Circulating NETosis biomarkers predict severity, 12-month functional outcome, and survival after spontaneous intracerebral hemorrhage

Background Spontaneous intracerebral hemorrhage (ICH) carries high early mortality and long-term disability; however, circulating biomarkers that capture both bleeding severity and prognosis are lacking. Neutrophil extracellular trap formation (NETosis), quantifiable in plasma as citrullinated histone H3 (H3Cit), cell-free DNA (cfDNA), and nucleosomes, is implicated in secondary brain injury after ICH. Methods Three hundred fifteen adults with first-ever spontaneous ICH within 24 h of onset and 160 community controls were enrolled. Plasma H3Cit, cfDNA and nucleosomes, admission hematoma volume, NIHSS, and GCS were recorded; the modified Rankin Scale (mRS) was assessed at 12 months. NETosis biomarkers entered linear, logistic, and Cox regressions as log2-transformed continuous variables and were adjusted progressively for time-to-sampling, demographics, vascular risk factors, surgical treatment, NIHSS, and hematoma volume. Incremental predictive value over a clinical baseline model was assessed using area under the receiver operating characteristic curve (AUC)/concordance index (C-index), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results All three biomarkers were higher in ICH than controls (all p < 0.001) and were several-fold higher in 12-month decedents than survivors, despite no difference in absolute neutrophil count. After adjustment, H3Cit was independently associated with hematoma volume, NIHSS and GCS; cfDNA and nucleosomes were independently associated with hematoma volume. After full adjustment for demographics, vascular risk factors, surgical treatment, NIHSS, and hematoma volume, each doubling of H3Cit (OR: 1.577, 95% CI: 1.368–1.838), cfDNA (OR: 1.687, 95% CI: 1.259–2.291), and nucleosomes (OR: 1.622, 95% CI: 1.297–2.057) independently predicted 12-month mRS ≥ 3 (all p < 0.001); per doubling, H3Cit (HR: 1.358, 95% CI: 1.207–1.528), cfDNA (HR: 1.619, 95% CI: 1.287–2.038), and nucleosomes (HR: 1.568, 95% CI: 1.310–1.877) independently predicted 12-month death. Adding H3Cit raised the AUC for mRS ≥ 3 from 0.814 to 0.869 (ΔAUC: 0.055, p < 0.001) and the C-index for death from 0.765 to 0.818 (ΔC: 0.052, p < 0.001); for cfDNA and nucleosomes, the gains in discrimination were not significant, although NRI and IDI improved. Conclusion Plasma H3Cit, cfDNA, and nucleosomes within 24 h of ICH track hemorrhage severity and predict 12-month mRS ≥ 3 and death beyond established clinical scores, with H3Cit showing the most consistent incremental value.

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Journal
Frontiers in Neurology
Published
2026-09-14
DOI
https://doi.org/10.3389/fneur.2026.1901302
Primary Topic
Neutrophil, Myeloperoxidase and Oxidative Mechanisms
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article
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article

Circulating NETosis biomarkers predict severity, 12-month functional outcome, and survival after spontaneous intracerebral hemorrhage

Chaoshuai Hu, Fan Wang, Shifang Zhou, Chi Ma et al.
Frontiers in Neurology
Neutrophil, Myeloperoxidase and Oxidative Mechanisms
article

Circulating NETosis biomarkers predict severity, 12-month functional outcome, and survival after spontaneous intracerebral hemorrhage

Chaoshuai Hu, Fan Wang, Shifang Zhou, Chi Ma, Ming Yang, Lei Hui
article en

Abstract

Background Spontaneous intracerebral hemorrhage (ICH) carries high early mortality and long-term disability; however, circulating biomarkers that capture both bleeding severity and prognosis are lacking. Neutrophil extracellular trap formation (NETosis), quantifiable in plasma as citrullinated histone H3 (H3Cit), cell-free DNA (cfDNA), and nucleosomes, is implicated in secondary brain injury after ICH. Methods Three hundred fifteen adults with first-ever spontaneous ICH within 24 h of onset and 160 community controls were enrolled. Plasma H3Cit, cfDNA and nucleosomes, admission hematoma volume, NIHSS, and GCS were recorded; the modified Rankin Scale (mRS) was assessed at 12 months. NETosis biomarkers entered linear, logistic, and Cox regressions as log2-transformed continuous variables and were adjusted progressively for time-to-sampling, demographics, vascular risk factors, surgical treatment, NIHSS, and hematoma volume. Incremental predictive value over a clinical baseline model was assessed using area under the receiver operating characteristic curve (AUC)/concordance index (C-index), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results All three biomarkers were higher in ICH than controls (all p < 0.001) and were several-fold higher in 12-month decedents than survivors, despite no difference in absolute neutrophil count. After adjustment, H3Cit was independently associated with hematoma volume, NIHSS and GCS; cfDNA and nucleosomes were independently associated with hematoma volume. After full adjustment for demographics, vascular risk factors, surgical treatment, NIHSS, and hematoma volume, each doubling of H3Cit (OR: 1.577, 95% CI: 1.368–1.838), cfDNA (OR: 1.687, 95% CI: 1.259–2.291), and nucleosomes (OR: 1.622, 95% CI: 1.297–2.057) independently predicted 12-month mRS ≥ 3 (all p < 0.001); per doubling, H3Cit (HR: 1.358, 95% CI: 1.207–1.528), cfDNA (HR: 1.619, 95% CI: 1.287–2.038), and nucleosomes (HR: 1.568, 95% CI: 1.310–1.877) independently predicted 12-month death. Adding H3Cit raised the AUC for mRS ≥ 3 from 0.814 to 0.869 (ΔAUC: 0.055, p < 0.001) and the C-index for death from 0.765 to 0.818 (ΔC: 0.052, p < 0.001); for cfDNA and nucleosomes, the gains in discrimination were not significant, although NRI and IDI improved. Conclusion Plasma H3Cit, cfDNA, and nucleosomes within 24 h of ICH track hemorrhage severity and predict 12-month mRS ≥ 3 and death beyond established clinical scores, with H3Cit showing the most consistent incremental value.

Frontiers in NeurologyVol. 17
First Affiliated Hospital of Henan University of Traditional Chinese Medicine (CN), First Affiliated Hospital of Zhengzhou University (CN)
Gender equality
Openalex Percentile: Top 18%
Neutrophil, Myeloperoxidase and Oxidative Mechanisms
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