This study demonstrates the potential of sparse plasma protein signatures to improve the prediction of disease onset for both common and rare diseases. By integrating measurements of approximately 3,000 plasma proteins with clinical information from the UK Biobank, researchers developed prediction models for 218 diseases. The models, which included as few as 5 to 20 proteins, outperformed models based on basic clinical information for 67 pathologically diverse diseases, with a median delta C-index of 0.07 (range = 0.02–0.31). These sparse protein models also outperformed models combining basic clinical information with clinical assay data for 52 diseases, including multiple myeloma, non-Hodgkin lymphoma, motor neuron disease, pulmonary fibrosis, and dilated cardiomyopathy. The study found that sparse protein signatures, including disease-specific proteins and shared protein predictors, offer clinically useful prediction of common and rare diseases. Additionally, the study identified specific proteins that are strongly predictive for only one disease, such as TNFRSF17 and TNFRSF13B, which are strong predictors of multiple myeloma and monoclonal gammopathy of undetermined significance, respectively. The findings highlight the potential of sparse plasma protein signatures to improve disease prediction and inform therapeutic clinical trials.This study demonstrates the potential of sparse plasma protein signatures to improve the prediction of disease onset for both common and rare diseases. By integrating measurements of approximately 3,000 plasma proteins with clinical information from the UK Biobank, researchers developed prediction models for 218 diseases. The models, which included as few as 5 to 20 proteins, outperformed models based on basic clinical information for 67 pathologically diverse diseases, with a median delta C-index of 0.07 (range = 0.02–0.31). These sparse protein models also outperformed models combining basic clinical information with clinical assay data for 52 diseases, including multiple myeloma, non-Hodgkin lymphoma, motor neuron disease, pulmonary fibrosis, and dilated cardiomyopathy. The study found that sparse protein signatures, including disease-specific proteins and shared protein predictors, offer clinically useful prediction of common and rare diseases. Additionally, the study identified specific proteins that are strongly predictive for only one disease, such as TNFRSF17 and TNFRSF13B, which are strong predictors of multiple myeloma and monoclonal gammopathy of undetermined significance, respectively. The findings highlight the potential of sparse plasma protein signatures to improve disease prediction and inform therapeutic clinical trials.