Title : Study residual cardiovascular risk of ApoB, HDL-C LDL-C CRP and Lpa in ischemic and non ischemic heart disease
Abstract:
Introduction: An understanding of the association between apolipoprotein B (Apo-B) and CRP (C-reactive protein) in the development of residual cardiovascular risk is very important when comparing it with atherogenic lipoproteins such as HDL-cholesterol, LDL-cholesterol. Apolipoprotein Apo-B and Lpa (lipoprotein pa) were better predictors of cardiovascular desease than total cholesterol, LDL cholesterol, and non-HDL cholesterol.
Method: 24 patients with and without ischemic heart disease were studied to evaluate residual cardiovascular risk. For this, the levels of apoliprotein B, HDL-C, LDL-C, Lpa and CRP were analyzed and compared. Negative values indicate low residual cardiovascular risk index. Positive values indicate low residual cardiovascular risk index
Results: Lpa levels showed higher residual risk in patients with ischemic heart disease respect non ischemic heart disease 0,88 ± 0,44 vs -0,79 ± 0,013 (p<0.05). But no differences were found regarding ApoB 0.76 ± 0.44 vs 0.31 ± 0.41 (p> 005). LDL-Col levels showed higher residual risk in ischemic heart disease respect non ichemic heart disease 1,32 ± 0,46 vs -0,66 ± 0,13 (p < 0.05). CRP values showed higher residual risk in ischemic heart disease respect non ichemic heart disease 0,79 ± 0,48 vs -0,44 ± 0,21 (p<0.05). In ischemic heart disease, the residual risk is higher when measuring Lpa respect to LDL-C 0,88 ± 0,44 vs -0,66 ± 0,13 (p< 0.05).
Conclusion: Lipoprotein a and C-reactive protein were shown to be more efficient in the evaluation of residual risk in coronary ischemic disease.
Audience Take Away:
- It is important to understand the dynamics in the evaluation of cardiovascular risk based on the measurement of biomarkers
- Of course, the simplicity of the evaluation regarding cardiovascular risk in a predictive way and its prevention in a non-invasive way
- With these results, a database can be created for the use of artificial intelligence and its use in predictive models
