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<front>
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<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>
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<issn pub-type="epub">1664-2392</issn>
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<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="doi">10.3389/fendo.2025.1671870</article-id>
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<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Effects of short-term very low-calorie diet on metabolic profile in patients with metabolic syndrome</article-title>
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<name><surname>Zhou</surname><given-names>Yi</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<name><surname>Li</surname><given-names>Shu-nan</given-names></name>
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<name><surname>Yuan</surname><given-names>Yuan</given-names></name>
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<name><surname>Chen</surname><given-names>Ying-xin</given-names></name>
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<name><surname>Sun</surname><given-names>Min-xing</given-names></name>
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<name><surname>Chen</surname><given-names>Hong-hua</given-names></name>
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<name><surname>Li</surname><given-names>Bo</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="author-notes" rid="fn004"><sup>&#x2021;</sup></xref>
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<contrib contrib-type="author" corresp="yes" equal-contrib="yes">
<name><surname>He</surname><given-names>Qi-da</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>*</sup></xref>
<xref ref-type="author-notes" rid="fn004"><sup>&#x2021;</sup></xref>
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<aff id="aff1"><label>1</label><institution>Department of Endocrinology, Xiamen Hospital of Traditional Chinese Medicine</institution>, <city>Xiamen</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff2"><label>2</label><institution>Department of General Medicine, School of Health, Fujian Medical University</institution>, <city>Fuzhou</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff3"><label>3</label><institution>Department of Traditional Chinese Medicine, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University</institution>, <city>Xiamen</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff4"><label>4</label><institution>Xiamen Clinical Nutrition Quality Control Center</institution>, <city>Xiamen</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff5"><label>5</label><institution>Fujian University of Traditional Chinese Medicine</institution>, <city>Fuzhou</city>,&#xa0;<country country="cn">China</country></aff>
<author-notes>
<corresp id="c001"><label>*</label>Correspondence: Qi-da He, <email xlink:href="mailto:hokida@163.com">hokida@163.com</email></corresp>
<fn fn-type="equal" id="fn003">
<label>&#x2020;</label>
<p>These authors have contributed equally to this work and share first authorship</p></fn>
<fn fn-type="equal" id="fn004">
<label>&#x2021;</label>
<p>These authors have contributed equally to this work</p></fn>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2025-12-11">
<day>11</day>
<month>12</month>
<year>2025</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2025</year>
</pub-date>
<volume>16</volume>
<elocation-id>1671870</elocation-id>
<history>
<date date-type="received">
<day>23</day>
<month>07</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>28</day>
<month>11</month>
<year>2025</year>
</date>
<date date-type="rev-recd">
<day>23</day>
<month>10</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Zhou, Li, Hu, Hu, Li, Fan, Yu, Yuan, Chen, Sun, Chen, Li and He.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Zhou, Li, Hu, Hu, Li, Fan, Yu, Yuan, Chen, Sun, Chen, Li and He</copyright-holder>
<license>
<ali:license_ref start_date="2025-12-11">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>Background</title>
<p>Accumulating evidence indicates that dietary modifications confer beneficial effects on metabolic syndrome (MetS). In clinical practice, short-term very low-calorie diets (VLCD) have been established as an effective intervention for improving MetS, even in the absence of exercise. However, the impact of a short-term VLCD on the serum metabolic profile of patients with MetS remains to be elucidated.</p>
</sec>
<sec>
<title>Methods</title>
<p>18 adult patients meeting the diagnostic criteria for MetS were enrolled and placed on a 9-day VLCD regimen. Anthropometric parameters, blood pressure, and lipid metabolism indices were measured before and after the intervention. Serum metabolic profiles were subsequently acquired using <sup>1</sup>H-NMR spectroscopy.</p>
</sec>
<sec>
<title>Results</title>
<p>Following the short-term VLCD intervention, patients with MetS exhibited significant reductions in body weight, waist circumference, and BMI (<italic>P</italic> &lt; 0.05). Blood pressure was also significantly lowered (<italic>P</italic> &lt; 0.05). Furthermore, the intervention regulated both glucose and lipid metabolism. Serum metabolomic analysis identified 20 characterized metabolites, all of which showed significantly decreased levels post-treatment (<italic>P</italic> &lt; 0.05). Pathway analysis indicated that the short-term VLCD modulated key metabolic pathways involved in energy and lipid metabolism, insulin sensitivity, anti-inflammatory and antioxidant responses, cellular signaling, and neurohormonal regulation.</p>
</sec>
<sec>
<title>Conclusions</title>
