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Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Transplant. Dec 18, 2025; 15(4): 105621
Published online Dec 18, 2025. doi: 10.5500/wjt.v15.i4.105621
YKL-40: Revolutionizing cardiac risk prediction and therapy in liver transplantation
Ileana Lulic, Ivan Durekovic, Jadranka Pavicic Saric, Iva Bacak Kocman, Zrinka Sarec, Department of Anesthesiology, Intensive Care and Pain Medicine, Clinical Hospital Merkur, Zagreb 10000, Croatia
Ileana Lulic, Dinka Lulic, Dunja Rogic, Department of Medical Biochemistry and Hematology, Faculty of Pharmacy and Biochemistry, Zagreb 10000, Croatia
Dinka Lulic, Immediate Medical Care Unit, Saint James Hospital, Sliema SLM-1030, Malta
Dunja Rogic, Department of Laboratory Diagnostics, University Hospital Centre Zagreb, Zagreb 10000, Croatia
ORCID number: Ileana Lulic (0000-0001-8828-9176); Dinka Lulic (0000-0002-8812-4731); Ivan Durekovic (0000-0001-9805-8187); Jadranka Pavicic Saric (0000-0003-4124-8056); Iva Bacak Kocman (0000-0002-6489-7537); Zrinka Sarec (0009-0005-5032-5422); Dunja Rogic (0000-0001-7097-276X).
Author contributions: Lulic I, Lulic D, and Rogic D participated in the conceptualization of this manuscript; Lulic I, Lulic D, Durekovic I, Pavicic Saric J, Bacak Kocman I, and Rogic D performed the literature review and data analysis; Lulic I, Lulic D, and Durekovic I designed the manuscript's original draft; Pavicic Saric J, Bacak Kocman I, Sarec Z, and Rogic D reviewed and edited the manuscript original draft; Lulic I, Lulic D, and Rogic D performed manuscript supervision and project administration; all of the authors approved the final version of the manuscript to be published.
Conflict-of-interest statement: There are no conflicts of interest to this manuscript.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ileana Lulic, MD, Postdoctoral Fellow, Department of Anesthesiology, Intensive Care and Pain Medicine, Clinical Hospital Merkur, Zajceva 19, Zagreb 10000, Croatia. ileanalulic@gmail.com
Received: February 2, 2025
Revised: March 25, 2025
Accepted: April 15, 2025
Published online: December 18, 2025
Processing time: 292 Days and 9.7 Hours

Abstract

Cardiovascular complications remain a leading cause of morbidity and mortality in liver transplantation (LT), exposing the limitations of current risk assessment strategies. YKL-40, a glycoprotein involved in inflammation, endothelial dysfunction, and fibrotic remodeling, has emerged as a biomarker associated with adverse cardiovascular outcomes and liver disease severity. Its inclusion in structured cardiovascular assessment frameworks may improve transplant-specific risk scores and support earlier identification of high-risk LT recipients. Mechanistically, YKL-40 contributes to vascular injury and myocardial dysfunction via nuclear factor-κB and STAT3 signaling, and is under investigation as a biomarker in preclinical studies aimed at cardiovascular risk modulation in LT. Integration of YKL-40 into predictive models, particularly those supported by artificial intelligence, may enhance individualized perioperative decision-making. This editorial outlines translational directions for integrating YKL-40 into cardiovascular risk models and therapeutic research, supporting its clinical adoption in precision-guided LT care.

Key Words: Liver transplantation; Translational research; Biomarkers; YKL-40; Cardiovascular risk stratification; Survival

Core Tip: YKL-40 is a biomarker of cardiovascular dysfunction in liver transplant (LT) recipients, reflecting underlying inflammation, endothelial activation, and fibrosis. Its integration into cardiovascular risk models may improve perioperative stratification, complement existing screening tools, and support early identification of high-risk patients. Mechanistic insights and artificial intelligence–driven analytics support its future role in targeted risk reduction, while clinical adoption will require prospective validation, actionable thresholds, and structured incorporation into multimodal assessment frameworks to improve outcomes in LT recipients.



INTRODUCTION

Liver transplantation (LT) is a life-saving intervention for patients with end-stage liver disease (ESLD). Advances in perioperative management have optimized short-term outcomes, yet long-term survival remains compromised by non-graft-related complications (Table 1)[1]. Cardiovascular disease (CVD) has emerged as the leading cause of morbidity and mortality in LT recipients, exceeding infections, renal dysfunction, and malignancies (Table 2)[2,3]. Studies indicate that CVD accounts for over 40% of early post-LT mortality, emphasizing its clinical significance[4].

Table 1 Essential perioperative strategies in liver transplantation.
Key perioperative considerations
Core objectives
Clinical strategies
Relevance to LT outcomes
Anesthetic managementOptimize hemodynamic stability while minimizing cardiovascular depressionUse of balanced anesthesia with reduced myocardial depressants, individualized ventilation strategiesReduces intraoperative cardiovascular instability and improves early graft function
Hemodynamic monitoring and supportMaintain adequate perfusion, prevent hypotension, and manage fluid shiftsContinuous invasive hemodynamic monitoring (arterial line, CVP), vasoactive drug titrationPrevents hemodynamic collapse, improves organ perfusion, and reduces renal dysfunction
Intraoperative transfusion strategiesReduce bleeding risk, optimize coagulation, and prevent transfusion-related complicationsGoal-directed transfusion strategies using viscoelastic tests (ROTEM/TEG), use of antifibrinolyticsMinimizes need for massive transfusion, reduces clotting disorders, and improves recovery
Metabolic and electrolyte managementCorrect metabolic derangements and optimize acid-base balancePerioperative glucose control, electrolyte replacement, correction of metabolic acidosis/ alkalosisPrevents metabolic crises, reduces acidosis-related cardiac dysfunction, and stabilizes electrolytes
Surgical techniquesEnhance graft implantation, reduce ischemia-reperfusion injury, and optimize surgical techniqueUse of minimally invasive techniques, normothermic regional perfusion. and/ or veno-venous bypass Optimizes graft survival, reduces ischemic complications, and improves surgical efficiency
Immunosuppressive strategiesPrevent rejection while minimizing cardiovascular and metabolic toxicityInduction therapy with IL-2 receptor antagonists, tailored CNI minimization in high-risk patientsEnhances long-term graft survival while mitigating CVD, metabolic, and renal adverse effects
Table 2 Non-graft-related causes of mortality in liver transplantation.
Cause of mortality
Condition
Pathophysiology and clinical impact
CVD and related complicationsCADAccelerated atherosclerosis, endothelial dysfunction, and pre-existing metabolic risk factors contribute to higher rates of MI, IHD, and HF post-LT
HF and cirrhotic cardiomyopathyMany LT candidates have underlying myocardial dysfunction due to cirrhosis-related hyperdynamic circulation, leading to increased HF risk after LT
Arrhythmias and sudden cardiac deathPost-LT arrhythmias, particularly AF, increase the likelihood of stroke, thromboembolic events, and sudden cardiac death, contributing to long-term mortality
Renal dysfunction and CKDCNI-induced nephrotoxicityProlonged exposure to CNIs (tacrolimus, cyclosporine) can lead to progressive renal dysfunction, increasing the risk of ESKD and cardiovascular mortality
Cardiorenal syndromeMany LT recipients develop concurrent renal and cardiac dysfunction, further complicating post-LT management
Metabolic disorders and graft function declineDM and metabolic syndromePTDM, dyslipidemia, and obesity contribute to long-term cardiovascular risk, exacerbating non-graft-related mortality
Late graft dysfunction and chronic rejectionWhile primary graft failure is a well-recognized early risk, chronic rejection and immune-mediated injury can lead to progressive hepatic dysfunction, affecting long-term survival
Infections and sepsisOpportunistic infectionsCMV, fungal infections, and multidrug-resistant bacterial infections are frequent causes of morbidity and mortality in immunosuppressed LT recipients
Sepsis and multi-organ failureInfection-related complications, particularly in the first year post-LT, remain a significant cause of non-graft-related mortality
Malignancies: De novo and recurrent cancersHCC recurrenceWhile LT is a curative treatment for selected patients with HCC, recurrence occurs in 10% to 20% of cases, significantly affecting survival
PTLDLinked to chronic immunosuppression, PTLD and other malignancies increase the risk of non-graft-related mortality
De novo cancersImmunosuppressive therapy contributes to higher rates of skin cancers, gastrointestinal malignancies, and hematologic malignancies, impacting long-term outcomes

The cardiovascular risk profile of LT recipients differs markedly from that of the general population. Metabolic risk factors such as hypertension (HTN), diabetes mellitus (DM), and hyperlipidemia (HLP) interact with cirrhosis-related changes, including cirrhotic cardiomyopathy (CCM), endothelial dysfunction, and portal HTN, to create a unique cardiovascular burden (Table 3)[5]. Existing cardiovascular risk assessment models fail to fully capture these complexities, leading to suboptimal perioperative risk stratification[6-8].

