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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Endocrinol.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Endocrinology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Endocrinol.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">1664-2392</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fendo.2026.1792072</article-id>
<article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Diagnostic value of 24-h urinary aldosterone and related biomarkers for screening primary aldosteronism in medication-exposed populations</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Chen</surname><given-names>Siyu</given-names></name>
<uri xlink:href="https://loop.frontiersin.org/people/3356267/overview"/>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="methodology" vocab-term-identifier="https://credit.niso.org/contributor-roles/methodology/">Methodology</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; original draft" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing &#x2013; original draft</role>
</contrib>
<contrib contrib-type="author">
<name><surname>Chen</surname><given-names>Ke</given-names></name>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Data curation" vocab-term-identifier="https://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; original draft" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing &#x2013; original draft</role>
</contrib>
<contrib contrib-type="author">
<name><surname>Wu</surname><given-names>Junyi</given-names></name>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Data curation" vocab-term-identifier="https://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
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<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; original draft" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing &#x2013; original draft</role>
</contrib>
<contrib contrib-type="author">
<name><surname>Yang</surname><given-names>Jiashu</given-names></name>
<uri xlink:href="https://loop.frontiersin.org/people/2512026/overview"/>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; original draft" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing &#x2013; original draft</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Data curation" vocab-term-identifier="https://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
</contrib>
<contrib contrib-type="author">
<name><surname>Shen</surname><given-names>Linlin</given-names></name>
<uri xlink:href="https://loop.frontiersin.org/people/2971376/overview"/>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; original draft" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing &#x2013; original draft</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Formal analysis" vocab-term-identifier="https://credit.niso.org/contributor-roles/formal-analysis/">Formal analysis</role>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Yuan</surname><given-names>Hui</given-names></name>
<xref ref-type="corresp" rid="c001"><sup>*</sup></xref>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Project-administration" vocab-term-identifier="https://credit.niso.org/contributor-roles/project-administration/">Project administration</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; review &amp; editing" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing &#x2013; review &amp; editing</role>
</contrib>
</contrib-group>
<aff id="aff1"><institution>Department of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical University</institution>, <city>Beijing</city>,&#xa0;<country country="cn">China</country></aff>
<author-notes>
<corresp id="c001"><label>*</label>Correspondence: Hui Yuan, <email xlink:href="mailto:yuanhui_AZYY@163.com">yuanhui_AZYY@163.com</email></corresp>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-02-27">
<day>27</day>
<month>02</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2026</year>
</pub-date>
<volume>17</volume>
<elocation-id>1792072</elocation-id>
<history>
<date date-type="received">
<day>20</day>
<month>01</month>
<year>2026</year>
</date>
<date date-type="accepted">
<day>16</day>
<month>02</month>
<year>2026</year>
</date>
<date date-type="rev-recd">
<day>12</day>
<month>02</month>
<year>2026</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2026 Chen, Chen, Wu, Yang, Shen and Yuan.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Chen, Chen, Wu, Yang, Shen and Yuan</copyright-holder>
<license>
<ali:license_ref start_date="2026-02-27">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. 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.</license-p>
</license>
</permissions>
<abstract>
<sec>
<title>Objective</title>
<p>To evaluate the screening value of 24-hour urinary aldosterone (24h-Uald) and the urinary aldosterone&#x2013;to&#x2013;renin ratio (UARR) for primary aldosteronism (PA in hypertensive patients under conditions of ongoing antihypertensive medication use.</p>
</sec>
<sec>
<title>Methods</title>
<p>Hypertensive patients who underwent PA screening at our hospital between August 2024 and August 2025 were retrospectively enrolled. Baseline clinical characteristics and biochemical parameters were collected while patients were receiving ongoing antihypertensive therapy. Multivariable logistic regression analysis was performed to assess the independent associations of 24h-Uald and UARR with PA. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis.</p>
</sec>
<sec>
<title>Results</title>
<p>Both 24h-Uald and UARR levels were significantly higher in patients with PA than in those with essential hypertension (EH) (P &lt; 0.05). After adjustment for potential confounders, including age, body mass index, renal function, and history of coronary artery disease, 24h-Uald and UARR remained independently associated with PA. ROC curve analysis demonstrated a modest diagnostic performance for 24h-Uald (AUC = 0.657), whereas UARR exhibited excellent diagnostic accuracy (AUC = 0.862).</p>
</sec>
<sec>
<title>Conclusions</title>
<p>Under conditions of ongoing antihypertensive medication use, the diagnostic performance of 24h-Uald alone for PA screening is limited. In contrast, UARR, incorporating renin measurements, demonstrates higher diagnostic accuracy and greater clinical utility.</p>
</sec>
</abstract>
<kwd-group>
<kwd>aldosterone-to-renin ratio</kwd>
<kwd>hypertension</kwd>
<kwd>medication effect</kwd>
<kwd>primary aldosteronism</kwd>
<kwd>urinary aldosterone</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Research-Oriented Wards Excellent Clinical Research Program (Grant No. BRWEP2024W012060108).</funding-statement>
</funding-group>
<counts>
<fig-count count="2"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="30"/>
<page-count count="9"/>
