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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Endocrinol.</journal-id>
<journal-title>Frontiers in Endocrinology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Endocrinol.</abbrev-journal-title>
<issn pub-type="epub">1664-2392</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
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<article-meta>
<article-id pub-id-type="doi">10.3389/fendo.2024.1342204</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Endocrinology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>CT semi-quantitative score used as risk factor for hyponatremia in patients with COVID-19: a cross-sectional study</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Wu</surname>
<given-names>Baofeng</given-names>
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<sup>1</sup>
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<xref ref-type="aff" rid="aff2">
<sup>2</sup>
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<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
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<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Li</surname>
<given-names>Ru</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
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<sup>&#x2020;</sup>
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<contrib contrib-type="author">
<name>
<surname>Hao</surname>
<given-names>Jinxuan</given-names>
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<sup>1</sup>
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<sup>2</sup>
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<contrib contrib-type="author">
<name>
<surname>Qi</surname>
<given-names>Yijie</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
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<contrib contrib-type="author">
<name>
<surname>Liu</surname>
<given-names>Botao</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
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<contrib contrib-type="author">
<name>
<surname>Wei</surname>
<given-names>Hongxia</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
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<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Zhe</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
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<xref ref-type="aff" rid="aff2">
<sup>2</sup>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Zhang</surname>
<given-names>Yi</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Liu</surname>
<given-names>Yunfeng</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
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<aff id="aff1">
<sup>1</sup>
<institution>Department of Endocrinology, First Hospital of Shanxi Medical University</institution>, <addr-line>Taiyuan</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>First Clinical Medical College, Shanxi Medical University</institution>, <addr-line>Taiyuan</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Department of Medical Imaging, Shanxi Medical University</institution>, <addr-line>Taiyuan</addr-line>, <country>China</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>Department of Pharmacology, Shanxi Medical University</institution>, <addr-line>Taiyuan</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Kamyar Asadipooya, University of Kentucky, United States</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Rita Indirli, University of Milan, Italy</p>
<p>Zhe Zhao, Max Planck Florida Institute for Neuroscience (MPFI), United States</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Yunfeng Liu, <email xlink:href="mailto:nectarliu@163.com">nectarliu@163.com</email>; Yi Zhang, <email xlink:href="mailto:yizhang313@163.com">yizhang313@163.com</email>
</p>
</fn>
<fn fn-type="equal" id="fn003">
<p>&#x2020;These authors have contributed equally to this work</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>14</day>
<month>06</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>15</volume>
<elocation-id>1342204</elocation-id>
<history>
<date date-type="received">
<day>21</day>
<month>11</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>03</day>
<month>06</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2024 Wu, Li, Hao, Qi, Liu, Wei, Li, Zhang and Liu</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Wu, Li, Hao, Qi, Liu, Wei, Li, Zhang and Liu</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Purpose</title>
<p>Chest computed tomography (CT) is used to determine the severity of COVID-19 pneumonia, and pneumonia is associated with hyponatremia. This study aims to explore the predictive value of the semi-quantitative CT visual score for hyponatremia in patients with COVID-19 to provide a reference for clinical practice.</p>
</sec>
<sec>
<title>Methods</title>
<p>In this cross-sectional study, 343 patients with RT-PCR confirmed COVID-19, all patients underwent CT, and the severity of lung lesions was scored by radiologists using the semi-quantitative CT visual score. The risk factors of hyponatremia in COVID-19 patients were analyzed and combined with laboratory tests. The thyroid function changes caused by SARS-CoV-2 infection and their interaction with hyponatremia were also analyzed.</p>
</sec>
<sec>
<title>Results</title>
<p>In patients with SARS-CoV-2 infection, the total severity score (TSS) of hyponatremia was higher [M(range), 3.5(2.5&#x2013;5.5) vs 3.0(2.0&#x2013;4.5) scores, <italic>P</italic>=0.001], implying that patients with hyponatremia had more severe lung lesions. The risk factors of hyponatremia in the multivariate regression model included age, vomiting, neutrophils, platelet, and total severity score. SARS-CoV-2 infection impacted thyroid function, and patients with hyponatremia showed a lower free triiodothyronine (3.1 &#xb1; 0.9 vs 3.7 &#xb1; 0.9, <italic>P</italic>=0.001) and thyroid stimulating hormone level [1.4(0.8&#x2013;2.4) vs 2.2(1.2&#x2013;3.4), <italic>P</italic>=0.038].</p>
</sec>
<sec>
<title>Conclusion</title>
<p>Semi-quantitative CT score can be used as a risk factor for hyponatremia in patients with COVID-19. There is a weak positive correlation between serum sodium and free triiodothyronine in patients with SARS-CoV-2 infection.</p>
</sec>
</abstract>
<kwd-group>
<kwd>COVID-19</kwd>
<kwd>SARS-CoV-2</kwd>
<kwd>hyponatremia</kwd>
<kwd>computed tomography</kwd>
<kwd>free triiodothyronine</kwd>
<kwd>pneumonia</kwd>
</kwd-group>
<counts>
<fig-count count="2"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="48"/>
<page-count count="10"/>
<word-count count="5704"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Endocrinology of Aging</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Hyponatremia is a common electrolyte disorder in hospitalized patients, often associated with poor prognosis (<xref ref-type="bibr" rid="B1">1</xref>). Severe hyponatremia may cause complications, such as cerebral edema, seizures, and coma. Patients with community-acquired pneumonia (CAP) are more likely to have hyponatremia (Na<sup>+</sup>&lt;135mmol/L), and hyponatremia is associated with more extended hospital stays, increased hospital costs, and increased mortality (<xref ref-type="bibr" rid="B2">2</xref>). The link between COVID-19 and hyponatremia is well known, and multiple studies have described the prevalence of hyponatremia in COVID-19 patients ranging from 20% to 35% (<xref ref-type="bibr" rid="B3">3</xref>). COVID-19 causes hyponatremia in patients likely to have the following several aspects: the first is due to the SARS-CoV-2 infection increases interleukin 6 (IL-6) (<xref ref-type="bibr" rid="B4">4</xref>), and IL-6 can cross the blood-brain barrier and directly stimulate the supraoptic and paraventricular nuclei cause the syndrome of inappropriate antidiuresis (SIAD) (<xref ref-type="bibr" rid="B5">5</xref>); Secondly, SARS-CoV-2 enters host cells through the angiotensin converting enzyme 2(ACE2), and its binding to ACE2 will down-regulate the activity of ACE2, causing an imbalance between ACE and ACE2, destroying the renin-angiotensin-aldosterone system (RAAS), and leading to the accumulation of angiotensin II (<xref ref-type="bibr" rid="B6">6</xref>). Animal studies have found that local application of various components of RAS to the paraventricular nucleus and supraventricular nucleus of the hypothalamus can trigger the release of hypothalamus antidiuretic hormone (ADH), which may also be the cause of hyponatremia in COVID-19 patients (<xref ref-type="bibr" rid="B7">7</xref>); Finally, electrolyte disturbances can also be caused by inappropriate use of diuretics and hypotonic fluids in patients with excessive fluid load for treatment.</p>