Table 3 Cardiovascular risk factors in liver transplantation: Traditional and transplant-specific considerations.
Risk factor
Description
Category
CVD risk level
HTNPrevalent in up to 70% of LT candidates, often secondary to cirrhosis-related hemodynamic changes and renal dysfunction. Increased after LT due to CNI therapyTraditionalHigh
DMStrongly associated with CAD in LT candidates. Increases post-LT MACE risk, especially in patients with NAFLD/ NASH-related cirrhosisTraditionalHigh
HLPOften masked by cirrhosis, where severe liver disease leads to low LDL and cholesterol levels. Becomes evident post-LT due to immunosuppressive drugs (CNIs, steroids, mTOR inhibitors)TraditionalModerate
Obesity and metabolic syndromeIncreasingly common due to the rising prevalence of NAFLD/NASH, which is now a leading LT indication. Contributes to insulin resistance, HTN, and CADTraditionalHigh
Smoking and CKDSmoking doubles post-LT cardiovascular risk. CKD is a strong predictor of post-LT cardiovascular events, often worsened by CNI nephrotoxicityTraditionalHigh
Cirrhotic cardiomyopathySubclinical cardiac dysfunction due to chronic cirrhosis-related myocardial remodeling. Manifests as blunted cardiac response to stress, leading to increased perioperative cardiovascular instabilityNontraditionalHigh
Portal hypertension and hyperdynamic circulationCharacterized by low SVR and high CO, which can mask underlying cardiac disease. Contributes to high-output HF and pulmonary hypertension in advanced cirrhosisNontraditionalModerate
NAFLD/ NASH-related cardiovascular riskStrongly associated with CAC and subclinical atherosclerosis. NAFLD-related CVD risk persists post-LT, even after resolution of liver diseaseNontraditionalHigh
HRS and ESLDWorsens fluid overload and increases cardiovascular complications, especially in patients requiring pre-transplant renal replacement therapyNontraditionalHigh
Inflammation and endothelial dysfunctionChronic systemic inflammation in cirrhosis promotes accelerated atherosclerosis. Circulating pro-inflammatory cytokines impair vascular function, increasing CAD riskNontraditionalModerate
QT prolongation and arrhythmiasCommon in cirrhosis due to electrolyte imbalances, autonomic dysfunction, and beta-adrenergic receptor desensitization. Increases perioperative arrhythmic risk, particularly in ALD patientsNontraditionalModerate

Biomarkers offer a promising avenue for refining risk assessment, with YKL-40 emerging as a key candidate due to its role in inflammation, fibrosis, and endothelial dysfunction[9]. Beyond prediction, YKL-40 actively contributes to disease progression, reinforcing its relevance as a biomarker[10].

We hypothesize that YKL-40 functions as a predictive biomarker for adverse cardiovascular events in LT recipients and may play a role in cardiovascular dysfunction. Integrating YKL-40 into risk prediction models may enhance perioperative stratification and inform earlier interventions. Additionally, targeting YKL-40-mediated pathways could provide novel therapeutic strategies to mitigate cardiovascular dysfunction and fibrosis, advancing precision medicine in LT.

CARDIAC DYSFUNCTION IN LT: AN OVERLOOKED CHALLENGE
Silent remodeling: Cirrhosis-induced cardiac dysfunction

Cirrhosis induces progressive cardiac remodeling, collectively known as CCM[11]. Early manifestations include diastolic dysfunction, characterized by impaired myocardial relaxation and increased ventricular stiffness. As cirrhosis advances, compensatory left ventricular hypertrophy and dilation develop, contributing to circulatory instability. The hyperdynamic circulatory state inherent to cirrhosis often masks underlying cardiac dysfunction, delaying recognition until perioperative stressors unmask overt impairment.

Surgical stress and hemodynamic instability in LT

LT imposes substantial circulatory stress. Rapid volume redistribution, vascular tone dysregulation, and metabolic shifts increase susceptibility to high-output heart failure (HF)[12]. Caval cross-clamping and reperfusion syndrome further destabilize hemodynamics, precipitating acute left ventricular failure and pulmonary edema[13,14]. Electrolyte disturbances exacerbate these risks, predisposing patients to arrhythmias and impaired myocardial contractility.

Arrhythmias in LT: The hidden electrical storm

Electrophysiological instability, particularly atrial fibrillation (AF), is a common complication in LT recipients[15]. Hemodynamic stress, neurohormonal activation, and persistent inflammation contribute to arrhythmic events. Pre-existing QT prolongation, autonomic dysfunction, and LT-related changes in myocardial conduction further increase arrhythmic risk.

A structured perioperative strategy, incorporating risk stratification, hemodynamic optimization, and rhythm surveillance, is essential to reducing cardiovascular complications and supporting sustained post-LT cardiac stability[16].

CORONARY RISK IN LT: ATHEROSCLEROSIS AND RELATED PATHWAYS
Pre-LT coronary disease: A silent threat

Coronary artery disease (CAD) is prevalent among LT candidates, with estimates ranging from 16.2% to 27%[17,18]. Risk is further amplified in individuals with non-alcoholic steatohepatitis due to associated metabolic syndrome components[19]. In addition to structural CAD, functional abnormalities such as coronary vasospasm and microvascular ischemia contribute to perioperative cardiovascular risk[20,21].

Post-LT atherosclerosis: The fast-track to cardiovascular complications

LT recipients are susceptible to accelerated atherosclerosis due to chronic endothelial injury, systemic inflammation, and immunosuppressive therapy. Progressive CAD increases the risk of late-onset myocardial infarction (MI), ischemic cardiomyopathy, and sudden cardiac death[22].

MAJOR ADVERSE CARDIOVASCULAR EVENTS AFTER LT: A PERSISTENT CHALLENGE

Major adverse cardiovascular events (MACE), including MI, HF, stroke, and cardiovascular death, represent a significant cause of late post-LT mortality. The cumulative effects of pre-LT CAD, post-LT atherosclerosis, and functional coronary abnormalities contribute to ischemic risk. Immunosuppressive therapy compounds this burden by promoting HTN, HLP, and endothelial dysfunction. Studies indicate that MACE rates in LT recipients range from 10% to 22% within five years post-LT, with late-onset CAD being a primary driver[5]. HF is particularly prevalent in recipients with pre-existing CCM, given its progression post-LT due to persistent hemodynamic stress and metabolic complications[23].

Given the multifactorial nature of coronary complications in this population, integrating biomarker-driven approaches, such as YKL-40 profiling, with advanced cardiovascular screening modalities may improve early risk identification and LT recipient long-term outcomes.

REVOLUTIONIZING CARDIOVASCULAR RISK PREDICTION IN LT

Accurate cardiovascular risk assessment in LT recipients is crucial due to the profound hemodynamic changes associated with cirrhosis. However, existing risk stratification tools have limitations. Traditional models fail to capture the full complexity of cardiovascular risk in LT candidates, often leading to misclassification of perioperative and long-term risk. A more dynamic, integrated predictive approach is needed[6]. Table 4 provides a structured comparison of predictive tools and scoring systems designed to assess cardiovascular risk in LT recipients.

