Urinary collagen peptides predict mortality
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Background
Organ fibrosis caused by the presence of excessive extracellular matrix (ECM) is strongly related to mortality. Urinary peptide signatures were reported predictive of death in SARS-CoV-2 and chronic kidney disease. Such signatures were composed for 68% of collagen fragments. In this study, we examined whether an exclusively collagen-based urinary peptide model, potentially representing organ fibrosis, could predict mortality in patients with critical and non-critical conditions.
Methods
Urinary proteomic data from 1,012 patients infected with SARS-CoV-2 were considered to evaluate the association of collagen peptide levels with short term mortality. Additional datasets from 9,193 patients were used for validation, including 1,719 patients sampled at intensive care unit (ICU) admission and 7,474 patients with other diseases (outside the ICU) were extracted from the Human Urinary Proteome Database.
Findings
A collagen peptide-based model based on 210 peptides (COL210) predicting mortality was developed for patients after SARS-CoV-2 infection. This model was validated in patients in (ICU HR : 2·64; 1·71-4·10; p<0·001) and outside the ICU (Non-ICU HR : 2·16 95% CI: 1·47–3·17; p<0·001), showing strong associations to mortality regardless underlying conditions.
Interpretation
This study demonstrates a link between the presence of ECM fragments in urine, specifically collagens, and increased mortality risk. The availability of such a non-invasive collagen-based predictor of mortality may serve as basis for proteomics guided targeted intervention.
Funding
This study was funded by “DisCo-I” (HORIZON–MSCA; 101072828), “SIGNAL” (BMBF; 01KU2307, FWF; project number I 6471 and Grant-DOI 10.55776/I6471 and ANR-22-PERM-0002-06), “UriCov” (BMG,2523FSB114), by “Accurate-CVD” (BMWK), by UPTAKE (BMBF; 01EK2105A-C) and MULTIR (101136926)
Research in Context
Evidence before this study
Fibrosis, marked by excessive extracellular matrix (ECM) deposition, is a key factor in chronic diseases and organ failure and is correlated with increased mortality in conditions such as idiopathic pulmonary fibrosis (IPF), chronic kidney disease (CKD), liver disease, cardiovascular disease (CVD), and cancer. A literature search conducted on 10/12/2024 using the MeSH terms “collagen” AND “fibrosis” AND “mortality” OR “death” OR “failure”, revealed a shift from static histological assessments of ECM deposition to a more dynamic approach based on the assessment of biomarkers of collagen turnover within specific fibrotic conditions, which offer real-time and potentially actionable insights into disease progression and predict adverse outcomes, including mortality. Key biomarkers include products of collagen synthesis (e.g., pro-peptides of type III, V, and VI collagen), but also of collagen degradation. These biomarkers have individually shown correlations with fibrosis severity and mortality, with links to disease progression and survival in IPF, CKD, CVD, and cancer. In heart failure, urinary peptides from collagen α-1 (I) chain predict adverse events and mortality. Collagen α-1 (XXIV) chain was also among the circulating plasma proteins that were related with transplant-free survival of patients with IPF.
Added value of this study
This work further adds to the growing relevance of collagen turnover in the context of mortality across fibrotic diseases, in an effort to generalize evidence for collagen degradation markers, independent from the underlying pathological condition. It additionally explores integration of collagen degradation fragments into risk models, potentially improving predictive accuracy and advancing precision medicine.
Implications of all the available evidence
Our study demonstrates that urinary collagen degradation markers integrated into a machine learning model (COL210) can identify “vulnerable” individuals from different fibrotic backgrounds, offering potential for improved prognostication and targeted interventions.
