Blood proteins predict the risk of developing more than 60 diseases, study finds

Good news! Very impressive!

"... The researchers report the ability of protein 'signatures' to predict the onset of 67 diseases including multiple myeloma, non-Hodgkin lymphoma, motor neuron disease, pulmonary fibrosis, and dilated cardiomyopathy.
The protein prediction models out-performed models based on standard, clinically recorded information. Prediction based on blood cell counts, cholesterol, kidney function and diabetes tests (glycated hemoglobin) performed less well than the protein prediction models for most examples. ..."

From the abstract:

"For many diseases there are delays in diagnosis due to a lack of objective biomarkers for disease onset. Here, in 41,931 individuals from the United Kingdom Biobank Pharma Proteomics Project, we integrated measurements of ~3,000 plasma proteins with clinical information to derive sparse prediction models for the 10-year incidence of 218 common and rare diseases (81–6,038 cases). We then compared prediction models developed using proteomic data with models developed using either basic clinical information alone or clinical information combined with data from 37 clinical assays. The predictive performance of sparse models including as few as 5 to 20 proteins was superior to the performance of models developed using basic clinical information for 67 pathologically diverse diseases (median delta C-index = 0.07; range = 0.02–0.31). Sparse protein models further outperformed models developed using basic information combined with clinical assay data for 52 diseases, including multiple myeloma, non-Hodgkin lymphoma, motor neuron disease, pulmonary fibrosis and dilated cardiomyopathy. For multiple myeloma, single-cell RNA sequencing from bone marrow in newly diagnosed patients showed that four of the five predictor proteins were expressed specifically in plasma cells, consistent with the strong predictive power of these proteins. External replication of sparse protein models in the EPIC-Norfolk study showed good generalizability for prediction of the six diseases tested. These findings show that sparse plasma protein signatures, including both disease-specific proteins and protein predictors shared across several diseases, offer clinically useful prediction of common and rare diseases."

Blood proteins predict the risk of developing more than 60 diseases, study finds Research on thousands of proteins measured from a drop of blood demonstrates the ability of proteins to predict the onset of many diverse diseases.

Blood proteins predict the risk of developing more than 60 diseases (original news release) Protein ‘signatures’, which can be obtained via a blood sample, can be used to predict the onset of 67 diseases including multiple myeloma, non-Hodgkin lymphoma, motor neurone disease, pulmonary fibrosis, and dilated cardiomyopathy. Using protein information to predict the onset of diseases could help with timely diagnosis, early initiation of treatment and improved patient outcomes.

Proteomic signatures improve risk prediction for common and rare diseases (open access)

Fig. 1: Study design

Fig. 2: Improvement in predictive performance of disease incidence by addition of proteomic information on top of basic clinical risk factors for 67 diseases.