm compared to eccrine coil diameters of ~500
µm, both located in the dermis and hypodermis.22 Apocrine
ducts are relatively short and found in close proximity to hair
follicles. The density of apocrine glands is highly variable
with reports of 8–43/cm2 body surface area in one study of the
axilla.30 Two different types of cells are visualised in apocrine
glands: columnar secretory cells and myoepithelial cells. The
secretory cells are generally noted to be full of mitochondria
and different granules with convoluted cell membranes and
microvilli presenting towards the lumen.22
Apoeccrine sweat glands are a mixed type gland as the name
suggests and were rst described in 1987 by Sato et al.30 They
are also presumed to develop during puberty and be restricted
to hairy body areas. As many as 50% of all axillary sweat
glands are thought to be apoeccrine. Component cells of
Sweat Collection for Metabolomics AnalysisHussain JN et al.
Clin Biochem Rev 38 (1) 2017 1716 Clin Biochem Rev 38 (1) 2017
apoeccrine glands include eccrine secretory cells, apocrine
secretory cells and myoepithelial cells.30 Identication of
these morphologically distinct glands can be made with
specic protein markers (i.e. phalloidin,S-100, CD15).22
Apocrine and apoeccrine glands are both classed as apocrine
glands. With apocrine glands, secretion occurs via pinching-
off of the cell’s plasma membrane producing membrane-
bound vesicles, which helps to account for comparatively
more viscous secretions.22,26 Sato et al. determined that Na+
and K+ concentrations obtained from the isolated ducts of the
apoeccrine glands are curiously more similar to that of eccrine
sweat compared to apocrine sweat.30 While the composition of
apoeccrine sweat has not yet been fully elucidated, apocrine
sweat has been demonstrated to contain carrier proteins for
volatile odour molecules like volatile organic compounds
(VOCs) and pheromones with amino acid conjugates
produced by bacterial enzymes.22 Apocrine bromhidrosis
(more commonly known as BO or body odour) is thought
to be linked to large amounts (i.e. over 106 bacteria/cm2) of
resident microora such as aerobic cocci, diphtheroid species,
Corynebacterium species and Staphylococcus epidermidis.26,31
Sebaceous Glands and Sebum
Sebaceous glands are located in the skin of all surface areas
except for the palms of hands and soles of feet. They are
particularly numerous on the forehead, scalp, midline back,
chest, perineum and surrounding the orices of the human
body. Densities of up to 400–900 glands/ cm2 occur on the
face, especially in the T-zone area of the face which starts from
the midpoint and sides of the forehead, and extends downward
toward the middle of the nose, including the sides of the
nose and the midline part of chin.32 Sebaceous gland density
decreases towards the extremities of the body. Sebaceous
glands can be divided into two types: pilosebaceous glands,
when associated with hair follicles, and free sebaceous glands
seen mostly at transitional zones between skin and mucous
membranes. A well-known example of a free sebaceous
gland is the Meibomian gland of the eyelid. All sebaceous
glands consist of single or multiple lobules, or acini, with
ducts emptying into a main sebaceous duct. Secretory lobules
contain sebaceous gland cells, or sebocytes, that are excreted
in their entirety as part of the holocrine gland status. Maturing
sebocytes have been visualised to accumulate high lipid
content as they migrate from periphery to gland duct.33
Like apocrine sweat, sebum is thought to play a role in the
generation of pheromones and body odour in its interactions
with skin-residing bacteria and yeast of the microbiome.34
Sebum, however, is even more lipid-based, containing
triglycerides and fatty acids (together 57% of contents) as
well as cholesterol, wax esters, squalene, keratin, cellular
debris, anti-microbial lipids, antioxidants, coenzyme Q10,
vitamin E and other various metabolites of fat-producing
cells.34,35 Interestingly, the lipids in sebum would seem to
originate from both sebocytes and keratinocytes, with studies
identifying differences based upon cholesterol and squalene
conversion enzymes.34
Collected Sweat
Setting aside the above distinctions in gland origins, the
vast majority of sweat studies in the literature have analysed
a collective form of sweat with eccrine gland secretions
predominating. Depending upon location of sweat sampling
and various cleaning and collection strategies used during
sampling or processing, trace amounts of sebum and/or
apocrine and apoeccrine gland secretions, cellular matter from
the epidermis and associated ~1012 skin microbes, as well
as other metabolites like xenobiotics may feature in sweat
samples.36 The systems-based approach of analysing sweat
with metabolomics offers the prospect of uniting all these
different subcomponents of sweat. With such a metabolomics
approach, studies of ‘normal’ sweat obtained from ‘healthy’
people have detected highly variable metabolite compositions
with large numbers of different small molecules, of both
microbial and human origin, in a primarily water-based
(~99%), relatively acidic (mean pH 6.3) solution (Figure 2).37-40
This rich complexity of sweat content hints at its functions
both at the level of the skin and at the level of the organism
as a whole.
Functions of Sweat
Temperature and Fluid Homeostasis
Sweat is integral to the regulation of body core temperature
by water evaporative heat dissipation. Blood ow regulation
and vasodilation of supercial blood vessels largely
contribute to this homeothermic control and the nding that
eccrine sweat production is under the control of cholinergic
and, to a lesser extent, adrenergic innervation is consistent
with this hypothesis.41 Various stimuli of this system include
temperature, emotions, intellectual stimulation and gustatory
stimulation.42 Sweat volumes vary widely as a result. Global
insensible uid losses can be approximately 1000 ml daily
for the whole human body, including more than half of uid
losses through the skin via perspiration with the remaining
losses being through the lungs.4,40 However, there are reports
of individuals perspiring up to several litres per hour, 12 L per
day under certain extreme physiological conditions.4,35,43
Eccrine sweat activity appears intermittent over a large
portion of the body: cycles of periodic discharges alternating
with pauses occurring from <1 to 12 geyser-like emissions
per hour with single sweat gland emissions recorded every
3.3 min in one recent study.41 This activity differs among
(a) Merocrine
secretion -
Eccrine Sweat Glands
(b) Apoocrine
secretion -
Apocrine and
Apoeccrine
Sweat Glands
(c) Holocrine
secretion -
Sebaceous Glands
Secretion
Secretory vesicle
Golgi complex
Nucleus
Pinched o
plasma
membrane-bound
vesicle is secretion
Sebocyte dies
and becomes
secretory product
– sebum
Sweat Gland Secretion Patterns
Figure 1. Sweat gland secretion patterns.
Sweat Collection for Metabolomics AnalysisHussain JN et al.
Clin Biochem Rev 38 (1) 2017 1918 Clin Biochem Rev 38 (1) 2017
individuals, environmental circumstances and body sites
with approximately 50% of total body volume of sweat being
thought to be produced by the trunk, 25% by the legs and
25% by the head and upper extremities.1,48 Even in cases of
profuse sweating, it is thought that only approximately 50%
of sweat pores release sweat at any given time, except for the
palmoplantar regions where the sweat gland activity is largely
synchronised.44 How these ndings t within sweat’s overall
functions in the human body is still unclear.
In contrast to eccrine glands, apocrine gland activity is
reported to be more continuous in its uid secretions, while
still receiving predominantly cholinergic and some adrenergic
innervation.22,42 Apoeccrine gland stimulation by physiological
and pharmacological stimuli appears to be distinct from those
controlling eccrine or apocrine glands. Apoeccrine glands
respond quickly to psychological stress and are thought to
more signicantly contribute to the abundant sweat produced
in the axilla.25,30
Sweat Electrolyte Regulation
Another important component of sweat that impacts human
uid balances is sodium. The concentrations of Na+ in sweat
can be highly variable, ~20–100 mmol/L, and some individuals
can lose an estimated 4–6 g of Na+ per day, equivalent to 12–
15 g of NaCl daily through sweating, especially if working in
moderately hot conditions.43 Eccrine gland duct cells reabsorb
several ions, including Na+ and Cl-, via a number of known
anion exchangers such as Na+/K+-ATPases (on basolateral
membranes), cystic brosis transmembrane conductance
regulators (CFTRs, mutations of which provide the basis for
Cystic Fibrosis Sweat Chloride testing), carbonic anhydrases
II, and vacuolar proton pumps (V-H+-ATPase).22 Sweat Na+
and Cl- concentrations have been documented to increase with
age to 12–19 years then stabilise thereafter.45,46 Sweat Na+,
Cl-, and K+ concentrations also have reported body regional
variations.47
Acid-base homeostatic mechanisms are presumed to be
involved in sweating since sweat is more acidic than plasma,
with pH ranges of 4–6.8 reported in various studies.1,4 It is
noted that with increased ow rates following exercise or at
temperatures above 31 °C, sweat pH increases to upper limits
of approximately 6.8, which is still more acidic than plasma.47
Some non-ionised basic drugs diffuse into sweat and become
ionised as a result of the lower pH of sweat, although the
exact mechanisms have not been fully elucidated. This has
led to projections of these non-ionised basic drugs displaying
free-drug (or molecule) sweat-to-plasma (S/P) ratios of >1, as
in the case of ammonia with reported S/P ratios of 20–50.48,49
Skin Protection
Sweat also provides lubricating, water-proong, antimicrobial
and skin barrier-promoting properties that support skin in the
rst line of defence against many environmental insults. In
extreme hot conditions, the lipid-rich secretions of apocrine
and sebaceous glands can emulsify sweat produced by
eccrine glands to create a hydrolipid lm that is not as readily
evaporated. This is thought to be of importance in delaying
dehydration. In colder conditions, the lipid nature of sweat
becomes more solid and, in coating the hair and skin, sources
of unwanted moisture like rain or snow can theoretically
be more effectively repelled.50 Palmar hydration, which is
directly linked to eccrine sweat production, increases the skin
friction coefcient which therefore improves the adherence
of hands to objects and contributes to a heightened sense of
touch.41 Sweat also contains antimicrobial peptides (AMPs)
like dermicidin, lactoferrin, and LL-37, an AMP of the
cathelicidin family, which serve to control certain pathogenic
bacterial counts on the skin surface.4,41 However, the precise
qualitative and quantitative content of skin microbiota and
associated microbe-microbe and microbe-host dynamics via
sweat are areas of active research with early ndings hinting
at rich metabolic inter-relationships with impacts on skin
integrity, especially in skin inammatory states.36
The free amino acid composition of sweat is curiously
different from other biouids. Data from a recent study
suggest the amino acid content of sweat is remarkably similar
to the amino acid content of an epidermal protein, prolaggrin.
Since prolaggrin is thought to be the key contributor of free
amino acids making up the natural moisturising factor within
the stratum corneum, it is postulated that sweat plays a role
via interactions with prolaggrin in maintaining the barrier
integrity of human skin.51
Immune System
Sweat has links to many immune-mediated mechanisms.
Skin epithelial cells interact with various external stimuli to
produce cytokines, and sweat directly activates epidermal
keratinocytes to produce various cytokines using in vitro
models with cultured human keratinocytes from surgically
discarded neonatal skin samples.39 It is postulated that sweat
may play both benecial and pathological roles in immune-
mediated communications. For example, sweat is well-
recognised in exacerbating atopic dermatitis (AD) lesions
and is associated with increased itching (pruritus) which
has associations with enhanced expression of IL-31 (newer
member of IL-6 family of cytokines) in tissue samples of
exacerbated AD lesions.52 Sweat also contains cystatin A, a
proteinase inhibitor of bacterial cysteine proteases. Given
these exogenous proteases are known to break down the
epidermal barrier, cystatin A in sweat may serve both immune
Lipid/Hydrophobic Content < 1% - Primarily Sebum,
Apocrine Sweat origins:
lipids,
glycoproteins,
steroid hormones,
nitrogen,
lactate,
pheromones,
VOCs,
proteins/enzymes/cytokines,
triglycerides,
fatty acids,
antioxidants,
vitamins,
cholesterol,
cholesterol esterases,
wax esters,
squalene,
and more.
Aqueous/Hydrophilic Content > 99% -
Primarily Eccrine Sweat origins:
water,
sodium,
potassium,
chloride,
bicarbonate,
urea,
glucose,
magnesium,
lactate,
iron,
copper,
zinc,
calcium,
phosphate,
manganese,
chromium,
cobalt,
nickel,
iodine,
molybdenum,
amino acids,
vitamins,
BPA,
Phthalates,
heavy metals –
lead,
cadmium,
mercury,
arsenic,
foreign antigens,
and more.