<p>Our study demonstrates that a short-term VLCD is an effective and safe intervention for improving anthropometric parameters, blood pressure, and lipid metabolism in patients with MetS. The observed therapeutic effects appear to be mediated through a remodeling of the serum metabolic profile and a concomitant modulation of key metabolic pathways. These findings provide a scientific rationale for the clinical application of short-term VLCD in MetS management.</p>
</sec>
</abstract>
<kwd-group>
<kwd>very low-calorie diet</kwd>
<kwd>metabolic syndrome</kwd>
<kwd>1H-NMR</kwd>
<kwd>metabolism</kwd>
<kwd>metabolic profile</kwd>
</kwd-group>
<funding-group>
<award-group id="gs1">
<funding-source id="sp1">
<institution-wrap>
<institution>Natural Science Foundation of Fujian Province</institution>
<institution-id institution-id-type="doi" vocab="open-funder-registry" vocab-identifier="10.13039/open_funder_registry">10.13039/501100003392</institution-id>
</institution-wrap>
</funding-source>
</award-group>
<funding-statement>The author(s) declared financial support was received for this work and/or its publication. This research was funded by The Natural Science Foundation of Fujian Province (No. 2023J011631).The funders had no input on any aspect of the study design or writing.</funding-statement>
</funding-group>
<counts>
<fig-count count="4"/>
<table-count count="2"/>
<equation-count count="0"/>
<ref-count count="49"/>
<page-count count="9"/>
<word-count count="3727"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Clinical Diabetes</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<title>Introduction</title>
<p>Metabolic syndrome (MetS) is characterized by the convergence of various metabolic risk factors. The diagnosis of MetS requires the concurrent presence of at least three of the following components: central adiposity, dyslipidemia, dysglycemia, and elevated blood pressure (<xref ref-type="bibr" rid="B1">1</xref>). Despite its well-defined clinical presentation, MetS remains a considerable management challenge. Its global prevalence, which has reached 20-25% in many developed nations (<xref ref-type="bibr" rid="B9">9</xref>, <xref ref-type="bibr" rid="B10">10</xref>), is rising and poses a significant public health burden (<xref ref-type="bibr" rid="B2">2</xref>, <xref ref-type="bibr" rid="B3">3</xref>). This is of particular concern as MetS substantially elevates the risk of type 2 diabetes, cardiovascular disease, and other chronic conditions (<xref ref-type="bibr" rid="B4">4</xref>&#x2013;<xref ref-type="bibr" rid="B8">8</xref>). In this context, dietary intervention has emerged as a cornerstone in the management of MetS (<xref ref-type="bibr" rid="B11">11</xref>).</p>
<p>As a therapeutic dietary intervention, a very low-calorie diet (VLCD) has garnered considerable attention in recent years (<xref ref-type="bibr" rid="B12">12</xref>). A VLCD denotes a nutrition plan with a caloric intake of 400&#x2013;800 kcal daily, wherein carbohydrates contribute approximately 50% of caloric content, and proteins are provided at a dosage of 0.8-1.5 grams per kilogram per day (<xref ref-type="bibr" rid="B13">13</xref>). And the method needs to be supplemented with essential fatty acids, vitamins, and minerals. However, long-term VLCD is believed to potentially lead to malnutrition (<xref ref-type="bibr" rid="B14">14</xref>). Numerous investigations have demonstrated the efficacy of short-term VLCD in enhancing insulin sensitivity and addressing various constituents of the metabolic syndrome (<xref ref-type="bibr" rid="B12">12</xref>, <xref ref-type="bibr" rid="B15">15</xref>, <xref ref-type="bibr" rid="B16">16</xref>). Nevertheless, the underlying mechanisms by which short-term VLCD exerts its therapeutic effects on MetS remain to be fully elucidated.</p>
<p>Metabolomics technology was a powerful tool for studying the characteristic metabolites associated with diseases. Moreover, metabolomics technology has been widely applied in studies associated with metabolic syndrome. Hydrogen-1 nuclear magnetic resonance (<sup>1</sup>H-NMR), as one of the components of metabolomics technology, has the advantages of convenient detection and high sensitivity. Hence, <sup>1</sup>H-NMR spectroscopy was applied to probe the impact of short-term VLCD on the metabolite profile associated with MetS in this study.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<title>Materials and methods</title>
<sec id="s2_1">
<title>Participants</title>
<p>This study is a single-arm pilot study without a control group, where the baseline clinical characteristics collected from all participants were compared before and after treatment. 18 patients diagnosed with MetS were enrolled from the Endocrinology Department of Xiamen Traditional Chinese Medicine Hospital from June 2023 to June 2024. All participants were vetted to ensure they satisfied the predefined inclusion and exclusion criteria. This study was meticulously conducted in adherence to the ethical principles enshrined in the Helsinki Declaration, and the ethical approval was reviewed and approved by the Medical Ethics Committee of Xiamen Traditional Chinese Medicine Hospital (Protocol Number: 2023-K029-01). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent was obtained from all individual participants.</p>
</sec>
<sec id="s2_2">
<title>Inclusion criteria</title>
<p>The following criteria were established for the inclusion of participants in the study: (i) all participants between the age of 18 to 65 years; (ii) central obesity (waist circumference of &#x2265;90 cm in males or &#x2265;80 cm in females, and at least two of the following factors: elevated triglyceride (TG) &#x2265;1.7 mmol/L, high-density lipoprotein (HDL) &lt;1.03 mmol/L for males and &lt;1.29 mmol/L for females, systolic blood pressure (SBP) &#x2265;130 mmHg or diastolic blood pressure (DBP) of &#x2265;85 mmHg, fasting blood glucose (FBG) &#x2265;5.6 mmol/L; (iii) the duration of both diabetes and hypertension should not exceed 5 years; (iv) all participants were required to provide informed consent and to confirm their voluntary participation in the study (<xref ref-type="bibr" rid="B17">17</xref>).</p>
