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\begin{document}

\title{Evaluation the potential of urinary volatilomic patterns of patients
infected with SARS-CoV-2 for COVID-19 diagnosis. An exploratory study}



\author[1]{José S. Câmara}%
\author[1]{Giulia Riccio}%
\author[2]{Joana Neto}%
\author[2]{Cristina V. Berenguer}%
\author[3]{Cristina P. Ornelas}%
\author[1]{Viviana Greco}%
\author[2]{Jorge A.M. Pereira}%
\author[2]{Rosa Perestrelo}%
\affil[1]{Universita Cattolica del Sacro Cuore - Campus di Roma}%
\affil[2]{Universidade da Madeira Centro de Quimica da Madeira}%
\affil[3]{Servico de Saude da Regiao Autonoma da Madeira}%


\vspace{-1em}



  
  \date{May 30, 2024}


\begingroup
\let\center\flushleft
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\endgroup





\selectlanguage{english}
\begin{abstract}
To assess whether SARS-CoV-2 infection induces changes in the urinary
volatilomic fingerprint suitable for non-invasive COVID-19 diagnosis and
management, urine samples from SARS-CoV-2 infected patients (62),
recovered COVID-19 patients (30), and non-infected individuals (41),
were analysed using solid-phase microextraction technique in headspace
mode, combined with gas chromatography hyphenated with mass spectrometry
(HS-SPME/GC-MS). A total of 101 volatile organic metabolites (VOMs) from
13 chemical families were characterized, with terpenes, phenolic
compounds, norisoprenoids, and ketones being the most represented
groups. Overall, the levels of terpenes and phenolic compounds decreased
in the control group, whereas those of norisoprenoids and ketones
increased significantly. In turn, a remarkable increase was noticed in
norisoprenoids and ketones and a milder increase in alcohols, furanic,
and sulfur compounds in the recovery group than in the COVID group.
Multivariate statistical analysis identified sets of VOMs that could
constitute the signatures of COVID-19 development and progression. These
signatures are composed of D-carvone,
3-methoxy-5-(trifluoromethyl)aniline (MTA),
1,1,6-trimethyl-dihydronaphthalene (TDN), 2-heptanone, and
2,5,5,8a-tetramethyl-1,2,3,5,6,7,8,8-octahydro-1-naphthalenyl ester
acetate (TONEA) for COVID-19 infection and nonanoic acid, \selectlanguage{greek}α-\selectlanguage{english}terpinene,
\selectlanguage{greek}β-\selectlanguage{english}damascenone, \selectlanguage{greek}α-\selectlanguage{english}isophorone, and trans-furan linalool for patients
recovering from the disease. The study reported in the current article
provides evidence that changes in the urinary volatilomic profile
triggered by SARS-CoV-2 infection constitute a promising and valuable
screening and/or diagnostic and management tool for COVID-19 in clinical
environment.%
\end{abstract}%



\sloppy


Evaluation the potential of urinary volatilomic patterns of patients
infected with SARS-CoV-2 for COVID-19 diagnosis. An exploratory study

Giulia Riccio \textsuperscript{1,2}, Joana Neto \textsuperscript{3},
Cristina V. Berenguer \textsuperscript{3}, Cristina P.
Ornelas\textsuperscript{4}, Viviana Greco \textsuperscript{1,2}, Jorge
A.M. Pereira \textsuperscript{3}, Rosa Perestrelo \textsuperscript{3},
Jos\selectlanguage{ngerman}é S. Câmara \textsuperscript{1,5,*}

\textsuperscript{1} Department of Basic Biotechnological Sciences,
Intensivological and Perioperative Clinics, Univesità Cattolica del
Sacro Cuore, 00168 Rome, Italy;\emph{giulia.riccio1@unicatt.it}(G.R.,
ORCID: 0000-0001-8396-7054 );\emph{viviana.greco@unicatt.it}(V.G.,
ORCID: 0000-0003-4521-0020)

\textsuperscript{2} Department of Diagnostic and Laboratory Medicine,
Unity of Chemistry, Biochemistry and Clinical Molecular Biology,
Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy

\textsuperscript{3} CQM -- Centro de Química da Madeira, NPRG, Campus da
Penteada, Universidade da Madeira, 9020-105 Funchal,
Portugal;\emph{cristina.berenguer@staff.uma.pt}(C.V.B.;
0000-0002-1762-1175);\emph{jorge.pereira@staff.uma.pt}(J.AM.P., ORCID:
0000-0003-0316-5348 );\emph{rmp@staff.uma.pt} (R.P., ORCID:
0000-0002-7223-1022)

