Abstract
Background/aims Exposure to bioactive compounds from nutrition, pharmaceuticals, environmental contaminants or other lifestyle habits may afect the human organism. To gain insight into the efects of these infuences, as well as the fundamental biochemical mechanisms behind them, individual molecular profling seems to be a promising tool and may support the further development of predictive, preventive and personalised medicine.
Methods We developed an assay, called metabo-tip for the analysis of sweat, collected from fngertips, using mass spectrom-
etry—by far the most comprehensive and sensitive method for such analyses. To evaluate this assay, we exposed volunteers
to various xenobiotics using standardised protocols and investigated their metabolic response.
Results As early as 15 min after the consumption of a cup of cofee, 50 g of dark chocolate or a serving of citrus fruits,
signifcant changes in the sweat composition of the fngertips were observed, providing relevant information in regard to the
ingested substances. This included not only health-promoting bioactive compounds but also potential hazardous substances.
Furthermore, the identifcation of metabolites from orally ingested medications such as metamizole indicated the applica-
bility of this assay to observe specifc enzymatic processes in a personalised fashion. Remarkably, we found that the sweat
composition fuctuated in a diurnal rhythm, supporting the hypothesis that the composition of sweat can be infuenced by
endogenous metabolic activities. This was further corroborated by the fnding that histamine was signifcantly increased in
the metabo-tip assay in individuals with allergic reactions.
Conclusion Metabo-tip analysis may have a large number of practical applications due to its analytical power, non-invasive
character and the potential of frequent sampling, especially regarding the individualised monitoring of specifc lifestyle and
infuencing factors. The extraordinarily rich individualised metabolomics data provided by metabo-tip ofer direct access to
individual metabolic activities and will thus support predictive preventive personalised medicine.
Keywords Exposomics · Histamine · Individualised metabolomics · Lifestyle · Mass spectrometry · Metabo-tip · Molecular
patterns · Multi-omics · Predictive preventive personalised medicine (PPPM) · Risk assessment · Sweat · Xenobiotics
- Christopher Gerner
christopher.gerner@univie.ac.at
1 Department of Analytical Chemistry, Faculty of Chemistry,
University Vienna, Vienna, Austria
2 Joint Metabolome Facility, University and Medical
University of Vienna, Vienna, Austria
3 Department of Laboratory Medicine, Medical University
of Vienna, Vienna, Austria
4 Max F. Perutz Laboratories, University of Vienna, Vienna,
Austria
5 Research Platform “Rhythms of Life”, University of Vienna,
Vienna, Austria
6 Department of Food Chemistry and Toxicology, Faculty
of Chemistry, University of Vienna, Vienna, Austria
/ Published online: 4 May 2021
EPMA Journal (2021) 12:141–153
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Introduction
It is now widely known that lifestyle choices are pivotal for
a person’s health and life quality, and it is estimated that at
least 40% and up to 95% of chronic illnesses can be traced
back to lifestyle risk factors (e.g. smoking, lack of physical
activity and dietary habits) [1, 2]. Moreover, a positive cor-
relation between the adoption of healthy lifestyle choices
and a reduced risk of mortality as well as postponing or
even avoiding many types of chronic illnesses such as can-
cer, cardiovascular diseases and metabolic syndrome has
been demonstrated [1, 3–8]. Insulin resistance, abnormal
lipid metabolism, diabetes and hypertension are risk fac-
tors highly correlated with obesity [9–11], while smoking
is known to cause several cancers, especially of the lung
and upper airways [12, 13]. In contrast, regular physical
activities as well as healthy eating habits such as the Medi-
terranean diet [14] have already shown to reduce the risk
of several chronic conditions such as cardiovascular dis-
ease [15, 16], to play a protective role in cancer prevention
[17], to promote longevity [18] and to decrease the risk of
developing metabolic syndrome and type 2 diabetes [19].
Observational studies suggest that lifestyle changes, mainly
dietary [20], can improve the immune system and reduce the
risk of recurrence of certain types of cancers, such as ovar-
ian cancer [21]. Therefore, fast and efcient monitoring of
biomarkers refecting individual lifestyle patterns could play
a preventive role in the development of chronic disorders.
Monitoring of individual metabolic responses
to xenobiotic exposures
The investigation of metabolic response to environmental
exposures is an emerging feld of research in toxicology [22,
23]. The so-called exposome includes exogenous exposure
not only to the environment, diet or lifestyle factors but
also to biological processes refecting internal responses to
exposure [24–26]. For example, a chronic low-dose expo-
sure to mycotoxins, which are frequently detected as natu-
ral contaminants in foods, has already been associated with
the onset of various diseases. The analysis of mycotoxins
as biomarkers for exposure to contaminated food was suc-
cessfully performed using plasma, serum, urine and milk
samples [27, 28]. The origin of many metabolites, which
are either endogenously produced, originate from the gut
microbiome, or come from the environment via nutrition or
smoking, is already being investigated in great detail [29].
