3f6ae40120d0bef6535881656838d48f 319517.pdf e13c97e13105fa47b49ec7978450aac395d818a6 319517.pdf fe41ef544c8b49047b9d056147dc88c978461530a64f185f420e1cf8d27d2e0e 319517.pdf Title: Metabolomic Signatures Predict Seven-Year Mortality in Clinically Stable COPD Patients Subject: Chronic Obstructive Pulmonary Disease (COPD) is a complex condition with high mortality. Early identification of patients at increased risk of death remains a major clinical challenge. This pilot study aimed to explore whether plasma metabolomic profiling could aid in the prediction of long-term (7-year) mortality and provide insight into potential underlying mechanisms. Plasma samples from 54 randomly selected stable COPD patients were analyzed using both untargeted and semi-targeted LC-MS approaches. After excluding patients with unclear death data, non-COPD-related deaths and metabolomic outliers, 41 individuals were included in the final analysis. During follow-up, 13 patients (32%) died, and 28 survived. Univariate analysis identified 12 metabolites—mainly amino acids—that differed significantly between the two groups. Functional analysis suggested a significant disruption in energy production pathways. Predictive models developed using machine learning algorithms, consisting of either ten metabolites alone or nine metabolites plus FEV1, achieved high accuracy for 7-year mortality prediction, with the latter model performing slightly better. Internal validation was conducted using five-fold cross-validation. While exploratory, these findings support the hypothesis that early metabolic alterations, particularly in energy pathways, may contribute to long-term mortality risk in stable COPD patients, and could complement traditional prognostic markers such as FEV1. Keywords: COPD; mortality; metabolomics; energy; amino acids; redox; microbiota Author: César Jessé Enríquez-Rodríguez, Bella Agranovich, Sergi Pascual-Guàrdia, Rosa Faner, Ramon Camps-Ubach, Ady Castro-Acosta, José Luis López-Campos, Germán Peces-Barba, Luis Seijo, Oswaldo Antonio Caguana-Vélez, Diego Rodríguez-Chiaradia, Esther Barreiro, Eduard Monsó, Borja G. Cosío, Ifat Abramovich, Alvar Agustí, Carme Casadevall, Joaquim Gea and on behalf of the BIOMEPOC Group Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Wed Jul 2 13:54:10 2025 CEST ModDate: Wed Jul 2 14:41:19 2025 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 21 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 2038186 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- WNWTFR+VnURWPalladioL-Bold Type 1 Custom yes yes yes 10 0 CJXOGM+URWPalladioL-Roma Type 1 Custom yes yes yes 16 0 TYVPCO+URWPalladioL-Bold Type 1 Custom yes yes yes 22 0 OBKIOM+URWPalladioL-Ital Type 1 Custom yes yes yes 27 0 ILXGXV+VnURWPalladioL Type 1 Custom yes yes yes 32 0 WBVKDJ+URWPalladioL-BoldItal Type 1 Custom yes yes yes 61 0 TWRXBB+CMSY10 Type 1 Builtin yes yes yes 66 0 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241 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-09-20 04:10:43 CEST RepresentationInformation: 319517.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2025-09-19 10:46:19 CEST Size: 2038186 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 452 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Results Destination: section.2 Children: Item: Title: Characterization of COPD Patients Destination: subsection.2.1 Item: Title: Differential Metabolite Profile of Deceased Patients Destination: subsection.2.2 Item: Title: Biological Function Analysis Destination: subsection.2.3 Item: Title: AI Modeling Destination: subsection.2.4 Item: Title: Integration of FEV1 into the Predictive Model Destination: subsection.2.5 Item: Title: Discussion Destination: section.3 Children: Item: Title: Dysregulation of Energy and Mitochondrial Metabolism Destination: subsection.3.1 Item: Title: Alterations in Amino Acid and Nitrogen Metabolism Destination: subsection.3.2 Item: Title: Redox Imbalance and Neuroendocrine