The following are key points to remember from this review article about omics phenotyping in heart failure (HF):

1. Omics aims at the collective characterization and quantification of pools of biological molecules of a given biological function, at different levels, including the molecular gene level (genomics: the study of genes and their function), the protein level (proteomics: the study of proteins), the transcript level (transcriptomic: the study of all RNA molecules, including noncoding RNAs), and the metabolic level (metabolomics: the study of molecules involved in cellular metabolism).
2. The omics approach incorporates data from studies of the genome, transcriptome, proteome, and metabolome to focus on the assessment of a varied range of biomolecules.
3. Genomics data obtained from targeted mutation testing, next-generation sequencing (including whole exome sequencing and whole genome sequencing), polygenic risk scores/genome-wide association studies, and epigenetic assays will likely add important new insights to our knowledge of the genetic basis of HF in the coming years.
4. Transcription of ribonucleic acid (RNA) follows DNA sequencing and epigenetic regulation. RNAs are detectable in the blood, free plasma, serum, and other fluids including urine. These molecules include mainly noncoding RNAs (ncRNAs), such as microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs. Many plasma-enriched ncRNAs are relatively stable, making them suitable for clinical assessment and disease monitoring. miRNAs and lncRNAs, a circRNA called MICRA, was shown to improve risk stratification, supporting the value of this novel biomarker in future prognostication strategies. Large multicenter studies to provide convincing evidence for clinical applicability in cardiovascular disease are needed.
5. The use of detailed protein panels—or proteomics—has been explored in multiple cohorts of patients with HF. These proteins fall into four dominant categories of signal networks: a) inflammation/apoptosis, b) extracellular matrix remodeling and angiogenesis, c) blood pressure and renin–angiotensin controls, and d) metabolic regulation. Proteomic analysis also has highlighted critical changes in innate immunity and inflammation, metabolism, novel signaling pathways, and accelerated senescence. In addition, the use of machine learning in proteomic truth tables has enabled to unveil the mechanism of action of new HF drugs.
6. Proteomic profiles can be used as sources of new candidate biomarkers to improve diagnosis and prognosis for different types of HF. The ultimate impact of proteomics will be the ability to bring together a minimum number of unique protein candidates for distinct biological pathways to inform decision making, in conjunction with clinical information and coherent complementary omics data.
7. The combination of novel critical pathophysiological pathways and robust protein signatures tied to the disease processes offers the chance to identify new therapeutic targets for HF.
8. Metabolomic profiling attempts to account for a large number (in the thousands) of small molecules of intermediary metabolism. The subtypes of these molecules are too many to list, but a few key ones are lipids, amino acids, organic acids, fatty acids, ketones, acylcarnitines, etc. Metabolite profiles in tissue and fluids are thought to represent both biologic processes and variability (the result of genomic and other biologic variation) as well as the impact of environmental factors and exposures on cellular and organ function. The cellular processes that feed and interact with the metabolite levels are also many and diverse but key drivers are metabolite transport mechanisms, mitochondrial function, cellular respiration, the citric acid cycle (Kreb’s cycle), and other forms of oxidation, such as beta-oxidation.
9. Omics has allowed the greater ability to analyze numerous markers allowing a more comprehensive picture of the factors and physiology involved in HF, and a rich repository of information for predictive modeling of phenotypes has become possible.
10. The space is only in its infancy; however, successful translation of omics or system biology tools as solutions for problems in clinical cardiology requires robust, reproducible observations that can be validated across multiple omics and HF independent populations to support clinical decision making.

Perspective:

This is an outstanding review on the utility of combining multiple omics technologies.

Clinical Topics: Arrhythmias and Clinical EP, Diabetes and Cardiometabolic Disease, Dyslipidemia, Heart Failure and Cardiomyopathies, Genetic Arrhythmic Conditions, Lipid Metabolism, Acute Heart Failure, Heart Failure and Cardiac Biomarkers

Keywords: Amino Acids, Angiotensins, Biological Products, Biomarkers, Blood Pressure, Epigenesis, Genetic, Fatty Acids, Genome-Wide Association Study, Genomics, Heart Failure, Ketones, Lipids, Metabolic Syndrome, Metabolomics, Metabolome, MicroRNAs, Mitochondria, Phenotype, Proteomics, RNA, Transcriptome

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