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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Nutr.</journal-id>
<journal-title>Frontiers in Nutrition</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Nutr.</abbrev-journal-title>
<issn pub-type="epub">2296-861X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fnut.2025.1464447</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Nutrition</subject>
<subj-group>
<subject>Systematic Review</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Predictive value of geriatric nutritional risk index in patients with pancreatic cancer: a meta-analysis</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Hua</surname> <given-names>Yaqi</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/2301267/overview"/>
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</contrib>
<contrib contrib-type="author">
<name><surname>Yuan</surname> <given-names>Yi</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<role content-type="https://credit.niso.org/contributor-roles/project-administration/"/>
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</contrib>
<contrib contrib-type="author">
<name><surname>Zhou</surname> <given-names>Chen</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author">
<name><surname>Liu</surname> <given-names>Liping</given-names></name>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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</contrib>
<contrib contrib-type="author">
<name><surname>Hu</surname> <given-names>Yan</given-names></name>
<xref ref-type="aff" rid="aff5"><sup>5</sup></xref>
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</contrib>
<contrib contrib-type="author">
<name><surname>Tu</surname> <given-names>Ping</given-names></name>
<xref ref-type="aff" rid="aff6"><sup>6</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1942744/overview"/>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Li</surname> <given-names>Dongying</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
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<aff id="aff1"><sup>1</sup><institution>Department of Intensive Care Unit, The 2nd Affiliated Hospital, Jiangxi Medical College, Nanchang University</institution>, <addr-line>Nanchang</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>School of Nursing, Jiangxi Medical College, Nanchang University</institution>, <addr-line>Nanchang</addr-line>, <country>China</country></aff>
<aff id="aff3"><sup>3</sup><institution>School of Nursing, University of South China</institution>, <addr-line>Hengyang, Hunan</addr-line>, <country>China</country></aff>
<aff id="aff4"><sup>4</sup><institution>Nursing Department, The 2nd Affiliated Hospital, Jiangxi Medical College, Nanchang University</institution>, <addr-line>Nanchang</addr-line>, <country>China</country></aff>
<aff id="aff5"><sup>5</sup><institution>Wound Ostomy Clinic, The 2nd Affiliated Hospital, Jiangxi Medical College, Nanchang University</institution>, <addr-line>Nanchang</addr-line>, <country>China</country></aff>
<aff id="aff6"><sup>6</sup><institution>Department of Post Anesthesia Care Unit, The 2nd Affiliated Hospital, Jiangxi Medical College, Nanchang University</institution>, <addr-line>Nanchang</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by" id="fn0001">
<p>Edited by: Nerina Denaro, IRCCS Ca &#x2018;Granda Foundation Maggiore Policlinico Hospital, Italy</p>
</fn>
<fn fn-type="edited-by" id="fn0002">
<p>Reviewed by: Uzoamaka Adaobi Okoli, University College London, United Kingdom</p>
<p>Shuang Wu, The University of Chicago, United States</p>
</fn>
<corresp id="c001">&#x002A;Correspondence: Dongying Li, <email>1162485657@qq.com</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>29</day>
<month>07</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>12</volume>
<elocation-id>1464447</elocation-id>
<history>
<date date-type="received">
<day>14</day>
<month>07</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>07</day>
<month>07</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2025 Hua, Yuan, Zhou, Liu, Hu, Tu and Li.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Hua, Yuan, Zhou, Liu, Hu, Tu and Li</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 id="sec1">
<title>Objective</title>
<p>While growing evidence supports the Geriatric Nutritional Risk Index (GNRI) as a prognostic indicator for various cancers, its predictive value in pancreatic cancer remains unclear. This meta-analysis systematically evaluates GNRI&#x2019;s ability to predict postoperative complications and long-term outcomes in pancreatic cancer patients.</p>
</sec>
<sec id="sec2">
<title>Methods</title>
<p>We conducted a comprehensive literature search across nine databases (Web of Science, PubMed, Embase, Cochrane Library, Scopus, WanFang, CNKI, VIP, and SinoMed) through June 1, 2025. Hazard ratios (HRs) with 95% confidence intervals (CIs) were used to assess overall survival (OS), while risk ratios (RRs) with 95% CIs evaluated postoperative complications.</p>
</sec>
<sec id="sec3">
<title>Results</title>
