{"title":"Minimally Invasive Versus Invasive Proteomics: Urine and Blood Biomarkers in Coronary Artery Disease.","authors":"Rui Vitorino","doi":"10.1002/prca.202400062","DOIUrl":null,"url":null,"abstract":"<p><p>Coronary artery disease (CAD) is a major cause of morbidity and mortality worldwide. This underlines the urgent need for effective biomarkers for early diagnosis, risk stratification, and therapeutic counseling. Proteomic signatures from plasma and urine have emerged as promising tools for these efforts, each offering unique advantages and challenges. This review provides a detailed comparison of urine and blood proteomic analyzes in the context of CAD and explores their respective advantages and limitations. Urine proteomics offers a minimally invasive, easily repeatable, and temporally stable sampling method, but faces challenges such as lower protein concentrations and potential contamination. Despite its invasive nature, blood proteomics captures high protein concentration and directly reflects systemic physiological changes, making it valuable for acute assessments. Advances in artificial intelligence (AI) have significantly improved the analysis and interpretation of proteomic data, enabling greater accuracy in diagnosis and predictive modeling. AI algorithms, particularly in pattern recognition and data integration, are helping to uncover subtle relationships between biomarkers and disease progression and supporting the discovery of plasma- and urine-based CAD biomarkers. This review demonstrates the potential of combining urine and blood proteomic data using AI to advance personalized approaches in CAD diagnosis and treatment. Future research should focus on standardization of collection protocols, validation of biomarkers in different populations, and the complexity of integrating data from different sources to maximize the potential of proteomics in the treatment of CAD.</p>","PeriodicalId":20571,"journal":{"name":"PROTEOMICS – Clinical Applications","volume":" ","pages":"e202400062"},"PeriodicalIF":2.1000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PROTEOMICS – Clinical Applications","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1002/prca.202400062","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/11/28 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Coronary artery disease (CAD) is a major cause of morbidity and mortality worldwide. This underlines the urgent need for effective biomarkers for early diagnosis, risk stratification, and therapeutic counseling. Proteomic signatures from plasma and urine have emerged as promising tools for these efforts, each offering unique advantages and challenges. This review provides a detailed comparison of urine and blood proteomic analyzes in the context of CAD and explores their respective advantages and limitations. Urine proteomics offers a minimally invasive, easily repeatable, and temporally stable sampling method, but faces challenges such as lower protein concentrations and potential contamination. Despite its invasive nature, blood proteomics captures high protein concentration and directly reflects systemic physiological changes, making it valuable for acute assessments. Advances in artificial intelligence (AI) have significantly improved the analysis and interpretation of proteomic data, enabling greater accuracy in diagnosis and predictive modeling. AI algorithms, particularly in pattern recognition and data integration, are helping to uncover subtle relationships between biomarkers and disease progression and supporting the discovery of plasma- and urine-based CAD biomarkers. This review demonstrates the potential of combining urine and blood proteomic data using AI to advance personalized approaches in CAD diagnosis and treatment. Future research should focus on standardization of collection protocols, validation of biomarkers in different populations, and the complexity of integrating data from different sources to maximize the potential of proteomics in the treatment of CAD.
期刊介绍:
PROTEOMICS - Clinical Applications has developed into a key source of information in the field of applying proteomics to the study of human disease and translation to the clinic. With 12 issues per year, the journal will publish papers in all relevant areas including:
-basic proteomic research designed to further understand the molecular mechanisms underlying dysfunction in human disease
-the results of proteomic studies dedicated to the discovery and validation of diagnostic and prognostic disease biomarkers
-the use of proteomics for the discovery of novel drug targets
-the application of proteomics in the drug development pipeline
-the use of proteomics as a component of clinical trials.