Predicting High Blood Pressure Using DNA Methylome-Based Machine Learning Models

Machine learning-based analyses of contributing factors for the development of hypertension: a comparative study

OBJECTIVES

Sufficient attention has not been given to machine learning (ML) models using longitudinal data for investigating important predictors of new onset of hypertension. We investigated the predictive ability of several ML models for the development of hypertension.

METHODS

A total of 15 965 Japanese participants (men/women: 9,466/6,499, mean age: 45 years) who received annual health examinations were randomly divided into a training group (70%, n = 11,175) and a test group (30%, n = 4,790). The predictive abilities of 58 candidates including fatty liver index (FLI), which is calculated by using body mass index, waist circumference and levels of γ-glutamyl transferase and triglycerides, were investigated by statistics analogous to the area under the curve (AUC) in receiver operating characteristic curve analyses using ML models including logistic regression, random forest, naïve Bayes, extreme gradient boosting and artificial neural network.

RESULTS

During a 10-year period (mean period: 6.1 years), 2,132 subjects (19.1%) in the training group and 917 subjects (19.1%) in the test group had new onset of hypertension. Among the 58 parameters, systolic blood pressure, age and FLI were identified as important candidates by random forest feature selection with 10-fold cross-validation. The AUCs of ML models were 0.765-0.825, and discriminatory capacity was significantly improved in the artificial neural network model compared to that in the logistic regression model.

CONCLUSIONS

The development of hypertension can be simply and accurately predicted by each ML model using systolic blood pressure, age and FLI as selected features. By building multiple ML models, more practical prediction might be possible.

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

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

This review explores the transformative role of artificial intelligence (AI) in hypertension care, summarizing and analyzing published works from the last three years in this field. Hypertension contributes to a significant healthcare burden both at an individual and global level. We focus on five key areas: risk prediction, diagnosis, education, monitoring, and management of hypertension, supplemented with a brief look into the works on hypertensive disease of pregnancy. For each area, we discuss the advantages and disadvantages of integrating AI. While AI, in its current rudimentary form, cannot replace sound clinical judgment, it can still enhance faster diagnosis, education, prevention, and management. The integration of AI in healthcare is poised to revolutionize hypertension care, although careful implementation and ongoing research are essential to mitigate risks.

Epigenetic Signatures in Hypertension

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.