Association of the hsCRP- triglyceride-glucose index (CTI) with early infarct growth rate in acute ischemic stroke due to large artery occlusion

Vollständiger Abstract

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Background Rapid infarct growth in acute ischemic stroke (AIS) due to large artery occlusion (LAO) is associated with poor clinical outcomes despite successful recanalization. The hypersensitive C-reactive protein (hsCRP)-triglyceride-glucose (TyG) index (CTI), a novel biomarker integrating insulin resistance and systemic inflammation, may reflect a meta-inflammatory state that exacerbates ischemic injury. However, its relationship with early infarct growth rate (IGR) remains unexplored. This study aimed to investigate whether CTI is independently associated with early IGR in patients with anterior circulation LAO. Methods We conducted a prospective cross-sectional study of 247 patients with AIS due to anterior circulation LAO. CTI was calculated as the product of the TyG index and high-sensitivity CRP from fasting blood samples. Early IGR was defined as the baseline infarct core volume (rCBF <30%) divided by the time from symptom onset to imaging. Patients were categorized into fast (≥10 mL/h) or slow (<10 mL/h) IGR groups. Multivariable logistic regression was used to assess the independent association between CTI and fast IGR. Results The fast IGR group had significantly higher CTI levels (median 5.396 vs. 5.177, p = 0.002). Spearman’s correlation revealed a positive association between CTI and IGR (r = 0.348, p < 0.001). After adjusting for confounders, CTI remained an independent predictor of fast IGR (adjusted OR = 1.635, 95% CI: 1.072–2.493, p = 0.022). Subgroup analyses confirmed the consistency of this association. ROC analysis showed an AUC of 0.613 (95% CI: 0.543–0.683) for CTI in discriminating fast IGR. Conclusion Elevated CTI is independently associated with faster early infarct growth in LAO stroke, suggesting its potential role as an adjunctive biomarker that may complement clinical and imaging assessment for risk stratification. However, the modest discriminative performance (AUC = 0.613) underscores the need for integration with established predictors rather than standalone use.