<p>Most patients with SARS-CoV-2 infection present with pneumonia, and the most common symptoms include fever, cough, dyspnea, and sore throat. Chest CT is an essential and helpful technique for diagnosing and evaluating lung diseases, including pneumonia. CT can detect the signs of pulmonary involvement of COVID-19 and can be used for highly sensitive diagnosis earlier than the reverse transcription-polymerase chain reaction (RT-PCR) test results, which is helpful to quickly and accurately determine the severity of the disease to carry out reasonable management and treatment of patients (<xref ref-type="bibr" rid="B8">8</xref>, <xref ref-type="bibr" rid="B9">9</xref>). Many chest CT scoring systems have been developed to assess the severity of lung involvement, and the TSS is widely used (<xref ref-type="bibr" rid="B10">10</xref>, <xref ref-type="bibr" rid="B11">11</xref>).</p>
<p>This study explored the association between semi-quantitative CT visual score and endocrine-related factors in patients with SARS-CoV-2 infection and hyponatremia, providing evidence for the vital role of CT score in pneumonia diagnosis, disease severity stratification, and prognosis analysis.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<label>2</label>
<title>Materials and methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Study design</title>
<p>This study was a cross-sectional study. Patients admitted to the First Hospital of Shanxi Medical University and diagnosed with COVID-19 from January 1 to January 31, 2023, were included. The study was approved by the Ethics Committee of the First Hospital of Shanxi Medical University (Approval number:2018K002). The patients/participants provided written informed consent to participate in this study.</p>
<p>Inclusion criteria:</p>
<p>1. SARS-CoV-2 infection was positive by RT-PCR 2. Chest CT showed definite pulmonary infection 3. Age &#x2265;18 years old</p>
<p>Exclusion criteria:</p>
<p>1. Patients who were missing CT imaging data and laboratory indicators 2. Patients with hypernatremia 3. Patients were readmitted due to COVID-19 and transferred patients</p>
<p>According to inclusion and exclusion criteria, 343 patients were included in the final study (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>). The purpose of this study is to explore the CT semi-quantitative score of COVID-19 patients with hyponatremia prediction effect. Therefore, patients included in the study must demonstrate the presence of SARS-CoV-2 infection and complete data on the underlying laboratory tests and examinations, and those who did not meet these criteria were excluded. Second, given the rarity of hypernatremia in COVID-19 patients (prevalence of 3.7% to 7%) (<xref ref-type="bibr" rid="B12">12</xref>, <xref ref-type="bibr" rid="B13">13</xref>), we also excluded patients with hypernatremia because only 15 patients had hypernatremia in this study, which could not meet the statistical requirements.</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Flowchart of the study design.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-15-1342204-g001.tif"/>
</fig>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Definition of covariates</title>
<p>The variables studied included age, sex, vital signs, symptoms, comorbidities, medication at admission, and laboratory parameters. The clinical symptoms we collected included fever, shortness of breath, cough/expectoration, muscle soreness, disturbance of consciousness, poor appetite, vomiting, and diarrhea. Comorbidities collected included diabetes, hypertension, coronary heart disease, cerebral infarction, thyroid dysfunction, and pulmonary disease. Medications on admission included diuretics, ACEI/ARBs, and glucocorticoids. Laboratory indicators included blood cell analysis, liver and kidney function indicators, coagulation indicators, electrolytes, inflammatory indicators, and other indicators. Blood cell analysis is measured by instrumental method (CAL8000), the determination of liver and kidney function by adopting the method of rate method and bromocresol green method, electrolytic determination with ion selective electrode (indirect method), coagulation function is measured by coagulation method and immunoturbidimetric method (ACL TOP 550), BNP and PCT are measured by microparticle chemiluminescence method and thyroid function is measured by electrochemiluminescence method (COBAS 6000).</p>
<p>Hyponatremia was defined as serum sodium less than 135 mmol/L, measured mainly by the indirect ion-selective electrode (ISE) method. Patients were further classified as having mild, moderate, or severe hyponatremia if their serum sodium levels were 130 to &lt;135 mmol/L, 125 to &lt;130 mmol/L, and &lt;125 mmol/L, respectively.</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>CT image acquisition and interpretation</title>
<p>All patients underwent a chest CT scan on admission. CT image data were obtained from one of four CT scanners (GE Lightspeed VCT 64, GE HealthCare, American; Somatom Force, Siemens Healthineers, Germany; IQon Spectral CT, Philips Healthcare, The Netherlands; NeuViz 128 CT, Neusoftmedical, China). The CT scan was performed with the patient supine and at the end of inspiration without administering intravenous contrast material. The scanning range was from the apex to the base of the lung. According to the international recommendations and other studies (<xref ref-type="bibr" rid="B14">14</xref>, <xref ref-type="bibr" rid="B15">15</xref>), the parameters used were tube voltage (120kV) and tube current (60&#x2013;100 mA), which were set by the automatic exposure control system (iDose) program, and the image quality was customized according to the needs of low dose patients. Thoracic VCAR pulmonary function analysis software (AW VolumeShare 7, GE company, American) was used to analyze the image data. The 0.625 mm slice thickness image at the end of deep inspiration was post-processed, and the threshold limit (-1024 to -200 HU) and automatic segmentation technology were used. The heart, trachea, rib, and other lung tissues were segmented to obtain a three-dimensional lung tissue model.</p>
</sec>
<sec id="s2_4">
<label>2.4</label>
<title>Semiquantitative CT visual score</title>
<p>In this study, we used the TSS to analyze chest CT findings in hospitalized patients. TSS is a digital scoring system based on visual evaluation that analyzes the range of lesions in CT images. Two radiologists with years of experience in imaging diagnosis performed scoring. To more clearly express mild and moderate hyponatremia CT score difference, our lung lesions (ground-glass opacity, consolidation, GGO + consolidation) to the following classification: 0:0%; 1:1&#x2013;10%; 2: 11&#x2013;20%; 3:21&#x2013;30%; 4:31&#x2013;40%; 5:41&#x2013;50%; 6: &gt;50%. According to these percentages, 0,1,2,3,4,5 and 6 points are given, respectively. The final TSS was the total score of the left and right lungs (range 0&#x2013;12).</p>
