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\begin{document}

\title{Sex differences in electrolyte imbalances caused by SARS-CoV-2: a
case-control study}



\author[1]{Arianna Pani}%
\author[2]{Elvira Inglese}%
\author[2]{Massimo Puoti}%
\author[1]{Valeria Cento}%
\author[1]{Claudia Alteri}%
\author[1]{Alessandra Romandini}%
\author[2]{Federica Di Ruscio}%
\author[1]{Michele Senatore}%
\author[2]{Mauro Moreno}%
\author[2]{Paolo Tarsia}%
\author[2]{Fabrizio Colombo}%
\author[2]{Oscar Epis}%
\author[3]{Valentina Panetta}%
\author[2]{Chiara Vismara}%
\author[2]{Andrea Bellone}%
\author[1]{Francesco Scaglione}%
\affil[1]{Università degli Studi di Milano}%
\affil[2]{Niguarda Hospital}%
\affil[3]{L’altrastatistica srl - Consultancy \& Training}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\selectlanguage{english}
\begin{abstract}
Background - Since SARS-CoV-2 spread, evidences regarding sex
differences in progression and prognosis of COVID-19 have emerged.
Besides this, studies on patients' clinical characteristics have
described electrolyte imbalances as one of the recurrent features of
COVID-19. Methods - We performed a case-control study on all patients
admitted to the emergency department (ED) from 1st March to 31st May
2020 who had undergone a blood gas analysis and a nasopharyngeal swab
test for SARS-CoV-2 by rtPCR. We defined positive patients as cases and
negatives as controls. The study was approved by the local ethics
committee Area 3 Milan. Data were automatically extracted from the
hospital laboratory SQL-based repository in anonymized form. We
considered as outcomes potassium (K+), sodium (Na+), chlorine (Cl-) and
calcium (Ca++) as continuous and as categorical variables, in their
relation with age, sex and SARS-CoV-2 infection status. Results - We
observed a higher prevalence of hypokalemia among patients positive for
SARS-CoV-2 (13.7\% vs 6\% of negative subjects). Positive patients had a
higher probability to be admitted to the ED with hypokalemia (OR 2.75,
95\% CI 1.8-4.1 p\textless{}0.0001) and women were twice as likely to be
affected than men (OR 2.43, 95\% CI 1.67-3.54 p\textless{}0.001). Odds
ratios for positive patients to manifest with an alteration in serum Na+
was (OR 1.6, 95\% CI 1.17-2.35 p\textless{}0.001) and serum chlorine (OR
1.6, 95\% CI 1.03-2.69 p\textless{}0.001). Notably, OR for positive
patients to be hypocalcemic was 7.2 (95\% IC 4.8-10.6
p\textless{}0.0001) with a low probability for women to be hypocalcemic
(OR 0.63, 95\% IC 0.4-0.8 p=0.005). Conclusions - SARS-CoV-2 infection
is associated with a higher prevalence of hypokalemia, hypocalcemia,
hypochloremia and sodium alterations. Hypokalemia is more frequent among
women and hypocalcemia among men.%
\end{abstract}%



\sloppy


Sex differences in electrolyte imbalances caused by SARS-CoV-2: a
case-control study Arianna Pani, MD*1, Elvira Inglese*2, Massimo Puoti,
MD3, Valeria Cento, MD. PhD1, Claudia Alteri, PhD1, Alessandra
Romandini\textsuperscript{1}, MD1, Federica Di Ruscio, MD2, Michele
Senatore\textsuperscript{1}, MD, Mauro Moreno, MD4, Paolo Tarsia, MD5,
Fabrizio Colombo, MD6, Oscar Massimiliano Epis, MD7, Valentina Panetta8,
Chiara Vismara\textsuperscript{2}, Andrea Bellone, MD9 and Francesco
Scaglione, MD, PhD1,2 *These two authors equally contributed 1Department
of Oncology and Hemato-Oncology, University of Milan, Milan, Italy;
\emph{E-mail: arianna.pani@unimi.it; E-mail: valeria.cento@unimi.it;
E-mail: claudia.alteri@unimi.it; E-mail: alessandra.romandini@unimi.it
Email: michele.senatore@unimi.it}2SC Clinical Chemistry and
Microbiology, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy;
\emph{E-mail: elvira.inglese@ospedaleniguarda.it; E-mail:
federica.diruscio@unimi.it; Email:chiara.vismara@ospedaleniguarda.it;
E-mail: francesco.scaglione@unimi.it}3Infectious Diseases Unit, ASST
Grande Ospedale Metropolitano Niguarda, Milan, Italy;\emph{E-mail:
massimo.puoti@ospedaleniguarda.it}4Healthcare Management Department,
ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy; \emph{E-mail:
mauro.moreno@ospedaleniguarda.it}5Pneumology Unit, ASST Grande Ospedale
Metropolitano Niguarda, Milan, Italy; \emph{E-mail:
paolo.tarsia@ospedaleniguarda.it}6Internal Medicine Department, ASST
Grande Ospedale Metropolitano Niguarda, Milan, Italy;\emph{E-mail:
fabrizio.colombo@ospedaleniguarda.it}7Rheumatology Unit, ASST Grande
Ospedale Metropolitano Niguarda, Milan, Italy; \emph{E-mail:
oscar.epis@ospedaleniguarda.it}8Biostatistics office, L'altrastatistica
srl -Consultancy \& Training, Rome, Italy 9Emergency Department, ASST
Grande Ospedale Metropolitano Niguarda, Milan, Italy;\emph{E-mail:
andrea.bellone@ospedaleniguarda.it}