\textsuperscript{4} Centro de Saúde do Bom Jesus, SESARAM EPERAM, Rua
das Hortas 67, 9050-024 Funchal.
Portugal;\emph{ornelas.cristina@gmail.com}(C.P.O., ORCID:)

\textsuperscript{5} Departamento de Química, Faculdade de Ciências
Exatas e Engenharia, Campus da Penteada, Universidade da Madeira,
9020-105 Funchal, Portugal (JSC, ORCID: 0000-0003-1965-3151)

* Correspondence:

Prof. José S. Câmara,\emph{jsc@staff.uma.pt}. Departamento de Química,
Faculdade de Ciências Exatas e Engenharia, Campus da Penteada,
Universidade da Madeira, 9020-105 Funchal, Portugal Phone: +351
291705112; Fax: +351 291705149)

\textbf{Running title:} Urinary volatilome for COVID-19 diagnosis



\textbf{ABSTRACT}

To assess whether SARS-CoV-2 infection induces changes in the urinary
volatilomic fingerprint suitable for non-invasive COVID-19 diagnosis and
management, urine samples from SARS-CoV-2 infected patients (62),
recovered COVID-19 patients (30), and non-infected individuals (41),
were analysed using solid-phase microextraction technique in headspace
mode, combined with gas chromatography hyphenated with mass spectrometry
(HS-SPME/GC-MS). A total of 101 volatile organic metabolites (VOMs) from
13 chemical families were characterized, with terpenes, phenolic
compounds, norisoprenoids, and ketones being the most represented
groups. Overall, the levels of terpenes and phenolic compounds decreased
in the control group, whereas those of norisoprenoids and ketones
increased significantly. In turn, a remarkable increase was noticed in
norisoprenoids and ketones and a milder increase in alcohols, furanic,
and sulfur compounds in the recovery group than in the COVID group.
Multivariate statistical analysis identified sets of VOMs that could
constitute the signatures of COVID-19 development and progression. These
signatures are composed of D-carvone,
3-methoxy-5-(trifluoromethyl)aniline (MTA),
1,1,6-trimethyl-dihydronaphthalene (TDN), 2-heptanone, and
2,5,5,8a-tetramethyl-1,2,3,5,6,7,8,8-octahydro-1-naphthalenyl ester
acetate (TONEA) for COVID-19 infection and nonanoic acid, \selectlanguage{greek}α-\selectlanguage{english}terpinene,
\selectlanguage{greek}β-\selectlanguage{english}damascenone, \selectlanguage{greek}α-\selectlanguage{english}isophorone, and trans-furan linalool for patients
recovering from the disease. The study reported in the current article
provides evidence that changes in the urinary volatilomic profile
triggered by SARS-CoV-2 infection constitute a promising and valuable
screening and/or diagnostic and management tool for COVID-19 in clinical
environment.

\textbf{Keywords:} VOMs; urine; COVID-19; volatilomics; HS-SPME/GC-MS

1. INTRODUCTION

In late 2019, COVID-19, a highly infectious disease caused by the severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2), triggered a
tremendous and severe pandemic outbreak. COVID-19 has been identified as
a hyperinflammatory syndrome characterised by aberrant immune activation
and excessive cytokine release (cytokine storm), ultimately leading to
failure in multiple organs {[}1,2{]}. As the respiratory virus spread to
all countries and/or regions in the world, intermittent lockdowns were
imposed as a desperate and unprecedented measure to contain the
propagation of the disease {[}3,4{]}. The effectiveness of this
procedure is still an open debate, although it is widely acknowledged
that the delay of virus propagation gave time to healthcare systems to
adapt and mitigate the mortality caused by COVID-19 {[}4,5{]}. According
to the World Health Organization (WHO), 768 million people were infected
and over 6.94 million deaths have been estimated (Figure 1) {[}6{]}.
This has caused a strong impact on society at economic, political,
social, educational, environmental, and cultural levels that will last
for decades {[}7-10{]}.

\textbf{Hosted file}

\verb`image1.emf`  available at \url{https://authorea.com/users/768720/articles/833462-evaluation-the-potential-of-urinary-volatilomic-patterns-of-patients-infected-with-sars-cov-2-for-covid-19-diagnosis-an-exploratory-study}

\textbf{Figure 1.} Impact of COVID-19 outbreak in terms of cases and
deaths {[}6{]}.

