{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T13:17:56Z","timestamp":1787836676511,"version":"build-2784847793"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"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. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Although hematoma expansion (HE) severely deteriorates outcomes in hypertensive intracerebral hemorrhage (HICH), predicting HE in patients comorbid with metabolic dysfunction-associated fatty liver disease (MAFLD) lacks specific machine learning tools. This study aimed to bridge this gap by exploring the interactive pathophysiological associations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data from 529 HICH-MAFLD patients were used to construct five predictive algorithms. The best-performing model was subjected to SHapley Additive exPlanations (SHAP) to map variable interactions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The XGBoost model achieved the best discrimination (AUC\u00a0=\u00a00.911). SHAP analysis revealed non-linear, synergistic statistical associations between the \u201cliver-brain axis\u201d components. Specifically, severe hepatic fibrosis (FIB-4 index) and systemic inflammation (hs-CRP) appeared to exponentially amplify the HE risk associated with local hemodynamic parameters (baseline hematoma volume and systolic blood pressure).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The SHAP-integrated XGBoost algorithm suggests a robust predictive framework for early HE. The statistical synergy unmasked between metabolic inflammation and local hemodynamics highlights the potential clinical significance of the \u201cliver-brain axis\u201d in guiding individualized risk stratification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1912965","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T04:49:50Z","timestamp":1787719790000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Metabolic dysfunction-associated fatty liver disease exacerbates hematoma expansion in intracerebral hemorrhage: an explainable machine learning approach"],"prefix":"10.3389","volume":"17","author":[{"given":"Zhuo","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Encephalopathy, Chengdu Xinjin District Hospital of Traditional Chinese Medicine","place":["Chengdu, 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