<p>From 233 initially identified studies, 10 met inclusion criteria (n&#x202F;=&#x202F;2,003 patients). Pooled analysis revealed that lower GNRI significantly predicted worse OS (HR&#x202F;=&#x202F;1.92, 95% CI 1.54&#x2013;2.41, <italic>p</italic> &#x003C;&#x202F;0.0001) and higher postoperative pancreatic fistula (POPF) incidence (RR&#x202F;=&#x202F;0.18, 95% CI 0.08&#x2013;0.43, <italic>p</italic> &#x003C;&#x202F;0.001). No significant association was found between GNRI and post-pancreatectomy hemorrhage (PPH) (RR&#x202F;=&#x202F;0.21, 95% CI 0.03&#x2013;1.53, <italic>p</italic> =&#x202F;0.13).</p>
</sec>
<sec id="sec4">
<title>Conclusion</title>
<p>GNRI shows promise as a clinically useful predictor of OS and POPF in pancreatic cancer patients. However, these findings require validation through prospective multicenter studies.</p>
</sec>
<sec id="sec5">
<title>Systematic review registration</title>
<p>Identifier CRD42023409362.</p>
</sec>
</abstract>
<kwd-group>
<kwd>geriatric nutritional risk index</kwd>
<kwd>pancreatic cancer</kwd>
<kwd>prognosis</kwd>
<kwd>survival</kwd>
<kwd>meta-analysis</kwd>
</kwd-group>
<counts>
<fig-count count="6"/>
<table-count count="2"/>
<equation-count count="0"/>
<ref-count count="43"/>
<page-count count="8"/>
<word-count count="4949"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Clinical Nutrition</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec6">
<title>Introduction</title>
<p>Pancreatic cancer (PC) is the third leading cause of cancer-related death in the US (<xref ref-type="bibr" rid="ref1">1</xref>). In 2020, there have been about 466,000 deaths worldwide from pancreatic cancer. According to the American Cancer Society, 64,050 new cases and 50,550 new deaths of pancreatic cancer occurred in the United States in 2023 (<xref ref-type="bibr" rid="ref2">2</xref>). Even though pancreatic cancer only represents 2.5% of all cancers, it is characterized by an insidious onset that typically renders it asymptomatic in its early stages, resulting in a late-stage diagnosis (<xref ref-type="bibr" rid="ref3">3</xref>). As such, it is associated with a poor prognosis, with a 5-year survival rate of only 12% (<xref ref-type="bibr" rid="ref2">2</xref>). Currently, numerous investigations have reported the prognostic indicators for pancreatic cancer, with emphasis on histochemical and molecular biological techniques such as CA19-9, circulating tumor DNA, and MicroRNA (<xref ref-type="bibr" rid="ref4 ref5 ref6">4&#x2013;6</xref>). Nonetheless, in clinical practice, these biomarkers are limited in application due to a lack of methodological standardization and quality control (<xref ref-type="bibr" rid="ref7">7</xref>). Therefore, it is urgent to find convenient, high-speed, and inexpensive prognostic factors for pancreatic cancer. The Geriatric Nutritional Risk Index (GNRI) is a valuable and simple tool for screening malnutrition, which includes two items: body weight and serum albumin, comprehensively reflecting the body&#x2019;s nutritional status (<xref ref-type="bibr" rid="ref8">8</xref>, <xref ref-type="bibr" rid="ref9">9</xref>). The nutritional status of patients to some extent reflects the progression of the disease (<xref ref-type="bibr" rid="ref10">10</xref>). GNRI has been used in various clinical settings and has shown good prognostic value in tumor patients (<xref ref-type="bibr" rid="ref11">11</xref>). Furthermore, its prognostic role has been verified in several types of cancers, such as urological cancers, gastrointestinal malignancy, and Non-Small Cell Lung Cancer, by meta-analyses (<xref ref-type="bibr" rid="ref12 ref13 ref14">12&#x2013;14</xref>). Nevertheless, the predictive potential of GNRI in pancreatic cancer has yet to be systematically evaluated. Thus, the objective of this meta-analysis is to examine the impact of GNRI on the prognosis of pancreatic cancer.</p>
</sec>
<sec sec-type="materials|methods" id="sec7">
<title>Materials and methods</title>
<p>This meta-analysis was conducted in accordance with the PRISMA guidelines and registered in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD42023409362.</p>
<sec id="sec8">
<title>Search strategy</title>
<p>A systematic literature search was performed in the following databases up to June 1, 2025: Web of Science, PubMed, Embase, Cochrane Library, Scopus, WanFang, CNKI, VIP, SinoMed. Search terms included: &#x201C;pancreas,&#x201D; &#x201C;cancer,&#x201D; and &#x201C;geriatric nutritional risk index.&#x201D; The PubMed search strategy was as follows: (neoplasms OR carcinoma OR cancer OR tumor OR malignancy OR adenoma OR neoplasm OR cancers) AND (pancreatic OR pancreas) AND (geriatric nutritional risk index OR GNRI). To ensure comprehensive coverage, we also manually screened the references of retrieved articles for additional eligible studies.</p>
</sec>
<sec id="sec9">
<title>Eligibility criteria</title>
<p>Articles meeting the following criteria were included: (1) P (Participant): Patients with pathologically confirmed pancreatic cancer; (2) I (Intervention): Patients have had a high GNRI index, GNRI calculated using the formula: GNRI&#x202F;=&#x202F;[1.489&#x202F;&#x00D7;&#x202F;serum albumin (g/L)]&#x202F;+&#x202F;[41.7&#x202F;&#x00D7;&#x202F;(current weight/ideal weight (kg))]; (3) C (Comparison): Patients have had a low GNRI index; (4) O (Outcome): Overall survival (OS) (with hazard ratio [HR] and 95% confidence interval [CI]); Postoperative complications (with risk ratio [RR] and 95% CI); (5) S (Study design): Retrospective or prospective studies with full-text availability. We excluded reviews, conference abstracts, case reports, letters, comments, meta-analyses, studies lacking complete data for analysis.</p>