The healthcare systems suffered immense pressure, leading to shortages
of beds, medical supplies, and healthcare workers. The closing and
layoffs of businesses have led to millions of job losses and the
adoption of remote work policies. In the education system, schools were
closed, and online learning was adopted as the new default. The
inequalities in access to technology and Internet connectivity worsened
social differences and posed huge challenges for teachers, students, and
parents. Social isolation, fear of infection, and financial stress
contributed to increased anxiety, depression, and emotional stress,
increasing the problems related to mental health, among others
{[}4,5{]}. The urgent need for fast and efficient means of diagnosis and
effective therapies has boosted several areas of science and research.
The importance of science in the context of COVID-19 was immense,
playing a crucial role in various aspects of understanding, managing,
and fighting the pandemic. Understanding the virus, vaccine development,
implementation of public health measures, epidemiological modelling, and
monitoring the emergence of new variants (Figure 2).

\textbf{Hosted file}

\verb`image2.emf`  available at \url{https://authorea.com/users/768720/articles/833462-evaluation-the-potential-of-urinary-volatilomic-patterns-of-patients-infected-with-sars-cov-2-for-covid-19-diagnosis-an-exploratory-study}

\textbf{Figure 2.} Impact of COVID-19 outbreak in different field of
society.

Different kinds of tests for COVID-19, namely molecular, antigen, and
serological tests, have been key factors in the diagnosis and management
of the pandemic. Real-time reverse transcription-polymerase chain
reaction (RT-PCR) and rapid antigen detection tests are the most
effective and rapid strategies developed to diagnose patients with
COVID-19 and control infections in hospitals and communities
{[}11,12{]}. However, the collection of nasopharyngeal exudates is an
invasive procedure that puts healthcare workers at risk of disease
transmission owing to patients' sneezing, coughing, or gag reflexes
{[}13{]}. Other limitations of the RT-PCR test are related to the long
turnaround time and the requirement for expensive laboratory equipment
and highly trained laboratory staff {[}12,14{]}. In turn, the results
obtained by the antigen detection test may be unreliable because there
is no RNA amplification, and in the case of low viral loads, the virus
may not be detected, leading to false-negative results {[}15{]}.
Additionally, the associated costs are relatively high. Besides the
enormous capabilities of RT-PCR and other diagnosis platforms, more
sensitive and accurate detection assays of SARS-CoV-2 are needed for an
early diagnosis. In this context the development of improved analytical
tests, simultaneously highly accurate, sensitive, and ultrafast, are
extremely important, to diagnose early-stage and even asymptomatic
individuals and, therefore, increase and improve the prevention and
treatment efficiency.

Studies on secondary volatile organic metabolites (VOMs) have shown
promising results in the identification of potential biomarkers for
oncological, inflammatory, and respiratory infectious diseases
{[}16-21{]}. VOMs are a rich source of information regarding the health
status of an individual, as changes in the levels of these metabolites
may be characteristic of specific disease processes {[}16,22{]}. VOMs
can be found in various biological matrices, such as blood, urine,
saliva, exhaled air, faeces, and skin exudates {[}18,20,23-27{]}.
Urinary VOMs are considered intermediate or final products of metabolic
pathways {[}16,28{]}. Additionally, urine sampling is non-invasive and
causes no discomfort to the patients. VOMs analyses require sensitive
procedures to avoid contamination and sample loss. Solid-phase
microextraction (SPME) is a simple, highly efficient, and
easy-to-perform extraction technique that does not require a
preconcentration step before analysis {[}29,30{]}. The combination of
SPME in headspace mode (HS) with gas chromatography coupled with mass
spectrometry (GC-MS) analysis allows reliable and reproducible results
and has been widely used for the analysis of urinary VOMs
{[}16,17,20,27{]}.

In this study a non-invasive approach, based on the establishment of the
urinary volatilomic profile of COVID-19 patients using the solid-phase
microextraction technique in headspace mode, (HS-SPME), combined with
gas chromatography hyphenated with mass spectrometry (GC-MS), was
proposed as useful and novel strategy to identify potential biomarkers
to diagnose COVID-19 infection. The chromatographic data obtained were
subjected to multivariable statistical analysis to identify volatile
signatures that could discriminate between the presence of COVID-19 and
its progression.

2. MATERIALS AND METHODS

2.1. Chemicals and reagents

Sodium chloride (NaCl, 99.5\%) was obtained from Panreac AppliChem ITW
Reagents (Barcelona, Spain). Ultrapure water, produced by a Milli-Q
water purification system (Millipore, Bedford, PA, USA), was used to
prepare the solutions of hydrochloric acid (HCl, 37\%) 5 M and 3-octanol
(internal standard, 99\%) 2.5 ppm, both acquired to Sigma-Aldrich (St.
Louis, MO, USA). Helium of purity 99.9\% (He, N60, Air Liquide, Alg\selectlanguage{ngerman}és,
Portugal) was used as the GC mobile phase. Glass vials, SPME holder, and
fused silica fibre coating partially cross-linked with 50/30 \selectlanguage{greek}µ\selectlanguage{english}m
divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) were
purchased from Supelco (Merck KGaA, Darmstadt, Germany). DVB/CAR/PDMS
fibres were conditioned according to the manufacturer's guidelines.
Before the first daily analysis, the fibres were conditioned for at
least 10 min at the operating temperature of the GC injector port.

