AUTHOR=Lu Yuexin , Song Enjie , Chen Ruilin , Jing Long , Tang Jiahao , Shi Shunan , Wang Ming , Wan Shu 

TITLE=Associations between triglyceride glucose-body mass index and 30-day mortality in patients with hemorrhagic stroke: analysis of the MIMIC-IV database

JOURNAL=Frontiers in Neurology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1602822

DOI=10.3389/fneur.2025.1602822

ISSN=1664-2295

ABSTRACT=BackgroundHemorrhagic stroke (HS), accounting for over 40% of stroke-related deaths, imposes a severe global health burden due to high mortality and disability rates. The triglyceride-glucose-body mass index (TyG-BMI), integrating lipid-glucose metabolism and obesity, has shown prognostic value in cardiovascular diseases but remains underexplored in HS populations.MethodsThis retrospective cohort study analyzed 413 HS patients from the MIMIC-IV database. Multivariable Cox regression, Kaplan–Meier analysis, and restricted cubic spline models assessed mortality risks at 30-day, 90-day, and 1-year intervals. Subgroup analysis and interaction tests were performed to evaluate the robustness of the results.ResultsCompared with the intermediate TyG-BMI group, the low TyG-BMI group exhibited a significantly increased risk of death at 30 days (adjusted hazard ratio [aHR] = 1.836, p = 0.010), 90 days (aHR = 1.694, p = 0.016), and 1 year (aHR = 1.642, p = 0.014). Similarly, the high TyG-BMI group also showed higher mortality risk at 30 days (aHR = 1.584, p = 0.039), 90 days (aHR = 1.571, p = 0.024), and 1 year (aHR = 1.484, p = 0.030). Kaplan–Meier analysis revealed the highest survival rate in the middle tertile group. Restricted cubic spline curve showed a U-shaped relationship emerged between TyG-BMI and mortality. No interactions between TyG-BMI and the stratified variables, except for coronary artery disease.ConclusionPatients with intermediate TyG-BMI levels have a lower 30-day mortality risk. As a composite metabolic-nutritional marker, TyG-BMI aids risk stratification in intensive care unit settings.