In particular, serum metabolomics has successfully revealed
several biomarkers which have improved our understanding
of disease mechanisms and which are being used in clinical
settings for the diagnosis of diseases as well as in the moni-
toring of therapeutic outcomes [30].
A fast and non‐invasive sweat collection procedure
in humans hyphenated with an ultra‐sensitive LC–
MS/MS approach
A comprehensive analysis of endogenous processes related
to the uptake and individual metabolism of xenobiotics
requires sensitive analytical techniques in addition to non-
invasive and fast sampling methods, allowing short interval
sampling and therefore enabling kinetic time-course meas-
urements in humans [31, 32]. The analysis of sweat from the
fngertips fulfls these requirements and supports compliance
of test subjects. Over decades, fngerprints have been used
to identify individuals and more recently play an important
role in lifestyle monitoring via imaging mass spectrometry
[33]. Exogenous compounds found in bug sprays and sun-
screens as well as food oils, alcohols and citrus fruits were
detected in fngerprints, ofering relevant chemical informa-
tion about the tested person [34]. Moreover, fngerprints are
used to detect illicit drugs and their metabolites [35, 36].
In contrast to the investigation of fngerprints, the analysis
of sweat has been successfully proven in diagnostic medi-
cine to enable the monitoring of individual metabolic and
health states [37, 38]. Sweat is mainly composed of water
(99%), but includes also numerous substances such as elec-
trolytes, lactate, pyruvate, urea, amino acids, proteins, pep-
tides, fatty acids, hormones and xenobiotics (e.g. cosmetics,
medications and drugs including ethanol) [39]. Antibodies
and cytokines detected in sweat may serve as potential bio-
markers for diseases [38] and, as already demonstrated, for
disease states in cystic fbrosis [40] and active tuberculosis
[41]. Moreover, cortisol has been successfully quantifed in
human eccrine sweat, demonstrating the potential of fnger
sweat analysis in regard to monitor endogenous processes
related to stress [42].
The power of individual metabolic profling
in the context of PPPM
Only recently, we successfully demonstrated kinetic time-
course measurements of metabolic activities in humans, indi-
cating that fnger sweat analyses may become a valuable tool
for precision medicine [32]. In contrast to other minimally
invasive approaches [43, 44], the presented non-invasive
metabolomics assay works quickly and easily while ofering
tremendous investigative power. We have termed it “metabo-
tip”. Indeed, the ingestion of many bioactive compounds
contained in food may become detectable via metabo-tip
within minutes after consumption. Applying mathematical
modelling strategies, it was possible to overcome critical
normalisation challenges and to obtain quantitative meas-
ures for individual metabolic properties [32]. In this project,
we investigated metabo-tip in regard to monitoring lifestyle
142 EPMA Journal (2021) 12:141–153
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parameters such as the presence of endogenous and exog-
enous bioactive compounds or exposure to toxins contained
in foods or beverages. These parameters could be described
by time-dependent metabolic patterns detected in sweat from
the fngertip. This basic research study was conducted to
explore potential felds of application of metabo-tip analysis
in the clinical routine, thereby supporting the further devel-
opment of predictive, preventive and personalised medicine
(PPPM). Thus, the potential of metabo-tip analysis in order
to monitor individual metabolic responses upon the uptake
of xenobiotics and potentially unexpected toxins was inves-
tigated. The observation of metabolic diurnal rhythms and
distinct individual responses to potentially adverse exposure
promises successful future applications of metabo-tip analy-
sis not only in the general assessment of individual lifestyle
parameters but also in the context of PPPM.
Material and methods
Reagents and chemicals
LC–MS grade formic acid, methanol and water used for
chromatographic separation as well as for preparation of
internal standards and samples were purchased from VWR
(Germany). Xenobiotic and metabolic standards were
obtained either from Sanova Pharma GmbH (Austria), from
Sigma-Aldrich (Austria), or from Honeywell Fluka (GER).
Filter papers (standardised to 1 cm2
) used for sampling
were stamped out of fuzz-free paper from Kimtech Science
(USA).
Cohort design
Several volunteers were recruited after giving written,
informed consent for the diferent studies as outlined in
Table 1. These experiments were approved by the ethical
committee of the University of Vienna (no. 00337). Volun-
teers may be part of more than one study (A–E). Gender dis-
tribution of participants was equally balanced between male
and female and their ages ranged from 20 to 50 years. Some
studies (C–E) required a fasting period of 12 h for cafein-
ated foods (chocolate, energy bars) and beverages (cofee,
tea, energy drinks) before beginning with the experiment in
order to avoid interferences with the investigation of xeno-
biotic metabolism. Finger sweat samples were collected just
before donors consumed a 50-g chocolate bar or a double
espresso (0 min). Finger sweat samples were thereafter col-
lected in short intervals as outlined in Table 1. In studies A
and B, the general lifestyle of volunteers was monitored.
