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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Oncol.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Oncology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Oncol.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2234-943X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fonc.2025.1620484</article-id>
<article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Renal function mediates the association between neutrophil percentage-to-albumin ratio and survival in cancer survivors: a large cross-sectional study</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Chen</surname><given-names>Weiming</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>
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<contrib contrib-type="author">
<name><surname>Cai</surname><given-names>Wenjing</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="author-notes" rid="fn003"><sup>&#x2020;</sup></xref>
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<contrib contrib-type="author">
<name><surname>Zeng</surname><given-names>Hao</given-names></name>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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<contrib contrib-type="author">
<name><surname>Wang</surname><given-names>Zhisheng</given-names></name>
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<name><surname>Luo</surname><given-names>Xin</given-names></name>
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<contrib contrib-type="author">
<name><surname>Xu</surname><given-names>Zengkai</given-names></name>
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<contrib contrib-type="author">
<name><surname>Wu</surname><given-names>Jiahuang</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<contrib contrib-type="author" corresp="yes">
<name><surname>Zhu</surname><given-names>Yong</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>*</sup></xref>
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<contrib contrib-type="author" corresp="yes">
<name><surname>Wang</surname><given-names>Hongjin</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>*</sup></xref>
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<aff id="aff1"><label>1</label><institution>Department of Cardio-Thoracic Surgery, Longyan First Affiliated Hospital of Fujian Medical University</institution>, <city>Longyan</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff2"><label>2</label><institution>Department of Thoracic Surgery, Fujian Medical University Union Hospital</institution>, <city>Fuzhou</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff3"><label>3</label><institution>Fujian Medical University</institution>, <city>Fuzhou</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff4"><label>4</label><institution>Department of Gastroenterology and Anorectal Surgery, Longyan First Affiliated Hospital of Fujian Medical University</institution>, <city>Longyan</city>,&#xa0;<country country="cn">China</country></aff>
<author-notes>
<corresp id="c001"><label>*</label>Correspondence: Hongjin Wang, <email xlink:href="mailto:surgeonwhj@163.com">surgeonwhj@163.com</email>; Yong Zhu, <email xlink:href="mailto:zhuyong@fjmu.edu.cn">zhuyong@fjmu.edu.cn</email></corresp>
<fn fn-type="other" id="fn003">
<label>&#x2020;</label>
<p>These authors share the first authorship</p></fn>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-01-14">
<day>14</day>
<month>01</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2025</year>
</pub-date>
<volume>15</volume>
<elocation-id>1620484</elocation-id>
<history>
<date date-type="received">
<day>29</day>
<month>04</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>25</day>
<month>12</month>
<year>2025</year>
</date>
<date date-type="rev-recd">
<day>30</day>
<month>09</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2026 Chen, Cai, Zeng, Wang, Luo, Xu, Wu, Zhu and Wang.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Chen, Cai, Zeng, Wang, Luo, Xu, Wu, Zhu and Wang</copyright-holder>
<license>
<ali:license_ref start_date="2026-01-14">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>Cancer survivors face heightened mortality risks due to recurrence, comorbidities, and insufficient prognostic tools. The neutrophil percentage-to-albumin ratio (NPAR), integrating inflammation and nutrition, has shown prognostic value in cancers but lacks validation in survivor populations.</p>
</sec>
<sec>
<title>Objective</title>
<p>To assess the predictive efficacy of NPAR on mortality in cancer survivors and to explore the mediating role of renal function.</p>
</sec>
<sec>
<title>Methods</title>
<p>Data from NHANES (2003-2018) were used to analyse 3,134 cancer survivors. Cox models assessed the association of NPAR with all-cause, cancer-specific, and non-cancer mortality. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive performance of NPAR. Restricted cubic splines assessed non-linear associations, while mediation analysis quantified the role of renal function. The external validation study was conducted between January 2016 and December 2018, involving an additional 985 cancer patients recruited from a tertiary hospital in China.</p>
</sec>
<sec>
<title>Results</title>
<p>This study demonstrated that each 1-unit increase in NPAR was associated with a 10% increase in the risk of all-cause mortality, a 6% increase in the risk of cancer-related mortality, and a 12% increase in the risk of non-cancer mortality. This dose-dependent association remained robust in multivariable-adjusted models. RCS analyses further revealed a nonlinear relationship between NPAR and risk of death, with a steep inflection point in risk of all-cause mortality when NPAR exceeded 12.84. ROC analysis showed that NPAR had an AUC of 0.608 for all-cause mortality, outperforming the systemic immune-inflammation index (SII). Finally, mediation analyses elucidated renal impairment as a key pathway through which NPAR affects prognosis: 23.08% of the NPAR-mediated risk of all-cause mortality was driven by a decline in eGFR, and 21.97% of the risk of non-cancer mortality was attributable to worsening renal function. In real-world data analysis, NPAR has also been demonstrated to correlate positively with all-cause mortality, cancer-specific mortality, and non-cancer-specific mortality among cancer patients.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>NPAR is a robust prognostic biomarker for mortality in cancer survivors, mediated in part by renal dysfunction. These findings highlight the clinical utility of NPAR and the need for interventions targeting the inflammation-nutrition-kidney pathways.</p>
</sec>
</abstract>
<kwd-group>
<kwd>neutrophil percentage to albumin ratio</kwd>
<kwd>cancer survivors</kwd>
<kwd>cancer-specific mortality</kwd>
<kwd>all-cause mortality</kwd>
<kwd>renal function</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was not received for this work and/or its publication.</funding-statement>
</funding-group>
<counts>
<fig-count count="8"/>
<table-count count="3"/>
<equation-count count="0"/>
<ref-count count="52"/>
<page-count count="15"/>
<word-count count="7175"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Cancer Epidemiology and Prevention</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<title>Introduction</title>
<p>Cancer poses a significant global public health threat, with its disease burden on the rise. In 2022, there are expected to be around 20 million new cancer cases and 10 million deaths worldwide. Projections indicate that by 2050, the annual number of new cases will increase by 77%, reaching 35 million (<xref ref-type="bibr" rid="B1">1</xref>). Although targeted therapy and immunotherapy have revolutionized cancer treatment, increasing the overall five-year relative survival rate to 68% (<xref ref-type="bibr" rid="B2">2</xref>), survivors still face many complex challenges, including the risk of recurrence, uncertainty about death, and secondary complications caused by psychological barriers (<xref ref-type="bibr" rid="B3">3</xref>). In this context, constructing a precise prognostic prediction system has become the focus of clinical research. In previous studies, the mechanism by which inflammation is associated with cancer progression and risk of death has been widely confirmed (<xref ref-type="bibr" rid="B4">4</xref>, <xref ref-type="bibr" rid="B5">5</xref>). The neutrophil-to-lymphocyte ratio (NLR), the platelet-to-lymphocyte ratio(PLR) (<xref ref-type="bibr" rid="B6">6</xref>), the systemic immune-inflammation index(SII) (<xref ref-type="bibr" rid="B7">7</xref>), the albumin-to-globulin ratio (AGR), and the prognostic nutritional index (PNI) (<xref ref-type="bibr" rid="B8">8</xref>) have all been shown to correlate with tumor prognosis.</p>