<word-count count="5106"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Cardiovascular Endocrinology</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>For Primary aldosteronism (PA) is one of the most common causes of secondary hypertension and is characterized by inappropriate aldosterone overproduction, leading to a spectrum of clinical manifestations, including hypertension and hypokalemia (<xref ref-type="bibr" rid="B1">1</xref>). Excessive and inappropriate aldosterone secretion promotes cardiovascular remodeling and increases the risk of adverse cardiovascular events, such as atrial fibrillation and heart failure, which occur more frequently in patients with primary aldosteronism than in those with essential hypertension (<xref ref-type="bibr" rid="B2">2</xref>&#x2013;<xref ref-type="bibr" rid="B4">4</xref>). In recent years, with the increasing understanding of PA, the clinical screening rate has been increasing year by year and is currently around 5% (<xref ref-type="bibr" rid="B5">5</xref>). Early screening and diagnosis of primary aldosteronism enable timely individualized treatment and may reduce the risk of disease-related complications.</p>
<p>The renin&#x2013;angiotensin&#x2013;aldosterone system (RAAS) plays a central role in maintaining fluid balance, regulating electrolyte homeostasis, and controlling blood pressure. The pathophysiology of PA is characterized by autonomous aldosterone secretion from the adrenal cortex, which leads to feedback suppression of renin release. Consequently, initial screening for PA relies primarily on the aldosterone-to-renin ratio (ARR), which reflects the inappropriate elevation of aldosterone relative to suppressed renin levels (<xref ref-type="bibr" rid="B6">6</xref>). This index provides a comprehensive response to the status of the RAAS axis. However, plasma aldosterone levels are susceptible to a wide range of medications and physiological factors (<xref ref-type="bibr" rid="B7">7</xref>), and therefore medication adjustments, postural and salt intake control, and other treatments are often required prior to screening. Multiple antihypertensive medications may result in false-positive or false-negative ARR, which in turn affects the judgment of PA (<xref ref-type="bibr" rid="B8">8</xref>). Current guidelines recommend that all hypertensive patients be screened for PA at least once. Interfering medications need to be discontinued for 2&#x2013;4 weeks, but discontinuation may lead to uncontrolled blood pressure and delayed diagnosis, so the necessity of switching medications before screening for ARR remains a controversial issue in clinical practice. Aldosterone secretion has a distinct circadian rhythm. Compared with a single blood sample, 24-h urinary aldosterone (24h-Uald) provides a more complete picture of a complete secretion cycle, while the urine matrix is relatively clean and less disturbed by external factors (<xref ref-type="bibr" rid="B9">9</xref>). Previous studies suggest that 24h-Uald has potential clinical value in the diagnosis of PA (<xref ref-type="bibr" rid="B10">10</xref>&#x2013;<xref ref-type="bibr" rid="B12">12</xref>).</p>
<p>Based on the above background, the aim of this study was to investigate the value of 24h-Uald and its related indexes in PA screening under the condition of taking drugs that interfere with the reliability of ARR results.</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 and patient population</title>
<p>This retrospective study was conducted at Beijing Anzhen Hospital, Capital Medical University. A total of 280 hypertensive patients who underwent screening for primary aldosteronism (PA) between August 2024 and August 2025 were included.</p>
<p>Inclusion criteria were as follows (<xref ref-type="bibr" rid="B1">1</xref>): Patients who met the diagnostic criteria for hypertension, defined as systolic blood pressure &gt;140 mmHg and/or diastolic blood pressure &gt;90 mmHg on three separate occasions on non-consecutive days in the absence of antihypertensive medications (<xref ref-type="bibr" rid="B2">2</xref>). Patients receiving medications that could interfere with the aldosterone-to-renin ratio (ARR) at admission and who had not yet undergone medication washout (<xref ref-type="bibr" rid="B3">3</xref>). Patients who subsequently completed medication washout and underwent repeat ARR screening and confirmatory testing for PA (<xref ref-type="bibr" rid="B4">4</xref>). Patients with complete clinical and laboratory data.</p>
<p>Exclusion criteria were as follows: (1) Missing key clinical or laboratory data. (2) Presence of other causes of secondary hypertension, including renal parenchymal hypertension, renal vascular hypertension, pheochromocytoma, and Cushing&#x2019;s syndrome. (3) Failure to complete medication washout and subsequent PA screening and confirmatory testing. (4) Pregnancy. (5) Severe comorbid conditions, including advanced hepatic or renal failure, severe cardiovascular or cerebrovascular disease, autoimmune disease, or malignancy.</p>
<p>Interfering medications and washout: We recorded detailed medication profiles for all patients at admission (pre-washout). Medications were categorized into three groups based on their potential pharmacological interference with the ARR:</p>
<p>Potential false-negative group, including angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), calcium channel blockers (CCBs), diuretics, mineralocorticoid receptor antagonists (MRAs, e.g., spironolactone), and amiloride (<xref ref-type="bibr" rid="B13">13</xref>). Potential false-positive group, including &#x3b2;-blockers and non-steroidal anti-inflammatory drugs (NSAIDs) (<xref ref-type="bibr" rid="B14">14</xref>). Mixed-interference group, referring to patients concurrently taking medications from both aforementioned categories.</p>
<p>To ensure diagnostic accuracy, specific washout periods were implemented: MRAs and diuretics were discontinued for at least 4 weeks, while other interfering medications (including ACEIs, ARBs, CCBs, and &#x3b2;-blockers) were discontinued for at least 2 weeks prior to biochemical testing and confirmatory trials (<xref ref-type="bibr" rid="B15">15</xref>).</p>
<p>Confirmatory testing: Patients underwent either the captopril challenge test (CCT) or the saline infusion test (SIT). For the CCT, 50 mg of captopril was administered orally, and blood samples were collected after 4 hours. PA was diagnosed based on a comprehensive assessment: aplasma aldosterone concentration (PAC) decrease of &#x2264;30% from baseline and/or a post-CCT PAC&#x2265; 11ng/dL. For the SIT, 2000 mL of 0.9% saline was infused intravenously over 4 hours, and a post-infusion PAC &gt;10 ng/dL supported the diagnosis. Patients were classified into the PA or essential hypertension (EH) group based on the results of confirmatory testing.</p>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Clinical and biochemical data</title>
<p>Clinical and biochemical data were retrieved from the hospital electronic medical record system. Baseline clinical information at admission (prior to medication washout) was collected, including sex, age, body mass index (BMI), systolic and diastolic blood pressure, and comorbidities such as hyperlipidemia, coronary heart disease (CHD), and diabetes mellitus. Biochemical parameters at admission were also recorded, including urea, creatinine, estimated glomerular filtration rate (eGFR), uric acid, serum potassium (K), plasma aldosterone concentration (PAC), direct renin concentration (DRC), aldosterone-to-renin ratio (ARR), 24h-Uald, 24-h urinary potassium, and 24-h urinary sodium. Boric acid was added to the 24-hour urine collection containers as a preservative to maintain the stability of aldosterone levels prior to analysis.</p>
