Blood protein levels predict leading incident diseases and mortality in UK Biobank
Gadd, D. A.; Hillary, R. F.; Kuncheva, Z.; Mangelis, T.; Admanit, R.; Gagnon, J.; Lin, T.; Ferber, K.; Runz, H.; Biogen Biobank Team, ; Marioni, R. E.; Foley, C. N.; Sun, B. B.
Show abstract
The circulating proteome offers insights into the biological pathways that underlie disease. Here, we test relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality, over 16 years of electronic health linkage in the UK Biobank (N=47,600). We report 3,201 associations between 961 protein levels and 21 incident outcomes, identifying proteomic indicators of multiple morbidities. Next, protein-based scores (ProteinScores) are developed using penalised Cox regression. When applied to test sets, six ProteinScores improve Area Under the Curve (AUC) estimates for the 10-year onset of incident outcomes beyond age, sex and a comprehensive set of 24 lifestyle factors, clinically-relevant biomarkers and physical measures. Furthermore, the ProteinScore for type 2 diabetes outperformed a polygenic risk score, a metabolomic score and HbA1c - a clinical marker used to monitor and diagnose type 2 diabetes. These data characterise early proteomic contributions to major age-related disease and demonstrate the value of the plasma proteome for risk stratification.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Systematic discovery of gene-environment interactions underlying the human plasma proteome in UK Biobank 96%
- Identification of plasma proteomic markers underlying polygenic risk of type 2 diabetes and related comorbidities 96%
- Metabolome-wide Mendelian randomization characterizes heterogeneous and shared causal effects of metabolites on human health 95%
Similar papers in this journal
- Causal effects of maternal circulating amino acids on offspring birthweight: a Mendelian randomisation study 94%
- Multi-ancestry omic Mendelian randomization revealing putative drug targets of COVID-19 severity 93%
- 1 H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints 93%
Similar papers in this journal
- Machine learning-guided deconvolution of plasma protein levels 94%
- Multi-cohort, cross-species urinary proteomics reveals signatures of LRRK2 dysfunction in Parkinsons disease 94%
- A tissue-aware machine learning framework enhances the mechanistic understanding and genetic diagnosis of Mendelian and rare diseases 93%
Similar papers in this journal
- The Interpretable Multimodal Machine Learning (IMML) framework reveals pathological signatures of distal sensorimotor polyneuropathy 96%
- Complex patterns of multimorbidity associated with severe COVID-19 and Long COVID 95%
- Deep Proteome Profiling of Metabolic Dysfunction-Associated Steatotic Liver Disease 94%
Similar papers in this journal
- Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations 94%
- Biological aging of human body and brain systems 94%
- A Neanderthal OAS1 isoform Protects Against COVID-19 Susceptibility and Severity: Results from Mendelian Randomization and Case-Control Studies 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.