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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Endocrinol.</journal-id>
<journal-title>Frontiers in Endocrinology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Endocrinol.</abbrev-journal-title>
<issn pub-type="epub">1664-2392</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fendo.2022.877794</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Endocrinology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Prognostic Value of Triglyceride to High-Density Lipoprotein Cholesterol Ratio (TG/HDL-C) in IgA Nephropathy Patients</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Pei</surname><given-names>Gaiqin</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="author-notes" rid="fn003"><sup>&#x2020;</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1110553"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Qin</surname><given-names>Aiya</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="author-notes" rid="fn003"><sup>&#x2020;</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1261702"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Dong</surname><given-names>Lingqiu</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1429453"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname><given-names>Siqing</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1429448"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Liu</surname><given-names>Xiang</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1177125"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Yang</surname><given-names>Dandan</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1840944"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Tan</surname><given-names>Jiaxing</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1843901"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhou</surname><given-names>Xiaoyuan</given-names>
</name>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Tang</surname><given-names>Yi</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="author-notes" rid="fn001"><sup>*</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1110559"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Qin</surname><given-names>Wei</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="author-notes" rid="fn001"><sup>*</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1037727"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>West China School of Medicine, Sichuan University</institution>, <addr-line>Chengdu, Sichuan</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>Department of Rehabilitation Medicine Center, West China Hospital, Sichuan University</institution>, <addr-line>Sichuan</addr-line>, <country>China</country></aff>
<aff id="aff3"><sup>3</sup><institution>Department of Nephrology, West China Hospital of Sichuan University</institution>, <addr-line>Chengdu, Sichuan</addr-line>, <country>China</country></aff>
<aff id="aff4"><sup>4</sup><institution>West China School of Public Health, West China Forth Hospital of Sichuan University</institution>, <addr-line>Chengdu</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Nehal Mohsen Elsherbiny, Mansoura University, Egypt</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Elena Rampanelli, Amsterdam University Medical Center, Netherlands; Enrique Rodilla, Hospital de Sagunto, Spain</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Yi Tang, <email xlink:href="mailto:tmka1986@163.com">tmka1986@163.com</email>; Wei Qin, <email xlink:href="mailto:qinweihx@scu.edu.cn">qinweihx@scu.edu.cn</email>
</p>
</fn>
<fn fn-type="equal" id="fn003">
<p>&#x2020;These authors have contributed equally to this work</p>
</fn>
<fn fn-type="other" id="fn002">
<p>This article was submitted to Renal Endocrinology, a section of the journal Frontiers in Endocrinology</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>20</day>
<month>06</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="collection">
<year>2022</year>
</pub-date>
<volume>13</volume>
<elocation-id>877794</elocation-id>
<history>
<date date-type="received">
<day>17</day>
<month>02</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>17</day>
<month>05</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2022 Pei, Qin, Dong, Wang, Liu, Yang, Tan, Zhou, Tang and Qin</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>Pei, Qin, Dong, Wang, Liu, Yang, Tan, Zhou, Tang and Qin</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Background</title>
<p>The triglycerides to high-density lipoprotein cholesterol (TG/HDL-C) ratio is an easy-to-use atherogenic and prognostic marker which has attracted increasing attention these days. However, whether TG/HDL-C correlate with outcomes in IgA nephropathy (IgAN) patients remains unknown. To clarify these issues, we conducted this study.</p>
</sec>
<sec>
<title>Methods</title>
<p>A total of 1146 patients from West China Hospital of Sichuan University were retrospectively analysed between 2008 and 2018.The demographic, clinical and pathological data of all patients at the time of biopsy were collected. Then, patients were divided into the high TG/HDL group (TG/HDL &#x2265; 1.495, N=382) and the low TG/HDL group (TG/HDL-C &lt; 1.495, N=764) based on the optimal cut-off value of the TG/HDL-C using receive operating curve. Cox proportional hazard models and Kaplan&#x2013;Meier curves were used to evaluate the renal outcomes of IgAN.</p>
</sec>
<sec>
<title>Results</title>