Table 4 Predictive tools and scoring systems designed to assess cardiovascular risk in liver transplant recipients.
Scoring system
Description
Risk categories
Clinical factors considered
Applicability to LT patients
RCRIDeveloped for the general surgical population to predict perioperative cardiac complicationsLow risk: 0-1 factors. Moderate risk: 2 factors. High risk: ≥ 3 factorsHistory of IHD. History of CHF. History of CVD. Insulin-dependent DM. CKD (creatinine > 2 mg/dL). Undergoing high-risk surgeryLimitations: Does not account for cirrhosis-specific hemodynamic alterations, potentially underestimating risk in LT candidates
CVROL scoreTailored specifically for LT candidates to predict post-LT cardiovascular eventsLow risk: Score ≤ 2. Moderate risk: Score 3-5. High risk: Score ≥ 6Age history of CAD DM hypertension Smoking status LVH elevated serum troponin levelsStrengths: Incorporates factors prevalent in LT candidates, providing a more accurate risk assessment
Framingham risk scoreEstimates 10-year cardiovascular risk in the general populationLow risk: < 10% risk. Intermediate risk: 10%-20% risk. High risk: > 20% riskAge gender total cholesterol. HDL cholesterol. SBP treatment for hypertension. Smoking statusLimitations: May not accurately reflect the altered cardiovascular physiology in LT candidates
CAR-OLT scoreDeveloped to predict cardiovascular complications post-LT, incorporating cirrhosis-specific factorsLow risk: Score ≤ 10. Moderate risk: Score 11-15. High risk: Score > 15Age history of CAD DM Beta-blocker use. Serum creatinine. LVH. Non-sinus rhythm. Low serum albuminStrengths: Addresses cirrhosis-specific hemodynamic changes, offering improved predictive accuracy for LT recipients
CAD-LT scorePredicts the risk of significant CAD in LT candidatesLow risk: Score ≤ 2. High risk: Score ≥ 3Age DM hypertension. Smoking status. DyslipidemiaStrengths: Assists in identifying LT candidates at higher risk for CAD, guiding further cardiac evaluation
Limitations of traditional risk models in LT

Current cardiovascular risk prediction tools were not designed for the unique physiology of cirrhosis. The Revised Cardiac Risk Index and the Cardiovascular Risk in Orthotopic LT score were developed for broader surgical populations and fail to account for cirrhosis-related cardiovascular adaptations[24]. These models primarily rely on static clinical parameters, such as HTN, DM, and renal dysfunction, without incorporating cirrhosis-related hemodynamic alterations. The presence of portal HTN, CCM, and systemic inflammation significantly alters cardiovascular physiology, yet these factors are not reflected in most scoring systems. As a result, many conventional models underestimate cardiovascular risk, leading to suboptimal preoperative decision-making and missed opportunities for early intervention. A multifaceted strategy incorporating hemodynamic profiling, biochemical markers, and transplant-specific cardiac metrics is critical for improving prognostic accuracy[25,26].

Beyond risk scores: Redefining cardiovascular screening in LT

Given the limitations of traditional scoring models, direct screening for CVD is essential[27]. Comprehensive screening aims to identify CAD, HF, and arrhythmias before surgery to guide perioperative management[28].

Functional stress tests remain widely used for ischemia detection but often yield false-negative results in cirrhotic patients due to hyperdynamic circulation and altered coronary flow[29,30]. Given these limitations, anatomical imaging modalities offer superior risk stratification in LT candidates, providing greater diagnostic precision and increasingly surpassing functional tests in clinical preference. Coronary computed tomography angiography (CCTA) offers a high-resolution assessment of CAD, allowing for early detection of coronary artery calcification and subclinical atherosclerosis[31]. CCTA-derived coronary artery calcium (CAC) scoring has emerged as a robust predictor of perioperative MACE in LT recipients, with CAC scores above 100 strongly associated with increased risk[32]. Combining structural imaging with next-generation prognostic tools strengthens preoperative evaluation, ensuring a more thorough cardiac risk assessment. Table 5 summarizes the strengths and limitations of screening methods in LT candidates.

Table 5 Diagnostic tests for cardiovascular disease detection in liver transplant candidates.
Diagnostic test
Purpose
Risk stratification
Key clinical parameters
Stress echocardiographyAssesses myocardial function under stress conditions to detect ischemia and evaluate contractile reserveLow risk: Normal stress echocardiography findings. High risk: Inducible ischemia or significant wall motion abnormalitiesUtilizes exercise or pharmacologic agents to induce stress. Non-invasive functional assessment. May have limitations in cirrhotic patients due to hyperdynamic circulation leading to false-negative results
Myocardial perfusion scintigraphy (nuclear stress test)Evaluates myocardial perfusion at rest and under stress to identify areas of reduced blood flow, aiding in the detection of silent ischemiaLow risk: No perfusion defects;
High risk: Reversible perfusion defects indicating ischemia
Involves administration of radioactive tracers. Non-invasive imaging technique. Accuracy may be compromised in cirrhotic patients due to splanchnic vasodilation and hyperdynamic circulation
CCTAProvides detailed anatomical visualization of coronary arteries to detect obstructive CADLow risk: No or minimal coronary artery disease. High risk: Presence of significant coronary artery stenosisHigh-resolution imaging modality. Non-invasive anatomical assessment. Effective in detecting CAD in LT candidates
CAC scoringQuantifies the burden of coronary artery calcification to stratify cardiovascular riskLow risk: CAC score of 0. Moderate risk: CAC score 1-100. High risk: CAC score > 100Derived from CCTA or dedicated calcium scoring CT. Non-invasive quantification of calcified plaque. Higher scores associated with increased risk of perioperative MACE
Cardiac MRIProvides detailed images of cardiac structures and function, aiding in the assessment of myocardial viability, fibrosis, and overall cardiac functionLow risk: Normal cardiac MRI findings; High risk: Presence of myocardial fibrosis, reduced ejection fraction, or other significant abnormalitiesOffers high spatial resolution images without ionizing radiation. Superior tissue characterization capabilities. Useful in detecting myocardial fibrosis and assessing ventricular function. Limited availability and higher cost may restrict widespread use
Bridging risk models and screening: A path to precision in LT

A layered diagnostic framework is crucial for enhancing cardiac risk evaluation in LT. Conventional scoring methods offer generalized insights, but incorporating physiological assessments, structural imaging, and biochemical profiling refines perioperative risk estimation. Merging these complementary techniques enables earlier identification of vulnerable patients, facilitates tailored intraoperative planning, and minimizes cardiovascular events following LT.

ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING: THE FUTURE OF CARDIOVASCULAR RISK PREDICTION IN LT

Traditional cardiovascular risk models are inherently limited by static variables and linear assumptions[33,34]. In contrast, machine learning offers a dynamic, data-driven alternative capable of capturing evolving risk patterns in real time. Deep learning models such as bidirectional gated recurrent units (BiGRU) analyze sequential patient data, enabling superior prediction of MACE in LT recipients compared to traditional models[35,36]. Extreme Gradient Boosting (XGBoost) further enhances post-LT risk stratification by identifying nonlinear interactions and complex trends, significantly improving predictive accuracy[37]. Recent studies demonstrate that BiGRU outperforms conventional models in predicting 30-day post-LT MACE, with an area under the curve (AUC) of 0.841, while extreme gradient boosting achieves an AUC of 0.89 in long-term risk prediction[38,39]. Table 6 details the clinical applications of AI-driven cardiovascular risk prediction in LT.

Table 6 Clinical applications of artificial intelligence in liver transplantation.

Clinical application
Description
1Personalized preoperative risk stratificationMachine learning enables data-driven candidate selection, identifying subclinical cardiovascular risk markers that traditional scoring systems may overlook
2Optimized post-LT monitoringAI-driven models facilitate early detection of cardiovascular decompensation, allowing for proactive, patient-specific management with tailored follow-up protocols
3AI-assisted decision supportIntegrating predictive models into EHRs can generate automated alerts, guiding transplant teams on cardiology referrals, prehabilitation strategies, and medication adjustments
4Resource allocation in low-resource settingsIn regions with limited access to advanced cardiac testing, AI-based risk prediction provides a cost-effective alternative to conventional cardiac workups, ensuring efficient resource distribution without compromising patient safety

Emerging predictive tools, such as Cardiac risk in orthotopic liver transplantation and BiGRU-based algorithms, provide individualized risk assessment frameworks. Incorporating YKL-40 into these models may enhance their predictive accuracy, allowing for early intervention and targeted risk mitigation strategies.