\textbf{Corresponding author}

Arianna Pani

Assistant Professor of~Pharmacology

Assistant Director of the Postgraduate School of Clinical Pharmacology
and Toxicology

Department of Oncology and~Hemato-Oncology

University of Milan

Mailing address:

Department of Oncology and Onco -- Hematology, Italy

e-mail- arianna.pani@unimi.it

Phone: ++39-0250316950

Fax: ++39-0250317050

ABSTRACT\textbf{Background} - Since SARS-CoV-2 spread, evidence
regarding sex differences in progression and prognosis of COVID-19 have
emerged. Besides this, studies on patients' clinical characteristics
have described electrolyte imbalances as one of the recurrent features
of COVID-19.\textbf{Methods -} We performed a case-control study on all
patients admitted to the emergency department (ED) from 1st March to
31st May 2020 who had undergone a blood gas analysis and a
nasopharyngeal swab test for SARS-CoV-2 by rtPCR. We defined positive
patients as cases and negatives as controls. The study was approved by
the local ethics committee Area 3 Milan. Data were automatically
extracted from the hospital laboratory SQL-based repository in
anonymized form. We considered as outcomes potassium (K+), sodium (Na+),
chlorine (Cl-) and calcium (Ca++) as continuous and as categorical
variables, in their relation with age, sex and SARS-CoV-2 infection
status.\textbf{Results -} We observed a higher prevalence of hypokalemia
among patients positive for SARS-CoV-2 (13.7\% vs 6\% of negative
subjects). Positive patients had a higher probability to be admitted to
the ED with hypokalemia (OR 2.75, 95\% CI 1.8-4.1 p\textless{}0.0001)
and women were twice as likely to be affected than men (OR 2.43, 95\% CI
1.67-3.54 p\textless{}0.001). Odds ratios for positive patients to
manifest with an alteration in serum Na+ was (OR 1.6, 95\% CI 1.17-2.35
p\textless{}0.001) and serum chlorine (OR 1.6, 95\% CI 1.03-2.69
p\textless{}0.001). Notably, OR for positive patients to be hypocalcemic
was 7.2 (95\% IC 4.8-10.6 p\textless{}0.0001) with a low probability for
women to be hypocalcemic (OR 0.63, 95\% IC 0.4-0.8
p=0.005).\textbf{Conclusions -} SARS-CoV-2 infection is associated with
a higher prevalence of hypokalemia, hypocal- cemia, hypochloremia and
sodium alterations. Hypokalemia is more frequent among women and
hypocal- cemia among men.\textbf{What is already known about this
topic?}Since SARS-CoV-2 spread, evidence regarding sex differences in
progression and prognosis of COVID-19 have emerged. Besides, studies on
patients' clinical characteristics have described electrolyte imbalances
as one of the recurrent features of COVID-19.\textbf{What does this
article add?}We observed a higher prevalence of hypokalemia in patients
positive for SARS-CoV-2, and women were twice as likely to be affected
than men. Positive patients also had a higher prevalence of
hypocalcemia, hypochloremia and sodium alteration. Hypokalemia appears
to be more frequent among women and hypocalcemia among men. INTRODUCTION
Since December 2019 the world has been struggling to fight the novel
coronavirus SARS-CoV-2. As soon as the virus spread in China,
researchers noticed the higher predisposition of men to contract
COVID-19 {[}1, 2{]}. Evidences emerged subsequently revealed also an
association between male sex and a more severe disease and death {[}3,
4{]}. Some authors have attributed the reasons for these differences to
sex differences in immune response {[}5{]}, to differences in the
prevalence of smoking subjects among men and women {[}6{]} and to
genetics {[}7{]}. Few studies have investigated the impact of sex
differences in clinical manifestations of the disease other than disease
severity and mortality. Early studies on COVID-19 have highlighted the
association between the disease and sodium (Na+), potassium (K+),
calcium (Ca++) and chloride (Cl-) abnormalities {[}1, 8, 9{]}. Chen et
al. {[}10{]} described a high prevalence of hypokalemic subjects among
patients hospitalized with COVID-19. Furthermore, the severity of the