<p>The median age of the patients was 33 (26-42) years, and 44.5% were men. By correlation analysis, we found that the TG/HDL-C ratio was negatively correlated with the eGFR (r = 0.250, <italic>P</italic> &lt; 0.001) but positively correlated with proteinuria (r = 0.230, <italic>P</italic>&lt; 0.001), BMI (r=0.380, P&lt;0.001) and serum uric (r =0.308, <italic>P</italic>&lt; 0.001). Patients with a higher TG/HDL-C ratio tended to have hypertension [odds ratio (OR), 1.987; 95% CI, 1.527-2.587; <italic>P</italic>&lt;0.001] and more severe pathologic lesions with tubular atrophy/interstitial fibrosis (OR, 1.610; 95% CI, 1.203-2.154; <italic>P</italic>=0.001). During a median follow-up period of 54.1 (35.6-73.2) months, a high TG/HDL ratio was strongly associated with worse renal survival in IgAN patients (log-rank: <italic>P &lt;</italic>0.001). Multivariate Cox analysis demonstrated that a high TG/HDL-C ratio (HR 1.775, 95% CI 1.056-2.798; <italic>P</italic>=0.029) was an independent predictive marker to ESRD.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>In this study, we addressed the importance of TG/HDL-C ratio as a predictive marker for IgAN progression.</p>
</sec>
</abstract>
<kwd-group>
<kwd>triglyceride</kwd>
<kwd>high-density lipoprotein cholesterol</kwd>
<kwd>IgA nephropathy</kwd>
<kwd>prognosis</kwd>
<kwd>TG/HDL-C ratio</kwd>
</kwd-group>
<contract-num rid="cn001">2020YFC2006503, 2020YFC2006500</contract-num>
<contract-sponsor id="cn001">National Key Research and Development Program of China<named-content content-type="fundref-id">10.13039/501100012166</named-content>
</contract-sponsor>
<counts>
<fig-count count="3"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="26"/>
<page-count count="7"/>
<word-count count="3806"/>
</counts>
</article-meta>
</front>
<body>
<sec id="s1">
<title>Background</title>
<p>Immunoglobulin A (IgA) nephropathy (IgAN), characterized by diffusely deposited IgA in the kidneys, is the most prevalent primary glomerulonephritis and a leading cause of end-stage renal disease (ESRD), in which 20&#x2013;40% of IgAN patients reach ESRD 10&#x2013;20&#x2009;years after the initial diagnosis (<xref ref-type="bibr" rid="B1">1</xref>). Recognizing risk factors of ESRD would be beneficial for to slowing the progression of IgAN.</p>
<p>Abnormal lipoprotein metabolism, which could lead to impaired renal function and accelerated atherosclerosis (<xref ref-type="bibr" rid="B2">2</xref>), is often characterized by the presence of high TG and low HDL-C in chronic kidney disease (CKD) (<xref ref-type="bibr" rid="B3">3</xref>). It is well known TG usually increases in the early stages of CKD and is associated with delayed catabolism. However, TG levels fluctuate substantially based on feeding status, thus limiting its utility as a predictive biomarker (<xref ref-type="bibr" rid="B4">4</xref>). The single HDL-C, despite the functions of anti-inflammatory and antithrombotic, remains controversial in predicting cardiovascular disease (CVD) or mortality (<xref ref-type="bibr" rid="B5">5</xref>). The combination of TG and HDL-C, which is the TG/HDL-C ratio, could therefore overcome the problem and has been proposed as a more practical atherogenic and insulin resistance marker (<xref ref-type="bibr" rid="B6">6</xref>, <xref ref-type="bibr" rid="B7">7</xref>). It has attracted increasing attention for the better predictive value for CVD (<xref ref-type="bibr" rid="B6">6</xref>, <xref ref-type="bibr" rid="B8">8</xref>) and disease prognosis such as peritoneal dialysis (<xref ref-type="bibr" rid="B7">7</xref>), coronavirus disease 2019 (<xref ref-type="bibr" rid="B9">9</xref>), type 2 diabetes (<xref ref-type="bibr" rid="B10">10</xref>), and CKD (<xref ref-type="bibr" rid="B11">11</xref>). However, whether the TG/HDL-C ratio could be another predictor of IgAN progression remains unknown. To clarify these issues, we conducted this study.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<title>Materials and Methods</title>
<sec id="s2_1">
<title>Patients</title>
<p>A total of 1449 patients from West China Hospital of Sichuan University between 2008 and 2018 were initially enrolled. patients with systemic disease, such as systemic lupus erythematosus, diabetes, Henoch-Sch&#xf6;nlein purpura, liver cirrhosis or disorder of liver function, malignancy, etc., and without complete clinical and pathologic data were excluded in this study. Patients were followed up for at least 12 months or until study-defined endpoints were reached. Finally, 1146 adult biopsy-proven IgAN patients (age &gt; 14 years) were enrolled. The research was in compliance with the Declaration of Helsinki and was approved by the ethical committees of West China Hospital of Sichuan University (2019-33). Informed consent was obtained from each patient or their legal guardians prior to treatment.</p>
</sec>
<sec id="s2_2">
<title>Clinical Data</title>
<p>Patient information, including age, sex, clinical manifestations, laboratory indexes, renal pathology reports, and treatment strategies, systolic/diastolic blood pressure (SBP/DBP), body mass index (BMI) was obtained from electronic medical records. Laboratory values included 24-h proteinuria (UPRO), hematuria level (URBC), hemoglobin (Hb), serum albumin (ALB), serum creatinine (Cr), estimated glomerular filtration rate (eGFR), uric acid (UA), triglycerides (TG), total cholesterol (TC), and high-density lipoprotein cholesterol (HDL-C). The TG/HDL-C ratio was obtained by dividing the serum triglyceride level by the plasma high-density lipoprotein cholesterol level. Anemia, hypertension and hyperuricaemia was defined as described previously (<xref ref-type="bibr" rid="B12">12</xref>, <xref ref-type="bibr" rid="B13">13</xref>). eGFR was calculated using the CKD-EPI equation (<xref ref-type="bibr" rid="B14">14</xref>). Renal biopsy samples were evaluated by an experienced pathologist and a nephrologist according to the Oxford classification (<xref ref-type="bibr" rid="B15">15</xref>)</p>
</sec>
<sec id="s2_3">
<title>Treatments</title>
<p>All patients received optimal support treatment, including a full dose of angiotensin-converting-enzyme inhibitor (ACEI) or angiotensin receptor blockers (ARBs). Glucocorticoids and immunosuppressant therapy included cyclophosphamide (2 mg/kg daily for 3 months), mycophenolate mofetil (1-2 g daily for 6-8 months), tacrolimus (0.03-0.05 mg/kg daily for 6-8 months) or cyclosporin was used based on pathological classification and clinical severity according to the guidelines.</p>