</sec>
<sec id="s2_5">
<label>2.5</label>
<title>Statistical analysis</title>
<p>The conformity of the data to a normal distribution was evaluated using skewness, kurtosis tests, and histogram plots. Normally distributed continuous variables are presented as mean and standard deviation (&#xb1; SD); a Student <italic>t</italic>-test was used. Non-normalized variables were presented as medians with interquartile ranges, and a Mann-Whitney <italic>U</italic> test was used. Categorical variables are described as the number (percentage), and Chi-square or Fisher&#x2019;s exact tests were used. Multivariate analysis was carried out using Logistic Regression (Forward Selection: Likelihood Ratio) to determine the significant risk factors of hyponatremia. The analysis of variance (ANOVA) was used in normal distribution variables, and the Kruskal Wallis test was used in non-normal distribution variables to compare the hyponatremia group (mild/moderate/severe) and the difference between normonatremia group. The data was entered and analyzed using the IBM SPSS 27 system (SPSS Inc., Chicago, IL, USA). A <italic>P</italic>-value &#x2264;5% was taken for statistical significance.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<sec id="s3_1">
<label>3.1</label>
<title>General characteristics of COVID-19 patients</title>
<p>A total of 343 eligible patients were included in the study, 58.6% male. The mean age of the patients was 74.5 &#xb1; 13.1 years, and 89.5% were older than 60 years. Cough/expectoration (74.9%), poor appetite (66.2%), and shortness of breath (44.6%) were the most common clinical symptoms observed. Hypertension and diabetes were the most common comorbidities, accounting for 49.0% and 25.9%, respectively. Of the 343 study patients, 43.4% had hyponatremia, whereas 56.6% had normonatremia (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>). Among the patients with hyponatremia, the prevalence of mild, moderate, and severe hyponatremia was 53.7%, 16.8%, and 29.5%, respectively (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table S1</bold>
</xref>).</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Demographic and clinical characteristics of the patients.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="center"/>
<th valign="top" align="center">Normonatremia (<italic>n</italic>=194)</th>
<th valign="top" align="center">Hyponatremia (<italic>n</italic>=149)</th>
<th valign="middle" align="center">
<italic>P</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<th valign="top" align="left" colspan="4">Demographic characteristics</th>
</tr>
<tr>
<td valign="top" align="left">Age (years)</td>
<td valign="top" align="center">72.5 (65.0&#x2013;82.8)</td>
<td valign="top" align="center">80.0 (70.0&#x2013;86.5)</td>
<td valign="top" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Male, <italic>n</italic> (%)</td>
<td valign="top" align="center">110.0 (56.7)</td>
<td valign="top" align="center">91.0 (61.1)</td>
<td valign="top" align="center">0.415</td>
</tr>
<tr>
<td valign="top" align="left">Body mass index (kg/m<sup>2</sup>)</td>
<td valign="top" align="center">23.9 (21.1&#x2013;26.8)</td>
<td valign="top" align="center">24.2 (21.3&#x2013;26.7)</td>
<td valign="top" align="center">0.769</td>
</tr>
<tr>
<th valign="top" align="left" colspan="4">Vital signs</th>
</tr>
<tr>
<td valign="top" align="left">Body temperature (&#xb0;C)</td>
<td valign="top" align="center">36.5 (36.3&#x2013;36.8)</td>
<td valign="top" align="center">36.6 (36.3&#x2013;37.0)</td>
<td valign="top" align="center">0.087</td>
</tr>
<tr>
<td valign="top" align="left">Pulse (Times/min)</td>
<td valign="top" align="center">80.0 (76.0&#x2013;90.0)</td>
<td valign="top" align="center">80.0 (73.5&#x2013;90.5)</td>
<td valign="top" align="center">0.430</td>
</tr>
<tr>
<td valign="top" align="left">SBP (mmHg)</td>
<td valign="top" align="center">130.0 (118.0&#x2013;139.8)</td>
<td valign="top" align="center">132.0 (118.0&#x2013;144.0)</td>
<td valign="top" align="center">0.325</td>
</tr>
<tr>
<td valign="top" align="left">DBP (mmHg)</td>
<td valign="top" align="center">76.0 (70.0&#x2013;82.0)</td>
<td valign="top" align="center">76.0 (69.0&#x2013;82.5)</td>
<td valign="top" align="center">0.839</td>
</tr>
<tr>
<th valign="top" align="left" colspan="4">Symptoms</th>
</tr>
<tr>
<td valign="top" align="left">Fever, <italic>n</italic> (%)</td>
<td valign="top" align="center">28.0 (14.4)</td>
<td valign="top" align="center">31.0 (20.8)</td>
<td valign="top" align="center">0.121</td>
</tr>
<tr>
<td valign="top" align="left">Shortness of breath, <italic>n</italic> (%)</td>
<td valign="top" align="center">93.0 (47.9)</td>
<td valign="top" align="center">60.0 (40.3)</td>
<td valign="top" align="center">0.157</td>
</tr>
<tr>
<td valign="top" align="left">Cough/Expectoration, <italic>n</italic> (%)</td>
<td valign="top" align="center">153.0 (78.9)</td>
<td valign="top" align="center">104.0 (69.8)</td>
<td valign="top" align="center">0.055</td>
</tr>
<tr>
<td valign="top" align="left">Muscle soreness, <italic>n</italic> (%)</td>
<td valign="top" align="center">26.0 (13.4)</td>
<td valign="top" align="center">14.0 (9.4)</td>
<td valign="top" align="center">0.252</td>
</tr>
<tr>
<td valign="top" align="left">Disturbance of consciousness, <italic>n</italic> (%)</td>
<td valign="top" align="center">12.0 (6.2)</td>
<td valign="top" align="center">16.0 (10.7)</td>
<td valign="top" align="center">0.127</td>
</tr>
<tr>
<td valign="top" align="left">Poor appetite, <italic>n</italic> (%)</td>
<td valign="top" align="center">124.0 (63.9)</td>
<td valign="top" align="center">103.0 (69.1)</td>
<td valign="top" align="center">0.312</td>
</tr>
<tr>
<td valign="top" align="left">Vomiting, <italic>n</italic> (%)</td>
<td valign="top" align="center">11.0 (5.7)</td>
<td valign="top" align="center">21.0 (14.1)</td>
<td valign="top" align="center">
<bold>0.008</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Diarrhea, <italic>n</italic> (%)</td>
<td valign="top" align="center">6.0 (3.1)</td>
<td valign="top" align="center">6.0 (4.0)</td>
<td valign="top" align="center">0.641</td>
</tr>
<tr>
<th valign="top" align="left" colspan="4">Comorbidities</th>
</tr>
<tr>
<td valign="top" align="left">Diabetes, <italic>n</italic> (%)</td>
<td valign="top" align="center">45.0 (23.2)</td>
<td valign="top" align="center">44.0 (29.5)</td>
<td valign="top" align="center">0.185</td>
</tr>
<tr>
<td valign="top" align="left">Hypertension, <italic>n</italic> (%)</td>
<td valign="top" align="center">97.0 (50.0)</td>
<td valign="top" align="center">71.0 (47.7)</td>
<td valign="top" align="center">0.666</td>
</tr>
<tr>
<td valign="top" align="left">Coronary heart disease, <italic>n</italic> (%)</td>
<td valign="top" align="center">31.0 (16.0)</td>
<td valign="top" align="center">16.0 (10.7)</td>
<td valign="top" align="center">0.162</td>
</tr>
<tr>
<td valign="top" align="left">Cerebral infarction, <italic>n</italic> (%)</td>
<td valign="top" align="center">28.0 (14.4)</td>
<td valign="top" align="center">22.0 (14.8)</td>
<td valign="top" align="center">0.931</td>
</tr>
<tr>
<td valign="top" align="left">Thyroid dysfunction, <italic>n</italic> (%)</td>
<td valign="top" align="center">7.0 (3.6)</td>
<td valign="top" align="center">4.0 (2.7)</td>
<td valign="top" align="center">0.863</td>
</tr>
<tr>
<td valign="top" align="left">Pulmonary disease, <italic>n</italic> (%)</td>
<td valign="top" align="center">5.0 (2.6)</td>