There were no dietary restrictions placed on the test subjects.
Collection of sweat from the fngertip
Finger sweat samples were collected as previously published
[32]. In short, flter papers were stamped out of fuzz-free
paper to get a circular area of 1 cm2
, flter papers were pre-
wetted with 3 μl of LC–MS grade water and stored in 0.5-ml
Eppendorf Tubes. For each sweat collection, donors cleaned
their hands with warm tap water and then dried their hands
with disposable paper towels. Donors kept their hands open
in the air at room temperature for 1 min. Then, the sampling
unit was placed between thumb and index fnger with clean
tweezers and held for 1 min. Filters were transferred back to
labelled 0.5-ml Eppendorf tubes using clean tweezers and
stored at 4 °C until sample preparation.
Sample preparation
Each finger sweat sample collected on filter paper was
extracted with 120 μl of extraction solution (an aqueous
solution of 1 pg μl−1 cafeine-trimethyl-D9 with 0.2% formic
acid). Metabolites were extracted via repeated pipetting of
the extraction solvent 15 times. The flter paper was pelleted
on the bottom of the Eppendorf Tube and the supernatant
was transferred into HPLC glass vials equipped with a 200-
μl V-Shape glass insert (Macherey–Nagel GmbH & Co.KG)
and analysed by LC–MS/MS. To determine background
metabolites or potential contaminants, blank flter papers
were additionally extracted in the same manner. Standard
solutions were prepared in methanol in a concentration of
Table 1 Overview of all studies discussed in this publication
Study Participants Design Sampling time points Restrictions
A 6 participants 30 consecutive days of sampling 2×per day (morning and evening) No restrictions
B 10 participants 10 consecutive days of sampling 3×per day (morning, lunch, evening) No restrictions, 4 smokers
C 10 participants 50-g chocolate bar 0, 15, 30, 45, 60, 90 and 120 min after
consumption
Fasting cafeinated foods and drinks 12 h
previous to the start of the experiment
D 6 participants 50-g chocolate bar or double
espresso or neither (control)
0, 20, 40, 60, 90, 120 and 120 min after
consumption
Fasting cafeinated foods and drinks 12 h
previous to the start of the experiment
E 20 participants Regular versus rare cofee drinkers Sampled one time to check baseline
levels
Fasting cafeinated foods and drinks 12 h
previous to the start of the experiment
EPMA Journal (2021) 12:141–153 143
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1 mg ml−1. After that, they were diluted to concentrations
of 100 pg μl−1 and 10 pg μl−1.
LC–MS/MS analysis
Chromatographic separation was performed on a Vanquish
UHPLC system (Thermo Fisher Scientifc) and analysed in
an untargeted fashion with a hybrid instrument consisting of
a quadrupole mass flter and an orbitrap mass analyser (Q
Exactive HF, Thermo Fisher Scientifc). A reversed phase
Kinetex XB-C18 column (100 Å, 2.6 μm, 100 ×2.1 mm,
Phenomenex Inc.) was used to separate analytes present in
fnger sweat samples. Mobile phase A consisted of water
with 0.2% formic acid and mobile phase B of methanol with
0.2% formic acid. The following gradient was applied: 1–5%
B in 0.3 min and then 5–40% B from 0.3–4.5 min, followed
by a column washing phase of 1.4 min at 80% B and a re-
equilibration phase of 1.6 min at 1% B resulting in a total
runtime of 7.5 min. Flow rate was set to 500 μl min−1 and
the injection volume was 10 μl. Electrospray ionisation was
144 EPMA Journal (2021) 12:141–153
1 3
performed in positive as well as negative ionisation mode.
MS scan range was m/z 100–1000 and the resolution was set
to 60,000 (at m/z 200) for MS1 and 15,000 (at m/z 200) for
MS2. A top 4 method was applied and dynamic exclusion
was set to 6 s. Collision energy was 30 eV. Instrument con-
trol was performed with Xcalibur software (Thermo Fisher
Scientifc).
Data analysis
Raw fles generated by the Q Exactive HF were searched
in the Compound Discoverer Software 3.1 (Thermo Fisher
Scientifc) applying a user-defned workfow. All identi-
fed compounds with a match factor≥80 were manually
reviewed using Xcalibur 4.0 Qual browser (Thermo Fisher
Scientifc) and the obtained MS2 spectra were compared
to reference spectra taken from mzcloud (Copyright ©
2013–2020 HighChem LLC, Slovakia). A maximum reten-
tion time shift of 0.1 min was allowed and the mass tolerance
was restricted to 10 ppm for MS1 and MS2. Identifed com-
pounds were also verifed using purchased analytical stand-
ards applying the same LC–MS method. The Tracefnder
Software 4.1 (Thermo Fisher Scientifc) was used for peak
integration and calculation of peak areas. Generated batch
tables were exported and further process with Microsoft
Excel, GraphPad Prism (for two-tailed, paired t-tests) and
the Perseus [45] (for principal component analysis) software.