</sec>
<sec id="s2_4">
<title>Outcome Definition</title>
<p>The renal outcome was progression to ESRD, defined by commencement of renal replacement therapy or an eGFR &lt;15 mL/min/1.73 m<sup>2</sup>.</p>
</sec>
<sec id="s2_5">
<title>Statistical Analysis</title>
<p>Continuous variables are expressed as the means &#xb1; SDs or medians (interquartile ranges). Categorical variables were expressed as numbers and percentages (%). Student&#x2019;s t test or the Mann&#x2013;Whitney U test was used for continuous variables, and the &#x3c7;2 test was used for categorical variables. The optimal thresholds of the TG/HDL ratio were obtained according to the highest Youden&#x2019;s index using receiver operating curve (ROC) analyses. Kidney survival in each group was estimated by the Kaplan&#x2013;Meier method. Univariate and multivariate Cox proportional hazard models were used to evaluate renal progression of IgAN. Three statistical models were used in analysis: model 1 (demographics + pathological features + TG/HDL-C), model 2 (demographics + clinical features +TG/HDL-C) and model 3 (demographics + clinical+ pathological features +TG/HDL-C). All the data were analysed by the software package SPSS 23.0 software package (SPSS, Chicago, IL, USA) and GraphPad Prism 8.0. Two-tailed <italic>P</italic>&lt;0.05 was considered statistically significant.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<title>Results</title>
<sec id="s3_1">
<title>Demographic and Clinicopathological Characteristics</title>
<p>A total of 1146 biopsy-proven IgA nephropathy patients from West China Hospital of Sichuan University were finally enrolled in this retrospective study (<xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1</bold></xref>). The demographic, clinical, and pathologic characteristics of the included patients are shown in <xref ref-type="table" rid="T1"><bold>Table&#xa0;1</bold></xref>. The median age of the patients was 33 (26-42) years, and 44.5% were men. The median follow-up period was 54.1(35.6-73.2) months. ROC analysis revealed that the optimal cut-off TG/HDL-C ratio with which to predict the progression of ESRD in patients with IgAN was 1.495 (<xref ref-type="supplementary-material" rid="SF1"><bold>Supplementary Figure&#xa0;1</bold></xref>). Thus, according to their TG/HDL-C ratio at the time of renal biopsy, patients were divided into two groups: a high TG/HDL-C group (TG/HDL &#x2265; 1.495, N=382) and a low TG/HDL group (TG/HDL-C &lt; 1.495, N=764). The median eGFR in the high TG/HDL-C and low TG/HDL-C groups was 9100.0 and 81.5 mL/min/1.73 m<sup>2</sup>, respectively. Interestingly, higher SBP, DBP and BMI levels were shown in the high TG/HDL-C group. Compared with the low TG/HDL-C group, patients in the high TG/HDL-C group had a higher incidence of anemia, hyperuricemia and hypertension (all <italic>P &lt;</italic>0.001), a higher proportion of males (<italic>P &lt;</italic>0.001), and worse renal function (<italic>P</italic>&lt;0.001). Moreover, higher levels of TG (<italic>P</italic>&lt;0.001), TC (<italic>P</italic>=0.003), UPRO (<italic>P</italic>&lt;0.001), and Cr (<italic>P</italic>&lt;0.001) and lower HDL-C (<italic>P</italic>&lt;0.001) and URBC (<italic>P</italic>=0.009) levels were observed in the high TG/HDL-C group. Regarding pathological lesions, patients with a high TG/HDL-C always had mesangial hypercellularity (<italic>P</italic>=0.06), segmental glomerulosclerosis (<italic>P</italic>=0.053) and tubular atrophy/interstitial fibrosis (<italic>P</italic>=0.002).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Flow diagram.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-13-877794-g001.tif"/>
</fig>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Demographic and clinicopathological characteristics of 1146 IgAN patients.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Parameters</th>
<th valign="top" align="center">Total N=1146</th>
<th valign="top" align="center">Group 1 (TG/HDL&lt;1.495) N=764</th>
<th valign="top" align="center">Group 2 (TG/HDL &#x2265; 1.495) N=382</th>
<th valign="top" align="center"><italic>P</italic>
</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Age (year)</td>
<td valign="top" align="center">33 (26-42)</td>
<td valign="top" align="center">31 (25-41)</td>
<td valign="top" align="center">36 (27-44)</td>
<td valign="top" align="center">&lt; 0.001</td>
</tr>
<tr>
<td valign="top" align="left">Gender (male, %)</td>
<td valign="top" align="center">510 (44.5)</td>
<td valign="top" align="center">303 (39.7)</td>
<td valign="top" align="center">207 (54.2)</td>
<td valign="top" align="center">&lt; 0.001</td>
</tr>
<tr>
<td valign="top" align="left">HTN (%)</td>
<td valign="top" align="center">335 (29.2)</td>
<td valign="top" align="center">186 (24.5)</td>
<td valign="top" align="center">149 (39.2)</td>
<td valign="top" align="center">&lt; 0.001</td>
</tr>
<tr>
<td valign="top" align="left">SBP (mmHg)</td>
<td valign="top" align="center">125 (115-138)</td>
<td valign="top" align="center">124 (115-137)</td>
<td valign="top" align="center">128 (117-139)</td>
<td valign="top" align="center">0.012</td>
</tr>
<tr>
<td valign="top" align="left">DBP (mmHg)</td>
<td valign="top" align="center">82 (75-90)</td>
<td valign="top" align="center">80 (74-90)</td>
<td valign="top" align="center">84 (76-92)</td>
<td valign="top" align="center">0.004</td>
</tr>
<tr>
<td valign="top" align="left">BMI (kg/m<sup>2</sup>)</td>
<td valign="top" align="center">23.1 (20.2-25.6)</td>
<td valign="top" align="center">22.0 (19.6-24.7)</td>
<td valign="top" align="center">24.9 (22.3-27.6)</td>
<td valign="top" align="center">&lt; 0.001</td>
</tr>
<tr>
<td valign="top" align="left"><bold>CKD stages (%)</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">&lt; 0.001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Stage 1</td>
<td valign="top" align="center">615 (53.7)</td>