<td valign="top" align="center">9.0 (6.0)</td>
<td valign="top" align="center">0.108</td>
</tr>
<tr>
<th valign="top" align="left" colspan="4">Medication at admission</th>
</tr>
<tr>
<td valign="top" align="left">Diuretics, <italic>n</italic> (%)</td>
<td valign="top" align="center">6.0 (3.1)</td>
<td valign="top" align="center">15.0 (10.1)</td>
<td valign="top" align="center">
<bold>0.008</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">ACEI/ARBs, <italic>n</italic> (%)</td>
<td valign="top" align="center">15.0 (7.7)</td>
<td valign="top" align="center">17.0 (11.4)</td>
<td valign="top" align="center">0.245</td>
</tr>
<tr>
<td valign="top" align="left">Glucocorticoids, <italic>n</italic> (%)</td>
<td valign="top" align="center">15.0 (7.7)</td>
<td valign="top" align="center">8.0 (5.4)</td>
<td valign="top" align="center">0.386</td>
</tr>
<tr>
<th valign="top" align="left" colspan="4">Laboratory tests</th>
</tr>
<tr>
<td valign="top" align="left">Leukocyte (&#xd7;10<sup>9</sup>/L)</td>
<td valign="top" align="center">5.7 (4.1&#x2013;7.8)</td>
<td valign="top" align="center">6.2 (4.8&#x2013;9.9)</td>
<td valign="top" align="center">
<bold>0.008</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Hemoglobin (g/L)</td>
<td valign="top" align="center">137.0 (124.0&#x2013;148.0)</td>
<td valign="top" align="center">134.0 (121.5&#x2013;144.0)</td>
<td valign="top" align="center">0.186</td>
</tr>
<tr>
<td valign="top" align="left">Platelets (&#xd7;10<sup>9</sup>/L)</td>
<td valign="top" align="center">194.0 (142.0&#x2013;246.0)</td>
<td valign="top" align="center">173.0 (124.5&#x2013;229.5)</td>
<td valign="top" align="center">
<bold>0.016</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Lymphocytes (&#xd7;10<sup>9</sup>/L)</td>
<td valign="top" align="center">1.0 (0.7&#x2013;1.4)</td>
<td valign="top" align="center">0.8 (0.5&#x2013;1.1)</td>
<td valign="top" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Neutrophils (&#xd7;10<sup>9</sup>/L)</td>
<td valign="top" align="center">3.8 (2.4&#x2013;6.5)</td>
<td valign="top" align="center">4.7 (3.3&#x2013;7.9)</td>
<td valign="top" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">NLR</td>
<td valign="top" align="center">3.8 (2.5&#x2013;6.8)</td>
<td valign="top" align="center">7.3 (3.5&#x2013;12.9)</td>
<td valign="top" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Blood glucose (mmol/L)</td>
<td valign="top" align="center">6.7 (5.8&#x2013;8.8)</td>
<td valign="top" align="center">7.0 (6.2&#x2013;9.2)</td>
<td valign="top" align="center">0.058</td>
</tr>
<tr>
<td valign="top" align="left">ALT (U/L)</td>
<td valign="top" align="center">20.5 (13.0&#x2013;35.3)</td>
<td valign="top" align="center">27.0 (18.0&#x2013;38.5)</td>
<td valign="top" align="center">
<bold>0.022</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">AST (U/L)</td>
<td valign="top" align="center">27.0 (21.0&#x2013;41.0)</td>
<td valign="top" align="center">37.0 (24.0&#x2013;57.0)</td>
<td valign="top" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Albumin (g/L)</td>
<td valign="top" align="center">35.8 (33.0&#x2013;39.3)</td>
<td valign="top" align="center">34.5 (31.8&#x2013;38.1)</td>
<td valign="top" align="center">
<bold>0.028</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">BUN (mmol/L)</td>
<td valign="top" align="center">5.3 (4.0&#x2013;7.3)</td>
<td valign="top" align="center">5.5 (4.1&#x2013;7.9)</td>
<td valign="top" align="center">0.558</td>
</tr>
<tr>
<td valign="top" align="left">SCr (&#x3bc;mol/L)</td>
<td valign="top" align="center">67.0 (57.0&#x2013;81.6)</td>
<td valign="top" align="center">71.0 (56.0&#x2013;88.5)</td>
<td valign="top" align="center">0.466</td>
</tr>
<tr>
<td valign="top" align="left">eGFR (mL/min/1.73m<sup>2</sup>)</td>
<td valign="top" align="center">91.5 (75.7&#x2013;100.1)</td>
<td valign="top" align="center">85.2 (68.9&#x2013;95.0)</td>
<td valign="top" align="center">
<bold>0.004</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Potassium (mmol/L)</td>
<td valign="top" align="center">3.9 (3.6&#x2013;4.3)</td>
<td valign="top" align="center">3.9 (3.5&#x2013;4.2)</td>
<td valign="top" align="center">0.197</td>
</tr>
<tr>
<td valign="top" align="left">Chlorine (mmol/L)</td>
<td valign="top" align="center">102.7 (100.4&#x2013;104.8)</td>
<td valign="top" align="center">93.8 (87.2&#x2013;98.1)</td>
<td valign="top" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">PT (s)</td>
<td valign="top" align="center">13.6 (12.8&#x2013;14.4)</td>
<td valign="top" align="center">13.4 (12.7&#x2013;14.3)</td>
<td valign="top" align="center">0.392</td>
</tr>
<tr>
<td valign="top" align="left">APTT (s)</td>
<td valign="top" align="center">31.6 (29.3&#x2013;33.7)</td>
<td valign="top" align="center">32.3 (29.9&#x2013;35.9)</td>
<td valign="top" align="center">
<bold>0.035</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">FDP (ug/ml)</td>
<td valign="top" align="center">4.7 (1.8&#x2013;163.5)</td>
<td valign="top" align="center">4.4 (1.9&#x2013;133.5)</td>
<td valign="top" align="center">0.504</td>
</tr>
<tr>
<td valign="top" align="left">D dimer (mg/L)</td>
<td valign="top" align="center">2.2 (0.3&#x2013;4.5)</td>
<td valign="top" align="center">1.8 (0.3&#x2013;4.4)</td>
<td valign="top" align="center">0.639</td>
</tr>
<tr>
<td valign="top" align="left">PCT (ng/ml)</td>
<td valign="top" align="center">0.26 (0.16&#x2013;0.35)</td>
<td valign="top" align="center">0.29 (0.20&#x2013;0.45)</td>
<td valign="top" align="center">
<bold>0.011</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">BNP (ng/L)</td>
<td valign="top" align="center">52.6 (26.8&#x2013;122.8)</td>
<td valign="top" align="center">109.0 (48.0&#x2013;270.9)</td>
<td valign="top" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">hs-cTnT (pg/ml)</td>
<td valign="top" align="center">9.5 (4.4&#x2013;19.6)</td>
<td valign="top" align="center">13.5 (7.3&#x2013;35.5)</td>
<td valign="top" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<th valign="top" align="left" colspan="4">CT assessment</th>
</tr>
<tr>
<td valign="top" align="left">TSS (scores)</td>
<td valign="top" align="center">3.0 (2.0&#x2013;4.5)</td>
<td valign="top" align="center">3.5 (2.5&#x2013;5.5)</td>
<td valign="top" align="center">
<bold>0.001</bold>
</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Values are expressed as mean (&#xb1; standard deviation), median (interquartile range), or number (percentage). Serum creatinine (SCr) measurements were used to calculate the estimated Glomerular Filtration Rate (eGFR) by using the 2021 Chronic Kidney Disease Epidemiology Collaboration (2021 CKD-EPI) Creatinine equation (<xref ref-type="bibr" rid="B16">16</xref>). SBP, systolic blood pressure; DBP, diastolic blood pressure; ACEI/ARB, angiotensin-converting enzyme inhibitor/angiotensin receptor blocker; NLR, neutrophil to lymphocyte ratio; ALT, Alanine aminotransferase; AST, aspartate aminotransferase; BUN, blood urea nitrogen; Cr, creatinine; eGFR, estimated glomerular filtration rate; PT, prothrombin time; APTT, activated partial thromboplastin time; FDP, fibrinogen degradation products; PCT, procalcitonin; BNP, brain natriuretic peptide; hs-cTnT, high-sensitivity cardiac troponin T; TSS, total severity score.</p>
</fn>
<fn>
<p>A P-value &lt;0.05 was considered statistically significant, shown in bold.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>Patients in the hyponatremia group were significantly older [M(range), 80.0(70.0&#x2013;86.5) vs 72.5(65.0&#x2013;82.8) years old, <italic>P</italic> &lt; 0.001] than those in the normonatremia group. Vomiting (14.1% vs 5.7%, <italic>P</italic>=0.008) and diuretic use (10.1% vs 3.1%, <italic>P</italic>=0.008) in the hyponatremia group were significantly different from those in normonatremia group. The remaining measures of vital signs, symptoms, coexisting conditions, and out-of-hospital medication use did not differ significantly between the two groups (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>).</p>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Laboratory findings and TSS</title>
