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Soluble interleukin 1 receptor-like 1 (ST2) is a circulating protein demonstrated to be associated with cardiovascular diseases; however, it has not been studied as a biomarker for peripheral artery disease (PAD). Using a prospectively recruited cohort of 476 patients (312 with PAD and 164 without PAD), we conducted a prognostic study of PAD using clinical/biomarker data. Plasma concentrations of three circulating proteins [ST2, cytokine-responsive gene-2 (CRG-2), vascular endothelial growth factor (VEGF)] were measured at baseline and the cohort was followed for 2 years. The outcome of interest was a 2-year major adverse limb event (MALE; composite of major amputation, vascular intervention, or acute limb ischemia). Using 10-fold cross-validation, a random forest model was trained using clinical characteristics and plasma ST2 levels. The primary model evaluation metric was the F1 score. Out of the three circulating proteins analyzed, ST2 was the only one that was statistically significantly higher in individuals with PAD compared to patients without PAD (mean concentration in plasma of 9.57 [SD 5.86] vs. 11.39 [SD 6.43] pg/mL, p < 0.001). Over a 2-year period, 28 (9%) patients with PAD experienced MALE. Our predictive model, incorporating clinical features and plasma ST2 levels, achieved an F1 score of 0.713 for forecasting 2-year MALE outcomes. Patients identified as high-risk by this model showed a significantly increased likelihood of developing MALE (HR 1.06, 95% CI 1.02–1.13, p = 0.003). By combining clinical characteristics and plasma ST2 levels, our proposed predictive model offers accurate risk assessment for 2-year MALE in PAD patients. This algorithm supports risk stratification in PAD, guiding clinical decisions regarding further vascular evaluation, specialist referrals, and appropriate medical or surgical interventions, thereby potentially enhancing patient outcomes.

Cytokine-induced neutrophil chemoattractant 1 (CINC-1), a cluster of differentiation 95 (CD95), fractalkine, and T-cell immunoglobulin and mucin domain 1 (TIM-1) are circulating proteins known to be involved in inflammation. While their roles have been studied in neurological conditions and cardiovascular diseases, their potential as peripheral artery disease (PAD) biomarkers remain unexplored. We conducted a cross-sectional diagnostic study using data from 476 recruited patients (164 without PAD and 312 with PAD). Plasma levels of CINC-1, CD95, fractalkine, and TIM-1 were measured at baseline. A PAD diagnosis was established at recruitment based on clinical exams and investigations, defined as an ankle-brachial index < 0.9 or toe-brachial index < 0.67 with absent/diminished pedal pulses. Using 10-fold cross-validation, we trained a random forest algorithm, incorporating clinical characteristics and biomarkers that showed differential expression in PAD versus non-PAD patients to predict a PAD diagnosis. Among the proteins tested, CINC-1, CD95, and fractalkine were elevated in PAD vs. non-PAD patients, forming a 3-biomarker panel. Our predictive model achieved an AUROC of 0.85 for a PAD diagnosis using clinical features and this 3-biomarker panel. By combining the clinical characteristics with these biomarkers, we developed an accurate predictive model for a PAD diagnosis. This algorithm can assist in PAD screening, risk stratification, and guiding clinical decisions regarding further vascular assessment, referrals, and medical/surgical management to potentially improve patient outcomes.

Summary: Peripheral artery disease (PAD) biomarkers have been studied in isolation; however, an algorithm that considers a protein panel to inform PAD prognosis may improve predictive accuracy. Biomarker-based prediction models were developed and evaluated using a model development (n = 270) and prospective validation cohort (n = 277). Plasma concentrations of 37 proteins were measured at baseline and the patients were followed for 2 years. The primary outcome was 2-year major adverse limb event (MALE; composite of vascular intervention or major amputation). Of the 37 proteins tested, 6 were differentially expressed in patients with vs. without PAD (ADAMTS13, ICAM-1, ANGPTL3, Alpha 1-microglobulin, GDF15, and endostatin). Using 10-fold cross-validation, we developed a random forest machine learning model that accurately predicts 2-year MALE in a prospective validation cohort of PAD patients using a 6-protein panel (AUROC 0.84). This algorithm can support PAD risk stratification, informing clinical decisions on further vascular evaluation and management.

Background and Objectives: Inflammatory proteins and their prognostic value in patients with carotid artery stenosis (CAS) have not been adequately studied. Herein, we identified CAS-specific biomarkers from a large pool of inflammatory proteins and assessed the ability of these biomarkers to predict adverse events in individuals with CAS. Materials and Methods: Samples of blood were prospectively obtained from 336 individuals (290 with CAS and 46 without CAS). Plasma concentrations of 29 inflammatory proteins were determined at recruitment, and the patients were followed for 24 months. The outcome of interest was a major adverse cardiovascular event (MACE; composite of stroke, myocardial infarction, or death). The differences in plasma protein concentrations between patients with vs. without a 2-year MACE were determined using the independent t-test or Mann–Whitney U test to identify CAS-specific prognostic biomarkers. Kaplan–Meier and Cox proportional hazards analyses with adjustment for baseline demographic and clinical characteristics were performed to assess the prognostic value of differentially expressed inflammatory proteins in predicting a 2-year MACE in patients with CAS. Results: The mean age of the cohort was 68.8 (SD 10.2) years and 39% were female. The plasma concentrations of two inflammatory proteins were significantly higher in individuals with a 2-year MACE relative to those without a 2-year MACE: IL-6 (5.07 (SD 4.66) vs. 3.36 (SD 4.04) pg/mL, p = 0.03) and CD163 (233.825 (SD 230.306) vs. 159.673 (SD 175.669) pg/mL, p = 0.033). Over a follow-up period of 2 years, individuals with elevated levels of IL-6 were more likely to develop MACE (HR 1.269 (95% CI 1.122–1.639), p = 0.042). Similarly, over a 2-year period, patients with high levels of CD163 were more likely to develop MACE (HR 1.413 (95% CI 1.022–1.954), p = 0.036). Conclusions: The plasma levels of inflammatory proteins IL-6 and CD163 are independently associated with adverse outcomes in individuals with CAS. These CAS-specific prognostic biomarkers may assist in the risk stratification of patients at an elevated risk of a MACE and subsequently guide further vascular evaluation, specialist referrals, and aggressive medical/surgical management, thereby improving outcomes for patients with CAS.