<td valign="top" align="center">455 (59.6)</td>
<td valign="top" align="center">160 (41.9)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Stage 2</td>
<td valign="top" align="center">290 (25.3)</td>
<td valign="top" align="center">178 (23.3)</td>
<td valign="top" align="center">112 (29.3)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Stage 3</td>
<td valign="top" align="center">202 (17.6)</td>
<td valign="top" align="center">118 (15.4)</td>
<td valign="top" align="center">84 (22.0)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Stage 4</td>
<td valign="top" align="center">39 (3.4)</td>
<td valign="top" align="center">13 (1.7)</td>
<td valign="top" align="center">26 (6.8)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left"><bold>Pathologic</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;M1 (%)</td>
<td valign="top" align="center">860 (75.1)</td>
<td valign="top" align="center">560 (73.3)</td>
<td valign="top" align="center">300 (78.5)</td>
<td valign="top" align="center">0.06</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;E1 (%)</td>
<td valign="top" align="center">52 (4.5)</td>
<td valign="top" align="center">35 (4.6)</td>
<td valign="top" align="center">17 (4.5)</td>
<td valign="top" align="center">1.00</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;S1 (%)</td>
<td valign="top" align="center">704 (61.4)</td>
<td valign="top" align="center">454 (59.4)</td>
<td valign="top" align="center">250 (65.4)</td>
<td valign="top" align="center">0.053</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;T1-2/T0 (%)</td>
<td valign="top" align="center">243 (21.2)</td>
<td valign="top" align="center">141 (18.5)</td>
<td valign="top" align="center">102 (26.7)</td>
<td valign="top" align="center">0.002</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;C1-2/C0 (%)</td>
<td valign="top" align="center">244 (21.3)</td>
<td valign="top" align="center">162 (21.2)</td>
<td valign="top" align="center">82 (21.5)</td>
<td valign="top" align="center">0.939</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Clinical</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Cr (umol/L)</td>
<td valign="top" align="center">83.3 (65.2-109.0)</td>
<td valign="top" align="center">79.0 (62.0-102.0)</td>
<td valign="top" align="center">93.8 (72.0-126.0)</td>
<td valign="top" align="center">&lt; 0.001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;eGFR (mL/min/1.73 m<sup>2</sup>)</td>
<td valign="top" align="center">93.6 (66.0-117.7)</td>
<td valign="top" align="center">100.0 (71.8-120.1)</td>
<td valign="top" align="center">81.5 (54.9-105.5)</td>
<td valign="top" align="center">&lt; 0.001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;ALB (g/L)</td>
<td valign="top" align="center">40.0 (36.0-43.1)</td>
<td valign="top" align="center">40.1 (36.0-43.2)</td>
<td valign="top" align="center">40.0 (35.8-43.0)</td>
<td valign="top" align="center">0.694</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;HDL (mmol/L)</td>
<td valign="top" align="center">1.39 (1.11-1.73)</td>
<td valign="top" align="center">1.54 (1.31-1.87)</td>
<td valign="top" align="center">1.07 (0.89-1.26)</td>
<td valign="top" align="center">&lt; 0.001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;TG (mmol/L)</td>
<td valign="top" align="center">1.5 (1.1-2.1)</td>
<td valign="top" align="center">1.2 (0.9-1.5)</td>
<td valign="top" align="center">2.5 (2.0-3.4)</td>
<td valign="top" align="center">&lt; 0.001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;TC (mmol/L)</td>
<td valign="top" align="center">4.8 (4.1-5.7)</td>
<td valign="top" align="center">4.7 (4.1-5.5)</td>
<td valign="top" align="center">5.0 (4.2-5.8)</td>
<td valign="top" align="center">0.003</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;UPRO (g/d)</td>
<td valign="top" align="center">1.5 (0.8-3.0)</td>
<td valign="top" align="center">1.3 (0.7-2.7)</td>
<td valign="top" align="center">2.0 (1.0-3.5)</td>
<td valign="top" align="center">&lt; 0.001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;URBC (/HP)</td>
<td valign="top" align="center">18.0 (6.0-61.0)</td>
<td valign="top" align="center">19.5 (6.3-68.0)</td>
<td valign="top" align="center">15.0 (5.0-47.5)</td>
<td valign="top" align="center">0.009</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Anemia (%)</td>
<td valign="top" align="center">161 (14.1)</td>
<td valign="top" align="center">88 (11.5)</td>
<td valign="top" align="center">73 (19.1)</td>
<td valign="top" align="center">&lt; 0.001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Hyperuricemia</td>
<td valign="top" align="center">466 (40.7)</td>
<td valign="top" align="center">262 (34.3)</td>
<td valign="top" align="center">204 (53.4)</td>
<td valign="top" align="center">&lt; 0.001</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Treatment</bold> (%)</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">0.039</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;SC</td>
<td valign="top" align="center">438 (38.2)</td>
<td valign="top" align="center">307 (40.2)</td>
<td valign="top" align="center">131 (34.3)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;GC only</td>
<td valign="top" align="center">432 (37.7)</td>
<td valign="top" align="center">289 (37.8)</td>
<td valign="top" align="center">143 (37.4)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;IT and/or GC</td>
<td valign="top" align="center">276 (24.1)</td>
<td valign="top" align="center">168 (22.0)</td>
<td valign="top" align="center">109 (28.3)</td>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left"><bold>Follow-up</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Duration (months)</td>
<td valign="top" align="center">54.1 (35.6-73.2)</td>
<td valign="top" align="center">56.4 (36.3-75.1)</td>
<td valign="top" align="center">48.3 (34.7-67.6)</td>
<td valign="top" align="center">&lt; 0.001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;ESRD</td>
<td valign="top" align="center">78 (6.8)</td>