</sec>
<sec id="sec10">
<title>Data abstraction</title>
<p>Two independent researchers used EndNote for literature management and screening. Extracted the following data: First author, study type, publication year, region, median follow-up, sample size, clinical staging, treatment, GNRI cutoff-value, outcomes (HR/RR with 95% CI). Resolved discrepancies through discussion or third-party arbitration until consensus was reached.</p>
</sec>
<sec id="sec11">
<title>Methodological quality assessment</title>
<p>Study quality was assessed using the Newcastle-Ottawa Scale (NOS), which evaluates: Selection (4 points), Comparability (2 points), Exposure/Outcome (3 points). A total score &#x2265; 7 indicated high-quality studies, while &#x003C;7 indicated lower quality.</p>
</sec>
<sec id="sec12">
<title>Statistical analysis</title>
<p>The meta-analysis used Stata (version 13.0) to extract research data and generate the forest map. In our meta-analysis, the HRs and RRs with corresponding 95% CIs were combined to explore the relationship between the preoperative GNRI and OS or postoperative complications in pancreatic cancer patients. The heterogeneity was detected by the Q test. A random-effects model was applied if <italic>p</italic> &#x003C;&#x202F;0.1 or I<sup>2</sup> &#x003E;&#x202F;50%; otherwise, a fixed-effects model was used. After combined analysis, it was considered statistically significant when <italic>p</italic> &#x003C;&#x202F;0.05. Sensitivity analysis was conducted to identify potential sources of heterogeneity and assess the influence of individual studies on the overall results. By using Begg&#x2019;s test and Egger&#x2019;s test to determine potential publication bias, when <italic>p</italic> &#x003C;&#x202F;0.05, it is considered that there is publication bias. If publication bias was detected, the trim-and-fill method was employed for adjustment and re-evaluation.</p>
</sec>
</sec>
<sec sec-type="results" id="sec13">
<title>Results</title>
<sec id="sec14">
<title>Literature search and study characteristics</title>
<p>The initial literature search yielded 233 potentially relevant studies. Following a systematic screening process, 10 studies involving a total of 2,003 cases were selected for final analysis (<xref ref-type="bibr" rid="ref15 ref16 ref17 ref18 ref19 ref20 ref21 ref22 ref23 ref24">15&#x2013;24</xref>) (<xref ref-type="fig" rid="fig1">Figure 1</xref>). The included studies comprised one prospective cohort study, nine retrospective cohort studies. Quality assessment using the predefined criteria revealed that all 10 studies scored &#x2265;7 points, indicating high methodological quality and a low risk of bias. <xref ref-type="table" rid="tab1">Table 1</xref> presents the baseline characteristics and primary outcome measures of the included studies.</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>PRISMA flow diagram of study selection process.</p>
</caption>
<graphic xlink:href="fnut-12-1464447-g001.tif">
<alt-text content-type="machine-generated">Flowchart depicting the process of selecting research papers. Initially, 230 records were identified from various databases, and three additional papers were obtained by other means. After removing 124 duplicates, 109 records remained. Following exclusions for non-relevant content, 36 papers were shortlisted. Further exclusion based on incomplete data and inconsistency left 10 papers for inclusion.</alt-text>
</graphic>
</fig>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Basic characteristics and quality evaluation of included studies.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Study</th>
<th align="center" valign="top">Design</th>
<th align="center" valign="top">Region</th>
<th align="center" valign="top">MFP (months)</th>
<th align="center" valign="top">Sample size</th>
<th align="center" valign="top">Age (years)</th>
<th align="center" valign="top">Clinical staging</th>
<th align="center" valign="top">Therapy</th>
<th align="center" valign="top">Cut-off value</th>
<th align="center" valign="top">Outcome</th>
<th align="center" valign="top">NOS (points)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Balzano G 2017 (<xref ref-type="bibr" rid="ref15">15</xref>)</td>
<td align="center" valign="middle">PS</td>
<td align="center" valign="middle">Italy</td>
<td align="center" valign="middle">64.8</td>
<td align="center" valign="middle">296</td>
<td align="center" valign="middle">68.9 (IQR: 14)</td>
<td align="center" valign="middle">I&#x2013;IV</td>
<td align="center" valign="middle">Surgery</td>
<td align="center" valign="middle">98</td>
<td align="center" valign="middle">OS</td>
<td align="center" valign="middle">7</td>
</tr>
<tr>
<td align="left" valign="middle">Hu Siping 2019 (<xref ref-type="bibr" rid="ref16">16</xref>)</td>
<td align="center" valign="middle">RS</td>
<td align="center" valign="middle">China</td>
<td align="center" valign="middle">12.6</td>
<td align="center" valign="middle">146</td>
<td align="center" valign="middle">67.7&#x202F;&#x00B1;&#x202F;5.8</td>
<td align="center" valign="middle">I&#x2013;IV</td>
<td align="center" valign="middle">Surgery</td>
<td align="center" valign="middle">100.2</td>
<td align="center" valign="middle">OS</td>
<td align="center" valign="middle">7</td>
</tr>
<tr>