2.2. Study design and samples

The sample consisted of a set of 133 adult individuals: 42 individuals
with no infection and never been infected by SARS-CoV-2(Control Group,
CTRL), 61 individuals infected with SARS-CoV-2 (COVID Group, COVID)
admitted to the Dr Nelio Mendon\selectlanguage{ngerman}ça Hospital between 15 May and 20 June
2020 (59 \% male and 41\% female, age average=56.8±18.6Y), and 30
recovered COVID-19 patients (Recovered Group, RECOV) (58 \% male and
42\% female, age average=60.7±16.3Y), with a recovery period of more
than 3 months. Urine samples were collected at Dr Nélio Mendonça
Hospital (Funchal, Portugal). Samples from healthy subjects were
obtained from blood donors, samples from the COVID-19 group were
collected from patients diagnosed with COVID-19, and samples from the
RECOV group were obtained from patients in their recovery period one
month after COVID-19 infection. Upon collection, the samples were frozen
at -20 °C until further analysis. This study was approved by the Ethics
Committee of Dr Nélio Mendonça Hospital. All the work described was
carried out following The Code of Ethics of the World Medical
Association (Declaration of Helsinki) for experiments involving humans.
Informed consent was obtained from all the subjects recruited for this
study, their privacy was strictly preserved, and any data beyond the
SARS-CoV-2 infection were included in the study. Therefore, dimensions
such as age, diet, previous diseases, sex, and sex were not considered
in this study.

2.3. Urinary volatilome analysis and data processing

HS-SPME extraction was performed according to previously optimised
conditions for urine sampling (9). Briefly, 4 mL of urine sample,
previously adjusted to pH 1--2 with 500 \selectlanguage{greek}µ\selectlanguage{english}L of HCl (5 M), 0.8 g NaCl and
5 \selectlanguage{greek}µ\selectlanguage{english}L 3-octanol 2.5 ppm were placed in an 8 mL glass vial. The vial was
placed in a water bath set at 50.0 \selectlanguage{ngerman}± 0.1 C with stirring at 800 rpm, and
the SPME fibre was exposed to the headspace for 60 min. After
extraction, the SPME fibre was collected and inserted into the injector
port of the GC-MS instrument for 6 min at 250 °C, where the analytes
were desorbed and transferred directly to the column. Each sample was
analysed in triplicate.

The GC-MS analysis was performed in a gas chromatograph Agilent
Technologies 6890N Network GC System (Palo Alto, CA, USA) equipped with
a BP-20 fused silica column (30 m × 0·25 mm ID × 0·25 \selectlanguage{greek}µ\selectlanguage{english}m (SGE, Dortmund,
Germany)), and connected to an Agilent 5975 quadrupole inert mass
selective detector. The separation of the VOMs was carried out with a
temperature gradient of 35 \selectlanguage{ngerman}°C for 2 min, followed by an increase to 220
°C (2.5 °C min\textsuperscript{-1}), remaining at this temperature for 5
min, for a total GC run time of 81 min. The column flow rate was
maintained at 1 mL min\textsuperscript{-1}. The injector port was
operated in the splitless mode and maintained at 250 °C. For the 5975MS
system, the temperatures of the transfer line, quadrupole, and
ionisation source were 270 °C, 150 °C, and 230 °C, respectively. Data
acquisition was performed in scan mode, 30-300 m/z, and the electron
multiplier was set to the auto-tune procedure, with the electron impact
mass spectra at 70 eV and the ionisation current at 10 \selectlanguage{greek}µ\selectlanguage{english}A. The VOMs were
identified by comparing the mass spectra obtained with those available
in Agilent MS Chemstation software (Palo Alto, CA, USA), which was
equipped with a NIST05 mass spectral library with a similarity threshold
of 80\%.

2.4. Statistical analyses

The data analyses were performed using Microsoft 365\selectlanguage{ngerman}® and MetaboAnalyst
5.0 (12). The data matrix was normalised using the median, log
transformation, and mean centring. The normalised data were processed
through univariate analysis, specifically a t-test (p-values \textless{}
0·05) to identify statistically significant VOMs. Subsequently,
multivariate analysis was performed using partial least squares
discriminant analysis (PLS-DA). Important variables of the generated
PLS-DA model were identified based on the variable importance in
projection (VIP) score. The model was further evaluated using a 10-fold
cross-validation (CV) and permutation tests. Finally, potential
biomarkers were validated through receiver operating characteristic
(ROC) curves created using the Monte Carlo CV (MCCV) methodology to
evaluate the accuracy and precision of the biomarkers.