<td valign="top" align="center">32 (4.2)</td>
<td valign="top" align="center">46 (12.0)</td>
<td valign="top" align="center">&lt; 0.001</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Data presented as median (first-third interquartile range) or number (percentage).</p>
</fn>
<fn>
<p>SBP, Systolic Blood Pressure; DBP, diastolic blood pressure; BMI, body mass index; CKD, chronic kidney disease; M, mesangial proliferation; E, endocapillary proliferation; S, segmental sclerosis; T, tubular atrophy/interstitial fibrosis; C, crescents; Cr, creatinine; eGFR, estimated glomerular filtration rate; ALB, albumin; HDL, high-density lipoprotein cholesterol; TG, triglycerides; TC, total cholesterol; UPRO, 24 h urine protein; URBC, urinary red blood cell counts; SC, supportive care; GC, corticosteroids; IT, immunosuppressive therapy; ESRD, end stage renal disease.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<title>Correlation of the TG/HDL-C Ratio With Clinical Parameters and Pathological Lesions</title>
<p>The correlations between the TG/HDL-C levels and clinicopathological findings are illustrated in <xref ref-type="table" rid="T2"><bold>Tables&#xa0;2</bold></xref><bold>, </bold>
<xref ref-type="table" rid="T3"><bold>3</bold></xref>. Our results showed that TG/HDL-C was significantly negatively correlated with the eGFR (r = -0.250, <italic>P</italic> &lt; 0.001) but positively correlated with proteinuria (r = 0.230, <italic>P</italic> &lt; 0.001), BMI (r=0.380, <italic>P</italic> &lt; 0.001) and serum uric (r =0.308, <italic>P</italic>&lt; 0.001). Logistic regression analysis was conducted to analyse the relationship between TG/HDL and clinicopathologic features. The high TG/HDL-C group IgAN patients were more likely to have hypertension [odds ratio (OR), 1.987; 95% CI, 1.527-2.587; <italic>P</italic>&lt;0.001] and pathologic lesions with tubular atrophy/interstitial fibrosis (OR, 1.610; 95% CI, 1.203-2.154; <italic>P</italic>&lt;0.001) and segmental sclerosis (OR, 1.293; 95% CI, 1.002-1.670; <italic>P</italic>=0.049)</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Correlation between related variables and TG/HDL-C.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left"/>
<th valign="top" align="center">Variables</th>
<th valign="top" align="center">Correlation coefficient (r)</th>
<th valign="top" align="center"><italic>P</italic> value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">TG/HDL-C</td>
<td valign="top" align="left">UPRO</td>
<td valign="top" align="center">0.230</td>
<td valign="top" align="center">&lt; 0.001**</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">Hb</td>
<td valign="top" align="center">0.034</td>
<td valign="top" align="center">0.254</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">UA</td>
<td valign="top" align="center">0.308</td>
<td valign="top" align="center">&lt; 0.001**</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">BMI</td>
<td valign="top" align="center">0.380</td>
<td valign="top" align="center">&lt; 0.001**</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">ALB</td>
<td valign="top" align="center">-0.035</td>
<td valign="top" align="center">0.242</td>
</tr>
<tr>
<td valign="top" align="left"/>
<td valign="top" align="left">eGFR</td>
<td valign="top" align="center">-0.250</td>
<td valign="top" align="center">&lt; 0.001**</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>**stands for P &lt; 0.01.</p>
</fn>
<fn>
<p>UPRO, 24 h urine protein; Hb, hemoglobin; UA, uric acid; BMI, body mass index; ALB, albumin; eGFR, estimated glomerular filtration rate.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>Logistics regression models for the relationship between TG/HDL-C and kidney pathologic lesion.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left"/>
<th valign="top" align="center">OR</th>
<th valign="top" align="center">95%CI</th>
<th valign="top" align="center"><italic>P</italic> value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">M</td>
<td valign="top" align="center">1.333</td>
<td valign="top" align="center">0.995-1.785</td>
<td valign="top" align="center">0.054</td>
</tr>
<tr>
<td valign="top" align="left">E</td>
<td valign="top" align="center">0.970</td>
<td valign="top" align="center">0.536-1.755</td>
<td valign="top" align="center">0.920</td>
</tr>
<tr>
<td valign="top" align="left">S</td>
<td valign="top" align="center">1.293</td>
<td valign="top" align="center">1.002-1.670</td>
<td valign="top" align="center">0.049*</td>
</tr>
<tr>
<td valign="top" align="left">T<sub>1-2</sub>/T<sub>0</sub>
</td>
<td valign="top" align="center">1.610</td>
<td valign="top" align="center">1.203-2.154</td>
<td valign="top" align="center">0.001**</td>
</tr>
<tr>
<td valign="top" align="left">C<sub>1-2</sub>/C<sub>0</sub>
</td>
<td valign="top" align="center">1.016</td>
<td valign="top" align="center">0.753-1.370</td>
<td valign="top" align="center">0.919</td>
</tr>
<tr>
<td valign="top" align="left">HTN</td>
<td valign="top" align="center">1.987</td>
<td valign="top" align="center">1.527-2.587</td>
<td valign="top" align="center">&lt; 0.001**</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>*stands for P &lt; 0.05, **stands for P &lt; 0.01.</p>
</fn>
<fn>
<p>M, mesangial proliferation; E, endocapillary proliferation; S, segmental sclerosis; T, tubular atrophy/interstitial fibrosis; C, crescents; HTN, hypertension.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_3">
<title>Renal Survival</title>
<p>During a median follow-up period of 54.1 (35.6-73.2) months, a total of 78 (6.8%) patients developed ESRD. For renal survival, our results reveal that TG/HDL-C &#x2265;1.495 was significantly associated with ESRD (<xref ref-type="fig" rid="f2"><bold>Figure&#xa0;2</bold></xref>, <italic>P</italic> &lt; 0.001). Subgroup analysis of mesangial hypercellularity and tubular atrophy/interstitial fibrosis, CKD stages and proteinuria for ESRD by Kaplan&#x2013;Meier analysis is shown in <xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3</bold></xref>. Our results indicated that a high TG/HDL-C ratio was a risk factor for ESRD in patients with IgAN, especially patients with eGFR&lt;60 mL/min/1.73 m<sup>2</sup> (<italic>P</italic>&lt;0.001) <bold>(</bold>