disease was associated with a higher degree of hypokalemia. Authors have
also shown, in a small sample of patients, that hypokalemic subjects had
a higher mean urinary K+ output than non-hypokalemic patients,
suggesting a possible implication for increased K+ urine loss. This
mechanism could be explained by the disruption caused by the binding of
SARS-CoV-2 to angiotensin-converting enzyme 2 (ACE2) with a possible
impact on the renin-angiotensin system (RAS). Despite the aforementioned
evidences, little is still known regarding electrolyte disturbances in
COVID-19 and in SARS-CoV-2 infection. The aim of this study was to
examine the prevalence of electrolyte imbalances among patients with
SARS- CoV-2 infection in a large cohort and its distribution according
to age and sex. Sex differences in electrolyte imbalances caused by
SARS-CoV2: a case-control study METHODS The study protocol was approved
by the Ethics Committee Area 3 Milano (prot. 92--15032020). Signed
informed content was obtained for each participant. This study was
conducted in accordance with the principles of the 1964 Declaration of
Helsinki. Strengthening the Reporting of Observational \emph{Studies} in
Epidemiology (STROBE) reporting guidelines.\textbf{DATA SOURCES}This
case-control study has been performed at the ASST GOM Niguarda Hospital
in Italy. At our hospital, we store all analytical results of the tests
performed in the laboratory in an SQL-based repository. With the purpose
to conduct this study, a query was created to extract data with no
personal information other than gender and date of birth. This query
extracted laboratory results based on the date of execution, the
requesting department and the type of service performed. The fields
extracted were: patient's ID (a numeric string that uniquely identifies
the patient without personal data), sex, birth date day of lab tests
execution (with a truncation for the extracted data referred to the
period 1st March - 31st May), tests ID, tests results, hospital ward. In
this way we were able to collect the biochemical and microbiological
profile of patients at admittance to the emergency department (ED) and
categorize patients based on their positivity (or negativity) to
SARS-CoV-2, sex and age.\textbf{STUDY PATIENTS AND COVARIATES}We
considered a cohort of patients admitted to the ED from
1\textsuperscript{st} March to 31\textsuperscript{st} May 2020 and
screened for SARS-CoV-2 infection. Inclusion criteria were: age over 18
years and the performance, during the per- manence in the Emergency
Department, of at least one blood gas analysis (both arterial and
venous) performed using POC Siemens RAPIDPoint 500 Blood Gas System.
Compared to central Laboratory analyzers, results obtained from POC
Siemens RAPIDPoint 500 blood gas system are stackable for all tested
parameters taken into consideration in this study. These results are
warranted for our own procedures and devices {[}11{]}. We collected data
regarding electrolyte profile, pH, blood glucose, white blood cell
counts, lymphocyte counts, neutrophils, creatinine. We defined cases and
controls according to the positivity or negativity to the nasopharyngeal
swab test for SARS-CoV-2 by rtPCR (GeneFinder TMCOVID-19 Plus RealAmp
Kit, ELITech; Allplex TM2019- nCoV Assay, Seegene) performed according
to the World Health Organization (WHO) guidance. Positive patients were
defined as cases and negatives as controls. Patients were then
categorized in three classes according to age (under 65 years, between
65 and 75 years and over 75 years) and according to sex and age range
combined into six groups: i) Females aged below 65 years; ii) Females
aged between 65 and 75 years; iii) Females aged over 75 years; iv) Males
aged below 65 years; v) Males aged between 65 and 75 years; vi) Males
aged over 75 years.\textbf{OUTCOMES}As outcomes, we considered potassium
(K+), sodium (Na+), chloride (Cl-) and calcium (Ca++) both as continuous
and as categorical variables, in their relationship with age, sex and
SARS-CoV-2 infection status. We defined imbalances of K+, Na+, Cl- and