GENERAL BIOLOGICAL FUNCTION OF YKL-40

YKL-40, also known as chitinase-3-like protein 1, functions as a biomarker and may also contribute to disease mechanisms, revealing its dual impact on pathophysiology[40]. As a biomarker, its elevated levels indicate the presence, severity, and prognosis of various conditions, offering insight into inflammatory and fibrotic mechanisms in disorders such as liver disease and cardiovascular dysfunction[9]. YKL-40 functions not only as a diagnostic biomarker but also plays a direct role in disease progression. By modulating pathways like nuclear factor-κB and STAT3 signaling, it influences tissue remodeling, immune activation, and vascular dysfunction, contributing to organ damage[41].

YKL-40 in LT: A biomarker of cardiovascular risk

YKL-40 is not limited to its general biological function, in the context of LT, it has emerged as a relevant biomarker in CVD[42]. Its role in this setting is not limited to passive risk assessment but extends to actively reflecting the dynamic interplay between systemic inflammation, endothelial dysfunction, and myocardial remodeling in this vulnerable population[43,44].

Elevated YKL-40 levels have been linked to an increased risk of HF, MACE, and atherosclerosis following LT[45-47]. Unlike conventional biomarkers that passively reflect myocardial injury, YKL-40 serves as a real-time insight into ongoing inflammatory and fibrotic activity[48]. Clinically, it holds the potential for refining risk stratification, classifying LT recipients into low-, moderate-, or high-risk groups for perioperative complications[49].

A Danish cohort study emphasizes the predictive strength of YKL-40, demonstrating that individuals with the highest circulating levels had a 1.66-fold increased risk of HF, 1.66 for Peripheral artery disease, and 2.18 for all-cause mortality[50]. These findings reinforce its potential as a cardiovascular risk marker in LT, supporting a more integrative approach to pre- and post-LT risk assessment.

N-terminal pro-brain natriuretic peptide (NT-proBNP) is currently the most validated biomarker used in cardiovascular risk assessment for LT candidates, particularly in the context of portopulmonary HTN. Recent consensus from the International LT Society endorses its use as part of structured risk assessment tools such as the Registry to Evaluate Early and Long-Term Pulmonary Arterial HTN Disease Management 2.0 risk score[51]. High-sensitivity C-reactive protein (hs-CRP), although widely used to quantify systemic inflammation, lacks specificity in ESLD and is not included in LT-specific cardiovascular algorithms. Evidence from non-transplant populations with type 2 diabetes indicates that NT-proBNP consistently demonstrates strong predictive value across multiple risk models, while hs-CRP provides limited incremental benefit due to inconsistent associations and modest risk discrimination[52]. In contrast, YKL-40, a glycoprotein associated with vascular inflammation and fibrosis, may offer superior diagnostic sensitivity over hs-CRP in CAD[53]. However, no studies have directly compared YKL-40 to NT-proBNP or hs-CRP in transplant cohorts. Furthermore, its predictive value for cardiovascular risk in LT recipients has not been validated in randomized controlled trials or large-scale observational studies, which emphasizes the need for prospective evaluation within transplant-specific risk models.

Beyond its role in arterial disease, YKL-40 has been implicated in hypercoagulability and venous thromboembolism, including deep vein thrombosis and pulmonary embolism[54]. In LT recipients, where prolonged immobilization, metabolic dysregulation, and systemic inflammation contribute to increased thrombotic risk, YKL-40 could serve as a valuable marker for early detection and preventive anticoagulation strategies. Table 7 presents emerging evidence on its diagnostic utility in cardiovascular risk stratification[46,48-50,54-56].

Table 7 YKL-40 as a biomarker in adverse cardiovascular events: Associations and clinical implications.

Adverse cardiovascular event
Cardiovascular condition overview
YKL-40 association
Ref.
1AFA common cardiac arrhythmia characterized by rapid and irregular beating of the atria, leading to inefficient blood flow and increasing the risk of stroke and HFGeneral population studies have reported that elevated YKL-40 Levels are associated with an approximately two-fold increased risk of AF. Findings from Kjaergaard et al[50] largely align with these observations. However, the lack of a significant association with AF in this study challenges previous hypotheses linking YKL-40 to thromboembolism. Notably, the prognostic value of YKL-40 for AF appears to be influenced by pre-existing CVEs, as no association was observed in individuals without prior CVEs at enrollment. This discrepancy with previous general population studies, which identified a stronger relationship between YKL-40 and AF in otherwise healthy cohorts, may be attributed to differences in study design. Specifically, earlier research did not adjust for age- and sex-related variations in YKL-40 Levels, potentially leading to overestimation of its predictive value for AFKjaergaard et al[50]. Marott et al[55]
2ISOccurs when an artery supplying blood to the brain is obstructed, typically by a thrombus or embolus, leading to brain tissue ischemia and potential infarctionGeneral population studies have shown that elevated YKL-40 Levels are associated with an approximately two-fold increased risk of IS. The study by Kjaergaard et al[50] reinforces this association, suggesting that YKL-40 may serve as a more effective prognostic biomarker for IS in individuals with lower CVE risk. This highlights the potential utility of YKL-40 in identifying subclinical vascular inflammation and endothelial dysfunction before overt CVEs developKjaergaard et al[48]. Kjaergaard et al[50]
3VTEIncludes DVT, where blood clots form in deep veins (commonly in the legs), and PE, where such clots travel to the lungs, causing potentially life-threatening complicationsGeneral population studies have reported that elevated YKL-40 Levels are associated with an approximately two-fold increased risk of VTE. Findings from Kjaergaard et al[50] partially align with these observations. However, the lack of a significant association between YKL-40 and VTE in this study challenges previous hypotheses linking YKL-40 to thromboembolic risk. This discrepancy suggests that while YKL-40 is a marker of systemic inflammation, its role in VTE pathogenesis may be less pronounced than previously thought, possibly influenced by differences in study populations or underlying risk factorsKjaergaard et al[50]. Kjaergaard et al[54]
4MIMI occurs when blood flow to a part of the heart muscle is blocked, leading to tissue damage or necrosisGeneral population studies have found no significant association between elevated YKL-40 Levels and MI. This suggests that YKL-40 may be more closely linked to thromboembolic processes rather than atherosclerotic plaque formation. Its role in CVD appears to be stronger in conditions driven by endothelial dysfunction and systemic inflammation rather than direct arterial occlusionChou et al[49]. Kjaergaard et al[50]
5HFA clinical syndrome where the heart is unable to pump sufficient blood to meet the body's needs, resulting in symptoms like shortness of breath, fatigue, and fluid retentionYKL-40 has been associated with increased mortality in HF populations. Elevated YKL-40 Levels have also been linked to a higher risk of HF, as demonstrated in a meta-analysis of population-based studies. The study by Kjaergaard et al[50] suggests that YKL-40 may serve as a more effective prognostic biomarker for HF in individuals with lower CVE risk, indicating its potential role in early disease identification and risk stratificationKjaergaard et al[50]. Henry et al[56]
6PADA circulatory condition characterized by narrowed arteries, reducing blood flow to the limbs, often leading to leg pain and mobility issuesElevated YKL-40 Levels have been observed in individuals with PAD, particularly among those with prediabetes or diabetes. However, no prospective studies have specifically evaluated YKL-40 as a risk factor for PAD development. The study by Kjaergaard et al[50] suggests that YKL-40 may serve as a more reliable prognostic biomarker for PAD in individuals with lower CVE risk, highlighting its potential role in early vascular risk assessmentWu et al[46]. Chou et al[49]. Kjaergaard et al[50]
YKL-40 in LT: A biomarker of cardiovascular pathology

Unlike conventional markers that simply reflect injury, YKL-40 plays an active role in promoting myocardial fibrosis, vascular dysfunction, and pro-thrombotic states, reinforcing its role as a biomarker with translational therapeutic relevance[57]. Elevated circulating levels correlate with progressive cardiac remodeling, endothelial activation, and heightened thrombotic risk, linking YKL-40 to long-term cardiovascular complications in LT[58].