Cellular Debris, Bacteria, Yeast, etc.
Metabolomic Sweat Content
Figure 2.
Metabolomic sweat content.
Sweat Collection for Metabolomics AnalysisHussain JN et al.
Clin Biochem Rev 38 (1) 2017 2120 Clin Biochem Rev 38 (1) 2017
and skin protective roles.53 Quantitative levels of IL-1α, IL-
1β, IL-6, TNF-α, IL-8 and TGF-β have been measured in
human sweat although the precise cellular origin of these
cytokines is still unknown and could be derived from sweat,
blood or epidermal cells.54
Excretion Functions and Drug Delivery Mechanisms
While the excretory function of sweat has previously been
considered negligible compared to the kidney, recent studies
challenge this notion. There is evidence that several toxic
elements and xenobiotics may be preferentially excreted
through human sweat.55-58 Some studies report arsenic,
cadmium, lead and mercury being excreted in appreciable
quantities via sweat, with the rates of excretion matching
or exceeding urinary excretion.58 Furthermore, while excess
dietary nicotinamide cannot be eliminated through urine
because of its reabsorption by the renal tubules, it can be
effectively excreted by sweat glands.35 Many pharmaceutical
drugs are also excreted via sweat and the role of sweat
patch technology in monitoring illicit drug use is based on
dozens of studies examining the pharmacodynamics and
pharmacokinetics of amphetamines, cocaine, cannabis,
opiates and associated metabolites excreted in sweat.1,48 Drug
binding to various skin fractions and reabsorption of drugs
from pooled sweat on skin has also been observed. The
relative concentration of unmetabolised drugs is reported to be
occasionally higher in sweat than in blood, urine or saliva.59,60
The above ndings suggest that molecules of drugs/
metabolites/xenobiotics may reach the skin surface from
blood by various proposed routes: via sweat or sebum by
active or passive inter- and/or transcellular mechanisms;
and transdermal migration through lipid bilayers of stratum
corneum.40 The second mechanism could be linked to the
concentration gradient in which only the free fraction of drug/
metabolite/xenobiotic, unbound to proteins, diffuses through
lipid membranes from plasma to sweat. Thus, it seems that
the physical nature (i.e. molecular mass, protein-binding,
pKa and lipophilicity) of each particular drug/metabolite/
xenobiotic plays a role in how much ends up in sweat. In
fact, dermatologists routinely take advantage of this scenario
when treating cutaneous fungal infections with oral antifungal
medications such as ketoconazole, terbinane or uconazole.
It is often recommended to exercise to induce sweating while
taking these oral antifungals since the drugs are transported
to the skin surface by eccrine sweat and/or sebum and then
often reabsorbed, thus optimising drug delivery to site of
infection.61-63
Metabolic and Infectious Diseases
The alteration of sweat with different pathological conditions
makes sweating a useful clinical indicator for various
conditions. Over-sweating (hyperhidrosis) and under-
sweating (hypohidrosis), whether regional or systemic, may
represent warning signs for systemic conditions or diseases.
Decreased sweat production involving the feet is the basis
for the Neuropad indicator test for diabetic peripheral
neuropathy.3 Impaired overall sweating is associated with pre-
eclampsia and thought to be related to decreased clearance of
plasma vasoactive amines.64 Female menopause is commonly
associated with ‘hot ushes’ linked to increased sweat
production.65
Night sweats can indicate serious systemic infections (e.g.
tuberculosis) and malignancy, while local hyperhidrosis
around a bite site can indicate toxic envenomations such as
occurs with Australian redback spider bites.66 Hypoglycaemia,
hyperthyroidism, hypercapnia and vagus nerve stimulation
can all lead to stimulation of eccrine sweat production and
alterations in local sweating may arise directly from certain
skin conditions.26 For example, some hyperkeratotic disorders
such as pityriasis versicolor and psoriasis interfere with
the excretion of sweat and are associated with decreased
sweat output as visualised with skin capacitance imaging of
lesions.44 Abnormalities in the transport of sweat onto the
skin’s surface may also cause a severe prickly sensation and
skin inammation resulting in the intra-epidermal retention
of sweat, such as occurs with miliaria rubra which has been
linked to elevated levels of IL-1 and IL-31detected in sweat.39
Therapeutic and Wellness Functions of Sweat
It is hypothesised that sweat produced by different activities
may differ in composition. For example, IL-1 concentrations
are increased in sweat induced by both exercise and sauna
bathing,39 yet exercise is linked to increases in the generation
of several end-metabolites like reactive oxygenated species
that are in turn linked to oxidative stresses. This is thought not
to be the case with sauna-induced sweat although this remains
to be validated by further studies.35
Lipid Homeostasis
Sebum production changes have been linked to diet. Caloric
deprivation in the setting of obesity decreases sebum
production while a high fat diet in the setting of psoriasis
increases it.67,68 Increases in energy intake have been associated
with increased excretion of triglycerides, cholesterol and
associated esters in sebum.35 As newer studies in sweat and
skin surface lipidomics are being done, more denitive
information regarding these links and potential mechanisms
of action are likely to emerge.69
Methods
Pubmed, Medline, Google Scholar, Embase, Science Direct,
Scopus, Ovid, Web of Science, Proquest, Toxline and UpToDate
databases were initially searched with keywords ‘sweat’ and
‘metabolomics’ with restrictions of English language and
of dates 2006–2016. These records were then supplemented
with searches for other research by key authors, searches of
citations and reference lists of key papers, and additional
searches with expanded keywords relating to sweat including
perspiration, sauna, exercise, secretion and/or excretion from
human skin and residual skin surface components as well
as expanded keywords relating to metabolomics including
exposomics, xenometabolomics, toxicometabolomics and
uxomics. Older studies of sweat (before 2006) have been
used in compiling background information, but not for the
detailed analysis of sweat collection methods.
Of the 1320 records identied for review as of 1 June 2016,
all 17 studies presenting quantitative human data utilising a
sweat metabolomics methodology of analysis between the
dates 2006–2016 were included, regardless of quality of
experimental design. An additional three sweat proteomics-
based studies were identied that utilised similar laboratory
platforms relevant to metabolomics and were also included in
the comparison analysis.
Results
The Table presents a summary of the pertinent information
regarding sweat induction and collection methods extracted
from the 20 identied studies for comparison.
Discussion
Sweat Induction Protocols
Induction of perspiration represents a phenomenon involving
a complex chain of metabolic reactions, with many possible
triggers, as already discussed. Exercise, temperature,
stress, psychological state, relative humidity, hormonal and
sympathetic/parasympathetic nervous system parameters,
diet, skin colonisation factors, xenobiotics exposure – both
purposeful and non-purposeful – can inuence sweat volumes
and content.40 Refer to the fourth column in the Table
describing sweat induction modes utilised in the reviewed
studies. A number of important factors are apparent when
obtaining sweat for metabolomics analysis: (i) ensuring
adequate amounts of sweat are available to complete the
analysis, including enough volume for controls and potential
further analysis; (ii) ensuring the mode of sweat induction
does not interfere with the utility of the results; and (iii)
ensuring that sweat induction and sweat collection happen
in a timely manner that optimises metabolic quenching and
metabolite stability.8,70
Pilocarpine Iontophoresis
Several active research groups rely on a chemical pilocarpine
iontophoresis method of inducing sweat.29,37,38,71,72 This
method takes advantage of the bioelectric properties of skin
which allow the application of low intensity electrical current
(i.e. 1.5 mA) for 5 min. The resulting opposition offered by
skin to this electrical current, called bio-impedance, is present
in intra-and extra-cellular uids and the capacitive reactance
of cell membranes. For a topically applied chemical such as
pilocarpine (0.5% pilocarpine nitrate solution), a drug with
cholinergic parasympathomimetic activity which aims to
stimulate primarily the muscarinic receptors of eccrine sweat
glands, to be absorbed through human skin, the electrical
current must overcome the bio-impedance imposed on its
ow to reach the target tissue of sweat glands with sufcient
intensity. This bio-impedance can be inuenced by a range of
factors, some of which are electricity source-dependent such
as the distance between electrodes positioning, pulsed direct
current vs constant direct current source, and size and content
of iontophoresis electrodes (typically containing 70% copper,
30% zinc with diameter of 30 mm).27,73
Some of the important host-dependent factors involved
with this mode of sweat induction include the amounts of
keratin and the variable thickness of stratum corneum (SC)
at different body sites, uctuating amounts of uid in skin
layers with overall hydration status, ambient temperature
increasing or decreasing hydration of keratin, adipose tissue
thickness (especially with some sweat glands residing in deep
dermis/ subcutaneous fat) and individual pain/tolerance to
the electric current. All of these factors can alter biological
responses, thereby potentially confounding metabolic results.
Therefore, the argument can be made that using pilocarpine
with iontophoresis induces production of a particular type of
primarily eccrine sweat but whether the detailed metabolomic
contents of this type of sweat are the same as physiologic
sweat and/or thermally-induced sweat and/or exercise-
induced sweat remains unknown.
After all, the original method of cholinergic stimulation
with pilocarpine iontophoresis on the skin to facilitate sweat
production dates back to the 1959 Gibson and Cooke publication
describing implementation and standardisation of the ‘classic
sweat test’ targeting sweat chloride levels for the purposes of
diagnosing cystic brosis (CF).74 The Webster Sweat Inducer
system coupled with a patented Macroduct Sweat Collector
used in more recent sweat metabolomics studies originates
from a further enhancement of the pilocarpine method, again
designed to specically improve the classic sweat test for
CF.75 The quantitative pilocarpine iontophoresis test (QPIT)
remains the gold standard for sweat induction in terms of
CF-related testing and now has over 50 years of progressive
standardisation.76 Despite better uniformity in collecting
sweat samples and improved reference intervals based on
age, dened rates of sweating and the volume of sweat to be
Sweat Collection for Metabolomics AnalysisHussain JN et al.
Clin Biochem Rev 38 (1) 2017 2322 Clin Biochem Rev 38 (1) 2017
Table. Sweat induction and collection methods for metabolomics.*
Study Aims n Sweat Induction Mode
Sweat Collection Sweat
Preparation
Protocols
Analytical Chemistry
Platforms
Methods Timing Amount Storage
Adewole et al.,
201672
Identify diagnostic
biomarkers of active
tuberculosis in eccrine sweat
83 Webster Sweat Inducer –
pilocarpine iontophoresis x
5 min
Macroduct® Sweat Collector – part of
Macroduct® Sweat Analysis System – covers
volar forearm x 15–35 min; sweat transferred
to micro-centrifuge tube
5 min induction
+ 15–35 min
collection
~10–30 µL Samples placed
on dry ice
immediately, stored
at -70°C until
analysed
Solubilised, reduced, alkylated, and digested
with protease; then dried, desalted, dried again,
then resuspended in ACN, formic acid
LC – MS/MS, untargeted
proteomics; FT mode for MS
detection, ion trap mode for
MS/MS detection
Jia et al., 201694 Assess feasibility of using
HPLC-MS/MS for accurate
quantifying of cortisol in
human eccrine sweat
4 Hot room set at 41°C and
~55% humidity
Leg skin cleansed with alcohol pads, followed
by dH2O and drying; sweat collected off skin
into Eppendorf LoBind micro-centrifuge tubes
25–30 min
collection times
>200 µL Samples placed
on dry ice
immediately, stored
at -80°C until
analysed
Sample mixed with ACN/ammonium acetate;
addition of internal standard (in ACN); ethyl
acetate extraction repeated twice, evaporated to
dryness, re-constituted in ACN
HPLC-MS/MS, SRM mode,
targeted
Sheng et al.,
201693
Monitor elimination of bio-
accumulated heavy metals in
humans with exercise
17 Exercise; no specic
instruction as to type or
location
Direct collection of sweat from any part of
the body into glass bottle with cover; then
transferred into 50 mL glass vials with lid.
