A groundbreaking study has charted how diseases leave distinct molecular “fingerprints” in human blood proteins, offering a roadmap for blood tests that can differentiate serious conditions from more common health fluctuations.
Published today in Science, the research presents the Human Disease Blood Atlas, a comprehensive map of how thousands of proteins in the bloodstream change due to aging, cancer, cardiovascular disorders, autoimmune conditions, and other diseases.
The project highlights that each person’s blood has a unique molecular profile, which evolves through childhood and stabilizes in adulthood, creating a personalized baseline for detecting early signs of disease.
The study’s senior author, Mathias Uhlén, and lead author, María Bueno Álvez, emphasize the role of machine learning in analyzing this complex data. This approach could pave the way for blood panels that accurately flag disease without misclassifying patients in real-world settings.
“By comparing these diseases side by side, we can separate universal false alarm bells of inflammation from truly disease specific signals,” says Uhlén, a professor at Stockholm’s KTH Royal Institute of Technology and director of the Human Protein Atlas project.
He adds, “The mapping of molecular fingerprints of disease is a crucial step for building blood tests that work in the clinic.” Many proteins that rise in cancer or autoimmune conditions also increase during infections, reflecting shared inflammatory pathways, whereas organ-specific patterns, such as those related to the liver, help pinpoint disease-specific markers.
The Human Disease Blood Atlas could address the long-standing challenge of identifying reliable, reproducible biomarkers. Traditionally, new protein markers have been compared only to healthy controls- a method prone to inconsistencies. The study found several biomarkers that are consistently altered across multiple conditions, suggesting these shared molecular features could serve as universal diagnostic, prognostic, or therapeutic targets.
“Because many proteins show variability across multiple conditions, such narrow comparisons often produce results that cannot be reproduced, contributing to the wider reproducibility crisis in today’s science,” says Bueno Álvez.
Among the key findings, certain protein patterns were observed to change significantly as individuals approached a cancer diagnosis, with some proteins rising even before diagnosis.
These results underscore the potential of proteomics for early cancer detection and the need for further investigation.
The study was conducted through SciLifeLab in Stockholm with contributions from over 100 researchers worldwide, offering an unprecedented resource for understanding how diseases imprint themselves on the human proteome.
Published today in Science, the research presents the Human Disease Blood Atlas, a comprehensive map of how thousands of proteins in the bloodstream change due to aging, cancer, cardiovascular disorders, autoimmune conditions, and other diseases.
The project highlights that each person’s blood has a unique molecular profile, which evolves through childhood and stabilizes in adulthood, creating a personalized baseline for detecting early signs of disease.
The study’s senior author, Mathias Uhlén, and lead author, María Bueno Álvez, emphasize the role of machine learning in analyzing this complex data. This approach could pave the way for blood panels that accurately flag disease without misclassifying patients in real-world settings.
“By comparing these diseases side by side, we can separate universal false alarm bells of inflammation from truly disease specific signals,” says Uhlén, a professor at Stockholm’s KTH Royal Institute of Technology and director of the Human Protein Atlas project.
He adds, “The mapping of molecular fingerprints of disease is a crucial step for building blood tests that work in the clinic.” Many proteins that rise in cancer or autoimmune conditions also increase during infections, reflecting shared inflammatory pathways, whereas organ-specific patterns, such as those related to the liver, help pinpoint disease-specific markers.
The Human Disease Blood Atlas could address the long-standing challenge of identifying reliable, reproducible biomarkers. Traditionally, new protein markers have been compared only to healthy controls- a method prone to inconsistencies. The study found several biomarkers that are consistently altered across multiple conditions, suggesting these shared molecular features could serve as universal diagnostic, prognostic, or therapeutic targets.
“Because many proteins show variability across multiple conditions, such narrow comparisons often produce results that cannot be reproduced, contributing to the wider reproducibility crisis in today’s science,” says Bueno Álvez.
Among the key findings, certain protein patterns were observed to change significantly as individuals approached a cancer diagnosis, with some proteins rising even before diagnosis.
These results underscore the potential of proteomics for early cancer detection and the need for further investigation.
The study was conducted through SciLifeLab in Stockholm with contributions from over 100 researchers worldwide, offering an unprecedented resource for understanding how diseases imprint themselves on the human proteome.
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