\textbf{3. RESULTS}

3.1. Characterization of the urinary volatilome

The volatile composition of 132 urine samples was analysed using
HS-SPME/GC-MS. Supplementary Figure S1 shows typical chromatographic
profiles obtained for the control, COVID-19 patient, and recovered
subject groups using HS-SPME/GC-MS methodology. Overall, a larger number
of peaks with higher intensities were observed in the chromatographic
profile of the COVID group than in the CTRL profile (Figure S1,
supplementary Material). Furthermore, the number and intensity of peaks
in the RECOV group were intermediate between those in the COVID and CTRL
groups.

A total of 101 VOMs belonging to 13 chemical families were identified.
Data regarding the VOMs detected in the analysed samples, frequency of
occurrence, and mean relative peak areas are available in Supplementary
Table S1. As shown in Figure 3, terpenes, phenolic compounds,
norisoprenoids, and ketones were the main contributors to the urinary
volatile profiles of the studied groups.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3.} Distribution of total peak area of the chemical
family in the control (CTRL, n = 42), COVID-19 (COVID, n = 61), and
recovered (RECOV, n = 30) groups. The average peak areas were normalised
areas obtained by the ratio between the peak areas of each VOM and the
respective IS peak areas. [?]VOMs indicate the sum of different VOMs
identified in each class; iVOMs indicate the number of VOMs identified
in each recruited group and class. The RSD was less than 30\%. Ter:
Terpenes; PC: Phenolic Compounds; Nor: Norisoprenoids; Ket: Ketones;
Alc: Alcohols; FC: Furanic Compounds; SC: Sulfur Compounds; ND:
Naphthalene Derivatives; CA: Carboxylic Acid; Est: Esters; BD: Benzene
Derivatives; Hc: Hydrocarbons; Ald: Aldehydes.

However, there were significant variations in the levels of these and
less represented chemical families among the groups studied, with
decreased levels of terpenes, phenolic compounds, benzene derivatives,
hydrocarbons, and aldehydes in COVID-19 patients compared to the control
subjects. In contrast, increased levels of norisoprenoids, ketones,
alcohols, furans, sulfur compounds, and naphthalene derivatives were
observed. For most chemical families in the RECOV group, the sum of the
average peak areas was like that of the control group, except for
benzene derivatives, terpenes, and phenolic compounds, whose levels were
like the COVID-19 group. It should also be highlighted that the
variations in the relative levels of the different chemical families are
broadly caused by an increase in the decrease of the same VOMs, as the
numbers for each chemical family in each group do not vary
significantly. The only exceptions are ketones, with 10, 9, and 13
ketones identified in CTRL, COVID-19 and RECOV groups, respectively.

Volatile organic metabolites can have various origins. They can be
endogenous because of bacterial activity or pH changes and can be the
product of metabolic pathways or oxidative stress. They can be
influenced by external factors including health status, diet, habits,
physical stress, and environmental exposure. For these reasons, the
human metabolome is highly complex, and it is difficult to understand
whether an increase or decrease in certain metabolites is related to a
specific disease or illness.

Therefore, it is crucial to establish a relationship between identified
VOMs and their potential endogenous origins. However, the origin of many
VOMs has not yet been clearly defined. Figure 4 shows the metabolomic
pathways responsible for the origin of endogenous VOMs.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4.} Potential origins of urinary VOMs.

3.2. Chemometric Analysis of Urine Samples\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

A data matrix of the relative peak areas of the 101 VOMs identified in
the three groups under study, the COVID-19, RECOV, and CTRL groups
(Table S1, Supplementary material), was processed using the
Metaboanalyst software package {[}31{]}. Only VOMs with a frequency of
occurrence (FO) higher than 80\% in the volatile composition of urine
were considered. To obtain a consistent distribution without redundant
values, the variables were normalised, and univariate analysis was
performed using a t-test (p \textless{} 0.05). Consequently, 17 VOMs
with insignificant contributions to the statistical analysis were
removed from the data matrix. The resulting data matrix was then
subjected to multivariate pattern recognition procedures. In Partial
Least Squares Discriminant Analysis (PLS-DA), the information present in
the VOMs fingerprint was utilised as multiple variables to visualise
group trends and clusters. This analysis revealed a clear separation
between the COVID and CTRL samples (Figure 5a). The score plot of the
top 10 variables of importance in projection (VIP \textgreater{} 1,
Figure S2, Supplementary material) was used to observe the relative
contributions of the metabolites to the variance between the COVID and
CTRL groups. Accordingly, 1,1,6-Trimethyl-dihydronaphthalene (TDN) and
2-heptanone showed a more significant contribution to the COVID groups,
D-carvone and 3-methoxy-5-(trifluoromethyl)aniline (MTA) showed a more
significant contribution to the CTRL group.

