Statin signature: using proteomics to detect pharmacological fingerprints
Lowering low-density lipoprotein cholesterol with statin therapy is a primary strategy for reducing cardiovascular morbidity and mortality. Yet, adherence to and persistence with statin medication is generally low, complicating the ability to evaluate whether treatment is effective.
Using modified-aptamer proteomics technology, SomaScan® assay v4.0, we assessed ∼5,000 proteins in 8,395 EDTA plasma samples from individuals aged 29-64 at visit 1 of the Fenland study, totaling ∼42 million protein measurements. A total of 305 individuals (3.6%) reported active statin medication use at this study visit. Predicted statin use was modeled with protein measurements using machine learning methods in 70% of Fenland as a training dataset. A hold-out dataset was used to assess model performance. A predictive model using elastic net logistic regression was optimized based on AUC and robustness to assay noise and sample handling conditions to detect a signature of statin usage.
A total of 839 proteins differed significantly between the ‘active statin’ and ‘no statin’ use groups in univariate analysis (FDR <0.01). Eight of the top 50 significant proteins have known mechanistic associations with statin pharmacology, including HMGCS1, PCSK9, and APOB. A six-protein model was developed with an AUC of 0.91 (sensitivity=0.82, specificity=0.88) and 0.90 (sensitivity=0.80, specificity=0.87) on the training and hold-out datasets, respectively, to predict whether a statin signature is present or not.
We successfully developed a blood-based protein-only model that detects mechanistically-relevant protein changes to predict active statin use in adults. The protein model could compliment self-reported medication status in clinical trials and in healthcare.
Lori K. Bogren
Michael A. Hinterberg
Stephen A. Williams
SomaLogic Operating Company, Inc, Boulder CO, USA
PosterComparison of Proteomic CV Risk to Established ASCVD 10-Year Risk Decision Points
The ASCVD pooled cohort equation (PCE) is well-established for CV risk assessment. Decision points for determining treatment plans are low, intermediate and high risk over 10 years, however this approach over and underestimates risk in certain subgroups. The validated CV Risk SomaSignal® Test (SST) provides 4-year risk probability of MACE allowing for timely assessment of risk, but the shorter timescale makes comparison to 10-year PCE risk less intuitive.
PosterUsing a proteomics-based cardiovascular risk test to identify systemic changes in a clinical trial of nonalcoholic fatty liver disease
Improvement in hepaKc inflammaKon, NAFLD acKvity score and fibrosis were associated with improved proteomic CV risk scores regardless of treatment provided.
PosterUtilization of proteomic surrogates for early detection of unexpected drug benefits
Detection of benefits and adverse effects of therapies in early clinical trial phases could improve the safety, efficiency, and cost of clinical trials. Earlier identification of their benefits beyond improved diabetic control may have had the potential to save loss of patients’ lives and years of sales.