</sec>
<sec id="s2_3">
<title>Exclusion criteria</title>
<p>The study applied the following exclusion criteria to ensure the selection of appropriate participants: (i) females who were pregnant or lactating; (ii) acute and chronic diabetes complications; (iii) participants with severe liver or kidney dysfunction, significant cardiovascular diseases, chronic infections, thyroid dysfunction, malignant tumor or other serious health issues; (iv) individuals with neurogenic anorexia or malnutrition; (v) neurological and Psychiatric Conditions.</p>
</sec>
<sec id="s2_4">
<title>Intervention methods</title>
<p>All participants were instructed to discontinue the use of hypoglycemic, lipid-lowering, and antihypertensive medications for one week. The subjects underwent a 9-day VLCD regimen. This regimen consisted of three phases: a 2-day buffer period at 600 kcal/day, a 5-day intensive phase (days 3-7) at 300 kcal/day, and a 2-day recovery phase (days 8-9) at 600 kcal/day. The diet maintained a consistent macronutrient distribution of 55% carbohydrates, 20% protein, and 25% fat throughout all phases (<xref ref-type="bibr" rid="B18">18</xref>). During the intervention, participants were advised to restrict physical activity to basic daily tasks and light exercise.</p>
</sec>
<sec id="s2_5">
<title>Baseline clinical characteristics</title>
<p>Among the 18 participants included in this study, there were 10 males (55.6%) and 8 females (44.4%). Among the 18 participants, due to personal willingness and case dropout, serum metabolomics profiling was ultimately conducted on 14 participants, comprising 7 males (50%) and 7 females (50%). Before and after the short-term VLCD intervention, a comprehensive array of baseline clinical characteristics was assessed in the patients. These baseline clinical characteristics included anthropometric measurements such as body weight (BW), waist circumference (WC), BMI, body fat mass, body fat percentage, and blood pressure.</p>
<p>Fasting peripheral venous blood samples were collected after an 8&#x2013;10 hour fast. Levels of fasting blood glucose (FBG) and fasting insulin (FINS) were quantified using a fully automated biochemical analyzer and a chemiluminescence immunoassay analyzer, respectively. The homeostasis model assessment of insulin resistance (HOMA-IR) was then calculated from FBG and FINS to evaluate insulin resistance. Lipid profiles, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL), were also analyzed. Additionally, the safety index of liver and kidney function was assessed.</p>
</sec>
<sec id="s2_6">
<title><sup>1</sup>H-NMR metabolomics assays</title>
<p>Following an 8&#x2013;10 hour fasting period, 3mL of peripheral venous blood was collected from each participant. The samples were centrifuged at 3000&#xd7;g for 10min to isolate serum. A 400&#x3bc;L serum was then mixed with 200&#x3bc;L phosphate buffer solution and subjected to high-speed centrifugation at 13,000&#xd7;g for 20min at 4&#xb0;C. Subsequently, 500&#x3bc;L of the resulting supernatant was transferred to a 5mm NMR tube for analysis. Finally, all processed samples were detected by a <sup>1</sup>H-NMR spectrometer (Bruker AVANCE-III 600MHz, Germany).</p>
</sec>
<sec id="s2_7">
<title>Data processing and analysis</title>
<p>The <sup>1</sup>H-NMR spectra of all samples underwent rigorous phase and baseline corrections using MestReNova v9.0.1 software. Subsequently, the data were imported into SIMCA-P 14.0 software. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was then conducted to differentiate between the metabolic profiles of the samples. Finally, differential metabolites with VIP&gt;1 and P&lt;0.05 were pointed out for before and after treatment.</p>
</sec>
<sec id="s2_8">
<title>Statistical analysis</title>
<p>SPSS software (version 26, IBM, United States) was applied for statistical analyses of clinical variables. The data were performed using paired t-tests for normally distributed parameters, while the Wilcoxon signed-rank test was employed for variables that deviated from normality. <italic>P</italic> &lt; 0.05 was considered statistically significant. All statistical analyses were conducted using SPSS software(IBM, United States).</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<title>Results</title>
<sec id="s3_1">
<title>Baseline clinical characteristics</title>
<p>Before and after the treatment of a short-term VLCD, the baseline clinical characteristics were observed. The results showed that levels of BW, WC, BMI, SBP, DBP, FBG, FINS, HOMA-IR, HDL, and TG were decreased significantly after treatment (<italic>P</italic> &lt; 0.05). However, there was no significant alteration in the levels of body fat mass, body fat percentage, LDL and TC after treatment by short-term VLCD (<italic>P</italic>&gt;0.05). Moreover, albumin (ALB), alanine aminotransferase (ALT), and creatinine (CR), which were related to the function of the liver and kidney, were not altered (<italic>P</italic>&gt;0.05) (<xref ref-type="table" rid="T1"><bold>Table&#xa0;1</bold></xref>). Notably, AST was significantly increased after treatment by short-term VLCD (<italic>P</italic> &lt; 0.05), but it remained in the normal range.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Baseline clinical characteristics of all participants.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Parameters</th>
<th valign="middle" align="center">Before treatment</th>