<p>It is worth noting that nutritional status is involved in the cancer process through bidirectional regulation of pro-inflammatory/anti-inflammatory, suggesting a complex interaction between inflammation-nutrition-cancer (<xref ref-type="bibr" rid="B9">9</xref>). Based on this, researchers are systematically integrating inflammatory and nutritional indicators to construct a new assessment system: neutrophils, as a core component of innate immunity, play a dual role in the tumor microenvironment, both anti-tumor and pro-cancer (<xref ref-type="bibr" rid="B10">10</xref>, <xref ref-type="bibr" rid="B11">11</xref>); albumin, as a classic nutritional marker, its dynamic changes have important predictive value for disease prognosis (<xref ref-type="bibr" rid="B12">12</xref>). The Bernard team innovatively proposed the neutrophil-albumin ratio (NPAR) concept, which has been shown to be significantly effective in predicting the response to neoadjuvant therapy in colorectal cancer (<xref ref-type="bibr" rid="B13">13</xref>). Subsequent studies have further verified the universality of NPAR for prognostic assessment of oral cancer (<xref ref-type="bibr" rid="B14">14</xref>), gastrointestinal tumors (<xref ref-type="bibr" rid="B15">15</xref>), breast cancer (<xref ref-type="bibr" rid="B16">16</xref>), lung cancer (<xref ref-type="bibr" rid="B17">17</xref>), pancreatic cancer (<xref ref-type="bibr" rid="B18">18</xref>), and bladder cancer. Among cancer patients, the occurrence of cachexia is common; this syndrome of malnutrition is associated with cancer, chronic kidney failure, and other chronic diseases (<xref ref-type="bibr" rid="B19">19</xref>). Additionally, the incidence of acute and chronic kidney failure in cancer patients is often associated with mortality (<xref ref-type="bibr" rid="B20">20</xref>). However, there is currently a lack of research on the correlation between nutrition, kidney function, and cancer.</p>
<p>To address this critical knowledge gap, this study systematically examined the predictive efficacy of NPAR for all-cause mortality and cause-specific mortality in cancer survivors, utilizing long-term follow-up data from the 2003&#x2013;2018 cohort of the National Health and Nutrition Examination Survey (NHANES). Notably, this study innovatively introduces the analysis of mediation effects, revealing for the first time the mediating mechanism of renal function indicators between NPAR and mortality, thereby providing a multi-dimensional theoretical foundation for the clinical application of NPAR as a new prognostic biomarker.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<title>Materials and methods</title>
<sec id="s2_1">
<title>Data source</title>
<p>This study used a retrospective cohort design, with data from the NHANES hosted by the US Centers for Disease Control and Prevention (CDC). This database collects the health status, lifestyle and laboratory test indicators of non-hospitalised adult populations in the United States through stratified multi-stage probability sampling methods, and is nationally representative. All procedures involving human participants in the original NHANES surveys were conducted in accordance with the ethical standards of the National Center for Health Statistics (NCHS) Research Ethics Review Board (ERB) and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.</p>
<p>Written informed consent was obtained from all NHANES participants prior to their inclusion in the survey. Detailed information regarding the NHANES informed consent process is publicly available on the NHANES website.</p>
<p>The protocol for each NHANES cycle is reviewed and approved annually by the NCHS Research Ethics Review Board. Because this present study involved the secondary analysis of existing, anonymized data, it was considered exempt from requiring additional ERB. All analyses were performed in accordance with relevant guidelines and regulations.</p>
</sec>
<sec id="s2_2">
<title>Study population</title>
<p>This study strictly screened cancer survivors who met the following inclusion criteria: (1) age &#x2265; 20 years; (2) diagnosed with cancer survivor status by NHANES; (3) complete NPAR calculation parameters (neutrophil percentage and serum albumin test values) and serum creatinine. Exclusion criteria included: (1) missing key covariates (demographic characteristics or comorbidity data); and (2) incomplete follow-up information or insufficient follow-up periods. After rigorous screening, the final cohort included 3,134 cancer survivors who met the study requirements (see <xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1</bold></xref> for the sample selection process).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Flowchart of sample selection process.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-15-1620484-g001.tif">
<alt-text content-type="machine-generated">Flowchart showing selection of study participants. From the National Health and Nutrition Examination Survey 2003-2018 (N = 80,312), 4,257 were diagnosed cancer survivors aged 20 or older. Excluding 1,123 due to missing waist circumference, weight, or covariate data, 3,134 patients were finally enrolled.</alt-text>
</graphic></fig>
</sec>
<sec id="s2_3">
<title>NPAR measurement</title>
<p>The NPAR is calculated as the quotient of the neutrophil percentage and the serum albumin concentration (g/dl). This indicator simultaneously reflects the body&#x2019;s inflammatory activation and nutritional reserve status. Subjects were divided into four quartile groups (Q1 &#x2264; 25%&lt;Q2 &#x2264; 50%&lt;Q3 &#x2264; 75%&lt;Q4) according to baseline NPAR.</p>
</sec>
<sec id="s2_4">
<title>Outcomes</title>
<p>Cancer survivors were identified through the NHANES questionnaire, which asked, &#x201c;Has a doctor or other health professional ever told you that you have cancer?&#x201d; The reliability and accuracy of self-reported cancer diagnoses in NHANES have been evaluated in previous studies, which suggest that there is generally good concordance between self-reported data and medical records for most common cancer types.</p>
</sec>
<sec id="s2_5">
<title>Ascertainment of mortality</title>
<p>Mortality data for this study were obtained from the National Death Index (NDI) death certificate records provided by the NCHS, with the associated mortality file updated to December 31, 2019. The study outcomes included all-cause mortality and cause-specific mortality attributable to cancer and non-cancer causes, with causes of death based on the International Classification of Diseases (ICD). All-cause mortality was defined as deaths from any cause, encompassing cancers (C00-C97), cardiovascular disease (CVD) (I00-I09, I11, I13, I20-I51), cerebrovascular disease (I60-I69), respiratory disease (J10-J18, J40-J47), and other causes. Death due to malignancy was classified as cancer mortality (C00-C97) during the follow-up period. The duration from the baseline interview to the date of death or December 31, 2019, was calculated for each participant.</p>
</sec>
<sec id="s2_6">
<title>Assessment of covariates</title>
<p>Covariates included demographic information, health behaviors, physical examination findings, and medical history. Demographic information was collected through self-administered NHANES questionnaires and encompassed basic details such as age, gender, race/ethnicity (non-Hispanic white, Hispanic, non-Hispanic black, and other), marital status, and the poverty-to-income ratio (PIR). Body mass index (BMI) was calculated as weight (in kilograms) divided by height squared (in&#xa0;meters) and classified as &lt;25 (normal), 25.0-29.9 (overweight), or &#x2265; 30 kg/m&#xb2; (obese). Smoking status was categorized into never-smokers, former smokers, and current smokers. Drinking status is divided into never-drinkers, former drinkers, and current drinkers. Hypertension was identified if blood pressure (BP) measurements were above 140/90 mm Hg or if patients were taking antihypertensive medication. Diagnosis of diabetes was based on laboratory measurements of fasting glucose and hemoglobin A1c, self-reported medication use, or a prior diagnosis by a healthcare provider. CVD was assessed through self-reported history and physical examinations. Chronic kidney disease (CKD) is identified by a glomerular filtration rate (eGFR) of less than 60 mL/min/1.73 m&#xb2; or a urinary albumin-to-creatinine ratio of greater than 30 mg/g. The CKD-EPI formula of the Chronic Kidney Disease Epidemiology Collaboration was applied to calculate the eGFR.</p>
</sec>
<sec id="s2_7">
<title>Statistical analysis</title>
<p>This study strictly followed the design characteristics of the NHANES stratified multi-stage probability sampling method, and composite sampling weights were used to correct selection bias and non-response bias. Grouping and analysis were based on the baseline NPAR quartiles (Q1&#x2264; 12.70, 12.70&lt; Q2&#x2264; 14.40, 14.40&lt; Q3 &#x2264; 16.17, Q4&lt;16.17), continuous variables are expressed as mean &#xb1; standard deviation, and the Wilcoxon rank sum test was used for intergroup comparisons; categorical variables are described by frequency/percentage, and the chi-square test was used to test for differences between groups. The association between NPAR and mortality risk was explored in three stages using a Cox proportional hazards model: model 1 (unadjusted), model 2 (adjusted for age, gender, and race), and model 3 (further adjusted for additional factors including PIR, marital status, educational level, BMI, smoking status, alcohol consumption, diabetes, hypertension, and CVD. Survival curves were plotted using the Kaplan-Meier method, and the Log-rank test was used to assess differences between groups. A restricted cubic spline (RCS, 4 knots) was used to analyze the dose-response relationship between NPAR and mortality risk. Subgroup analyses included stratification variables such as age, gender, race, BMI, smoking status, alcohol consumption, hypertension, diabetes, CVD, and CKD. In addition, the mediating effect of renal function (quantified by eGFR) in the association between NPAR and mortality was further assessed. Finally, we performed receiver operating characteristic (ROC) curve analysis to evaluate the discriminatory ability of NPAR and the systemic immune-inflammation index (SII) in predicting all-cause, cancer-specific, and non-cancer mortality. The area under the curve (AUC) was calculated for each biomarker, and comparisons were made to assess their prognostic performance.</p>
