AI Insight
This study used NMR-based metabolomics and machine learning to characterize the serum lipid profile of adults with haemophagocytic lymphohistiocytosis (HLH), comparing them to patients with sepsis, rheumatic disease, post-surgical inflammation, and healthy controls. The researchers identified elevated apolipoprotein B (ApoB)-containing lipoproteins and lipoprotein remodelling toward triglyceride enrichment and cholesterol depletion as hallmark features of HLH, with machine learning models achieving classification accuracy above 91% when distinguishing HLH from healthy controls. These metabolomic findings were corroborated by whole-blood RNA sequencing, demonstrating coordinated dysregulation of lipid, amino acid, and glucose metabolism at both the transcriptomic and metabolomic levels.
Why it matters
HLH is a rare and frequently fatal condition that is notoriously difficult to distinguish from other hyperinflammatory syndromes such as sepsis; the identification of ApoB and the ApoB-to-ApoA1 ratio as candidate biomarkers, validated by ELISA, could offer a more accessible and specific diagnostic tool to guide earlier and more accurate diagnosis in clinical settings.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Haemophagocytic lymphohistiocytosis (HLH) is a rare, life-threatening hyperinflammatory syndrome characterised by uncontrolled immune activation. Reduced high- and low-density lipoprotein cholesterol and hypertriglyceridaemia are reported in HLH, suggesting lipid metabolism disturbances although in-depth serum metabolomic analysis is lacking in HLH. Here a lipid-focused NMR spectroscopy platform was used to define the serum metabolomic landscape of adults hospitalised with HLH compared to adults with sepsis (HLH-mimic) and rheumatic disease (potential HLH drivers/triggers), following surgical resection of solid organ cancer (non-infectious acute inflammation controls) and healthy controls (HCs). Serum metabolites distinguished HLH from HCs with high accuracy (>91.36%) using multiple machine learning models. The top classifying features included elevated apolipoprotein-B (ApoB)-containing low, intermediate, and very low-density lipoprotein particles; and lipoprotein remodelling towards triglyceride enrichment and cholesterol depletion. Differentially abundant metabolites in HLH compared to all control groups were enriched in pathways related to lipid metabolism including: ‘Lipid particles composition’, ‘Plasma lipoprotein clearance’, ‘Plasma lipoprotein remodelling’, ‘Glucose homeostasis’ and ‘Amino acid metabolism’. Metabolomic results were validated using matched whole blood RNA-sequencing which identified differentially expressed genes enriched in metabolic modules associated with lipid, amino acid, and glucose metabolism, supporting a coordinated metabolic dysregulation in HLH from a transcriptomic to metabolomic level. Finally, twenty-seven metabolites including ApoB-containing, triglyceride-rich lipoproteins and saturated fatty acids distinguished HLH from all disease controls (AUC>0.70) either alone or combined as a metabolomic signature. Elevated ApoB and ApoB:ApoA1 ratio in HLH vs sepsis and HCs were validated by ELISA, supporting their utility as biomarkers to distinguish HLH from other hyperinflammatory syndromes.