Ca++, according to ranges presented in \textbf{Table 1}. Leveraging the
fact that a blood gas analysis was performed on the majority of patients
accepted to the ED, we decided to consider the electrolyte values
deriving from this analysis, to avoid potential bias due to the
comparison of different methods. We analyzed potential effect modifiers
such as renal disease, by calculating eGFR using the MDRD formula
{[}12{]}. To get closer to precision medicine, it would be preferable to
have all the information available in electronic form and the Human
Phenotype Ontologies (HPO) is a useful tool for this purpose, making
information available at different levels of granularity. The power of
HPO has been demonstrated to enrich clinical data, including infectious
diseases {[}13{]} and for this, to describe outcomes we use the terms of
the ''Human Phenotype Ontology'' (HPO) to better promote the link
between SARS-CoV2 to their phenotypes in support of infectious disease
research. {[}Table 1 about here.{]}\textbf{STATISTICAL ANALYSIS}Patients
characteristics were summarized with mean and standard deviation for
continuous variables and with number and percentages for categorical.
Differences between the two cohorts have been tested with t-test and
chi-square test, or Fisher exact test, respectively. Assumption of
normality distribution was evaluated by Shapiro-Wilk test. A three-way
ANOVA test was performed to evaluate differences among sex, class of age
and presence of SARS-CoV-2 infection on potassium values. Tukey's
multiple comparisons test was performed for post-hoc comparison. Several
logistic regression analyses were performed to evaluate the relationship
between SARS-CoV-2 infection and electrolyte imbalances separately. Odds
ratios (ORs) and confidence intervals 95\% (CI 95\%) were reported.
Multivariable logistic regression considering SARS-CoV-2 positivity, age
group and sex on hypokalemia, hypochloremia, hypocalcemia and abnormal
natremia were performed. Odds ratios and CI 95\% were reported. P values
\textless{}0.05 were considered statistically significant. Sex
differences in electrolyte imbalances caused by SARS-CoV2: a
case-control study RESULTS For the retrospective cohort study of
consecutive patients admitted to the ED from 1\textsuperscript{st} March
1 to 31\textsuperscript{st} May 2020 who had undergone blood gas
analysis, 1347 patients were included. 710 (53\%) patients resulted
positive to the nasopharyngeal swabs rtPCR test for SARS-CoV-2 and 619
resulted negative. Patients baseline characteristics between the two
groups are summarized in \textbf{table 2}. The case cohort was
characterized by a lower prevalence of female subjects (39\%, 274/710),
lower mean pH and lactate values (7.23 \selectlanguage{ngerman}\emph{±} 0.37 and 1.72 \emph{±}
1.45 mEq/l) and an electrolyte panel significantly different from
controls for K+ and Ca (3.80 \emph{±} 0.49 vs 4.11 \emph{±} 0.62 mEq/l
and 1.20 \emph{±} 0.10 vs 1.12 \emph{±} 0.07 mEq/l respectively). Both
cohorts were homogeneous for age, eGFR and renal disease prevalence. The
prevalence of patients aged between 65 and 75 years was significantly
higher in the SARS-CoV-2 positive cohort (20.6\%, 145), while there was
a lower prevalence of patients aged under 75 years (30.2\%, 212).
{[}Table 2 about here.{]}\textbf{POTASSIUM}We analyzed the distribution
of serum K+ values as a continuous parameter with a three-way ANOVA on
the basis of SARS-CoV-2 positivity or negativity, three classes of ages
(years \textgreater{}65; 65 to 75; \textgreater{}75) and sex. Results
showed a clusterization (\textbf{Figure 1}) of serum K+ values according
to those parameters, and in particular to SARS-CoV-2 status. We then
divided cases and controls cohorts according to their kalemic state
(\textbf{Table 3}) and observed a higher prevalence of patients with
hypokalemia in the group with SARS-CoV-2 infection. {[}Table 3 about
here.{]} Further analyses of the population according to sex and class
