Prediction of Blood Pressure and Blood Pressure Change With a Genetic Risk Score

Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

Prognostic risk models for incident hypertension: A PRISMA systematic review and meta-analysis

OBJECTIVE

Our goal was to review the available literature on prognostic risk prediction for incident hypertension, synthesize performance, and provide suggestions for future work on the topic.

METHODS

A systematic search on PUBMED and Web of Science databases was conducted for studies on prognostic risk prediction models for incident hypertension in generally healthy individuals. Study-quality was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST) checklist. Three-level meta-analyses were used to obtain pooled AUC/C-statistic estimates. Heterogeneity was explored using study and cohort characteristics in meta-regressions.

RESULTS

From 5090 hits, we found 53 eligible studies, and included 47 in meta-analyses. Only four studies were assessed to have results with low risk of bias. Few models had been externally validated, with only the Framingham risk model validated more than thrice. The pooled AUC/C-statistics were 0.82 (0.77-0.86) for machine learning models and 0.78 (0.76-0.80) for traditional models, with high heterogeneity in both groups (I2 > 99%). Intra-class correlations within studies were 60% and 90%, respectively. Follow-up time (P = 0.0405) was significant for ML models and age (P = 0.0271) for traditional models in explaining heterogeneity. Validations of the Framingham risk model had high heterogeneity (I2 > 99%).

CONCLUSION

Overall, the quality of included studies was assessed as poor. AUC/C-statistic were mostly acceptable or good, and higher for ML models than traditional models. High heterogeneity implies large variability in the performance of new risk models. Further, large heterogeneity in validations of the Framingham risk model indicate variability in model performance on new populations. To enable researchers to assess hypertension risk models, we encourage adherence to existing guidelines for reporting and developing risk models, specifically reporting appropriate performance measures. Further, we recommend a stronger focus on validation of models by considering reasonable baseline models and performing external validations of existing models. Hence, developed risk models must be made available for external researchers.

Development of risk models of incident hypertension using machine learning on the HUNT study data

In this study, we aimed to create an 11-year hypertension risk prediction model using data from the Trøndelag Health (HUNT) Study in Norway, involving 17 852 individuals (20-85 years; 38% male; 24% incidence rate) with blood pressure (BP) below the hypertension threshold at baseline (1995-1997). We assessed 18 clinical, behavioral, and socioeconomic features, employing machine learning models such as eXtreme Gradient Boosting (XGBoost), Elastic regression, K-Nearest Neighbor, Support Vector Machines (SVM) and Random Forest. For comparison, we used logistic regression and a decision rule as reference models and validated six external models, with focus on the Framingham risk model. The top-performing models consistently included XGBoost, Elastic regression and SVM. These models efficiently identified hypertension risk, even among individuals with optimal baseline BP (< 120/80 mmHg), although improvement over reference models was modest. The recalibrated Framingham risk model outperformed the reference models, approaching the best-performing ML models. Important features included age, systolic and diastolic BP, body mass index, height, and family history of hypertension. In conclusion, our study demonstrated that linear effects sufficed for a well-performing model. The best models efficiently predicted hypertension risk, even among those with optimal or normal baseline BP, using few features. The recalibrated Framingham risk model proved effective in our cohort.

Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1% to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8% to 5.1% (SBP) and 4.7% to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs.

Evaluating the use of blood pressure polygenic risk scores across race/ethnic background groups

We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare "clumping-and-thresholding" (PRSice2) and LD-based (LDPred2) methods to construct PRSs from each of multiple GWAS, as well as multi-PRS approaches that sum PRSs with and without weights, including PRS-CSx. We use datasets from the MGB Biobank, TOPMed study, UK biobank, and from All of Us to train, assess, and validate PRSs in groups defined by self-reported race/ethnic background (Asian, Black, Hispanic/Latino, and White). For both SBP and DBP, the PRS-CSx based PRS, constructed as a weighted sum of PRSs developed from multiple independent GWAS, perform best across all race/ethnic backgrounds. Stratified analysis in All of Us shows that PRSs are better predictive of BP in females compared to males, individuals without obesity, and middle-aged (40-60 years) compared to older and younger individuals.

A cardiologist's guide to machine learning in cardiovascular disease prognosis prediction

A modern-day physician is faced with a vast abundance of clinical and scientific data, by far surpassing the capabilities of the human mind. Until the last decade, advances in data availability have not been accompanied by analytical approaches. The advent of machine learning (ML) algorithms might improve the interpretation of complex data and should help to translate the near endless amount of data into clinical decision-making. ML has become part of our everyday practice and might even further change modern-day medicine. It is important to acknowledge the role of ML in prognosis prediction of cardiovascular disease. The present review aims on preparing the modern physician and researcher for the challenges that ML might bring, explaining basic concepts but also caveats that might arise when using these methods. Further, a brief overview of current established classical and emerging concepts of ML disease prediction in the fields of omics, imaging and basic science is presented.