Mechanistically, YKL-40 fuels CVD progression by modulating multiple pathological pathways[59-62]. Novel approaches, including monoclonal antibodies and RNA-based therapies, are being explored to inhibit YKL-40-driven signaling pathways, with early data suggesting potential benefits in mitigating vascular and myocardial deterioration[63].

Clinical translation of YKL-40 in cardiovascular risk prediction and disease modulation

YKL-40’s dual role as a biomarker and active disease driver presents an opportunity for both enhanced risk stratification and therapeutic intervention in LT recipients. Its integration into multimodal cardiovascular risk models could improve early detection, perioperative decision-making, and long-term post-LT outcomes. However, its clinical application remains constrained by the lack of large-scale prospective validation studies and the potential confounding effects of hepatic fibrosis, systemic inflammation, and metabolic dysregulation[64]. These limitations complicate its specificity as a cardiovascular biomarker, reinforcing the need for rigorous validation, standardized cutoff values, and prospective trials to determine its independent predictive value.

In a recent prospective study, Lv et al[65] developed and validated a blood-based biomarker panel to detect liver allograft fibrosis in pediatric LT recipients. The panel, known as Pediatric LT-Liver Fibrosis Evaluation (PT-LIFE), combined three biomarkers, YKL-40, leukocyte cell-derived chemotaxin 2, and fibulin-3, into a single diagnostic score ranging from 0 to 1. Two thresholds were introduced to guide clinical interpretation. A score below 0.12 was associated with minimal or no fibrosis and was used to rule out disease with high sensitivity and negative predictive value. A score of 0.29 or above identified patients with moderate to advanced fibrosis and supported a rule-in diagnosis with high specificity. These thresholds allowed clear classification in over 70% of cases, reducing the need for invasive liver biopsy during follow-up. While PT-LIFE was designed to assess graft fibrosis rather than cardiovascular risk, it represents one of the first validated examples of how YKL-40 can be applied in a transplant-specific clinical context. Importantly, the model was tested in independent cohorts, but its use remains limited to pediatric populations. This study highlights practical aspects that must be addressed before broader clinical use of YKL-40, including assay standardization, transplant population-specific cutoffs, and integration into structured decision-making protocols.

Beyond its utility in risk prediction, YKL-40 is increasingly recognized as a therapeutic target, shifting the focus from passive biomarker interpretation to active disease modification. Preclinical studies exploring monoclonal antibodies and RNA-based inhibitors have demonstrated potential benefits in mitigating vascular inflammation, myocardial fibrosis, and thrombotic risk, key contributors to post-LT cardiovascular morbidity. In addition, a recent murine study by Jin et al[66] showed that exogenous administration of YKL-40 attenuated liver ischemia-reperfusion injury by reducing hepatocellular damage and local inflammation, highlighting its potential cytoprotective effects in the LT setting. While these findings do not address cardiovascular endpoints, they support the emerging concept of YKL-40 pathway modulation as a potential therapeutic strategy in LT. Further investigation is warranted to evaluate its feasibility as a precision medicine tool and define its role in personalized cardiovascular management. The prospect of targeted modulation of YKL-40 pathways opens new avenues for preventive and therapeutic approaches, moving beyond risk assessment toward direct disease intervention.

Cost-effectiveness and implementation strategies

The potential clinical benefits of YKL-40 testing must be weighed against economic and logistical considerations to determine its feasibility in routine cardiovascular risk prediction. Conducting cost-benefit analyses is essential to assess whether the added diagnostic and prognostic value of YKL-40 testing justifies its financial implications[67]. These evaluations will guide resource allocation decisions and influence the scalability of YKL-40 testing across different healthcare systems, particularly in resource-limited settings. Balancing clinical utility with economic sustainability will be key to integrating YKL-40 into standardized cardiovascular screening and treatment protocols in LT recipients.

CONCLUSION

YKL-40 is emerging as a transplant-relevant biomarker associated with cardiovascular complications in LT recipients, reflecting underlying processes such as systemic inflammation, endothelial dysfunction, and fibrotic remodeling. Rather than replacing existing tools, it offers an opportunity to improve cardiovascular risk stratification through mechanistic precision. Its integration may support earlier identification of high-risk LT candidates and inform targeted perioperative strategies, aligning biomarker use with outcome-driven transplant care. To establish clinical utility, future research must focus on transplant-specific validation through prospective longitudinal studies. Defining clinically actionable thresholds, standardizing assay protocols, and evaluating integration into multimodal predictive models, especially those enhanced by artificial intelligence, represent essential steps in translating YKL-40 from investigational use to clinical decision-making.

Footnotes

Provenance and peer review: Invited article; Externally peer reviewed.

Peer-review model: Single blind

Corresponding Author's Membership in Professional Societies: European Resuscitation Council, 488606.