<th valign="middle" align="center">After treatment</th>
<th valign="middle" align="center">P value</th>
</tr>
</thead>
<tbody>
<tr>
<th valign="middle" colspan="4" align="left">Anthropometric parameters</th>
</tr>
<tr>
<td valign="middle" align="center">BW (kg)</td>
<td valign="middle" align="center">90.43 &#xb1; 17.52</td>
<td valign="middle" align="center">85.56 &#xb1; 16.64*</td>
<td valign="middle" align="center">0.000</td>
</tr>
<tr>
<td valign="middle" align="center">BMI (kg/m2)</td>
<td valign="middle" align="center">32.28 &#xb1; 5.33</td>
<td valign="middle" align="center">30.28 &#xb1; 5.02*</td>
<td valign="middle" align="center">0.000</td>
</tr>
<tr>
<td valign="middle" align="center">Body fat mass (kg)</td>
<td valign="middle" align="center">34.78 &#xb1; 11.32</td>
<td valign="middle" align="center">33.95 &#xb1; 10.91</td>
<td valign="middle" align="center">0.307</td>
</tr>
<tr>
<td valign="middle" align="center">Body fat percentage (%)</td>
<td valign="middle" align="center">38.49 &#xb1; 6.15</td>
<td valign="middle" align="center">37.85 &#xb1; 6.88</td>
<td valign="middle" align="center">0.422</td>
</tr>
<tr>
<td valign="middle" align="center">WC (cm)</td>
<td valign="middle" align="center">101.89 &#xb1; 12.83</td>
<td valign="middle" align="center">95.40 &#xb1; 12.42*</td>
<td valign="middle" align="center">0.000</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Blood pressure</th>
</tr>
<tr>
<td valign="middle" align="center">DBP (mmHg)</td>
<td valign="middle" align="center">84.44 &#xb1; 11.64</td>
<td valign="middle" align="center">74.44 &#xb1; 5.68*</td>
<td valign="middle" align="center">0.003</td>
</tr>
<tr>
<td valign="middle" align="center">SBP (mmHg)</td>
<td valign="middle" align="center">134.11 &#xb1; 14.06</td>
<td valign="middle" align="center">116.44 &#xb1; 8.10*</td>
<td valign="middle" align="center">0.000</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Glucose metabolism index</th>
</tr>
<tr>
<td valign="middle" align="center">FBG (mmol/L)</td>
<td valign="middle" align="center">6.04 &#xb1; 2.27</td>
<td valign="middle" align="center">4.29 &#xb1; 0.65*</td>
<td valign="middle" align="center">0.001</td>
</tr>
<tr>
<td valign="middle" align="center">FINS (&#x3bc;IU/mL)</td>
<td valign="middle" align="center">25.29 &#xb1; 20.69</td>
<td valign="middle" align="center">16.63 &#xb1; 16.20</td>
<td valign="middle" align="center">0.043</td>
</tr>
<tr>
<td valign="middle" align="center">HOMA-IR</td>
<td valign="middle" align="center">6.52 &#xb1; 5.19</td>
<td valign="middle" align="center">3.37 &#xb1; 3.66*</td>
<td valign="middle" align="center">0.008</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Lipid metabolism index</th>
</tr>
<tr>
<td valign="middle" align="center">HDL (mmol/L)</td>
<td valign="middle" align="center">1.18 &#xb1; 0.22</td>
<td valign="middle" align="center">1.03 &#xb1; 0.14*</td>
<td valign="middle" align="center">0.003</td>
</tr>
<tr>
<td valign="middle" align="center">LDL (mmol/L)</td>
<td valign="middle" align="center">3.02 &#xb1; 0.77</td>
<td valign="middle" align="center">2.99 &#xb1; 0.76</td>
<td valign="middle" align="center">0.793</td>
</tr>
<tr>
<td valign="middle" align="center">TC (mmol/L)</td>
<td valign="middle" align="center">4.79 &#xb1; 0.99</td>
<td valign="middle" align="center">4.52 &#xb1; 0.93</td>
<td valign="middle" align="center">0.159</td>
</tr>
<tr>
<td valign="middle" align="center">TG (mmol/L)</td>
<td valign="middle" align="center">1.63 &#xb1; 0.71</td>
<td valign="middle" align="center">0.99 &#xb1; 0.28*</td>
<td valign="middle" align="center">0.001</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Safety index</th>
</tr>
<tr>
<td valign="middle" align="center">ALB(g/L)</td>
<td valign="middle" align="center">47.39 &#xb1; 9.14</td>
<td valign="middle" align="center">44.33 &#xb1; 2.33</td>
<td valign="middle" align="center">0.162</td>
</tr>
<tr>
<td valign="middle" align="center">ALT (IU/L)</td>
<td valign="middle" align="center">37.33 &#xb1; 20.53</td>
<td valign="middle" align="center">37.11 &#xb1; 19.26</td>
<td valign="middle" align="center">0.939</td>
</tr>
<tr>
<td valign="middle" align="center">AST (IU/L)</td>
<td valign="middle" align="center">24.78 &#xb1; 9.90</td>
<td valign="middle" align="center">29 &#xb1; 9.75*</td>
<td valign="middle" align="center">0.02</td>
</tr>
<tr>
<td valign="middle" align="center">CR (&#x3bc;mol/L)</td>
<td valign="middle" align="center">67.59 &#xb1; 13.37</td>
<td valign="middle" align="center">67.61 &#xb1; 15.71</td>
<td valign="middle" align="center">0.991</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Data are presented as mean &#xb1; standard error. *Means the significantly different between before and after treatment. ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; BW, bodyweight; Cr, creatinine; Alb, albumin; DBP, diastolic blood pressure; FBG, fasting blood glucose; FINS, fasting insulin; HDL, high-density lipoprotein; HOMA-IR, homeostasis model assessment of insulin resistance; LDL, low-density lipoprotein; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; WC, waist circumference.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<title>Effects of short-term VLCD on serum metabolic profile in patients with MetS</title>
<p>Although a total of 18 participants were enrolled in this study, there were only 14 participants who agreed to be assayed for serum metabolites. <sup>1</sup>H-NMR was utilized to identify the characteristic metabolites in the serum. Meanwhile, the differential metabolites were fingered in the <sup>1</sup>H-NMR spectra (<xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1</bold></xref>).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p><sup>1</sup>H-NMR spectra of differential metabolites in serum. 1.Low density lipoprotein; 2.Very low-density lipoprotein; 3.Leucine; 4.Valine; 5.Diethyl methylmalonate; 6.Threonine; 7.Lactic acid; 8.Alanine; 9.Glutamic acid; 10.Acetone; 11.Phosphocholine; 12.Glycerol phosphocholine; 13.&#x3b2;-Glucose; 14.Betaine; 15.&#x3b1;-Glucose; 16.Glycine; 17.Glycerol; 18.Glutamine; 19.Serine; 20.Methylhistidine.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-16-1671870-g001.tif">
