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Article Abstract

Background: The triglyceride‒glucose body mass index (TyG-BMI) has been recognized as a significant predictor of cardiovascular disease risk and plays a crucial role in assessing insulin resistance. However, the correlation between the TyG-BMI and clinical outcomes in patients with sepsis and acute heart failure (AHF) has not been sufficiently explored. This study aimed to investigate the associations between TyG-BMI and clinical outcomes in patients with sepsis and AHF.

Methods: We conducted a retrospective analysis of ICU-admitted patients via data from the MIMIC-IV database. Multivariable logistic regression, sensitivity analysis, and restricted cubic spline (RCS) models were used to assess the relationship between TyG-BMI and all-cause mortality. K‒M survival analysis and Boruta analysis were employed to evaluate the predictive value of the TyG-BMI. Subgroup analyses considered the effects of age, sex, ethnicity, and comorbidities.

Results: Among the 1,729 patients, a higher TyG-BMI was associated with lower all-cause mortality at 90 and 180 days. Each standard deviation increase in the TyG-BMI was linked to 0.2% and 0.3% reductions in 90-day and 180-day all-cause mortality, respectively. Kaplan‒Meier analysis revealed significantly lower all-cause mortality in patients with higher TyG-BMIs (P < 0.0001). The RCS model revealed a nonlinear relationship between the TyG-BMI and mortality. Boruta analysis identified the TyG-BMI as an important clinical feature. Sensitivity analyses revealed that the association remained significant after patients with myocardial infarction, malignancies, or missing data were excluded. The subgroup analysis revealed that for the 90-day and 180-day mortality rates, significant interactions were found only in the subgroup of patients with kidney diseases (P < 0.05).

Conclusion: The TyG-BMI may have potential value in predicting mortality in ICU patients with sepsis and AHF, supporting early risk assessment and clinical intervention. This study provides critical insights into patient prognosis.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12063255PMC
http://dx.doi.org/10.1186/s12872-025-04804-7DOI Listing

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