<p>Among the laboratory indicators in <xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>, the median (IQR) findings of complete blood count (white blood cell/platelet/hemoglobin/neutrophil), renal function indexes (BUN, SCr), ALT, potassium, coagulation indicators (PT, APTT, and FDP), and BNP were within normal limits. Compared with the normonatremia group, the white blood cells and neutrophils in the hyponatremia group increased, while the platelets and lymphocytes decreased, and the difference was statistically significant (all <italic>P</italic>&lt; 0.05, <xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>). Compared with the normonatremia group, the median eGFR and chlorine in the hyponatremia group were lower than the lower limit of normal, and the difference was statistically significant (all <italic>P</italic>&lt; 0.05, <xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>). The median blood glucose, D dimer, and PCT in the hyponatremia group were increased, which were higher than the upper limit of normal, and the difference was statistically significant (all <italic>P</italic>&lt; 0.05, <xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>).</p>
<p>Excellent agreement was achieved between the two radiologists in the assessment of lung lesions, with an average measurement intraclass correlation coefficient (ICC) of 0.953 (95% CI, 0.942&#x2013;0.962; <italic>P</italic> &lt; 0.001) (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table S2</bold>
</xref>). Thus indicating a high reliability of the semi-quantitative method, in the following analysis, the average of the CT scores of the two radiologists was selected instead of using the scores of radiologist 1 or radiologist 2. For TSS, the hyponatremia group showed higher scores than the normonatremia group [M(range), 3.5(2.5&#x2013;5.5) vs 3.0(2.0&#x2013;4.5) scores, <italic>P</italic>=0.001] (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>), indicating more severe lung lesions. The hyponatremia was further divided into mild, moderate, and severe groups, and the difference in CT scores between different degrees of hyponatremia group and normonatremia group was analyzed. The results showed that the difference between the normonatremia and mild hyponatremia groups (<italic>P</italic>=0.001) and the normonatremia and moderate hyponatremia groups (<italic>P</italic>=0.023) were significant (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Differences in CT scores of different degrees of hyponatremia. The difference between the normonatremia and mild hyponatremia groups, and the normonatremia and moderate hyponatremia groups were significant. **<italic>P</italic>&lt;0.01, ***<italic>P</italic>&lt;0.001.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-15-1342204-g002.tif"/>
</fig>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Risk factors for hyponatremia in patients with SARS-CoV-2 infection</title>
<p>Univariate and multivariate logistic regression analyses were performed to explore the risk factors of hyponatremia in patients with SARS-CoV-2 infection, combined with the statistics of the difference between the normonatremia group and the hyponatremia group. By univariate logistic regression analysis, statistically significant risk factors for hyponatremia included age, vomiting, diuretics, platelets, lymphocytes, neutrophils, NLR, eGFR, APTT, BNP, PCT, and TSS, as shown in <xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>.</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Logistic regression analysis to predict the indicators of hyponatremia in patients with COVID-19.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="center"/>
<th valign="middle" colspan="2" align="center">Univariate model</th>
<th valign="middle" colspan="2" align="center">Multivariate model</th>
</tr>
<tr>
<th valign="top" align="center"/>
<th valign="middle" align="center">OR (95% CI)</th>
<th valign="middle" align="center">
<italic>P</italic>-value</th>
<th valign="middle" align="center">OR (95% CI)</th>
<th valign="middle" align="center">
<italic>P</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<th valign="top" align="left" colspan="5">Demographic characteristics</th>
</tr>
<tr>
<td valign="top" align="left">Age (years)</td>
<td valign="middle" align="center">1.036 (1.017&#x2013;1.056)</td>
<td valign="middle" align="center">
<bold>&lt;0.001</bold>
</td>
<td valign="middle" align="center">1.039 (1.018&#x2013;1.061)</td>
<td valign="middle" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>Symptoms</bold>
</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Vomiting</td>
<td valign="middle" align="center">2.729 (1.272&#x2013;5.858)</td>
<td valign="middle" align="center">
<bold>0.010</bold>
</td>
<td valign="middle" align="center">2.920 (1.233&#x2013;6.913)</td>
<td valign="middle" align="center">
<bold>0.015</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Diarrhea</td>
<td valign="middle" align="center">1.315 (0.415&#x2013;4.162)</td>
<td valign="middle" align="center">0.642</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<th valign="top" align="left" colspan="5">Medication at admission</th>
</tr>
<tr>
<td valign="top" align="left">Diuretics</td>
<td valign="middle" align="center">3.507 (1.327&#x2013;9.274)</td>
<td valign="middle" align="center">
<bold>0.011</bold>
</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="top" align="left">ACEI/ARBs</td>
<td valign="middle" align="center">1.537 (0.741&#x2013;3.189)</td>
<td valign="middle" align="center">0.248</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Glucocorticoids</td>
<td valign="middle" align="center">0.677 (0.279&#x2013;1.642)</td>
<td valign="middle" align="center">0.388</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<th valign="top" align="left" colspan="5">Laboratory parameters</th>
</tr>
<tr>
<td valign="top" align="left">Platelets (&#xd7;10<sup>9</sup>/L)</td>
<td valign="middle" align="center">0.997 (0.994&#x2013;0.999)</td>
<td valign="middle" align="center">
<bold>0.016</bold>
</td>
<td valign="middle" align="center">0.995 (0.991&#x2013;0.998)</td>
<td valign="middle" align="center">
<bold>0.002</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">Lymphocytes (&#xd7;10<sup>9</sup>/L)</td>
<td valign="middle" align="center">0.494 (0.329&#x2013;0.749)</td>
<td valign="middle" align="center">
<bold>&lt;0.001</bold>
</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Neutrophils (&#xd7;10<sup>9</sup>/L)</td>
<td valign="middle" align="center">1.038 (1.013&#x2013;1.171)</td>
<td valign="middle" align="center">
<bold>0.001</bold>
</td>
<td valign="middle" align="center">1.167 (1.079&#x2013;1.263)</td>
<td valign="middle" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">NLR</td>
<td valign="middle" align="center">1.053 (1.024&#x2013;1.083)</td>
<td valign="middle" align="center">
<bold>&lt;0.001</bold>
</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="top" align="left">ALT (mmol/L)</td>
<td valign="middle" align="center">1.000 (0.996&#x2013;1.004)</td>
<td valign="middle" align="center">0.848</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="top" align="left">AST (mmol/L)</td>
<td valign="middle" align="center">1.002 (0.999&#x2013;1.005)</td>
<td valign="middle" align="center">0.196</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="top" align="left">Albumin</td>
