{"id":895,"date":"2025-04-23T06:00:35","date_gmt":"2025-04-23T06:00:35","guid":{"rendered":"https:\/\/perfusfind.com\/ic\/?p=895"},"modified":"2025-04-22T22:19:52","modified_gmt":"2025-04-22T22:19:52","slug":"metabolic-septic-shock-sub-phenotypes-stability-over-time-and-association-with-clinical-outcome","status":"publish","type":"post","link":"https:\/\/perfusfind.com\/ic\/index.php\/2025\/04\/23\/metabolic-septic-shock-sub-phenotypes-stability-over-time-and-association-with-clinical-outcome\/","title":{"rendered":"Metabolic septic shock sub-phenotypes, stability over time and association with clinical outcome"},"content":{"rendered":"
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Summary of Metabolic Septic Shock Sub-Phenotypes, Stability Over Time and Association with Clinical Outcome<\/strong><\/p>\n<\/blockquote>\n

Abstract Summary:<\/strong> Antcliffe et al. examined metabolic sub-phenotypes in septic shock using metabolomic data from two clinical trials (LeoPARDS and VANISH). They identified three distinct metabolic clusters, primarily characterized by lipid metabolite levels, especially lysophospholipids. Persistence in low lysophospholipid clusters correlated significantly with increased mortality. These findings suggest metabolic monitoring could aid in identifying high-risk septic shock patients who might benefit from tailored therapeutic interventions.<\/p>\n

Key Points:<\/strong><\/p>\n

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    Study Design and Cohorts:<\/strong> The analysis included data from two randomized trials (LeoPARDS and VANISH), involving 663 patients and 1,895 serum samples, using nuclear magnetic resonance (NMR) spectroscopy and ultra-high performance liquid chromatography mass spectrometry (UHPLC-MS) for metabolite profiling.<\/p>\n<\/li>\n

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    Metabolic Sub-Phenotypes Identified:<\/strong> Three distinct metabolic clusters were robustly identified using machine learning techniques, characterized mainly by varying levels of lipid metabolites, notably lysophospholipids and triglycerides.<\/p>\n<\/li>\n

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    Cluster Stability and Evolution:<\/strong> Initial cluster proportions changed significantly over time, with the metabolically favorable cluster (high lysophospholipid levels) becoming more prevalent and associated with improved survival outcomes.<\/p>\n<\/li>\n

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    Clinical Outcome Associations:<\/strong> Persistence in metabolically unfavorable clusters (characterized by low lysophospholipids) significantly correlated with higher mortality rates (LeoPARDS: OR 3.66, p=0.0001; VANISH: OR 4.13, p=0.03).<\/p>\n<\/li>\n

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    Inflammation and Protein Profiles:<\/strong> Low lysophospholipid clusters exhibited higher inflammatory protein levels, including IL-6, sTNFR1, and Ang-2, indicating greater inflammatory and endothelial dysfunction.<\/p>\n<\/li>\n

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    Gene Expression Insights:<\/strong> Differential gene expression analysis revealed distinct pathways enriched in the high triglyceride, low lysophospholipid cluster, notably related to cell division and immune response.<\/p>\n<\/li>\n

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    Metabolite Signatures:<\/strong> Lysophosphatidylcholines and triglycerides emerged as key discriminators of cluster membership, suggesting their potential as therapeutic targets or biomarkers.<\/p>\n<\/li>\n

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    Implications for Therapy:<\/strong> The findings imply potential therapeutic interventions, such as lysophospholipid supplementation, could benefit patients persistently in high-risk metabolic sub-phenotypes.<\/p>\n<\/li>\n

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    Limitations and Future Directions:<\/strong> The authors acknowledged study limitations, including targeted metabolite measurement constraints, potential confounding factors (medications, feed), and survivorship bias influencing longitudinal analyses.<\/p>\n<\/li>\n

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    Conclusion and Clinical Utility:<\/strong> Metabolic sub-phenotyping presents a promising approach to stratify septic shock patients, enhance prognostic accuracy, and guide personalized therapeutic strategies based on metabolic trajectories.<\/p>\n<\/li>\n<\/ol>\n

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    Heat map (A<\/strong>) showing the distribution of metabolite classes between the three metabolomic clusters (cluster 1 in red, cluster 2 in blue, cluster 3 in yellow) defined by k-means clustering. B<\/strong> Volcano plots comparing metabolite differences in the baseline sample between clusters 1 vs 2, clusters 1 vs 3 and clusters 2 vs 3 in the LeoPARDS dataset. Metabolites were considered significant if they had an adjusted p<\/em> value of\u2009<\u20090\u00b705 and a fold change\u2009>\u20091\u00b75 (the dashed lines represent these thresholds). Significant metabolites are colored by class as shown in the key. (A1<\/em> apolipoprotein A-1, AB<\/em> apolipoprotein B, CAR<\/em> carnitine, CE<\/em> cholesterol ester, CH<\/em> cholesterol, FC<\/em> free cholesterol, H<\/em> high density lipoprotein subspecies, HDFC<\/em> high density lipoprotein free cholesterol, IDCH<\/em> intermediate density cholesterol, IDFC<\/em> intermediate density lipoprotein free cholesterol, IDPL<\/em> intermediate density lipoprotein phospholipids, IDTG<\/em> intermediate density lipoprotein triglycerides, L<\/em> low density lipoprotein subspecies, LPC<\/em> Lysophosphatidylcholine, LPE<\/em> Lysophosphatidylethanolamine, LPG<\/em> Lysophosphatidyl glycerol, PL<\/em> phospholipids, SM<\/em> Sphingomyelins, TG<\/em> Triglycerides, V<\/em> very low density lipoprotein subfractions<\/figcaption><\/figure>\n

    Conclusion:<\/strong> The study robustly establishes three distinct metabolic clusters in septic shock, highlighting the prognostic significance of metabolic trajectories. Persistent low lysophospholipid levels indicate poor outcomes, offering a novel approach for monitoring and potentially guiding personalized treatments in septic shock.<\/p>\n

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    Metabolic septic shock sub-phenotypes, stability over time and association with clinical outcome<\/figcaption><\/figure>\n
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    Watch the following video on :Pro-Con: Are Personalized Interventions in Sepsis Even Possible? (Session 7 \u2013\u00a02022 WSC Spotlight)” by World Sepsis Congress (WSC)<\/strong><\/p>\n