Associations of polygenic risk scores for preeclampsia and blood pressure with hypertensive disorders of pregnancy

BACKGROUND

Preexisting hypertension increases risk for preeclampsia. We examined whether a generic blood pressure polygenic risk score (BP-PRS), compared with a preeclampsia-specific polygenic risk score (PE-PRS), could better predict hypertensive disorders of pregnancy.

METHODS

Our study sample included 141 298 genotyped FinnGen study participants with at least one childbirth and followed from 1969 to 2021. We calculated PRSs for SBP and preeclampsia using summary statistics for greater than 1.1 million single nucleotide polymorphisms.

RESULTS

We observed 8488 cases of gestational hypertension (GHT) and 6643 cases of preeclampsia. BP-PRS was associated with GHT [multivariable-adjusted hazard ratio for 1SD increase in PRS (hazard ratio 1.38; 95% CI 1.35-1.41)] and preeclampsia (1.26, 1.23-1.29), respectively. The PE-PRS was also associated with GHT (1.16; 1.14-1.19) and preeclampsia (1.21, 1.18-1.24), but with statistically more modest magnitudes of effect (P = 0.01). The model c-statistic for preeclampsia improved when PE-PRS was added to clinical risk factors (P = 4.6 × 10-15). Additional increment in the c-statistic was observed when BP-PRS was added to a model already including both clinical risk factors and PE-PRS (P = 1.1 × 10-14).

CONCLUSION

BP-PRS is strongly associated with hypertensive disorders of pregnancy. Our current observations suggest that the BP-PRS could capture the genetic architecture of preeclampsia better than the current PE-PRSs. These findings also emphasize the common pathways in the development of all BP disorders. The clinical utility of a BP-PRS for preeclampsia prediction warrants further investigation.

Polygenic risk scores for the prediction of cardiometabolic disease

Cardiometabolic diseases contribute more to global morbidity and mortality than any other group of disorders. Polygenic risk scores (PRSs), the weighted summation of individually small-effect genetic variants, represent an advance in our ability to predict the development and complications of cardiometabolic diseases. This article reviews the evidence supporting the use of PRS in seven common cardiometabolic diseases: coronary artery disease (CAD), stroke, hypertension, heart failure and cardiomyopathies, obesity, atrial fibrillation (AF), and type 2 diabetes mellitus (T2DM). Data suggest that PRS for CAD, AF, and T2DM consistently improves prediction when incorporated into existing clinical risk tools. In other areas such as ischaemic stroke and hypertension, clinical application appears premature but emerging evidence suggests that the study of larger and more diverse populations coupled with more granular phenotyping will propel the translation of PRS into practical clinical prediction tools.

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms

Objective

The prevention of hypertension in primary care requires an effective and suitable hypertension risk assessment model. The aim of this study was to develop and compare the performances of three machine learning algorithms in predicting the risk of hypertension for residents in primary care in Shanghai, China.

Methods

A dataset of 40,261 subjects over the age of 35 years was extracted from Electronic Healthcare Records of 47 community health centers from 2017 to 2019 in the Pudong district of Shanghai. Embedded methods were applied for feature selection. Machine learning algorithms, XGBoost, random forest, and logistic regression analyses were adopted in the process of model construction. The performance of models was evaluated by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, accuracy and F1-score.

Results

The XGBoost model outperformed the other two models and achieved an AUC of 0.765 in the testing set. Twenty features were selected to construct the model, including age, diabetes status, urinary protein level, BMI, elderly health self-assessment, creatinine level, systolic blood pressure measured on the upper right arm, waist circumference, smoking status, low-density lipoprotein cholesterol level, high-density lipoprotein cholesterol level, frequency of drinking, glucose level, urea nitrogen level, total cholesterol level, diastolic blood pressure measured on the upper right arm, exercise frequency, time spent engaged in exercise, high salt consumption, and triglyceride level.

Conclusions

XGBoost outperformed random forest and logistic regression in predicting the risk of hypertension in primary care. The integration of this risk assessment model into primary care facilities may improve the prevention and management of hypertension in residents.