Specialty type: Transplantation

Country of origin: Croatia

Peer-review report’s classification

Scientific Quality: Grade A

Novelty: Grade B

Creativity or Innovation: Grade B

Scientific Significance: Grade B

P-Reviewer: Lampridis S S-Editor: Liu H L-Editor: A P-Editor: Guo X

References
1.  Collett D, Friend PJ, Watson CJ. Factors Associated With Short- and Long-term Liver Graft Survival in the United Kingdom: Development of a UK Donor Liver Index. Transplantation. 2017;101:786-792.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 75]  [Cited by in RCA: 74]  [Article Influence: 9.3]  [Reference Citation Analysis (0)]
2.  Harrington CR, Levy P, Cabrera E, Gao J, Gregory DL, Padilla C, Crespo G, VanWagner LB. Evolution of pretransplant cardiac risk factor burden and major adverse cardiovascular events in liver transplant recipients over time. Liver Transpl. 2023;29:581-590.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 5]  [Cited by in RCA: 9]  [Article Influence: 4.5]  [Reference Citation Analysis (0)]
3.  Izzy M, Fortune BE, Serper M, Bhave N, deLemos A, Gallegos-Orozco JF, Guerrero-Miranda C, Hall S, Harinstein ME, Karas MG, Kriss M, Lim N, Palardy M, Sawinski D, Schonfeld E, Seetharam A, Sharma P, Tallaj J, Dadhania DM, VanWagner LB. Management of cardiac diseases in liver transplant recipients: Comprehensive review and multidisciplinary practice-based recommendations. Am J Transplant. 2022;22:2740-2758.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 6]  [Cited by in RCA: 26]  [Article Influence: 8.7]  [Reference Citation Analysis (0)]
4.  VanWagner LB, Lapin B, Levitsky J, Wilkins JT, Abecassis MM, Skaro AI, Lloyd-Jones DM. High early cardiovascular mortality after liver transplantation. Liver Transpl. 2014;20:1306-1316.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 148]  [Cited by in RCA: 165]  [Article Influence: 15.0]  [Reference Citation Analysis (0)]
5.  Harinstein ME, Gandolfo C, Gruttadauria S, Accardo C, Crespo G, VanWagner LB, Humar A. Cardiovascular disease assessment and management in liver transplantation. Eur Heart J. 2024;45:4399-4413.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 9]  [Reference Citation Analysis (0)]
6.  Barman PM, VanWagner LB. Cardiac Risk Assessment in Liver Transplant Candidates: Current Controversies and Future Directions. Hepatology. 2021;73:2564-2576.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 16]  [Cited by in RCA: 59]  [Article Influence: 14.8]  [Reference Citation Analysis (0)]
7.  Artzner T, Michard B, Besch C, Levesque E, Faitot F. Liver transplantation for critically ill cirrhotic patients: Overview and pragmatic proposals. World J Gastroenterol. 2018;24:5203-5214.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in CrossRef: 17]  [Cited by in RCA: 16]  [Article Influence: 2.3]  [Reference Citation Analysis (0)]
8.  Blazevic N, Rogic D, Pelajic S, Miler M, Glavcic G, Ratkajec V, Vrkljan N, Bakula D, Hrabar D, Pavic T. YKL-40 as a biomarker in various inflammatory diseases: A review. Biochem Med (Zagreb). 2024;34:010502.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 18]  [Cited by in RCA: 21]  [Article Influence: 21.0]  [Reference Citation Analysis (0)]
9.  Kjaergaard AD, Johansen JS, Bojesen SE, Nordestgaard BG. Role of inflammatory marker YKL-40 in the diagnosis, prognosis and cause of cardiovascular and liver diseases. Crit Rev Clin Lab Sci. 2016;53:396-408.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 29]  [Cited by in RCA: 49]  [Article Influence: 5.4]  [Reference Citation Analysis (0)]
10.  Libreros S, Iragavarapu-Charyulu V. YKL-40/CHI3L1 drives inflammation on the road of tumor progression. J Leukoc Biol. 2015;98:931-936.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 59]  [Cited by in RCA: 78]  [Article Influence: 7.8]  [Reference Citation Analysis (0)]
11.  Rahman S, Mallett SV. Cirrhotic cardiomyopathy: Implications for the perioperative management of liver transplant patients. World J Hepatol. 2015;7:507-520.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 37]  [Cited by in RCA: 43]  [Article Influence: 4.3]  [Reference Citation Analysis (0)]
12.  Singh SA, Shrivastava P, Agarwal A, Nandakumar K, Nasa VK, Premkumar GV, Rajakumar A, Panchwagh A, Vohra V, Ranade S, Kumar L, Saraf N, Shah VR, Sudhidharan S. LTSI Consensus Guidelines: Preoperative Pulmonary Evaluation in Adult Liver Transplant Recipients. J Clin Exp Hepatol. 2023;13:523-531.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 2]  [Cited by in RCA: 3]  [Article Influence: 1.5]  [Reference Citation Analysis (0)]
13.  Chan T, DeGirolamo K, Chartier-Plante S, Buczkowski AK. Comparison of three caval reconstruction techniques in orthotopic liver transplantation: A retrospective review. Am J Surg. 2017;213:943-949.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 13]  [Cited by in RCA: 20]  [Article Influence: 2.5]  [Reference Citation Analysis (0)]
14.  Puttappa A, Gaurav R, Kakhandki V, Swift L, Fear C, Webster R, Radwan A, Mohammed M, Butler A, Klinck J, Watson C. Normothermic regional and ex situ perfusion reduces postreperfusion syndrome in donation after circulatory death liver transplantation: A retrospective comparative study. Am J Transplant. 2025;.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 3]  [Reference Citation Analysis (0)]
15.  Brankovic M, Lee P, Pyrsopoulos N, Klapholz M. Cardiac Syndromes in Liver Disease: A Clinical Conundrum. J Clin Transl Hepatol. 2023;11:975-986.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in RCA: 3]  [Reference Citation Analysis (0)]
16.  Gitto S, Fiorillo C, Argento FR, Fini E, Borghi S, Falcini M, Roccarina D, La Delfa R, Lillo L, Zurli T, Forte P, Ghinolfi D, De Simone P, Chiesi F, Ingravallo A, Vizzutti F, Aspite S, Laffi G, Lynch E, Petruccelli S, Carrai P, Palladino S, Sofi F, Stefani L, Amedei A, Baldi S, Toscano A, Lau C, Marra F, Becatti M. Oxidative stress-induced fibrinogen modifications in liver transplant recipients: unraveling a novel potential mechanism for cardiovascular risk. Res Pract Thromb Haemost. 2024;8:102555.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 1]  [Cited by in RCA: 9]  [Article Influence: 9.0]  [Reference Citation Analysis (0)]
17.  Cheng XS, VanWagner LB, Costa SP, Axelrod DA, Bangalore S, Norman SP, Herzog CA, Lentine KL; American Heart Association Council on the Kidney in Cardiovascular Disease and Council on Cardiovascular Radiology and Intervention. Emerging Evidence on Coronary Heart Disease Screening in Kidney and Liver Transplantation Candidates: A Scientific Statement From the American Heart Association: Endorsed by the American Society of Transplantation. Circulation. 2022;146:e299-e324.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 54]  [Cited by in RCA: 90]  [Article Influence: 30.0]  [Reference Citation Analysis (0)]
18.  Xiao J, Yong JN, Ng CH, Syn N, Lim WH, Tan DJH, Tan EY, Huang D, Wong RC, Chew NWS, Tan EXX, Noureddin M, Siddiqui MS, Muthiah MD. A Meta-Analysis and Systematic Review on the Global Prevalence, Risk Factors, and Outcomes of Coronary Artery Disease in Liver Transplantation Recipients. Liver Transpl. 2022;28:689-699.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 7]  [Cited by in RCA: 21]  [Article Influence: 7.0]  [Reference Citation Analysis (0)]
19.  Wong MYZ, Yap JJL, Sultana R, Cheah M, Goh GBB, Yeo KK. Association between non-alcoholic fatty liver disease and subclinical atherosclerosis in Western and Asian cohorts: an updated meta-analysis. Open Heart. 2021;8:e001850.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in RCA: 25]  [Reference Citation Analysis (0)]
20.  Bertic M, Chue CD, Virani S, Davis MK, Ignaszewski A, Sedlak T. Coronary Vasospasm Following Heart Transplantation: Rapid Progression to Aggressive Cardiac Allograft Vasculopathy. Can J Cardiol. 2018;34:1687.e9-1687.e11.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 1]  [Cited by in RCA: 4]  [Article Influence: 0.6]  [Reference Citation Analysis (0)]
21.  Billig S, Hein M, Kirchner C, Schumacher D, Habigt MA, Mechelinck M, Fuchs D, Klinge U, Theißen A, Beckers C, Bleilevens C, Kramann R, Uhlig M. Coronary Microvascular Dysfunction in Acute Cholestasis-Induced Liver Injury. Biomedicines. 2024;12:876.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 1]  [Reference Citation Analysis (0)]
22.  Opałka B, Żołnierczuk M, Grabowska M. Immunosuppressive Agents-Effects on the Cardiovascular System and Selected Metabolic Aspects: A Review. J Clin Med. 2023;12:6935.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in RCA: 19]  [Reference Citation Analysis (0)]