Referenced methods from Genuis et al. 2011
utilised
Same day as
urine sample
collection
>20 mL Stored at -20°C
until analysis
Samples dried in oven for standardised weight,
ashed in furnace, cooled in dryer; residue
reconstituted in HNO3 with heat
Flame atomic absorption
spectrophotometry, targeted
Tang et al.,
201695
Compare levels of 5 heavy
metals (Cr, Cu, Zn, Cd, Pb) in
human sweat and urine after
physical exercise
9 Exercise; playing badminton
x 2 h
Upper bodies cleansed with ultrapure
H2O before exercise; sweat scraped into
polyethylene sample bottles. Samples allowed
to stand for 30 min, then ltered using 9-mm
lter paper into test tube
~2 h >20 mL Stored at 4°C until
testing
3 methods:
(i) direct dilution with HNO3
(ii) wet digestion with HNO3 + HClO4, heated
to 200°C; cooled, with HNO3 re-added, nal
dilution with ultrapure H2O
(iii) microwave digestion with HNO3 added,
microwaved, cooled, diluted with ultrapure H2O
ICP-MS, targeted
Delgado-
Povedano,
Calderon-
Santiago et al.,
201671
Develop and validate a
method for metabolomic
analysis of human sweat
using GC-TOF/MS
6 Webster Sweat Inducer –
Pilogel® Iontophoretic discs;
1.5 mA electric current x 5
min
Macroduct® Sweat Collector – part of
Macroduct® Sweat-Analysis System – covers
forearm skin x 15 min; sample transferred into
micro-Eppendorf tube
5 min induction
+ 15 min
collection
>70 µL each
participant –
pooled into one
sample
Frozen at -80°C Pooled sweat into each of 3 protocols:
(i) deproteinisation with methanol-ACN;
(ii) extraction with dichloro-methane;
(iii) extraction with ethyl acetate.
Each followed with methoxymation + silylation
GC-TOF/MS, full scan
mode, untargeted
Calderon-
Santiago et al.,
201538
Identify metabolic markers
of lung cancer in sweat to
develop screening tool for
diagnosis of lung cancer
96 Webster Sweat Inducer –
Pilogel® Iontophoretic discs;
1.5 mA electric current x 5
min
Macroduct® Sweat Collector – part of
Macroduct® Sweat Analysis System – covers
forearm skin x 15 min; sample transferred into
micro-Eppendorf tube
5 min induction
+ 15 min
collection
>10 µL Frozen at -80°C
until analysed
Diluted with formic acid and vortexed LC-QTOF MS/MS,
untargeted
Porucznik et
al., 201589
Targeted detection of BPA in
sweat in comparison to urine
for biomonitoring
50 Passive sampling – no
articial modes of sweat
induction
Sweat patches (PharmChek®) applied after
skin cleansed with alcohol wipes, to either
upper-outer arm or front/back midriff
7 days Not specied Sweat patches
stored and
transported in
sterile, BPA-
free 4-oz poly-
propylene
sample cups;
no temperature
specied
Sweat patches extracted with methanol;
evaporated in Turbovap®; reconstituted with
ammonium bicarbonate:
ACN (mobile phase)
UHPLC-MS-MS, targeted;
using methods initially
designed for urine samples
Sweat Collection for Metabolomics AnalysisHussain JN et al.
Clin Biochem Rev 38 (1) 2017 2524 Clin Biochem Rev 38 (1) 2017
Study Aims n Sweat Induction Mode
Sweat Collection Sweat
Preparation
Protocols
Analytical Chemistry
Platforms
Methods Timing Amount Storage
Dutkiewicz et
al., 201459
Untargeted metabolomics
proling of human sweat to
evaluate hydrogel micropatch
collection linked with direct
mass spectrometry
9 Passive sampling – in room
temperature, ~25°C, 45%
relative humidity
Skin pre-wiped with cellulose tissue soaked
with isopropanol: H2O; fabricated agarose
hydrogel micropatch embedded with PTFE
probe attached to forearm area with adhesive
bandage tape
1 min–1 h ‘single droplet’
– unable
to estimate
volume of
sweat sample
accurately
Hydrogel
micropatch probe
covered with glass
slide, stored at 4°C
Direct coupling of hydrogel micropatch probe
to nanospray desorption electrospray ionisation
mass spectrometer
ESI + IT + FT-ICR-MS
Calderon-
Santiago et al.,
201437
Untargeted global
metabolomics proling of
human sweat to optimise
laboratory methods and
chemometrics
96 Webster Sweat Inducer –
Pilogel® Iontophoretic discs;
1.5mA electric current x 5
min
Macroduct® Sweat Collector – part of
Macroduct® Sweat Analysis System – covers
forearm skin x 15 min; sample transferred into
micro-Eppendorf tube
5 min induction
+ 15 min
collection
>5μL Frozen at -80°C Pooled samples diluted with formic acid:H2O
with additional protocols:
(i) hydrolysis with 0.1M NaOH or HCL in H2O,
vortexed, evaporated to dryness, reconstituted
in chromatographic mobile phase A;
(ii) solid phase extraction using C18 and
hydrophilic centrifugal Micro SpinColumn™
LC-QTOF MS/MS,
untargeted
Shetage et al.,
201423
Identify collection methods
for RSSC and evaluate effects
of ethnicity, gender and age
on amount and composition
315 Passive sampling at room
temperature: 18–25°C, 50–
60% relative humidity
Forehead pre-wiped with cotton soaked in
diethyl ether, allowed to dry. Cigarette paper
applied, held in place with elastic headband, in
duplicate x 1 h, fresh cigarette paper replaced
every hour for total 3 h
3 h Totals not
specied:
peak amounts
0.11–0.12 +/-
0.06–0.07 mg/
cm2 of RSSC
collected in rst
hour
None specied Cigarette papers dehydrated x 2 h; extracted
with hexane; extract ltered through 0.2 micron
PTFE membrane, concentrated by purging
nitrogen
GC/MS, untargeted
Mark et al.,
201351
Detailed amino acid analysis
of sweat to better understand
key biological mechanisms
governing its composition
12 Hot room; 40°C, 60% relative
humidity x 15–40 min
Sweat droplets removed from axilla
with positive displacement pipette using
polypropylene tips; sample transferred directly
into ‘low binding’ Eppendorf tube kept at 4°C
~20 min >500 µL Frozen at -70°C Two methods:
i) ninhydrin derivatisation for amino acid
automated analyser
(ii )oximation and trimethyl-silylation for
GC-TOF/MS
Targeted amino acid analysis;
automated amino acid
analyser + GC-TOF/MS,
targeted
Raiszadeh et
al., 201229
Untargeted and targeted
analysis of healthy control
and schizophrenic patient
sweat, to identify candidate
biomarkers of disease
78 Webster Sweat Inducer –
pilocarpine iontophoresis
applied to volar forearm
Macroduct™ Sweat Collector – Macroduct
™ Sweat Stimulation and Sweat Collection
System (Elitech/WESCOR, Inc., Logan, UT,
USA); sample transferred into
micro-centrifuge tubes
30 min 50–60 µL Stored on dry ice Pooled samples:
reduction (dithio-threitol/urea), alkylation
(iodo-acetamide), overnight enzymatic
digestion (trypsin/ammonium bi-carbonate),
quenching (glacial acetic acid, then angiotensin
II), desalting (C-18 Zip Tips), drying in vacuum
concentrator, reconstitution in 0.1% formic acid
LC-MS/MS;
LC-MS/MS + spectral
counting;
MRM-MS verication
Genuis et al.,
201257
Targeted proling of phthalate
compounds in blood, sweat
and urine
20 Self-determined by
participants – infrared sauna,
steam sauna, exercise
Direct collection from any body site into 500
mL glass jar using stainless steel spatula;
participant-delivered to commercial laboratory;
transferred to 4 mL glass jars at laboratory
No time
parameters
around sweat
collection
except
conditional
within 1 week
of blood
collection
(before/after)
100 mL Stored at -20°C;
shipped frozen
on dry ice from
Canada to Sweden
for analysis
Not specied HPLC/MS, targeted;
GC/MS, targeted
Genuis et al.,
201255
Targeted proling of BPA in
blood, sweat and urine
LC-MS-MS, targeted
Genuis et al.,
201156
Targeted proling of 120
compounds (toxicants) in
blood, sweat and urine
ICP-MS, targeted
Sweat Collection for Metabolomics AnalysisHussain JN et al.
Clin Biochem Rev 38 (1) 2017 2726 Clin Biochem Rev 38 (1) 2017
collected at standardised sites as well as newer conrmatory
CFTR-based testing, there are still complicating factors.77-79
Documented reports of false positive and false negative sweat
chloride tests are in the literature, hypothesised to be due
to such wide ranging factors as contaminating topical gels,
interfering dermatological lesions (i.e. atopic dermatitis),
autonomic nervous system dysfunction, prostaglandin
use and other medication uses (e.g. topiramate), arsenic
toxicity, malnutrition states, immunoglobulin deciencies,
autoimmune disorders such as systemic lupus erythematosus,
and various abnormal endocrine states such as untreated
hypothyroidism and Addison’s Disease.27, 80, 81
Exercise/Sauna/Hot Rooms
Other forms of sweat induction used in the studies presented
in the Table include exercise and sauna activity or exposure
to elevated temperatures with varying humidity levels. Older
non-metabolomics studies have suggested distinct differences
in metabolic content when sweat is obtained from exercise or
sauna activity, especially in Ca2+ and Mg2+ concentrations.82
As this is an active area of ongoing research, it cannot be
assumed that metabolomics studies using exercise and/ or
sauna-produced sweat have interchangeable results. A further
potential confounder for sweat studies is the humidity level
of sauna or hot rooms as this may contaminate sweat samples
with condensation of airborne water droplets potentially
containing bacteria, viruses, fungi, and/or xenobiotics. This is
Study Aims n Sweat Induction Mode
Sweat Collection Sweat
Preparation
Protocols
Analytical Chemistry
Platforms
Methods Timing Amount Storage
Lee et al.,
201191
Untargeted metabolomics
analysis to determine
biochemical composition of
exercise sweat
48 Exercise on ergometer for 60
min
Collection with skin patch placed on the lower
back
Sweat patches
removed at
3 time-points:
10–20min,
30–40min,
50–60min of
exercise; placed
on dry ice
Not specied Frozen at -80°C
until analysis.
Not specied GC/MS and LC/MS/MS,
untargeted
Kutyshenko et
al., 201110
Untargeted metabolomics
analysis to determine
biochemical composition of
human sweat
10 Natural environmental heat Direct collection from forehead, upper chest,
upper/lower back, arms using glass pipette or
glass roller, rolled in tray with dH2O and/or
sterile spray gun lled with D2O sprayed
3–5 min
collection
+ 7–10 min
sample
preparation
>0.56 mL Sample storage not
specied; analysis
performed 10–15
min after sweat
collection
Diluted with D2O, centrifuged, transferred to
standard NMR tube
1H NMR Spectroscopy
– high resolution, both
one dimensional and two
dimensional, untargeted
Michael-Jubeli
et al., 201169
Develop simple analytical
protocol for qualitative
characterisation of individual
SSLs and quantitative
evaluation of lipid classes
1 Passive sampling Lipid-free absorbent papers placed on 6 areas
– forehead, back, thorax, forearm, thigh, calf
– maintained for 30 min with medical tape;
removed with tweezers and placed into closed
vials. Collections repeated 4 times
30 min
collection
Not specied Storage of
unprocessed
samples not
specied
Extracted with diethyl ether twice, concentrated
with rotary evaporation, transferred into
2 mL vials, dried under nitrogen stream;
dried extract stored at -20°C until analysis;
extracts derivatised/trimethyl-silylated; rotary
evaporated, residue dissolved in isooctane
HTGC-MS, with electron
impact and chemical
ionisation
Penn et al.,
200792
Test the validity of individual
odour hypothesis by analysing
VOCs in sweat, urine and
saliva
197 Passive sampling Axillary sweat sampled with devised twister
PDMS-coated stir bars, held by special rollers,
placed directly on skin; samples transferred to
glass vials
Once each
fortnight
sampling over
10-week period;
unspecied
sweat collection
timing
Not specied Stored at ~4°C;
shipped in cooler
each week from
Austria to USA for
analysis
Samples directly analysed with SBSE in
connection with thermal desorption GC-MS
SBSE with thermal
desorption GC-MS
Harker et al.,
200628
Untargeted metabolomics
analysis of human eccrine
sweat
60 Hot room at 43.3°C, 65%
relative humidity x 15–40 min
Underarm area wiped, then sweat collected
with plastic-tipped pipette, sample transferred
into sealed glass vials
15 min
collection
>50µL Frozen at -20°C
until analysis
Samples diluted and deuterated phosphate
buffer (pH 7.4, 0.1M); transferred into 5 mm
OK NMR tubes
1NMR Spectroscopy – high
resolution, one dimensional,
untargeted
BPA – Bisphenol A; PTFE – polytetra-uoro-ethylene; PDMS – polydimethyl-siloxane; RSCC – residual skin surface components; SSLs – surface skin lipids; VOCs – volatile organic compounds; ACN – acetonitrile; SBSE – stir bar sorptive extraction; LC-MS/MS – Liquid
Chromatography-Tandem Mass Spectrometry; HPLC-MS/MS – High Performance Liquid Chromatography-Tandem Mass Spectrometry; SRM – selected reaction monitoring; ICP-MS – Inductively Coupled Plasma mass Spectrometry; GC-TOF/MS – Gas Chromatography-
Time of Flight/ Mass Spectrometry; LC-QTOF MS/MS – Liquid Chromatography-Quadripole Time Of Flight-Tandem Mass Spectrometry; ESI – Electrospray Ionisation; IT – Ion Trap; FT-ICR-MS – Fourier Transform Ion Cyclotron Resonance mass spectrometry; MRM-
MS – Multiple Reaction Monitoring-Mass Spectrometry; UHPLC-MS-MS – Ultra High Performance Liquid Chromatography-Tandem Mass Spectrometry; GC / MS – Gas Chromatography / Mass Spectrometry; HTGC-MS High Temperature Gas Chromatography-Mass
Spectrometry; 1H NMR – Proton (Hydrogen -1 nuclei) Nuclear Magnetic Resonance Spectroscopy; ICP-MS – Inductively Coupled Plasma Mass Spectrometry; PTFE – polytetrauoroethylene; dH20 – distilled water; -D2O – deuterated, heavy water
*See Appendix (online supplement) for an expanded version of this table including more detailed information of sweat preparation protocols, chemometrics, databases and key ndings pertaining to studies.
