A study using data from the UK Biobank Pharma Proteomics Project (UKB-PPP) demonstrates that sparse plasma protein signatures can significantly improve the prediction of 218 common and rare diseases compared to traditional clinical models. By integrating measurements of approximately 3,000 plasma proteins with clinical data, researchers developed prediction models for 218 diseases, showing that models incorporating as few as 5-20 proteins outperformed models based on basic clinical information for 67 diseases. These models also performed better than models combining clinical data with 37 clinical assays for 52 diseases, including multiple myeloma, non-Hodgkin lymphoma, motor neuron disease, pulmonary fibrosis, and dilated cardiomyopathy. For multiple myeloma, four of the five predictor proteins were expressed specifically in plasma cells, supporting their predictive power. External validation in the EPIC-Norfolk study confirmed the generalizability of these models for six diseases. The study highlights the potential of plasma proteomic signatures to provide clinically useful predictions for both common and rare diseases, offering insights into underlying biological mechanisms and improving early diagnosis and treatment. The findings suggest that sparse protein signatures, including disease-specific and shared proteins, can enhance risk prediction and inform therapeutic trials. The study also emphasizes the importance of further research to explore the application of proteomic signatures in rare diseases and to develop practical tools for clinical use.A study using data from the UK Biobank Pharma Proteomics Project (UKB-PPP) demonstrates that sparse plasma protein signatures can significantly improve the prediction of 218 common and rare diseases compared to traditional clinical models. By integrating measurements of approximately 3,000 plasma proteins with clinical data, researchers developed prediction models for 218 diseases, showing that models incorporating as few as 5-20 proteins outperformed models based on basic clinical information for 67 diseases. These models also performed better than models combining clinical data with 37 clinical assays for 52 diseases, including multiple myeloma, non-Hodgkin lymphoma, motor neuron disease, pulmonary fibrosis, and dilated cardiomyopathy. For multiple myeloma, four of the five predictor proteins were expressed specifically in plasma cells, supporting their predictive power. External validation in the EPIC-Norfolk study confirmed the generalizability of these models for six diseases. The study highlights the potential of plasma proteomic signatures to provide clinically useful predictions for both common and rare diseases, offering insights into underlying biological mechanisms and improving early diagnosis and treatment. The findings suggest that sparse protein signatures, including disease-specific and shared proteins, can enhance risk prediction and inform therapeutic trials. The study also emphasizes the importance of further research to explore the application of proteomic signatures in rare diseases and to develop practical tools for clinical use.