Scholarlink

Background <p>Sepsis represents a profound metabolic crisis, yet the prognostic significance of glycolipid biomarkers remains unclear. Most prior studies examined single metabolic indicators, overlooking their combined effects on organ dysfunction and survival. This study aimed to identify glycolipid-related biomarkers predicting outcomes in sepsis and to define a reproducible metabolic phenotype linked to multi-organ dysfunction and mortality.</p> Methods <p>We conducted a multicenter retrospective cohort study of 2,970 adults with sepsis admitted to three tertiary hospitals in China (2015–2023), with external validation using the international MIMIC-IV database (2008–2019). A comprehensive panel of glycolipid markers was evaluated through univariable Cox and LASSO regression, supported by tree-based machine learning and logistic regression for sepsis-associated acute kidney injury. Low-density lipoprotein cholesterol (LDL-C) and the triglyceride–glucose (TyG) index emerged as the most consistent prognostic biomarkers and were used to define metabolic phenotypes.</p> Results <p>Among 2,970 patients (median age 68 years [IQR 60–80]; 62.5% male), LDL-C and TyG independently predicted mortality across statistical and machine-learning analyses. Patients with a high TyG–low LDL phenotype exhibited the most severe multi-organ dysfunction—renal, hepatic, coagulative, and inflammatory—and the highest mortality risk (HR 1.31, 95% CI 1.04–1.65). Validation in MIMIC-IV confirmed the reproducibility of this phenotype across populations and healthcare systems.</p> Conclusions <p>Integrating LDL-C and TyG identifies a reproducible metabolic phenotype marking sepsis patients at high risk of multi-organ failure and death, offering a simple framework for early risk stratification and personalized management in critical care.</p> Graphical abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Metabolic phenotyping of sepsis in large multicenter cohorts: identification of a reproducible high-risk subgroup

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月5日

查看全文
  • Yining Zhang,
  • Guoxiang Liu,
  • Zhaoming Shang,
  • Xianxian Yu,
  • Huailong Shang,
  • Xiao Cui,
  • Jiameng Chen,
  • Jiawei Ye,
  • Jiyuan Zhang,
  • Yidan Zhai,
  • Junwei Qian,
  • Chaoping Ma,
  • Wenjie Liu,
  • Mingquan Chen,
  • Bing Zhao,
  • Chengjin Gao

摘要

Background

Sepsis represents a profound metabolic crisis, yet the prognostic significance of glycolipid biomarkers remains unclear. Most prior studies examined single metabolic indicators, overlooking their combined effects on organ dysfunction and survival. This study aimed to identify glycolipid-related biomarkers predicting outcomes in sepsis and to define a reproducible metabolic phenotype linked to multi-organ dysfunction and mortality.

Methods

We conducted a multicenter retrospective cohort study of 2,970 adults with sepsis admitted to three tertiary hospitals in China (2015–2023), with external validation using the international MIMIC-IV database (2008–2019). A comprehensive panel of glycolipid markers was evaluated through univariable Cox and LASSO regression, supported by tree-based machine learning and logistic regression for sepsis-associated acute kidney injury. Low-density lipoprotein cholesterol (LDL-C) and the triglyceride–glucose (TyG) index emerged as the most consistent prognostic biomarkers and were used to define metabolic phenotypes.

Results

Among 2,970 patients (median age 68 years [IQR 60–80]; 62.5% male), LDL-C and TyG independently predicted mortality across statistical and machine-learning analyses. Patients with a high TyG–low LDL phenotype exhibited the most severe multi-organ dysfunction—renal, hepatic, coagulative, and inflammatory—and the highest mortality risk (HR 1.31, 95% CI 1.04–1.65). Validation in MIMIC-IV confirmed the reproducibility of this phenotype across populations and healthcare systems.

Conclusions

Integrating LDL-C and TyG identifies a reproducible metabolic phenotype marking sepsis patients at high risk of multi-organ failure and death, offering a simple framework for early risk stratification and personalized management in critical care.

Graphical abstract