\textbf{Figure 5.} Multivariate analysis of the COVID-19 and control
group data. \textbf{a} ) Partial least-squares discriminant analysis
(PLS-DA) was applied to the obtained data. \textbf{b} ) 10-fold CV
performance of PLS-DA classification using different numbers of
components; \textbf{c} ) multivariate analysis of COVID (infected) and
RECOV (infected at the end) group data. Partial least-squares
discriminant analysis (PLS-DA); \textbf{d} ) 10-fold CV performance of
PLS-DA classification using a different number of components (*
represents the best Q2 value, the best classifier).

The robustness of the model obtained was then evaluated using a 10-fold
cross-validation performance assay to determine the goodness of fit (R2)
and the predictive ability for distinguishing between the studied groups
(Q2). As can be observed in Figure 5b, the R2 and Q2 values obtained
were close to 1, which is the highest possible robustness. A random
permutation test involving 1000 permutations was performed to assess the
statistical significance of class discrimination between the COVID and
CTRL groups, further supporting the discriminatory ability of the
statistical model obtained in this study (Supplementary Figure S2b).

The same multivariate analysis was performed to compare the data from
SARS-CoV-2 infected urine samples with those recovered from COVID-19
urine samples. In addition, PLS-DA segregated the COVID and RECOV
samples into two well-separated clusters corresponding to the infected
and recovered patients, respectively (Figure 5c). The 10-fold CV
performance and permutation test showed the good robustness of the
PLS-DA model (Figure 5d). The VIP score plot assay revealed that
\selectlanguage{greek}β-\selectlanguage{english}damascenone and \selectlanguage{greek}α-\selectlanguage{english}isophorone gave higher discrimination between the
COVID group, and nonanoic acid and \selectlanguage{greek}α-\selectlanguage{english}terpinene provided the most
significant contribution to discriminate the RECOV group (Figure S3a,
Supplementary material). Similarly, a random permutation test involving
1000 permutations was performed to assess the statistical significance
of the class discrimination between the COVID and RECOV groups, further
supporting the discriminatory ability of the statistical model (Figure
S3b, Supplementary material).

Hierarchical clustering analysis of the volatilomic data was carried out
for the two comparisons, COVID-CTRL and COVID-RECOV, through the heat
map and dendrogram (Figure 6). A heatmap was created using Spearman's
distance correlation to build a visual representation of the dataset,
focusing on the 15 most relevant metabolites to discriminate between the
two groups. The heat map provides an intuitive description of the
relationship between the samples and detected VOMs. The coloured
representation of the cells corresponds to the concentration of the
detected VOMs for each sample (dark blue, less concentrated; dark red,
more concentrated).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image13/image13}

\end{center}
\end{figure}

\textbf{Figure 6.} Hierarchical clustering analysis. Heatmap
visualisation using the top 15 metabolites with more significance by
Spearman's correlation of a) COVID (infected) and CTRL (control) samples
and b) COVID (infected) and RECOV (infected in the recovery stage)
samples. Dendrogram analysis using Spearman's distance measure and
average linkage for c) COVID-CTRL and d) COVID-RECOV.

In the comparison between COVID-19 and control samples, the analysis
revealed two well-defined clusters (Figure 6a). The urinary VOMs
2-methoxythiophene, toluene, \selectlanguage{greek}α-\selectlanguage{english}isophorone, TDN, and hemimellitene showed
a high correlation with the urine profile of COVID-19 patients.
Piperitone, \selectlanguage{greek}β-\selectlanguage{english}ionone, D-carvone, and eudalene were more closely related
to the urinary profile of the CTRL (control) group. The dendrogram
completely split the samples into two groups, matching the real groups
studied (Figure 6b). In the comparison between COVID-19 and recovered
samples, although the heat map perfectly clustered the volatilomic data,
the cluster accuracy was visually lower than that of the first analysis
(COVID-CTRL), highlighting that the COVID-19 patients' urinary profile
is closer to that of the RECOV group. Urinary VOMs such as
hemimellitene, furan, \selectlanguage{greek}β-\selectlanguage{english}damascenone, and \selectlanguage{greek}α-\selectlanguage{english}isophorone showed a higher
correlation with the COVID patients' group, instead,
2,4-dimethylbenzaldehyde, nonanoic acid, 1-methylcycloheptene, and
\selectlanguage{greek}α-\selectlanguage{english}terpinene were more related to the recovered patient's volatile
profile (Figure 6c). The dendrogram only partially divided the samples
of the two different groups (Figure 6d).