<td valign="middle" align="center">0.968 (0.928&#x2013;1.009)</td>
<td valign="middle" align="center">0.124</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="top" align="left">eGFR (mL/min/1.73m<sup>2</sup>)</td>
<td valign="middle" align="center">0.990 (0.981&#x2013;1.000)</td>
<td valign="middle" align="center">
<bold>0.039</bold>
</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="top" align="left">APTT (s)</td>
<td valign="middle" align="center">1.036 (0.999&#x2013;1.075)</td>
<td valign="middle" align="center">0.058</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="top" align="left">PCT (ng/ml)</td>
<td valign="middle" align="center">1.062 (1.003&#x2013;1.125)</td>
<td valign="middle" align="center">
<bold>0.040</bold>
</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="top" align="left">BNP (ng/L)</td>
<td valign="middle" align="center">1.001 (1.000&#x2013;1.002)</td>
<td valign="middle" align="center">
<bold>0.007</bold>
</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="top" align="left">hs-cTnT (pg/ml)</td>
<td valign="middle" align="center">1.001 (0.999&#x2013;1.003)</td>
<td valign="middle" align="center">0.226</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<th valign="top" align="left" colspan="5">CT assessment</th>
</tr>
<tr>
<td valign="top" align="left">TSS (scores)</td>
<td valign="middle" align="center">1.220 (1.094&#x2013;1.361)</td>
<td valign="middle" align="center">
<bold>&lt;0.001</bold>
</td>
<td valign="middle" align="center">1.203 (1.069&#x2013;1.354)</td>
<td valign="middle" align="center">
<bold>0.002</bold>
</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>OR, odds ratio; CI, confidence intervals; ACEI/ARB, angiotensin-converting enzyme inhibitor/angiotensin receptor blocker; AST, aspartate aminotransferase; eGFR, estimated glomerular filtration rate; APTT, activated partial thromboplastin time; PCT, procalcitonin; BNP, brain natriuretic peptide; TSS, total severity score.</p>
</fn>
<fn>
<p>A P-value &lt;0.05 was considered statistically significant, shown in bold.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>Based on our clinical observations, fluid loss due to diarrhea/vomiting and medications such as diuretics, ACEI/ARBs, or glucocorticoids can cause electrolyte disturbances. After excluding the variables with higher degree of collinearity, multivariate logistic regression analysis was performed, and the results showed that (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>) age (OR=1.039, 95%CI 1.018&#x2013;1.061, <italic>P</italic>&lt;0.001), vomiting (OR=2.920, 95%CI 1.233&#x2013;6.913, <italic>P</italic>=0.015), neutrophil count (OR=1.167, 95%CI 1.079&#x2013;1.263, <italic>P</italic>&lt;0.001), TSS score (OR=1.203, 95%CI 1.069&#x2013;1.354, <italic>P</italic>=0.002), and platelet count (OR=0.995, 95%CI 0.991&#x2013;0.998, <italic>P</italic>=0.002) were independent risk factors for hyponatremia in COVID-19 patients.</p>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>The thyroid function between normonatremia group and hyponatremia group</title>
<p>To explore the role of endocrine-related factors in developing hyponatremia in COVID-19 patients, we performed a subgroup analysis of 104 patients with available thyroid function data. The results showed (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>) that the levels of free triiodothyronine (FT3), free thyroxine (FT4), and thyroid stimulating hormone (TSH) were within the normal range. However, the levels of FT3, FT4, and TSH in the hyponatremia group were lower than those in the normonatremia group, and the difference in FT3 (3.1 &#xb1; 0.9 vs 3.7 &#xb1; 0.9, <italic>P</italic>=0.001) and TSH [M (range), 1.4 (0.8&#x2013;2.4) vs 2.2 (1.2&#x2013;3.4) uIU/ml, <italic>P</italic>=0.038] between the two groups were statistically significant. The result may indicate that SARS-CoV-2 infection affects pituitary and thyroid function differently.</p>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>Comparison of thyroid function between Normonatremia group and hyponatremia group.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="center"/>
<th valign="top" align="center">Normonatremia<break/> (<italic>n</italic>=50)</th>
<th valign="top" align="center">Hyponatremia<break/> (<italic>n</italic>=54)</th>
<th valign="top" align="center">
<italic>P</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<th valign="top" align="left" colspan="4">Thyroid function</th>
</tr>
<tr>
<td valign="top" align="left">FT3 (pmol/L)</td>
<td valign="top" align="center">3.7 &#xb1; 0.9</td>
<td valign="top" align="center">3.1 &#xb1; 0.9</td>
<td valign="top" align="center">
<bold>0.001</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">FT4 (pmol/L)</td>
<td valign="top" align="center">16.9 (15.2&#x2013;18.8)</td>
<td valign="top" align="center">15.7 (13.2&#x2013;21.1)</td>
<td valign="top" align="center">0.507</td>
</tr>
<tr>
<td valign="top" align="left">FT3/FT4</td>
<td valign="top" align="center">0.23 (0.18&#x2013;0.25)</td>
<td valign="top" align="center">0.21 (0.16&#x2013;0.27)</td>
<td valign="top" align="center">0.486</td>
</tr>
<tr>
<td valign="top" align="left">TSH (uIU/ml)</td>
<td valign="top" align="center">2.2 (1.2&#x2013;3.4)</td>
<td valign="top" align="center">1.4 (0.8&#x2013;2.4)</td>
<td valign="top" align="center">
<bold>0.038</bold>
</td>
</tr>
<tr>
<th valign="top" align="left" colspan="4">CT assessment</th>
</tr>
<tr>
<td valign="top" align="left">TSS</td>
<td valign="top" align="center">2.3 (2.0&#x2013;4.0)</td>
<td valign="top" align="center">2.8 (2.0&#x2013;3.6)</td>
<td valign="top" align="center">0.172</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Values are expressed as mean (&#xb1; standard deviation) or median (interquartile range). FT3, free triiodothyronine; FT4, free thyroxine; TSH, thyroid stimulating hormone; TSS, total severity score.</p>
</fn>
<fn>
<p>A P-value &lt;0.05 was considered statistically significant, shown in bold.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>We performed a correlation analysis further to explore the relationship between FT3 and serum sodium. Spearman correlation analysis showed that there was a positive correlation between FT3 and serum sodium (<italic>r<sub>s</sub>=0.358, P</italic>&lt; 0.001) (<xref ref-type="table" rid="T4">
<bold>Table&#xa0;4</bold>
</xref>). The higher the level of FT3, the higher the serum sodium of patients, but this correlation was weak.</p>
<table-wrap id="T4" position="float">
<label>Table&#xa0;4</label>
<caption>
<p>Correlation between FT3 and serum sodium.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="left"/>
<th valign="middle" rowspan="2" align="left">Correlation coefficient(<italic>r</italic>)</th>
<th valign="middle" rowspan="2" align="left">Spearman<break/>
<italic>P</italic>-value</th>
<th valign="middle" colspan="2" align="left">95% CI</th>
</tr>
<tr>
<th valign="middle" align="left">Low</th>
<th valign="middle" align="left">High</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">FT3-Serum sodium</td>
<td valign="middle" align="center">0.358</td>
<td valign="middle" align="center">&lt;0.001</td>
<td valign="middle" align="center">0.172</td>