<xref ref-type="fig" rid="f3"><bold>Figures&#xa0;3A&#x2013;C</bold></xref><bold>)</bold>, 24-hour urine protein &#x2265;1 g/day (<italic>P</italic>&lt; 0.001) <bold>(</bold>
<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3D</bold></xref><bold>)</bold>, or mesangial hypercellularity and tubular atrophy/interstitial fibrosis <bold>(</bold>
<xref ref-type="fig" rid="f3"><bold>Figures&#xa0;3E, F</bold></xref><bold>)</bold> in pathologic lesions.</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Kaplan-Meier analysis for the endpoint of ESRD stratified by the cutoff point of the TG/HDL-C.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-13-877794-g002.tif"/>
</fig>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Subgroup Kaplan-Meier analysis for endpoint of ESRD; eGFR <bold>(A&#x2013;C)</bold>, UPRO <bold>(D)</bold>, M <bold>(E)</bold>, T <bold>(F)</bold>; eGFR, estimated glomerular filtration rate; UPRO, 24 h urine protein; M, mesangial proliferation; T, tubular atrophy/interstitial fibrosis.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fendo-13-877794-g003.tif"/>
</fig>
</sec>
<sec id="s3_4">
<title>TG/HDL-C as an Independent Risk Factor for Progression of IgAN to ESRD</title>
<p>We performed Cox regression analyses to evaluate risk factors for ESRD in patients with IgAN, which showed that a high TG/HDL-C ratio was significantly associated with a higher risk of ESRD (HR=3.290, 95% CI: 2.093-5.173, <italic>P</italic>&lt;0.001) in univariate analysis. Then, three models were used for multivariate Cox regression (<xref ref-type="table" rid="T4"><bold>Table&#xa0;4</bold></xref> and <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Tables&#xa0;1, 2</bold></xref>), which indicated that high TG/HDL-C was an independent risk factor of renal endpoints (model 1: HR 4.158, 95% CI 1.970-8.775, <italic>P</italic>&lt;0.001; model 2: HR 3.944, 95% CI 1.825-8.523, <italic>P</italic>&lt;0.001; model 3: HR 1.775, 95% CI 1.056-2.798, <italic>P</italic>=0.029). Moreover, when stratified by the quartiles of baseline level of TG/HDL-C as Q1, Q2, Q3 and Q4 group (<xref ref-type="supplementary-material" rid="SF2"><bold>Supplementary Figure&#xa0;2</bold></xref>), significant difference were also shown between Q4 (highest quartiles) and other groups (Q1, Q2, and Q3)</p>
<table-wrap id="T4" position="float">
<label>Table&#xa0;4</label>
<caption>
<p>Analysis of factors associated with renal outcomes in model 3 (demographics+ clinical indicators+ pathological features+ TG/HDL-C).</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" rowspan="2" align="left">Parameter</th>
<th valign="top" colspan="3" align="center">&#x2003;Univariate</th>
<th valign="top" colspan="3" align="center">Multivariate</th>
</tr>
<tr>
<th valign="top" align="center">HR</th>
<th valign="top" align="center">95%CI</th>
<th valign="top" align="center"><italic>P</italic> value</th>
<th valign="top" align="center">HR</th>
<th valign="top" align="center">95%CI</th>
<th valign="top" align="center"><italic>P</italic> value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">high TG/HDL-C</td>
<td valign="top" align="center">3.290</td>
<td valign="top" align="center">2.093-5.173</td>
<td valign="top" align="center">&lt; 0.001</td>
<td valign="top" align="center">1.775</td>
<td valign="top" align="center">1.056-2.798</td>
<td valign="top" align="center">0.029</td>
</tr>
<tr>
<td valign="top" align="left">male</td>
<td valign="top" align="center">1.902</td>
<td valign="top" align="center">1.213-2.982</td>
<td valign="top" align="center">0.005</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">Age (per year)</td>
<td valign="top" align="center">0.991</td>
<td valign="top" align="center">0.971-1.012</td>
<td valign="top" align="center">0.410</td>
<td valign="top" align="center">0.951</td>
<td valign="top" align="center">0.928-0.976</td>
<td valign="top" align="center">&lt; 0.001</td>
</tr>
<tr>
<td valign="top" align="left">BMI (kg/m<sup>2</sup>)</td>
<td valign="top" align="center">1.048</td>
<td valign="top" align="center">0.956-1.149</td>
<td valign="top" align="center">0.319</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">SBP (mmHg)</td>
<td valign="top" align="center">1.033</td>
<td valign="top" align="center">1.022-1.044</td>
<td valign="top" align="center">&lt; 0.001</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">DBP (mmHg)</td>
<td valign="top" align="center">1.049</td>
<td valign="top" align="center">1.034-1.063</td>
<td valign="top" align="center">&lt; 0.001</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">M1</td>
<td valign="top" align="center">8.806</td>
<td valign="top" align="center">2.776-27.937</td>
<td valign="top" align="center">&lt; 0.001</td>
<td valign="top" align="center">4.320</td>
<td valign="top" align="center">1.348-13.847</td>
<td valign="top" align="center">0.014</td>
</tr>
<tr>
<td valign="top" align="left">E1</td>
<td valign="top" align="center">2.232</td>
<td valign="top" align="center">1.073-4.642</td>
<td valign="top" align="center">0.032</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">S1</td>
<td valign="top" align="center">1.614</td>
<td valign="top" align="center">1.001-2.602</td>
<td valign="top" align="center">0.049</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">T<sub>1-2</sub>/T<sub>0</sub>
</td>
<td valign="top" align="center">11.811</td>
<td valign="top" align="center">7.154-19.499</td>
<td valign="top" align="center">&lt; 0.001</td>
<td valign="top" align="center">2.475</td>
<td valign="top" align="center">1.398-13.847</td>
<td valign="top" align="center">0.002</td>
</tr>
<tr>
<td valign="top" align="left">C<sub>1-2</sub>/C<sub>0</sub>
</td>
<td valign="top" align="center">1.290</td>
