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Updated: Jun 16, 2026

Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model
Published on: November 29, 2024
Cardioprotective therapy in type 2 diabetes guided by a proteomic risk model: A randomized trial
Rosalynn Gill1, Meredith A Carpenter1, Amy Holstein1
1SomaLogic Operating Company, Standard BioTools, 2945 Wilderness Pl, Boulder, CO 80301, USA.
Background:
Cardioprotective (CP) therapies such as GLP-1 RA, SGLT2i, and PCSK9i medications remain underutilized in cardiovascular (CV) risk management. Individualized risk assessment using blood-based proteomic testing, such as Residual Cardiovascular Risk (RCVR) scores, may help guide treatment decisions.
Objective:
To evaluate whether access to RCVR scores improves risk-concordant prescribing of CP medications in clinical practice.
Methods:
In this multicenter, open label, behavioral randomized trial, adults with type 2 diabetes eligible for CP medications underwent SomaScan™ proteomic testing. Participants were randomized to an informed (RCVR results shared with patient and clinicians) or uninformed arm (results withheld until study end). CP prescribing rate and sensitivity-to-change of RCVR to CP initiation were assessed by trial arm. Chi-square trend in proportions, one-sided 2-sample tests for equality of proportions, and one-sided paired Wilcoxon signed-rank tests were used for statistical analysis.
Results:
Among 377 baseline participants analyzed (59 % informed; mean age 66 ± 10.3 years; 45 % female), CP prescribing was significantly higher in the informed versus uninformed arm (32.1 % vs 9.6 %; OR 5.45; 95 % CI: 2.31-12.89; p < 0.001); per-risk-bin rates were also significantly higher in all elevated risk bins (p<0.05). Among 173 participants with paired RCVR scores from baseline-6-months, CP initiation (n = 24) in the informed arm (n = 92) corresponded to a 3.9 % reduction in proteomic-predicted CV risk (p = 0.036).
Conclusions:
Disclosure of proteomic RCVR scores was associated with higher rates of CP prescribing and short-term reductions in proteomic risk scores among CP medication users; proteomics-based risk assessment may support personalized CV prevention.
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