<p>All analyses were performed using R 4.2.1 software, and a two-sided <italic>P &lt;</italic>0.05 was considered statistically significant.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<title>Results</title>
<sec id="s3_1">
<title>Baseline characteristics of study participants</title>
<p>The study ultimately included 3,134 cancer survivors, stratified by NPAR quartile completion cohort (Q1-Q4). Baseline analysis revealed significant intergroup heterogeneity: the mean age (65.97 &#xb1; 13.40 vs 60.19 &#xb1; 13.99 years) and BMI (30.24 &#xb1; 7.34 vs 27.96 &#xb1; 5.32 kg/m&#xb2;) of patients in the Q4 group (highest NPAR level) were significantly higher than those in the Q1 group (both <italic>P &lt;</italic>0.001), which was accompanied by a higher burden of chronic diseases (hypertension 67.74% vs 53.24%; diabetes 30.51% vs 17.14%; CVD 25.09% vs 15.50%; CKD 26.02% vs 13.32%). It is worth noting that this group exhibits contradictory behavioral characteristics: there is a significant increase in the proportion of people living alone (37.24% vs. 29.00%, <italic>P</italic>=0.028) but a significant decrease in alcohol intake (60.36% of current drinkers vs. 70.92%, <italic>P</italic>=0.003). Survival outcome analysis showed that the all-cause mortality rate (32.80%), cancer-specific mortality rate (10.32%), and non-cancer mortality rate (22.48%) in the Q4 group were significantly higher than those in other groups (all <italic>P &lt;</italic>0.001). See <xref ref-type="table" rid="T1"><bold>Table&#xa0;1</bold></xref> for details.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Baseline characteristics of study participants by NPAR quartiles.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Characteristic</th>
<th valign="middle" align="center">Overall N=17,551,655</th>
<th valign="middle" align="center">Q1&#x2264; 12.70 N=4,544,951</th>
<th valign="middle" align="center">12.70&lt;Q2 &#x2264; 14.40 N=4,776,283</th>
<th valign="middle" align="center">14.40&lt;Q3 &#x2264; 16.17 N=4,331,234</th>
<th valign="middle" align="center">Q4&gt;16.17 N=3,899,186</th>
<th valign="middle" align="center"><italic>P</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Age, years, mean (SD)</td>
<td valign="middle" align="center">62.51 &#xb1; 14.32</td>
<td valign="middle" align="center">60.19 &#xb1; 13.99</td>
<td valign="middle" align="center">60.81 &#xb1; 14.01</td>
<td valign="middle" align="center">63.69 &#xb1; 15.08</td>
<td valign="middle" align="center">65.97 &#xb1; 13.40</td>
<td valign="middle" align="center">&lt;0.001</td>
</tr>
<tr>
<td valign="middle" align="left">Gender</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">0.4</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Male</td>
<td valign="middle" align="center">1,483 (42.98%)</td>
<td valign="middle" align="center">359 (43.35%)</td>
<td valign="middle" align="center">351 (41.57%)</td>
<td valign="middle" align="center">382 (41.09%)</td>
<td valign="middle" align="center">391 (46.39%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Female</td>
<td valign="middle" align="center">1,651 (57.02%)</td>
<td valign="middle" align="center">427 (56.65%)</td>
<td valign="middle" align="center">431 (58.43%)</td>
<td valign="middle" align="center">400 (58.91%)</td>
<td valign="middle" align="center">393 (53.61%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">Race</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">0.2</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Mexican American</td>
<td valign="middle" align="center">197 (2.20%)</td>
<td valign="middle" align="center">46 (1.89%)</td>
<td valign="middle" align="center">47 (2.13%)</td>
<td valign="middle" align="center">52 (2.55%)</td>
<td valign="middle" align="center">52 (2.25%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Other Hispanic</td>
<td valign="middle" align="center">162 (2.09%)</td>
<td valign="middle" align="center">45 (2.57%)</td>
<td valign="middle" align="center">51 (2.27%)</td>
<td valign="middle" align="center">40 (1.88%)</td>
<td valign="middle" align="center">26 (1.57%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Non-Hispanic white</td>
<td valign="middle" align="center">2,230 (87.51%)</td>
<td valign="middle" align="center">516 (85.71%)</td>
<td valign="middle" align="center">570 (88.88%)</td>
<td valign="middle" align="center">572 (87.82%)</td>
<td valign="middle" align="center">572 (87.60%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Non-Hispanic black</td>
<td valign="middle" align="center">411 (4.76%)</td>
<td valign="middle" align="center">140 (6.58%)</td>
<td valign="middle" align="center">85 (3.75%)</td>
<td valign="middle" align="center">89 (3.83%)</td>
<td valign="middle" align="center">97 (4.91%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Other race</td>
<td valign="middle" align="center">134 (3.43%)</td>
<td valign="middle" align="center">39 (3.25%)</td>
<td valign="middle" align="center">29 (2.97%)</td>
<td valign="middle" align="center">29 (3.92%)</td>
<td valign="middle" align="center">37 (3.67%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">Education level</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">0.2</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Less than high school</td>
<td valign="middle" align="center">653 (12.69%)</td>
<td valign="middle" align="center">153 (12.83%)</td>
<td valign="middle" align="center">150 (10.59%)</td>
<td valign="middle" align="center">169 (13.11%)</td>
<td valign="middle" align="center">181 (14.65%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;High school or GED</td>
<td valign="middle" align="center">1,677 (53.96%)</td>
<td valign="middle" align="center">423 (51.67%)</td>
<td valign="middle" align="center">419 (53.84%)</td>
<td valign="middle" align="center">415 (54.08%)</td>
<td valign="middle" align="center">420 (56.63%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Above high school</td>
<td valign="middle" align="center">804 (33.35%)</td>
<td valign="middle" align="center">210 (35.51%)</td>
<td valign="middle" align="center">213 (35.57%)</td>
<td valign="middle" align="center">198 (32.80%)</td>
<td valign="middle" align="center">183 (28.72%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">Marital status</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">0.028</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Married or living with a &#x2003;partner</td>
<td valign="middle" align="center">1,904 (66.04%)</td>
<td valign="middle" align="center">509 (71.00%)</td>
<td valign="middle" align="center">481 (66.37%)</td>
<td valign="middle" align="center">469 (63.44%)</td>
<td valign="middle" align="center">445 (62.76%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Living alone</td>
<td valign="middle" align="center">1,230 (33.96%)</td>
<td valign="middle" align="center">277 (29.00%)</td>
<td valign="middle" align="center">301 (33.63%)</td>
<td valign="middle" align="center">313 (36.56%)</td>
<td valign="middle" align="center">339 (37.24%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">PIR</td>
<td valign="middle" align="center">3.30 &#xb1; 1.56</td>
<td valign="middle" align="center">3.44 &#xb1; 1.56</td>
<td valign="middle" align="center">3.38 &#xb1; 1.59</td>
<td valign="middle" align="center">3.21 &#xb1; 1.54</td>
<td valign="middle" align="center">3.12 &#xb1; 1.54</td>
<td valign="middle" align="center">0.004</td>
</tr>
<tr>
<td valign="middle" align="left">BMI, kg/m2, mean (SD)</td>
<td valign="middle" align="center">28.93 &#xb1; 6.45</td>
<td valign="middle" align="center">27.96 &#xb1; 5.32</td>
<td valign="middle" align="center">28.34 &#xb1; 6.01</td>
<td valign="middle" align="center">29.42 &#xb1; 6.89</td>
<td valign="middle" align="center">30.24 &#xb1; 7.34</td>
<td valign="middle" align="center">&lt;0.001</td>
</tr>
<tr>
<td valign="middle" align="left">Alcohol consumption</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">0.003</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Never drinkers</td>
<td valign="middle" align="center">383 (9.52%)</td>
<td valign="middle" align="center">103 (10.21%)</td>
<td valign="middle" align="center">95 (8.92%)</td>
<td valign="middle" align="center">88 (9.03%)</td>
<td valign="middle" align="center">97 (9.99%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Former drinkers</td>
<td valign="middle" align="center">746 (23.37%)</td>
<td valign="middle" align="center">170 (18.87%)</td>
<td valign="middle" align="center">165 (19.79%)</td>
<td valign="middle" align="center">186 (26.40%)</td>
<td valign="middle" align="center">225 (29.65%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Current drinkers</td>
<td valign="middle" align="center">2,005 (67.11%)</td>
<td valign="middle" align="center">513 (70.92%)</td>