of age (\textbf{Table 4}) allowed us to observe a higher prevalence of
hypokalemic patients among woman positive for SARS-CoV2. The prevalence
was similar for all classes of age. The difference between positive and
negative patients was statistically significant for men below 65 years
(p=0.013), women below 65 years (p\textless{}0.001). In women over 75
years it was possible to note a difference which did not reach
statistical significance (p=0.069).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. \textbf{Heatmap of Post hoc Tukey's HDS test results.} A
Tukey's multiple comparisons test was performed to compare potassium
between all groups. Euclidean distance and average linkage was used for
clustering process. The test results were pre-processed using k-means
before applying clustering and gener- ating the heatmap and the
dendogram. All test results were transformed for the analysis in Z-score
intensity signal. Z score is constructed by taking the ratio of weighted
mean difference and combined standard de- viation according to Box and
Tiao (1992). The application of a classical method of data
normalization, z-score transformation, provides a way of standardizing
data across a wide range of experiments. {[}Table 4 about here.{]} At
binary logistic regression, the OR for patients with SARS-CoV-2 to
present with hypokalemia at admission to the ED is twice than the one
for non-infected patients (\textbf{Table 5}). At multivariable logistic
regression, adjusting for age and sex, the probability to be admitted to
the ED with hypokalemia was nearly three times higher for patients with
SARS-CoV-2 infection (OR 2.75, 95\% CI 1.8-4.1 p\textless{}0.001)
(\textbf{Figure 2}). For women, the probability to be admitted to the ED
with hypokalemia, adjusting for SARS-CoV-2 positivity and age, is more
than twice than the one for men (OR 2.43, 95\% CI 1.67-3.54
p\textless{}0.001) (\textbf{Figure 2}). {[}Table 5 about here.{]} Sex
differences in electrolyte imbalances caused by SARS-CoV2: a
case-control study\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{OTHER ELECTROLYTES}Regarding natremia, we observed a higher
prevalence of both patients with hyperrnatremia and hyponatremia among
positive subjects (table a in Supplementary Materials). Women tended to
be more hypernatremic while men were more hyponatremic. However, these
differences among groups were not statistically significant (table b in
Supplementary Materials). At logistic regression, patients with
SARS-CoV-2 infection had a slightly higher probability to presents to
the ER with an alteration in natremia (OR 1.6, 95\% CI 1.17-2.35
p\textless{}0.001). We detected also a mild tendency of infected
patients to present with hypochloremia (table c Supple- mentary
Materials) with no differences for sex and age groups (table d
Supplementary Material). Lastly, nearly 30\% of patients with SARS-CoV-2
infection presented to the ED with hypocalcemia versus 5.3\% of negative
subjects and only 0.4\% were hypercalcemic among cases, compared to
7.4\% of controls (table e Supplementary Materials). Differences among
sex and age groups were visible with a lower prevalence of hypocalcemia
among women compared to men, independently from infection status, and
very significant differences between cases and controls for each of the
six subgroups based on age group and sex. Prevalence of hypocalcemia
among male patients with SARS-CoV-2 infection ranged from 28\% to 34\%,
compared to 16\% to 32\% of females (table f in Supplementary
Materials). At binary logistic regression the OR for being hypocalcemic
if positive to SARS-CoV-2 was 7.2 (95\% IC 4.8-10.6 p\textless{}0.0001)
and at multivariable logistic regression women had a lower probability
to have hypocalcemia (OR 0.63, 95\% IC 0.4-0.8 p=0.005) (\textbf{Figure
2}) DISCUSSION In this cohort study, we evaluated the association
between electrolyte imbalances and SARS- CoV-2 infection, deepening its
distribution according to age and sex. To our knowledge, this is the
first study conducted on a very large sample describing electrolytes