Dysregulation Destination: subsection.3.3 Item: Title: Microbiota-Derived Metabolites and Host Microbiota Interactions Destination: subsection.3.4 Item: Title: Metabolic Signature of Poor Prognosis Destination: subsection.3.5 Item: Title: Strengths and Potential Limitations Destination: subsection.3.6 Item: Title: Materials and Methods Destination: section.4 Children: Item: Title: Participants and Ethics Destination: subsection.4.1 Item: Title: Blood Sample Collection Destination: subsection.4.2 Item: Title: Metabolomics Workflow Destination: subsection.4.3 Children: Item: Title: Metabolite Collection and LC-MS Parameters Destination: subsubsection.4.3.1 Item: Title: Metabolite Identification and Quantification Destination: subsubsection.4.3.2 Item: Title: Metabolomic Outlier Identification and Quality Control Destination: subsubsection.4.3.3 Item: Title: Data Analysis Destination: subsection.4.4 Children: Item: Title: Sample Size Calculation Destination: subsubsection.4.4.1 Item: Title: Clinical Data Analysis Destination: subsubsection.4.4.2 Item: Title: Metabolomic Data Preprocessing and Filtering Destination: subsubsection.4.4.3 Item: Title: Covariate Adjustment and Differential Analysis Destination: subsubsection.4.4.4 Item: Title: Data Export for Machine Learning Destination: subsubsection.4.4.5 Item: Title: Generation of Predictive Models Destination: subsection.4.5 Children: Item: Title: Model Development Destination: subsubsection.4.5.1 Item: Title: Initial Model Fitting Destination: subsubsection.4.5.2 Item: Title: Internal Validation Destination: subsubsection.4.5.3 Item: Title: Model Evaluation Destination: subsubsection.4.5.4 Item: Title: Biological Function Analysis Destination: subsubsection.4.5.5 Item: Title: Conclusions Destination: section.5 Item: Title: Appendix A Destination: appendix.A. Item: Title: Appendix B Destination: appendix.B. Item: Title: References Destination: appendix.C. Info: Title: Metabolomic Signatures Predict Seven-Year Mortality in Clinically Stable COPD Patients Author: César Jessé Enríquez-Rodríguez, Bella Agranovich, Sergi Pascual-Guàrdia, Rosa Faner, Ramon Camps-Ubach, Ady Castro-Acosta, José Luis López-Campos, Germán Peces-Barba, Luis Seijo, Oswaldo Antonio Caguana-Vélez, Diego Rodríguez-Chiaradia, Esther Barreiro, Eduard Monsó, Borja G. Cosío, Ifat Abramovich, Alvar Agustí, Carme Casadevall, Joaquim Gea and on behalf of the BIOMEPOC Group Subject: Chronic Obstructive Pulmonary Disease (COPD) is a complex condition with high mortality. Early identification of patients at increased risk of death remains a major clinical challenge. This pilot study aimed to explore whether plasma metabolomic profiling could aid in the prediction of long-term (7-year) mortality and provide insight into potential underlying mechanisms. Plasma samples from 54 randomly selected stable COPD patients were analyzed using both untargeted and semi-targeted LC-MS approaches. After excluding patients with unclear death data, non-COPD-related deaths and metabolomic outliers, 41 individuals were included in the final analysis. During follow-up, 13 patients (32%) died, and 28 survived. Univariate analysis identified 12 metabolites mainly amino acids that differed significantly between the two groups. Functional analysis suggested a significant disruption in energy production pathways. Predictive models developed using machine learning algorithms, consisting of either ten metabolites alone or nine metabolites plus FEV1, achieved high accuracy for 7-year mortality prediction, with the latter model performing slightly better. Internal validation was conducted using five-fold cross-validation. While exploratory, these findings support the hypothesis that early metabolic alterations, particularly in energy pathways, may contribute to long-term mortality risk in stable COPD patients, and could complement traditional prognostic markers such as FEV1. 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