ACD/Labs’ ChemSketch (Freeware) 2020.1.1 was used to
draw structures.
Results
Both food and beverage consumption leave
characteristic imprints on fnger sweat
composition and enable conclusions to be drawn
about the general lifestyle of individuals
Figure 1a shows extracted ion chromatograms identifying
cafeine, 7-, 3- and 1-methylxanthine as well as catechin
and epicatechin in a fnger sweat sample of an individual
15 min after consumption of 50-g dark chocolate. None of
these substances was detectable in the fnger sweat of indi-
viduals before consumption; the signifcant increase of these
compounds in fnger sweat after chocolate consumption
could be reproduced in a controlled study with ten diferent
individuals (Fig. 1b). Furthermore, the levels of diferent
cafeine metabolites after chocolate intake were compared
to those after drinking a cup of cofee. Although both caf-
feine and theobromine are contained in chocolate as well
as cofee, diferent levels of these two compounds found
in fnger sweat enabled the identifcation of the respective
consumption groups (Fig. 1c). Paraxanthine is the catabolic
metabolite of cafeine and apparently accumulates in regular
cofee consumers [46, 47]. In our experiment, we could con-
frm this fnding in individuals consuming cofee regularly
through elevated baseline levels of paraxanthine in fnger
sweat (Fig. 1d). Thus, a metabolic property detected in fn-
ger sweat may be related to individual lifestyle preferences,
such as cofee consumption. Obviously, nutrition is a major
contributor to individual lifestyle habits. The monitoring of
individual metabolic responses such as food intolerances
upon the intake of specifc foods and beverages as outlined
below may gain great relevance in the further development
of PPPM.
Cigarette smoke, pharmaceuticals and exposure
to environmental toxins leave characteristic
imprints on fnger sweat composition
Not only a specifc diet but also personal habits such as
smoking and regular use of pharmacological drugs will
result in characteristic changes in fnger sweat. Moreover,
metabo-tip has the potential to detect even trace amounts
of pesticides as demonstrated in Fig. 2. The consumption
of an orange allowed for the identifcation of natural bio-
active compounds such as favonoids (nobiletin, hesperidin
and tangeritin) together with pesticides such as enilconazole
(Fig. 2a). In addition, metabo-tip was applied to detect smok-
ing of tobacco (cigarettes) in fnger sweat via characteristic
Fig. 1 Imprints of food and beverages on fnger sweat composition
via high-resolution LC–MS/MS. a Characteristic metabolic profle
in sweat after the consumption of chocolate exemplifed by extracted
ion chromatograms of cafeine (retention time (RT)=3.25 min, m/z
195.0877, red peak), theobromine (RT=2.09 min, m/z 181.0720, blue
peak), 7-, 3- and 1-methylxanthine (RT=1.45, 1.59 and 1.69 min,
m/z 167.0564, green peaks) and catechin and epicatechin (RT=2.68
and 3.47 min, m/z 291.0863 orange peaks) for a single donor. All of
the shown molecules are known chocolate constituents or metabo-
lites thereof, demonstrating that xenobiotics and their metabolites
contained in foods and beverages can be identifed in the sweat of
volunteers after consumption. b Controlled study (study C) of 10
donors eating a 50-g chocolate bar. Normalised areas under the curve
(nAUC) before (0 min) and 15 min after consumption are shown for
cafeine, theobromine, 7-methylxanthine, 3-methylxanthine, epicat-
echin and catechin, demonstrating an increase in all individuals after
15 min. The increase was signifcant for all molecules except for cat-
echin. *p-value≤0.05, **p-value≤0.01, ns not signifcant. c Time-
course measurements of cafeine and theobromine shown for three
donors consuming either a double espresso (blue), a 50-g choco-
late bar (red) or neither (control, black) demonstrate an increase of
those metabolites in a highly characteristic fashion, demonstrating
that metabo-tip analysis can indeed identify consumption behaviour.
nAUC, normalised area under curve. d Paraxanthine is the main
metabolite of cafeine and accumulates in the sweat of regular cofee
drinkers. Here, we show the comparison of paraxanthine baseline lev-
els of regular (at least 1 cup of cofee per day) with rare cofee con-
sumers, which allows the distinction based on the volunteers’ dietary
preferences. ***p-value≤0.001. nAUC, normalised area under the
curve
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EPMA Journal (2021) 12:141–153 145
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compounds such as nicotine and anatabine (Fig. 2b). The
simultaneous detection of precursor molecules and their
metabolites such as nicotine, cotinine and 3-hydroxycoti-
nine facilitates individual metabolic profling (Fig. 2b). In
the case that xenobiotics are not directly detectable, evi-
dence can be found by the detection of specifc metabolites.