Sweat Collection for Metabolomics AnalysisHussain JN et al.
Clin Biochem Rev 38 (1) 2017 2928 Clin Biochem Rev 38 (1) 2017
especially the case if direct analysis of sweat without detailed
cleaning and extraction methods is employed. There is also a
documented progressive decline in sweat rates when the skin
is thoroughly wetted and/or with higher humidity conditions,
referred to as hidromeiosis. This can be followed by an after-
effect of increased sweating when the skin is outside the
exposed high humidity environment. The timing and rate of
sweating therefore depends upon ambient temperature and
humidity.83
Passive Sampling/Physiological Sweating
Some of the studies in the Table used only passive sampling
of sweat without any imposed modes of sweat induction.
Most of the time, longer periods of sweat collection were
deemed necessary to obtain the same or smaller amounts of
sweat or residual skin surface components (RSCCs). This
may compromise the results, especially in terms of metabolic
quenching of enzymatic reactions and metabolite stability.
With untargeted metabolomics analysis, this is highly relevant
due to unknown metabolites and thus unknown metabolite
stability proles. With more targeted metabolomics, as in
the case of sweat testing for various drugs of abuse, this
would depend on desired levels of quantication and the
relative stability of the target molecule in certain specied
conditions, necessitating controlled stability studies to be
done, as discussed more extensively in a recent review by
de Giovanni and Fucci.1 With newer technological trends
of sweat analysis requiring smaller amounts of sweat, the
need for complicated sweat induction methods will likely be
reduced or redundant.6,59,84,85
Sweat Collection Protocols
A brief overview of the sweat collection methods in the Table
reveals a diverse range of techniques employed to collect sweat
for metabolomics studies. These techniques vary from simple,
direct collection of sweat off skin into microcentrifuge tubes
or glass jars, to elaborate specically-designed implements
(i.e. glass pipettes and rollers, hydrogel micropatches) to
more commercially-available products like the Macroduct®
Sweat Collector or PharmChek® sweat patches. Large
variations between individuals in the amounts and location of
sweat produced create major difculties for those attempting
to design a universal sweat collection device. Skin irritation,
alterations of skin pH, disruptions to skin barrier properties
and interactions with differing individuals’ skin microbiota
are just some of the difculties to be encountered in designing
an ideal sweat collecting apparatus.1
Commercial Sweat Collection Devices
The Macroduct (ELITECH Wescor® Inc., Logan, UT, USA)
is a popular commercially-available sweat collector that
employs a plastic capillary-coil device of 29 mm diameter to
wick the sweat off the skin surface, usually of the forearms.86
Since its introduction in 1986, it has been used in several
sweat studies, including several addressing sweat metabolome
optimisation.37,71 The Macroduct® is a component of the
Macroduct® Sweat Analysis system, involving a commercial
apparatus covering the skin after iontophoresis stimulation
using pilocarpine29,37,38,71,72 that was developed to reduce
the problems encountered with older lter pad- and tissue
paper-based sweat collections of sweat chloride testing for
CF.86 Macroduct® helps to overcome issues of background
contamination, encapsulation (which increases local skin
temperature and sweat gland secretion) and hidromeiosis
(the progressive decline in sweat rates that occurs when
skin is thoroughly wetted and/or with higher humidity)
encountered with the older methods.86 The Macroduct® has
a capacity of ~0.1 mL of sweat collection per device session.
Some researchers have considered placing more than one
Macroduct® simultaneously to collect larger amounts of
sweat that are then pooled for analysis, but differing sweat
rates at different collection sites, amplication of inconsistent
dilutional effects and difculty in attaching the Macroduct®
to other body sites produces confounding results.29
The same corporation (ELITECH Wescor®, Inc., Logan,
UT, USA) developed a larger version of the Macroduct®
called the Megaduct®. This is a round, plastic concave-based
device with a larger collection area of 22.1 cm2 and a central
aperture through which sweat collects into coiled capillary
tubing. While the Megaduct® has an increased sweat volume
capacity of ~0.5 mL,40,87 its utility is limited by the duration of
heat and/or exercise necessary to sweat long enough to ll the
Megaduct® reservoir. For example, in one study, it required
65–75 min to collect the full 0.5 mL reservoir of sweat in 10
healthy men, with varying exercise intensities (VO2 = 0.5–2.0
L/min), temperatures (20–40 °C) in a controlled 50% humidity
environment.87 Increasing sweat collection times to this range
(>60 min) can potentially impact the power of metabolic
ndings, especially with the issues of metabolic quenching
and time course of metabolic changes.87 For example, it is
known that concentrations of sweat electrolytes and minerals
such as zinc and iron change in relatively short periods of
time (<30 min).87,88 Furthermore, both the Macroduct® and
Megaduct® are designed primarily for forearm placement. As
discussed already, the human body does not have a uniform
sweat rate or composition over all skin locations. In fact,
results of one study suggest forearm sweat rate is 30–60%
less than that of the chest or back.87
Another popular commercial sweat collection device is the
PharmCheck® or PharmChek® (PharmChem Inc., Fort
Worth, TX, USA) sweat patch which has been available
since 1990 (refer to the sweat collection method used by
Porucznik et al., 2015 in the Table).89 This device is a non-
occlusive patch consisting of a medical-grade cellulose paper
absorption pad covered by a thin layer of polyurethane and
acrylate adhesive. To use it, the skin site must rst be cleaned
with a tolerable solvent (e.g. isopropyl alcohol swabs) and
thoroughly dried before application. The adhesive lm of the
patch is a semipermeable barrier that allows oxygen, carbon
dioxide and water (vaporised by body heat) to diffuse freely.
Larger non-volatile molecules (such as drugs, metabolites,
metals and other xenobiotics) are retained on the inert
cellulose absorption pad of the patch. Contaminants from
the environment cannot penetrate the adhesive barrier from
the outside once it is in place, enabling the patch to be worn
during normal activities, including bathing, swimming and
other exercise. It has a release liner that allows removal of
the collection pad only once from the adhesive layer after
use thereby preventing removal, reapplication or tampering
with the patch. Underneath the polyurethane layer is a unique
9-digit number printed on the patch that is visible through a
purpose-made window for legal (or research) applications.
These features make this device useful in sweat testing for
illicit drugs.1
Some of the disadvantages of PharmChek® include high
inter-subject variability (potentially due to variable body
site placement), high cost, possibility of environmental
contamination either before patch application or after patch
removal, risk of accidental removal before desired monitoring
period and differing rates of drug/metabolite/xenobiotic
penetration through the membrane, depending upon charged
or uncharged state. Molecules in an uncharged state have
been recorded to migrate more rapidly than charged species
in studies of PharmChek®.1,90
Non-Commercial Sweat Collection Techniques
A newer form of sweat patch with commercial potential
described by Dutkiewicz et al. is a specically-designed
agarose hydrogel micropatch with polytetrauoroethylene
(PTFE) support that has been developed for simplied
collection of very small amounts of sweat that can be analysed
directly within minutes using various MS platforms.59
This new method of sweat collection shows promise, but
still requires further validation and optimisation of signal
sensitivity and performance at higher temperatures and at
increased sweat rates.59
Other noncommercial techniques of sweat collection for
metabolomics studies are also documented in the Table. Lee
et al. describe a ‘sweat collection patch’ placed on the lower
back with sweat collected at three time points (10–20 min,
30–40 min, 50–60 min) while participants exercised on a
cycling ergometer.91 Sweat was frozen on dry ice, and then
stored at –80 °C until prepared and analysed. Unfortunately,
there is limited mention of skin preparation, the type of sweat
collection patch used, how the sweat is frozen, either intact
in patch or transferred to another collection tube, or how
sweat is prepared for untargeted metabolomics analysis.91
Occlusive skin patches consisting of 2–3 layers of lter paper
or gauze have been used in other sweat collection studies
but limitations of excessive pH variations and skin irritation
with some degree of presumed skin disruption have been
signicant detractors.40
Shetage et al. and Michael-Jubeli et al. both use passive
sampling with ‘cigarette paper’ and ‘lipid-free absorbent paper’
to collect the desired RSCCs or surface skin lipids (SSLs),
respectively. These collection methods have advantages of
economics and simplicity but still have the disadvantages of
encapsulation and hidromeiosis already discussed as well as
long collection times of 3 h and 30 min respectively.23,69
Kutyshenko et al. describe specially-designed glass rollers
and glass pipettes for sweat collection. The rollers were used
on lower sweat-producing regions (e.g. arms) moisturised
with a sterile distilled water spray gun beforehand whilst the
glass pipettes were used on heavier sweat-producing areas,
namely forehead, chest and back.10 Although the use of glass
is compelling with its relative inertness and is certainly of
benet when metabolomically targeting plastics-related
xenobiotics, the confounders of varied locations of sweat
harvesting, dilutional effects of adding sprayed distilled water
and a lack of standardisation of temperature and humidity are
likely to complicate the untargeted ndings of this study.
Penn et al. describe another unique, specially-designed
method of collecting sweat with a polydimethylsiloxane-
coated stir bar that is rolled directly onto skin. The fact that
sweat samples can then directly go through the necessary
extraction step with a thermal desorption GC-MS setup is
attractive. However, the fact that samples had to be shipped
at 4 °C overseas to a special laboratory is a limitation and
raises the issues of sample contamination and metabolite
degradation during transportation.92
Unsupervised Sweat Collection Techniques
In studies by Genuis et al. and Sheng et al., participants were
instructed to collect perspiration from any site on their body
directly into a laboratory-provided, pre-cleaned, acid- and
water-rinsed 500 mL glass jar or by using a stainless steel
spatula against their skin to transfer perspiration directly
into the same laboratory glass jar.55-57,93 Sweat was collected
within one week before or after specied blood collection and
participants delivered the collected sweat sample themselves
to a laboratory without any specied storage or transport
Sweat Collection for Metabolomics AnalysisHussain JN et al.
Clin Biochem Rev 38 (1) 2017 3130 Clin Biochem Rev 38 (1) 2017
timeframes. The choice of glass for storage container concurs
with the previous study discussed above. Including the option
of stainless steel spatulas for sweat collection is intriguing.
A grade of stainless steel was reportedly chosen to match
the composition of laboratory needles used in standard
blood collections since sweat was being directly compared
to similarly-targeted detections of compounds in blood and
urine in these studies. Recognising that stainless steel contains
varying amounts of primarily iron, nickel and chromium that
can also be found in trace amounts of physiological sweat, this
is indeed important to factor in with future sweat metabolomics
studies.56 However, the relative absence of controlled timing,
temperature, humidity and storage conditions of sweat
samples and the presumed delay of a metabolic quenching
step make the sweat collection protocol of these series of
studies less than ideal for metabolomics studies.
