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Global Translational Medicine Epigenetics on cardiovascular diseases

Figure 5. The effect of epigenetics on cardiovascular diseases.
Abbreviation: ncRNAs: Non-coding RNAs.

cardiovascular health (CVH) by applying the Life’s Simple Alexandar et al. [146] have established a literature-
7 metrics. There are four health elements and three based database, CardioGenBase, collecting gene-disease
health behaviors of lifestyle [142,143] . Joyce et al. [143] provided association from PubMed and MEDLINE, and containing
evidence that GrimAge acceleration (GrimAA), a measure approximately 1500 CVD genes from around 24,000
of epigenetic aging, maybe a practical biomarker of CVD research articles. CardioGenBase is an indispensable
risk; this gives a biological understanding of the epigenetic online resource to uphold genome-wide analysis of genetic,
mechanisms associated with age-related CVH decline and epigenetic, and pharmacological studies [146] .
CVDs.
In addition to data retrieval and manipulation,
5. Systems medicine and interdisciplinary computational analysis is crucial to accurately approaching
integration unprocessed information material and overcoming
the complex fragmentation of data required. The real
The epigenetic impact is a constant and uninterrupted constraint is not the availability of data but the limited
process with broad cell and tissue applications, increasing processing power, which is the critical technology needed.
our understanding of disease progression and treatment. CVDs should be explored through different levels or
At the moment, it is not fully integrated into medical sections of information, from genetic and molecular realms
practice or extensively used in clinical applications. to clinical phenotypes, unraveling and connecting the
Epigenetic data are increasing due to the progress in high- available biological networks in pursuit of defining clinical
throughput sequencing and microarray technologies. complexity. Thus, the holistic multiscale and integrative
The question now is how to enlarge and analyze the approach of SB will increase the prospects for true systems
data findings and identify and interpret their functional medicine. Regardless of important breakthroughs in
repercussions in normal development and disease . clinical medicine through the reductionist methodology,
[56]
The extensive parallel sequencing technologies merged there are still unanswered questions and limitations for
with analytical molecular approaches and computational common complex diseases [147] .
techniques have permitted clinicians and researchers to
realize chronic mechanisms involved in the pathology Ahn et al. [148] argue that reductionism has limitations and
of CVDs. As a consequence of the absence of suitable a different explanation is needed. The systems perspective
and effective computational methods, most studies respects the holistic and compounded attributes, solving
concentrate on a single epigenetic element in isolation the problem through the use of computational and
despite the fact that numerous connections from multiple mathematical tools. In another paper, the same authors
constituents and genotypes are taking place in vivo [144] . claim that reductionism splits up the problem into its
An effective data informative statement is required parts, missing important information about the whole,
for data standardization to enhance the reproduction and disregards interactions between elements [149] . Systems
of epigenetic discoveries [145] . A great number of medicine looks beyond linear relationships and isolated
epigenetic data are assembled in the domain of CVDs, parameters, using multiple parameters from multiple
but comprehension of the basic mechanistic features of time points and spatial conditions to accomplish a holistic
CVDs remains unrevealed; a new friendly strategy and perspective of an individual [147] . Ung et al. [150] argue
extensive information processing to elucidate disease that it remains ambiguous how epigenetic regulations
pathophysiology is needed. consensually guide the advancement of biological memories

Volume 2 Issue 4 (2023) 12 https://doi.org/10.36922/gtm.1868