For the classification of true positives and false positives and their
predictive ability, multivariate exploratory receiver operating
characteristic (ROC) curves were created using the Monte Carlo
cross-validation (MCCV) methodology. The features importance, selected
using 2/3 of the samples, were utilized to construct classification
models, which were validated on the remaining 1/3 of the samples that
were not initially used. This process was repeated several times to
determine the performance of each model and to calculate the confidence
intervals. From these samples, the top three, five, ten, twenty, thirty,
and 61 important features were identified, and the built curves were
reported (Figures 7a and 7c). Figure 7a displays the ROC curves for
different sets of important features for the COVID-CTRL (COVID-19
patients and control subjects). The area under the curve (AUC) values
obtained, ranging from 0.988 to 1, indicated excellent discriminative
accuracy between the two groups. The plot in Figure 7c illustrates the
ROC curves for the patient comparison (COVID-19 patients and infected
subjects during the recovery period). In this case, the area under the
curve (AUC) values fell in the range of 0.937-0.987, which shows an
optimal ability to discriminate between the groups. These values were
calculated using 95\% confidence intervals to demonstrate the
reliability of the results. Figure 7b and Figure 7d illustrate the
predictive accuracy of the biomarker models as the number of features
increased. As more features were included in the models, predictive
accuracy improved. This suggests that the selected features contribute
to the differentiation between the control and COVID-19 groups, and
COVID-19 and recovered groups. The predicted class probabilities was
assessed through the performance of the classification model for
COVID-CTRL groups (Figure 7e) and COVID-RECOVERED groups (Figure 7f).
Overall, the results demonstrate the promising performance of the
biomarker models, with high accuracy in distinguishing between the two
groups.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image21/image21}

\end{center}
\end{figure}

\textbf{Figure 7.} Receiver Operating Characteristic (ROC) curves
analysis for the predictive model with a combination of metabolites
calculated from the logistic regression analysis using different
metabolites selected by the VIP (\textgreater{} 1.0) values respectively
for a) COVID-CTRL and c) COVID-RECOV. Predictive accuracy plot of
biomarker models with an increasing number of features to discriminate
between b) COVID and CTRL and d) COVID and RECOV. The most accurate
biomarker model is highlighted by the red dots. Predicted class
probabilities based on a structure of a 2 \selectlanguage{ngerman}× 2 matrix to assess the
performance of a classification model: e) COVID-CTRL groups; and f)
COVID-RECOVERED groups.

The boxplots of the most important variables (VOMS) for discriminating
between COVID and CTRL groups, and between COVID and RECOV, were plotted
(Figure 8).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image22/image22}

\end{center}
\end{figure}

\textbf{Figure 8.} Boxplots of the most important variables (VOMS) for
discriminating between (a) COVID-CTRL and (b) COVID - RECOV. MTA -
3-methoxy-5-(trifluoromethyl)aniline; TND -
1,1,6-Trimethyl-dihydronaphthalene.

Some VOMs, such as D-carvone, MTA, TDN, and \selectlanguage{greek}α-\selectlanguage{english}terpinene, are associated
with diet {[}32-35{]}, so their interpretation as potential biomarkers
of COVID-19 infection and progression is not straightforward.