<td valign="top" align="center">0.786-2.115</td>
<td valign="top" align="center">0.314</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">UPRO&gt;1.0g</td>
<td valign="top" align="center">3.310</td>
<td valign="top" align="center">1.823-6.007</td>
<td valign="top" align="center">&lt; 0.001</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">URBC&gt;5/HP</td>
<td valign="top" align="center">0.947</td>
<td valign="top" align="center">0.559-1.605</td>
<td valign="top" align="center">0.840</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">Anemia</td>
<td valign="top" align="center">3.717</td>
<td valign="top" align="center">2.339-5.908</td>
<td valign="top" align="center">&lt; 0.001</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">Hyperuricemia</td>
<td valign="top" align="center">4.457</td>
<td valign="top" align="center">2.778-7.573</td>
<td valign="top" align="center">&lt; 0.001</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">hypoalbuminemia</td>
<td valign="top" align="center">2.255</td>
<td valign="top" align="center">1.302-3.907</td>
<td valign="top" align="center">0.004</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">CKD stages</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;CKD 2 vs 1</td>
<td valign="top" align="center">6.993</td>
<td valign="top" align="center">2.260-21.268</td>
<td valign="top" align="center">0.001</td>
<td valign="top" align="center">6.800</td>
<td valign="top" align="center">2.147-21.538</td>
<td valign="top" align="center">0.001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;CKD 3 vs 1</td>
<td valign="top" align="center">30.497</td>
<td valign="top" align="center">10.970-85.394</td>
<td valign="top" align="center">&lt; 0.001</td>
<td valign="top" align="center">23.898</td>
<td valign="top" align="center">7.714-74.031</td>
<td valign="top" align="center">&lt; 0.001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;CKD 4 vs 1</td>
<td valign="top" align="center">136.066</td>
<td valign="top" align="center">46.960-394.247</td>
<td valign="top" align="center">&lt; 0.001</td>
<td valign="top" align="center">63.129</td>
<td valign="top" align="center">19.189-207.689</td>
<td valign="top" align="center">&lt; 0.001</td>
</tr>
<tr>
<td valign="top" align="left">Treatment</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">0.010</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">0.036</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;GC/SC</td>
<td valign="top" align="center">0.738</td>
<td valign="top" align="center">0.414-1.317</td>
<td valign="top" align="center">0.304</td>
<td valign="top" align="center">1.191</td>
<td valign="top" align="center">0.705-2.011</td>
<td valign="top" align="center">0.514</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;IT/SC</td>
<td valign="top" align="center">1.710</td>
<td valign="top" align="center">1.019-2.869</td>
<td valign="top" align="center">0.042</td>
<td valign="top" align="center">0.563</td>
<td valign="top" align="center">0.319-0.992</td>
<td valign="top" align="center">0.047</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>SBP, Systolic Blood Pressure; DBP, diastolic blood pressure; BMI, body mass index; M, mesangial proliferation; E, endocapillary proliferation; S, segmental sclerosis; T, tubular atrophy/interstitial fibrosis; C, crescents; UPRO, 24 h urine protein; URBC, urinary red blood cell counts; SC, supportive care; GC, corticosteroids; IT, immunosuppressive therapy.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<title>Discussion</title>
<p>Dyslipidemia is common in China, with a prevalence of 41.9&#x2009;%, and is commonly characterized by the presence of high TG and low HDL-C in CKD (<xref ref-type="bibr" rid="B16">16</xref>). Recently, the combination of TG and HDL-C, the TG/HDL-C ratio, has attracted increasing attention for its better predictive power for cardiovascular events and insulin resistance than the lonely combination (<xref ref-type="bibr" rid="B17">17</xref>, <xref ref-type="bibr" rid="B18">18</xref>). Noticeably, several studies have shown a positively relationship between the TG/HDL-C ratio and renal function decline in CKD patients (<xref ref-type="bibr" rid="B16">16</xref>, <xref ref-type="bibr" rid="B18">18</xref>&#x2013;<xref ref-type="bibr" rid="B20">20</xref>). However, whether TG/HDL-C has a role in IgAN progression remains unknown.</p>
<p>In this study, 78 (6.8%) patients developed ESRD in our 1146 biopsy-proven IgAN patients. A higher TG/HDL-C ratio in patients with IgAN was associated with more severe clinical features and pathologic lesions. Our further analysis revealed a higher serum TG/HDL-C level was an independent risk factor for the progression to ESRD (HR 1.775, 95% CI 1.056-2.798, <italic>P</italic>=0.029).</p>
<p>Previous studies have reported that the reduction in renal function in patients is related to high TG/HDL-C levels (<xref ref-type="bibr" rid="B11">11</xref>, <xref ref-type="bibr" rid="B16">16</xref>), but no study has focused on IgAN patients. To our knowledge, this is the first study assessing the correlation between the TG/HDL-C ratio and ESRD in IgAN patients. Moreover, the TG/HDL-C ratio has a greater influence in advanced IgAN patients (eGFR&lt; 60 mL/min/1.73 m<sup>2</sup>) or these with 24-hour urine protein of &#x2265;1 g/day in our study. This might be due to the slow progression of mild renal disease, especially within a limited follow-up period (<xref ref-type="bibr" rid="B12">12</xref>).</p>
