The aim of this review was to perform a critical appraisal of serum inflammatory biomarkers used for the prediction of cardiovascular disease (CVD) risk. We conducted a systematic review of studies listed on MEDLINE and Scopus from January 2000 to December 2023, focused on the prognostic value of serum inflammatory biomarkers [i.e., C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α)] in individuals without known CVD. Eligible studies used a multivariate prediction model and included discrimination or risk reclassification analysis. The Quality in Prognostic Studies (QUIPS) tool was used to evaluate study quality and potential bias. Thirty-five studies (i.e., total 208,897 participants) that evaluated the added prognostic value of CRP, IL-6, TNF-α on CVD risk prediction were retrieved. Significant improvements in CVD risk model’s predictive ability were observed in 7 out of 32 studies relating CRP and 1 out of 8 studies relating IL-6 with CVD risk. The single study found no added prognostic value of TNF-α use in CVD risk model. The integration of serum inflammatory biomarkers into CVD risk prediction models does not appear to improve risk discrimination models, suggesting that these biomarkers may act as surrogate markers, but not as predictors of atherosclerotic CVD.
VaduganathanMMensahGATurcoJVFusterVRothGA. The global burden of cardiovascular diseases and risk: a compass for future health. J Am Coll Cardiol. 2022;80:2361-2371.
2.
DunbarSBKhavjouOABakasT, et al.Projected costs of informal caregiving for cardiovascular disease: 2015 to 2035: a policy statement from the American heart association. Circulation. 2018;137:e558-e577.
3.
TownsendNKazakiewiczDLucy WrightF, et al.Epidemiology of cardiovascular disease in Europe. Nat Rev Cardiol. 2022;19:133-143.
4.
Luengo-FernandezRWalli-AttaeiMGrayA, et al.Economic burden of cardiovascular diseases in the European Union: a population-based cost study. Eur Heart J. 2023;44:4752-4767.
5.
BaysHEKulkarniAGermanC, et al.Ten things to know about ten cardiovascular disease risk factors - 2022. Am J Prev Cardiol. 2022;10:100342.
6.
Nilsson WadströmBPerssonMEngströmGNilssonPM. Aortic stiffness, inflammation, and incidence of cardiovascular events in elderly participants from the general population. Angiology. 2022;73:51-59.
7.
VasiliadisIKolovouGMikhailidisDP. Cardiotoxicity and cancer therapy. Angiology. 2014;65:369-371.
8.
LiuYGuanSXuHZhangNHuangMLiuZ. Inflammation biomarkers are associated with the incidence of cardiovascular disease: a meta-analysis. Front Cardiovasc Med. 2023;10:1175174.
9.
AlfaddaghAMartinSSLeuckerTM, et al.Inflammation and cardiovascular disease: from mechanisms to therapeutics. Am J Prev Cardiol. 2020;4:100130.
10.
WongNDBudoffMJFerdinandK, et al.Atherosclerotic cardiovascular disease risk assessment: an American society for preventive cardiology clinical practice statement. Am J Prev Cardiol. 2022;10:100335.
GrahamIM. The importance of total cardiovascular risk assessment in clinical practice. Eur J Gen Pract. 2006;12:148-155.
13.
CooneyMTDudinaAD’AgostinoRGrahamIM. Cardiovascular risk-estimation systems in primary prevention: do they differ? do they make a difference? can we see the future?Circulation. 2010;122:300-310.
14.
RosselloXDorresteijnJAJanssenA, et al.Risk prediction tools in cardiovascular disease prevention: a report from the ESC prevention of CVD programme led by the European association of preventive Cardiology (EAPC) in collaboration with the acute cardiovascular care association (ACCA) and the association of cardiovascular nursing and allied professions (ACNAP). Eur J Prev Cardiol. 2019;18:1534-1544.
15.
WilsonPWD’AgostinoRBLevyDBelangerAMSilbershatzHKannelWB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837-1847.
16.
RidkerPMBuringJERifaiNCookNR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds risk score. JAMA. 2007;297:611-619.
17.
RidkerPMPaynterNPRifaiNGazianoJMCookNR. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds risk score for men. Circulation. 2008;118:2243-2251, 4p following 51.
18.
Hippisley-CoxJCouplandCRobsonJBrindleP. Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database. BMJ. 2010;341:c6624.
19.
GoffDCJrLloyd-JonesDMBennettG, et al.2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American college of cardiology/American heart association task force on practice guidelines. Circulation. 2014;129:S49-S73.
20.
SCORE2 Working Group and ESC Cardiovascular Risk Collaboration. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur Heart J. 2021;42:2439-2454.
21.