<td valign="middle" align="center">0.519</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>CI, confidence intervals; FT3, free triiodothyronine.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>Hyponatremia is a common electrolyte disorder associated with high morbidity and mortality, about 30% in hospitalized patients, and the incidence is higher in intensive care units (<xref ref-type="bibr" rid="B17">17</xref>). Compared to patients with pneumonia, COVID-19 patients with a significantly higher risk of hyponatremia (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B18">18</xref>). Therefore, active prevention and treatment of hyponatremia greatly help the prognosis of the disease. Frontera and colleagues found that among patients with COVID-19, moderate (Na 121&#x2013;129 mEq/L) and severe (Na &#x2264; 120mEq/L) hyponatremia accounted for 7% and 1% of the study population, respectively (<xref ref-type="bibr" rid="B19">19</xref>). In our study, which included only patients with COVID-19, the incidence of hyponatremia was 43.4%. The prevalence of mild, moderate, and severe hyponatremia was 23.3%, 7.3%, and 12.8%, respectively. The higher incidence of hyponatremia may be related to the advanced age of patients and more comorbidities, and these factors are often associated with poor prognosis. Secondly, because our data came from a large tertiary general hospital, there were more critically ill patients, so the incidence of hyponatremia in our study was high.</p>
<p>In this study, older age, vomiting, increased neutrophil count, and higher TSS score were associated with a higher risk of hyponatremia in COVID-19 patients. Among patients with SARS-CoV-2 infection, older age, and more coexisting conditions are associated with more severe disease, and these same factors are present in patients with hyponatremia. The study by Muhammad Anees et&#xa0;al. found that an elevated NLR was a risk factor for hyponatremia in hospitalized patients with COVID-19 (<xref ref-type="bibr" rid="B20">20</xref>). Although NLR was not proven to be a risk factor for the development of hyponatremia in our study by multivariate logistic regression, neutrophil count was proved to be a risk factor for the development of hyponatremia in COVID-19 patients by univariate or multivariate logistic regression.</p>
<p>The increase in platelet count can reduce the risk of hyponatremia, but the reduction effect is weak. Thrombocytopenia is another feature of SARS-CoV-2 infection, and in a retrospective study of 1,476 hospitalized COVID-19 patients, 20.7% were found to have thrombocytopenia, and thrombocytopenia was associated with increased mortality (<xref ref-type="bibr" rid="B21">21</xref>). We found that thrombocytopenia was more in the hyponatremia group, and the difference was statistically significant compared with the normonatremia group. The causes of thrombocytopenia were related to the direct effect of the virus on bone marrow cells and the formation of autoantibodies by platelets and their participation in immune regulation (<xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B23">23</xref>).</p>
<p>ADH is generally produced by the supraoptic and paraventricular hypothalamic nuclei, stored and released from the posterior pituitary. ADH can also be derived from non-pituitary sources, and excessive release of the hormone from other sites results in SIAD. SIAD can be induced by various factors, including tumors, infections such as pneumonia and meningitis, and neurological diseases such as stroke (<xref ref-type="bibr" rid="B24">24</xref>). The effect of SIAD on hyponatremia in community-acquired pneumonia has been confirmed by studies (<xref ref-type="bibr" rid="B25">25</xref>), and SIAD is considered the leading cause of hyponatremia in COVID-19 patients. IL-6 is one of the most critical cytokines in inflammatory syndrome, causing pathological changes after SARS-CoV-2 infection (<xref ref-type="bibr" rid="B26">26</xref>). Elevated IL-6 levels can induce ADH secretion by directly stimulating the hypothalamus and inducing alveolar basement membrane damage and pulmonary hypoxia (<xref ref-type="bibr" rid="B27">27</xref>). Second, after SARS-CoV-2 infection, activated immune cells (mainly T and B lymphocytes) and released proinflammatory cytokines stimulate immune cells to release stored ADH (<xref ref-type="bibr" rid="B28">28</xref>).</p>
<p>In the study by A Berni et&#xa0;al., IL-6 was elevated in 17 of 29 patients with SARS-CoV-2 infection and inversely correlated with serum sodium concentration (<xref ref-type="bibr" rid="B29">29</xref>). In our study, IL-6 was also elevated in the hyponatremia group, and the difference was statistically significant compared with the normonatremia group. Furthermore, in the linear analysis, we also found a weak negative correlation between IL-6 and serum sodium (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Tables S3</bold>
</xref>, <xref ref-type="supplementary-material" rid="SM1">
<bold>S4</bold>
</xref>). This result of our study may further support the idea of a nonosmotic release of ADH associated with IL-6.</p>
<p>With the rapid spread of COVID-19 worldwide, many scoring systems for lung assessment have been released. Chest CT severity score, total severity score, modified total severity score, and other scoring methods have excellent reliability in clinical assessment (<xref ref-type="bibr" rid="B11">11</xref>, <xref ref-type="bibr" rid="B30">30</xref>). Peijie Lyu and colleagues found that qualitative or quantitative chest CT measures can assess the clinical severity of COVID-19 pneumonia (<xref ref-type="bibr" rid="B31">31</xref>). Miklos Szabo et&#xa0;al. found that the chest CT scoring system (CCTS) and specific chest CT patterns can predict ventilation requirements and mortality in COVID-19 (<xref ref-type="bibr" rid="B32">32</xref>). In this study, we used the TSS semi-quantitative method to assess the severity of lung lesions in patients with COVID-19 and then correlate it with hyponatremia. Because our study included a small number of patients with severe or critical lung illness, we modified the CT score of the lung to make it easier to identify mild and moderate pulmonary infections and to explore their effect on hyponatremia. The results showed that there was a significant difference in total severity score between the normonatremia group and the hyponatremia group, which suggested that CT score may be a risk factor for hyponatremia, and the results of multivariate logistic regression also proved this, the higher the TSS score, the higher the risk of hyponatremia.</p>
<p>CT score can predict the severity of pneumonia after SARS-CoV-2 infection (<xref ref-type="bibr" rid="B31">31</xref>&#x2013;<xref ref-type="bibr" rid="B33">33</xref>), and hyponatremia can be caused by SARS-CoV-2 infection (<xref ref-type="bibr" rid="B34">34</xref>, <xref ref-type="bibr" rid="B35">35</xref>). Many studies have confirmed these conclusions. To the best of our knowledge, this study is the first to correlate CT score with hyponatremia, and further exploration showed a weak inverse association between TSS and serum sodium (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table S5</bold>