<alt-text content-type="machine-generated">NMR spectrum with multiple peaks labeled one through twenty, showing chemical shifts in parts per million (ppm) on the x-axis from 0 to 8.5. A significant peak appears at approximately 8.0 ppm. The y-axis ranges from 0 to 9000, indicating signal intensity.</alt-text>
</graphic></fig>
<p>To point out biomarkers that reflect changes in metabolite profiles before and after the short-term VLCD, an OPLS-DA model was employed for a more in-depth analysis. Obviously, there was a significant discrete between the before and after treatment by short-term VLCD (<xref ref-type="fig" rid="f2"><bold>Figure&#xa0;2</bold></xref>). The results shown that the samples from the two groups did not exhibit any crossover or significant overlap, indicating a clear separation and good dispersion (<italic>P</italic> &lt; 0.05). This finding was crucial as it points to specific metabolites that may serve as biomarkers for the metabolic changes induced by the short-term VLCD treatment.</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>OPLS-DA and S-plots from the before treatment and after treatment. <bold>(A)</bold> means OPLS-DA; <bold>(B)</bold> means S-plots.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-16-1671870-g002.tif">
<alt-text content-type="machine-generated">Two-part scatter plot. (A) Shows green dots labeled &#x201c;Before&#x201d; overlapping with orange dots labeled &#x201c;After&#x201d; within an ellipse, indicating variance. (B) Displays a correlation graph with dots transitioning from green to blue, showing a trend with respect to the p[1] and p(corr)[1] axes. A color bar on the right ranges from negative to positive values, transitioning from red to green.</alt-text>
</graphic></fig>
<p>Serum metabolomic profiling identified 20 differential metabolites that were significantly altered following the short-term VLCD intervention in patients with MetS (P&lt;0.05). All identified differential metabolites were including acetone, alanine, betaine, diethyl methylmalonate, &#x3b1;-glucose, &#x3b2;-glucose, glycerophosphocholine, glutamate, glutamine, glycerol, glycine, lactic acid, low-density lipoprotein, methyl histidine, phosphatidylcholine, very low-density lipoprotein, leucine, serine, threonine, and valine, were significantly decreased after treatment (<xref ref-type="table" rid="T2"><bold>Table&#xa0;2</bold></xref>, <xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3</bold></xref>). Subsequently, KEGG enrichment analysis of these metabolites revealed 13 major metabolic pathways that were significantly enriched, based on a threshold of -log(p)&gt;1 (<xref ref-type="fig" rid="f4"><bold>Figure&#xa0;4</bold></xref>).</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Differential metabolic markers in serum.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">No.</th>
<th valign="middle" align="center">Metabolite</th>
<th valign="middle" align="center">Chemical shift</th>
<th valign="middle" align="center">VIP index</th>
<th valign="middle" align="center">Log2 fold change</th>
<th valign="middle" align="center">Trend</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center">&#x3b1;-Glucose</td>
<td valign="middle" align="center">3.54</td>
<td valign="middle" align="center">1.38</td>
<td valign="middle" align="center">0.34</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">2</td>
<td valign="middle" align="center">&#x3b2;-Glucose</td>
<td valign="middle" align="center">3.25</td>
<td valign="middle" align="center">1.39</td>
<td valign="middle" align="center">0.40</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">3</td>
<td valign="middle" align="center">Alanine</td>
<td valign="middle" align="center">1.48</td>
<td valign="middle" align="center">1.34</td>
<td valign="middle" align="center">0.40</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">4</td>
<td valign="middle" align="center">Acetone</td>
<td valign="middle" align="center">2.23</td>
<td valign="middle" align="center">1.26</td>
<td valign="middle" align="center">0.46</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">5</td>
<td valign="middle" align="center">Betaine</td>
<td valign="middle" align="center">3.27</td>
<td valign="middle" align="center">1.45</td>
<td valign="middle" align="center">0.34</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">6</td>
<td valign="middle" align="center">Diethyl methylmalonate</td>
<td valign="middle" align="center">1.23</td>
<td valign="middle" align="center">1.74</td>
<td valign="middle" align="center">0.39</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">7</td>
<td valign="middle" align="center">Glutamate</td>
<td valign="middle" align="center">2.13</td>
<td valign="middle" align="center">1.05</td>
<td valign="middle" align="center">0.39</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">8</td>
<td valign="middle" align="center">Glycerophosphocholine</td>
<td valign="middle" align="center">3.23</td>
<td valign="middle" align="center">1.98</td>
<td valign="middle" align="center">0.40</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">9</td>
<td valign="middle" align="center">Glycine</td>
<td valign="middle" align="center">3.56</td>
<td valign="middle" align="center">1.51</td>
<td valign="middle" align="center">0.31</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">10</td>
<td valign="middle" align="center">Glycerol</td>