23.  Chuzi S, Tanaka Y, Bavishi A, Bruce M, Van Wagner LB, Wilcox JE, Ahmad FS, Ladner DP, Lagu T, Khan SS. Association Between End-Stage Liver Disease and Incident Heart Failure in an Integrated Health System. J Gen Intern Med. 2023;38:2445-2452.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 7]  [Reference Citation Analysis (0)]
24.  VanWagner LB, Ning H, Whitsett M, Levitsky J, Uttal S, Wilkins JT, Abecassis MM, Ladner DP, Skaro AI, Lloyd-Jones DM. A point-based prediction model for cardiovascular risk in orthotopic liver transplantation: The CAR-OLT score. Hepatology. 2017;66:1968-1979.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 79]  [Cited by in RCA: 97]  [Article Influence: 12.1]  [Reference Citation Analysis (0)]
25.  VanWagner LB, Harinstein ME, Runo JR, Darling C, Serper M, Hall S, Kobashigawa JA, Hammel LL. Multidisciplinary approach to cardiac and pulmonary vascular disease risk assessment in liver transplantation: An evaluation of the evidence and consensus recommendations. Am J Transplant. 2018;18:30-42.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 104]  [Cited by in RCA: 118]  [Article Influence: 16.9]  [Reference Citation Analysis (0)]
26.  Biolato M, Miele L, Avolio AW, Marrone G, Liguori A, Galati F, Petti A, Tomasello L, Pedicino D, Lombardo A, D'aiello A, Pompili M, Agnes S, Gasbarrini A, Grieco A. Diagnostic accuracy and cost-effectiveness of the CAR-OLT score in predicting cardiac risk for liver transplantation. World J Transplant. 2025;15:99208.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in RCA: 1]  [Reference Citation Analysis (0)]
27.  Moody WE, Holloway B, Arumugam P, Gill S, Wahid YS, Boivin CM, Thomson LE, Berman DS, Armstrong MJ, Ferguson J, Steeds RP. Prognostic value of coronary risk factors, exercise capacity and single photon emission computed tomography in liver transplantation candidates: A 5-year follow-up study. J Nucl Cardiol. 2021;28:2876-2891.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 5]  [Cited by in RCA: 8]  [Article Influence: 2.0]  [Reference Citation Analysis (0)]
28.  Hughes DL, Rice JD, Burton JR, Jin Y, Peterson RA, Ambardekar AV, Pomposelli JJ, Pomfret EA, Kriss MS. Presence of any degree of coronary artery disease among liver transplant candidates is associated with increased rate of post-transplant major adverse cardiac events. Clin Transplant. 2020;34:e14077.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 4]  [Cited by in RCA: 9]  [Article Influence: 1.8]  [Reference Citation Analysis (0)]
29.  Iaconi M, Maritti M, Ettorre GM, Tritapepe L. Echocardiographic evaluation in patient candidate for liver transplant: from pathophysiology to hemodynamic optimization. J Anesth Analg Crit Care. 2024;4:75.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 3]  [Reference Citation Analysis (0)]
30.  Duvall WL, Singhvi A, Tripathi N, Henzlova MJ. SPECT myocardial perfusion imaging in liver transplantation candidates. J Nucl Cardiol. 2020;27:254-265.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 14]  [Cited by in RCA: 21]  [Article Influence: 4.2]  [Reference Citation Analysis (0)]
31.  Pagano G, Sastre L, Blasi A, Brugaletta S, Mestres J, Martinez-Ocon J, Ortiz-Pérez JT, Viñals C, Prat-Gonzàlez S, Rivas E, Perea RJ, Rodriguez-Tajes S, Muxí Á, Ortega E, Doltra A, Ruiz P, Vidal B, Martínez-Palli G, Colmenero J, Crespo G. CACS, CCTA and mCAD-LT score in the pre-transplant assessment of coronary artery disease and the prediction of post-transplant cardiovascular events. Liver Int. 2024;44:1912-1923.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 10]  [Reference Citation Analysis (0)]
32.  Zorzi A, Brunetti G, Cardaioli F, D'Arcangelo F, Fabris T, Gambato M, Iliceto S, Martini A, Mattesi G, Peluso C, Polacco M, Sartori C, Lorenzoni G, Feltracco P, Angeli P, Burra P, Cillo U, Pontisso P. Coronary artery calcium on standard chest computed tomography predicts cardiovascular events after liver transplantation. Int J Cardiol. 2021;339:219-224.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 13]  [Cited by in RCA: 15]  [Article Influence: 3.8]  [Reference Citation Analysis (0)]
33.  Raval Z, Harinstein ME, Skaro AI, Erdogan A, DeWolf AM, Shah SJ, Fix OK, Kay N, Abecassis MI, Gheorghiade M, Flaherty JD. Cardiovascular risk assessment of the liver transplant candidate. J Am Coll Cardiol. 2011;58:223-231.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 172]  [Cited by in RCA: 181]  [Article Influence: 12.9]  [Reference Citation Analysis (0)]
34.  Stevens D, Lane DA, Harrison SL, Lip GYH, Kolamunnage-Dona R. Modelling of longitudinal data to predict cardiovascular disease risk: a methodological review. BMC Med Res Methodol. 2021;21:283.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in RCA: 7]  [Reference Citation Analysis (0)]
35.  Spann A, Yasodhara A, Kang J, Watt K, Wang B, Goldenberg A, Bhat M. Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review. Hepatology. 2020;71:1093-1105.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 73]  [Cited by in RCA: 113]  [Article Influence: 22.6]  [Reference Citation Analysis (0)]
36.  Nitski O, Azhie A, Qazi-Arisar FA, Wang X, Ma S, Lilly L, Watt KD, Levitsky J, Asrani SK, Lee DS, Rubin BB, Bhat M, Wang B. Long-term mortality risk stratification of liver transplant recipients: real-time application of deep learning algorithms on longitudinal data. Lancet Digit Health. 2021;3:e295-e305.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 12]  [Cited by in RCA: 66]  [Article Influence: 16.5]  [Reference Citation Analysis (0)]
37.  Ma B, Yan G, Chai B, Hou X. XGBLC: an improved survival prediction model based on XGBoost. Bioinformatics. 2022;38:410-418.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 3]  [Cited by in RCA: 20]  [Article Influence: 6.7]  [Reference Citation Analysis (0)]
38.  Abdelhameed A, Bhangu H, Feng J, Li F, Hu X, Patel P, Yang L, Tao C. Deep Learning-Based Prediction Modeling of Major Adverse Cardiovascular Events After Liver Transplantation. Mayo Clin Proc Digit Health. 2024;2:221-230.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 1]  [Cited by in RCA: 3]  [Article Influence: 3.0]  [Reference Citation Analysis (0)]
39.  Soldera J, Corso LL, Rech MM, Ballotin VR, Bigarella LG, Tomé F, Moraes N, Balbinot RS, Rodriguez S, Brandão ABM, Hochhegger B. Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model: A cohort study. World J Hepatol. 2024;16:193-210.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 4]  [Cited by in RCA: 7]  [Article Influence: 7.0]  [Reference Citation Analysis (1)]
40.  Prakash M, Bodas M, Prakash D, Nawani N, Khetmalas M, Mandal A, Eriksson C. Diverse pathological implications of YKL-40: answers may lie in 'outside-in' signaling. Cell Signal. 2013;25:1567-1573.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 60]  [Cited by in RCA: 77]  [Article Influence: 6.4]  [Reference Citation Analysis (0)]
41.  Johansen JS. Studies on serum YKL-40 as a biomarker in diseases with inflammation, tissue remodelling, fibroses and cancer. Dan Med Bull. 2006;53:172-209.  [PubMed]  [DOI]
42.  Rathcke CN, Vestergaard H. YKL-40--an emerging biomarker in cardiovascular disease and diabetes. Cardiovasc Diabetol. 2009;8:61.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 132]  [Cited by in RCA: 156]  [Article Influence: 9.8]  [Reference Citation Analysis (0)]
43.  Qu Z, Lu Y, Ran Y, Xu D, Guo Z, Cheng M. Chitinase3 likeprotein1: A potential predictor of cardiovascular disease (Review). Mol Med Rep. 2024;30:176.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in RCA: 4]  [Reference Citation Analysis (0)]
44.  Wienke J, Mertens JS, Garcia S, Lim J, Wijngaarde CA, Yeo JG, Meyer A, van den Hoogen LL, Tekstra J, Hoogendijk JE, Otten HG, Fritsch-Stork RDE, de Jager W, Seyger MMB, Thurlings RM, de Jong EMGJ, van der Kooi AJ, van der Pol WL; Dutch Juvenile Myositis Consortium, Arkachaisri T, Radstake TRDJ, van Royen-Kerkhof A, van Wijk F. Biomarker profiles of endothelial activation and dysfunction in rare systemic autoimmune diseases: implications for cardiovascular risk. Rheumatology (Oxford). 2021;60:785-801.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 7]  [Cited by in RCA: 24]  [Article Influence: 6.0]  [Reference Citation Analysis (0)]