<td align="left" valign="middle">Funamizu N 2020 (1) (<xref ref-type="bibr" rid="ref17">17</xref>)</td>
<td align="center" valign="middle">RS</td>
<td align="center" valign="middle">Japan</td>
<td align="center" valign="middle">-</td>
<td align="center" valign="middle">37</td>
<td align="center" valign="middle">73 (35&#x2013;82)</td>
<td align="center" valign="middle">I&#x2013;IV</td>
<td align="center" valign="middle">Surgery</td>
<td align="center" valign="middle">96</td>
<td align="center" valign="middle">POPF</td>
<td align="center" valign="middle">7</td>
</tr>
<tr>
<td align="left" valign="middle">Funamizu N 2020 (2) (<xref ref-type="bibr" rid="ref18">18</xref>)</td>
<td align="center" valign="middle">RS</td>
<td align="center" valign="middle">Japan</td>
<td align="center" valign="middle">-</td>
<td align="center" valign="middle">121</td>
<td align="center" valign="middle">76.1&#x202F;&#x00B1;&#x202F;2.0 (10) /70.8&#x202F;&#x00B1;&#x202F;1.1 (111)</td>
<td align="center" valign="middle">I&#x2013;IV</td>
<td align="center" valign="middle">Surgery</td>
<td align="center" valign="middle">92</td>
<td align="center" valign="middle">PPH</td>
<td align="center" valign="middle">7</td>
</tr>
<tr>
<td align="left" valign="middle">Hu SP 2020 (<xref ref-type="bibr" rid="ref19">19</xref>)</td>
<td align="center" valign="middle">RS</td>
<td align="center" valign="middle">China</td>
<td align="center" valign="middle">72.9</td>
<td align="center" valign="middle">282</td>
<td align="center" valign="middle">58.7&#x202F;&#x00B1;&#x202F;13.5</td>
<td align="center" valign="middle">I&#x2013;IV</td>
<td align="center" valign="middle">Surgery</td>
<td align="center" valign="middle">98</td>
<td align="center" valign="middle">OS</td>
<td align="center" valign="middle">8</td>
</tr>
<tr>
<td align="left" valign="middle">Itoh S 2021 (<xref ref-type="bibr" rid="ref20">20</xref>)</td>
<td align="center" valign="middle">RS</td>
<td align="center" valign="middle">Japan</td>
<td align="center" valign="middle">20.4</td>
<td align="center" valign="middle">589</td>
<td align="center" valign="middle">71 (63&#x2013;77)</td>
<td align="center" valign="middle">I&#x2013;III</td>
<td align="center" valign="middle">Surgery</td>
<td align="center" valign="middle">98</td>
<td align="center" valign="middle">OS</td>
<td align="center" valign="middle">7</td>
</tr>
<tr>
<td align="left" valign="middle">Sakamoto T 2021 (<xref ref-type="bibr" rid="ref21">21</xref>)</td>
<td align="center" valign="middle">RS</td>
<td align="center" valign="middle">Japan</td>
<td align="center" valign="middle">26.6</td>
<td align="center" valign="middle">105</td>
<td align="center" valign="middle">73.4 (65&#x2013;84)</td>
<td align="center" valign="middle">I&#x2013;IV</td>
<td align="center" valign="middle">Mixed</td>
<td align="center" valign="middle">98</td>
<td align="center" valign="middle">OS</td>
<td align="center" valign="middle">8</td>
</tr>
<tr>
<td align="left" valign="middle">Funamizu N 2022 (<xref ref-type="bibr" rid="ref22">22</xref>)</td>
<td align="center" valign="middle">RS</td>
<td align="center" valign="middle">Japan</td>
<td align="center" valign="middle">&#x2264;60</td>
<td align="center" valign="middle">139</td>
<td align="center" valign="middle">70 (34&#x2013;89)</td>
<td align="center" valign="middle">I&#x2013;IV</td>
<td align="center" valign="middle">Surgery</td>
<td align="center" valign="middle">99</td>
<td align="center" valign="middle">OS</td>
<td align="center" valign="middle">7</td>
</tr>
<tr>
<td align="left" valign="middle">Grinstead C 2022 (<xref ref-type="bibr" rid="ref23">23</xref>)</td>
<td align="center" valign="middle">RS</td>
<td align="center" valign="middle">America</td>
<td align="center" valign="middle">2.9</td>
<td align="center" valign="middle">98</td>
<td align="center" valign="middle">66&#x202F;&#x00B1;&#x202F;9.8</td>
<td align="center" valign="middle">III&#x2013;IV</td>
<td align="center" valign="middle">Mixed</td>
<td align="center" valign="middle">98</td>
<td align="center" valign="middle">OS</td>
<td align="center" valign="middle">8</td>
</tr>
<tr>
<td align="left" valign="middle">Zhang Bolin 2022 (<xref ref-type="bibr" rid="ref24">24</xref>)</td>
<td align="center" valign="middle">RS</td>
<td align="center" valign="middle">China</td>
<td align="center" valign="middle">-</td>
<td align="center" valign="middle">190</td>
<td align="center" valign="middle">&#x2265;65</td>
<td align="center" valign="middle">I&#x2013;IV</td>
<td align="center" valign="middle">Surgery</td>
<td align="center" valign="middle">98</td>
<td align="center" valign="middle">PPH; POPF</td>
<td align="center" valign="middle">7</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>PS, Prospective cohort study; RS, Retrospective cohort study; POPF, Postoperative pancreatic fistula; PPH, Post-pancreatectomy hemorrhage; OS, Overall Survival; NOS, The Newcastle-Ottawa Scale.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec15">
<title>GNRI and overall survival</title>
