Proteomics, outcomes and precision treatment of patients with chronic limb threatening ischemia (CLTI).

About This Grant

PROJECT ABSTRACT There is a fundamental gap in predicting adverse outcomes and guiding precision-based treatment for the most severe and lethal form of peripheral artery disease (PAD), chronic limb threatening ischemia (CLTI). Patients face a dual threat: profound morbidity and mortality from major adverse limb and cardiovascular (CV) events (MALE and MACE). Yet clinical management remains reactive because we lack tools to predict these distinct outcomes. Circulating proteins are central integrators of these vascular pathologies, directly interacting with the arterial wall. Multiplex proteomics (Olink Explore) quantifies thousands of proteins linked to vascular pathology, yet its potential in CLTI remains largely untapped, leaving a critical gap in predicting outcomes and guiding treatment—especially for antithrombotic agents like rivaroxaban, which reduce events but carry bleeding risk. We will overcome this gap by leveraging harmonized clinical testing and adjudicated outcomes from two NHLBI cohorts (TIDE and PACE) plus VOYAGER-PAD—the largest trial of low-dose rivaroxaban in PAD, including 1,533 CLTI patients—to discover and validate signatures predictive of MALE, MACE, and therapeutic response to rivaroxaban—informing strategies that optimize benefit and minimize harm. To address gaps in high-molecular weight protein coverage with the Olink panel, we will integrate lipid and lipoprotein measurements from clinical assays (outside the panel). Early analyses suggest thrombosis drives limb events, whereas atherosclerosis-related inflammation underlies cardiac events. Guided by these signals, we will prioritize putatively causal proteins via Mendelian Randomization and validate top candidates with ELISA. Central hypothesis: multiplex proteomics will (1) identify proteomic signatures predicting MALE/MACE, (2) improve risk stratification beyond clinical factors, and (3) define patients most likely to benefit from low-dose rivaroxaban while minimizing bleeding risk. We will test this via three aims: derive a CLTI-specific proteomic signature of MALE (Aim 1) and MACE (Aim 2) beyond clinical measures among TIDE/PACE participants. Validation for Aims 1 and 2 will occur in the placebo arm of VOYAGER-PAD CLTI trial. Aim 3: determine whether proteomic signatures identify CLTI patients with a favorable therapeutic response to low-dose rivaroxaban (benefit >> risk) by applying causal machine learning to model heterogeneity of treatment effect and deliver a net-benefit classifier. This project applies state-of-the-art proteomic assays and machine learning to an understudied, undertreated population, will develop and validate tools for risk prediction and treatment optimization in CLTI, shifting care from reactive salvage to precision biological management, reducing preventable amputations, CV events, and deaths. Ultimately, it will establish a precision-guided, scalable pathway to lessen the national burden of CLTI.