For example, the pain killer metamizole was not detectable
in sweat, but after consumption of metamizole, a group of
metamizole-derived metabolites could be detected in fnger
sweat, as demonstrated in Fig. 2c. The power of metabo-
tip with respect to the detection of even trace amounts of
substances with noxious efects, as summarised in Fig. 3,
demonstrates its great potential as tool for PPPM supporting
clinical practice. In this way, not only patient’s compliance
regarding the intake of specifc prescribed medications may
be monitored but also a molecular risk assessment analysis
146 EPMA Journal (2021) 12:141–153
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upon the intake of toxins such as cigarette smoke can be
performed.
Sweat composition provides a wide spectrum
of information regarding individual lifestyle
Metabo-tip not only allows the detection of specifc com-
pounds after consumption of a certain food or medication
but also enables comprehensive, untargeted screening of
exogenous bioactive compounds, for instance from cos-
metics or environmental pollutants. In addition, it detects
specifc markers for general lifestyle habits (Fig. 3, Sup-
plementary Table). Based on previously published stud-
ies [48, 49], it could be assumed that basic compounds are
more likely to get transported into sweat ducts; however,
our results do not seem to discriminate between analytes
based on their pKA values, therefore allowing for a com-
prehensive metabolic profle (Supplementary Table with
listed pKA values). The combined detection of exogenous
compounds as well as endogenously produced metabolites
results in a specifc molecular signature in the fnger sweat
of each individual. Exposure to bioactive compounds as well
as to various medications and drugs can be easily detected.
Moreover, environmental pollutants such as fungicides,
mycotoxins and compounds derived from plastic contain-
ers such as bisphenol S and melamine are detectable (Sup-
plementary Table) and may be of great relevance concern-
ing individual health. Thus, the wide array of information
obtained from fnger sweat analysis may reveal information
about the exposition to toxins and general health status of an
individual. This important property of metabo-tip may open
up many remarkable opportunities for a more systematic
investigation of exposure to toxins and individual responses
thereupon, thus improving risk assessment and extending
our opportunities to assess an individual’s health status.
Metabo‐tip analysis reveals diurnal fuctuations
in metabolism and individual endogenous
responses
Principal component analysis (PCA) of fnger sweat samples
collected from three donors in the morning and evening over
a time period of 30 days revealed fuctuating diferences in
fnger sweat composition with the possibility to distinguish
between these individuals based on their general lifestyle
(Fig. 4a, b). Donor A and donor B could not be completely
separated by PCA which might be contributed to a similar
lifestyle as these donors share the same household. Intrigu-
ingly, a regular shift of metabolites in a diurnal rhythm was
observed (Fig. 4c). Analysis of the PCA loadings plot sug-
gested cafeine, theobromine, theophylline, paraxanthine and
amino acids as most infuential compounds (Fig. 4d).
This time series analysis was also used to assess the bio-
genic monoamine histamine, which serves both as a neuro-
transmitter and as a modulator in infammatory responses.
Figure 4e exemplifes the levels of histamine of an individ-
ual determined by metabo-tip analysis on consecutive days.
Histamine peaks correlated with symptoms associated with
food intolerance and allergy reported on the correspond-
ing days. This observation indicated that specifc individual
responses to food consumption are detectable by metabo-tip
analysis. Obviously, diferent individuals may respond rather
diferently to a given food, typically documented by gossips
rather than molecular analyses. The capability of metabo-
tip to detect individual molecular responses opens another
highly relevant feld of PPPM research.
Discussion
Diagnosis and treatment of chronic disease are often dif-
cult, because these diseases often have multiple causes—as
evidenced in case of arteriosclerosis, type II diabetes, cancer
or neurodegenerative diseases. Actually they may be caused
or promoted by an imbalance of metabolic homoeostasis
due to long-lasting stress. The investigation of metabolic
imbalances in humans has been difcult though such investi-
gation is crucial to predictive preventive personalised medi-
cine (PPPM) improvement [50]. Due to its dynamic nature,
metabolomic measurements call for repeated analyses in a
narrow timeframe. Blood sampling is therefore hardly fea-
sible as it leads to compliance issues. Non-invasive methods
Fig. 2 Metabo-tip reveals environmental pollution in nutrition,
smoking habits and medication. a The intake of an orange (donor
from study A) results in the detection of distinct metabolites in fn-
ger sweat. Extracted ion chromatograms are shown for nobiletin
(RT=5.58 min, m/z 403.1387, blue peak), hesperidin (RT=5.27 min,
m/z 611.1970, green peak), tangeritin (RT=5.55 min, m/z 373.1282,
orange peak) and enilconazole (RT=5.31 min, m/z 297.0556, red
peak), which is a fungicide predominantly used in the agriculture of
citrus fruits. b The imprint of smoking on the fnger sweat (donor
from study B). Not only nicotine but also its metabolites were
detected in sweat as well as anatabine, which is found in tobacco. The
