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Record W4405366119 · doi:10.3390/biomedicines12122842

Prediction of Major Adverse Cardiovascular Events in Patients with Peripheral Artery Disease Using Circulating Immunomodulatory Proteins

2024· article· en· W4405366119 on OpenAlexaff
Ben Li, Farah Shaikh, Houssam Younes, Batool Abuhalimeh, Jason Chin, Khurram Rasheed, Abdelrahman Zamzam, Rawand Abdin, Mohammad Qadura

Bibliographic record

VenueBiomedicines · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsMcMaster UniversityUniversity of TorontoSt. Michael's Hospital
FundersBlair Foundation
KeywordsMaceMedicineMyocardial infarctionCohortInternal medicineCoronary artery diseaseCardiologyBiomarkerProportional hazards modelStroke (engine)Prospective cohort studyCohort studyPeripheralDiseasePercutaneous coronary interventionBiology

Abstract

fetched live from OpenAlex

Background/Objectives: The leading cause of death for people with peripheral artery disease (PAD) is major adverse cardiovascular events (MACE), including heart attacks and strokes. However, research into biomarkers that could help predict MACE in patients with PAD has been limited. Immunomodulatory proteins are known to significantly influence systemic atherosclerosis, suggesting they could be useful prognostic indicators for MACE in patients with PAD. In this study, we evaluated a broad panel of immunomodulatory proteins to identify those linked to MACE in individuals with PAD. Methods: We conducted a prognostic study involving a prospectively recruited cohort of 406 patients consisting of 254 with PAD and 152 without PAD. At the baseline, we measured the plasma concentrations of 17 circulating immunomodulatory proteins and followed the cohort for two years. The primary outcome was 2-year MACE, a composite of myocardial infarction, stroke, or death. Plasma protein concentrations were compared between patients with PAD with and without 2-year MACE using Mann–Whitney U tests. We further examined the prognostic potential of differentially expressed proteins through a Cox proportional hazards analysis, determining their independent associations with 2-year MACE while controlling for all the baseline demographic and clinical characteristics, including the existing coronary artery and cerebrovascular diseases. Additionally, A Kaplan–Meier analysis was performed to evaluate the 2-year freedom from MACE in patients with low versus high levels of the differentially expressed proteins based on the median plasma concentrations. Results: The mean age of the cohort was 68.8 years (SD 11.1), with 134 patients (33%) being female. During the two-year follow-up, 63 individuals (16%) developed MACE. The following proteins were significantly elevated in patients with PAD who experienced 2-year MACE compared to those who did not: galectin-1 (0.17 [SD 0.06] vs. 0.10 [SD 0.07] pg/mL, p = 0.012), alpha-1-microglobulin (16.68 [SD 7.48] vs. 14.74 [SD 6.71] pg/mL, p = 0.019), and galectin-9 (0.14 [SD 0.09] vs. 0.09 [SD 0.05] pg/mL, p = 0.033). The Cox proportional hazards analysis indicated that these three proteins were independently associated with 2-year MACE after adjusting for all the baseline demographic and clinical factors: galectin-1 (HR 1.45 [95% CI 1.09–1.92], p = 0.019), alpha-1-microglobulin (HR 1.31 [95% CI 1.06–1.63], p = 0.013), and galectin-9 (HR 1.35 [95% CI 1.02–1.78], p = 0.028). Over the two-year follow-up, patients with higher levels of galectin-1, galectin-9, and alpha-1-microglobulin had a lower freedom from MACE. Additional analysis showed that these three proteins were not significantly associated with 2-year MACE in patients without PAD. Conclusions: Among the 17 immunomodulatory proteins evaluated, galectin-1, galectin-9, and alpha-1-microglobulin were found to be independently and specifically associated with 2-year MACE in patients with PAD. Assessing the plasma concentrations of these proteins can aid in risk stratification for MACE in patients with PAD, helping to inform clinical decisions regarding multidisciplinary referrals to cardiologists, neurologists, and vascular medicine specialists. This information can also guide the aggressiveness of medical management, ultimately improving cardiovascular outcomes for patients with PAD.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.206
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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