<td valign="middle" align="center">3.57</td>
<td valign="middle" align="center">1.61</td>
<td valign="middle" align="center">0.35</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">11</td>
<td valign="middle" align="center">Glutamine</td>
<td valign="middle" align="center">3.78</td>
<td valign="middle" align="center">1.36</td>
<td valign="middle" align="center">0.35</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">12</td>
<td valign="middle" align="center">LDL</td>
<td valign="middle" align="center">0.86</td>
<td valign="middle" align="center">1.72</td>
<td valign="middle" align="center">0.43</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">13</td>
<td valign="middle" align="center">Leucine</td>
<td valign="middle" align="center">0.96</td>
<td valign="middle" align="center">1.06</td>
<td valign="middle" align="center">0.28</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">14</td>
<td valign="middle" align="center">Lactic acid</td>
<td valign="middle" align="center">1.33</td>
<td valign="middle" align="center">2.67</td>
<td valign="middle" align="center">0.43</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">15</td>
<td valign="middle" align="center">Methyl histidine</td>
<td valign="middle" align="center">7.78</td>
<td valign="middle" align="center">1.05</td>
<td valign="middle" align="center">0.54</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">16</td>
<td valign="middle" align="center">Phosphatidylcholine</td>
<td valign="middle" align="center">3.22</td>
<td valign="middle" align="center">1.41</td>
<td valign="middle" align="center">0.38</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">17</td>
<td valign="middle" align="center">Serine</td>
<td valign="middle" align="center">3.83</td>
<td valign="middle" align="center">1.26</td>
<td valign="middle" align="center">0.34</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">18</td>
<td valign="middle" align="center">Threonine</td>
<td valign="middle" align="center">1.33</td>
<td valign="middle" align="center">2.67</td>
<td valign="middle" align="center">0.43</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">19</td>
<td valign="middle" align="center">VLDL</td>
<td valign="middle" align="center">0.89</td>
<td valign="middle" align="center">2.47</td>
<td valign="middle" align="center">0.37</td>
<td valign="middle" align="center">Down</td>
</tr>
<tr>
<td valign="middle" align="center">20</td>
<td valign="middle" align="center">Valine</td>
<td valign="middle" align="center">0.99</td>
<td valign="middle" align="center">1.06</td>
<td valign="middle" align="center">0.22</td>
<td valign="middle" align="center">Down</td>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>The heat-map of differential metabolites.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-16-1671870-g003.tif">
<alt-text content-type="machine-generated">Heatmap showing metabolite levels before and after an event. Rows represent various metabolites like glucose and alanine, while columns show group changes. Color gradient from red to blue indicates metabolite level changes.</alt-text>
</graphic></fig>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>The metabolic pathways of differential metabolites. Blue means the differential metabolites. <bold>(A)</bold> means the metabolic pathway; Red arrow means reduced levels after treatment. Arrows mean a decrease in the level of metabolites. <bold>(B)</bold> means the scatter plot of the metabolic pathway;1.Valine, leucine and isoleucine biosynthesis; 2.Glyoxylate and dicarboxylate metabolism; 3.Glycine, serine and threonine metabolism; 4.Nitrogen metabolism; 5.Arginine biosynthesis; 6.Histidine metabolism; 7.Neomycin, kanamycin and gentamicinbiosynthesis; 8.Galactose metabolism; 9.Glutathione metabolism; 10.Alanine, aspartate and glutamate metabolism; 11.Porphyrin metabolism; 12.Glycerophospholipid metabolism; 13.Valine, leucine and isoleucine degradation.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-16-1671870-g004.tif">
<alt-text content-type="machine-generated">Diagram (A) shows a metabolic pathway with interactions including compounds like glucose, glycerol, and glutamate, with arrows indicating reactions. Diagram (B) displays a scatter plot of 13 points, numbered one to thirteen, representing pathway impact versus negative logarithm of P-value. Points are color-coded on a gradient from yellow to red, indicating varying levels of significance.</alt-text>
</graphic></fig>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<title>Discussion</title>
<p>This study revealed that a short-term VLCD could significantly improve various parameters in patients with MetS, including BW, WC, BMI, SBP, DBP, FBG, FINS, HOMA-IR, HDL and TG. The findings suggest that a short-term VLCD treatment could effectively alleviate conditions such as hyperglycemia, hypertension, hypertriglyceridemia, overweight, and insulin resistance in MetS patients.</p>
<p>Notably, short-term VLCD treatment resulted in a reduction of HDL levels. The underlying mechanism might involve a sudden decrease in caloric intake, which triggers a stress response and subsequently leads to the suppression of HDL synthesis. Additionally, inadequate fat intake could also play a role by impeding HDL production (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B20">20</xref>). Consequently, the observed short-term decline in HDL levels may signify a dynamic adaptive response of the body to acute energy deprivation, rather than a deterioration of its cardiovascular protective capabilities. Nevertheless, this hypothesis necessitates further validation. Additionally, the results demonstrated that a short-term VLCD treatment had no adverse effects on liver and kidney function. However, the elevated levels of AST imply that prolonged adherence to a VLCD might potentially impair liver function. Collectively, these findings support the notion that a short-term VLCD treatment is a more suitable therapeutic approach for MetS patients compared to a long-term VLCD.</p>