<p>The urinary aldosterone-to-renin ratio (UARR) was calculated using the following formula: UARR = 24h-Uald/DRC.</p>
<p>Here, 24-h Uald represents the 24-hour urinary aldosterone level (numerator), and DRC denotes the plasma direct renin concentration (denominator).</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Statistical analysis</title>
<p>Sample size calculation was performed using PASS software, and the adequacy of the sample size was determined based on the calculated statistical power. Data analysis was conducted using R software (version 4.4.1). The normality of continuous variables was assessed using the Shapiro&#x2013;Wilk test. Normally distributed continuous variables were expressed as mean &#xb1; standard deviation (mean &#xb1; SD), whereas non-normally distributed continuous variables were presented as median (P25, P75). Categorical variables were expressed as counts and percentages. Differences between two groups for continuous variables were compared using the independent samples t-test or the Mann&#x2013;Whitney U test, as appropriate, and categorical variables were compared using the chi-square test. The associations between diagnostic indicators and clinical parameters were assessed using Spearman&#x2019;s rank correlation analysis. Multivariable logistic regression analysis was used to assess the association between 24-h urinary aldosterone and related indicators with PA. The diagnostic performance of each indicator was evaluated using receiver operating characteristic (ROC) curve analysis, and differences in area under the curve (AUC) between ROC curves were compared using the DeLong test.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<sec id="s3_1">
<label>3.1</label>
<title>Baseline characteristics of patients with PA and EH</title>
<p>A total of 280 hypertensive patients who had not yet undergone medication washout and were awaiting diagnosis were included in this study. After medication washout, 204 patients were diagnosed with EH and 76 patients were diagnosed with PA. The baseline characteristics of the patients are presented in <xref ref-type="table" rid="T1"><bold>Table&#xa0;1</bold></xref>. Regarding medication status at admission, no significant differences were observed between the PA and EH groups in the distribution of potential false-negative, potential false-positive, and mixed-interference medications (P = 0.219). Compared with the EH group, patients with PA were older and had higher rates of diabetes and CHD. In addition, PAC, ARR, K, 24h-Uald, and the UARR were significantly higher in the PA group, while BMI, eGFR, and renin levels were significantly lower (P &lt; 0.05). There were no significant differences between the two groups in terms of sex, systolic blood pressure, diastolic blood pressure, history of hyperlipidemia, urea, creatinine, uric acid, 24-h urinary sodium, or 24-h urinary potassium. The differences in 24-h Uald and log-transformed UARR between PA and EH patients are shown (<xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1</bold></xref>).</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Clinical and biochemical characteristics of patients with PA and EH.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="center">Characteristics</th>
<th valign="top" align="center">EH (n=204)</th>
<th valign="top" align="center">PA(n=76)</th>
<th valign="top" align="center"><italic>P value</italic></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="center">Age (y)</td>
<td valign="top" align="center">52.00 (42.00, 61.00)</td>
<td valign="top" align="center">55.00 (47.75, 62.00)</td>
<td valign="top" align="center">0.03454</td>
</tr>
<tr>
<td valign="top" align="center">Male</td>
<td valign="top" align="center">126(61.8%)</td>
<td valign="top" align="center">39(51.3%)</td>
<td valign="top" align="center">0.114</td>
</tr>
<tr>
<td valign="top" align="center">BMI (kg/m<sup>2</sup>)</td>
<td valign="top" align="center">27.70 (24.87, 30.50)</td>
<td valign="top" align="center">26.00 (24.00, 28.75)</td>
<td valign="top" align="center">0.04549</td>
</tr>
<tr>
<td valign="top" align="center">SBP (mm Hg)</td>
<td valign="top" align="center">148.00 (134.00, 162.25)</td>
<td valign="top" align="center">150.50 (139.00, 166.50)</td>
<td valign="top" align="center">0.3504</td>
</tr>
<tr>
<td valign="top" align="center">DBP (mm Hg)</td>
<td valign="top" align="center">96.06 &#xb1; 15.62</td>
<td valign="top" align="center">95.97 &#xb1; 13.14</td>
<td valign="top" align="center">0.9615</td>
</tr>
<tr>
<td valign="top" align="center">Hyperlipidemia</td>
<td valign="top" align="center">113(55.4%)</td>
<td valign="top" align="center">50(65.8%)</td>
<td valign="top" align="center">0.1167</td>
</tr>
<tr>
<td valign="top" align="center">Diabetes</td>
<td valign="top" align="center">30(14.7%)</td>
<td valign="top" align="center">19(25%)</td>
<td valign="top" align="center">0.0438</td>
</tr>
<tr>
<td valign="top" align="center">CHD</td>
<td valign="top" align="center">8(3.9%)</td>
<td valign="top" align="center">12(15.8%)</td>
<td valign="top" align="center">&lt;0.01</td>
</tr>
<tr>
<td valign="top" align="center">Urea(mmol/L)</td>
<td valign="top" align="center">5.38 (4.53, 6.26)</td>
<td valign="top" align="center">5.38 (4.75, 6.51)</td>
<td valign="top" align="center">0.2672</td>
</tr>
<tr>
<td valign="top" align="center">Creatinine (&#x3bc;mol/L)</td>
<td valign="top" align="center">71.05 (59.25, 81.58)</td>
<td valign="top" align="center">71.65 (59.45, 82.22)</td>
<td valign="top" align="center">0.4978</td>
</tr>
<tr>
<td valign="top" align="center">eGFR (mL/min/1.73 m<sup>2</sup>)</td>
<td valign="top" align="center">97.34 (89.27, 108.27)</td>
<td valign="top" align="center">95.18 (81.61, 101.72)</td>
<td valign="top" align="center">0.01328</td>
</tr>
<tr>
<td valign="top" align="center">Uric acid (&#x3bc;mol/L)</td>
<td valign="top" align="center">363.05 (295.75, 430.75)</td>
<td valign="top" align="center">335.50 (276.00, 414.48)</td>
<td valign="top" align="center">0.07645</td>
</tr>
<tr>
<td valign="top" align="center">K<sup>+</sup>(mmol/L)</td>
<td valign="top" align="center">3.93 &#xb1; 0.33</td>
<td valign="top" align="center">3.79 &#xb1; 0.42</td>
<td valign="top" align="center">&lt;0.01</td>
</tr>
<tr>
<td valign="top" align="center">PAC (ng/dl)</td>
<td valign="top" align="center">12.50 (8.25, 17.92)</td>
<td valign="top" align="center">17.70 (13.02, 24.97)</td>
<td valign="top" align="center">&lt;0.01</td>
</tr>
<tr>
<td valign="top" align="center">DRC (ulU/ml)</td>
<td valign="top" align="center">17.75 (6.10, 38.67)</td>
<td valign="top" align="center">3.50 (1.15, 6.65)</td>
<td valign="top" align="center">&lt;0.01</td>
