Patients suffering from rheumatologic diseases are known to have an increased risk for cardiovascular disease (CVD). Although the pathological mechanisms behind this excess risk have been increasingly better understood, there still seems to be a general lack of consensus in early detection and treatment of endothelial dysfunction and CVD risk in patients suffering from rheumatologic diseases and in particular in those who haven't yet shown symptoms of CVD. Traditional CVD prediction scores, such as Systematic Coronary Risk Evaluation (SCORE), Framingham, or PROCAM Score have been proposed as valid assessment tools of CVD risk in the general population. However, these risk calculators developed for the general population do not factor in the effect of the inflammatory burden, as well as other factors that can increase CVD risk in patients with rheumatic diseases, such as glucocorticoid therapy, abnormal lipoprotein function, endothelial dysfunction or accelerated atherosclerosis. Thus, their sole use could lead to underestimation of CVD risk in patients with rheumatic diseases. Therefore, there is a need for new biomarkers which will allow a valid and early assessment of CVD risk. In recent years, different research groups, including ours, have examined the value of different CVD risk factors such as carotid sonography, carotid-femoral pulse wave velocity, flow-mediated arterial dilation and others in the assessment of CVD risk. Moreover, various novel CVD laboratory markers have been examined in the setting of autoimmune diseases, such as Paraoxonase activity, Endocan and Osteoprotegerin. Dyslipidemia in rheumatoid arthritis (RA) is for instance better quantified by lipoproteins and apolipoproteins than by cholesterol levels; screening as well as pre-emptive carotid sonography hold promise to identify patients earlier, when prophylaxis is more likely to be effective. The early detection of subtle changes indicating CVD in asymptomatic patients has been facilitated through improved imaging methods; the inclusion of artificial intelligence (AI) shows promising results in more recent studies. Even though the pathophysiology of coronary artery disease in patients with autoimmune rheumatic diseases has been examined in multiple studies, as we continuously gain an increased understanding of this comorbidity, particularly in subclinical cases we still seem to fail in the stratification of who really is at risk—and who is not. A the time being, a multipronged and personalized approach of screening patients for traditional CVD risk factors, integrating modern imaging and further CV diagnostic tools and optimizing treatment seems to be a solid approach. There is promising research on novel biomarkers, likewise, methods using artificial intelligence in imaging provide encouraging data indicating possibilities of risk stratification that might become gold standard in the near future. The present review concentrates on showcasing the newest findings concerning CVD risk in patients with rheumatologic diseases and aims to evaluate screening methods in order to optimize CVD risk evaluation and thus avoiding underdiagnosis and undertreatment, as well as highlighting which patient groups are most at risk.

Novel Surrogate Markers of Cardiovascular Risk in the Setting of Autoimmune Rheumatic Diseases: Current Data and Implications for the Future

Cavagna L;
2022-01-01

Abstract

Patients suffering from rheumatologic diseases are known to have an increased risk for cardiovascular disease (CVD). Although the pathological mechanisms behind this excess risk have been increasingly better understood, there still seems to be a general lack of consensus in early detection and treatment of endothelial dysfunction and CVD risk in patients suffering from rheumatologic diseases and in particular in those who haven't yet shown symptoms of CVD. Traditional CVD prediction scores, such as Systematic Coronary Risk Evaluation (SCORE), Framingham, or PROCAM Score have been proposed as valid assessment tools of CVD risk in the general population. However, these risk calculators developed for the general population do not factor in the effect of the inflammatory burden, as well as other factors that can increase CVD risk in patients with rheumatic diseases, such as glucocorticoid therapy, abnormal lipoprotein function, endothelial dysfunction or accelerated atherosclerosis. Thus, their sole use could lead to underestimation of CVD risk in patients with rheumatic diseases. Therefore, there is a need for new biomarkers which will allow a valid and early assessment of CVD risk. In recent years, different research groups, including ours, have examined the value of different CVD risk factors such as carotid sonography, carotid-femoral pulse wave velocity, flow-mediated arterial dilation and others in the assessment of CVD risk. Moreover, various novel CVD laboratory markers have been examined in the setting of autoimmune diseases, such as Paraoxonase activity, Endocan and Osteoprotegerin. Dyslipidemia in rheumatoid arthritis (RA) is for instance better quantified by lipoproteins and apolipoproteins than by cholesterol levels; screening as well as pre-emptive carotid sonography hold promise to identify patients earlier, when prophylaxis is more likely to be effective. The early detection of subtle changes indicating CVD in asymptomatic patients has been facilitated through improved imaging methods; the inclusion of artificial intelligence (AI) shows promising results in more recent studies. Even though the pathophysiology of coronary artery disease in patients with autoimmune rheumatic diseases has been examined in multiple studies, as we continuously gain an increased understanding of this comorbidity, particularly in subclinical cases we still seem to fail in the stratification of who really is at risk—and who is not. A the time being, a multipronged and personalized approach of screening patients for traditional CVD risk factors, integrating modern imaging and further CV diagnostic tools and optimizing treatment seems to be a solid approach. There is promising research on novel biomarkers, likewise, methods using artificial intelligence in imaging provide encouraging data indicating possibilities of risk stratification that might become gold standard in the near future. The present review concentrates on showcasing the newest findings concerning CVD risk in patients with rheumatologic diseases and aims to evaluate screening methods in order to optimize CVD risk evaluation and thus avoiding underdiagnosis and undertreatment, as well as highlighting which patient groups are most at risk.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1461147
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