<p>MetS is intricately linked to multiple organs and metabolic targets, aligning seamlessly with the principles of metabolomics. The utilization of <sup>1</sup>H-NMR spectroscopy enables the detection of numerous metabolites without the need for intricate sample preparation protocols, yielding high-resolution data. Following short-term VLCD treatment, the serum metabolic profile of MetS patients exhibited marked alterations. Notably, these differentially expressed metabolites are closely associated with key metabolic pathways, including energy-related metabolism, lipid-related metabolism, insulin sensitivity-related metabolism, inflammation-related metabolism, oxidative stress-related metabolism, cell signaling, and neurotransmitter-related metabolism. Below, we provide a detailed analysis of each differential metabolite identified:</p>
<sec id="s4_1">
<title>Energy-related metabolism</title>
<p>A short-term VLCD treatment is recognized as an effective approach for weight reduction through caloric restriction, potentially enhancing energy metabolism. Implementation of a short-term VLCD treatment results in decreased blood glucose levels, thereby reducing the dependency on glucose as a primary energy source. Moreover, this dietary intervention may enhance insulin sensitivity and diminish insulin resistance, facilitating more efficient insulin utilization and promoting glucose uptake into cells. Lactic acid can be integrated into the tricarboxylic acid (TCA) cycle either via gluconeogenesis or through the enzymatic conversion of lactate to pyruvate by lactate dehydrogenase and pyruvate dehydrogenase, thereby contributing to ATP synthesis (<xref ref-type="bibr" rid="B21">21</xref>). Similarly, acetone and glycerol can be metabolized to generate energy (<xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B23">23</xref>). Leucine and valine not only serve as energy substrates but also enter the TCA cycle, providing additional energy for physiological processes (<xref ref-type="bibr" rid="B24">24</xref>, <xref ref-type="bibr" rid="B25">25</xref>). Threonine participates in protein synthesis and gluconeogenesis, aiding in the maintenance of energy equilibrium (<xref ref-type="bibr" rid="B26">26</xref>). Metabolic disturbances are widely acknowledged as a hallmark feature of patients with MetS (<xref ref-type="bibr" rid="B17">17</xref>). Notably, short-term VLCD treatment can modulate key aspects of energy metabolism, including glucose, lactate, acetone, and glycerol pathways. The findings of this study demonstrate that short-term VLCD treatment facilitates effective weight management, enhances insulin sensitivity, and reduces insulin resistance which are pivotal in the therapeutic management of MetS.</p>
</sec>
<sec id="s4_2">
<title>Lipid-related metabolism</title>
<p>LDL and VLDL are the principal lipid carriers in the bloodstream. VLDL is synthesized in the liver and plays a crucial role in transporting triglycerides to peripheral tissues. In the context of obesity, the apolipoprotein C-II (Apo C-II) present on the VLDL surface activates lipoprotein lipase, promoting the hydrolysis of triglycerides. Subsequently, a significant proportion of VLDL is converted into LDL. An excessive accumulation of LDL, particularly oxidized LDL, predisposes to induce arteriosclerosis (<xref ref-type="bibr" rid="B27">27</xref>). A short-term VLCD effectively modulates the metabolism of LDL and VLDL, thereby contributing to the management of MetS and reducing cardiovascular risk. Additionally, glycerol serves as a critical intermediate in fatty acid metabolism. By regulating glycerol metabolism and reducing fat accumulation, a short-term VLCD treatment offers an effective therapeutic approach for addressing MetS.</p>
</sec>
<sec id="s4_3">
<title>Insulin sensitivity-related metabolism</title>
<p>Leucine and valine are metabolized within muscle tissue, where they not only generate energy and promote muscle mass augmentation but also stimulate insulin secretion. Nevertheless, their metabolic intermediates carry the potential to induce insulin resistance (<xref ref-type="bibr" rid="B25">25</xref>, <xref ref-type="bibr" rid="B28">28</xref>). It has been demonstrated that regulating branched-chain amino acid (BCAA) metabolism can alleviate insulin resistance (<xref ref-type="bibr" rid="B29">29</xref>). Glycine enhances insulin secretion by activating receptors on pancreatic &#x3b2;-cells (<xref ref-type="bibr" rid="B30">30</xref>), while serine modulates insulin sensitivity in the liver (<xref ref-type="bibr" rid="B31">31</xref>). Glutamine facilitates the release of glucagon-like peptide-1 and boosts insulin secretion (<xref ref-type="bibr" rid="B32">32</xref>), whereas glutamate can elevate glucagon secretion and worsen insulin resistance (<xref ref-type="bibr" rid="B33">33</xref>, <xref ref-type="bibr" rid="B34">34</xref>). A short-term VLCD treatment significantly modulates the levels of these relevant metabolites. By optimizing their metabolic profiles, this dietary intervention may enhance insulin sensitivity and mitigate insulin resistance in patients with MetS.</p>
</sec>
<sec id="s4_4">
<title>Inflammation and oxidative stress-related metabolism</title>
