Back

Multi-omics data integration from patients with carotid stenosis illuminates key molecular signatures of atherosclerotic instability

Das, V.; Narayanan, S.; Zhang, X.; Bergman, O.; Djordjevic, D.; Kronqvist, M.; Chemaly, M.; Karadimou, G.; Vuckovic, S.; Prasad, I.; Buckler, A. J.; Knape, K. C.; Michaelsen, N. B.; Hedin, U.; Matic, L.

2025-05-13 cardiovascular medicine
10.1101/2025.05.12.25327328
Show abstract

BackgroundUnderstanding the pathophysiology of unstable atherosclerosis is imperative to prevent myocardial infarction and stroke. We used multi-omics integration to identify key molecular targets with diagnostic and therapeutic potential. MethodsBiobank of Karolinska Endarterectomies encompassing patients with symptomatic (S) and asymptomatic (AS) carotid atherosclerosis, was the main resource. Plaques, peripheral blood monocytes and plasma sampled locally from around plaque or periphery of n>700 individuals, were profiled by transcriptomics, proteomics and metabolomics. A supervised feature-selection method DIABLO was used for per patient data integration. Multi-omics layers were integrated separately across local and peripheral disease sites, and their intersection, with stratification for symptomatology. Identified analytes were investigated using scRNAseq, clinical and outcome data. ResultsIn peripheral circulation, FABP4, IL6, Bilirubin and Sphingomyelin were the most prominent analytes. F11, ANGPTL3, ICOSLG, ITGB1 and Sphingomyelin were enriched in the local disease site, while FABP4, C1R, IL6, Bilirubin and Sphingomyelin appeared at the intersection. Coagulation, necroptosis, inflammation and cholesterol metabolism were confirmed as key pathways determining symptomatology. Clinical analyses showed an impact of lipid-lowering therapy on ICOSLG expression, anti-hypertensives on plasma FABP4 and BLVRB levels, anti-diabetics on plasma Sphingomyelins, while no medications affected ANGPTL3. Association with future adverse events was shown for plasma Bilirubin, Sphingomyelin, ANGPTL3 and ICOSLG plaque levels. Open-source target genetic analyses suggested causal involvement of F11, C1S, EGFR, IL6, ANGPTL3 in the disease. ConclusionsUsing an innovative, deep-data framework, this study provides confirmatory and novel information on mechanisms behind atherosclerotic instability. The findings raise possibilities for translational prioritizations to aid personalized medicine. Structured Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/25327328v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1372bdborg.highwire.dtl.DTLVardef@1203b97org.highwire.dtl.DTLVardef@1283922org.highwire.dtl.DTLVardef@1beead9_HPS_FORMAT_FIGEXP M_FIG C_FIG Key QuestionThis study performed first-of-a-kind, orthogonal, per-patient multi-omics integration from a large carotid stenosis biobank, with an aim to identify key molecular signatures and pathways of human atherosclerotic instability. Key FindingThe complex multi-omics design coupled with deep-data analyses, enabled the discovery of numerous confirmatory and novel molecular signatures implicated in patient symptomatology. Extended validation analyses elucidated their cellular sources, associations with plaque morphology, clinical biochemistry, medication and long-term patient outcomes. Take-home MessageThe findings are interesting for further investigation with respect to druggable targeting or plasma biomarkers, altogether leading to improved patient phenotyping and precision medicine potential in cardiovascular disease.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Arteriosclerosis, Thrombosis, and Vascular Biology
based on 11 papers
Top 0.1%
17.8%
2
Atherosclerosis
based on 16 papers
Top 0.2%
10.6%
3
Circulation: Genomic and Precision Medicine
based on 30 papers
Top 0.8%
7.9%
4
Journal of the American Heart Association
based on 92 papers
Top 3%
7.9%
5
Hypertension
based on 20 papers
Top 0.5%
6.7%
50% of probability mass above
6
eLife
based on 262 papers
Top 4%
4.7%
7
Frontiers in Cardiovascular Medicine
based on 33 papers
Top 3%
4.7%
8
Circulation
based on 37 papers
Top 3%
2.6%
9
Nature Communications
based on 483 papers
Top 24%
2.6%
10
Biomedicines
based on 21 papers
Top 0.9%
2.4%
11
European Heart Journal
based on 14 papers
Top 4%
1.6%
12
Journal of Thrombosis and Haemostasis
based on 10 papers
Top 0.7%
1.6%
13
Nature Genetics
based on 72 papers
Top 6%
1.6%
14
Scientific Reports
based on 701 papers
Top 71%
1.6%
15
iScience
based on 74 papers
Top 4%
1.4%
16
BMC Medicine
based on 155 papers
Top 17%
1.3%
17
Genome Medicine
based on 56 papers
Top 6%
1.3%
18
Communications Medicine
based on 63 papers
Top 2%
0.9%
19
European Journal of Preventive Cardiology
based on 12 papers
Top 2%
0.8%
20
EBioMedicine
based on 21 papers
Top 1%
0.8%
21
Frontiers in Genetics
based on 32 papers
Top 5%
0.8%
22
European Heart Journal - Digital Health
based on 15 papers
Top 3%
0.8%
23
Open Heart
based on 18 papers
Top 5%
0.7%
24
PLOS ONE
based on 1737 papers
Top 96%
0.7%