3. DISCUSSION

This study focused on the analysis of the volatile composition of the
urine of patients infected with SARS-CoV-2 upon recovery. Overall, 101
different VOMs were identified in the urine of the recruited subjects,
and statistically significant differences were found between these
groups and control subjects. Accordingly, a volatile signature composed
of D-carvone, MTA, TDN, 2-heptanone, and TONEA and nonanoic acid,
\selectlanguage{greek}α-\selectlanguage{english}terpinene, \selectlanguage{greek}β-\selectlanguage{english}damascenone, \selectlanguage{greek}α-\selectlanguage{english}isophorone, and t-furan linalool were
defined for COVID-19 patients and patients recovered from the disease,
respectively. Correspondent boxplots show sharp variations in the levels
of the referred VOMs between analysed groups. The interpretation of
these variations in urine composition is hindered by the fact that they
undergo modifications due to various factors, including metabolic
processes, pH fluctuations, bacterial activity, and the degradation of
urine components. Additionally, external factors such as diet, lifestyle
habits, health conditions, physical stress, and the environment also
affect the urine composition {[}16{]}. Terpenes are often associated
with exogenous sources such as beverages, foods, and flavouring
ingredients, although they can also be produced through the mevalonic
acid pathway {[}28,35{]}. Carotenoid-rich foods are a source of volatile
norisoprenoids, which are produced through enzymatic degradation and
have been reported in other studies involving volatile urinary
fingerprinting {[}36{]}. Phenolic compounds are often found in urine as
by-products of metabolic processes, but they can also be produced
through the consumption of food, beverages, industrial chemicals, and
environmental pollutants {[}37{]}. Ketones are a key subgroup of
chemicals detected in urine, and multiple metabolic pathways are
involved in their production, including carbohydrate metabolism,
decarboxylation of oxo-acids, and lipid peroxidation {[}38,39{]}. Some
studies have suggested that a considerable portion of ketones in urine
may stem from gut bacterial activity, external sources such as
foodstuffs, beverages, flavouring ingredients, or pollution {[}31,40{]}.
To our knowledge, this is the first study to reveal changes in the
urinary volatilomic profile following SARS-CoV-2 infection and recovery
from COVID-19. Such changes define volatile signatures with the
potential to be used in noninvasive COVID-19 diagnosis and management.
In this context, the number of samples constitutes a limitation of this
study, which can be circumvented by future disease outbreaks. In this
scenario, the experimental conditions, safety protocols, and
collaboration between the research entities involved in this study can
be promptly activated, allowing the recruitment of more subjects and
relevant clinical information. Age, diet, previous clinical condition,
sex, and gender, for instance, were interferents that could not be
included in the present study.

4. CONCLUSION

A novel, fast, and sensitive analytical approach was developed and
successfully applied in diagnosis of COVID-19 infection. This
exploratory study using HS-SPME/GC-MS, unveiled significant differences
in the volatilomic patterns of COVID-19 patients and recovered patients
compared to control subjects, evidencing that SARS-CoV-2 infection
triggered metabolic changes that also affect the urinary volatile
composition of the infected patients. This constitutes a signature with
potential for COVID-19 diagnosing and monitoring of the disease
progression with potential to be successfully used in clinical
applications. Overall, the results show the feasibility of using urine
samples for the non-invasive COVID-19 diagnosis and further studies with
larger cohorts are desirable, envisaging the developing of this
complementary tool for COVID-19 diagnosis.

\textbf{AUTHOR CONTRIBUTIONS}

Conceptualisation, J.S.C.; investigation, G.R. and J.N.; sample
collection, C.P.O.; writing of the original draft preparation, G.R. and
J.N.; review and editing, C.P.O.; V.G.; J.A.M.P., R.P. and J.S.C.;
Visualization, J.A.M.P., R.P. and J.S.C.; Supervision: J. S. C., R. P.,
and J. A. M. P.; Funding Acquisition: J.S.C. All authors have read and
agreed to the published version of the manuscript. All authors confirm
that they have full access to all data in the study and accept the
responsibility to submit the manuscript for publication.

\textbf{ACKNOWLEDGEMENTS}

This work was supported by FCT-Funda\selectlanguage{ngerman}ção para a Ciência e a Tecnologia
through the CQM Base Fund-UIDB/00674/2020 and Programmatic
Fund-UIDP/00674/2020; and by ARDITI-Agência Regional para o
Desenvolvimento da Investigação Tecnologia e Inovação through the
project M1420-01-0145-FEDER-000005-Centro de Química da Madeira-CQM+
(Madeira 14-20 Program). Giulia Riccio was supported by a PhD
scholarship given by Università Cattolica del Sacro Cuore, 00168 Rome,
Italy.

The authors would like to thank all staff members at SESARAM, EPE-RAM
and Dr. Nélio Mendonça Hospital for their help in conducting this
research. We would also like to thank all the donors who participated in
this study. The authors also acknowledge financial support from the
Fundação para a Ciência e Tecnologia and Madeira 14-2020 program to the
Portuguese Mass Spectrometry Network through the PROEQUIPRAM program
M14-20 M1420-01-0145-FEDER-000008. Jorge A. M. Pereira was supported by
a research contract given by Centro de Química da Madeira and
Universidade da Madeira.

\textbf{INFORMED CONSENT STATEMENT}

An individual informed consent was obtained from all the subjects
involved in the study (Supplementary Material).

\textbf{DATA AVAILABILITY STATEMENT}

The study protocol and datasets generated and analysed during the
current study will be available with publication from the corresponding
author upon reasonable request.

\textbf{CONFLICTS OF INTEREST}

The authors declare that there are no conflicts of interest.

\textbf{ETHICS STATEMENT}

This project was reviewed and approved by Ethics Committee of the Dr
Nélio Mendonça Hospital.

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