<p>Glycine mitigates the levels of pro-inflammatory cytokines through the regulation of NF-&#x3ba;B, while methylhistidine exerts an inhibitory effect on pro-inflammatory cytokines (<xref ref-type="bibr" rid="B35">35</xref>, <xref ref-type="bibr" rid="B36">36</xref>). Glutamine influences glycolytic processes and the subsequent inflammatory response (<xref ref-type="bibr" rid="B37">37</xref>), and glutamate modulates inflammatory signaling pathways through its specific receptors (<xref ref-type="bibr" rid="B38">38</xref>). Betaine participates in methylation reactions, thereby reducing the production of inflammatory mediators (<xref ref-type="bibr" rid="B39">39</xref>). Glutathione, a tripeptide composed of glutamic acid, cysteine, and glycine, exhibits potent antioxidant properties (<xref ref-type="bibr" rid="B40">40</xref>). Betaine enhances non-enzymatic antioxidant defenses, further contributing to cellular protection (<xref ref-type="bibr" rid="B41">41</xref>). MetS is intricately associated with chronic inflammation and oxidative stress (<xref ref-type="bibr" rid="B42">42</xref>&#x2013;<xref ref-type="bibr" rid="B44">44</xref>). The short-term VLCD treatment significantly modulates the levels of these pertinent metabolites, indicating its potential anti-inflammatory and antioxidant effects. This observation is consistent with previous studies demonstrating that caloric restriction diets reduce inflammatory cytokine levels and mitigate oxidative damage. Collectively, these findings underscore the multiple metabolic pathways through which a short-term VLCD treatment contributes to the amelioration of MetS (<xref ref-type="bibr" rid="B45">45</xref>, <xref ref-type="bibr" rid="B46">46</xref>).</p>
</sec>
<sec id="s4_5">
<title>Neurotransmitter-related metabolism</title>
<p>Glutamate functions as the predominant excitatory neurotransmitter in the central nervous system, significantly influencing the release of neurohormones (<xref ref-type="bibr" rid="B47">47</xref>). Glutamine plays a pivotal role in the synthesis and transmission of glutamate, ensuring its availability for neurotransmission. Betaine can modulate the methylation processes of neurohormone receptors, thereby affecting their function and expression (<xref ref-type="bibr" rid="B48">48</xref>). MetS is closely associated with dysregulation of neurohormones, which are crucial for maintaining energy balance, regulating appetite, and orchestrating other physiological processes (<xref ref-type="bibr" rid="B49">49</xref>). The administration of a short-term VLCD treatment effectively modulates the levels of these relevant metabolites. By optimizing neurohormone signaling pathways, this dietary intervention exerts a beneficial effect on the management and alleviation of MetS.</p>
<p>In conclusion, the metabolic profiles associated with MetS underwent significant alterations, with short-term VLCD treatment effectively regulating characteristic metabolites involved in energy metabolism, lipid metabolism, insulin sensitivity, inflammation, oxidative stress, cell signaling, and neurotransmitter-related pathways. Furthermore, short-term VLCD treatment may improve anthropometric parameters, blood pressure, and lipid metabolism indices in MetS patients by modulating multiple metabolites.</p>
<p>In this study, the observed alterations cannot be solely ascribed to the short-term VLCD treatment for MetS, since factors such as time effects, regression to the mean, or medication withdrawal may also play a role. It should be noted that the sample size in this study was relatively limited. In the future, a 9-month follow-up observation will be established to assess the durability of therapeutic efficacy and its long-term effects on the cardiovascular system. Moreover, 50 to 100 participants will be enrolled, and targeted quantitative techniques applied to further validate the results of this study.</p>
</sec>
</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 Medical Ethics Committee of Xiamen Traditional Chinese Medicine Hospital. 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.</p></sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>YZ: Conceptualization, Writing &#x2013; original draft, Data curation, Funding acquisition, Writing &#x2013; review &amp; editing, Formal Analysis. SL: Conceptualization, Writing &#x2013; review &amp; editing. TH: Writing &#x2013; review &amp; editing, Data curation, Methodology. SH: Methodology, Data curation, Writing &#x2013; review &amp; editing. NL: Data curation, Writing &#x2013; review &amp; editing, Methodology. YF: Writing &#x2013; review &amp; editing, Methodology, Data curation. JY: Writing &#x2013; review &amp; editing, Methodology, Data curation. YY: Writing &#x2013; review &amp; editing, Data curation, Methodology. YC: Writing &#x2013; review &amp; editing, Data curation, Methodology. MS: Writing &#x2013; review &amp; editing, Methodology, Data curation. HC: Methodology, Writing &#x2013; review &amp; editing, Data curation. BL: Data curation, Formal Analysis, Writing &#x2013; review &amp; editing. QH: Data curation, Writing &#x2013; original draft, Formal Analysis, Writing &#x2013; review &amp; editing.</p></sec>
<ack>
<title>Acknowledgments</title>
<p>The authors acknowledge the participants and are grateful to the study teams for their support and contribution to this study.</p>
</ack>
<sec id="s9" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors 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) declare 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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<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1916976">Yingnan Jia</ext-link>, Fudan University, China</p></fn>
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<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1437349">Mengnan Liu</ext-link>, Southwest Medical University, China</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3214344">AmirHosein Jafarpour</ext-link>, Shiraz University of Medical Sciences, Iran</p></fn>
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