<td valign="middle" align="center">522 (71.29%)</td>
<td valign="middle" align="center">508 (64.57%)</td>
<td valign="middle" align="center">462 (60.36%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">Smoking status</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">0.3</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Never smoker</td>
<td valign="middle" align="center">1,388 (45.22%)</td>
<td valign="middle" align="center">388 (48.46%)</td>
<td valign="middle" align="center">340 (43.20%)</td>
<td valign="middle" align="center">344 (47.24%)</td>
<td valign="middle" align="center">316 (41.69%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Former smoker</td>
<td valign="middle" align="center">1,260 (38.70%)</td>
<td valign="middle" align="center">286 (37.31%)</td>
<td valign="middle" align="center">315 (39.78%)</td>
<td valign="middle" align="center">332 (38.44%)</td>
<td valign="middle" align="center">327 (39.29%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Current smoker</td>
<td valign="middle" align="center">486 (16.07%)</td>
<td valign="middle" align="center">112 (14.24%)</td>
<td valign="middle" align="center">127 (17.01%)</td>
<td valign="middle" align="center">106 (14.31%)</td>
<td valign="middle" align="center">141 (19.02%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<th valign="middle" colspan="7" align="left">Medical history</th>
</tr>
<tr>
<td valign="middle" align="left">Hypertension</td>
<td valign="middle" align="center">2,012 (57.95%)</td>
<td valign="middle" align="center">487 (54.04%)</td>
<td valign="middle" align="center">473 (53.24%)</td>
<td valign="middle" align="center">509 (58.44%)</td>
<td valign="middle" align="center">543 (67.74%)</td>
<td valign="middle" align="center">&lt;0.001</td>
</tr>
<tr>
<td valign="middle" align="left">Diabetes</td>
<td valign="middle" align="center">838 (22.18%)</td>
<td valign="middle" align="center">172 (17.14%)</td>
<td valign="middle" align="center">191 (18.55%)</td>
<td valign="middle" align="center">222 (23.98%)</td>
<td valign="middle" align="center">253 (30.51%)</td>
<td valign="middle" align="center">&lt;0.001</td>
</tr>
<tr>
<td valign="middle" align="left">CVD</td>
<td valign="middle" align="center">775 (19.48%)</td>
<td valign="middle" align="center">150 (15.50%)</td>
<td valign="middle" align="center">189 (18.12%)</td>
<td valign="middle" align="center">202 (20.10%)</td>
<td valign="middle" align="center">234 (25.09%)</td>
<td valign="middle" align="center">0.002</td>
</tr>
<tr>
<td valign="middle" align="left">CKD</td>
<td valign="middle" align="center">734 (18.33%)</td>
<td valign="middle" align="center">141 (13.32%)</td>
<td valign="middle" align="center">156 (15.68%)</td>
<td valign="middle" align="center">198 (19.60%)</td>
<td valign="middle" align="center">239 (26.02%)</td>
<td valign="middle" align="center">&lt;0.001</td>
</tr>
<tr>
<td valign="middle" align="left">All-cause mortality, n (%)</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">&lt;0.001</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Alive</td>
<td valign="middle" align="center">2,216 (79.13%)</td>
<td valign="middle" align="center">620 (84.96%)</td>
<td valign="middle" align="center">593 (83.75%)</td>
<td valign="middle" align="center">539 (78.67%)</td>
<td valign="middle" align="center">464 (67.20%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Death</td>
<td valign="middle" align="center">918 (20.87%)</td>
<td valign="middle" align="center">166 (15.04%)</td>
<td valign="middle" align="center">189 (16.25%)</td>
<td valign="middle" align="center">243 (21.33%)</td>
<td valign="middle" align="center">320 (32.80%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">Cancer mortality, n (%)</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">&lt;0.001</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Alive</td>
<td valign="middle" align="center">2,853 (93.54%)</td>
<td valign="middle" align="center">722 (94.27%)</td>
<td valign="middle" align="center">726 (95.39%)</td>
<td valign="middle" align="center">718 (94.23%)</td>
<td valign="middle" align="center">687 (89.68%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Death</td>
<td valign="middle" align="center">281 (6.46%)</td>
<td valign="middle" align="center">64 (5.73%)</td>
<td valign="middle" align="center">56 (4.61%)</td>
<td valign="middle" align="center">64 (5.77%)</td>
<td valign="middle" align="center">97 (10.32%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">Non-cancer mortality, n (%)</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">&lt;0.001</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Alive</td>
<td valign="middle" align="center">2,497 (85.59%)</td>
<td valign="middle" align="center">684 (90.69%)</td>
<td valign="middle" align="center">649 (88.36%)</td>
<td valign="middle" align="center">603 (84.44%)</td>
<td valign="middle" align="center">561 (77.52%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Death</td>
<td valign="middle" align="center">637 (14.41%)</td>
<td valign="middle" align="center">102 (9.31%)</td>
<td valign="middle" align="center">133 (11.64%)</td>
<td valign="middle" align="center">179 (15.56%)</td>
<td valign="middle" align="center">223 (22.48%)</td>
<td valign="middle" align="center"/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>NPAR, neutrophil percentage-to-albumin ratio; PIR, poverty income ratio; BMI, body mass index; CVD, cardiovascular disease; CKD, Chronic kidney disease.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<title>Relationship between NPAR and mortality</title>
<p>During the median follow-up of 7.24 years, a total of 918 deaths were recorded, including 281 cancer-related deaths (30.6%) and 637 non-cancer-related deaths (69.4%). Stratified analysis showed a significant time-dose effect of NPAR level on the risk of death: the cumulative incidence of all-cause death (320 cases), cancer-related death (97 cases), and non-cancer-related death (223 cases) in the Q4 group (highest NPAR) was significantly higher than that in other groups (see <xref ref-type="table" rid="T1"><bold>Table&#xa0;1</bold></xref> for details).</p>
<p>The continuous variable model showed that for every one-unit increase in NPAR, the risk of all-cause mortality increased by 10%&#x2013;15% (models 1&#x2013;3: HR=1.10&#x2013;1.15, all <italic>P &lt;</italic>0.001), and the risk of non-cancer-related mortality increased by 12%&#x2013;17% (HR=1.12&#x2013;1.17, all <italic>P&#xa0;&lt;</italic>0.001). It is worth noting that in the basic model (model 1: HR=1.10, 95%CI 1.04-1.17, <italic>P</italic> =0.001) and the population school correction model (model 2: HR=1.07, 1.01-1.14, <italic>P</italic> =0.025), NPAR remained a significant predictor of cancer-related mortality risk in the population-adjusted model (model 2: HR=1.07, 1.01-1.14, <italic>P</italic> =0.025), but the association weakened to borderline significance in the fully adjusted model (model 3) (HR=1.06, 1.00-1.13, <italic>P</italic> =0.054) (<xref ref-type="table" rid="T2"><bold>Table&#xa0;2</bold></xref>).</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Cox proportional hazards regression analysis of NPAR and mortality.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Exposure</th>
<th valign="middle" align="center">Model 1 HR (95% CI) <italic>P</italic>-value</th>
<th valign="middle" align="center">Model 2 HR (95% CI) <italic>P</italic>-value</th>
<th valign="middle" align="center">Model 3 HR (95% CI) <italic>P</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<th valign="middle" colspan="4" align="left">All-cause mortality</th>
</tr>
<tr>
<td valign="middle" align="left">NPAR (continuous)</td>
<td valign="middle" align="center">1.15 (1.12, 1.19) &lt;0.001</td>
<td valign="middle" align="center">1.11 (1.08, 1.14) &lt;0.001</td>
<td valign="middle" align="center">1.10(1.07, 1.13) &lt;0.001</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">NPAR quartile</th>
</tr>
<tr>
<td valign="middle" align="left">Quartile 1</td>
<td valign="middle" align="center">Reference</td>
<td valign="middle" align="center">Reference</td>
<td valign="middle" align="center">Reference</td>
</tr>
<tr>
<td valign="middle" align="left">Quartile 2</td>
<td valign="middle" align="center">1.13 (0.90, 1.41) 0.309</td>
<td valign="middle" align="center">1.10 (0.89, 1.35) 0.374</td>
<td valign="middle" align="center">1.05 (0.85, 1.29) 0.632</td>
</tr>
<tr>
<td valign="middle" align="left">Quartile 3</td>
<td valign="middle" align="center">1.54 (1.20, 1.97) &lt;0.001</td>
<td valign="middle" align="center">1.14 (0.92, 1.41) 0.243</td>
<td valign="middle" align="center">1.10 (0.89, 1.36) 0.370</td>
</tr>
<tr>
<td valign="middle" align="left">Quartile 4</td>
<td valign="middle" align="center">2.81 (2.25, 3.51) &lt;0.001</td>
<td valign="middle" align="center">2.04 (1.68, 2.49) &lt;0.001</td>
<td valign="middle" align="center">1.89 (1.55, 2.31) &lt;0.001</td>
</tr>
<tr>
<td valign="middle" align="left">P for trend</td>
<td valign="middle" align="center">&lt;0.001</td>
<td valign="middle" align="center">&lt;0.001</td>
<td valign="middle" align="center">&lt;0.001</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Cancer mortality</th>
</tr>
<tr>
<td valign="middle" align="left">NPAR (continuous)</td>
<td valign="middle" align="center">1.10 (1.04, 1.17) 0.001</td>
