Distinguishing post-COVID from long-COVID in adults: Development and validation of a biomarker signature using targeted proteomics and machine learning in a cross-sectional observational study
Background
COVID-19 can have diverse clinical manifestations, ranging from asymptomatic infection to critical illness with multiorgan involvement. While many patients recover fully, others develop long-COVID, a heterogeneous condition marked by persistent symptoms beyond the acute phase. The immunological pathomechanisms between long-COVID and other post-acute recovery states remain unclear.
Objective
To characterize and compare clinical, pulmonary, and proteomic profiles of patients with long-COVID (LC) and those recovering from severe COVID-19 without long-COVID (post-severe-COVID, PC), and to evaluate the predictive potential of machine learning–based biomarker analysis.
Methods
In this monocentric, prospective observational study with a cross-sectional design, patients undergoing rehabilitation were included at admission. Clinical data, detailed symptom profiles, and lung function testing, including diffusing capacity of the lungs, were collected. Serum proteomics covering immune response and inflammation panels was performed, and a Random Forest classifier was applied to identify biomarkers differentiating LC and PC.
Results
LC (n = 24) patients were younger (52 years vs. 58 years in PC), predominantly female (66.7% vs. 30.0% in PC), and reported fatigue, neurocognitive symptoms, and exercise intolerance, whereas PC (n = 40) patients showed greater pulmonary impairment, as shown by reduced diffusing capacity (46% vs. 72.5% in LC p <0.001).
Proteomic profiling revealed distinct immune and inflammatory signatures between groups. Applying a random forest classification algorithm, we were able to distinguish between the LC and the PC group with a high degree of accuracy of around 89%, using LAMP3 (Lysosome-associated membrane glycoprotein 3), CKAP4 (cytoskeleton associated protein 4) and KRT19 (Keratin 19).
Conclusions
This study introduces a novel characterization of patients recovering from severe COVID-19 without long-COVID, enabling clearer differentiation between persistent and recovering trajectories.
Combining clinical data, pulmonary function, and proteomic machine learning analysis provides insight into post-acute COVID-19 biology and identifies candidate biomarkers for improved diagnosis.
Web | DOI | PMC | PDF | PLOS One | Open Access
Meyer, Franziska; Traidl, Stephan; Ameri, Milad; Dreher, Anita; Abu-Rashed-Kufs, Nevine; Vontobel, Jan; Möhrenschlager, Matthias; Duchna, Hans-Werner; Sandberg, Felicia; Brüggen, Marie-Charlotte
Background
COVID-19 can have diverse clinical manifestations, ranging from asymptomatic infection to critical illness with multiorgan involvement. While many patients recover fully, others develop long-COVID, a heterogeneous condition marked by persistent symptoms beyond the acute phase. The immunological pathomechanisms between long-COVID and other post-acute recovery states remain unclear.
Objective
To characterize and compare clinical, pulmonary, and proteomic profiles of patients with long-COVID (LC) and those recovering from severe COVID-19 without long-COVID (post-severe-COVID, PC), and to evaluate the predictive potential of machine learning–based biomarker analysis.
Methods
In this monocentric, prospective observational study with a cross-sectional design, patients undergoing rehabilitation were included at admission. Clinical data, detailed symptom profiles, and lung function testing, including diffusing capacity of the lungs, were collected. Serum proteomics covering immune response and inflammation panels was performed, and a Random Forest classifier was applied to identify biomarkers differentiating LC and PC.
Results
LC (n = 24) patients were younger (52 years vs. 58 years in PC), predominantly female (66.7% vs. 30.0% in PC), and reported fatigue, neurocognitive symptoms, and exercise intolerance, whereas PC (n = 40) patients showed greater pulmonary impairment, as shown by reduced diffusing capacity (46% vs. 72.5% in LC p <0.001).
Proteomic profiling revealed distinct immune and inflammatory signatures between groups. Applying a random forest classification algorithm, we were able to distinguish between the LC and the PC group with a high degree of accuracy of around 89%, using LAMP3 (Lysosome-associated membrane glycoprotein 3), CKAP4 (cytoskeleton associated protein 4) and KRT19 (Keratin 19).
Conclusions
This study introduces a novel characterization of patients recovering from severe COVID-19 without long-COVID, enabling clearer differentiation between persistent and recovering trajectories.
Combining clinical data, pulmonary function, and proteomic machine learning analysis provides insight into post-acute COVID-19 biology and identifies candidate biomarkers for improved diagnosis.
Web | DOI | PMC | PDF | PLOS One | Open Access