<p>Seven studies involving 1,655 patients examined the relationship between GNRI and overall survival (OS) (<xref ref-type="bibr" rid="ref15">15</xref>, <xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref19 ref20 ref21 ref22 ref23">19&#x2013;23</xref>). The pooled analysis demonstrated a significant association between low GNRI and poorer OS in pancreatic cancer patients (HR&#x202F;=&#x202F;1.92, 95% CI 1.54&#x2013;2.41, <italic>p</italic>&#x202F;&#x003C;&#x202F;0.0001), with substantial heterogeneity observed (I<sup>2</sup>&#x202F;=&#x202F;81%, <italic>P</italic> for Q-test&#x202F;=&#x202F;0.05; <xref ref-type="fig" rid="fig2">Figure 2</xref>). To address potential confounding factors, we performed subgroup analyses stratified by cut-off value, sample size, primary therapy and publishing time. These subgroup analyses consistently identified GNRI as an independent prognostic factor for OS across all strata (<xref ref-type="table" rid="tab2">Table 2</xref>).</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Forest plots for the meta-analysis regarding the association between GNRI and OS in patients with pancreatic cancer.</p>
</caption>
<graphic xlink:href="fnut-12-1464447-g002.tif">
<alt-text content-type="machine-generated">Forest plot showing hazard ratios (HR) with 95% confidence intervals (CI) for seven studies: Balzano G 2017, Hu Siping 2019, Hu SP 2020, Itoh S 2021, Sakamoto T 2021, Funamizu N 2022, Grinstead C 2022, and an overall summary. Weights range from 6.27% to 23.26%. The summary HR is 1.92 (1.54, 2.41) with I-squared at 52.1% and a p-value of 0.051.</alt-text>
</graphic>
</fig>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Stratification analysis of the meta-analysis for overall survival in patients with pancreatic cancer.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Factors</th>
<th align="center" valign="top" rowspan="2">No. of studies</th>
<th align="center" valign="top" rowspan="2">No. of patients</th>
<th align="center" valign="top" rowspan="2">Effects model</th>
<th align="center" valign="top" rowspan="2">HR (95%CI)</th>
<th align="center" valign="top" rowspan="2">
<italic>p</italic>
</th>
<th align="center" valign="top" colspan="2">Heterogeneity</th>
</tr>
<tr>
<th align="center" valign="top">I<sup>2</sup> (%)</th>
<th align="center" valign="top">
<italic>P</italic>
<sub>Q</sub>
</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Overall</td>
<td align="center" valign="middle">7 (<xref ref-type="bibr" rid="ref15">15</xref>, <xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref19 ref20 ref21 ref22 ref23">19&#x2013;23</xref>)</td>
<td align="center" valign="middle">1,655</td>
<td align="center" valign="middle">REM</td>
<td align="center" valign="middle">1.92 (1.54&#x2013;2.41)</td>
<td align="center" valign="middle">&#x003C;0.0001</td>
<td align="center" valign="middle">81</td>
<td align="center" valign="middle">0.05</td>
</tr>
<tr>
<td align="left" valign="middle">Cut-off value</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">98</td>
<td align="center" valign="middle">5 (<xref ref-type="bibr" rid="ref15">15</xref>, <xref ref-type="bibr" rid="ref19 ref20 ref21">19&#x2013;21</xref>, <xref ref-type="bibr" rid="ref23">23</xref>)</td>
<td align="center" valign="middle">1,370</td>
<td align="center" valign="middle">REM</td>
<td align="center" valign="middle">1.68 (1.44&#x2013;1.97)</td>
<td align="center" valign="middle">&#x003C;0.0001</td>
<td align="center" valign="middle">61</td>
<td align="center" valign="middle">0.04</td>
</tr>
<tr>
<td align="left" valign="middle">99</td>
<td align="center" valign="middle">1 (<xref ref-type="bibr" rid="ref22">22</xref>)</td>
<td align="center" valign="middle">139</td>
<td align="center" valign="middle">-</td>
<td align="center" valign="middle">2.49 (1.37&#x2013;4.54)</td>
<td align="center" valign="middle">0.003</td>
<td align="center" valign="middle">-</td>
<td align="center" valign="middle">-</td>
</tr>
<tr>
<td align="left" valign="middle">100.2</td>
<td align="center" valign="middle">1 (<xref ref-type="bibr" rid="ref16">16</xref>)</td>
<td align="center" valign="middle">146</td>
<td align="center" valign="middle">-</td>
<td align="center" valign="middle">2.87 (1.49&#x2013;5.51)</td>
<td align="center" valign="middle">0.002</td>
<td align="center" valign="middle">-</td>
<td align="center" valign="middle">-</td>
</tr>
<tr>
<td align="left" valign="middle">Sample size</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2264;200</td>
<td align="center" valign="middle">4 (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref21 ref22 ref23">21&#x2013;23</xref>)</td>
<td align="center" valign="middle">488</td>
<td align="center" valign="middle">FEM</td>
<td align="center" valign="middle">2.50 (1.90&#x2013;3.30)</td>
<td align="center" valign="middle">&#x003C;0.00001</td>
<td align="center" valign="middle">10</td>
<td align="center" valign="middle">0.34</td>
</tr>
<tr>
<td align="left" valign="middle">&#x2265;200</td>
<td align="center" valign="middle">3 (<xref ref-type="bibr" rid="ref15">15</xref>, <xref ref-type="bibr" rid="ref19">19</xref>, <xref ref-type="bibr" rid="ref20">20</xref>)</td>
<td align="center" valign="middle">1,167</td>
<td align="center" valign="middle">FEM</td>
<td align="center" valign="middle">1.57 (1.33&#x2013;1.84)</td>
<td align="center" valign="middle">&#x003C;0.0001</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">0.60</td>
</tr>
<tr>
<td align="left" valign="middle">Primary therapy</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Surgery</td>
<td align="center" valign="middle">5 (<xref ref-type="bibr" rid="ref15">15</xref>, <xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref19">19</xref>, <xref ref-type="bibr" rid="ref20">20</xref>, <xref ref-type="bibr" rid="ref22">22</xref>)</td>