simultaneous detection of nicotine and its metabolites not only allows
individual metabolic profling but also rules out that nicotine is only
present in fnger sweat samples due to skin contamination by holding
a cigarette. Extracted ion chromatograms of nicotine (RT=0.60 min,
m/z 163.1230, blue peak), cotinine (RT=0.95 min, m/z 177.1022,
green peak), 3-hydroxycotinine (RT=0.59 min, m/z 193.0972, orange
peak) and anatabine (RT=0.65 min, m/z 161.1073, red peak) are
depicted. c Evidence for the intake of a pain killer by the successful
detection of its metabolites in fnger sweat. Extracted ion chromato-
grams of metamizole (m/z 312.1013, empty trace) and its metabolites
N-methyl-4-aminoantipyrine (RT=1.89 min, m/z 218.1288, blue
peak), 4-aminoantipyrine (RT=2.01 min, m/z 204.1131, green peak),
N-formyl-4-aminoantipyrine (RT=3.09 min, m/z 232.1086, orange
peak) and N-acetyl-4-aminoantipyrine (RT=3.65 min, m/z 246.1237,
red peak). Even though metamizole was not directly found in the
volunteer’s sweat, metabo-tip analysis revealed the usage of the pain
killer via the successful detection of the metamizole metabolites
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EPMA Journal (2021) 12:141–153 147
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would be preferable but the analysis of urine, for instance,
does not allow repeated time series analyses in a narrow
timeframe because urine is collected in the bladder prior
to sampling. Saliva may sufer from contaminants such as
bacteria and food as well as from mucous glycoproteins
which may interfere with metabolite extraction and which
may impair sensitivity of measurement. Sweat is only avail-
able in limited amounts, and the risk of potential contamina-
tion from the skin also causes quantifcation challenges. To
overcome some of these obstacles, we investigated sweat
collected from clean fngertips via metabo-tip. This proce-
dure is fast, non-invasive and easy to use, thereby supporting
multiple measurements within a short timeframe, and ensur-
ing good compliance by the test subjects [31]. A determina-
tion of individual enzymatic activities and a management
strategy to cope with normalisation issues, related to the
total amount of sweat collected per sample, has already been
demonstrated [32]. Here, we evaluated whether this ana-
lytical method could detect specifc substances that would
refect upon lifestyle habits which may be relevant to health
to be determined.
Extending current PPPM strategies by monitoring
individual metabolic responses to the uptake
of xenobiotics
We demonstrated that the uptake of as little as 0.2 mg of
a chemical compound may be sufcient to be detected by
metabo-tip analysis, and investigated if this approach was
limited to basic compounds showing higher solubility in
the slightly acidic sweat pH [49, 51]. Plotting the identifed
substances versus pKA values did not show any relation,
suggesting no discrimination of substances based on their
pKA (Supplementary Table). Thus, it seems that metabo-
tip analysis may provide a representative overview of stable
substances we are exposed to. Present data therefore sug-
gest a broad applicability of metabo-tip. The detection of
bioactive compounds such as favonoids and even pesticides
Fig. 3 Monitoring lifestyle parameters via fnger sweat analysis.
Sweat used for metabo-tip analysis is collected by holding a flter
paper between thumb and index fnger without forcing sweat forma-
tion. Metabo-tip analysis reveals the highly dynamic nature of sweat
composition and how it changes when specifc foods and beverages
are consumed (e.g. polyphenols found in chocolate and wine, xan-
thines like cafeine from cofee, favonoids from citrus fruits and
mycotoxins found in bread/wheats), when supplements for example
vitamins or melatonin as sleep aid supplement are taken or when
certain types of hygiene products have been used such as climbazole
found in dandruf shampoos or oxybenzone from sunscreen lotions.
Moreover, the general health status of a person can be assessed by
revealing individual medication (e.g. for pain and fever, for colds,
hormones, or cannabidiol) even allowing risk assessment for thera-
peutic strategies. Metabo-tip analysis may even reveal biomarker pat-
terns associated with certain diseases or general lifestyle habits, like
smoking. Exposure to environmental toxins derived from consumed
products like pesticides (e.g. enilconazole, fenpropimorph or propa-
mocarb) as well as toxic compounds in plastics found in the packag-
ing of foods and beverages (e.g. bisphenol S and melamine) was also
successfully detected in the sweat of test subjects. The Supplemen-
tary Table lists all compounds detected by metabo-tip analysis and
shows the potential of metabo-tip analysis to support the development
for novel PPPM strategies
148 EPMA Journal (2021) 12:141–153
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after the consumption of oranges (Fig. 2a) demonstrated the
practicability of the assay.
Furthermore, the metabolism of xenobiotics could be
observed using metabo-tip analysis as demonstrated in
the case of xanthines, nicotine and metamizole (Figs. 1
and 2). Individual diferences in the expression of P450
isoenzymes [52] may account for individual variations in
drug efects and toxicity [53]. As demonstrated by us pre-
viously, proteomics and eicosanoid analysis revealed that
the consumption of cofee may have signifcant pro- or
anti-infammatory efects in an individualised fashion [54].