Life-Course Associations between Blood Pressure-Related Polygenic Risk Scores and Hypertension in the Bogalusa Heart Study

Genetic information may help to identify individuals at increased risk for hypertension in early life, prior to the manifestation of elevated blood pressure (BP) values. We examined 369 Black and 832 White Bogalusa Heart Study (BHS) participants recruited in childhood and followed for approximately 37 years. The multi-ancestry genome-wide polygenic risk scores (PRSs) for systolic BP (SBP), diastolic BP (DBP), and hypertension were tested for an association with incident hypertension and stage 2 hypertension using Cox proportional hazards models. Race-stratified analyses were adjusted for baseline age, age2, sex, body mass index, genetic principal components, and BP. In Black participants, each standard deviation increase in SBP and DBP PRS conferred a 38% (p = 0.009) and 22% (p = 0.02) increased risk of hypertension and a 74% (p < 0.001) and 50% (p < 0.001) increased risk of stage 2 hypertension, respectively, while no association was observed with the hypertension PRSs. In Whites, each standard deviation increase in SBP, DBP, and hypertension PRS conferred a 24% (p < 0.05), 29% (p = 0.01), and 25% (p < 0.001) increased risk of hypertension, and a 27% (p = 0.08), 29% (0.01), and 42% (p < 0.001) increased risk of stage 2 hypertension, respectively. The addition of BP PRSs to the covariable-only models generally improved the C-statistics (p < 0.05). Multi-ancestry BP PRSs demonstrate the utility of genomic information in the early life prediction of hypertension.

Prediction of hypertension using traditional regression and machine learning models: A systematic review and meta-analysis

OBJECTIVE

We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approaches and compare their predictive performance.

METHODS

We systematically searched MEDLINE, EMBASE, Web of Science, Scopus, and the grey literature for studies predicting the risk of hypertension among the general adult population. Summary statistics from the individual studies were the C-statistic, and a random-effects meta-analysis was used to obtain pooled estimates. The predictive performance of pooled estimates was compared between traditional regression-based models and machine learning-based models. The potential sources of heterogeneity were assessed using meta-regression, and study quality was assessed using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) checklist.

RESULTS

Of 14,778 articles, 52 articles were selected for systematic review and 32 for meta-analysis. The overall pooled C-statistics was 0.75 [0.73-0.77] for the traditional regression-based models and 0.76 [0.72-0.79] for the machine learning-based models. High heterogeneity in C-statistic was observed. The age (p = 0.011), and sex (p = 0.044) of the participants and the number of risk factors considered in the model (p = 0.001) were identified as a source of heterogeneity in traditional regression-based models.

CONCLUSION

We attempted to provide a comprehensive evaluation of hypertension risk prediction models. Many models with acceptable-to-good predictive performance were identified. Only a few models were externally validated, and the risk of bias and applicability was a concern in many studies. Overall discrimination was similar between models derived from traditional regression analysis and machine learning methods. More external validation and impact studies to implement the hypertension risk prediction model in clinical practice are required.

Identifying the predictive effectiveness of a genetic risk score for incident hypertension using machine learning methods among populations in rural China

Current studies have shown the controversial effect of genetic risk scores (GRSs) in hypertension prediction. Machine learning methods are used extensively in the medical field but rarely in the mining of genetic information. This study aims to determine whether genetic information can improve the prediction of incident hypertension using machine learning approaches in a prospective study. The study recruited 4592 subjects without hypertension at baseline from a cohort study conducted in rural China. A polygenic risk score (PGGRS) was calculated using 13 SNPs. According to a ratio of 7:3, subjects were randomly allocated to the train and test datasets. Models with and without the PGGRS were established using the train dataset with Cox regression, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM) methods. The discrimination and reclassification of models were estimated using the test dataset. The PGGRS showed a significant association with the risk of incident hypertension (HR (95% CI), 1.046 (1.004, 1.090), P = 0.031) irrespective of baseline blood pressure. Models that did not include the PGGRS achieved AUCs (95% CI) of 0.785 (0.763, 0.807), 0.790 (0.768, 0.811), 0.838 (0.817, 0.857), and 0.854 (0.835, 0.873) for the Cox, ANN, RF, and GBM methods, respectively. The addition of the PGGRS led to the improvement of the AUC by 0.001, 0.008, 0.023, and 0.017; IDI by 1.39%, 2.86%, 4.73%, and 4.68%; and NRI by 25.05%, 13.01%, 44.87%, and 22.94%, respectively. Incident hypertension risk was better predicted by the traditional+PGGRS model, especially when machine learning approaches were used, suggesting that genetic information may have the potential to identify new hypertension cases using machine learning methods in resource-limited areas. CLINICAL TRIAL REGISTRATION: The Henan Rural Cohort Study has been registered at the Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). http://www.chictr.org.cn/showproj.aspx?proj=11375 .