</tr>
<tr>
<td valign="top" align="center">ARR</td>
<td valign="top" align="center">0.80 (0.20, 1.80)</td>
<td valign="top" align="center">5.25 (2.58, 11.45)</td>
<td valign="top" align="center">&lt;0.01</td>
</tr>
<tr>
<td valign="top" align="center">24h- Uald (&#x3bc;g/24h)</td>
<td valign="top" align="center">5.95 (3.75, 9.02)</td>
<td valign="top" align="center">8.21 (5.13, 13.13)</td>
<td valign="top" align="center">&lt;0.01</td>
</tr>
<tr>
<td valign="top" align="center">24h- Urine Na<sup>+</sup> (mmol/24h)</td>
<td valign="top" align="center">123.55 (83.90, 171.77)</td>
<td valign="top" align="center">114.50 (86.18, 152.65)</td>
<td valign="top" align="center">0.3813</td>
</tr>
<tr>
<td valign="top" align="center">24-h Urine K<sup>+</sup> (mmol/24h)</td>
<td valign="top" align="center">37.60 (30.90, 49.10)</td>
<td valign="top" align="center">37.60 (31.32, 46.58)</td>
<td valign="top" align="center">0.7312</td>
</tr>
<tr>
<td valign="top" align="center">UARR</td>
<td valign="top" align="center">0.36(0.11, 0.93)</td>
<td valign="top" align="center">2.49(1.36, 6.52)</td>
<td valign="top" align="center">&lt;0.01</td>
</tr>
<tr>
<td valign="top" align="center">Medication Status</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">0.219</td>
</tr>
<tr>
<td valign="top" align="center">Potential false-negative drug <xref ref-type="table-fn" rid="fnT1_1"><sup>a</sup></xref></td>
<td valign="top" align="center">120(58.82%)</td>
<td valign="top" align="center">36(47.37%)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="center">Potential false-positive drug<xref ref-type="table-fn" rid="fnT1_2"><sup>b</sup></xref></td>
<td valign="top" align="center">17(8.33%)</td>
<td valign="top" align="center">9(11.84%)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="center">Mixed-interference drug<xref ref-type="table-fn" rid="fnT1_3"><sup>c</sup></xref></td>
<td valign="top" align="center">67(32.84%)</td>
<td valign="top" align="center">31(40.79%)</td>
<td valign="top" align="center"/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="fnT1_1"><label>a</label>
<p>Potential false-negative drugs include ACEIs, ARBs, CCBs, diuretics, MRAs, and amiloride.</p></fn>
<fn id="fnT1_2"><label>b</label>
<p>Potential false-positive drugs include &#x3b2;-blockers and NSAIDs.</p></fn>
<fn id="fnT1_3"><label>c</label>
<p>Mixed-interference group refers to the concurrent use of medications from both of the aforementioned categories.</p></fn>
</table-wrap-foot>
</table-wrap>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Differences in 24-h Uald levels and UARR between PA and EH patients. <bold>(A)</bold> Comparison of 24-h Uald levels between PA and EH patients. <bold>(B)</bold> Comparison of log-transformed UARR between PA and EH patients. Ratios were log-transformed for graphical clarity.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-17-1792072-g001.tif">
<alt-text content-type="machine-generated">Boxplot panel A presents 24-hour urinary aldosterone levels and panel B presents logarithmic urinary aldosterone-to-renin ratio for EH (yellow) and PA (pink); PA values are significantly higher than EH, as indicated by asterisks.</alt-text>
</graphic></fig>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Association between 24-h Uald, UARR and the risk of PA</title>
<p>To evaluate the biochemical stability of the indicators, we first analyzed the correlations between 24h-Uald, UARR, and renal-related clinical parameters. Spearman correlation analysis showed that 24h-Uald was significantly correlated with serum K (rho = -0.25, P &lt; 0.01) and marginally correlated with eGFR (rho = 0.12, P = 0.052). In contrast, UARR demonstrated superior stability, showing no significant association with serum K (P = 0.18) and only a very weak correlation with eGFR (rho = -0.12, P = 0.045). These findings suggest that UARR is more robust against fluctuations in serum potassium and renal function than 24h-Uald.</p>
<p>Multivariable logistic regression analysis was performed to identify factors associated with PA (<xref ref-type="table" rid="T2"><bold>Table&#xa0;2</bold></xref>). Model 1 included age, BMI, history of CHD, eGFR, and K. The results showed that BMI(OR 0.93, 95% CI 0.86&#x2013;1.00, P = 0.04), CHD history (OR4.10, 95% CI1.49&#x2013;11.32, P &lt; 0.01), eGFR (OR 0.97, 95% CI0.95&#x2013;0.99, P =0.01) and K(OR 0.23, 95% CI 0.10&#x2013;0.51, P&lt; 0.01) were significantly associated with PA, whereas age was not statistically significant. In Model 2, after 24h-Uald was added to Model 1, 24h-Uald remained an independent predictor of PA (OR1.17, 95% CI1.09&#x2013;1.26, P &lt; 0.01), while BMI, CHD history, eGFR and K continued to be independent factors. In Model 3, UARR was added to Model 1, UARR was independently and positively associated with PA (OR1.38, 95% CI1.19&#x2013;1.59, P &lt; 0.01), while CHD history, eGFR and K remained statistically significant in this model.</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Clinical and biochemical characteristics in relation to PA.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="center">Variable</th>
<th valign="middle" colspan="2" align="center">Model 1</th>
<th valign="middle" colspan="2" align="center">Model 2</th>
<th valign="middle" colspan="2" align="center">Model 3</th>
</tr>
<tr>
<th valign="middle" align="center">OR</th>
<th valign="middle" align="center"><italic>P value</italic></th>
<th valign="middle" align="center">OR</th>
<th valign="middle" align="center"><italic>P value</italic></th>
<th valign="middle" align="center">OR</th>
<th valign="middle" align="center"><italic>P value</italic></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">Age</td>
<td valign="middle" align="center">1.00(0.95-1.03)</td>
<td valign="middle" align="center">0.76</td>
<td valign="middle" align="center">1.02(0.98-1.05)</td>
<td valign="middle" align="center">0.40</td>
<td valign="middle" align="center">0.99 (0.96&#x2013;1.03)</td>
<td valign="middle" align="center">0.59</td>
</tr>
<tr>
<td valign="middle" align="center">BMI</td>
<td valign="middle" align="center">0.93(0.86-1.00)</td>
<td valign="middle" align="center">0.04</td>
<td valign="middle" align="center">0.88(0.81-0.96)</td>
<td valign="middle" align="center">&lt;0.01</td>
<td valign="middle" align="center">0.95(0.88-1.03)</td>
<td valign="middle" align="center">0.24</td>
</tr>
<tr>
<td valign="middle" align="center">CHD</td>
<td valign="middle" align="center">4.10(1.49-11.32)</td>
<td valign="middle" align="center">&lt;0.01</td>
<td valign="middle" align="center">2.94(1.00-8.64)</td>
<td valign="middle" align="center">0.05</td>
<td valign="middle" align="center">4.20(1.42-12.37)</td>
<td valign="middle" align="center">0.01</td>
</tr>
<tr>
<td valign="middle" align="center">eGFR</td>
<td valign="middle" align="center">0.97(0.95-0.99)</td>
<td valign="middle" align="center">0.01</td>
<td valign="middle" align="center">0.97(0.95-1.00)</td>
<td valign="middle" align="center">0.02</td>
<td valign="middle" align="center">0.97(0.95-1.00)</td>
<td valign="middle" align="center">0.02</td>