In that study, age, sex, cofee consumption habits as well
as the metabolic kinetics of cafeine in blood of each indi-
vidual were ruled out as predictive parameters accounting
for pro- or anti-infammatory efects. Thus, the combi-
nation of diferent “-omics” approaches, especially the
inclusion of metabolic profling, may be of great relevance
regarding PPPM and individual risk assessment [55]. As
metabo-tip analysis allows for the profling of xenobiotic
exposure and the associated metabolic activities, it will
greatly support more systematic studies to improve current
PPPM strategies.
Fig. 4 Diurnal metabolic fuctuations and individual endogenous
responses. Principle component analyses (PCA) of fnger sweat sam-
ples collected from three donors (study A) in the morning (a) and
evening (b) over 30 days are depicted. Donor C can be completely
separated from the other donors, while donors A and B share a rather
similar lifestyle living in a joint household. c PCA of the fnger
sweat samples of three donors reveals diurnal metabolic fuctuations
demonstrated by a consistent shift of component 1 to the right from
morning to evening in each individual. d Loadings plot for the PCA
depicted in b, showing the strong infuence of histamine, cofee or
chocolate consumption (cafeine, theobromine, theophylline and par-
axanthine), and general diet (amino acids and vitamins like pantoth-
enic acid) on components 1 and 2. Estradiol is a hormone and has
only a minimal infuence on components 1 and 2; however, this shows
that further improvements of metabo-tip analysis may even allow
to robustly distinguish male and female donors based on hormones
as discriminators. e Time-course analysis of histamine over a time
period of 30 days. Asterisk marks timepoints showing high histamine
levels of volunteers accompanied by reported indispositions in rela-
tion to food or beverage intake
EPMA Journal (2021) 12:141–153 149
1 3
Metabo‐tip: a novel tool for the detection
of endogenous metabolic responses
We have observed an apparent correlation between allergic
symptoms and histamine detected by metabo-tip analysis
(Fig. 4), which opens the opportunity to defne biochemi-
cal signatures of some adverse efects. Metabo-tip analyses
will allow us to investigate apparent detrimental efects with
scientifc rigour, enabling reasonable conclusions to be made
regarding risk assessment. Furthermore, the presented ana-
lytical workfow ofers the opportunity to monitor individual
responses to plant-derived bioactive compounds, especially
favonoids, which are intensely discussed in the context of
PPPM because of their benefcial impact on carcinogenesis
[56, 57]. Specifcally, the detection of histamine points to the
enormous power of metabo-tip for clinical studies related to
food intolerance. So far, only volunteers reporting no acute
medical disorders were included in our studies. Thus, a sys-
tematic and large-scale evaluation of the predictive power of
metabo-tip analysis in patients sufering from various medi-
cal disorders needs to be performed in order to evaluate the
power of metabo-tip for clinical routine applications.
The opportunities offered by metabo-tip analysis are
raising many questions. Only large-scale studies will allow
us to decide, e.g., whether the apparent diurnal rhythm
observed by metabo-tip in this study (Fig. 4) was related to
endogenous factors (e.g. through circadian regulation), or
rather exogenous factors (such as regular nutrient patterns
or repeated exposure to xenobiotics). Furthermore, technical
improvements regarding robustness, sensitivity and through-
put may also support the development of stress monitoring
devices as well as therapy monitoring strategies, bringing
about crucial progress in the feld of PPPM.
Sweat may serve as specifc source for reactive
oxygen species‐associated metabolites
We have recently reported that erythrocytes interfere with
reactive oxygen species (ROS) formation so efciently that
ROS-dependent T-cell activation is blocked by the mere
presence of erythrocytes [58]. This observation clearly
implies that the formation of ROS-dependent metabolites
in blood may be inhibited as well. Infammatory activation
of leukocytes typically takes place in the interstitium, and
actually sweat refects the constitution of interstitial fuid
[59, 60] which is free from erythrocytes. Therefore, the anal-
ysis of sweat may provide the advantage of including ROS-
associated metabolites in the analysis which may otherwise
be missing in blood. On the other hand, labile and reactive
metabolites may react already at the skin surface due to the
presence of oxygen and get lost for meaningful evaluation.
These aspects highlight the huge potential of metabo-tip
only to be realised by more basic research.
Ways to translate metabo‐tip into clinical practice
and its potential impact on supporting PPPM
concepts
Up until now, metabo-tip was exclusively used in the frame-
work of basic research projects to identify and uncover its
power and applicability in various research areas. In this
context, a comprehensive metabolomic screening approach
was conducted which requires specially trained personnel
in the feld of LC–MS/MS and data analysis. The transla-
tion of metabo-tip analysis as tool for PPPM in the clinical
routine will require some practical adaptions. Based on the
screening approach, a panel of molecules serving as bio-
markers should be selected for targeted analysis, facilitat-
ing data management and evaluation. Regarding the gen-
eral cost–beneft of metabo-tip analysis in clinical practice,
it will prove most cost-efective in the long run since the
principal costs will fall to the purchase and installation of a
suitable LC–MS/MS system.
