The COV50 classifier predicts frailty and future death, independent from SARS-CoV-2 infection

BackgroundThere is evidence of pre-established vulnerability in individuals that increases the risk of their progression to severe disease or death, though the mechanisms that cause this are still not fully understood. Previous research has demonstrated that a urinary peptide classifier (COV50) predicts disease progression and death from SARS-CoV-2 at an early stage, indicating that the outcome prediction may be partly due to already present vulnerabilities. The aim of this study is to examine the ability of COV50 to predict future non-COVID-19-related mortality, and evaluate whether the pre-established vulnerability can be generic and explained on a molecular level by urinary peptides. MethodsUrinary proteomic data from 9193 patients (1719 patients sampled at intensive care unit (ICU) admission and 7474 patients with other diseases (non-ICU)) were extracted from the Human Urinary Proteome Database. The previously developed COV50 classifier, a urinary proteomics biomarker panel consisting of 50 peptides, was applied to all datasets. The association of COV50 scoring with mortality was evaluated. ResultsIn the ICU group, an increase in the COV50 score of one unit resulted in a 20% higher relative risk of death (adj. HR 1{middle dot}2 [95% CI 1{middle dot}17-1{middle dot}24]). The same increase in COV50 in non-ICU patients resulted in a higher relative risk of 61% (adj. HR 1{middle dot}61 [95% CI 1{middle dot}47-1{middle dot}76]), in line with adjusted meta-analytic HR estimate of 1{middle dot}55. The most notable and significant changes associated with future fatal events were reductions of specific collagen fragments, most of collagen alpha I(I). ConclusionThe COV50 classifier is predictive of death in the absence of SARS-CoV-2 infection, suggesting that it detects pre-existing vulnerability. Prediction is based mainly on collagen fragments, possibly reflecting disturbances in the integrity of the extracellular matrix. These data may serve as basis for proteomics guided intervention aiming towards manipulating/improving collagen turnover, thereby reducing the risk of death.