<td valign="middle" align="center">1.07 (1.01, 1.14) 0.025</td>
<td valign="middle" align="center">1.06 (1.00, 1.13) 0.054</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">NPAR quartile</th>
</tr>
<tr>
<td valign="middle" align="left">Quartile 1</td>
<td valign="middle" align="center">Reference</td>
<td valign="middle" align="center">Reference</td>
<td valign="middle" align="center">Reference</td>
</tr>
<tr>
<td valign="middle" align="left">Quartile 2</td>
<td valign="middle" align="center">0.83 (0.55,1.25) 0.372</td>
<td valign="middle" align="center">0.82 (0.55, 1.22) 0.327</td>
<td valign="middle" align="center">0.81 (0.54, 1.21) 0.303</td>
</tr>
<tr>
<td valign="middle" align="left">Quartile 3</td>
<td valign="middle" align="center">1.08 (0.72, 1.62) 0.706</td>
<td valign="middle" align="center">0.91 (0.60, 1.40) 0.674</td>
<td valign="middle" align="center">0.88 (0.57, 1.36) 0.564</td>
</tr>
<tr>
<td valign="middle" align="left">Quartile 4</td>
<td valign="middle" align="center">2.23 (1.59, 3.13) &lt;0.001</td>
<td valign="middle" align="center">1.78 (1.27, 2.48) &lt;0.001</td>
<td valign="middle" align="center">1.67 (1.18, 2.35) 0.003</td>
</tr>
<tr>
<td valign="middle" align="left">P for trend</td>
<td valign="middle" align="center">&lt;0.001</td>
<td valign="middle" align="center">0.001</td>
<td valign="middle" align="center">0.004</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Non-cancer mortality</th>
</tr>
<tr>
<td valign="middle" align="left">NPAR (continuous)</td>
<td valign="middle" align="center">1.17 (1.13, 1.22) &lt;0.001</td>
<td valign="middle" align="center">1.12 (1.08, 1.17) &lt;0.001</td>
<td valign="middle" align="center">1.12 (1.08, 1.16) &lt;0.001</td>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">NPAR quartile</th>
</tr>
<tr>
<td valign="middle" align="left">Quartile 1</td>
<td valign="middle" align="center">Reference</td>
<td valign="middle" align="center">Reference</td>
<td valign="middle" align="center">Reference</td>
</tr>
<tr>
<td valign="middle" align="left">Quartile 2</td>
<td valign="middle" align="center">1.31 (1.01, 1.70) 0.045</td>
<td valign="middle" align="center">1.28 (1.00, 1.64) 0.049</td>
<td valign="middle" align="center">1.20 (0.94, 1.53) 0.144</td>
</tr>
<tr>
<td valign="middle" align="left">Quartile 3</td>
<td valign="middle" align="center">1.82 (1.34, 2.48) &lt;0.001</td>
<td valign="middle" align="center">1.28 (0.98, 1.62) 0.074</td>
<td valign="middle" align="center">1.22 (0.95, 1.55) 0.116</td>
</tr>
<tr>
<td valign="middle" align="left">Quartile 4</td>
<td valign="middle" align="center">3.16 (2.39, 4.18) &lt;0.001</td>
<td valign="middle" align="center">2.22 (1.72, 2.85) &lt;0.001</td>
<td valign="middle" align="center">2.04 (1.61, 2.58) &lt;0.001</td>
</tr>
<tr>
<td valign="middle" align="left">P for trend</td>
<td valign="middle" align="center">&lt;0.001</td>
<td valign="middle" align="center">&lt;0.001</td>
<td valign="middle" align="center">&lt;0.001</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Model 1: No covariates were adjusted.</p></fn>
<fn>
<p>Model 2: Age, gender, and race were adjusted.</p></fn>
<fn>
<p>Model 3: Age, gender, race, PIR, marital status, educational level, BMI, smoking status, alcohol consumption, diabetes, hypertension, and CVD were adjusted.</p></fn>
<fn>
<p>NPAR, neutrophil percentage-to-albumin ratio; PIR, poverty income ratio; BMI, body mass index; CVD, cardiovascular disease; HR, hazard ratio; CI, confidence interval.</p></fn>
</table-wrap-foot>
</table-wrap>
<p>From a clinical significance perspective, each unit increase in NPAR is associated with a quantifiable increase in risk that holds clear clinical reference value. In the fully adjusted model (Model 3), an increase of 1 unit in NPAR is directly associated with a 10% increase in all-cause mortality risk (HR=1.10) and a 12% increase in non-cancer mortality risk (HR=1.12). This suggests that even a small rise in NPAR (for example, from 10 to 11) may lead to a quantifiable adverse change in the mortality risk of cancer survivors. Furthermore, the association between NPAR and cancer-specific mortality risk (HR=1.06, P=0.054), while approaching statistical significance, indicates a potential link between NPAR levels and tumor-related prognosis.</p>
<p>When NPAR was grouped into quartiles, the gradient effect of mortality risk was more significant: compared with Q1, the risk of all-cause mortality in Q4 increased by 89% (model 3 HR=1.89, 95%CI 1.55-2.31), and the risk of non-cancer-related mortality increased by 104% (HR=2.04, 1.61-2.58) (both <italic>P &lt;</italic>0.001). The risk of cancer-related death decreased but still maintained an independent risk increase of 67% (Q4 vs Q1: HR=1.67, 1.18-2.35, <italic>P</italic>&#xa0;=0.003) (<xref ref-type="table" rid="T2"><bold>Table&#xa0;2</bold></xref>).</p>
</sec>
<sec id="s3_3">
<title>Survival outcomes by NPAR levels</title>
<p>In the Kaplan-Meier survival analysis, all-cause survival rates for patients tended to decline with increasing NPAR, and this observation was statistically significant (P&lt;0.001) (<xref ref-type="fig" rid="f2"><bold>Figure&#xa0;2</bold></xref>). Patients in the Q4 group exhibited the poorest cancer-specific survival rates, with the difference being statistically significant (<italic>P</italic>&lt;0.001) (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3</bold></xref>). Furthermore, non-cancer-specific survival rates also decreased with rising NPAR levels, with a statistically significant difference observed (P&lt;0.001) (<xref ref-type="fig" rid="f4"><bold>Figure&#xa0;4</bold></xref>).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Kaplan-Meier curves for all-cause mortality by NPAR quartiles. NPAR, neutrophil percentage-to-albumin ratio.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-15-1620484-g002.tif">
<alt-text content-type="machine-generated">Kaplan-Meier survival curve showing survival probability over 16 years for four quantiles (Q1 to Q4) with distinct lines. The survival curve shows a significant difference (p &lt; 0.0001) among quantiles, with Q4 having the lowest survival probability. A table beneath the plot lists the number at risk for each quantile.</alt-text>
</graphic></fig>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Kaplan-Meier curves for cancer-specific mortality by NPAR quartiles. NPAR, neutrophil percentage-to-albumin ratio.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-15-1620484-g003.tif">
<alt-text content-type="machine-generated">Kaplan-Meier survival graph showing survival probability over 16 years for four quantiles (Q1 to Q4) with survival ranging from near 100% to below 75%. Statistical significance is indicated by p &lt; 0.0001. Below the graph, a table lists the number of individuals at risk at various time points for each quantile.</alt-text>
</graphic></fig>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>Kaplan-Meier curves for non-cancer-specific mortality by NPAR quartiles. NPAR, neutrophil percentage-to-albumin ratio.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-15-1620484-g004.tif">
<alt-text content-type="machine-generated">Kaplan-Meier survival curve showing four NPAR quantiles over a 16-year follow-up. Survival probability decreases over time for all groups. Q1 (blue) has the highest survival, followed by Q3 (green), Q2 (red), and Q4 (black). The p-value is less than 0.0001, indicating significant differences. The table below shows the number at risk for each quantile at various time points.</alt-text>
</graphic></fig>
</sec>
<sec id="s3_4">
<title>Nonlinear association between NPAR and mortality</title>
<p>The results of restricted cubic splines (RCS) analysis demonstrate that the relationship between NPAR and mortality risk is not merely linear. When NPAR is below 12.84, each unit increase in NPAR is associated with a gradual decline in all-cause mortality risk (HR=0.92, 95% CI: 0.88-0.97, <italic>P</italic>&lt;0.001). However, once NPAR surpasses this threshold of 12.84, each unit increase in NPAR leads to a dramatic 16% rise in all-cause mortality risk (HR=1.16, 95% CI: 1.13-1.19, <italic>P &lt;</italic>0.001). This nonlinear characteristic underscores the importance of closely monitoring cancer survivors with NPAR levels of 12.84 or higher in clinical practice. In this population, the risk effect associated with each unit increase in NPAR (HR=1.16) is notably greater than the overall linear estimate (HR=1.10), necessitating more proactive interventions to address inflammatory and nutritional status in order to reduce mortality risk (<xref ref-type="fig" rid="f5"><bold>Figure&#xa0;5</bold></xref>, <xref ref-type="table" rid="T3"><bold>Table&#xa0;3</bold></xref>).</p>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>Restricted cubic spline analysis of the association between NPAR and mortality in Cancer Survivors. Model 1 represents unadjusted covariates; Model 2 is adjusted for age, gender, and race; and Model 3 is adjusted for age, gender, race, PIR, marital status, educational level, BMI, smoking status, alcohol consumption, diabetes, hypertension, and CVD. The solid line and purple area represent estimates and their corresponding 95% confidence intervals (CIs), respectively. NPAR, neutrophil percentage-to-albumin ratio; PIR, ratio of income to poverty; BMI, body mass index; CVD, cardiovascular disease; HR, hazard ratio; CI, confidence interval.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-15-1620484-g005.tif">
