Hypertension (HTN) is the leading cause of cardiovascular disease morbidity and mortality worldwide. Despite effective treatments, most people with HTN do not have their blood pressure under control. Precision health strategies emphasizing predictive, preventive, and personalized care through digital tools offer notable opportunities to optimize the management of HTN.

This scoping review aimed to fill a research gap in understanding the current state of precision health research using digital tools for the management of HTN in adults.

This study used a scoping review framework to systematically search for articles in 5 databases published between 2013 and 2023. The included articles were thematically analyzed based on their precision health focus: personalized interventions, prediction models, and phenotyping. Data were extracted and summarized for study and sample characteristics, precision health focus, digital health technology, disciplines involved, and characteristics of personalized interventions.

After screening 883 articles, 46 were included; most studies had a precision health focus on personalized digital interventions (34/46, 74%), followed by prediction models (8/46, 17%) and phenotyping (4/46, 9%). Most studies (38/46, 82%) were conducted in or used data from North America or Europe, and 63% (29/46) of the studies came exclusively from the medical and health sciences, with 33% (15/46) of studies involving 2 or more disciplines. The most commonly used digital technologies were mobile phones (33/46, 72%), blood pressure monitors (18/46, 39%), and machine learning algorithms (11/46, 24%). In total, 45% (21/46) of the studies either did not report race or ethnicity data (14/46, 30%) or partially reported this information (7/46, 15%). For personalized intervention studies, nearly half (14/30, 47%) used 2 or less types of data for personalization, with only 7% (2/30) of the studies using social determinants of health data and no studies using physical environment or digital literacy data. Personalization characteristics of studies varied, with 43% (13/30) of studies using fully automated personalization approaches, 33% (10/30) using human-driven personalization, and 23% (7/30) using a hybrid approach.

This scoping review provides a comprehensive mapping of the literature on the current trends and gaps in digital precision health research for the management of HTN in adults. Personalized digital interventions were the primary focus of most studies; however, the review highlighted the need for more precise definitions of personalization and the integration of more diverse data sources to improve the tailoring of interventions and promotion of health equity. In addition, there were significant gaps in the reporting of race and ethnicity data of participants, underuse of wearable devices for passive data collection, and the need for greater interdisciplinary collaboration to advance precision health research in digital HTN management.

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Hypertension, a leading cause of cardiovascular disease and premature death, remains incompletely understood despite extensive research. Indeed, even though numerous drugs are available, achieving adequate blood pressure control remains a challenge, prompting recent interest in artificial intelligence. To promote the use of machine learning in cardiovascular medicine, this review provides a brief introduction to machine learning and reviews its notable applications in hypertension management and research, such as disease diagnosis and prognosis, treatment decisions, and omics data analysis. The challenges and limitations associated with data-driven predictive techniques are also discussed. The goal of this review is to raise awareness and encourage the hypertension research community to consider machine learning as a key component in developing innovative diagnostic and therapeutic tools for hypertension. By integrating traditional cardiovascular risk factors with genomics, socioeconomic, behavioral, and environmental factors, machine learning may aid in the development of precise risk prediction models and personalized treatment approaches for patients with hypertension.

Research on the genetic mechanisms of hypertension has been a hot topic in the cardiovascular field.

To study the correlation between senile hypertension and traditional Chinese medicine (TCM) constitution and lipoprotein lipase (LPL) gene polymorphism and to provide the theoretical basis for TCM prevention and treatment of hypertension.

The elderly population in communities in Shanghai (hypertensive: 264 cases; non-hypertensive: 159 cases) was taken as the research object. Essential data and information on TCM constitution were collected. The LPL gene mutation was detected using the second-generation sequencing method. Statistical analysis was performed to clarify the relationship between hypertension and senile hypertension. The correlation of TCM constitution with risk factors and LPL gene polymorphisms was studied.

