{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T05:28:40Z","timestamp":1786512520006,"version":"build-2736575974"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Nutr."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Post-stroke infection (PSI) is a major cause of poor prognosis in acute ischemic stroke (AIS). The independent and combined predictive value of nutritional, electrolyte, inflammatory, and insulin resistance indices for PSI requires further validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed 6,149 AIS patients from the Second Affiliated Hospital of Nanchang University (June 2017\u2013April 2025). Multivariate logistic regression, restricted cubic spline (RCS), and ROC curves assessed the predictive value of nutritional (GNRI, PNI), electrolyte (serum sodium, potassium), inflammatory (NLR, PLR), and insulin resistance (TyG, TyG-BMI) indices. Hierarchical clustering selected clinical features; five ML algorithms (XGBoost, RF, LightGBM, Extra Trees, CatBoost) with 5-fold cross-validation built predictive models. SHapley Additive exPlanations (SHAP) identified key predictors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      NLR, PLR and TyG were elevated in PSI patients, while GNRI, PNI, and serum sodium were decreased (all\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.05). RCS identified malnutrition (GNRI \u226498.799, PNI\u202f\u2264\u202f46.898), a U-shaped sodium\u2013PSI relationship (inflection: 139.611\u202fmmol\/L), elevated NLR (&amp;gt;2.703), and elevated TyG as risk factors; After adjusting for confounding factors such as comorbidities, the statistical significance of potassium metabolic disturbances disappeared (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.151), suggesting that their impact on PSI may be due to confounding by underlying diseases rather than an independent causal association. The non-linear potassium\u2013PSI association (inflection: 3.529\u202fmmol\/L) was not significant in the fully adjusted model. TyG-BMI was not independently associated with PSI. The CatBoost model integrating all four biomarker categories with clinical features performed best (AUC\u202f=\u202f0.750, 95% CI: 0.722\u20130.779). SHAP identified TyG (|SHAP|\u202f=\u202f0.409), GNRI (0.358), NLR (0.293), invasive procedure (0.194), coronary heart disease (0.179), and atrial fibrillation (0.137) as top features.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Nutritional, electrolyte, inflammatory, and insulin resistance indices are significantly associated with PSI, and their integration with clinical features produces a predictive model with good discriminative performance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnut.2026.1863416","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T04:47:55Z","timestamp":1786510075000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Independent and combined predictive value of nutritional, electrolyte, inflammatory, and insulin resistance indices for post-stroke infection in acute ischemic stroke: a retrospective study"],"prefix":"10.3389","volume":"13","author":[{"given":"Jianmo","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Canwei","family":"Yi","sequence":"additional","affiliation":[{"name":"Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University","place":["Nanchang, China"]},{"name":"Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haowen","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengfei","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaofa","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongfei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University","place":["Nanchang, China"]},{"name":"Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiming","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University","place":["Nanchang, China"]},{"name":"Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University","place":["Nanchang, China"]},{"name":"Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weijiang","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University","place":["Nanchang, China"]},{"name":"Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chumin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingping","family":"Yi","sequence":"additional","affiliation":[{"name":"Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"594","DOI":"10.11622\/smedj.2020044","article-title":"Influence of chronic kidney disease and haemodialysis on stroke outcome","volume":"62","author":"Pande","year":"2021","journal-title":"Singapore Med J"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1985","DOI":"10.1093\/pm\/pnaa107","article-title":"Chronic pain increases the risk for major adverse cardiac and cerebrovascular events: a Nationwide population-based study in Asia","volume":"21","author":"Chung","year":"2020","journal-title":"Pain Med"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"3331","DOI":"10.1161\/STROKEAHA.113.002108","article-title":"Preceding and poststroke infections in young adults with first-ever ischemic stroke: effect on short-term and long-term 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