Identification of circulating lipidomic biomarkers of malnutrition risk among oncology patients in the Total Cancer Care (TCC) Study: a cross-sectional analysis.

Background: Early identification of malnutrition is critical for improving clinical outcomes in oncology patients. However, there are no established biomarkers for malnutrition screening. Objective: This study aimed to identify circulating lipid species associated with malnutrition risk among oncology patients through lipidomic analysis. Methods: A cross-sectional study was conducted using plasma samples from oncology patients classified as at risk (n = 90) or not at risk (n = 90) for malnutrition using the Malnutrition Screening Tool (MST) (MST score = 0 versus ≥2). All participants had head and neck, lung, or gastrointestinal cancer. Targeted lipidomics were conducted using LC-MS. Elastic net regression adjusted for confounding variables identified lipids associated with malnutrition risk. A weighted Lipid Malnutrition Risk Score was derived and evaluated using Receiver Operating Characteristic Area Under the Curve (ROC- AUC). Conditional multivariable logistic regression assessed the association of the lipid score with malnutrition risk. Lipid enrichment analysis was performed using Lipid Ontology (LION) enrichment framework. Results: Elastic net regression identified 12 lipids species that were inversely associated with malnutrition risk: cholesterol ester 20:0, ceramide 18:2;O2/26:0, lysophosphatidylcholine 26:0/0:0, lysophosphatidylinositol 18:2/0:0, phosphatidylcholine 34:5, phosphatidylcholine 40:8, phosphatidylethanolamine P-18:0/20:3, phosphatidylethanolamine P-18:1/18:2, phosphatidylethanolamine P-18:1/20:4, sulfated hexosylceramide 18:1;O2/16:0, sphingomyelin 18:2;O2/23:0, and triglyceride (O-50:1). One lipid, dihexosylceramide 18:1;O2/20:0, was positively associated with malnutrition risk. The weighted Lipid Malnutrition Risk Score was associated with increased risk for malnutrition risk (OR = 3.57, 95% CI 1.97-6.47, p < 0.001). Addition of the lipids score to established malnutrition risk factors improved model predictive performance, increasing the ROC-AUC from 0.78 (95% CI 0.71-0.84) to 0.90 (95% CI 0.86-0.94). LION enrichment analysis indicated downregulation of membrane structure and signaling lipids and upregulation of storage lipids. Conclusion: This study highlights the potential of lipidomics to identify biomarkers of malnutrition risk among oncology patients. Large, prospective studies are warranted to validate and expand upon these findings.