David B. Antcliffe, Elsa Harte, Humma Hussain, Beatriz Jiménez, Charlotte Browning, Anthony C. Gordon
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Patients were included soon after the onset of shock and had serum collected at up to four time points. Metabolic clusters were identified from 474 metabolites using k-means clustering in LeoPARDS and predicted in VANISH with an elastic net classifier.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Three sub-phenotypes were found. The main determinants of cluster membership were lipid species, especially lysophospholipids. Low lysophospholipid sub-phenotypes were associated with higher circulating cytokine levels. Persistence of low lysophospholipid sub-phenotypes was associated with higher mortality compared to the high lysophospholipid sub-phenotype (LeoPARDS: cluster 2 odds ratio 3.66 (95% CI 1.88–7.20), <i>p</i> = 0.0001, cluster 3 2.49 (1.29–4.81), <i>p</i> = 0.006; VANISH: cluster 2 4.13 (1.17–15.61), <i>p</i> = 0.03), cluster 3 3.22 (1.09–9.92), <i>p</i> = 0.04, vs cluster 1). We found no heterogeneity of treatment effect for any of the trial interventions by baseline metabolic sub-phenotype.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>Three metabolic subgroups exist in septic shock which evolve over time. Persistence of low lysophospholipid sub-phenotypes is associated with mortality. Monitoring these subgroups could help identify patients at risk of poor outcome and direct novel therapies such as lysophospholipid supplementation.</p><h3 data-test=\"abstract-sub-heading\">Registration</h3><p>Clinicaltirals.gov Identifiers, VANISH: ISRCTN 20769191, LeoPARDS: ISRCTN12776039.</p>","PeriodicalId":13665,"journal":{"name":"Intensive Care Medicine","volume":"73 1","pages":""},"PeriodicalIF":27.1000,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Metabolic septic shock sub-phenotypes, stability over time and association with clinical outcome\",\"authors\":\"David B. Antcliffe, Elsa Harte, Humma Hussain, Beatriz Jiménez, Charlotte Browning, Anthony C. 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引用次数: 0
摘要
目的：机器学习有望从基因表达和蛋白质数据中检测出脓毒症患者的有用亚组。这种方法很少用于代谢组学数据集。代谢组学数据很有趣，因为它们捕获了基因组、蛋白质组和环境的影响。我们的目的是发现感染性休克的代谢亚表型，检查它们的时间稳定性和与临床结果的关联。方法对感染性休克的两项双盲随机试验(LeoPARDS（470例患者1402份样本）和VANISH（173例患者493份样本）进行分析。患者在休克发作后不久被纳入，并在多达四个时间点收集血清。利用LeoPARDS中的k-means聚类方法从474种代谢物中识别出代谢簇，并在VANISH中使用弹性网络分类器进行预测。结果共发现3个亚表型。集群成员的主要决定因素是脂质种类，特别是溶血磷脂。低溶血磷脂亚表型与较高的循环细胞因子水平相关。与高溶血磷脂亚表型相比，低溶血磷脂亚表型的持续存在与更高的死亡率相关(豹：集群2优势比3.66 (95% CI 1.88-7.20), p = 0.0001，集群3优势比2.49 (1.29-4.81),p = 0.006；VANISH：第2组4.13 （1.17-15.61),p = 0.03)，第3组3.22 (1.09-9.92),p = 0.04，与第1组相比）。我们发现，根据基线代谢亚表型，任何试验干预措施的治疗效果都没有异质性。结论感染性休克存在三个代谢亚群，并随时间变化而变化。低溶血磷脂亚表型的持续存在与死亡率有关。监测这些亚组可以帮助识别有不良预后风险的患者，并指导新的治疗方法，如溶血磷脂补充剂。
Metabolic septic shock sub-phenotypes, stability over time and association with clinical outcome
Purpose
Machine learning has shown promise to detect useful subgroups of patients with sepsis from gene expression and protein data. This approach has rarely been deployed in metabolomic datasets. Metabolomic data are of interest as they capture effects from the genome, proteome, and environmental. We aimed to discover metabolic sub-phenotypes of septic shock, examine their temporal stability and association with clinical outcome.
Methods
Analysis was performed in two double-blind randomized trials in septic shock (LeoPARDS (1402 samples from 470 patients) and VANISH (493 samples from 173 patients)). Patients were included soon after the onset of shock and had serum collected at up to four time points. Metabolic clusters were identified from 474 metabolites using k-means clustering in LeoPARDS and predicted in VANISH with an elastic net classifier.
Results
Three sub-phenotypes were found. The main determinants of cluster membership were lipid species, especially lysophospholipids. Low lysophospholipid sub-phenotypes were associated with higher circulating cytokine levels. Persistence of low lysophospholipid sub-phenotypes was associated with higher mortality compared to the high lysophospholipid sub-phenotype (LeoPARDS: cluster 2 odds ratio 3.66 (95% CI 1.88–7.20), p = 0.0001, cluster 3 2.49 (1.29–4.81), p = 0.006; VANISH: cluster 2 4.13 (1.17–15.61), p = 0.03), cluster 3 3.22 (1.09–9.92), p = 0.04, vs cluster 1). We found no heterogeneity of treatment effect for any of the trial interventions by baseline metabolic sub-phenotype.
Conclusion
Three metabolic subgroups exist in septic shock which evolve over time. Persistence of low lysophospholipid sub-phenotypes is associated with mortality. Monitoring these subgroups could help identify patients at risk of poor outcome and direct novel therapies such as lysophospholipid supplementation.
期刊介绍：
Intensive Care Medicine is the premier publication platform fostering the communication and exchange of cutting-edge research and ideas within the field of intensive care medicine on a comprehensive scale. Catering to professionals involved in intensive medical care, including intensivists, medical specialists, nurses, and other healthcare professionals, ICM stands as the official journal of The European Society of Intensive Care Medicine. ICM is dedicated to advancing the understanding and practice of intensive care medicine among professionals in Europe and beyond. The journal provides a robust platform for disseminating current research findings and innovative ideas in intensive care medicine. Content published in Intensive Care Medicine encompasses a wide range, including review articles, original research papers, letters, reviews, debates, and more.
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