<alt-text content-type="machine-generated">Graphs showing hazard ratios (HR) with 95% confidence intervals (CI) for all-cause, cancer-specific, and non-cancer-specific mortality across three models, with NPAR as the variable. All graphs indicate significant non-linear relationships (P &lt; 0.0001). Blue shading represents the CI range around the red HR lines.</alt-text>
</graphic></fig>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>Threshold effect analysis of NPAR index on all-cause mortality in cancer survivors.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">All-cause mortality</th>
<th valign="middle" align="center">HR (95% CI) <italic>P</italic> value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Fitting by the standard linear model</td>
<td valign="middle" align="center">1.10 (1.07, 1.12) &lt;0.001</td>
</tr>
<tr>
<th valign="middle" colspan="2" align="left">Fitting by the two-piecewise linear model</th>
</tr>
<tr>
<td valign="middle" align="left">Inflection point</td>
<td valign="middle" align="center">12.84</td>
</tr>
<tr>
<td valign="middle" align="left">NPAR index&lt;12.84</td>
<td valign="middle" align="center">0.92 (0.88, 0.97) &lt;0.001</td>
</tr>
<tr>
<td valign="middle" align="left">NPAR index &#x2265; 12.84</td>
<td valign="middle" align="center">1.16 (1.13, 1.19) &lt;0.001</td>
</tr>
<tr>
<td valign="middle" align="left">P for Log-likelihood ratio</td>
<td valign="middle" align="center">&lt;0.001</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>The model was adjusted for age, gender, race, education, marital level, PIR, BMI, drinker, smoker, diabetes, hypertension, and CVD. NPAR, neutrophil percentage-to-albumin ratio; PIR, poverty income ratio; BMI, body mass index; CVD, cardiovascular disease; HR, hazard ratio; CI, confidence interval.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_5">
<title>Subgroup associations of NPAR With mortality</title>
<p>Subgroup analyses revealed no significant heterogeneity in the association of NPAR with the risk of all-cause and idiosyncratic mortality (both interaction <italic>P</italic> &gt; 0.05). However, specific clinical subgroups showed a more significant risk gradient: for all-cause mortality, females (HR=1.13 vs. males HR=1.10), normal weight individuals (HR=1.12 vs. overweight/obese HR=1.10), those with combined hypertension (HR=1.12), diabetes mellitus (HR=1.15), or CVD (HR=1.13), as well as non-CKD patients exhibited a more pronounced increase in risk. In contrast, for cancer-specific mortality, individuals aged &lt;60 years (HR=1.15 vs. &#x2265;60 years HR=1.05), females (HR=1.12), overweight/obese individuals (HR=1.10), and those with comorbid hypertension (HR=1.11), diabetes (HR=1.10), CVD (HR=1.15), or CKD (HR=1.11) displayed a stronger NPAR-mortality risk association (<xref ref-type="fig" rid="f6"><bold>Figure&#xa0;6</bold></xref>).</p>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>Subgroup analysis between NPAR and all-cause mortality <bold>(A)</bold>, cancer-specific mortality <bold>(B)</bold>, and non-cancer-specific mortality <bold>(C)</bold>. NPAR, neutrophil percentage-to-albumin ratio; BMI, body mass index; CVD, cardiovascular disease; CKD, Chronic kidney disease; HR, hazard ratio; CI, confidence interval.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-15-1620484-g006.tif">
<alt-text content-type="machine-generated">Three forest plots labeled A, B, and C compare hazard ratios (HR) with 95% confidence intervals (CI) for subgroups based on age, sex, race, BMI, hypertension, diabetes, alcohol consumption, smoking status, CVD, and CKD. Each plot includes P-values for interaction. A red vertical line marks HR of 1.0, indicating the threshold between low and high risk. Subgroups are plotted with male and female differences, highlighting notable findings with arrows, such as significant race and diabetes interactions.</alt-text>
</graphic></fig>
</sec>
<sec id="s3_6">
<title>Mediating role of renal function</title>
<p>This study innovatively introduced the causal mediation analysis framework, which revealed for the first time the key mediating role of renal function (eGFR) in the association between NPAR and mortality. For all-cause mortality (<xref ref-type="fig" rid="f7"><bold>Figure&#xa0;7A</bold></xref>), NPAR exerted an indirect effect on all-cause mortality through eGFR, with an indirect effect value of 0.0187 (95% CI: 0.0119-0.0264, <italic>P</italic>&lt;0.001). The direct effect of NPAR on all-cause mortality was 0.0624 (95% CI: 0.0423 - 0.0837, <italic>P</italic>&lt;0.001), and the proportion of mediation accounted for by eGFR was 23.08%. Regarding cancer-specific mortality (<xref ref-type="fig" rid="f7"><bold>Figure&#xa0;7B</bold></xref>), the indirect effect of NPAR on cancer-specific mortality via eGFR was 0.0040 (95% CI: 0.0023 - 0.0062, <italic>P</italic>&lt;0.001). However, the direct effect of NPAR on cancer-specific mortality was 0.0079 (95% CI: -0.0053 - 0.0216, <italic>P</italic>=0.238), and the proportion of mediation by eGFR was 33.86%. Notably, no significant direct effect of NPAR on cancer-specific mortality was observed. For non-cancer-specific mortality (<xref ref-type="fig" rid="f7"><bold>Figure&#xa0;7C</bold></xref>), NPAR had an indirect effect on non-cancer-specific mortality through eGFR, with a value of 0.0148 (95% CI: 0.0093 - 0.0211, <italic>P</italic>&lt;0.001). The direct effect of NPAR on non-cancer-specific mortality was 0.0526 (95% CI: 0.0352 - 0.0696, <italic>P</italic>&lt;0.001), and the proportion of mediation by eGFR was 21.97%.</p>
<fig id="f7" position="float">
<label>Figure&#xa0;7</label>
<caption>
<p>Analysis of the mediation by eGFR of the associations of NPAR with all-cause mortality <bold>(A)</bold>, cancer-specific mortality <bold>(B)</bold>, and non-cancer-specific mortality <bold>(C)</bold> in cancer survivors. NPAR, neutrophil percentage-to-albumin ratio; eGFR, glomerular filtration rate.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-15-1620484-g007.tif">
<alt-text content-type="machine-generated">Diagram illustrating mediation analysis in three scenarios labeled A, B, and C. In each, NPAR influences an outcome through eGFR. A shows all-cause mortality with indirect effect 0.0187 and direct effect 0.0624. B shows cancer-specific mortality with indirect effect 0.0040 and direct effect 0.0079. C shows non-cancer mortality with indirect effect 0.0148 and direct effect 0.0526. Proportions of mediation are 23.08%, 33.86%, and 21.97% respectively. All indirect effects are significant with P &lt; 0.001.</alt-text>
</graphic></fig>
</sec>
<sec id="s3_7">
<title>Discriminatory performance of NPAR and SII</title>
<p>ROC curve analyses were performed to evaluate the discriminatory ability of NPAR and SII in predicting all-cause mortality, cancer-specific mortality and non-cancer. For all-cause mortality (<xref ref-type="fig" rid="f8"><bold>Figure&#xa0;8A</bold></xref>), the AUC for NPAR was 0.608 (95% CI: 0.586&#x2013;0.630), while the AUC for SII was 0.581 (95% CI: 0.559&#x2013;0.604). When assessing cancer-specific mortality (<xref ref-type="fig" rid="f8"><bold>Figure&#xa0;8B</bold></xref>), NPAR exhibited an AUC of 0.553 (95% CI: 0.516&#x2013;0.591) and SII an AUC of 0.524 (95% CI: 0.485&#x2013;0.563). In the context of non-cancer-specific mortality prediction (<xref ref-type="fig" rid="f8"><bold>Figure&#xa0;8C</bold></xref>), the AUC for NPAR was 0.611 (95% CI: 0.587&#x2013;0.636) and for SII it was 0.592 (95% CI: 0.567&#x2013;0.617). Overall, NPAR demonstrated marginally superior discriminatory capacity compared with SII across all the mortality endpoints examined.</p>
<fig id="f8" position="float">
<label>Figure&#xa0;8</label>
<caption>
<p>ROC curve analysis of the predictive value of NPAR and SII for all-cause mortality <bold>(A)</bold>, cancer-specific mortality <bold>(B)</bold> and non-cancer mortality <bold>(C)</bold>.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-15-1620484-g008.tif">
<alt-text content-type="machine-generated">Three ROC curve analyses labeled A, B, and C, each comparing sensitivity versus 1-specificity. In graph A, NPAR has an AUC of 0.608 and SII has 0.581. In graph B, NPAR has 0.553 and SII has 0.524. In graph C, NPAR has 0.611 and SII has 0.592. Each graph includes a diagonal reference line.</alt-text>
</graphic></fig>
</sec>
<sec id="s3_8">
<title>External validation analyses</title>
<p>To further validate the relationship between NPAR and cancer patient prognosis, a real-world data analysis was conducted at Longyan First Hospital with ethical approval obtained (Ethics Approval Number: LYREC2024-k028-01). A total of 1,194 patients diagnosed with cancer were enrolled between January 2016 and December 2018. Among these, 59 lacked neutrophil count or serum albumin data, 126 had missing follow-up data, and 24 lacked covariates. Ultimately, 985 participants were included in the analysis. <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table&#xa0;1</bold></xref> presents baseline characteristics of study participants stratified by NPAR.</p>
