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Different plasma biomarker patterns associated with coronary atherosclerosis in low- versus high-risk individuals -
Sofia Cederström1, Yunzhang Wang1, Pia Lundman1
1Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.
Insights
Plasma biomarkers for coronary artery disease (CAD) differ between low and high cardiovascular disease (CVD) risk groups. Identifying these distinct patterns, including branched-chain amino acids and high-density lipoprotein (HDL) metabolites, aids in understanding CAD risk factors for primary prevention.
Area of Science:
- Biochemistry
- Cardiovascular Medicine
- Biomarker Discovery
Background:
- Coronary artery disease (CAD) and cardiovascular disease (CVD) events are linked to systemic blood biomarkers.
- Targeted omics techniques offer potential for identifying these biomarkers.
- Understanding these associations is crucial for primary prevention strategies.
Purpose of the Study:
- To investigate associations between plasma protein and metabolite biomarkers and subclinical CAD.
- To compare these associations in low-risk versus high-risk CVD populations.
- To identify distinct biomarker patterns related to subclinical atherosclerosis.
Main Methods:
- Utilized the Swedish CArdioPulmonary bioImage Study (SCAPIS) cohort.
- Stratified participants into low (<5% SCORE2) and high (>7.5% SCORE2) CVD risk groups.
- Employed random forest and ordinal logistic regression to analyze 409 proteins and metabolites against coronary artery calcium score (CACS) and segments with coronary atherosclerosis (SIS).
Main Results:
- In low-risk individuals, branched-chain amino acids correlated with CACS and SIS; high-density lipoprotein (HDL) metabolites (excluding phospholipids) were linked to lower CACS and SIS.
- In high-risk individuals, inflammatory proteins showed negative associations with coronary atherosclerosis; HDL metabolites lacked a protective effect.
- Renin and MMP12 were associated with subclinical CAD in both risk groups.
Conclusions:
- Distinct plasma biomarker patterns are associated with subclinical CAD in low- and high-risk populations.
- These findings can enhance the understanding of protective and risk factors for CAD in primary prevention.
- Biomarker profiles differ significantly based on cardiovascular risk stratification.
Background And Aims:
Targeted omics techniques can be used to identify systemic blood biomarkers associated with coronary artery disease (CAD) and cardiovascular disease (CVD) events. Our aim was to study associations between plasma protein, metabolite biomarkers and subclinical CAD in a low and a high CVD risk population.
Methods:
The Swedish CArdioPulmonary bioImage Study (SCAPIS) was used for inclusion of a study group without a history of atherosclerotic CVD stratified by cardiovascular risk; one low-risk group with a Systematic COronary Risk Evaluation 2 (SCORE2) of less than 5 % and one high-risk group with a SCORE2 above 7.5 %. In a cross-sectional study design, random forest and ordinal logistic regression models were used to analyse the relative importance of 409 proteins and metabolites for associations with the degree of coronary computed tomography angiography-detected subclinical coronary atherosclerosis, measured as coronary artery calcium score (CACS) and number of segments with coronary atherosclerosis (SIS) in the two groups.
Results:
In the low-risk group (n = 2063), branched-chain amino acids were associated with both CACS and SIS, while increased high-density lipoprotein (HDL) metabolites, with the exception of phospholipids, were associated with a lower CACS and SIS. In the high-risk group (n = 576), proteins involved in inflammatory processes showed negative associations with coronary atherosclerosis, while HDL metabolites did not show any protective effect. Renin and MMP12 were associated with subclinical CAD in both groups.
Conclusions:
Different plasma biomarker patterns associated with subclinical CAD (CACS and SIS) were identified in individuals with a low and a high CVD risk, which could be used to better understand protective and risk factors for CAD in primary prevention.
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