The primary TCM constitutions in the hypertension group were phlegm-dampness constitution (51.52%), yin-deficiency constitution (17.42%), balanced constitution (15.53%), and yin-deficiency (9.43%). Logistic regression analysis showed that the phlegm-dampness constitution (P< 0.05, OR = 2.587) and yin-deficiency constitution (P< 0.01, OR = 2.693) were the risk constitutions of hypertension in the elderly. A total of 37 LPL gene mutation loci (SNP: 22; new discovery: 15) were detected in the LPL gene, and the mutation rates of rs254, rs255, rs3208305, rs316, rs11570891, rs328, rs11570893, and rs13702 were relatively high, which were 26.24%, 26.24%, 16.08%, 14.66%, 13.24%, 12.06%, and 10.64%. In the phlegm-dampness group, the proportion of rs254 CC type, rs255 TT type, and rs13702 TT type in the hypertensive group (77.21%, 77.21%, and 93.38%) was higher than that in the non-hypertensive group (56.41%, 56.41%, and 82.05%), The difference was statistically significant (P< 0.05).

The phlegm-dampness constitution and yin-deficiency constitution are the risk factors of hypertension in the elderly; in the phlegm-dampness population, rs254 CC type, rs255 TT type, and rs13702 TT type are the risk factors for elderly hypertension.

Anastomotic leakage (AL) occurs frequently after sphincter-preserving surgery for rectal cancer and has a significant mortality rate. There are many factors that influence the incidence of AL, and each patient's unique circumstances add to this diversity. The early identification and prediction of AL after sphincter-preserving surgery are of great significance for the application of clinically targeted preventive measures. Developing an AL predictive model coincides with the aim of personalised healthcare, enhances clinical management techniques, and advances the medical industry along a more precise and intelligent path.

To develop nomogram, decision tree, and random forest prediction models for AL following sphincter-preserving surgery for rectal cancer and to evaluate the predictive efficacy of the three models.

The clinical information of 497 patients with rectal cancer who underwent sphincter-preserving surgery at Jincheng People's Hospital of Shanxi Province between January 2017 and September 2022 was analyzed in this study. Patients were divided into two groups: AL and no AL. Using univariate and multivariate analyses, we identified factors influencing postoperative AL. These factors were used to establish nomogram, decision tree, and random forest models. The sensitivity, specificity, recall, accuracy, and area under the receiver operating characteristic curve (AUC) were compared between the three models.

AL occurred in 10.26% of the 497 patients with rectal cancer. The nomogram model had an AUC of 0.922, sensitivity of 0.745, specificity of 0.966, accuracy of 0.936, recall of 0.987, and accuracy of 0.946. The above indices in the decision tree model were 0.919, 0.833, 0.862, 0.951, 0.994, and 0.955, respectively and in the random forest model were 1.000, 1.000, 1.000, 0.951, 0.994, and 0.955, respectively. The DeLong test revealed that the AUC value of the decision-tree model was lower than that of the random forest model (P < 0.05).

The random forest model may be used to identify patients at high risk of AL after sphincter-preserving surgery for rectal cancer owing to its strong predictive effect and stability.

Arterial hypertension (AH) is a progressive issue that grows in importance with the increased average age of the world population. The potential role of artificial intelligence (AI) in its prevention and treatment is firmly recognized. Indeed, AI application allows personalized medicine and tailored treatment for each patient. Specifically, this article reviews the benefits of AI in AH management, pointing out diagnostic and therapeutic improvements without ignoring the limitations of this innovative scientific approach. Consequently, we conducted a detailed search on AI applications in AH: the articles (quantitative and qualitative) reviewed in this paper were obtained by searching journal databases such as PubMed and subject-specific professional websites, including Google Scholar. The search terms included artificial intelligence, artificial neural network, deep learning, machine learning, big data, arterial hypertension, blood pressure, blood pressure measurement, cardiovascular disease, and personalized medicine. Specifically, AI-based systems could help continuously monitor BP using wearable technologies; in particular, BP can be estimated from a photoplethysmograph (PPG) signal obtained from a smartphone or a smartwatch using DL. Furthermore, thanks to ML algorithms, it is possible to identify new hypertension genes for the early diagnosis of AH and the prevention of complications. Moreover, integrating AI with omics-based technologies will lead to the definition of the trajectory of the hypertensive patient and the use of the most appropriate drug. However, AI is not free from technical issues and biases, such as over/underfitting, the "black-box" nature of many ML algorithms, and patient data privacy. In conclusion, AI-based systems will change clinical practice for AH by identifying patient trajectories for new, personalized care plans and predicting patients' risks and necessary therapy adjustments due to changes in disease progression and/or therapy response.

Globally, blood pressure management strategies were ineffective, and a low percentage of patients receiving hypertension treatment had their blood pressure controlled. In this study, we aimed to build a medication prediction model by correlating patient attributes with medications to help physicians quickly and rationally match appropriate medications.