A risk scoring system to predict the risk of new-onset hypertension among patients with type 2 diabetes

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.

Genome-Wide and Candidate Gene Association Analyses Identify a 14-SNP Combination for Hypertension in Patients With Type 2 Diabetes

BACKGROUND

High blood pressure is common and comorbid with type 2 diabetes (T2D). Almost 50% of patients with T2D have high blood pressure. Patients with both conditions of hypertension (HTN) and T2D are at risk for cardiovascular diseases and mortality. The study aim was to investigate genetic risk factors for HTN in T2D patients.

METHODS

This study included 999 T2D (cohort 1) patients for the first genome scan stage and 922 T2D (cohort 2) patients for the replication stage. Here, we investigated the genetic susceptibility and cumulative weighted genetic risk score for HTN in T2D patients of Han Chinese descent in Taiwan.

RESULTS

Thirty novel genetic single nucleotide polymorphisms (SNPs) were associated with HTN in T2D after adjusting for age and body mass index (P value <1 × 10-4). Eight blood pressure-related and/or HTN-related genetic SNPs were associated with HTN in T2D after adjusting for age and body mass index (P value <0.05). Linkage disequilibrium and cumulative weighted genetic risk score analyses showed that 14 of the 38 SNPs were associated with risk of HTN in a dose-dependent manner in T2D (Cochran-Armitage trend test: P value <0.0001). The 14-SNP cumulative weighted genetic risk score was also associated with increased regression tendency of systolic blood pressure in T2D (SBP = 122.05 + 0.8 × weighted genetic risk score; P value = 0.0001).

CONCLUSIONS

A cumulative weighted genetic risk score composed of 14 SNPs is important for HTN, increased tendency of systolic blood pressure, and may contribute to HTN risk in T2D in Taiwan.

Polygenic Risk Scores Predict Hypertension Onset and Cardiovascular Risk

[Figure: see text].

A novel predicted model for hypertension based on a large cross-sectional study

Hypertension is a global public health issue and leading risk for death and disability. It is urgent to search novel methods predicting hypertension. Herein, we chose 73158 samples of physical examiners in central China from June 2008 to June 2018. After strict exclusion processes, 33570 participants with hypertension and 35410 healthy controls were included. We randomly chose 70% samples as the train set and the remaining 30% as the test set. Clinical parameters including age, gender, height, weight, body mass index, triglyceride, total cholesterol, low-density lipoprotein, blood urea nitrogen, uric acid, and creatinine were significantly increased, while high-density lipoprotein was decreased in the hypertension group versus controls. Nine optimal markers were identified by a logistic regression model, and achieved AUC value of 76.52% in the train set and 75.81% in the test set for hypertension. In conclusions, this study is the first to establish predicted models for hypertension using the logistic regression model in Central China, which provide risk factors and novel prediction method to predict and prevent hypertension.

Genetic Susceptibility, Dietary Protein Intake, and Changes of Blood Pressure: The POUNDS Lost Trial

High blood pressure (BP) is closely related to obesity, and weight loss lowers BP. Evidence has shown considerable interpersonal variation of changes in BP among people experiencing weight loss, and such variation might be partly determined by genetic factors. We assessed the changes in systolic and diastolic BP (SBP/DBP) among 692 participants randomly assigned to 1 of 4 diets varying in macronutrient content for 2 years. Two separate polygenic scores (SBP/DBP-PGS derived from 52/50 single nucleotide polymorphisms) were built for each participant based on 66 BP-associated single nucleotide polymorphisms. During a 2-year intervention, participants in the bottom versus upper tertile of SBP/DBP-PGS had a greater decrease in SBP (△SBP at 6, 12, and 24 months: -3.84 versus -1.61, -4.76 versus -2.75, -2.49 versus -1.63; P=0.001) or in DBP (△DBP at 6, 12, and 24 months: -3.09 versus -1.34, -2.69 versus -1.44, -1.82 versus -0.53; P<0.001). We also found gene-diet interaction on changes in SBP from baseline to 24 months (Pinteraction=0.009). Among participants assigned to a high-protein diet, those with a lower SBP-polygenic scores had greater decreases in SBP at months 6 (P=0.018), months 12 (P=0.007), and months 24 (P=0.089); while no significant difference was observed across the SBP-polygenic scores tertile groups among those assigned to an average-protein diet (all P values >0.05). Our data indicate that genetic susceptibility may affect BP changes in response to weight-loss diet interventions, and protein intake may modify the genetic associations with changes in BP. This trial was registered at URL: http://www.clinicaltrials.gov. Unique identifier: NCT00072995.