alter- ations in patients infected with SARS-CoV-2 at admission to the
ED and compared with a similar cohort of non-infected patients.
Differently from the results reported by Chen et al. {[}10{]}, we
observed a lower prevalence of hypokalemic patients and a lower severity
of hypokalemia. This is because we considered not only patients
hospitalized for COVID-19, but a population of subjects positive to
SARS-CoV-2 with a highly heterogeneous grade of the disease. Moreover,
the novelty of this study lies in the description of the important and
significant differences observed among female and male patients. Among
women, the difference in the prevalence of hypokalemic patients between
cases and controls tended to decrease with increasing age. This could be
an expression of a different hormonal influence between pre- and
postmenopausal women. This is supported by the influence of female sex
hormones, mainly estrogens, on ACE2 which are able to impact the
ACE/ACE2 activity ratio and the expression of angiotensin 1-7 {[}14,
15{]}, also according to age {[}16{]}. Regarding calcium we observed a
reverse trend, with higher prevalence of hypocalcemia among male
patients; a trend is also observed in the non-positive population, but
exacerbated by SARS-CoV-2 infection. As reported elsewhere {[}17{]}, sex
differences in hypocalcemia could be correlated to the more frequent use
of vitamin D and/or calcium among women for osteoporosis prevention
{[}18{]}. From a pathophysiological perspective SARS-CoV-2 is able to
penetrate human cells by binding to ACE2 through a receptor binding
domain present on the spike glycoprotein {[}19{]}. ACE2 is a receptor
implied in the RAS system, in particular in the modulation of renal
sodium transporters {[}20, 21{]}, especially the Na+/K+ ATPase on the
baso-lateral membrane of epithelial tubular cells {[}22, 23{]}. As
explained in \textbf{Figure 3}, ACE2 cleaves angiotensin II in
angiotensin 1-7, which is able to exert a natriuretic function by acting
on the AT1 receptor. Furthemore, the action of ACE2 is also important to
the vasodilation triggered by Ca++ release from the endoplasmic
reticulum induced by angiotensin 1-7. The disruption of this pathway
caused by the binding of SARS-CoV-2 on ACE2 could underlie the
electrolytes alterations observed in this study and others as well {[}1,
8-10{]}. Indeed, recent studies reported evidences of kidney damage
manifesting as tubular dysfunction and necrosis, endothelial alterations
and deposition of complement complex on tubules {[}24, 25{]}.
Electrolyte imbalances, especially of potassium and calcium, associated
with SARS-CoV-2 infection could also help explain the numerous reports
of QT prolongations, arrhythmias and cardiac deaths Sex differences in
electrolyte imbalances caused by SARS-CoV2: a case-control study\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3. Actions of ACE2 on potassium, sodium and calcium imbalance and
impact of SARS-CoV2 infection. Created with BioRender.com reported in
patients with COVID-19 {[}26-28{]} and ascribed to treatments with
macrolides and hydroxychloroquine {[}29, 30{]}. This highlights the
importance to continuosly monitor and manage hypokalemia and
electrolytes imbalances {[}31, 32{]} The limitations of this study lie
in its retrospective nature and in the non-correlation of lab values
with disease severity and clinical outcomes. Besides this, we believe
that our findings are important to better understand the pathophysiology
of COVID-19 disease, and especially sex differences in the manifestation
of the disease which certainly impact on better prognosis for women.
Clinical implications of this study are relevant from different
perspectives. Monitoring kalemia, calcemia and electrolytes in general,
is important in patients with COVID-19 and immediate supplementation
should be suggested. The role of electrolytes imbalances should be
further investigated also to understand the association with
cardiovascular events reported in other studies.

