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Circulating Biomarkers for Cardiovascular Disease Risk Prediction in Patients With Cardiovascular Disease

ABSTRACT: Cardiovascular disease (CVD) is the leading cause of death globally. Risk assessment is crucial for identifying at-risk individuals who require immediate attention as well as to guide the intensity of medical therapy to reduce subsequent risk of CVD. In the past decade, many risk prediction models have been proposed to estimate the risk of developing CVD. However, in patients with a history of CVD, the current models that based on traditional risk factors provide limited power in predicting recurrent cardiovascular events. Several biomarkers from different pathophysiological pathways have been identified to predict cardiovascular events, and the incorporation of biomarkers into risk assessment may contribute to enhance risk stratification in secondary prevention. This review focuses on biomarkers related to cardiovascular and metabolic diseases, including B-type natriuretic peptide, high-sensitivity cardiac troponin I, adiponectin, adipocyte fatty acid-binding protein, heart-type fatty acid-binding protein, lipocalin-2, fibroblast growth factor 19 and 21, retinol-binding protein 4, plasminogen activator inhibitor-1, 25-hydroxyvitamin D, and proprotein convertase subtilisin/kexin type 9, and discusses the potential utility of these biomarkers in cardiovascular risk prediction among patients with CVD. Many of these biomarkers have shown promise in improving risk prediction of CVD. Further research is needed to assess the validity of biomarker and whether the strategy for incorporating biomarker into clinical practice may help to optimize decision-making and therapeutic management.

SUBMITTER: Wong Y

PROVIDER: S-EPMC8517145 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The high risk of cardiovascular disease (CVD) and premature death in patients with CKD associates with a plethora of elevated circulating biomarkers that may reflect distinct signaling pathways or simply, are epiphenomena of CKD. We compared the predictive strength of 12 biomarkers analyzed concomitantly in patients with stage 5 CKD.From 1994 to 2014, 543 patients with stage 5 CKD (median age =56 years old; 63% men; 199 patients had CVD) took part in our study on malnutrition, inflammation, and CVD in incident dialysis patients. Circulating levels of albumin, ferritin, high-sensitivity C-reactive protein (hsCRP), IGF-1, IL-6, orosomucoid, troponin T (TnT), TNF, soluble intracellular adhesion molecule, soluble vascular cellular adhesion molecule 1 (sVCAM-1), and platelet and white blood cell (WBC) counts were analyzed as predictors of the presence of clinically overt CVD at baseline, protein-energy wasting (PEW), and subsequent all-cause mortality. During follow-up for a median of 28 months, there were 149 deaths, 81 of which were caused by CVD.Most biomarkers were elevated compared with reference values and--except for albumin, ferritin, and IGF-1-higher in patients with CVD. In receiver operating characteristic analysis, age, IL-6, TnT, hsCRP, and IGF-1 were classifiers of baseline CVD and predictors of all-cause mortality. In addition to age, diabetes mellitus, smoking (for CVD), and PEW, only IL-6, relative risk (RR) 1.10 and 95% confidence interval ([95% CI], 1.02 to 1.19), sVCAM-1 RR 1.09 (95% CI, 1.01 to 1.17), and serum albumin RR 0.89 (95% CI, 0.83 to 0.95) associated with baseline CVD, and only WBC, hazard ratio (HR) 1.94 (95% CI, 1.34 to 2.82), IL-6 HR 1.79 (95% CI, 1.20 to 2.67), and TNF HR 0.65 (95% CI, 0.44 to 0.97) predicted all-cause mortality.In addition to age and comorbidities, only IL-6, sVCAM-1, and albumin could-independently of other biomarkers-classify clinical CVD, and only IL-6, WBC, and TNF could-independently of other biomarkers-predict all-cause mortality risk. These data underscore the robustness of IL-6 as a classifier of clinically overt CVD and predictor of all-cause mortality in patients with stage 5 CKD.

Project description:BackgroundDiabetic kidney disease (DKD) patients are facing an extremely high risk of cardiovascular disease (CVD), which is a major cause of death for DKD patients. We aimed to build a deep learning model to predict CVD risk among DKD patients and perform risk stratifying, which could help them perform early intervention and improve personal health management.MethodsA retrospective cohort study was conducted to assess the risk of the occurrence of composite cardiovascular disease, which includes coronary heart disease, cerebrovascular diseases, congestive heart failure, and peripheral artery disease, in DKD patients. A least absolute shrinkage and selection operator (LASSO) regression was used to perform the variable selection. A deep learning-based survival model called DeepSurv, based on a feed-forward neural network was developed to predict CVD risk among DKD patients. We compared the model performance with the conventional Cox proportional hazards (CPH) model and the Random survival forest (RSF) model using the concordance index (C-index), the area under the curve (AUC), and integrated Brier scores (IBS).ResultsWe recruited 890 patients diagnosed with DKD in this retrospective study. During a median follow-up of 10.4 months, there are 289 patients who sustained a subsequent CVD. Seven variables, including age, high density lipoprotein (HDL), hemoglobin (Hb), systolic blood pressure (SBP), smoking status, 24 h urinary protein excretion, and total cholesterol (TC), chosen by LASSO regression were used to develop the predictive model. The DeepSurv model showed the best performance, achieved a C-index of 0.767(95% confidence intervals [CI]: 0.717-0.817), AUC of 0.780(95%CI: 0.721-0.839), and IBS of 0.067 in the validation set. Then we used the cut-off value determined by ROC (receiver operating characteristic) curve to divide the patients into different risk groups. Moreover, the DeepSurv model was also applied to develop an online calculation tool for patients to conduct risk monitoring.ConclusionA deep-learning-based predictive model using seven clinical variables can effectively predict CVD risk among DKD patients and perform risk stratification. An online calculator allows its easy implementation.

Project description:BackgroundCardiovascular disease is one of the major causes of death worldwide. Assessing the risk for cardiovascular disease is an important aspect in clinical decision making and setting a therapeutic strategy, and the use of serological biomarkers may improve this. Despite an overwhelming number of studies and meta-analyses on biomarkers and cardiovascular disease, there are no comprehensive studies comparing the relevance of each biomarker. We performed a systematic review of meta-analyses on levels of serological biomarkers for atherothrombosis to compare the relevance of the most commonly studied biomarkers.Methods and findingsMedline and Embase were screened on search terms that were related to "arterial ischemic events" and "meta-analyses". The meta-analyses were sorted by patient groups without pre-existing cardiovascular disease, with cardiovascular disease and heterogeneous groups concerning general populations, groups with and without cardiovascular disease, or miscellaneous. These were subsequently sorted by end-point for cardiovascular disease or stroke and summarized in tables. We have identified 85 relevant full text articles, with 214 meta-analyses. Markers for primary cardiovascular events include, from high to low result: C-reactive protein, fibrinogen, cholesterol, apolipoprotein B, the apolipoprotein A/apolipoprotein B ratio, high density lipoprotein, and vitamin D. Markers for secondary cardiovascular events include, from high to low result: cardiac troponins I and T, C-reactive protein, serum creatinine, and cystatin C. For primary stroke, fibrinogen and serum uric acid are strong risk markers. Limitations reside in that there is no acknowledged search strategy for prognostic studies or meta-analyses.ConclusionsFor primary cardiovascular events, markers with strong predictive potential are mainly associated with lipids. For secondary cardiovascular events, markers are more associated with ischemia. Fibrinogen is a strong predictor for primary stroke.

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Circulating Biomarkers for Cardiovascular Disease Risk Prediction in Patients With Cardiovascular Disease

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