A Prediction Model of Essential Hypertension Based on Genetic and Environmental Risk Factors in Northern Han Chinese

Background: Essential hypertension (EH) is a chronic disease of universal high prevalence and a well-established independent risk factor for cardiovascular and cerebrovascular events. The regulation of blood pressure is crucial for improving life quality and prognoses in patients with EH. Therefore, it is of important clinical significance to develop prediction models to recognize individuals with high risk for EH. Methods: In total, 965 subjects were recruited. Clinical parameters and genetic information, namely EH related SNPs were collected for each individual. Traditional statistic methods such as t-test, chi-square test and multi-variable logistic regression were applied to analyze baseline information. A machine learning method, mainly support vector machine (SVM), was adopted for the development of the present prediction models for EH. Results: Two models were constructed for prediction of systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively. The model for SBP consists of 6 environmental factors (age, BMI, waist circumference, exercise [times per week], parental history of hypertension [either or both]) and 1 SNP (rs7305099); model for DBP consists of 6 environmental factors (weight, drinking, exercise [times per week], TG, parental history of hypertension [either and both]) and 3 SNPs (rs5193, rs7305099, rs3889728). AUC are 0.673 and 0.817 for SBP and DBP model, respectively. Conclusions: The present study identified environmental and genetic risk factors for EH in northern Han Chinese population and constructed prediction models for SBP and DBP.

DNA Methylation Analysis Identifies Loci for Blood Pressure Regulation

Genome-wide association studies have identified hundreds of genetic variants associated with blood pressure (BP), but sequence variation accounts for a small fraction of the phenotypic variance. Epigenetic changes may alter the expression of genes involved in BP regulation and explain part of the missing heritability. We therefore conducted a two-stage meta-analysis of the cross-sectional associations of systolic and diastolic BP with blood-derived genome-wide DNA methylation measured on the Infinium HumanMethylation450 BeadChip in 17,010 individuals of European, African American, and Hispanic ancestry. Of 31 discovery-stage cytosine-phosphate-guanine (CpG) dinucleotides, 13 replicated after Bonferroni correction (discovery: N = 9,828, p < 1.0 × 10-7; replication: N = 7,182, p < 1.6 × 10-3). The replicated methylation sites are heritable (h2 > 30%) and independent of known BP genetic variants, explaining an additional 1.4% and 2.0% of the interindividual variation in systolic and diastolic BP, respectively. Bidirectional Mendelian randomization among up to 4,513 individuals of European ancestry from 4 cohorts suggested that methylation at cg08035323 (TAF1B-YWHAQ) influences BP, while BP influences methylation at cg00533891 (ZMIZ1), cg00574958 (CPT1A), and cg02711608 (SLC1A5). Gene expression analyses further identified six genes (TSPAN2, SLC7A11, UNC93B1, CPT1A, PTMS, and LPCAT3) with evidence of triangular associations between methylation, gene expression, and BP. Additional integrative Mendelian randomization analyses of gene expression and DNA methylation suggested that the expression of TSPAN2 is a putative mediator of association between DNA methylation at cg23999170 and BP. These findings suggest that heritable DNA methylation plays a role in regulating BP independently of previously known genetic variants.

Recent development of risk-prediction models for incident hypertension: An updated systematic review

BACKGROUND

Hypertension is a leading global health threat and a major cardiovascular disease. Since clinical interventions are effective in delaying the disease progression from prehypertension to hypertension, diagnostic prediction models to identify patient populations at high risk for hypertension are imperative.

METHODS

Both PubMed and Embase databases were searched for eligible reports of either prediction models or risk scores of hypertension. The study data were collected, including risk factors, statistic methods, characteristics of study design and participants, performance measurement, etc.

RESULTS

From the searched literature, 26 studies reporting 48 prediction models were selected. Among them, 20 reports studied the established models using traditional risk factors, such as body mass index (BMI), age, smoking, blood pressure (BP) level, parental history of hypertension, and biochemical factors, whereas 6 reports used genetic risk score (GRS) as the prediction factor. AUC ranged from 0.64 to 0.97, and C-statistic ranged from 60% to 90%.

CONCLUSIONS

The traditional models are still the predominant risk prediction models for hypertension, but recently, more models have begun to incorporate genetic factors as part of their model predictors. However, these genetic predictors need to be well selected. The current reported models have acceptable to good discrimination and calibration ability, but whether the models can be applied in clinical practice still needs more validation and adjustment.