<td align="center" valign="middle">1,452</td>
<td align="center" valign="middle">FEM</td>
<td align="center" valign="middle">1.67 (1.44&#x2013;1.93)</td>
<td align="center" valign="middle">&#x003C;0.0001</td>
<td align="center" valign="middle">10</td>
<td align="center" valign="middle">0.35</td>
</tr>
<tr>
<td align="left" valign="middle">Mixed</td>
<td align="center" valign="middle">2 (<xref ref-type="bibr" rid="ref21">21</xref>, <xref ref-type="bibr" rid="ref23">23</xref>)</td>
<td align="center" valign="middle">203</td>
<td align="center" valign="middle">FEM</td>
<td align="center" valign="middle">3.47 (2.09&#x2013;5.76)</td>
<td align="center" valign="middle">&#x003C;0.00001</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">0.37</td>
</tr>
<tr>
<td align="left" valign="middle">Publishing time</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x2264;2020</td>
<td align="center" valign="middle">3 (<xref ref-type="bibr" rid="ref15">15</xref>, <xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref19">19</xref>)</td>
<td align="center" valign="middle">724</td>
<td align="center" valign="middle">FEM</td>
<td align="center" valign="middle">1.73 (1.42&#x2013;2.11)</td>
<td align="center" valign="middle">&#x003C;0.0001</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">0.40</td>
</tr>
<tr>
<td align="left" valign="middle">&#x003E;2020</td>
<td align="center" valign="middle">4 (<xref ref-type="bibr" rid="ref20 ref21 ref22 ref23">20&#x2013;23</xref>)</td>
<td align="center" valign="middle">931</td>
<td align="center" valign="middle">REM</td>
<td align="center" valign="middle">2.43 (1.47&#x2013;4.02)</td>
<td align="center" valign="middle">0.001</td>
<td align="center" valign="middle">72</td>
<td align="center" valign="middle">0.01</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>FEM, fixed-effects model; REM, random-effects model.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec16">
<title>GNRI and postoperative pancreatic fistula</title>
<p>Two studies involving 227 patients evaluated the predictive value of GNRI for postoperative pancreatic fistula (POPF) (<xref ref-type="bibr" rid="ref17">17</xref>, <xref ref-type="bibr" rid="ref24">24</xref>). As illustrated in <xref ref-type="fig" rid="fig3">Figure 3</xref>, the analysis revealed no significant heterogeneity (I<sup>2</sup>&#x202F;=&#x202F;0%; <italic>P</italic> for Q-test&#x202F;=&#x202F;0.71). The pooled results demonstrated that higher GNRI levels were significantly associated with reduced POPF incidence in pancreatic cancer patients (RR&#x202F;=&#x202F;0.18, 95% CI 0.08&#x2013;0.43, <italic>p</italic>&#x202F;&#x003C;&#x202F;0.001).</p>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Forest plots for the meta-analysis regarding the association between GNRI and POPF in patients with pancreatic cancer.</p>
</caption>
<graphic xlink:href="fnut-12-1464447-g003.tif">
<alt-text content-type="machine-generated">Forest plot showing relative risks (RR) and 95% confidence intervals (CI) for two studies: Funamizu N 2022 and Zhang Bolin 2022. Funamizu N 2022 has an RR of 0.13 (0.02, 0.90) with a weight of 19.72%. Zhang Bolin 2022 has an RR of 0.20 (0.07, 0.51) with a weight of 80.28%. The overall RR is 0.18 (0.08, 0.43) with I-squared at 0.0% and a p-value of 0.707.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec17">
<title>GNRI and post-pancreatectomy hemorrhage</title>
<p>Two studies comprising 311 patients assessed the prognostic value of the GNRI for post-pancreatectomy hemorrhage (PPH) (<xref ref-type="bibr" rid="ref18">18</xref>, <xref ref-type="bibr" rid="ref24">24</xref>). As depicted in <xref ref-type="fig" rid="fig4">Figure 4</xref>, significant heterogeneity was observed (I<sup>2</sup>&#x202F;=&#x202F;73%; <italic>P</italic> for Q-test&#x202F;=&#x202F;0.05). The meta-analysis revealed no significant association between elevated GNRI levels and reduced PPH incidence in pancreatic cancer patients (RR&#x202F;=&#x202F;0.21, 95% CI 0.03&#x2013;1.53, <italic>p</italic>&#x202F;=&#x202F;0.13).</p>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>Forest plots for the meta-analysis regarding the association between GNRI and PPH in patients with pancreatic cancer.</p>
</caption>
<graphic xlink:href="fnut-12-1464447-g004.tif">
<alt-text content-type="machine-generated">Forest plot showing relative risk (RR) and confidence intervals (CI) for two studies: Funamizu N 2022 with RR 0.07 (95% CI: 0.01 to 0.37), and Zhang Bolin 2022 with RR 0.54 (95% CI: 0.16 to 1.85). The overall RR is 0.21 (95% CI: 0.03 to 1.53), with heterogeneity indicated by I-squared 73.5% and p-value 0.052.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec18">
<title>Sensitivity analysis</title>
<p>We performed sensitivity analyses to assess the robustness of the association between GNRI and OS. Specifically, each included study was sequentially removed from the meta-analysis to evaluate its individual impact on the pooled results. The sensitivity analysis demonstrated that the exclusion of any single study did not significantly alter the overall effect estimate (<xref ref-type="fig" rid="fig5">Figure 5</xref>), indicating stable and reliable findings regarding the prognostic value of GNRI for OS in pancreatic cancer patients.</p>
<fig position="float" id="fig5">
<label>Figure 5</label>
<caption>