AUTHOR CONTRIBUTIONS

A.P., E.I., F.S. contributed to study conception and design, data
acquisition, analysis and interpretation, and paper drafting. V.C.,
C.A., A.B., contributed to data acquisition and analysis. V.A.F.
contributed to study design and data acquisition. M.P., C.V., F.D.R.,
O.E., F.C., P.T., gave critical revision for intellectual content and
contributed with patient enrollment and management. V.P., A.R.,
contributed to data analysis and interpretation, paper drafting, and
gave critical revision. M.M., contributed to data interpretation, paper
drafting, and gave critical revision for intellectual content. F.S. gave
critical revision for intellectual content.

TRANSPARENCY DECLARATION

The manuscript is an honest, accurate, and transparent account of the
study being reported; that no important aspects of the study have been
omitted; and that any discrepancies from the study as planned (and, if
relevant, registered) have been explained.

FUNDINGS

No fundings

Sex differences in electrolyte imbalances caused by SARS-CoV2: a
case-control study LIST OF TABLES

\begin{enumerate}
\tightlist
\item
  Normality and abnormality ranges considered for electrolytes 12
\item
  Characteristics of study population 13
\item
  Study population distribution according to kalemia 14
\item
  Comparison between kalemia and SARS-CoV-2 infection according to age
  and sex\ldots{}\ldots{}\ldots{}.. 18
\item
  Univariable logistic regression of probability to develop hypokalemia
  for patients with SARS-CoV-2 infection 15
\end{enumerate}

Table 1. Normality and abnormality ranges considered for electrolytes
and relative HPO term\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
& Imbalance type & (HPO term) & Range\tabularnewline
\midrule
\endhead
Potassium (K+) & Hypokalemia & HP:0002900 & \textless{} 3.5
mmol/l\tabularnewline
& Normokalemia & NOT HP:0011042 & 3.5 - 5.2 mmol/l\tabularnewline
& Hyperkalemia & HP:0002153 & \textgreater{} 5.2 mmol/l\tabularnewline
Sodium (Na+) & Hyponatremia & HP:0002902 & \textless{} 132
mmol/l\tabularnewline
& Normonatremia & NOT HP:0010931 & 132-143 mmol/l\tabularnewline
& Hypernatremia & HP:0003228 & \textgreater{} 143 mmol/l\tabularnewline
Chloride (Cl-) & Hypochloremia & HP:0003113 & \textless{} 96
mmol/l\tabularnewline
& Normochloremia & NOT HP:0011422 & 96-107 mmol/l\tabularnewline
& Hyperchloremia & HP:0011423 & \textgreater{} 107 mmol/l\tabularnewline
Calcium (Ca++) & Hypocalcemia & HP:0002901 & \textless{} 1.1
mmol/l\tabularnewline
& Normocalcemia & NOT HP:0004363 & 1.1 - 1.4 mmol/l\tabularnewline
& Hypercalcemia & HP:0003072 & \textgreater{} 1.4 mmol/l\tabularnewline
\bottomrule
\end{longtable}

Sex differences in electrolyte imbalances caused by SARS-CoV2: a
case-control study Table 2. Characteristics of study population\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
SARS-CoV2 - (619)\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
SARS-CoV2\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
+\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
P value\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
(710)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Female (\%, n)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
48.2 (297)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
39.0 (274)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Age (mean, sd)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
61.9 (21.15)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
63.7 (16.2)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textless{}0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Class of age (\%, n)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textless{}65 years\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
50.0 (308)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
50.3 (353)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.002\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
65-75\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
13.8 (85)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
20.6 (145)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textgreater{}75\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
36.7 (226)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
30.2 (212)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
pH (mean, sd)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
7.31 (0.19)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
7.23 (0.37)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textless{}0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lactate, mEq/l (mean, sd)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.17 (1.58)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.72 (1.45)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Leucocytes\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
10.30 (9.38)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
8.08 (6.01)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.627\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Neutrophils\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
7.10 (4.00)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5.98 (4.02)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.431\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Haemoglobin\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
14.03 (2.23)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
13.93 (1.92)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.010\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Calcium, mEq/l (mean, sd)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.20 (0.10)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.12 (0.07)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textless{}0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Chloride, mEq/l (mean, sd)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
102.86 (4.28)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
101.62 (4.17)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.321\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Potassium, mEq (mean, sd)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
4.11 (0.62)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
3.80 (0.49)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textless{}0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Sodium, mEq/l (mean, sd)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
137.43 (4.36)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
135.84 (4.50)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.319\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Blood glucose, mg/dl (mean,\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
131.78 (60.60)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
143.06 (63.29)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.275\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
sd)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Creatinine, mg/dl (mean, sd)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.11 (0.63)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.15 (0.96)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.116\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
eGFR, mL/min/1.73 mq\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
74.06 (44.10)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
74.36 (44.41)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.907\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
(mean, sd)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Renal Disease (\%, n)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
eGFR\textless{}90\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
24.7 (138)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
23.7 (156)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.227\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
60\textless{}eGFR\textless{}89\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
4.5 (243)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
49.0 (323)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
30\textless{}eGFR\textless{}59\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
25.8 (144)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
20.8 (137)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
15\textless{}eGFR\textless{}29\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
4.7 (26)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
4.7 (31)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
eGFR\textless{}15\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.4 (8)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.8 (12)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Table 3. Study population distribution according to kalemia\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
& SARS-CoV2 - (619) & \vtop{\hbox{\strut SARS-CoV2
+}\hbox{\strut (710)}} & Total (1329)\tabularnewline
\midrule
\endhead
Hyperkalemia (n, \%) & 30 (4.8) & 10 (1.4) & 40 (3.0)\tabularnewline
Hypokalemia (n, \%) & 37 (6.0) & 98 (13.7) & 135 (10.2)\tabularnewline
Normokalemia (n, \%) & 552 (89.2) & 602 (84.8) & 1154
(86.8)\tabularnewline
\bottomrule
\end{longtable}