45.  Qiu QC, Wang L, Jin SS, Liu GF, Liu J, Ma L, Mao RF, Ma YY, Zhao N, Chen M, Lin BY. CHI3L1 promotes tumor progression by activating TGF-β signaling pathway in hepatocellular carcinoma. Sci Rep. 2018;8:15029.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 41]  [Cited by in RCA: 67]  [Article Influence: 9.6]  [Reference Citation Analysis (0)]
46.  Wu S, Hsu LA, Cheng ST, Teng MS, Yeh CH, Sun YC, Huang HL, Ko YL. Circulating YKL-40 level, but not CHI3L1 gene variants, is associated with atherosclerosis-related quantitative traits and the risk of peripheral artery disease. Int J Mol Sci. 2014;15:22421-22437.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 15]  [Cited by in RCA: 27]  [Article Influence: 2.5]  [Reference Citation Analysis (0)]
47.  Song M, Zhang G, Shi H, Zhu E, Deng L, Shen H. Serum YKL-40 in coronary heart disease: linkage with inflammatory cytokines, artery stenosis, and optimal cut-off value for estimating major adverse cardiovascular events. Front Cardiovasc Med. 2023;10:1242339.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 4]  [Reference Citation Analysis (0)]
48.  Kjaergaard AD, Johansen JS, Bojesen SE, Nordestgaard BG. Elevated plasma YKL-40, lipids and lipoproteins, and ischemic vascular disease in the general population. Stroke. 2015;46:329-335.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 34]  [Cited by in RCA: 48]  [Article Influence: 4.8]  [Reference Citation Analysis (0)]
49.  Chou HH, Teng MS, Juang JJ, Chiang FT, Tzeng IS, Wu S, Ko YL. Circulating YKL-40 levels but not CHI3L1 or TRIB1 gene variants predict long-term outcomes in patients with angiographically confirmed multivessel coronary artery disease. Sci Rep. 2024;14:29416.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in RCA: 2]  [Reference Citation Analysis (0)]
50.  Kjaergaard AD, Vaag A, Jensen VH, Olsen MH, Højlund K, Vestergaard P, Hansen T, Thomsen RW, Jessen N. YKL-40, cardiovascular events, and mortality in individuals recently diagnosed with type 2 diabetes: A Danish cohort study. Diabetes Res Clin Pract. 2025;219:111970.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 1]  [Cited by in RCA: 1]  [Article Influence: 1.0]  [Reference Citation Analysis (0)]
51.  DuBrock HM, Savale L, Sitbon O, Raevens S, Kawut SM, Fallon MB, Heimbach JK, Chadha RM, Crespo G, Ramsay MA, Krowka MJ. International liver transplantation society practice guideline update on portopulmonary hypertension. Liver Transpl. 2025;.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 2]  [Cited by in RCA: 4]  [Article Influence: 4.0]  [Reference Citation Analysis (0)]
52.  Ahmad A, Lim LL, Morieri ML, Tam CH, Cheng F, Chikowore T, Dudenhöffer-Pfeifer M, Fitipaldi H, Huang C, Kanbour S, Sarkar S, Koivula RW, Motala AA, Tye SC, Yu G, Zhang Y, Provenzano M, Sherifali D, de Souza RJ, Tobias DK; ADA/EASD PMDI, Gomez MF, Ma RCW, Mathioudakis N. Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis. Commun Med (Lond). 2024;4:11.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 5]  [Cited by in RCA: 28]  [Article Influence: 28.0]  [Reference Citation Analysis (0)]
53.  Song CL, Bin-Li, Diao HY, Wang JH, Shi YF, Lu Y, Wang G, Guo ZY, Li YX, Liu JG, Wang JP, Zhang JC, Zhao Z, Liu YH, Li Y, Cai D, Li Q. Diagnostic Value of Serum YKL-40 Level for Coronary Artery Disease: A Meta-Analysis. J Clin Lab Anal. 2016;30:23-31.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 3]  [Cited by in RCA: 9]  [Article Influence: 1.0]  [Reference Citation Analysis (0)]
54.  Kjaergaard AD, Johansen JS, Bojesen SE, Nordestgaard BG. Observationally and Genetically High YKL-40 and Risk of Venous Thromboembolism in the General Population: Cohort and Mendelian Randomization Studies. Arterioscler Thromb Vasc Biol. 2016;36:1030-1036.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 11]  [Cited by in RCA: 16]  [Article Influence: 1.8]  [Reference Citation Analysis (0)]
55.  Marott SC, Benn M, Johansen JS, Jensen GB, Tybjærg-Hansen A, Nordestgaard BG. YKL-40 levels and atrial fibrillation in the general population. Int J Cardiol. 2013;167:1354-1359.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 24]  [Cited by in RCA: 25]  [Article Influence: 1.9]  [Reference Citation Analysis (0)]
56.  Henry A, Gordillo-Marañón M, Finan C, Schmidt AF, Ferreira JP, Karra R, Sundström J, Lind L, Ärnlöv J, Zannad F, Mälarstig A, Hingorani AD, Lumbers RT; HERMES and SCALLOP Consortia. Therapeutic Targets for Heart Failure Identified Using Proteomics and Mendelian Randomization. Circulation. 2022;145:1205-1217.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 48]  [Cited by in RCA: 112]  [Article Influence: 37.3]  [Reference Citation Analysis (0)]
57.  Deng Y, Li G, Chang D, Su X. YKL-40 as a novel biomarker in cardio-metabolic disorders and inflammatory diseases. Clin Chim Acta. 2020;511:40-46.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 7]  [Cited by in RCA: 23]  [Article Influence: 4.6]  [Reference Citation Analysis (0)]
58.  Batinic K, Höbaus C, Grujicic M, Steffan A, Jelic F, Lorant D, Hörtenhuber T, Hoellerl F, Brix JM, Schernthaner G, Koppensteiner R, Schernthaner GH. YKL-40 is elevated in patients with peripheral arterial disease and diabetes or pre-diabetes. Atherosclerosis. 2012;222:557-563.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 32]  [Cited by in RCA: 41]  [Article Influence: 3.2]  [Reference Citation Analysis (0)]
59.  Zhao H, Huang M, Jiang L. Potential Roles and Future Perspectives of Chitinase 3-like 1 in Macrophage Polarization and the Development of Diseases. Int J Mol Sci. 2023;24:16149.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in RCA: 18]  [Reference Citation Analysis (0)]
60.  Kim HM, Lee BW, Song YM, Kim WJ, Chang HJ, Choi DH, Yu HT, Kang E, Cha BS, Lee HC. Potential association between coronary artery disease and the inflammatory biomarker YKL-40 in asymptomatic patients with type 2 diabetes mellitus. Cardiovasc Diabetol. 2012;11:84.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 20]  [Cited by in RCA: 27]  [Article Influence: 2.1]  [Reference Citation Analysis (0)]
61.  Kong P, Cui ZY, Huang XF, Zhang DD, Guo RJ, Han M. Inflammation and atherosclerosis: signaling pathways and therapeutic intervention. Signal Transduct Target Ther. 2022;7:131.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 307]  [Cited by in RCA: 641]  [Article Influence: 213.7]  [Reference Citation Analysis (0)]
62.  Murphy JF, Lennon F, Steele C, Kelleher D, Fitzgerald D, Long AC. Engagement of CD44 modulates cyclooxygenase induction, VEGF generation, and proliferation in human vascular endothelial cells. FASEB J. 2005;19:446-448.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 54]  [Cited by in RCA: 57]  [Article Influence: 2.9]  [Reference Citation Analysis (0)]
63.  Zhang H, Zhou W, Cao C, Zhang W, Liu G, Zhang J. Amelioration of atherosclerosis in apolipoprotein E-deficient mice by combined RNA interference of lipoprotein-associated phospholipase A2 and YKL-40. PLoS One. 2018;13:e0202797.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 8]  [Cited by in RCA: 12]  [Article Influence: 1.7]  [Reference Citation Analysis (0)]
64.  Lulic I, Lulic D, Pavicic Saric J, Bacak Kocman I, Rogic D. Personalized translational medicine: Investigating YKL-40 as early biomarker for clinical risk stratification in hepatocellular carcinoma recurrence post-liver transplantation. World J Transplant. 2025;15:103036.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in RCA: 2]  [Reference Citation Analysis (0)]
65.  Lv Z, Yong JK, Liu Y, Zhou Y, Pan Y, Xiang X, Li L, Wang Y, Zhao Y, Liu Z, Zhang Z, Xia Q, Feng H. A blood-based PT-LIFE (Pediatric Liver Transplantation-LIver Fibrosis Evaluation) biomarker panel for noninvasive evaluation of pediatric liver fibrosis after liver transplantation: A prospective derivation and validation study. Am J Transplant. 2025;25:501-515.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 2]  [Reference Citation Analysis (0)]
66.  Jin G, Guo N, Liu Y, Zhang L, Chen L, Dong T, Liu W, Zhang X, Jiang Y, Lv G, Zhao F, Liu W, Hei Z, Yang Y, Ou J. 5-aminolevulinate and CHIL3/CHI3L1 treatment amid ischemia aids liver metabolism and reduces ischemia-reperfusion injury. Theranostics. 2023;13:4802-4820.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in RCA: 21]  [Reference Citation Analysis (0)]
67.  Garay OU, Ambühl LE, Bird TG, Barnes E, Irving WL, Walkley R, Rowe IA. Cost-Effectiveness of Hepatocellular Carcinoma Surveillance Strategies in Patients With Compensated Liver Cirrhosis in the United Kingdom. Value Health. 2024;27:1698-1709.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 7]  [Reference Citation Analysis (0)]