We collected clinical data from elderly hypertensive patients during hospitalization and combined statistical methods and machine learning (ML) algorithms to filter out typical indicators. We constructed five ML models to evaluate all datasets using 5-fold cross-validation. Include random forest (RF), support vector machine (SVM), light gradient boosting machine (LightGBM), artificial neural network (ANN), and naive Bayes (NB) models. And the performance of the models was evaluated using the micro-F1 score.

Our experiments showed that by statistical methods and ML algorithms for feature selection, we finally selected Age, SBP, DBP, Lymph, RBC, HCT, MCHC, PLT, AST, TBIL, Cr, UA, Urea, K, Na, Ga, TP, GLU, TC, TG, γ-GT, Gender, HTN CAD, and RI as feature metrics of the models. LightGBM had the best prediction performance with the micro-F1 of 78.45%, which was higher than the other four models.

LightGBM model has good results in predicting antihypertensive medication regimens, and the model can be beneficial in improving the personalization of hypertension treatment.

To provide an overview of the literature regarding the use of machine learning algorithms to predict hypertension. A systematic review was performed to select recent articles on the subject.

The screening of the articles was conducted using a machine learning algorithm (ASReview). A total of 21 articles published between January 2018 and May 2021 were identified and compared according to variable selection, train-test split, data balancing, outcome definition, final algorithm, and performance metrics. Overall, the articles achieved an area under the ROC curve (AUROC) between 0.766 and 1.00. The algorithms most frequently identified as having the best performance were support vector machines (SVM), extreme gradient boosting (XGBoost), and random forest. Machine learning algorithms are a promising tool to improve preventive clinical decisions and targeted public health policies for hypertension. However, technical factors such as outcome definition, availability of the final code, predictive performance, explainability, and data leakage need to be consistently and critically evaluated.

Hypertension (HTN), which frequently co-exists with diabetes mellitus, is the leading major cause of cardiovascular disease and death globally. This study aimed to develop and validate a risk scoring system considering the effects of glycemic and blood pressure (BP) variabilities to predict HTN incidence in patients with type 2 diabetes. This research is a retrospective cohort study that included 3416 patients with type 2 diabetes without HTN and who were enrolled in a managed care program in 2001-2015. The patients were followed up until April 2016, new-onset HTN event, or death. HTN was defined as diastolic BP (DBP) ≥ 90 mm Hg, systolic BP (SBP) ≥ 140 mm Hg, or the initiation of antihypertensive medication. Cox proportional hazard regression model was used to develop the risk scoring system for HTN. Of the patients, 1738 experienced new-onset HTN during an average follow-up period of 3.40 years. Age, sex, physical activity, body mass index, type of DM treatment, family history of HTN, baseline SBP and DBP, variabilities of fasting plasma glucose, SBP, and DBP and macroalbuminuria were significant variables for the prediction of new-onset HTN. Using these predictors, the prediction models for 1-, 3-, and 5-year periods demonstrated good discrimination, with AUC values of 0.70-0.76. Our HTN scoring system for patients with type 2 DM, which involves innovative predictors of glycemic and BP variabilities, has good classification accuracy and identifies risk factors available in clinical settings for prevention of the progression to new-onset HTN.

We aimed to evaluate the effects of serum testosterone, obesity and their interaction on blood pressure (BP) parameters and hypertension among Chinese rural adults.

A total of 6199 adults were recruited from the Henan Rural Cohort Study. Serum testosterone was measured by liquid chromatography-tandem mass spectrometry. Logistic regression and linear regression were used to evaluate the association between testosterone, hypertension and BP parameters (including systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), and mean arterial pressure (MAP)). A generalized linear model was performed to identify the interactive effects of testosterone and obesity on hypertension.

High levels of serum testosterone were associated with a lower prevalence of hypertension in males (odds ratio (OR): 0.69, 95% confidence interval (CI): 0.53, 0.89). After stratification by obesity, observed associations were only found in non-obese males. Each one-unit increase in ln-testosterone was associated with a 1.23 mmHg decrease in SBP, 0.97 mmHg decrease in DBP, and 1.05 mmHg decrease in MAP among males. Moreover, interactive effects between testosterone and obesity on hypertension and BP parameters were found, indicating that protective effects of serum testosterone on hypertension and BP parameters were counteracted and accompanied by increased values of obesity-related indicators in males, and additional testosterone increased BP parameters and prevalence of hypertension at high levels of waist-to-hip ratio and waist-to-height ratio in females.

