Epigenetic Signatures in Hypertension

Abstract

Clear epigenetic signatures were found in hypertensive and pre-hypertensive patients using DNA methylation data and neural networks in a classification algorithm. It is shown how by selecting an appropriate subset of CpGs it is possible to achieve a mean accuracy classification of 86% for distinguishing control and hypertensive (and pre-hypertensive) patients using only 2239 CpGs. Furthermore, it is also possible to obtain a statistically comparable model achieving an 83% mean accuracy using only 22 CpGs. Both of these approaches represent a substantial improvement over using the entire amount of available CpGs, which resulted in the neural network not generating accurate classifications. An optimization approach is followed to select the CpGs to be used as the base for a model distinguishing between hypertensive and pre-hypertensive individuals. It is shown that it is possible to find methylation signatures using machine learning techniques, which can be applied to distinguish between control (healthy) individuals, pre-hypertensive individuals and hypertensive individuals, illustrating an associated epigenetic impact. Identifying epigenetic signatures might lead to more targeted treatments for patients in the future.

Transforming Healthcare: The AI Revolution in the Comprehensive Care of Hypertension

“…Another model differentiated between hypertensive and pre-hypertensive patients with 88.3% accuracy using 1120 CpGs. This method, unaffected by external factors, shows promise for personalized treatment based on DNA methylation profiles, despite challenges in model complexity and interpretation [ 10 ].…”

“…Applying ML to large amounts of genetic data to reveal outcomes is very difficult to implement in daily clinical practice, and the correlations and causations remain unclear. This process requires sophisticated analyses due to the large volumes of data and complex relationships involved, making it impractical for routine use [ 10 ]. Furthermore, none of these devices have been approved by governing bodies for clinical use.…”

“… Summary of the applications of AI in the risk prediction, diagnosis, management, and treatment of hypertension [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. …”

Epigenetics of hypertension as a risk factor for the development of coronary artery disease in type 2 diabetes mellitus

“…Epigenetics, which explores the dynamic interplay between genetic factors and the environment, emerges as a critical player in the regulation of blood pressure. Epigenetic changes can be influenced by environmental stimuli such as nutrition, aging, and pharmaceuticals, and importantly, these changes possess the potential for reversibility ( 8 , 9 ). This characteristic sets the stage for a spectrum of treatment possibilities distinct from those for genetic disorders.…”

“…This characteristic sets the stage for a spectrum of treatment possibilities distinct from those for genetic disorders. Epigenetics has garnered global attention, as evidenced by initiatives like the International Human Epigenome Consortium and the Human Epigenome Project ( 8 , 10 ).…”