Clinical Study Protocol of the ‘Biomarkers of Severity of COVID-19 Patients’ (BIOMARCOVID) Project

Introduction

The coronavirus disease 2019 (COVID-19) pandemic has challenged health care systems worldwide, in certain areas exceeding hospital capacities and human resources. This has underscored the importance of having better tools to predict the outcome of potentially severe respiratory infections such as SARS-CoV-2. Predicting COVID-19 severity may allow physicians to better manage ICU beds and increase the chances of patient survival through appropriate management. During the toughest months of the pandemic, most physicians tried to identify patients that might develop severe forms based primarily on clinical features on admission (e.g., BMI, age). In this context, significant research has focused on identifying comorbidities, clinical manifestations, and routine blood biomarkers to predict disease severity. However, despite the demonstrated value of untargeted metabolomics in assessing severity, limited data exist on its use for identifying novel metabolite biomarkers that could improve both the sensitivity and specificity of outcome prediction. Our goal is to identify metabolite biomarkers that could enhance the predictive accuracy of standard medical biology data and clinical parameters.

Methods and analysis

This is a retrospective, observational, monocentric cohort study conducted at the Centre Hospitalier Universitaire Grenoble Alpes (CHUGA). The maximum number of eligible patients admitted for PCR-confirmed COVID-19 between March and December 2020 will be included. Severity outcome is defined using the WHO 10-category ordinal scale (mild: categories 4–5; severe: >5). Blood samples were collected within 48 hours of admission and analyzed for 62 routine blood tests and untargeted multiplatform LC-MS/MS metabolomics across four national platforms. Statistical analysis will include logistic regression with variable selection for the primary aim, and multi-block chemometric integration of clinical, biological, and metabolomics data as a secondary aim.

Ethics and dissemination

A study steering committee has been formed to ensure the accuracy of the collected data by thoroughly reviewing it prior to the data lock. All aspects of the study comply with ethical standards, including approval by the CHUGA institutional review board and adherence to CNIL Reference Methodology MR004 for the protection of participants’ rights, privacy, and confidentiality. This study is registered on the French Health Data Hub (number F20210218154851). Results will be disseminated through peer-reviewed publications, presentations at national and international scientific and clinical conferences, and reports shared with key healthcare system stakeholders.

ARTICLE SUMMARY

Strengths and limitations of this study

• Blood samples were collected within 48 hours of admission and before any severe symptom onset, aliquoted within 4 hours of collection and stored at −80°C, ensuring high pre-analytical quality.

• This study uses a multiplatform untargeted LC-MS/MS metabolomics approach across four complementary analytical platforms (CEA Saclay, INRAE Clermont, MetaToul, GEMELI), providing broad metabolome coverage.

• The integration of three heterogeneous data blocks (Metabolome, Biologicome, Clinicome) via multi-block chemometrics enables a systems-level view of COVID-19 severity.

• The sample size is limited by the complexity and cost of metabolomics analyses, and no formal sample size calculation was performed; findings should be considered exploratory and hypothesis-generating.

• As a monocentric, retrospective study conducted during a single epidemic wave (March–December 2020), generalizability may be limited by incomplete data across some routine blood tests and by differences in settings, subsequent variants, or treated populations.