PanagiotakosDBStavrinosV. Methodological issues in cardiovascular epidemiology: the risk of determining absolute risk through statistical models. Vasc Health Risk Manag. 2006;2:309-315.
22.
StroupDFBerlinJAMortonSC, et al.Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis of observational studies in epidemiology (MOOSE) group. JAMA. 2000;283:2008-2012.
23.
PageMJMcKenzieJEBossuytPM, et al.The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.
24.
HaydenJAvan der WindtDACartwrightJLCôtéPBombardierC. Assessing bias in studies of prognostic factors. Ann Intern Med. 2013;158:280-286.
25.
QureshiWTRanaJSYeboahJBin NasirUAl-MallahMH. Risk stratification for primary prevention of coronary artery disease: roles of C-reactive protein and coronary artery calcium. Curr Cardiol Rep. 2015;17:110.
26.
MenottiALantiMPudduPEKromhoutD. Coronary heart disease incidence in northern and southern European populations: a reanalysis of the seven countries study for a European coronary risk chart. Heart. 2000;84:238-244.
27.
PyöräläK. Assessment of coronary heart disease risk in populations with different levels of risk. Eur Heart J. 2000;21:348-350.
28.
SingCFHavilandMBTempletonARZerbaKEReillySL. Biological complexity and strategies for finding DNA variations responsible for inter-individual variation in risk of a common chronic disease, coronary artery disease. Ann Med. 1992;24:539-547.
29.
AntonopoulosASAngelopoulosAPapanikolaouP, et al.Biomarkers of vascular inflammation for cardiovascular risk prognostication: a meta-analysis. JACC Cardiovasc Imaging. 2022;15:460-471.
30.
KaptogeSDi AngelantonioEPennellsL, et al.C-reactive protein, fibrinogen, and cardiovascular disease prediction. N Engl J Med. 2012;367:1310-1320.
31.
KrintusMKozinskiMKubicaJSypniewskaG. Critical appraisal of inflammatory markers in cardiovascular risk stratification. Crit Rev Clin Lab Sci. 2014;51:263-279.
32.
Herrero-FernandezBGomez-BrisRSomovilla-CrespoBGonzalez-GranadoJM. Immunobiology of atherosclerosis: a complex net of interactions. Int J Mol Sci. 2019;20:5293.
33.
TsouprasALordanRZabetakisI. Inflammation, not cholesterol, is a cause of chronic disease. Nutrients. 2018;10:604.
34.
KongPCuiZYHuangXFZhangDDGuoRJHanM. Inflammation and atherosclerosis: signaling pathways and therapeutic intervention. Signal Transduct Targeted Ther. 2022;7:131.
35.
WongBWMeredithALinDMcManusBM. The biological role of inflammation in atherosclerosis. Can J Cardiol. 2012;28:631-641.
36.
OckeneISMatthewsCERifaiNRidkerPMReedGStanekE. Variability and classification accuracy of serial high-sensitivity C-reactive protein measurements in healthy adults. Clin Chem. 2001;47:444-450.
37.
PearsonTAMensahGAAlexanderRW, et al.Markers of inflammation and cardiovascular disease: application to clinical and public health practice: a statement for healthcare professionals from the centers for disease control and prevention and the American heart association. Circulation. 2003;107:499-511.
38.
RobertsWL. CDC/AHA workshop on markers of inflammation and cardiovascular disease: application to clinical and public health practice: laboratory tests available to assess inflammation--performance and standardization: a background paper. Circulation. 2004;110:e572-e576.
39.
GrahamIM. Are there risks in comparing risks?Eur Prev Cardiol. 2022;29:664-665.
40.
GrahamIMCooneyMT. Risks in estimating risk. Eur Heart J. 2014;35:537-539.
41.
PencinaMJD’AgostinoRBSr. Evaluating discrimination of risk prediction models: the C statistic. JAMA. 2015;314:1063-1064.
42.
GrootenWJATseliEÄngBO, et al.Elaborating on the assessment of the risk of bias in prognostic studies in pain rehabilitation using QUIPS-aspects of interrater agreement. Diagn Progn Res. 2019;3:5.
43.
DamenJAAMoonsKGMvan SmedenMHooftL. How to conduct a systematic review and meta-analysis of prognostic model studies. Clin Microbiol Infect. 2023;29:434-440.
44.
PangCLPilkingtonNWeiYPetersJRoobottomCHydeC. A methodology review on the incremental prognostic value of computed tomography biomarkers in addition to Framingham risk score in predicting cardiovascular disease: the use of association, discrimination and reclassification. BMC Cardiovasc Disord. 2018;18:39.
45.
HanleyJAMcNeilBJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983;148:839-843.
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