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Observational evidence supports long-term prognostic value of LDL-cholesterol, lipoprotein(a), and high-sensitivity C-reactive protein (hsCRP), aligning with trial data on primary prevention statin benefit and residual CVD risk despite well-controlled LDL-C. Findings support universal LDL-C and Lp(a), with hsCRP for most patients, to enable earlier lifestyle guidance and more precise preventive pharmacotherapy allocation.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/bundled-measurement-of-ldl-cholesterol-lipoproteina-and-high-sensitivity-c-reactive-protein-across-the-spectrum-of-cardiovascular-risk-research-focus/451136/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/bundled-measurement-of-ldl-cholesterol-lipoproteina-and-high-sensitivity-c-reactive-protein-across-the-spectrum-of-cardiovascular-risk-research-focus/451136.png","ImageObject",300,407,{"name":92,"@type":93},"Đào","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why do the PREVENT equations require recalibration of cardiovascular risk definitions?","Question",{"text":112,"@type":113},"PREVENT focuses on improved calibration of predicted versus observed risk, aiming to address overestimation seen with the Pooled Cohort Equations and to refine prevention thresholds and treatment decisions.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What roles do LDL-cholesterol, lipoprotein(a), and hsCRP play in predicting CVD risk?",{"text":117,"@type":113},"Long-term observational data show prognostic value for all three markers, supporting their use in predicting CVD and identifying residual risk, including among individuals with well-controlled LDL-C.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the document propose using biomarkers to personalize preventive recommendations?",{"text":121,"@type":113},"After calculating risk with PREVENT, it recommends supplemental biomarker testing and subclinical atherosclerosis measures not included in PREVENT, particularly hsCRP, Lp(a), and coronary artery calcium (CAC), to personalize and recalibrate recommendations.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},451136,1790774719,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":81,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":39,"preview_page_count":4},1374402968488,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","American Journal of Preventive Cardiology 25 (2026) 101384  \nContents lists available at ScienceDirect  \nAmerican Journal of Preventive Cardiology  \njournal [homepage:](homepage: www.journals.elsevier.com/american-journal-of-preventive-cardiology)[ www.journals.elsevier.com/american-journal-of-preventive-cardiology](homepage: www.journals.elsevier.com/american-journal-of-preventive-cardiology)  \n| Bundled measurement of LDL-cholesterol, lipoprotein(a), and high sensitivity C-reactive protein across the spectrum of cardiovascular risk | |\n| --- | --- |\n| Alexander C. Razavia,* , Aaron L. Troy b, Laurence S. Sperling a, Roger S. Blumenthal b\u003Cbr>a Emory Center for Heart Disease Prevention, Emory University School of Medicine, Atlanta, GA, USA\u003Cbr>b Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, MD, USA |  |\n| A B S T R A C T |  |\n| In the era of the Predicting Risk of Cardiovascular Disease (CVD) EVENTs (PREVENT) equations, recalibrating definitions for low, borderline, intermediate, and high risk will be of primary importance. Similarly, the cardiovascular-kidney-metabolic construct calls for more robust assessment of residual risk among individuals with clinical CVD. Recent observational studies demonstrate long-term prognostic value of LDL-cholesterol, lipoprotein(a) [Lp(a)], and high-sensitivity C-reactive protein (hsCRP) for the prediction of CVD. These findings support prior randomized clinical trial data demonstrating a benefit of primary prevention statin therapy for individuals with elevated hsCRP, and that elevated Lp(a) and hsCRP are associated with residual CVD risk in those with clinical CVD and well-controlled LDL-C. Overall, such evidence supports universal measurement of LDL-C, Lp(a), and, for most patients, hsCRP across the spectrum of CVD to facilitate earlier lifestyle guidance and more precise allocation of preventive pharmacotherapies. |  |\n\nIn 2023, the American Heart Association introduced the Predicting Risk of cardiovascular disease (CVD) EVENTs (PREVENT) equations, ushering a new era in cardiovascular disease prevention [1]. Translating the PREVENT equation and its underlying inflammatory and cardiovascular-kidney-metabolic (CKM) framework into public health impact warrants an updated approach to prevention, including universal low-density lipoprotein-cholesterol (LDL-C) and lipoprotein(a) [Lp(a)] measurement and targeted assessment of inflammatory biomarkers and subclinical atherosclerosis.  \nThe PREVENT equations are informed by the CKM staging construct that incorporates the link between inflammation and CVD, as individuals progress from dysfunctional, pro-inflammatory, visceral adipose tissue to clinical cardiovascular, kidney, and metabolic diseases [2]. Compared to the 2013 Pooled Cohort Equations, PREVENT was developed and validated using larger and more contemporary cohorts to support estimation of 10-year and 30-year risk across a broader age range (30 to 79 years), prediction of heart failure (HF) in addition to myocardial infarction and stroke, and incorporation of body mass index (BMI), estimated glomerular filtration rate, and statin use to improve risk estimation [3].  \nOne of the key aims ofthe PREVENT calculator was an improvement in calibration, as the Pooled Cohort Equation calculator often overestimated risk. Given the resultant recalibration of predicted versus observed risk, the PREVENT risk equations yield a 10-year risk estimation that is approximately 30–50 % lower than the Pooled Cohort  \nEquations for men and women of similar age and risk factor burden. These changes warrant updated risk thresholds for CVD risk calculated using the PREVENT equations, specifically to prevent the undertreatment of individuals who may benefit from intensive lifestyle modification and pharmacotherapy. For example, recent evidence suggests PREVENT may underestimate risk across various geographic and healthcare settings [4]. This v","cbCaicI5YWsqarjY","https://ap.wps.com/l/cbCaicI5YWsqarjY","pdf",494005,"English","# Abstract\n# Background and rationale\n## PREVENT equations and CKM framework\n## Calibration versus Pooled Cohort Equations\n# Universal measurement strategy\n## LDL-C and lipoprotein(a)\n## Targeted inflammatory biomarkers and subclinical atherosclerosis\n# Observational evidence for prognostic value\n## Women’s Health Study (WHS)\n## EPIC Norfolk Study","[{\"question\":\"Why do the PREVENT equations require recalibration of cardiovascular risk definitions?\",\"answer\":\"PREVENT focuses on improved calibration of predicted versus observed risk, aiming to address overestimation seen with the Pooled Cohort Equations and to refine prevention thresholds and treatment decisions.\"},{\"question\":\"What roles do LDL-cholesterol, lipoprotein(a), and hsCRP play in predicting CVD risk?\",\"answer\":\"Long-term observational data show prognostic value for all three markers, supporting their use in predicting CVD and identifying residual risk, including among individuals with well-controlled LDL-C.\"},{\"question\":\"How does the document propose using biomarkers to personalize preventive recommendations?\",\"answer\":\"After calculating risk with PREVENT, it recommends supplemental biomarker testing and subclinical atherosclerosis measures not included in PREVENT, particularly hsCRP, Lp(a), and coronary artery calcium (CAC), to personalize and recalibrate recommendations.\"}]","Bundled measurement of LDL-cholesterol, lipoprotein(a), and high sensitivity C-reactive protein across the spectrum of cardiovascular risk - Research focus | PDF",1790734449]