Direct Sweat Sampling Techniques
In a yet different approach, Harker et al. detailed subjects
rinsing and drying their axillae with water just before entering
a hot room (set at 43.3 °C and 65% relative humidity) for
15–40 min.28 Subsequently, the underarms were ‘wiped’ in an
unspecied way and sweat was collected with a plastic-tipped
pipette and transferred directly into glass vials with a collection
period of approximately 15 min. The glass vials were then
immediately sealed and stored frozen at –20 °C until analysis.
This method included several commendable presampling
controls by limiting use of pharmaceutical medications and
topical applications of antiperspirant and soap products,
detailing dietary limitations and specifying shaving of the
axillary hair. However, the wiping of underarms immediately
before sweat collection introduces potential issues of altered
skin integrity on a molecular level which may impact the
content of sweat analysis with 1H NMR spectroscopy. As
mentioned in the section discussing sweat functions, the skin
integrity is thought to inuence pathways of water and other
molecules/metabolites either via transmembrane proteins or
lipid membranes or sweat glands during uid transport from
plasma to skin surface. Not specifying the exact material the
underarms were wiped with and mixing plastic with glass
in the sweat collection and storage before analysis create
additional uncertainty.28
Some of the researchers associated with the Harker et al.
study went on to publish another study of axillary sweat with
similar direct sweat collection techniques, but optimised and
targeted for amino acid analysis with a different metabolomics
platform of GC-TOF (Time of Flight)/MS instead of 1H NMR
spectroscopy.51 While direct sampling with minimal handling
time and prompt metabolic quenching are advantages of this
method, wiping the armpit before sampling and the use of
a positive displacement pipette that might disrupt the skin
surface along with the requirement for trained personnel to
perform the sampling task remain as drawbacks.51 The choice
of harvesting sweat from the axillae, rich in apocrine and
apoeccrine sweat as well as eccrine sweat, in both of these
studies complicates the comparisons to be made with other
metabolomics studies harvesting sweat from other specic
areas of the body with minimal apocrine or apoeccrine
contributions.
Some similar advantages and disadvantages are appreciated
with the sweat collection methods of Jia et al.94 Leg skin
cleansing with alcohol pads followed by distilled water
rinsing and drying precede the direct collection of sweat into
microcentrifuge tubes which were placed immediately on dry
ice to effect metabolic quenching.94 The cleansing and rinsing
beforehand, as well as the physical, direct contact with the
microcentrifuge tube may however disrupt the skin surface
and again potentially alter skin integrity with its possible
effects on uid migrations from plasma to skin surface.
Summary
A diverse range of sweat induction modes and sweat
collection methods are presented in the Table, all with
their own advantages and disadvantages. Issues of variable
location, timing and amounts of sweat induction and sampling
as well as inconsistent sample processing steps and storage
conditions confound most comparisons between methods.
Optimising these parameters and exploring newer identied
concepts surrounding sweat collection based upon updated
information about sweat glands and the collective contents
of their secretions will generate more meaningful results to
build and improve our knowledge of the sweat metabolome.
Standard operating protocols (SOPs) for collecting human
biouids like urine, blood and sweat for metabolomics studies
are crucial to help control for the wide variety of factors that
can inuence metabolite concentrations. The SOPs for human
sweat collection require updating beyond cystic brosis and
illicit drug testing models to optimise metabolomics results.
The following considerations need attention in future studies:
Specifying body sites of human sweat collection is
of utmost importance in future comparisons of both
targeted and untargeted metabolomics studies. Not all
sweat collected anywhere on the body can be assumed
homogenous in metabolic content.
Until further comparative studies are done, consideration
should be given to subclassifying sweat based upon
induction approaches – i.e. pilocarpine-induced sweat
vs physiological sweat vs thermally-induced sweat vs
exercise-induced sweat.
• Measures to ensure adequate skin integrity other than
mere visual inspection at sweat collection sites need
further development and study.
• Examining the molecular content of sweat induction,
collection and storage devices for potential adsorption
and metabolic reactivity requires further attention and
attempts at standardisation.
Minimising the timing of sweat collection, transport
and storage as well as ensuring a timely and adequate
metabolic quenching step is important for comparing
future sweat metabolome studies.
Environmental factors of temperature and humidity
signicantly impact the metabolic parameters of sweat and
need to be specied and ideally standardised. Furthermore,
the potential inter-relationship between overall core body
temperature and local skin temperature at sweat collection
sites may impact sweat metabolomics results.
Attention to controlling individual variables such as
diet content, fasting vs postprandial state, exercise state,
emotional state, pharmaceutical and/or recreational drug/
supplement use and underlying medical conditions that
could impact the pH or overall metabolic state is important
when interpreting any metabolomics results. This applies
to controls instituted both preceding and during collection
of sweat.
Dening the minimum amounts of sweat necessary to
overcome intra-individual and inter-individual global
metabolomic differences, stretching beyond guidelines
based upon CF-specic testing of pilocarpine-induced
sweat, is still a work in progress. Clarications between
physiological sweating and exercise- or thermally-induced
sweating within this context are also necessary.
Age-specic inuences on sweat metabolomics results
will require further investigation.
Attempts at simultaneously characterising the individual
skin microbiota (colonising bacteria, viruses, fungi, etc.)
both quantitatively and qualitatively at sites of sweat
collection might further elucidate suspected important
metabolic relationships.
Conclusion
Better standardising of human sweat induction and collection
methods to address the important challenges identied in this
review is a key step to furthering sweat metabolomics. If this
can be achieved, it is anticipated that sweat may become a
more utilised biouid capable of delivering easily accessible,
individualised and instantaneously useful metabolic
information that signicantly enhances our knowledge of
human health and disease.
Acknowledgements: This manuscript was developed as
part of study conducted by Dr Joy Hussain during her PhD
candidature. We wish to thank the Jacka Foundation of
Natural Therapies for providing an academic scholarship to
support her candidature and therefore this study.
Competing Interests: None declared.
References
1. De Giovanni N, Fucci N. The current status of sweat
testing for drugs of abuse: a review. Curr Med Chem
2013;20:545-61.
2. Papanas N, Papatheodorou K, Christakidis D, Papazoglou
D, Giassakis G, Piperidou H, et al. Evaluation of a new
indicator test for sudomotor function (Neuropad (R)) in
the diagnosis of peripheral neuropathy in type 2 diabetic
patients. Exp Clin Endocrinol Diabetes 2005;113:195-8.
3. Quattrini C, Jeziorska M, Tavakoli M, Begum P, Boulton
A, Malik R. The Neuropad test: a visual indicator test for
human diabetic neuropathy. Diabetologia 2008;51:1046-
50.
4. Peng Y, Cui X, Liu Y, Li Y, Liu J, Cheng B. Systematic
review focusing on the excretion and protection roles of
sweat in the skin. Dermatology 2014;228:115-20.
5. Gao W, Emaminejad S, Nyein HYY, Challa S, Chen
K, Peck A, et al. Fully integrated wearable sensor ar-
rays for multiplexed in situ perspiration analysis. Nature
2016;529:509-14.
6. Dam VAT, Zevenbergen MAG, van Schaijk R. Toward
wearable patch for sweat analysis. Sens Actuators B
Chem 2016;236:834-8.
7. Nayak R, Wang L, Padhye R. Electronic textiles for mili-
tary personnel. Woodhead Publishing Ltd: Cambridge,
UK; 2015.
8. Álvarez-Sánchez B, Priego-Capote F, de Castro ML. Me-
tabolomics analysis I. Selection of biological samples and
practical aspects preceding sample preparation. Trends
Anal Chem 2010;29:111-9.
9. Williams RJ. Biochemical Individuality: The Basis for
the Genetotrophic concept: John Wiley & Sons, 1956;
University of Texas Press, 1969 to 1979; Keats Publish-
ing, 1998; 1956.
10. Kutyshenko VP, Molchanov M, Beskaravayny P, Uver-
sky VN, Timchenko MA. Analyzing and mapping sweat
metabolomics by high-resolution NMR spectroscopy.
PLoS One 2011;6:e28824.
11. Gehlaut R, Yadav S. Metabolomics a new tool to molecu-
lar imaging technology. Int J Therapeut Appl 2012;2:33-
42.
12. Orešič M. Metabolomics, a novel tool for studies of nutri-
tion, metabolism and lipid dysfunction. Nutr Metab Car-
diovasc Dis 2009;19:816-24.
13. Emwas A-HM, Salek RM, Grifn JL, Merzaban J. NMR-
based metabolomics in human disease diagnosis: applica-
tions, limitations, and recommendations. Metabolomics
2013;9:1048-72.
14. Kell DB, Goodacre R. Metabolomics and systems phar-
macology: why and how to model the human meta-
bolic network for drug discovery. Drug Discov Today
2014;19:171-82.
15. Kosmides AK, Kamisoglu K, Calvano SE, Corbett SA,
Sweat Collection for Metabolomics AnalysisHussain JN et al.
Clin Biochem Rev 38 (1) 2017 3332 Clin Biochem Rev 38 (1) 2017
Androulakis IP. Metabolomic ngerprinting: challenges
and opportunities. Crit Rev Biomed Eng 2013;41:205-21.
16. Vulimiri SV, Berger A, Sonawane B. The potential of me-
tabolomic approaches for investigating mode (s) of action
of xenobiotics: case study with carbon tetrachloride. Mu-
tat Res 2011;722:147-53.
17. Shen H, Xu W, Peng S, Scherb H, She J, Voigt K, et al.
Pooling samples for top-down molecular exposomics re-
search: the methodology. Environ Health 2014;13:8.
18. Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young
N, et al. HMDB: the human metabolome database. Nu-
cleic Acids Res 2007;35(Suppl 1):D521-6.
19. HMDB. The Human Metabolome Database: TMIC, The
Metabolomics Innovation Centre http://www.hmdb.ca/.
(Accessed 7 November 2016).
20. Roessner U, Bowne J. What is metabolomics all about?
Biotechniques 2009;46:363-5.
21. Goncharov N, Ukolov A, Orlova T, Migalovskaia E,
Voitenko N. Metabolomics: on the way to an integration
of biochemistry, analytical chemistry, and informatics.
Biol Bull Rev 2015;5:296-307.
22. Wilke K, Martin A, Terstegen L, Biel SS. A short history
of sweat gland biology. Int J Cosmet Sci 2007;29:169-79.
23. Shetage SS, Traynor MJ, Brown MB, Raji M, Graham-
Kalio D, Chilcott RP. Effect of ethnicity, gender and age
on the amount and composition of residual skin surface
components derived from sebum, sweat and epidermal
lipids. Skin Res Technol 2014;20:97-107.
24. Sato K. The physiology, pharmacology, and biochemistry
of the eccrine sweat gland. Rev Physiol Biochem Phar-
macol 1977;79:51-131.
25. Piérard GE, Elsner P, Marks R, Masson P, Paye M; EEM-
CO Group. EEMCO guidance for the efcacy assessment
of antiperspirants and deodorants. Skin Pharmacol Appl
Skin Physiol 2003;16:324-42.
26. Noël F, Piérard-Franchimont C, Piérard GE, Quatresooz
P. Sweaty skin, background and assessments. Int J Der-
matol 2012;51:647-55.
27. Beauchamp M, Lands LC. Sweat-testing: a review
of current technical requirements. Pediatr Pulmonol
2005;39:507-11
28. Harker M, Coulson H, Fairweather I, Taylor D, Daykin
CA. Study of metabolite composition of eccrine sweat
from healthy male and female human subjects by 1H
NMR spectroscopy. Metabolomics 2006;2:105-12.
29. Raiszadeh MM, Ross MM, Russo PS, Schaepper MA,
Zhou W, Deng J, et al. Proteomic analysis of eccrine
sweat: implications for the discovery of schizophrenia
biomarker proteins. J Proteome Res 2012;11:2127-39.
30. Sato K, Leidal R, Sato F. Morphology and development
of an apoeccrine sweat gland in human axillae. Am J
Physiol 1987;252:R166-80.