</tr>
<tr>
<td valign="middle" align="center">K</td>
<td valign="middle" align="center">0.23(0.10-0.51)</td>
<td valign="middle" align="center">&lt;0.01</td>
<td valign="middle" align="center">0.37(0.16-0.88)</td>
<td valign="middle" align="center">0.02</td>
<td valign="middle" align="center">0.29(0.12-0.71)</td>
<td valign="middle" align="center">&lt;0.01</td>
</tr>
<tr>
<td valign="middle" align="center">24-h Uald</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">1.17(1.09-1.26)</td>
<td valign="middle" align="center">&lt;0.01</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center">UARR</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">1.38(1.19-1.59)</td>
<td valign="middle" align="center">&lt;0.01</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Diagnostic value of 24-h Uald and UARR for PA</title>
<p>ROC curves for ARR, 24h-Uald, and the UARR were plotted without stratifying patients by medication status (<xref ref-type="fig" rid="f2"><bold>Figure&#xa0;2A</bold></xref>). The areas under the ROC curves (AUCs) for 24h-Uald, UARR, and ARR were 0.657, 0.862, and 0.867, respectively, indicating that UARR had good discriminative ability. The AUC of UARR was comparable to that of ARR, with no statistically significant difference (P = 0.618). The corresponding optimal cut-off values, sensitivities, and specificities are presented in <xref ref-type="table" rid="T3"><bold>Table&#xa0;3</bold></xref>. Using the Youden index to determine the optimal thresholds, the cut-off value for 24h-Uald was 7.435 &#x3bc;g/24 h (sensitivity 0.632, specificity 0.652), the cut-off value for UARR was 0.893 (sensitivity 0.833, specificity 0.791), and the cut-off value for ARR was 1.65 (sensitivity 0.882, specificity 0.740).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>ROC curves of 24-h Uald, UARR, and ARR for the diagnosis of PA. <bold>(A)</bold> ROC curves without stratification by antihypertensive medications. <bold>(B)</bold> ROC curves in patients receiving potential false-negative medications. <bold>(C)</bold> ROC curves in patients receiving mixed medications (containing both false-negative and false-positive effects).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-17-1792072-g002.tif">
<alt-text content-type="machine-generated">Three panels labeled A, B, and C display receiver operating characteristic (ROC) curves with yellow, pink, and blue lines for ARR, UALD, and UARR, respectively. All panels plot sensitivity against one minus specificity, demonstrating model performance for each metric. The blue and yellow lines consistently show higher sensitivities across varying specificities compared to the pink line, indicating superior diagnostic accuracy for UARR and ARR over UALD in each panel.</alt-text>
</graphic></fig>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>Diagnostic performance of 24-h Uald, UARR, and ARR for diagnosing PA.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Medications</th>
<th valign="middle" align="center">Biomarker</th>
<th valign="middle" align="center">AUC</th>
<th valign="middle" align="center">Threshold</th>
<th valign="middle" align="center">Sensitivity</th>
<th valign="middle" align="center">Specificity</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" rowspan="3" align="center">Overall medication use</td>
<td valign="middle" align="center">ARR</td>
<td valign="middle" align="center">0.867</td>
<td valign="middle" align="center">1.650</td>
<td valign="middle" align="center">0.882</td>
<td valign="middle" align="center">0.740</td>
</tr>
<tr>
<td valign="middle" align="center">24-h Uald (&#x3bc;g/24h)</td>
<td valign="middle" align="center">0.657</td>
<td valign="middle" align="center">7.435</td>
<td valign="middle" align="center">0.632</td>
<td valign="middle" align="center">0.652</td>
</tr>
<tr>
<td valign="middle" align="center">UARR</td>
<td valign="middle" align="center">0.862</td>
<td valign="middle" align="center">0.893</td>
<td valign="middle" align="center">0.842</td>
<td valign="middle" align="center">0.745</td>
</tr>
<tr>
<td valign="top" rowspan="3" align="center">False-negative medications</td>
<td valign="middle" align="center">ARR</td>
<td valign="middle" align="center">0.863</td>
<td valign="middle" align="center">1.450</td>
<td valign="middle" align="center">0.833</td>
<td valign="middle" align="center">0.791</td>
</tr>
<tr>
<td valign="middle" align="center">24-h Uald (&#x3bc;g/24h)</td>
<td valign="middle" align="center">0.688</td>
<td valign="middle" align="center">7.900</td>
<td valign="middle" align="center">0.694</td>
<td valign="middle" align="center">0.667</td>
</tr>
<tr>
<td valign="middle" align="center">UARR</td>
<td valign="middle" align="center">0.874</td>
<td valign="middle" align="center">0.893</td>
<td valign="middle" align="center">0.806</td>
<td valign="middle" align="center">0.842</td>
</tr>
<tr>
<td valign="top" rowspan="3" align="center">Confounding medications</td>
<td valign="middle" align="center">ARR</td>
<td valign="middle" align="center">0.90</td>
<td valign="middle" align="center">2.45</td>
<td valign="middle" align="center">0.90</td>
<td valign="middle" align="center">0.76</td>
</tr>
<tr>
<td valign="middle" align="center">24-h Uald (&#x3bc;g/24h)</td>
<td valign="middle" align="center">0.654</td>
<td valign="middle" align="center">7.42</td>
<td valign="middle" align="center">0.61</td>
<td valign="middle" align="center">0.67</td>
</tr>
<tr>
<td valign="middle" align="center">UARR</td>
<td valign="middle" align="center">0.871</td>
<td valign="middle" align="center">1.32</td>
<td valign="middle" align="center">0.87</td>
<td valign="middle" align="center">0.75</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>Baseline characteristics of patients with PA and EH under the influence of different types of drugs</title>
<p>All patients included in this study were evaluated before medication washout. Since most patients were taking multiple antihypertensive drugs, medications were categorized into three groups based on their potential interference with ARR results: false-negative drugs, false-positive drugs, and mixed-interference drugs (i.e., drugs with both false-negative and false-positive effects). Because the sample size in the false-positive drug group was limited, this group was not analyzed.</p>
<p>Clinical characteristics of PA and EH patients under false-negative and mixed-interference drug conditions are shown in <xref ref-type="table" rid="T4"><bold>Table&#xa0;4</bold></xref>. Under false-negative drug conditions, PAC, ARR, 24h-Uald, 24-h urinary potassium, and UARR were significantly higher in the PA group than in the EH group, while the prevalence of hyperlipidemia, renin levels, and 24-h urinary sodium were significantly lower in PA (P &lt; 0.05). Under mixed-interference drug conditions, patients with PA were older and had higher rates of diabetes and coronary heart disease. In addition, PAC, ARR, 24h-Uald, and UARR were significantly higher in the PA group, whereas eGFR, uric acid, and renin levels were significantly lower compared with the EH group (P &lt; 0.05).</p>