</xref>), suggesting that not only can CT score predict the risk of hyponatremia, but it also seems to predict the severity of hyponatremia. Of course, we still need to do much validation. Given the widespread and convenient use of chest CT examination in clinical practice, our results are encouraging, which means that CT score can not only predict the occurrence of hyponatremia after SARS-CoV-2 infection but also provide new ideas for evaluating the association between other lung infections and hyponatremia. Although derived from inpatients, our findings may also be helpful in the outpatient setting since chest CT is routinely performed based on lung lesions. CT scores can predict the development of hyponatremia before serologic tests, which may facilitate early intervention in the outpatient setting.Multiple studies have reported impaired thyroid function in COVID-19 patients, including decreased TSH and T3 levels, decreased TSH levels alone, decreased TSH and increased T4 levels, and decreased TSH and FT4 (<xref ref-type="bibr" rid="B36">36</xref>&#x2013;<xref ref-type="bibr" rid="B39">39</xref>). The causes of thyroid dysfunction may be related to a direct effect of COVID-19 on thyroid follicular cells or to disturbances in immune function (<xref ref-type="bibr" rid="B40">40</xref>, <xref ref-type="bibr" rid="B41">41</xref>). Our study found that TSH and FT3 in patients with hyponatremia were lower than those with normal serum sodium. Similarly, W Gao et&#xa0;al. found that FT3 concentration was significantly lower in patients with severe COVID-19 than in non-severe patients, and FT3 reduction could be used as an independent predictor of all-cause mortality in patients with severe COVID-19 (<xref ref-type="bibr" rid="B42">42</xref>). We considered that the reasons for the lower TSH and FT3 in the hyponatremia group were as follows (<xref ref-type="bibr" rid="B1">1</xref>): After SARS-CoV-2 infection, the pituitary cells of patients were damaged (<xref ref-type="bibr" rid="B43">43</xref>), resulting in the increased release of ADH and the decreased secretion of TSH. The increased release of ADH can cause dilute hyponatremia, while the decreased secretion of TSH can cause a decrease in FT4 and FT3 (<xref ref-type="bibr" rid="B2">2</xref>). Cytokine IL-6 is involved in SARS-CoV-2-related cytokine storm (<xref ref-type="bibr" rid="B44">44</xref>). Elevated IL-6 can cause the non-osmotic release of ADH and increase the occurrence of hyponatremia. McGonagle et&#xa0;al. found that increased IL-6 and TNF-&#x3b1; were associated with decreased FT3 levels in patients with severe COVID-19 (<xref ref-type="bibr" rid="B45">45</xref>). In patients with SARS-CoV-2 infection, elevated IL-6 is associated with subacute thyroiditis, Graves&#x2019; disease, and Hashimoto&#x2019;s thyroiditis (<xref ref-type="bibr" rid="B46">46</xref>), while abnormalities of the hypothalamic-pituitary-thyroid axis can cause a series of changes in TSH and thyroid hormones. Hyponatremia and low FT3 together affect the severity and prognosis of the disease (<xref ref-type="bibr" rid="B3">3</xref>). The patients in our hyponatremia group had a poorer general condition (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table S6</bold>
</xref>), were at higher risk for multiorgan dysfunction, and were more likely to be treated with glucocorticoids according to guideline recommendations. In contrast, the administration of glucocorticoids decreases TSH levels and inhibits the conversion of T4 to T3 while stimulating the conversion of T4 to rT3 (<xref ref-type="bibr" rid="B47">47</xref>, <xref ref-type="bibr" rid="B48">48</xref>), the changes similar to those observed in non-thyroidal illness syndrome. Decreases in TSH and T3 are common, and the degree of decrease in T3 correlates with disease severity. Although the results of these studies were based on patients with SARS-CoV-2 infection, it also suggests that we should be aware of thyroid abnormalities in other lung lesions.</p>
<p>Our study also has several additional limitations: First, we did not observe the dynamic evolution of CT scores and hyponatremia in this cross-sectional study, and the lack of a certain follow-up period may make our conclusions partial. Second, the semi-quantitative CT score used in this study is subject to error and unvalidated, as well as the lack of specific serologic measures of specificity (e.g., ADH), which could attenuate the association between CT score and hyponatremia. Third, this study lacked a study of patients with hypernatremia because hypernatremia may be associated with worse outcomes (ICU admission, intubation, death). Finally, there were no statistics on vaccination status, such as the occurrence of autoimmune thyroid disease after COVID-19 vaccination, in some studies, so it is difficult to rule out the effect of this confounding factor.</p>
<p>In conclusion, in this study, for the first time, the semi-quantitative CT visual score was associated with hyponatremia, and the endocrine factor (thyroid function) was analyzed to clarify the relationship further. It was found that the CT score level can be used to evaluate the occurrence of hyponatremia, which can achieve early detection, prediction, and intervention in clinical practice. It is helpful to reduce the occurrence of clinical complications. Although our study population was derived from patients with SARS-CoV-2 infection, it provides a new perspective for analyzing patients with other lung lesions or endocrine abnormalities.</p>
</sec>
<sec id="s5" sec-type="conclusions">
<label>5</label>
<title>Conclusion</title>
<p>In our study, CT semi-quantitative score was associated with hyponatremia for the first time, and the endocrine factor (thyroid function) was analyzed to clarify further the association, and high TSS was found to be a risk factor for hyponatremia. Although our study population was derived from patients with SARS-CoV-2 infection, it provides a new perspective for analyzing patients with other lung lesions or endocrine abnormalities. The haze brought by COVID-19 has gradually dissipated, but new variants still exist, and the research on long COVID-19 is in the early stages. We hope our research can provide a reference for disease prevention, diagnosis, and treatment.</p>
</sec>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Material</bold></xref>. Further inquiries can be directed to the corresponding authors.</p>
</sec>
<sec id="s7" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>The studies involving humans were approved by the Ethics Committee of the First Hospital of Shanxi Medical University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.</p>
</sec>
<sec id="s8" sec-type="author-contributions">
<title>Author contributions</title>
<p>BW: Visualization, Writing &#x2013; review &amp; editing, Writing &#x2013; original draft, Investigation, Formal analysis, Data curation. RL: Formal analysis, Data curation, Writing &#x2013; review &amp; editing, Writing &#x2013; original draft, Methodology, Investigation. JH: Writing&#xa0;&#x2013; review &amp; editing, Visualization, Formal analysis, Data curation. YQ: Writing &#x2013; review &amp; editing, Visualization, Formal analysis, Data curation. BL: Writing &#x2013; review &amp; editing, Software, Formal analysis, Data curation. HW: Writing &#x2013; review &amp; editing, Software, Formal analysis, Data curation. ZL: Writing &#x2013; review &amp; editing, Software, Formal analysis, Data curation. YZ: Writing &#x2013; review &amp; editing, Supervision, Methodology, Funding acquisition, Conceptualization. YL: Writing &#x2013; review &amp; editing, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization.</p>
</sec>
</body>
<back>
<sec id="s9" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (Grant numbers 81973378 and 82073909), Shanxi Scholarship Council of China (Grant number:2020-0172), the Shanxi Provincial Central Leading Local Science and Technology Development Fund Project (Grant number: YDZJSX2022A059), Four &#x201c;Batches&#x201d; Innovation Project of Invigorating Medical through Science and Technology of Shanxi Province (Grant number:2023XM022).</p>
</sec>
<sec id="s10" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s11" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s12" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fendo.2024.1342204/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fendo.2024.1342204/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Table_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
</sec>
<ref-list>
<title>References</title>
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</name>
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<surname>Guignard</surname> <given-names>V</given-names>
</name>
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