<p>Sensitivity analysis for the association between GNRI and OS.</p>
</caption>
<graphic xlink:href="fnut-12-1464447-g005.tif">
<alt-text content-type="machine-generated">Forest plot showing meta-analysis estimates when each named study is omitted. Studies listed include Balzano G 2017, Hu Siping 2019, Hu SP 2020, Itoh S 2021, Sakamoto T 2021, Funamizu N 2022, and Grinstead C 2022. The plot displays lower and upper confidence interval limits and estimates for each study, centered around a vertical line at approximately 0.65. The x-axis ranges from 0.38 to 1.00.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec19">
<title>Publication bias</title>
<p>In our meta-analysis of OS, both Begg&#x2019;s test (<italic>p</italic>&#x202F;=&#x202F;0.007) and Egger&#x2019;s test (<italic>p</italic>&#x202F;=&#x202F;0.011) indicated the presence of significant publication bias (<xref ref-type="fig" rid="fig6">Figures 6A</xref>,<xref ref-type="fig" rid="fig6">B</xref>). To address this potential bias, we applied the trim-and-fill method. This adjustment resulted in the imputation of three additional studies to achieve symmetry in the funnel plot (<xref ref-type="fig" rid="fig6">Figure 6C</xref>). Importantly, the corrected hazard ratio remained statistically significant (HR&#x202F;=&#x202F;1.65, 95% CI 1.29&#x2013;2.11, <italic>p</italic>&#x202F;&#x003C;&#x202F;0.001), confirming the robustness of our primary findings.</p>
<fig position="float" id="fig6">
<label>Figure 6</label>
<caption>
<p>Plots for publication bias test in meta-analysis for overall survival. <bold>(A)</bold> Begg&#x2019;s funnel plot; <bold>(B)</bold> Egger&#x2019;s publication bias plot; <bold>(C)</bold> The trim-and-fill methods.</p>
</caption>
<graphic xlink:href="fnut-12-1464447-g006.tif">
<alt-text content-type="machine-generated">Three-panel image showing statistical plots. Panel A: Begg's funnel plot with logHR versus standard error, including pseudo 95% confidence limits. Panel B: Regression plot depicting standardized normal deviate of effect estimate versus precision, with a regression line and 95% confidence interval for the intercept. Panel C: Filled funnel plot with theta versus standard error including pseudo 95% confidence limits.</alt-text>
</graphic>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="sec20">
<title>Discussion</title>
<p>The advancement of tumor nutrition theory has established a significant association between nutritional status and cancer prognosis, leading to the clinical application of various nutritional risk assessment tools. Currently, the primary nutritional indices include: Prognostic Nutritional Index (PNI), Nutritional Risk Index (NRI), Geriatric Nutritional Risk Index (GNRI), Controlling Nutritional Status (CONUT) score (<xref ref-type="bibr" rid="ref25 ref26 ref27">25&#x2013;27</xref>). Among these, GNRI has demonstrated superior predictive performance. Wang et al. compared these four indices in 192 esophageal cancer patients and found GNRI to be the most effective prognostic indicator for perioperative management (<xref ref-type="bibr" rid="ref28">28</xref>). Originally developed by Bouillanne et al. to assess morbidity and mortality in hospitalized elderly patients (<xref ref-type="bibr" rid="ref9">9</xref>), GNRI has since been adapted for prognostic evaluation in chronic diseases (<xref ref-type="bibr" rid="ref11">11</xref>, <xref ref-type="bibr" rid="ref26">26</xref>). GNRI&#x2019;s clinical utility stems from its composite nature, incorporating both serum albumin levels and body weight measurements to provide a dynamic, objective assessment of nutritional status (<xref ref-type="bibr" rid="ref29">29</xref>, <xref ref-type="bibr" rid="ref30">30</xref>). Serum albumin serves as a crucial prognostic biomarker in oncology, with demonstrated value in predicting patient survival across multiple cancer types. Its clinical importance is evidenced by its incorporation into standard cancer staging systems (<xref ref-type="bibr" rid="ref31">31</xref>, <xref ref-type="bibr" rid="ref32">32</xref>). As a multifunctional indicator, serum albumin reflects both nutritional status and systemic inflammation (<xref ref-type="bibr" rid="ref33">33</xref>). Hypoalbuminemia (low serum albumin) signifies two clinically important pathological states: First, it indicates malnutrition, which compromises immune function and prolongs disease course (<xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>); second, it reflects systemic inflammation, as inflammatory processes suppress hepatic albumin synthesis while increasing vascular permeability, thereby exacerbating albumin loss (<xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref36">36</xref>). Notably, the inflammatory cytokines associated with hypoalbuminemia may directly promote tumor progression and correlate with poorer clinical outcomes (<xref ref-type="bibr" rid="ref37">37</xref>). This relationship was quantitatively demonstrated by Yang et al. in a large-scale study (<italic>n</italic>&#x202F;=&#x202F;82,061), which established a significant inverse linear correlation between serum albumin levels and cancer risk (<xref ref-type="bibr" rid="ref38">38</xref>). Concurrently, weight loss&#x2014;a key component of cancer cachexia diagnosis (<xref ref-type="bibr" rid="ref39">39</xref>, <xref ref-type="bibr" rid="ref40">40</xref>)&#x2014;contributes to metabolic dysregulation