<table-wrap id="T4" position="float">
<label>Table&#xa0;4</label>
<caption>
<p>Clinical and biochemical characteristics of Patients with PA and EH stratified by drug.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center"/>
<th valign="middle" colspan="3" align="center">False-negative medications (n= 156)</th>
<th valign="middle" colspan="3" align="center">Confounding medications (n= 98)</th>
</tr>
<tr>
<th valign="middle" align="center"/>
<th valign="middle" align="center">EH(n=121)</th>
<th valign="middle" align="center">PA(n=35)</th>
<th valign="middle" align="center"><italic>P value</italic></th>
<th valign="middle" align="center">EH(n=67)</th>
<th valign="middle" align="center">PA(n=31)</th>
<th valign="middle" align="center"><italic>P Value</italic></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">Age</td>
<td valign="middle" align="center">51(41, 61.25)</td>
<td valign="middle" align="center">53(45, 60.25)</td>
<td valign="middle" align="center">0.60</td>
<td valign="middle" align="center">52.72 &#xb1; 11.74</td>
<td valign="middle" align="center">57.55 &#xb1; 9.69</td>
<td valign="middle" align="center">0.05</td>
</tr>
<tr>
<td valign="middle" align="center">Mela (man)</td>
<td valign="middle" align="center">75(62.5%)</td>
<td valign="middle" align="center">18(50%)</td>
<td valign="middle" align="center">0.25</td>
<td valign="middle" align="center">41(61.2%)</td>
<td valign="middle" align="center">19(61.3%)</td>
<td valign="middle" align="center">1.00</td>
</tr>
<tr>
<td valign="middle" align="center">BMI</td>
<td valign="middle" align="center">27.65(24.75, 30.70)</td>
<td valign="middle" align="center">26.25(23.70, 28.48)</td>
<td valign="middle" align="center">0.18</td>
<td valign="middle" align="center">27.93 &#xb1; 4.06</td>
<td valign="middle" align="center">26.64 &#xb1; 3.70</td>
<td valign="middle" align="center">0.14</td>
</tr>
<tr>
<td valign="middle" align="center">SBP</td>
<td valign="middle" align="center">149.50(139.75, 162.25)</td>
<td valign="middle" align="center">148.50(138.00, 168.25)</td>
<td valign="middle" align="center">0.82</td>
<td valign="middle" align="center">144.00 &#xb1; 22.88</td>
<td valign="middle" align="center">152.19 &#xb1; 20.27</td>
<td valign="middle" align="center">0.10</td>
</tr>
<tr>
<td valign="middle" align="center">DBP</td>
<td valign="middle" align="center">97(87.75, 105.25)</td>
<td valign="middle" align="center">98(85, 108)</td>
<td valign="middle" align="center">0.74</td>
<td valign="middle" align="center">93.81 &#xb1; 17.52</td>
<td valign="middle" align="center">94.32 &#xb1; 13.03</td>
<td valign="middle" align="center">0.87</td>
</tr>
<tr>
<td valign="middle" align="center">Hyperlipidemia</td>
<td valign="middle" align="center">55(45.8%)</td>
<td valign="middle" align="center">23(63.9%)</td>
<td valign="middle" align="center">0.09</td>
<td valign="middle" align="center">46(68.7%)</td>
<td valign="middle" align="center">21(67.7%)</td>
<td valign="middle" align="center">1.00</td>
</tr>
<tr>
<td valign="middle" align="center">Diabetes</td>
<td valign="middle" align="center">15(12.5%)</td>
<td valign="middle" align="center">7(19.4%)</td>
<td valign="middle" align="center">0.44</td>
<td valign="middle" align="center">10(14.9%)</td>
<td valign="middle" align="center">10(32.3%)</td>
<td valign="middle" align="center">0.08</td>
</tr>
<tr>
<td valign="middle" align="center">Coronary heart Disease</td>
<td valign="middle" align="center">5(4.2%)</td>
<td valign="middle" align="center">2(5.6%)</td>
<td valign="middle" align="center">0.66</td>
<td valign="middle" align="center">3(4.5%)</td>
<td valign="middle" align="center">7(22.6%)</td>
<td valign="middle" align="center">0.01</td>
</tr>
<tr>
<td valign="middle" align="center">Urea(mmol/L)</td>
<td valign="middle" align="center">5.30(4.45, 6.10)</td>
<td valign="middle" align="center">5.42(4.95, 6.22)</td>
<td valign="middle" align="center">0.29</td>
<td valign="middle" align="center">5.51(4.74, 6.50)</td>
<td valign="middle" align="center">5.39(4.72, 6.83)</td>
<td valign="middle" align="center">0.90</td>
</tr>
<tr>
<td valign="middle" align="center">Creatinine (&#x3bc;mol/L)</td>
<td valign="middle" align="center">70.80(58.83, 81.98)</td>
<td valign="middle" align="center">70.30(63.63,84.20)</td>
<td valign="middle" align="center">0.61</td>
<td valign="middle" align="center">71.08 &#xb1; 14.39</td>
<td valign="middle" align="center">74.65 &#xb1; 16.87</td>
<td valign="middle" align="center">0.28</td>
</tr>
<tr>
<td valign="middle" align="center">eGFR (mL/min/1.73 m<sup>2</sup>)</td>
<td valign="middle" align="center">98.87(90.30, 107.83)</td>
<td valign="middle" align="center">97.33(79.500.04, 105.36)</td>
<td valign="middle" align="center">0.15</td>
<td valign="middle" align="center">96.70 &#xb1; 13.42</td>
<td valign="middle" align="center">90.26 &#xb1; 13.32</td>
<td valign="middle" align="center">0.03</td>
</tr>
<tr>
<td valign="middle" align="center">Uric acid (&#x3bc;mol/L)</td>
<td valign="middle" align="center">350.85(292.98, 424.13)</td>
<td valign="middle" align="center">343.00(290.30, 399.45)</td>
<td valign="middle" align="center">0.51</td>
<td valign="middle" align="center">382.29 &#xb1; 95.03</td>
<td valign="middle" align="center">341.85 &#xb1; 88.59</td>
<td valign="middle" align="center">0.05</td>
</tr>
<tr>
<td valign="middle" align="center">K<sup>+</sup>(mmol/L)</td>
<td valign="middle" align="center">3.95(3.74, 4.17)</td>
<td valign="middle" align="center">3.85(3.65, 4.08)</td>
<td valign="middle" align="center">0.13</td>
<td valign="middle" align="center">3.88 &#xb1; 0.39</td>
<td valign="middle" align="center">3.75 &#xb1; 0.36</td>
<td valign="middle" align="center">0.13</td>
</tr>
<tr>
<td valign="middle" align="center">Pac(ng/dl)</td>
<td valign="middle" align="center">13.25(8.68, 18.05)</td>
<td valign="middle" align="center">17.85(13.40, 22.43)</td>
<td valign="middle" align="center">&lt;0.01</td>
<td valign="middle" align="center">10.00(7.75, 17.95)</td>
<td valign="middle" align="center">17.80(13.85, 27.55)</td>
<td valign="middle" align="center">&lt;0.01</td>
</tr>
<tr>
<td valign="middle" align="center">DRC (ulU/ml)</td>
<td valign="middle" align="center">22.80(10.08, 52.40)</td>
<td valign="middle" align="center">4.50(2.45, 9.43)</td>
<td valign="middle" align="center">&lt;0.01</td>
<td valign="middle" align="center">14.80(3.60, 32.85)</td>
<td valign="middle" align="center">2.00(0.75, 4.70)</td>
<td valign="middle" align="center">&lt;0.01</td>
</tr>
<tr>
<td valign="middle" align="center">ARR</td>
<td valign="middle" align="center">0.70(0.20, 1.30)</td>
<td valign="middle" align="center">3.80(1.78, 5.90)</td>
<td valign="middle" align="center">&lt;0.01</td>
<td valign="middle" align="center">0.80(0.35, 2.30)</td>
<td valign="middle" align="center">7.60(4.70, 19.70)</td>
<td valign="middle" align="center">&lt;0.01</td>
</tr>
<tr>
<td valign="middle" align="center">24h- Uald(&#x3bc;g/24h)</td>
<td valign="middle" align="center">6.05(3.86, 9.30)</td>
<td valign="middle" align="center">9.36(5.89, 13.75)</td>
