Assessing Type 2 Diabetes and GLP-1 agonist response trajectories with a proteogenomic atlas of disease progression
Tangirala, S.; Isaac, S.; Gehad, Y.; Roquefort, F.; Gabrieli, P.; Miller, G.; Thaker, V. V.; Tierney, B. T.; Patel, C. J.
Show abstract
Mapping patient progression from a healthy metabolic state to Type 2 Diabetes (T2D) provides opportunities for precision medicine-driven preventative interventions. We constructed the Metabolic Atlas of Progression to Diabetes (MAP-D), leveraging proteomic data on 2,923 proteins measured in a median of 47,963 UK Biobank participants to compute associations with hallmarks of metabolic disease (body mass index, HDL, LDL, triglyceride to HDL ratio [TRIG/HDL]), systolic and diastolic blood pressure), and glycated hemoglobin A1C [HbA1c]) in individuals with normoglycemia, prediabetes, and type 2 diabetes. The MAP-D contains proteomic signatures that discriminate between patient subpopulations (e.g., individuals with obesity and normoglycemia, individuals with obesity and T2D) along known (e.g., leptin [LEP], growth hormone receptor [GHR]) and potentially unexplored (e.g., B-cell differentiation antigen [CD72], ADAMTS-like protein 2 [ADAMTSL2]) axes of disease. MAP-D proteins improved prediction of BMI, HDL, LDL, TRIG-HDL ratio and HbA1c in T2D compared to demographics alone (full model R2 of up to 0.8; {Delta}R{superscript 2} up to 0.7). Further, we integrated the MAP-D with proteomic data from semaglutide (GLP-1 receptor agonist [GLP1RA]) intervention trials and found signatures of therapeutic efficacy and reversion to a healthy metabolic state. A subset of proteins were "therapeutically intransient", or were associated with metabolic disease but not affected by semaglutide. This suggests that divergent pathogenic pathways that contain proteins (e.g., EGFR) are associated with future cardiovascular, kidney, or liver complications. More importantly, these proteins are known therapeutic targets of approved drugs (e.g., nitroglycerin) indicating that combined GLP1RA therapies may yield better disease outcomes. In total, we propose the MAP-D as a resource for characterizing circulating metabolic disease pathways and improving disease management. The atlas is available as a resource at https://btierneyshiny.shinyapps.io/mapd-visualizer/ 1,2.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Integrative proteogenomic analyses provide novel interpretations of type 1 diabetes risk loci through circulating proteins 96%
- Characterizing common and rare variations in non-traditional glycemic biomarkers using multivariate approaches on multi-ancestry ARIC study 95%
- Deep serum proteomics reveal biomarkers and causal candidates for type 2 diabetes 95%
Similar papers in this journal
- Identification of plasma proteomic markers underlying polygenic risk of type 2 diabetes and related comorbidities 96%
- Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population 96%
- Novel mediation analysis of human plasma proteome and metabolome reveals mediators of improved glycemia after gastric bypass surgery 95%
Similar papers in this journal
- High-throughput Genetic Clustering of Type 2 Diabetes Loci Reveals Heterogeneous Mechanistic Pathways of Metabolic Disease 95%
- The power of TOPMed imputation for the discovery of Latino enriched rare variants associated with type 2 diabetes 95%
- Epigenome-wide association study of incident type 2 diabetes in Black and White participants from the Atherosclerosis Risk in Communities Study 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.