affecting carbohydrate, lipid, and protein metabolism (<xref ref-type="bibr" rid="ref41">41</xref>, <xref ref-type="bibr" rid="ref42">42</xref>). These metabolic disturbances impair immune competence and tissue repair capacity, creating a vicious cycle that accelerates functional decline and worsens cancer prognosis (<xref ref-type="bibr" rid="ref41 ref42 ref43">41&#x2013;43</xref>). The combination of these albumin-related and weight-related pathophysiological mechanisms explains the consistent clinical observation that lower GNRI scores (incorporating both parameters) predict poorer outcomes. Building on these principles, Balzano et al. (<xref ref-type="bibr" rid="ref15">15</xref>) achieved a milestone in pancreatic cancer research by successfully incorporating GNRI into a predictive scoring system for postoperative mortality.</p>
<p>Our meta-analysis incorporated 10 relevant studies comprising 2,003 pancreatic cancer patients. The results demonstrate that the GNRI serves as an independent prognostic factor for pancreatic cancer outcomes. Subgroup analyses stratified by cut-off value, sample size, primary therapy, and publication time consistently confirmed GNRI&#x2019;s independent predictive value for OS across all subgroups. Furthermore, while GNRI was identified as an independent risk factor for POPF, it showed no statistically significant association with PPH. Sensitivity analyses confirmed the stability and reliability of these findings. Although we detected publication bias, trim-and-fill adjustment maintained the significant association between low GNRI and poor OS, supporting the robustness of our conclusions. These results suggest that GNRI may be a potential indicator for predicting postoperative complications and prognosis in patients with pancreatic cancer.</p>
<p>Several limitations should be acknowledged. First, the number of included studies was relatively small (<italic>n</italic>&#x202F;=&#x202F;10), particularly for POPF and PPH analyses (<italic>n</italic>&#x202F;=&#x202F;2), and further research is needed to explore the role of GNRI in these complications. Second, heterogeneity may exist due to variations in GNRI cutoff values, treatment protocols, and demographic characteristics across the included studies. Additionally, all studies were retrospective in design; therefore, future prospective randomized controlled trials are required to validate the predictive value of GNRI. Finally, Begg&#x2019;s and Egger&#x2019;s tests indicated potential publication bias, which may reflect the limited number of available studies and should be considered in future investigations.</p>
</sec>
<sec sec-type="conclusions" id="sec21">
<title>Conclusion</title>
<p>This study provides a comprehensive evaluation of the prognostic significance of the GNRI in pancreatic cancer. Our findings demonstrate a statistically significant association between reduced GNRI levels and adverse clinical outcomes, particularly in OS and POPF incidence. The current analysis offers robust clinical evidence supporting the utility of GNRI as a practical prognostic indicator for pancreatic cancer patients. However, these conclusions require validation through large-scale, multicenter prospective cohort studies to strengthen their clinical applicability.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="sec22">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.</p>
</sec>
<sec sec-type="author-contributions" id="sec23">
<title>Author contributions</title>
<p>YaqH: Conceptualization, Formal analysis, Methodology, Project administration, Resources, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing. YY: Project administration, Resources, Software, Visualization, Writing &#x2013; original draft. CZ: Data curation, Formal analysis, Methodology, Project administration, Writing &#x2013; original draft. LL: Methodology, Project administration, Writing &#x2013; original draft. YanH: Supervision, Validation, Visualization, Writing &#x2013; review &#x0026; editing. PT: Supervision, Validation, Visualization, Writing &#x2013; review &#x0026; editing. DL: Conceptualization, Formal analysis, Funding acquisition, Supervision, Validation, Visualization, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing.</p>
</sec>
<sec sec-type="funding-information" id="sec24">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by Science and Technology Project of Jiangxi Health Commission (202130363). The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.</p>
</sec>
<ack>
<p>We would like to thank the researchers and study participants for their contributions.</p>
</ack>
<sec sec-type="COI-statement" id="sec25">
<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 sec-type="disclaimer" id="sec26">
<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>
<fn-group>
<title>Abbreviations</title>
<fn fn-type="abbr">
<p>PC, Pancreatic cancer; GNRI, Geriatric nutritional risk index; PRISMA, Preferred Reporting Items for Systematic Review and Meta-Analysis; OS, Overall survival; HR, Hazard ratio; RR, Risk ratio; 95% CI, 95% Confidence interval; NOS, Newcastle&#x2013;Ottawa Scale; RS, Retrospective cohort study; POPF, Postoperative pancreatic fistula; PPH, Post-pancreatectomy hemorrhage; FEM, Fixed-effects model; REM, Random-effects model.</p>
</fn>
</fn-group>
<ref-list>
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