31. Barzantny H, Brune I, Tauch A. Molecular basis of human
body odour formation: insights deduced from corynebac-
terial genome sequences. Int J Cosmet Sci 2012;34:2-11.
32. Tsatsou F, Zouboulis CC. Anatomy of the Sebaceous
Gland. In: Pathogenesis and Treatment of Acne and Ro-
sacea. Tsatsou F, Zouboulis CC, Kligman AM, editors.
Springer; 2014. pp. 27-31.
33. Jung Y, Tam J, Jalian HR, Anderson RR, Evans CL. Lon-
gitudinal, 3D in vivo imaging of sebaceous glands by co-
herent anti-stokes Raman scattering microscopy: normal
function and response to cryotherapy. J Invest Dermatol
2015;135:39-44.
34. Shi VY, Leo M, Hassoun L, Chahal DS, Maibach HI, Si-
vamani RK. Role of sebaceous glands in inammatory
dermatoses. J Am Acad Dermatol 2015;73:856-63.
35. Zhou S-S, Li D, Zhou Y-M, Cao J-M. The skin function:
a factor of anti-metabolic syndrome. Diabetol Metabc
Syndr 2012;4:4-15.
36. Fyhrquist N, Salava A, Auvinen P, Lauerma A. Skin Bi-
omes. Curr Allergy Asthma Rep 2016;16:40.
37. Calderón-Santiago M, Priego-Capote F, Jurado-Gámez
B, Luque de Castro MD. Optimization study for metabo-
lomics analysis of human sweat by liquid chromatogra-
phy-tandem mass spectrometry in high resolution mode.
J Chromatogr A 2014;1333:70-8.
38. Calderón-Santiago M, Priego-Capote F, Turck N, Robin
X, Jurado-Gámez B, Sanchez JC, et al. Human sweat
metabolomics for lung cancer screening. Anal Bioanal
Chem 2015;407:5381-92.
39. Dai X, Okazaki H, Hanakawa Y, Murakami M, Tohyama
M, Shirakata Y, et al. Eccrine sweat contains IL-1α, IL-
1β and IL-31 and activates epidermal keratinocytes as a
danger signal. PLoS One 2013;8:e67666.
40. Jadoon S, Karim S, Akram MR, Kalsoom Khan A,
Zia MA, Siddiqi AR, et al. Recent developments in
sweat analysis and its applications. Int J Anal Chem
2015;2015:164974.
41. Chen X, Gasecka P, Formanek F, Galey JB, Rigneault H.
In vivo single human sweat gland activity monitoring us-
ing coherent anti-Stokes Raman scattering and two-pho-
ton excited autouorescence microscopy. Brit J Dermatol
2016;174:803-12.
42. Shields SA, MacDowell KA, Fairchild SB, Campbell
ML. Is mediation of sweating cholinergic, adrenergic,
or both? A comment on the literature. Psychophysiology
1987;24:312-9.
43. Bates GP, Miller VS. Sweat rate and sodium loss during
work in the heat. J Occup Med Toxicol 2008;3:4.
44. Xhauaire-Uhoda E, Paquet P, Quatresooz P, Piérard GE.
Characterization of the skin using capacitance imaging.
Expert Rev Dermatol 2010;5:149-58.
45. Kirk JM, Keston M, McIntosh I, al Essa S. Variation of
sweat sodium and chloride with age in cystic brosis and
normal populations: further investigations in equivocal
cases. Ann Clin Biochem 1992;29:145-52.
46. Mishra A, Greaves R, Massie J. The limitations of sweat
electrolyte reference intervals for the diagnosis of cys-
tic brosis: a systematic review. Clin Biochem Rev
2007;28:60-76.
47. Taylor NA, Machado-Moreira CA. Regional variations
in transepidermal water loss, eccrine sweat gland den-
sity, sweat secretion rates and electrolyte composition
in resting and exercising humans. Extrem Physiol Med
2013;2:4.
48. Cone EJ, Hillsgrove MJ, Jenkins AJ, Keenan RM, Dar-
win WD. Sweat testing for heroin, cocaine, and metabo-
lites. J Anal Toxicol 1994;18:298-305.
49. Brusilow SW, Gordes EH. Ammonia secretion in sweat.
Am J Physiol 1968;214:513-7.
50. Porter AM. Why do we have apocrine and sebaceous
glands? J R Soc Med 2001;94:236-7.
51. Mark H, Harding CR. Amino acid composition, includ-
ing key derivatives of eccrine sweat: potential biomark-
ers of certain atopic skin conditions. Int J Cosmet Scie
2013;35:163-8.
52. Cornelissen C, Lüscher-Firzlaff J, Baron JM, Lüscher B.
Signaling by IL-31 and functional consequences. Eur J
Cell Biol 2012;91:552-66.
53. Tanei R, Hasegawa Y. Atopic dermatitis in older adults:
A viewpoint from geriatric dermatology. Geriatr Gerontol
Int 2016;16(Suppl 1):75-86.
54. Marques-Deak A, Cizza G, Eskandari F, Torvik S, Chris-
tie IC, Sternberg EM, et al. Measurement of cytokines in
sweat patches and plasma in healthy women: Validation
in a controlled study. J Immunol Methods 2006;315:99-
109.
55. Genuis SJ, Beesoon S, Birkholz D, Lobo RA. Human
excretion of bisphenol A: blood, urine, and sweat (BUS)
study. J Environ Public Health 2012;2012:185731.
56. Genuis SJ, Birkholz D, Rodushkin I, Beesoon S. Blood,
urine, and sweat (BUS) study: monitoring and elimina-
tion of bioaccumulated toxic elements. Arch Environ
Contam Toxicol 2011;61:344-57.
57. Genuis SJ, Beesoon S, Lobo RA, Birkholz D. Hu-
man elimination of phthalate compounds: blood,
urine, and sweat (BUS) study. Scientic World Journal
2012;2012:615068.
58. Sears ME, Kerr KJ, Bray RI. Arsenic, cadmium, lead, and
mercury in sweat: a systematic review. J Environ Public
Health 2012;2012:184745.
59. Dutkiewicz EP, Lin J-D, Tseng T-W, Wang Y-S, Urban
PL. Hydrogel Micropatches for Sampling and Proling
Skin Metabolites. Anal Chem 2014;86:2337-44.
60. Cone EJ. New developments in biological measures of
drug prevalence. NIDA Res Monogr 1997;167:108-29.
61. Harris R, Jones H, Artis W. Orally administered ketocon-
azole: route of delivery to the human stratum corneum.
Antimicrob Agents Chemother 1983;24:876-82.
62. Lever L, Dykes P, Thomas R, Finlay A. How orally ad-
ministered terbinane reaches the stratum corneum. J
Dermatolog Treat 1990;1(Suppl 2):23-5.
63. Faergemann J, Laufen H. Levels of uconazole in se-
rum, stratum corneum, epidermis-dermis (without stra-
tum corneum) and eccrine sweat. Clin Exp Dermatol
1993;18:102-6.
64. Zhou S-S, Zhou Y-M, Li D, Chen N-N. Preeclampsia and
future cardiovascular risk: A point of view from the clear-
ance of plasma vasoactive amines. Hypertens Pregnancy
2016;35:1-14.
65. Bouloux P-M. M58–Sweating and Flushing: Evaluation
and Management. In: ENDO2013, Meet the Professor -
Clinical Case Management. Royal Free Campus, UCL;
2013.
66. Nicholson GM, Graudins A, Wilson HI, Little M, Broady
KW. Arachnid toxinology in Australia: from clinical toxi-
cology to potential applications. Toxicon 2006;48:872-
98.
67. Pochi PE, Downing DT, Strauss JS. Sebaceous gland re-
sponse in man to prolonged total caloric deprivation. J
Invest Dermatol 1970;55:303-9.
68. Wilkinson DI. Psoriasis and dietary fat: the fatty acid
composition of surface and scale (ether-soluble) lipids. J
Invest Dermatol 1966;47:185-92.
69. Michael-Jubeli R, Bleton J, Baillet-Guffroy A. High-tem-
perature gas chromatography-mass spectrometry for skin
surface lipids proling. J Lipid Res 2011;52:143-51.
70. Álvarez-Sánchez B, Priego-Capote F, de Castro ML. Me-
tabolomics analysis II. Preparation of biological samples
prior to detection. Trends Anal Chem 2010;29:120-7.
71. Delgado-Povedano M, Calderón-Santiago M, Priego-
Capote F, de Castro ML. Development of a method for
enhancing metabolomics coverage of human sweat by
gas chromatography–mass spectrometry in high resolu-
tion mode. Anal Chim Acta 2016;905:115-25.
72. Adewole OO, Erhabor GE, Adewole TO, Ojo AO, Harriet
O, Wolfe LM, et al. Proteomic proling of eccrine sweat
reveals its potential as a diagnostic biouid for active tu-
berculosis. Proteomics Clin Appl 2016;10:547-53.
73. Gomez CC, Servidoni Mde F, Marson FA, Canavezi PJ,
Vinagre AM, Costa ET, et al. Pulsed direct and constant
direct currents in the pilocarpine iontophoresis sweat
chloride test. BMC Pulm Med 2014;14:198.
74. Gibson LE, Cooke RE. A test for concentration of electro-
lytes in sweat in cystic brosis of the pancreas utilizing
pilocarpine by iontophoresis. Pediatrics 1959;23:545-9.
75. Webster HL. Laboratory diagnosis of cystic brosis. Crit
Sweat Collection for Metabolomics AnalysisHussain JN et al.
Clin Biochem Rev 38 (1) 2017 3534 Clin Biochem Rev 38 (1) 2017
Rev Clin Lab Sci 1983;18:313-38.
76. Taylor CJ, Hardcastle J, Southern KW. Physiological
measurements conrming the diagnosis of cystic brosis:
the sweat test and measurements of transepithelial poten-
tial difference. Paediatr Respir Rev 2009;10:220-6.
77. Collie JT, Massie RJ, Jones OA, LeGrys VA, Greaves RF.
Sixty-ve years since the New York heat wave: advanc-
es in sweat testing for cystic brosis. Pediatr Pulmonol
2014;49:106-17.
78. Quinton PM. A synopsis of methods of sweat tests in pa-
thology. Clin Biochem 2014;9:757-8.
79. Mishra A, Greaves R, Smith K, Carlin JB, Woot-
ton A, Stirling R, et al. Diagnosis of cystic brosis by
sweat testing: age-specic reference intervals. J Pediatr
2008;153:758-63.
80. Guglani L, Stabel D, Weiner DJ. False-positive and false-
negative sweat tests: systematic review of the evidence.
Pediatr Allergy Immunol Pulmonol 2015;28:198-211.
81. DeMarco ML, Dietzen DJ, Brown SM. Sweating the
small stuff: adequacy and accuracy in sweat chloride de-
termination. Clin Biochem 2015;48:443-7.
82. Verde T, Shephard R, Corey P, Moore R. Sweat composi-
tion in exercise and in heat. J Appl Physiol 1982;53:1540-
5.
83. Iwase S, Kawahara Y, Nishimura N, Sugenoya J. Effect
of micro mist sauna bathing on thermoregulatory and cir-
culatory functions and thermal sensation in humans. Int J
Biometeorol 2016;60:699-709.
84. Al-omari M, Liu G, Mueller A, Mock A, Ghosh RN,
Smith K, et al. A portable optical human sweat sensor. J
Appl Phys 2014;116:183102.
85. Li Z, Tatlay J, Li L. Nanoow LC-MS for high-perfor-
mance chemical isotope labeling quantitative metabolo-
mics. Anal Chem 2015;87:11468-74.
86. Cole D, Boucher MJ. Use of a new sample-collection de-
vice (Macroduct) in anion analysis of human sweat. Clin
Chem 1986;32:1375-8.
87. Ely M, Ely B, Chinevere T, Lacher C, Lukaski H, Cheu-
vront S. Evaluation of the Megaduct sweat collector for
mineral analysis. Physiol Meas 2012;33:385-94.
88. DeRuisseau KC, Cheuvront SN, Haymes EM, Sharp RG.
Sweat iron and zinc losses during prolonged exercise. Int
J Sport Nutr Exerc Metab 2002;12:428-37.
89. Porucznik CA, Cox KJ, Wilkins DG, Anderson DJ, Bai-
ley NM, Szczotka KM, et al. A preliminary study of bio-
monitoring for bisphenol-A in human sweat. J Anal Toxi-
col 2015;39:562-6.