<td valign="middle" align="center">&lt;0.01</td>
<td valign="middle" align="center">5.62(3.42, 8.56)</td>
<td valign="middle" align="center">8.20(4.61, 12.38)</td>
<td valign="middle" align="center">0.01</td>
</tr>
<tr>
<td valign="middle" align="center">24h- Urine Na(mmol/24h)</td>
<td valign="middle" align="center">126.85(86.15, 171.10)</td>
<td valign="middle" align="center">117.85(88.95, 182.65)</td>
<td valign="middle" align="center">&lt;0.01</td>
<td valign="middle" align="center">122.20(83.20, 176.05)</td>
<td valign="middle" align="center">113.6(79.30, 129.90)</td>
<td valign="middle" align="center">0.29</td>
</tr>
<tr>
<td valign="middle" align="center">24h- Urine K(mmol/24h)</td>
<td valign="middle" align="center">38.30(32.15, 45.73)</td>
<td valign="middle" align="center">42.20(32.23, 55.50)</td>
<td valign="middle" align="center">0.35</td>
<td valign="middle" align="center">37.00(29.00, 50.05)</td>
<td valign="middle" align="center">36.00(31.15, 43.95)</td>
<td valign="middle" align="center">0.65</td>
</tr>
<tr>
<td valign="middle" align="center">UARR</td>
<td valign="middle" align="center">0.31(0.09, 0.70)</td>
<td valign="middle" align="center">1.80(1.00, 3.35)</td>
<td valign="middle" align="center">&lt;0.01</td>
<td valign="middle" align="center">0.41(0.13, 1.41)</td>
<td valign="middle" align="center">3.24(1.69, 10.11)</td>
<td valign="middle" align="center">&lt;0.01</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s3_5">
<label>3.5</label>
<title>Diagnostic value of UARR and 24-h Uald under the influence of different types of drugs</title>
<p>ROC curves for ARR, 24h-Uald, and the UARR were plotted separately for each medication category (<xref ref-type="fig" rid="f2"><bold>Figures&#xa0;2B, C</bold></xref>). The corresponding optimal cut-off values, sensitivities, and specificities are shown in <xref ref-type="table" rid="T3"><bold>Table&#xa0;3</bold></xref>. In the false-negative drug interference group, the AUCs for 24h-Uald, UARR, and ARR were 0.688, 0.874, and 0.863, respectively. UARR demonstrated good diagnostic performance, and its AUC was comparable to that of ARR, with no statistically significant difference (P = 0.381). Using the Youden index, the optimal cut-off values were 7.900 &#x3bc;g/24 h for 24h-Uald (sensitivity 0.694, specificity 0.667), 0.893 for UARR (sensitivity 0.806, specificity 0.842), and 1.45 for ARR (sensitivity 0.833, specificity 0.791).</p>
<p>In the mixed-interference drug group, the AUCs for 24h-Uald, UARR, and ARR were 0.654, 0.871, and 0.900, respectively. UARR also demonstrated good diagnostic performance, and its AUC was comparable to that of ARR, with no statistically significant difference (P = 0.113). Using the Youden index, the optimal cut-off values were 7.420 &#x3bc;g/24 h for 24h-Uald (sensitivity 0.610, specificity 0.670), 1.32 for UARR (sensitivity 0.870, specificity 0.750), and 2.45 for ARR (sensitivity 0.900, specificity 0.760).</p>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>In this study, we evaluated the predictive value of 24-h Uald and UARR for PA screening in patients who had not undergone medication washout. The results showed that 24-h urinary aldosterone alone had limited diagnostic performance in distinguishing PA from EH, whereas UARR demonstrated better discriminative ability.</p>
<p>Whether to switch antihypertensive medications before PA screening has long been a focus of clinical debate. Some patients, due to comorbid cardiovascular diseases or refractory hypertension, are unable or unsuitable to undergo medication adjustment (<xref ref-type="bibr" rid="B16">16</xref>). Notably, pivotal studies have demonstrated that the ARR remains a robust and accurate screening tool even in patients maintaining their antihypertensive medications (<xref ref-type="bibr" rid="B17">17</xref>, <xref ref-type="bibr" rid="B18">18</xref>). This established reliability of ratio-based assessments provides a strong theoretical foundation for the clinical application of UARR without medication washout. However, despite the robustness of the ratio, plasma aldosterone remains susceptible to various influencing factors such as momentary body position and sodium intake. Under conditions without medication adjustment, these factors may increase the difficulty of interpreting screening results (<xref ref-type="bibr" rid="B7">7</xref>). Furthermore, plasma biomarkers are highly sensitive to the patient&#x2019;s acute state at the moment of sampling. Markou et&#xa0;al. (<xref ref-type="bibr" rid="B19">19</xref>) demonstrated that acute psychological or physiological stress can trigger transient aldosterone hypersecretion via the ACTH pathway, potentially leading to fluctuations in the ARR. In contrast, 24-h Uald reflects the integrated secretion over a full day (<xref ref-type="bibr" rid="B20">20</xref>), effectively buffering these episodic secretory pulses. In our study population without medication adjustment, both 24-h Uald and UARR levels were higher in the PA group than in the EH group. Notably, UARR demonstrated higher specificity than ARR (0.791 vs. 0.740), further supporting the advantage of urinary markers in reducing stress interference and assessing the true biochemical load. Current guidelines recommend that all hypertensive patients should undergo at least one PA screening. Given its diagnostic stability and ease of implementation, UARR is particularly well-suited for large-scale screening in primary care settings.</p>
<p>After adjusting for age, BMI, renal function, serum potassium and a history of CHD, both 24-h Uald and UARR remained independently associated with PA, suggesting that they may provide supplementary information for PA screening in patients receiving antihypertensive medications. Notably, the prevalence of prior coronary artery disease and diabetes mellitus was higher in the PA group than in the EH group, and a history of CHD remained positively associated with PA in the multivariate analysis. However, the significance of this association was markedly reduced in Model 2. This finding suggests that the interpretation of CHD history should be approached with caution. It is possible that PA had been present for a prolonged period, leading to chronic aldosterone excess. Aldosterone may promote plaque formation through mechanisms such as activation of vascular endothelial cells and facilitation of inflammatory cell migration (<xref ref-type="bibr" rid="B21">21</xref>&#x2013;<xref ref-type="bibr" rid="B23">23</xref>), thereby contributing to the development of CHD. However, CHD is often considered a high-risk condition, and patients with coronary artery disease are more likely to undergo PA screening, which may lead to selection bias and contribute to the higher prevalence observed in this study. Therefore, these findings suggest an association between a history of CHD and PA, but the directionality and underlying mechanisms remain difficult to ascertain.</p>