<p>Consistently, participants with higher NPAR levels exhibited increased all-cause mortality, cancer-specific mortality, and non-cancer-specific mortality (all <italic>P</italic>&lt;0.001) (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table S2</bold></xref>). After adjusting for covariates including age, sex, BMI, smoking status, alcohol consumption, hypertension, diabetes, and heart disease, the positive association remained statistically significant (all <italic>P</italic>&lt;0.001). When NPAR was grouped into quartiles, compared with Q1, Q4 was associated with a 163% increased risk of all-cause mortality (Model 3 HR=2.63, 95% CI 1.87-3.71), cancer-related mortality risk increased by 224% (HR=3.24, 1.76-5.98), and non-cancer-related mortality risk increased by 84% (HR=1.84, 1.25-2.70).</p>
<p>Furthermore, Kaplan-Meier survival analysis of the external validation cohort revealed that overall survival, cancer-specific survival, and non-cancer-specific survival rates all exhibited a declining trend with increasing NPAR, patients in the Q4 group demonstrated the lowest survival rates, with the difference being statistically significant (all <italic>P</italic>&lt;0.001) (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Figure S1-3</bold></xref>).</p>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<title>Discussion</title>
<p>This study utilized data from NHANES and revealed for the first time that NPAR is an independent predictor of all-cause and cancer-specific mortality risk in cancer survivors. The core findings showed that for every 1-unit increase in NPAR, the risk of all-cause mortality increased by 10%, the risk of cancer-related mortality increased by 6%, and the risk of non-cancer mortality increased by 12%. This dose-dependent association remained robust in multivariate adjusted models. RCS analysis further revealed a nonlinear relationship between NPAR and mortality risk, with a steep inflection point in the risk of all-cause mortality when NPAR exceeded 12.84. Finally, mediation analysis clarified that renal function impairment is a key pathway through which NPAR affects prognosis: 23.08% of the risk of all-cause mortality mediated by NPAR is driven by a decline in eGFR, and 21.97% of the risk of non-cancer mortality is attributed to worsening renal function.</p>
<p>Neutrophils represent a significant component of the innate immune system, with their hematopoietic stem cells generated through evolutionary processes. In the tumor microenvironment, neutrophils are classified as tumor-associated neutrophils (<xref ref-type="bibr" rid="B21">21</xref>). Numerous studies have demonstrated that neutrophils exhibit plasticity within the tumor host; they can be categorized into anti-tumor (N1) and pro-tumor (N2) phenotypes based on their roles in tumor dynamics. Neutrophils of the N1 phenotype may exert anti-tumor effects via reactive oxygen species (ROS)-associated pathways, while pro-tumor mechanisms of N2 phenotype neutrophils may involve neutrophil elastase (NE) and matrix metalloproteinases (<xref ref-type="bibr" rid="B22">22</xref>&#x2013;<xref ref-type="bibr" rid="B24">24</xref>). Albumin can bind to pro-inflammatory cytokines, such as IL-6, and neutralize lipopolysaccharides (LPS), thereby preventing the conversion of neutrophils to the N2 phenotype. <italic>In vitro</italic> studies (<xref ref-type="bibr" rid="B25">25</xref>) have demonstrated that albumin can inhibit the formation of NETs (neutrophil extracellular traps) by N2 neutrophils, thereby reducing their protective effect on tumor cells. A decrease in albumin levels, which is commonly observed in high NPAR states, increases the availability of free IL-6, which in turn induces N2 polarization. This creates a vicious cycle: N2 neutrophils secrete more IL-6, further inhibiting hepatic albumin synthesis, leading to even higher NPAR levels and ultimately resulting in poorer prognosis (<xref ref-type="bibr" rid="B25">25</xref>). The N1/N2 ratio has also been suggested as a prognostic factor in hepatocellular carcinoma, where a higher N1/N2 ratio in the tumor microenvironment correlates positively with prognosis. Conversely, a high N1/N2 ratio in peritumoral tissues has been associated with poorer prognostic outcomes (<xref ref-type="bibr" rid="B26">26</xref>). Additionally, tumor-associated neutrophils may be influenced by metabolic processes, leading to increased expression of hypoxia-inducible factor 1&#x3b1; (HIF-1&#x3b1;), which promotes angiogenesis, glycolysis, and gene upregulation, ultimately advancing tumor&#xa0;progression (<xref ref-type="bibr" rid="B27">27</xref>&#x2013;<xref ref-type="bibr" rid="B30">30</xref>). Furthermore, past studies have demonstrated that neutrophil infiltration is regulated by chemokine pathways such as CXCL/CXCR2, which are associated with either improved or worsened outcomes in different cancer types, highlighting their complex roles in cancer progression (<xref ref-type="bibr" rid="B31">31</xref>, <xref ref-type="bibr" rid="B32">32</xref>).</p>
<p>Neutrophils and lymphocytes serve as critical and easily accessible markers of inflammation; the significance of the neutrophil-to-lymphocyte ratio in prognosing various diseases has garnered considerable interest. Elevations in this ratio may constitute an important risk factor for increased cardiovascular mortality among cancer survivors (<xref ref-type="bibr" rid="B33">33</xref>). Importantly, inflammation among cancer survivors is routinely correlated with nutritional status, and recent studies indicate that cancer patients exhibit a heightened risk of malnutrition, with their nutritional and immune statuses serving as significant predictors of tumor development and outcomes (<xref ref-type="bibr" rid="B12">12</xref>, <xref ref-type="bibr" rid="B33">33</xref>, <xref ref-type="bibr" rid="B34">34</xref>). In the context of disease prediction, albumin levels are closely correlated with inflammation and can serve as a marker for clinical stability when assessing nutritional status (<xref ref-type="bibr" rid="B35">35</xref>, <xref ref-type="bibr" rid="B36">36</xref>). Albumin and globulin represent the primary components of serum proteins, with numerous studies confirming their efficacy as indicators of nutritional and inflammatory status in cancer progression. Their prognostic significance has been established across multiple cancers, including non-small cell lung cancer, gastric cancer, colorectal cancer, and breast cancer (<xref ref-type="bibr" rid="B37">37</xref>&#x2013;<xref ref-type="bibr" rid="B40">40</xref>).</p>
<p>Several comprehensive indicators have been used to assess inflammation and nutritional status in cancer patients, including hemoglobin, albumin, lymphocytes, and platelets (HALP score) (<xref ref-type="bibr" rid="B41">41</xref>), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) (<xref ref-type="bibr" rid="B6">6</xref>), prognostic nutritional index (PNI) (<xref ref-type="bibr" rid="B8">8</xref>), and the systemic immune-inflammation index (SII) (<xref ref-type="bibr" rid="B7">7</xref>). Compared to these indices, NPAR has distinct advantages as it combines a direct measure of innate immune activity (neutrophil percentage) with reliable indicators of nutritional and inflammatory status (albumin). While NLR and PLR reflect immune imbalance, and PNI and HALP include nutritional components, NPAR provides a more comprehensive assessment of systemic inflammation and catabolic status, which may explain its robust predictive performance in our cohort.</p>
<p>The NPAR integrates inflammatory and nutritional factors to provide a comprehensive assessment of an individual&#x2019;s inflammatory and nutritional status, and it was first introduced in 2016 by Bernard et&#xa0;al., demonstrating its potential predictive value in cancer (<xref ref-type="bibr" rid="B13">13</xref>). Numerous studies have supported the predictive validity of NPAR in various chronic diseases, revealing a positive correlation between high NPAR and both all-cause mortality and cardiovascular mortality in patients with hypertension and diabetes (<xref ref-type="bibr" rid="B42">42</xref>, <xref ref-type="bibr" rid="B43">43</xref>). Additionally, elevated NPAR has been implicated as a risk factor for stroke (<xref ref-type="bibr" rid="B44">44</xref>). In the realm of tumor-specific survival, a growing body of research has established NPAR&#x2019;s predictive value in patients with bladder cancer undergoing surgery after neoadjuvant chemotherapy, linking higher NPAR to lower overall tumor survival. Conversely, in oral squamous cell carcinoma, high NPAR has been associated with poorer overall survival and disease-free survival in patients (<xref ref-type="bibr" rid="B14">14</xref>, <xref ref-type="bibr" rid="B45">45</xref>). A multicenter cohort study has shown high NPAR to be independently associated with all-cause mortality in individuals with cancer (<xref ref-type="bibr" rid="B46">46</xref>), aligning with our findings that indicate that higher NPAR correlates with poorer prognoses among cancer survivors. In terms of all-cause mortality, these results suggest that higher NPAR may be associated with an increased mortality risk, and we have identified possible inflection points. Furthermore, our additional analyses concerning cancer-specific and non-cancer mortality led to similar conclusions, indicating that elevated NPAR is associated with an increased risk of both cancer-specific and non-cancer-related deaths.</p>