<p>Here, higher SBP, DBP and BMI levels were shown in the high TG/HDL-C group. Further analysis showed TG/HDL was positively correlated with BMI (r=0.380, <italic>P</italic> &lt; 0.001). In our multivariate Cox regression analyses, BMI was not independent risk factor for renal progression. It is well known obesity often result in excessive accumulation of energy, accompanied by abnormalities in lipid parameter levels. Some studies showed it was associated with the early development of hypertension and CKD progression (<xref ref-type="bibr" rid="B21">21</xref>). However, emerging epidemiologic evidence indicated that obesity was not a direct and independent risk factor for IgAN (<xref ref-type="bibr" rid="B22">22</xref>). It was possible that high BMI indirectly accelerated the progression of IgAN by inducing metabolic syndrome on patients, in which TG/HDL-C is strongly associated with it (<xref ref-type="bibr" rid="B23">23</xref>). Additionally, we would like to stress that high TG/HDL-C patients tend to have hypertension and more severe renal pathologic lesions of segmental glomerulosclerosis and tubular atrophy/interstitial fibrosis. Considering that abnormalities in lipid parameter levels could accelerated atherosclerosis is plausible to believe that pathology of IgAN patients with a high TG/HDL-C level.</p>
<p>Abnormalities in lipid parameter levels could lead to impaired renal function and accelerated atherosclerosis (<xref ref-type="bibr" rid="B2">2</xref>). TG usually increases in the early stages of CKD and is associated with delayed catabolism and decreased activity of hepatic TG lipase and peripheral lipoprotein lipase. However, based on feeding status, TG levels could fluctuate substantially, thus limiting its utility as a predictive biomarker (<xref ref-type="bibr" rid="B4">4</xref>). HDL-C, inversely associated with outcomes, decreases in patients with CKD (<xref ref-type="bibr" rid="B11">11</xref>). The combination of these two markers could therefore overcome this problem and lead to a far more consistent, stable, fasting measurement of dyslipidaemia, which has indeed attracted much more attention in disease prognosis, including cardiovascular disease (<xref ref-type="bibr" rid="B6">6</xref>), diabetes (<xref ref-type="bibr" rid="B10">10</xref>), and peritoneal dialysis (<xref ref-type="bibr" rid="B7">7</xref>). Here, the potential mechanisms by which the TG/HDL-C ratio serves as a prognostic biomarker in IgAN patients may be as follows. TG/HDL-C is a reliable indicator of insulin resistance, which induces oxidative stress. Oxidative stress impairs the activation of nuclear factor erythroid-2-related factor-2, which protects against kidney tissue injury (<xref ref-type="bibr" rid="B16">16</xref>). The filtered proteins, such as fatty acids, phospholipids, and cholesterol contained in the filtered proteins (albumin and lipoproteins), could include direct toxic effects of lipids on glomerular cells and promote matrix production (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B24">24</xref>). Moreover, these materials act as damage-associated molecular patterns (DAMPs) and are recognized by Toll-like receptors (TLR2 and TLR4), which can activate inflammatory responses, causing tubulointerstitial fibrosis and injury in the reabsorption process (<xref ref-type="bibr" rid="B25">25</xref>, <xref ref-type="bibr" rid="B26">26</xref>). Additionally, further tissue injury is contributed owing to impaired HDL-mediated reverse cholesterol transport by limiting the unloading of the excess cellular cholesterol and phospholipid burden (<xref ref-type="bibr" rid="B25">25</xref>). Thus, in the future, recognizing the TG/HDL-C ratio as a potentially modifiable risk factor for patients may allow the utilization of preventative strategies to optimize both treatment and survival outcomes.</p>
<p>Some limitations warrant consideration. First, this was a retrospective study based on a single-center database. Second, the median follow-up time of 54 months was relatively short. Further multicenter validation in different ethnic populations with longer follow-ups was needed.</p>
</sec>
<sec id="s5">
<title>Conclusion</title>
<p>The TG/HDL-C ratio may be considered as a prognostic biomarker in IgAN patients.</p>
</sec>
<sec id="s6" 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="s7" sec-type="ethics-statement">
<title>Ethics Statement</title>
<p>The studies involving human participants were reviewed and approved by the ethical committees of West China Hospital of Sichuan University (2019-33). The patients/participants provided their written informed consent to participate in this study.</p>
</sec>
<sec id="s8" sec-type="author-contributions">
<title>Author Contributions</title>
<p>GP, AQ, SW, LD, XL, and DY collected and analysed the data. GP and AQ wrote the main manuscript text. YT, XZ and JT reviewed and edited the manuscript. WQ supervised all the work and revised the manuscript. All authors contributed to the article and approved the submitted version.</p>
</sec>
<sec id="s9" sec-type="funding-information">
<title>Funding</title>
<p>This study is partly supported by the National Key R&amp;D Program of China (2020YFC2006503) &amp;(2020YFC2006500).</p>
</sec>
<sec id="s10" sec-type="COI-statement">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s11" sec-type="disclaimer">
<title>Publisher&#x2019;s Note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
</body>
<back>
<ack>
<title>Acknowledgments</title>
<p>We are indebted to all the people who kindly participated in this study.</p>
</ack>
<sec id="s12" sec-type="supplementary-material">
<title>Supplementary Material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fendo.2022.877794/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fendo.2022.877794/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Image_1.tif" id="SF1" mimetype="image/tiff">
<label>Supplementary Figure&#xa0;1</label>
<caption>
<p>ROC curves (AUC) of TG/HDL-C for prediction of ESRD.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Image_2.tif" id="SF2" mimetype="image/tiff">
<label>Supplementary Figure&#xa0;2</label>
<caption>
<p>Kaplan-Meier analysis for the endpoint of ESRD stratified by quartile: Q1, first quartile; Q2, median; Q3, third quartile, Q4, fourth quartile.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="DataSheet_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
</sec>
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