Elevated levels of serum testosterone were associated with decreased BP parameters and prevalent hypertension in males, and obesity modifying effects of serum testosterone on BP parameters and hypertension.

Prevention and treatment of hypertension (HTN) are a challenging public health problem. Recent evidence suggests that artificial intelligence (AI) has potential to be a promising tool for reducing the global burden of HTN, and furthering precision medicine related to cardiovascular (CV) diseases including HTN. Since AI can stimulate human thought processes and learning with complex algorithms and advanced computational power, AI can be applied to multimodal and big data, including genetics, epigenetics, proteomics, metabolomics, CV imaging, socioeconomic, behavioral, and environmental factors. AI demonstrates the ability to identify risk factors and phenotypes of HTN, predict the risk of incident HTN, diagnose HTN, estimate blood pressure (BP), develop novel cuffless methods for BP measurement, and comprehensively identify factors associated with treatment adherence and success. Moreover, AI has also been used to analyze data from major randomized controlled trials exploring different BP targets to uncover previously undescribed factors associated with CV outcomes. Therefore, AI-integrated HTN care has the potential to transform clinical practice by incorporating personalized prevention and treatment approaches, such as determining optimal and patient-specific BP goals, identifying the most effective antihypertensive medication regimen for an individual, and developing interventions targeting modifiable risk factors. Although the role of AI in HTN has been increasingly recognized over the past decade, it remains in its infancy, and future studies with big data analysis and N-of-1 study design are needed to further demonstrate the applicability of AI in HTN prevention and treatment.

The latest works on CRISPR genome editing tools mainly employs deep learning techniques. However, deep learning models lack explainability and they are harder to reproduce. We were motivated to build an accurate genome editing tool using sequence-based features and traditional machine learning that can compete with deep learning models.

In this paper, we present CRISPRpred(SEQ), a method for sgRNA on-target activity prediction that leverages only traditional machine learning techniques and hand-crafted features extracted from sgRNA sequences. We compare the results of CRISPRpred(SEQ) with that of DeepCRISPR, the current state-of-the-art, which uses a deep learning pipeline. Despite using only traditional machine learning methods, we have been able to beat DeepCRISPR for the three out of four cell lines in the benchmark dataset convincingly (2.174%, 6.905% and 8.119% improvement for the three cell lines).

CRISPRpred(SEQ) has been able to convincingly beat DeepCRISPR in 3 out of 4 cell lines. We believe that by exploring further, one can design better features only using the sgRNA sequences and can come up with a better method leveraging only traditional machine learning algorithms that can fully beat the deep learning models.

We aimed to study the relationship between rs11174811 and rs3803107 single nucleotide polymorphisms (SNPs) in miRNA target sites of the 3' UTR in the arginine vasopressin receptor 1a gene (AVPR1A) and the risk of hypertension in the Chinese Han population. The genotypes at rs11174811 and rs3803107 were analyzed by direct sequencing in 425 Chinese Han patients with hypertension and 425 healthy subjects. AVPR1A expression was investigated by transfecting miR-526b, miR-375, and miR-186 mimics into human umbilical vein endothelial cells (HUVECs) containing AVPR1A rs11174811 CC, CA/AA and AVPR1A rs3803107 GG, GA/AA genotypes. The A alleles of rs11174811 (adjusted OR = 1.424, 95% CI: 1.231-1.599, P<0.001) and rs3803107 (adjusted OR = 1.222, 95% CI: 1.092-1.355; P=0.001) were high risk factors for hypertension. Plasma levels of miR-526b, miR-375, and miR-186 were higher in the study group than in the control group (P<0.001). The expression levels of AVPR1A mRNA in AVPR1A rs11174811 and rs3803107 mutant HUVECs were higher than those in wild-type cells (t = 8.811, 4.068 and P=0.001, 0.015, respectively). The single nucleotide polymorphisms rs11174811 and rs3803107 in the AVPR1A gene are associated with an increased risk of hypertension in the Chinese Han population. This may be related to the effect of these variants on the regulation of AVPR1A expression by miRNAs.