<p>A novel finding of this study is the mediating role of renal function in the association between NPAR and mortality. Renal impairment may exacerbate systemic inflammation and malnutrition through several mechanisms. The interaction mechanism between renal dysfunction and systemic inflammation, as well as malnutrition, suggests that renal impairment (evidenced by decreased estimated glomerular filtration rate) is not an isolated pathological state but forms a &#x201c;vicious cycle&#x201d; through various pathways involving systemic inflammation and malnutrition, thereby exacerbating the mortality risk among cancer survivors. From an inflammatory amplification perspective, the kidneys typically maintain inflammatory homeostasis by clearing circulating inflammatory mediators (such as tumor necrosis factor-alpha and interleukin-6), metabolizing reactive oxygen species (ROS), and inhibiting the overactivation of neutrophils. When estimated GFR (eGFR) declines, this &#x201c;inflammatory clearance capacity&#x201d; is severely impaired: accumulated inflammatory factors (such as interleukin-6) further activate neutrophils and promote the formation of neutrophil extracellular traps (NETs), a process associated with elevated NPAR in this study. Activated NETs exacerbate Toll-like receptor 4/NF-&#x3ba;B signaling in renal tubules, triggering a more intense local renal inflammatory response and establishing a positive feedback loop of &#x201c;renal inflammation &#x2192; systemic inflammation &#x2192; renal injury&#x201d; (<xref ref-type="bibr" rid="B47">47</xref>, <xref ref-type="bibr" rid="B48">48</xref>).Second, hypoalbuminemia creates a vicious cycle by inhibiting vascular endothelial growth factor (VEGF) signaling and mTORC1-mediated autophagy repair mechanisms, a process that has been validated in mouse models (<xref ref-type="bibr" rid="B49">49</xref>, <xref ref-type="bibr" rid="B50">50</xref>). Notably, the S100A8/A9 proteins secreted by neutrophils establish positive feedback with hypoalbuminemia through bidirectional regulation &#x2014; the former inhibits HNF-4&#x3b1;, a key factor in liver albumin synthesis, while the latter promotes the further release of NETs by upregulating HMGB1 (<xref ref-type="bibr" rid="B51">51</xref>). Importantly, the downregulation of Klotho expression in renal tubules not only aggravates phosphate retention and vascular calcification but also maintains the stemness of tumor cells through the Wnt/&#x3b2;-catenin pathway (<xref ref-type="bibr" rid="B52">52</xref>), suggesting that the imbalance of the Klotho/FGF23 axis may be a central mediator in the &#x201c;renal-cancer dialogue.&#x201d; Future studies should combine single-cell sequencing to elucidate cell interactions in the renal microenvironment and develop targeted intervention strategies aimed at clearing NETs or regulating the albumin-VEGF axis.</p>
<p>However, the underlying mechanisms through which NPAR contributes to increased mortality remain incompletely understood. Current studies have predominantly focused on inflammatory mechanisms, nutritional status, and immune dysregulation in the context of tumorigenesis, while the role of renal function has been relatively underexplored. Importantly, our mediation analysis identifies renal impairment (reflected by reduced eGFR) as a critical intermediary pathway linking NPAR to poor outcomes. Neutrophils may exert pro-tumorigenic effects through the secretion of cytokines and chemokines, the formation of neutrophil extracellular traps (NETs), and intercellular interactions, thereby promoting tumor angiogenesis, cancer cell proliferation, and metastasis (<xref ref-type="bibr" rid="B11">11</xref>). Notably, impaired renal clearance capacity due to decreased eGFR exacerbates systemic inflammation by failing to effectively remove pro-inflammatory cytokines (e.g., IL-6, TNF-&#x3b1;), which in turn further activates neutrophils and promotes NET formation. Meanwhile, albumin&#x2014;a key component of NPAR&#x2014;is not only a marker of nutritional status but also possesses anti-inflammatory properties. It has been demonstrated that serum albumin can inhibit NET formation <italic>in vitro</italic> by neutralizing activators such as lipopolysaccharide (LPS) (<xref ref-type="bibr" rid="B25">25</xref>). In the setting of renal dysfunction, hypoalbuminemia may intensify this vicious cycle by impairing antioxidant and endotoxin-neutralizing capacities, thereby amplifying neutrophil-mediated inflammation and organ damage. Therefore, renal dysfunction serves as both a contributor to and a consequence of imbalanced inflammatory-nutritional status, forming a self-reinforcing cycle that ultimately increases mortality risk. Nevertheless, the precise mechanisms underlying NPAR&#x2019;s predictive value, particularly its interplay with renal pathways, warrant further investigation in future studies.</p>
<p>It is important to acknowledge the potential heterogeneity across different cancer types in our study. The biological behavior, tumor microenvironment, and treatment modalities vary substantially among malignancies (e.g., solid tumors vs. hematologic cancers, or hormone-driven vs. inflammation-driven cancers). These differences could undoubtedly influence the relationship between systemic inflammation/nutrition and patient survival. This study, by design, focused on the general population of cancer survivors to provide an overarching insight into the NPAR-mortality link. This approach has the advantage of identifying a biomarker with broad applicability but may obscure its cancer-specific performance. Future research is imperative to validate and calibrate the prognostic value of NPAR within homogenous cohorts of specific cancer types to guide precise clinical application.</p>
<p>This study does have some limitations. First, it is a retrospective study, and its design does not completely eliminate the possibility of selection bias. Although various confounding factors have been adjusted for, residual confounding (such as unmeasured lifestyle factors and genetic background) might still affect the results. Second, due to the observational design, the possibility of reverse causation cannot be fully excluded (for instance, elevated NPAR before death may reflect end-stage pathological states). Third, conclusions drawn from the NHANES U.S. population may not be directly applicable to other ethnic groups or healthcare systems, requiring further validation for external applicability. Finally, although the NHANES is nationally representative and features a rigorous research design, it lacks internal validation from independent clinical cohorts. Future studies should validate our findings in prospective, multi-center cohorts to enhance clinical translatability.</p>
</sec>
<sec id="s5" sec-type="conclusions">
<title>Conclusion</title>
<p>This study identifies the NPAR as a novel prognostic biomarker for mortality in cancer survivors. Elevated NPAR independently correlates with increased risks of all-cause, cancer-specific, and non-cancer mortality. Mediation analysis identifies renal dysfunction as a key pathway connecting NPAR to adverse outcomes, highlighting the interplay between systemic inflammation, nutrition, and kidney health. These findings support NPAR&#x2019;s clinical utility for risk stratification and advocate for interventions targeting the inflammation-nutrition-kidney nexus to improve survival.</p>
</sec>
</body>
<back>
<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 humans were approved by The National Center for Health Statistic. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.</p></sec>
<sec id="s8" sec-type="author-contributions">
<title>Author contributions</title>
<p>WMC: Methodology, Writing &#x2013; original draft, Visualization, Data curation, Formal Analysis, Project administration, Validation. WJC: Methodology, Formal Analysis, Writing &#x2013; original draft, Conceptualization. HZ: Conceptualization, Methodology, Writing &#x2013; original draft, Formal Analysis. ZW: Software, Resources, Visualization, Conceptualization, Writing &#x2013; review &amp; editing. XL: Methodology, Writing &#x2013; original draft, Investigation. ZX: Supervision, Software, Writing &#x2013; original draft, Methodology. JW: Writing &#x2013; original draft, Software. YZ: Writing &#x2013; review &amp; editing, Methodology, Writing &#x2013; original draft, Project administration, Formal Analysis. HW: Writing &#x2013; original draft, Data curation, Visualization, Investigation, Conceptualization, Project administration, Supervision, Software, Writing &#x2013; review &amp; editing, Resources, Methodology.</p></sec>
<ack>
<title>Acknowledgments</title>
<p>We extend our gratitude to all colleagues who dedicated their time and effort to this research.</p>
</ack>
<sec id="s10" 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="s11" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declared that generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p></sec>
<sec id="s12" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p></sec>
<sec id="s13" 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/fonc.2025.1620484/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fonc.2025.1620484/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="DataSheet1.pdf" id="SM1" mimetype="application/pdf"/></sec>
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