Table 4. Comparison between kalemia and SARS-CoV2 infection according to
age and sex.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllllllllllllll@{}}
\toprule
& & Hyperkalemia & Hyperkalemia & Hyperkalemia & Hyperkalemia &
Hyperkalemia & Hyperkalemia & Hyperkalemia & Hypokalemia & Hypokalemia &
Hypokalemia & Hypokalemia & Hypokalemia & Not abnormal kalemia & Not
abnormal kalemia & Not abnormal kalemia & Not abnormal kalemia & Not
abnormal kalemia & Not abnormal kalemia & Not abnormal kalemia & P
Value\tabularnewline
\midrule
\endhead
& & SARS-CoV2 - & SARS-CoV2 - & SARS-CoV2 - & SARS-CoV2 - & SARS-Cov2+ &
SARS-Cov2+ & SARS-Cov2+ & SARS-CoV2 - & SARS-CoV2 - & SARS-CoV2 - &
SARS-Cov2+ & SARS-Cov2+ & SARS-CoV2 - & SARS-CoV2 - & SARS-CoV2 - &
SARS-Cov2+ & SARS-Cov2+ & SARS-Cov2+ & SARS-Cov2+ &\tabularnewline
& & N & N & \% & \% & N & N & \% & N & \% & N & N & \% & N & \% & \% &
\% & N & N & \% &\tabularnewline
\textless{}65 & M & 2 & 1.40 & 1.40 & 4 & 4 & 1.80 & 1.80 & 2 & 1.40 &
18 & 18 & 8.00 & 140, & 140, & 97.20 & 202 & 202 & 90.20 & 90.20 &
0.013\tabularnewline
65\textless{}x\textless{}75 & M & 1 & 1.70 & 1.70 & 1 & 1 & 1.20 & 1.20
& 5 & 8.60 & 7 & 7 & 8.10 & 52 & 52 & 89.70 & 78 & 78 & 90.70 & 90.70 &
1.00\tabularnewline
\textgreater{}75 & M & 2 & 1.70 & 1.70 & 2 & 2 & 1.70 & 1.70 & 8 & 6.80
& 12 & 12 & 10.30 & 107 & 107 & 91.50 & 103 & 103 & 88.80 & 88.80 &
0.713\tabularnewline
\textless{}65 & F & 19 & 11.80 & 11.80 & 1 & 1 & 0.80 & 0.80 & 8 & 5.00
& 28 & 28 & 21.90 & 134 & 134 & 83.20 & 99 & 99 & 77.30 & 77.30 &
\textless{}0.001\tabularnewline
65\textless{}x\textless{}75 & F & 1 & 3.70 & 3.70 & 0 & 0 & 0.00 & 0.00
& 2 & 7.40 & 12 & 12 & 21.80 & 24 & 24 & 88.90 & 43 & 43 & 78.20 & 78.20
& 0.108\tabularnewline
\textgreater{}75 & F & 5 & 4.60 & 4.60 & 1 & 1 & 1.10 & 1.10 & 12 &
11.00 & 19 & 19 & 20.90 & 92 & 92 & 84.40 & 71 & 71 & 78.00 & 78.00 &
0.069\tabularnewline
\bottomrule
\end{longtable}

Sex differences in electrolyte imbalances caused by SARS-CoV2: a
case-control study Table 5. Univariable logistic regression of
probability to develop hypokalemia for patients with SARS- CoV-2
infection Non Hypokalemia Hypokalemia OR CI 95\% p value\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
SARS-CoV-2 & \vtop{\hbox{\strut n}\hbox{\strut 612}} & \% 86.2 & n 98 &
\% 13.0 & 2.52 & 1.7-3.7 & \textless{}0.001\tabularnewline
\midrule
\endhead
positive & & & & & & &\tabularnewline
SARS-CoV-2 & 582 & 94.0 & 37 & 6.0 & 1 & &\tabularnewline
negative & & & & & & &\tabularnewline
\bottomrule
\end{longtable}

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6. Paleiron N, Mayet A, Marbac V, Perisse A, Barazzutti H, Brocq FX,
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