Multiomic analyses uncover immunological signatures in acute and chronic coronary syndromes.
Kami PekayvazCorinna LosertViktoria KnottenbergChristoph GoldIrene V van BloklandRoy OelenHilde E GrootJan-Walter BenjaminsSophia BrambsRainer W J KaiserAdrian GottschlichGordon Victor HoffmannLuke EiversAlejandro Martinez NavarroNils BrunsSusanne StillerSezer AkgölKeyang YueVivien PolewkaRaphael EscaigMarkus JoppichAleksandar JanjicOliver PoppSebastian KoboldTobias PetzoldRalf ZimmerWolfgang EnardKathrin SaarPhilipp MertinsNorbert HübnerPim van der HarstLude H FrankeMonique G P van der WijstSteffen MassbergMatthias HeinigLeo NicolaiKonstantin StarkPublished in: Nature medicine (2024)
Acute and chronic coronary syndromes (ACS and CCS) are leading causes of mortality. Inflammation is considered a key pathogenic driver of these diseases, but the underlying immune states and their clinical implications remain poorly understood. Multiomic factor analysis (MOFA) allows unsupervised data exploration across multiple data types, identifying major axes of variation and associating these with underlying molecular processes. We hypothesized that applying MOFA to multiomic data obtained from blood might uncover hidden sources of variance and provide pathophysiological insights linked to clinical needs. Here we compile a longitudinal multiomic dataset of the systemic immune landscape in both ACS and CCS (n = 62 patients in total, n = 15 women and n = 47 men) and validate this in an external cohort (n = 55 patients in total, n = 11 women and n = 44 men). MOFA reveals multicellular immune signatures characterized by distinct monocyte, natural killer and T cell substates and immune-communication pathways that explain a large proportion of inter-patient variance. We also identify specific factors that reflect disease state or associate with treatment outcome in ACS as measured using left ventricular ejection fraction. Hence, this study provides proof-of-concept evidence for the ability of MOFA to uncover multicellular immune programs in cardiovascular disease, opening new directions for mechanistic, biomarker and therapeutic studies.
Keyphrases
- ejection fraction
- aortic stenosis
- end stage renal disease
- cardiovascular disease
- acute coronary syndrome
- chronic kidney disease
- newly diagnosed
- coronary artery
- left ventricular
- coronary artery disease
- liver failure
- electronic health record
- drug induced
- big data
- type diabetes
- genome wide
- prognostic factors
- heart failure
- respiratory failure
- polycystic ovary syndrome
- intensive care unit
- oxidative stress
- acute myocardial infarction
- drinking water
- patient reported outcomes
- metabolic syndrome
- dna methylation
- immune response
- cardiovascular risk factors
- aortic valve
- deep learning
- hypertrophic cardiomyopathy
- dendritic cells
- peripheral blood
- risk factors
- patient reported
- hepatitis b virus