<p>In addition, lower eGFR levels were independently associated with PA, which is consistent with previous studies (<xref ref-type="bibr" rid="B24">24</xref>). Since 24-h Uald is an excretory product of renal filtration, its urinary excretion is influenced by glomerular filtration capacity (<xref ref-type="bibr" rid="B25">25</xref>). Moreover, chronic aldosterone excess can lead to renal structural and functional damage through proinflammatory and profibrotic mechanisms, resulting in a bidirectional interaction between aldosterone excess and renal impairment (<xref ref-type="bibr" rid="B26">26</xref>).Notably, the correlation analysis in this study revealed that while 24-h Uald levels exhibited a clear dependency on serum potassium and renal function, UARR demonstrated a more stable profile. Specifically, UARR showed no significant correlation with serum potassium and only a marginal correlation with eGFR. In model 3, the correlation of eGFR was still statistically significant, but the significance level was substantially reduced. This may be due to the inclusion of renin as the denominator in the UARR index, the level of which not only reflects the suppressed state of the RAAS system, but also to some extent reflects the renal perfusion and reserve function. Consequently, the incorporation of renin into the UARR index functions as a robust correction mechanism, effectively mitigating the diagnostic bias introduced by variations in renal function and electrolyte status.</p>
<p>A similar corrective effect was observed in the analysis of BMI. In model 2, BMI was significantly associated with PA, which is consistent with previous findings showing a positive correlation between BMI and 24-hour urinary aldosterone (<xref ref-type="bibr" rid="B27">27</xref>, <xref ref-type="bibr" rid="B28">28</xref>). However, in model 3, after incorporating renin indicators, BMI was no longer an independent correlate of PA. Previous studies have shown that adipocytes promote aldosterone synthesis and secretion through multiple pathways, and that, in addition to traditional RAAS activation, adipokines can directly stimulate the adrenal cortex, leading to increased aldosterone levels (<xref ref-type="bibr" rid="B29">29</xref>, <xref ref-type="bibr" rid="B30">30</xref>). Although aldosterone secretion is elevated in obese individuals, this increase is often insufficient to cause the profound renin suppression observed in PA patients; therefore, BMI is not significantly correlated with renin concentration. Consequently, by using renin as the denominator, the UARR metric effectively adjusts for obesity-mediated physiological fluctuations in aldosterone, thereby attenuating the independent predictive contribution of BMI in multivariable analyses.</p>
<p>Multivariable analysis showed that both 24-h Uald and UARR were independently associated with PA, but their diagnostic performance differed substantially in ROC curve analysis. In this study, the AUC of 24-h Uald alone without medication washout was only 0.657, indicating limited efficacy in distinguishing PA from EH. In contrast, the AUC of UARR reached 0.862, which was comparable to that of ARR. This result differs from the findings of You et&#xa0;al. (<xref ref-type="bibr" rid="B10">10</xref>), who reported that the AUC of 24-h Uald under the influence of medication could be as high as 0.82. The weaker performance of 24-h Uald in our study (AUC 0.657 overall and 0.688 in the false-negative drug group) may be due to differences in subgroup definitions. You et&#xa0;al. mainly classified drugs based on their direction of interference with renin, whereas the &#x201c;false-negative group&#x201d; in the present study included a complex combination of CCBs, ARBs, ACEIs, and diuretics. Although this grouping increases the heterogeneity of interfering factors, it more closely reflects the real-world scenario of multi-drug combination therapy in clinical practice and highlights the limitations of 24-h Uald for PA screening in the context of complex medication use.</p>
<p>UARR introduces plasma renin concentration as the denominator, which effectively mitigates the bias caused by antihypertensive drugs on the RAAS axis. In EH patients, drug-induced increases in aldosterone are usually accompanied by parallel fluctuations in renin, resulting in a relatively stable ratio. In contrast, in PA patients, even when renin-stimulating medications are administered, renin levels remain suppressed due to autonomous aldosterone secretion from the adrenal glands. In further subgroup analyses, we also found that the diagnostic AUC of UARR was higher than that of ARR, with improved specificity in patients taking medications that produce false-negative ARR results, which may further explain this observation. Therefore, UARR more sensitively reflects the underlying imbalance of the RAAS system than simple excretory metrics, and demonstrates greater diagnostic value in clinical settings with complex medication use.</p>
<p>The main limitation of this study is the relatively small sample size. In addition, due to the limited number of patients taking only false-positive drugs, it was difficult to evaluate the diagnostic performance in this specific subgroup. In future studies, we plan to expand the sample size and further refine subgroup classification to more comprehensively assess the clinical diagnostic efficacy of the relevant indicators.</p>
<p>In conclusion, this retrospective study evaluated the screening value of 24-h Uald and UARR for PA under conditions of ongoing medication use. The diagnostic performance of 24-h Uald alone was limited, whereas UARR, which incorporates renin metrics, substantially improved screening specificity. These findings provide useful evidence for PA screening in patients who are unable to discontinue antihypertensive therapy.</p>
</sec>
</body>
<back>
<sec id="s5" sec-type="data-availability">
<title>Data availability statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p></sec>
<sec id="s6" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>The studies involving humans were approved by the Ethics Committee of Beijing Anzhen Hospital, Capital Medical University. The studies were conducted in accordance with the local legislation and institutional requirements. This is a retrospective study of de-identified clinical data, posing no more than minimal risk to patients, and obtaining consent was impracticable.</p></sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>SC: Methodology, Writing &#x2013; original draft. KC: Data curation, Writing &#x2013; original draft. JW: Data curation, Formal analysis, Writing &#x2013; original draft. JY: Writing &#x2013; original draft, Data curation. LS: Writing &#x2013; original draft, Formal Analysis. HY: Project administration, Writing &#x2013; review &amp; editing.</p></sec>
<sec id="s9" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The author(s) declared that this work 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="s10" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declared that generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</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>
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