90. Kidwell DA, Smith FP. Susceptibility of PharmChek
drugs of abuse patch to environmental contamination.
Forensic Sci Int 2001;116:89-106.
91. Lee DP, Kennedy AD, O’Neal EK, Bishop PA, Haub MD,
Strecker KL, et al. Global untargeted metabolic proling
of human sweat from exercising men and women. J Int
Soc Sports Nutr 2011;8:9.
92. Penn DJ, Oberzaucher E, Grammer K, Fischer G, Soini
HA, Wiesler D, et al. Individual and gender ngerprints
in human body odour. J R Soc Interface 2007;4:331-40.
93. Sheng J, Qiu W, Xu B, Xu H, Tang C. Monitoring of
heavy metal levels in the major rivers and in residents’
blood in Zhenjiang City, China, and assessment of heavy
metal elimination via urine and sweat in humans. Environ
Sci Pollut Res 2016;23:11034-45.
94. Jia M, Chew W, Feinstein Y, Skeath P, Sternberg E.
Quantication of cortisol in human eccrine sweat by liq-
uid hromatography-tandem mass spectrometry. Analyst
2016;141:2053-60.
95. Tang S, Yu X, Wu C. Comparison of the levels of ve
heavy metals in human urine and sweat after strenuous
exercise by ICP-MS. J Appl Math Phys 2016;4:183-8.
... To the best of our knowledge, current techniques of metabolic phenotyping are largely focussed on generating static diagnostic pictures because the commonly used biological fluids (e.g. plasma, urine) [15][16][17] or tissues do not routinely allow for time-course studies. The implementation of dynamic metabolic responses as a biomarker strategy may be desirable, but requires a considerable number of data points on a single individual. ...
... Eccrine sweat from the fingertips is mainly composed of water (~99%), but contains electrolytes, urea, lactate, amino acids, metal ions 23,24 and a variety of endogenous metabolites, including peptides, organic acids, carbohydrates, lipids, lipid-derived metabolites, as well as xenobiotics 21,22,[25][26][27] . Sweat composition is highly dynamic, changes significantly with pathological states and may reveal habits of diet, metabolic conditions or use of drugs and supplements 17,24,28 . In fact, the analysis of sweat has already been reported to assess individual metabolic characteristics 29,30 . ...
... In fact, the analysis of sweat has already been reported to assess individual metabolic characteristics 29,30 . Clinical assays based on the analysis of sweat exist and include the screening of newborn children for elevated chloride and sodium levels to confirm cystic fibrosis via pilocarpine stimulated iontophoresis or forensic and criminal investigations to test for illicit drug use 17,22,[31][32][33] . Furthermore, it has already been successfully demonstrated that the analysis of proteins contained in sweat enables not only the diagnosis of active tuberculosis but can also be used to screen for lung cancer 16,34,35 , highlighting the potential of sweat analysis for precision medicine 36 . ...
Article
Full-text available
Metabolic biomonitoring in humans is typically based on the sampling of blood, plasma or urine. Although established in the clinical routine, these sampling procedures are often associated with a variety of compliance issues, which are impeding time-course studies. Here, we show that the metabolic profiling of the minute amounts of sweat sampled from fingertips addresses this challenge. Sweat sampling from fingertips is non-invasive, robust and can be accomplished repeatedly by untrained personnel. The sweat matrix represents a rich source for metabolic phenotyping. We confirm the feasibility of short interval sampling of sweat from the fingertips in time-course studies involving the consumption of coffee or the ingestion of a caffeine capsule after a fasting interval, in which we successfully monitor all known caffeine metabolites as well as endogenous metabolic responses. Fluctuations in the rate of sweat production are accounted for by mathematical modelling to reveal individual rates of caffeine uptake, metabolism and clearance. To conclude, metabotyping using sweat from fingertips combined with mathematical network modelling shows promise for broad applications in precision medicine by enabling the assessment of dynamic metabolic patterns, which may overcome the limitations of purely compositional biomarkers. Biomonitoring of sweat from fingertips overcomes current limitations in time-resolved metabolomic profiling of humans and may prove to become a powerful, noninvasive tool for precision medicine. Here, in a feasibility study of short interval sampling of sweat from fingertips, the authors assay individual dynamic metabolic patterns of endogenous and exogenous molecules.
... Besides the regulation of body temperature, sweat also plays an important role in protecting, lubricating and waterproofing the skin. Moreover, sweat forms part of the immune system since it contains cytokines and other related molecules involved in the immune-mediated mechanisms that occur in the skin, being one of the first barriers during an immune response [13]. ...
... Different images of the alginate beads were taken during the experiments at 1, 2, 3, 4, 5, 6,7,8,9,10,13,16,19,25,30,35 and 50 min, using a 20 MP (megapixel) + 2 MP dual camera with an f/1.8 aperture (Huawei, Shenzhen, China) placed 25 cm over the sample. The same light conditions and camera settings were kept during all experiments using a photography chamber (MVpower Kit Photography Illumination Studio, Cube 80 × 80 × 80 cm 3 , 3× softbox 50 × 70 cm 2 , 3× light sources 135 W and 4× background colours, black, white, blue and red, Kissta). ...
Article
Full-text available
Lactate is present in sweat at high concentrations, being a metabolite of high interest in sport science and medicine. Therefore, the potential to determine lactate concentrations in physiological fluids, at the point of need with minimal invasiveness, is very valuable. In this work, the synthesis and performance of an alginate bead biosystem was investigated. Artificial sweat with different lactate concentrations was used as a proof of concept. The lactate detection was based on a colorimetric assay and an image analysis method using lactate oxidase, horseradish peroxidase and tetramethyl benzidine as the reaction mix. Lactate in artificial sweat was detected with a R² = 0.9907 in a linear range from 10 mM to 100 mM, with a limit of detection of 6.4 mM and a limit of quantification of 21.2 mM. Real sweat samples were used as a proof of concept to test the performance of the biosystem, obtaining a lactate concentration of 48 ± 3 mM. This novel sensing configuration, using alginate beads, gives a fast and reliable method for lactate sensing, which could be integrated into more complex analytical systems.
... Depending on how sweat sample is collected, it can also include secretions generated by the sebaceous glands. While hydrophilic composition represents the largest portion of this fluid, hydrophobic content may consist of less than 1% of sweat fraction, being mainly derived from sebum and apocrine secretion [77]. Within the organic portion of sweat, there are endogenous and exogenous substances excreted by the organism [78]. ...
... Since iontophoresis is based on the application of electrical current to the skin, the process may be uncomfortable. On the other hand, this method allows prompt collection of sweat and standard sample volumes can be used for the following analysis [77]. ...
Article
Full-text available
Purpose The use of alternative matrices in toxicological analyses has been on the rise in clinical and forensic settings. Specimens alternative to blood and urine are useful in providing additional information regarding drug exposure and analytical benefits. The goal of this paper is to present a critical review on the most recent literature regarding the application of six common alternative matrices, i.e., oral fluid, hair, sweat, meconium, breast milk and vitreous humor in forensic toxicology. Methods The recent literature have been searched and reviewed for the characteristics, advantages and limitations of oral fluid, hair, sweat, meconium, breast milk and vitreous humor and its applications in the analysis of traditional drugs of abuse and novel psychoactive substances (NPS). Results This paper outlines the properties of six biological matrices that have been used in forensic analyses, as alternatives to whole blood and urine specimens. Each of this matrix has benefits in regards to sampling, extraction, detection window, typical drug levels and other aspects. However, theses matrices have also limitations such as limited incorporation of drugs (according to physical–chemical properties), impossibility to correlate the concentrations for effects, low levels of xenobiotics and ultimately the need for more sensitive analysis. For more traditional drugs of abuse (e.g., cocaine and amphetamines), there are already data available on the detection in alternative matrices. However, data on the determination of emerging drugs such as the NPS in alternative biological matrices are more limited. Conclusions Alternative biological fluids are important specimens in forensic toxicology. These matrices have been increasingly reported over the years, and this dynamic will probably continue in the future, especially considering their inherent advantages and the possibility to be used when blood or urine are unavailable. However, one should be aware that these matrices have limitations and particular properties, and the findings obtained from the analysis of these specimens may vary according to the type of matrix. As a potential perspective in forensic toxicology, the topic of alternative matrices will be continuously explored, especially emphasizing NPS.
... Studies have adopted varying methods to collect sweat for composition analyses [14][15][16], including the direct collection of sweat from the skin using glass jars or tubes and sweat collection using made-to-measure apparatuses (e.g., sweat pouches, glass pipettes, and arm bags) and commercial products. However, pH alteration, skin irritation, barrier property disturbance, and variations in the body region for sweating and the aim of examination have resulted in difficulty in designing a universal apparatus to fit all test conditions [17]. ...
Article
Full-text available
Physiologists have long regarded sweating as an effective and safe means of detoxification, and heavy metals are excreted through sweat to reduce the levels of such metals in the body. However, the body can sweat through many means. To elucidate the difference in the excretion of heavy metals among sweating methods, 12 healthy young university students were recruited as participants (6 men and 6 women). Sweat samples were collected from the participants while they were either running on a treadmill or sitting in a sauna cabinet. After they experienced continuous sweating for 20 min, a minimum of 7 mL of sweat was collected from each participant, and the concentrations of nickel (Ni), lead (Pb), copper (Cu), arsenic (As), and mercury (Hg) were analyzed. The results demonstrated that the sweating method affected the excretion of heavy metals in sweat, with the concentrations of Ni, Pb, Cu, and As being significantly higher during dynamic exercise than during sitting in the sauna (all p < 0.05). However, the concentrations of Hg were unaffected by the sweating method. This study suggests that the removal of heavy metals from the body through dynamic exercise may be more effective than removal through static exposure to a hot environment.
... Urine, sweat, and tears show high glucose concentration and remarkable correlation with blood levels; however, they require collection protocols that may generate discomfort or are not applicable to all patients. In case of sweat, it includes exercise or high temperature exposure, while tears require induced lacrimation or the use of an extra microfluidic collection device [15,16]. Additionally, urine diagnostic value is limited, since the glucose concentration in urine may be variable and is not representative of a certain instant but is the average of the production/accumulation period, which can be several hours. ...
Article
Full-text available
Nowadays, there is an increasing interest in Point-of-care (POC) devices for the noninvasive glucose assessment. Despite the recent progress in glucose self-monitoring, commercially available devices still use invasive samples such as blood or interstitial fluids, and they are not equipment-free and affordable for the whole population. Here, we report a fully integrated strip test for the semi-quantitative detection of glucose in whole saliva. The colorimetric mechanism consists of an enzyme-mediated reshaping of multibranched gold nanoparticles (MGNPs) into nanospheres with an associated plasmonic shift and consequent blue-to-red color change, clearly detectable in less than 10 min.
... Fourth, it is essential to harmonize analytical approaches by following recommendations edited by the Metabolomics Society [120,121]. For instance, analyte concentration in sweat can vary greatly depending on the collection, handling, processing, storage, and skin microbiome [122,123]. Finally, it is important to conduct regression analyses rather than simple correlation analyses and thereby adjust for relevant confounders [90]. ...
Article
Full-text available
Background Cardiorespiratory fitness (CRF) is a potent health marker, the improvement of which is associated with a reduced incidence of non-communicable diseases and all-cause mortality. Identifying metabolic signatures associated with CRF could reveal how CRF fosters human health and lead to the development of novel health-monitoring strategies. Objective This article systematically reviewed reported associations between CRF and metabolites measured in human tissues and body fluids. Methods PubMed, EMBASE, and Web of Science were searched from database inception to 3 June, 2021. Metabolomics studies reporting metabolites associated with CRF, measured by means of cardiopulmonary exercise test, were deemed eligible. Backward and forward citation tracking on eligible records were used to complement the results of database searching. Risk of bias at the study level was assessed using QUADOMICS. Results Twenty-two studies were included and 667 metabolites, measured in plasma ( n = 619), serum ( n = 18), skeletal muscle ( n = 16), urine ( n = 11), or sweat ( n = 3), were identified. Lipids were the metabolites most commonly positively ( n = 174) and negatively ( n = 274) associated with CRF. Specific circulating glycerophospholipids ( n = 85) and cholesterol esters ( n = 17) were positively associated with CRF, while circulating glycerolipids ( n = 152), glycerophospholipids ( n = 42), acylcarnitines ( n = 14), and ceramides ( n = 12) were negatively associated with CRF. Interestingly, muscle acylcarnitines were positively correlated with CRF ( n = 15). Conclusions Cardiorespiratory fitness was associated with circulating and muscle lipidome composition. Causality of the revealed associations at the molecular species level remains to be investigated further. Finally, included studies were heterogeneous in terms of participants’ characteristics and analytical and statistical approaches. PROSPERO Registration Number CRD42020214375.