Thus, a large number of PPPM-related applications,
potentially ofered by metabo-tip analysis, warrant larger
scale studies. The non-invasive and painless sampling proce-
dure in combination with high-throughput analysis enables
the systematic monitoring of individuals in short intervals.
Hence, the opportunity of frequent sampling makes it even
possible to carry out specifc metabolic in vivo studies in
humans. Up until now, the most comprehensive metabolic
studies were conducted using in vitro cell culture model
systems. As demonstrated previously, those experiments
often sufer from unexpected infuencing factors such as
the composition of fetal calf serum [61]. The advantage of
metabo-tip analysis is the facilitated use of humans as model
system for comprehensive metabolic studies in vivo. This
possibility enables the monitoring of a plethora of metabolic
intervention studies in individuals in the context of PPPM
such as the monitoring of food intolerances, the examination
of individual lifestyle changes in association with the general
health status and risk assessment and the monitoring of the
intake of prescribed medications in clinical practice.
Strengths and limitations
Metabo-tip analysis is a fast, non-invasive, painless and easy
to use strategy, thereby supporting metabolic measurements
in a short timeframe, and ensuring optimal compliance by
the test subjects. These strengths enable the individualised
monitoring of metabolites and the assessment of metabolic
kinetics of individuals as response to various stimuli. Nev-
ertheless, some limitations of metabo-tip analysis have to be
considered. Up until now, metabo-tip was applied for basic
research projects only. The applicability of metabo-tip in
clinical routine has still to be evaluated systematically by
150 EPMA Journal (2021) 12:141–153
1 3
setting up targeted LC–MS/MS methods applied to larger
study cohorts. The small amount of sweat used for metabo-
tip analysis requires highly sensitive analytical approaches
for metabolomic analyses with the drawback that even small
contaminations on the fngertip of test subjects can already
impede and distort the analysis. Furthermore, the absolute
quantifcation of metabolites represents a true challenge.
However, using the kinetic profle of biochemically related
pairs of molecules such as cafeine and its metabolites, i.e.
xanthines, has already been proven to support normalisation
and determine individual sweat fux rates.
Conclusions and outlook
We conclude that metabo-tip ofers a great opportunity to
support the further development of PPPM strategies due
to its non-invasive character and the possibility of pain-
less sampling in a narrow timeframe while providing rich
molecular information. In this way, personalised molecular
profles can be generated allowing us to observe individual
metabolic reactions to exogenous and/or endogenous stimuli
or challenges. Ongoing research will establish the relation
of individual molecular patterns obtained by metabo-tip
with disease states or risk for diseases. We thus suggest that
metabo-tip may have a large number of clinical applications
ranging from diagnostic applications such as the early detec-
tion of food intolerances to the monitoring and validation of
therapeutic strategies.
Abbreviations CV: Coefcient of variation; LC–MS: Liquid chroma-
tography mass spectrometry; m/z: Mass over charge; nAUC: Normal-
ised area under the curve; ns: Not signifcant; PCA: Principle com-
ponent analysis; pKA: Acid dissociation constant; PPPM: Predictive
preventive personalised medicine; ROS: Reactive oxygen species;
RT: Retention time
Supplementary Information The online version contains supplemen-
tary material available at https://doi.org/10.1007/s13167-021-00241-6 .
Acknowledgements We acknowledge support by the Mass Spectrom-
etry Centre of the Faculty of Chemistry, University of Vienna, and the
Joint Metabolome Facility, University of Vienna and Medical Univer-
sity of Vienna. Both facilities are members of the Vienna Life Science
Instruments (VLSI). We would also like to thank the Hochschulju-
biläumsstiftung (HJS) for their fnancial support during the course of
this research project. Figure 3 was created with the help of the Mind
the Graph platform (www.mindthegraph.com) and Servier Medical Art
(www.servier.com).
Author contribution J.B. performed research, interpreted data, ana-
lysed data and wrote the manuscript. A.B. interpreted data and wrote
the manuscript. T.S. interpreted data and wrote the manuscript. F.R.
performed research and wrote the manuscript. G.F. performed research.
S.M.M. performed research, analysed and interpreted data and wrote
the manuscript. C.G. conceptualised the project, interpreted data and
wrote the manuscript.
Funding Open access funding provided by University of Vienna.
Data availability Raw data is secured and available on request.
Code availability Not applicable.
Declarations
Ethics approval This study was approved by the ethical committee of
the University of Vienna (no. 00337).
Consent to participate Volunteers have given their written, informed
consent to participate in this study.
Consent for publication Volunteers have given their written, informed
consent for publishing the data.
Conflict of interest The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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