Article
Full-text available
Sweat sensors allow for new unobtrusive ways to continuously monitor an athlete's performance and health status. Significant advances have been made in the optimization of sensitivity, selectivity, and durability of electrochemical sweat sensors. However, comparing the in situ performance of these sensors in detail remains challenging because standardized sweat measurement methods to validate sweat sensors in a physiological setting do not yet exist. Current collection methods, such as the absorbent patch technique, are prone to contamination and are labor-intensive, which limits the number of samples that can be collected over time for offline reference measurements. We present an easy-to-fabricate sweat collection system that allows for continuous electrochemical monitoring, as well as chronological sampling of sweat for offline analysis. The patch consists of an analysis chamber hosting a conductivity sensor and a sequence of 5 to 10 reservoirs that contain level indicators that monitor the filling speed. After testing the performance of the patch in the laboratory, elaborate physiological validation experiments (3 patch locations, 6 participants) were executed. The continuous sweat conductivity measurements were compared with laboratory [Na+] and [Cl-] measurements of the samples, and a strong linear relationship (R2 = 0.97) was found. Furthermore, sweat rate derived from ventilated capsule measurement at the three locations was compared with patch filling speed and continuous conductivity readings. As expected from the literature, sweat conductivity was linearly related to sweat rate as well. In short, a successfully validated sweat collection patch is presented that enables sensor developers to systematically validate novel sweat sensors in a physiological setting.
Article
Sweat is a potential biological fluid for the non-invasive analytical assessment of diverse molecules, including biomarkers. Notwithstanding, the sampling methodology is critical, and it must be assessed prior to using sweat for clinical diagnosis. In the current work, the analytical methodology was further developed taking into account the sampling step, in view of the identification and level variations of sweat components that have potential to be stress biomarkers using separation by liquid chromatography and detection by tandem mass spectrometry, in order to attain a screening profile of 26 molecules in just one stage. As such, the molecule identification was used as a test for the evaluation of the sampling procedures, including the location on the body, using patches for long-term sampling and vials for direct sampling, through a qualitative approach. From this evaluation it was possible to conclude that the sampling may be performed on the chest or back skin. Additionally, possible interference was evaluated. The long-term sampling with patches can be used under both rest and exercise conditions with variation of the detected molecule’s levels. The direct sampling, using vials, has the advantage of not having interferences but the disadvantage of only being effective after exercise in order to have enough sample for sweat analysis.
Chapter
The pattern of abundances and molecular identifications of all small molecules within a biological system is referred to as the metabolome. Both targeted and untargeted approaches can be used to study differences in patterns between samples of interest. A wide variety of sample types and analytical platforms are employed in the field of metabolomics. Sample types include biofluids, tissues, and cellular extracts. Each has specific requirements regarding sample collection, storage, and metabolite extraction. The choice of analytical technique for metabolite identification and quantification directly influences sample preparation procedures. Common assays utilize either liquid or gas chromatography coupled to mass spectrometry. Multivariable statistical analysis is often applied to the complex datasets generated in order to identify specific metabolites that differ between samples. Biological insight can be gained through study of the relevant biochemical pathways in which the differing metabolites are involved.
Article
Full-text available
Wearable sweat sensors have received significant research interest and have become popular as sweat contains considerable health information about physiological and psychological states. However, measured biomarker concentrations vary with sweat rates, which has a significant effect on the accuracy and reliability of sweat biosensors. Wearable sweat loss measuring devices (SLMDs) have recently been proposed to overcome the limitations of biomarker tracking and reduce inter- and intraindividual variability. In addition, they offer substantial potential for monitoring human body homeostasis, because sweat loss plays an indispensable role in thermoregulation and skin hydration. Previous studies have not carried out a comprehensive and systematic review of the principles, importance, and development of wearable SLMDs. This paper reviews wearable SLMDs with a new health perspective from the role of sweat loss to advanced mechanisms and designs. Two types of sweat and their measurement significance for practical applications are highlighted. Then, a comprehensive review of advances in different wearable SLMDs based on hygrometers, absorbent materials, and microfluidics is presented by describing their respective device architectures, present situations, and future directions. Finally, concluding remarks on opportunities for future application fields and challenges for future sweat sensing are presented.
Article
Full-text available
The cutaneous microbiome has been investigated broadly in recent years and some traditional perspectives are beginning to change. A diverse microbiome exists on human skin and has a potential to influence pathogenic microbes and modulate the course of skin disorders, e.g. atopic dermatitis. In addition to the known dysfunctions in barrier function of the skin and immunologic disturbances, evidence is rising that frequent skin disorders, e.g. atopic dermatitis, might be connected to a dysbiosis of the microbial community and changes in the skin microbiome. As a future perspective, examining the skin microbiome could be seen as a potential new diagnostic and therapeutic target in inflammatory skin disorders.
Article
Full-text available
Atopic dermatitis (AD) in older adults represents a newly defined subgroup of AD. The prevalence of elderly AD is approximately 1–3% among elderly populations in industrialized countries. Elderly patients with AD show some common clinical characteristics, such as a male predominance, a lower incidence of lichenified eczema at the elbow and knee folds, and particular patterns of onset and clinical course. Both immunoglobulin (Ig)E-allergic and non-IgE-allergic types are observed in elderly AD. Elderly patients with IgE-allergic AD show high rates of positivity for specific IgE antibodies against house dust mites, associations with IgE allergic and asthmatic complications, histopathological features with a predominance of IgE-mediated allergic inflammation in the lesional skin, and a significantly lower incidence of malignancy as compared with control subjects. The etiology of elderly AD might be associated with immunosenescence, age-related changes to the sex hormone milieu, age-related barrier dysfunctions in the skin and gut, functional disturbance of sweat production, and environmental stimuli in the lifestyle of elderly individuals. Powerful anti-inflammatory treatments, such as oral corticosteroids, might be required together with standard treatments to manage moderate to severe cases of elderly AD. Finally, most elderly patients with AD reach the end of life with this disease, which should now be considered a lifelong allergic disease. Geriatr Gerontol Int 2016; 16 (Suppl. 1): 75–86.
Article
Full-text available
The coastal areas of China face great challenges, owing to heavy metal contamination caused by rapid industrialization and urbanization. To our knowledge, this study is the first report of the levels of heavy metals in the major rivers of Zhenjiang, one of the most important cities of the Yangtze River Delta in China. In addition, we measured heavy metal levels in the blood of 76 residents of Zhenjiang. The results suggest that the presence of heavy metals in the blood may threaten human health and the distribution appeared to correspond to most highly populated areas and/or areas with high traffic. We also found that the concentration of heavy metals in human blood showed an accumulation effect with increase in age. Moreover, the levels of most heavy metals were lower in participants who regularly exercised than in those who did not. We studied heavy metal levels in the urine and sweat of another 17 volunteers to monitor the elimination of bioaccumulated heavy metal. Heavy metals were found in the urine and sweat of all the 17 participants and were more concentrated in sweat. Induced micturition and sweating appear to be potential methods for the elimination of heavy metals from the human body.
Article
Full-text available
Wearable sensor technologies play a significant role in realizing personalized medicine through continuously monitoring an individual’s health state. To this end, human sweat is an excellent candidate for non-invasive monitoring as it contains physiologically rich information. Previously reported sweat-based and other non-invasive biosensors either can only monitor a single analyte at a time or lack on-site signal processing circuitry and sensor calibration mechanisms for accurate analysis of the physiological state. Given the complexity of sweat secretion, simultaneous and multiplexed screening of target biomarkers is critical and full system integration to ensure the accuracy of measurements is a necessity. Here, a mechanically flexible and fully-integrated perspiration analysis system is presented that simultaneously and selectively measures sweat metabolites (e.g. glucose and lactate) and electrolytes (e.g. sodium and potassium ions), as well as the skin temperature to calibrate the sensors' response. Our work bridges the technological gap between signal transduction, conditioning, processing and wireless transmission in wearable biosensors by merging plastic-based sensors that interface with the skin, and silicon integrated circuits consolidated on a flexible circuit board for complex signal processing. The envisioned application could not have been realized by either of the technologies alone due to their respective inherent limitations. This wearable system is used to measure the detailed sweat profile of human subjects engaged in prolonged indoor and outdoor physical activities, and infer real-time assessment of the physiological state of the subjects. The platform enables a wide range of personalized diagnostic and physiological monitoring applications.
Article
Purpose: Excessive sweating is a common symptom of the disease and an unexplored biofluid for TB diagnosis, we conducted a proof-of concept- study to identify potential diagnostic biomarkers of active TB in eccrine sweat. Experimental design: We performed a global proteomic profile of eccrine sweat sampled from patients with active pulmonary TB, other lung diseases (non-TB disease), and healthy controls. A comparison of proteomics between Active-TB, Non-TB and Healthy Controls was done in search for potential biomarkers of active TB. Results: Sweat specimens were pooled from 32 active TB patients, 27 patients with non-TB diseases and 24 apparently healthy controls, all were negative for HIV. Over one hundred unique proteins were identified in the eccrine sweat of all three groups. Twenty six proteins were exclusively detected in the sweat of patients with active TB while the remaining detected proteins overlapped between three groups. Gene ontology evaluation indicated that the proteins detected uniquely in sweat of active TB patients were involved in immune response and auxiliary protein transport. Gene products for cellular components (e.g. ribosomes) were detected only in active TB patients. Data are available via ProteomeXchange with identifier PXD003224 CONCLUSIONS AND CLINICAL RELEVANCE: : Proteomics of sweat from active TB patients is a viable approach for biomarker identification which could be used to develop a non-sputum based test for detection of active TB This article is protected by copyright. All rights reserved.
Article
Objective: To summarize the reported evidence on the relationship between vasoactive amines and preeclampsia. Methods: A literature search was conducted in MEDLINE/PubMed and EMBASE. Results: The summarized results are as follows: (1) Menstruation can effectively eliminate vasoactive amines norepinephrine, serotonin and histamine. (2) Pregnancy increases norepinephrine production due to fetal brain development and decreases vasoactive-amine elimination due to amenorrhea. (3) Preeclampsia is associated with a low renal and/or sweating capacity, or in rare cases, with increased norepinephrine production due to maternal pheochromocytoma and fetal neuroblastoma. Conclusion: Preeclampsia is mainly due to decreased excretion of norepinephrine and other vasoactive amines.
Article
Cortisol has long been recognized as the “stress biomarker” in evaluating stress related disorders. Plasma, urine or saliva are the current source for cortisol analysis. The sampling of these biofluids is either invasive or has reliability problems that could lead to inaccurate results. Sweat has drawn increasing attention as a promising source for non-invasive stress analysis. A sensitive HPLC-MS/MS method was developed for the quantitation of cortisol ((11β)-11,17,21-Trihydroxypregn-4-ene-3,20-dione) in human eccrine sweat. At least one unknown isomer that has previously not been reported and could potentially interfere with quantification was separated from cortisol with mixed mode RP HPLC. Detection of cortisol was carried out using atmospheric pressure chemical ionization (APCI) and selected reaction monitoring (SRM) in positive ion mode, using cortisol-9,11,12,12-D4 as internal standard. LOD and LOQ were estimated to be 0.04 ng/ml and 0.1 ng/ml, respectively. Linear range of 0.10 – 25.00 ng/ml was obtained. Intraday precision (2.5% - 9.7%) and accuracy (0.5% - 2.1%), interday precision (12.3% - 18.7%) and accuracy (7.1% - 15.1%) were achieved. This method has been successfully applied to the cortisol analysis of human eccrine sweat samples. This is the first demonstration that HPLC-MS/MS can be used for the sensitive and highly specific determination of cortisol in human eccrine sweat in the presence of at least one isomer that has similar hydrophobicity as cortisol. This study demonstrated that human eccrine sweat could be used as a promising source for non-invasive assessment of stress biomarkers such as cortisol and other steroid hormones.