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

Body mass index modifies genetic susceptibility to high systolic blood pressure in adolescents and young adults: results from an 18-year longitudinal study

Genome-wide association studies (GWAS) in adults have identified single nucleotide polymorphisms (SNPs) associated with systolic blood pressure (SBP), but it is unclear whether the findings apply in youth. Further, the role of body mass index (BMI) in these associations is understudied. Our objective was to determine whether BMI modifies genetic susceptibility to high SBP in young people. The sample comprised 714 participants of European ancestry recruited in 1999-2000 from 10 Montreal-area high schools for a longitudinal study. SBP was measured at ages 12, 15, 17, 24, and 30. Blood and saliva samples were collected at ages 14, 20, and 25. Two evidence-based genetic risk scores (GRS) were constructed based on GWAS results in adults: GRS22 used 22 SNPs and GRS182 added 160 additional SNPs to GRS22. Sex-specific associations between each GRS and repeated measures of SBP were estimated using linear mixed models including BMI and a GRS*BMI product term. GRS182 explained a greater proportion of SBP variance than GRS22, and a greater proportion in females than males. The associations increased monotonically with BMI values between 22 kg/m2 and 35 kg/m2. Results indicate that BMI modifies the association between a GRS and SBP levels from adolescence to adulthood.

2025 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2025 AHA Statistical Update is the product of a full year's worth of effort in 2024 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. This year's edition includes a continued focus on health equity across several key domains and enhanced global data that reflect improved methods and incorporation of ≈3000 new data sources since last year's Statistical Update.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Prospective associations of genetic susceptibility to high blood pressure and muscle strength with incident cardiovascular disease outcomes

BACKGROUND

This study explored the prospective associations of genetic susceptibility to high blood pressure (BP) and muscle strength with cardiovascular disease (CVD) mortality, incident coronary heart disease (CHD) and incident stroke.

METHODS

This study included 349 085 white British individuals from the UK Biobank study. Genetic risk of high BP was estimated using a weighted polygenic risk score that incorporated 136 and 135 nonoverlapping single-nucleotide polymorphisms for systolic BP and diastolic BP, respectively. Muscle strength was assessed using a hand dynamometer and expressed relative to fat-free mass. Sex- and age-specific tertiles were used to classify muscle strength into three categories. Cox regressions with age as the underlying timescale were fit for CVD mortality ( n  = 8275), incident CHD ( n  = 14 503), and stroke ( n  = 7518).

RESULTS

Compared with the lowest genetic risk of high BP (bottom 20%), the highest (top 20%) had greater hazards of each outcome. Low muscle strength was associated with higher hazards of CVD mortality [hazard ratio (HR): 1.51, 95% confidence interval (CI): 1.43-1.59], incident CHD (HR: 1.16, 95% CI: 1.11-1.21), and stroke (HR: 1.20, 95% CI: 1.14-1.27), independently of confounders and genetic predisposition to high BP, compared with high muscle strength. Joint analyses revealed that the estimated 10-year absolute risks of each outcome were lower for high muscle strength combined with high genetic risk, compared with low muscle strength combined with low or medium genetic risk.

CONCLUSION

Individuals who are genetically predisposed to high BP but have high muscle strength could have lower risk of major CVD events, compared with those who have low or medium genetic risk but low muscle strength.

Advancements in genetic research by the Hispanic Community Health Study/Study of Latinos: A 10-year retrospective review

The Hispanic Community Health Study/Study of Latinos (HCHS/SOL) is a multicenter, longitudinal cohort study designed to evaluate environmental, lifestyle, and genetic risk factors as they relate to cardiometabolic and other chronic diseases among Hispanic/Latino populations in the United States. Since the study's inception in 2008, as a result of the study's robust genetic measures, HCHS/SOL has facilitated major contributions to the field of genetic research. This 10-year retrospective review highlights the major findings for genotype-phenotype relationships and advancements in statistical methods owing to the HCHS/SOL. Furthermore, we discuss the ethical and societal challenges of genetic research, especially among Hispanic/Latino adults in the United States. Continued genetic research, ancillary study expansion, and consortia collaboration through HCHS/SOL will further drive knowledge and advancements in human genetics research.

Comparison of methods for building polygenic scores for diverse populations

Polygenic scores (PGSs) are a promising tool for estimating individual-level genetic risk of disease based on the results of genome-wide association studies (GWASs). However, their promise has yet to be fully realized because most currently available PGSs were built with genetic data from predominantly European-ancestry populations, and PGS performance declines when scores are applied to target populations different from the populations from which they were derived. Thus, there is a great need to improve PGS performance in currently under-studied populations. In this work we leverage data from two large and diverse cohorts the Million Veterans Program (MVP) and All of Us (AoU), providing us the unique opportunity to compare methods for building PGSs for multi-ancestry populations across multiple traits. We build PGSs for five continuous traits and five binary traits using both multi-ancestry and single-ancestry approaches with popular Bayesian PGS methods and both MVP META GWAS results and population-specific GWAS results from the respective African, European, and Hispanic MVP populations. We evaluate these scores in three AoU populations genetically similar to the respective African, Admixed American, and European 1000 Genomes Project superpopulations. Using correlation-based tests, we make formal comparisons of the PGS performance across the multiple AoU populations. We conclude that approaches that combine GWAS data from multiple populations produce PGSs that perform better than approaches that utilize smaller single-population GWAS results matched to the target population, and specifically that multi-ancestry scores built with PRS-CSx outperform the other approaches in the three AoU populations.

The expected polygenic risk score (ePRS) framework: an equitable metric for quantifying polygenetic risk via modeling of ancestral makeup

Polygenic risk scores (PRSs) depend on genetic ancestry due to differences in allele frequencies between ancestral populations. This leads to implementation challenges in diverse populations. We propose a framework to calibrate PRS based on ancestral makeup. We define a metric called "expected PRS" (ePRS), the expected value of a PRS based on one's global or local admixture patterns. We further define the "residual PRS" (rPRS), measuring the deviation of the PRS from the ePRS. Simulation studies confirm that it suffices to adjust for ePRS to obtain nearly unbiased estimates of the PRS-outcome association without further adjusting for PCs. Using the TOPMed dataset, the estimated effect size of the rPRS adjusting for the ePRS is similar to the estimated effect of the PRS adjusting for genetic PCs. Similarly, we applied the ePRS framework to six cardiovascular-related traits in the All of Us dataset, and the results are consistent with those from the TOPMed analysis. The ePRS framework can protect from population stratification in association analysis and provide an equitable strategy to quantify genetic risk across diverse populations.

Utility of a Systolic Blood Pressure Polygenic Risk Score With Chlorthalidone Response

Importance

The clinical utility of polygenic risk scores (PRS) for blood pressure (BP) response to antihypertensive treatment (AHT) has not been elucidated.

Objective

To investigate the ability of a systolic BP (SBP) PRS to predict AHT response and apparent treatment-resistant hypertension (aTRH).

Design, Setting, and Participants

The Genetics of Hypertension Associated Treatments (GenHAT) study was an ancillary pharmacogenomic study to the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). ALLHAT, which enrolled participants aged 55 years or older with hypertension (HTN) starting in February 1994, completed follow-up in March 2002. The current study was conducted from a subset of Black GenHAT participants randomized to the treatment groups of either chlorthalidone (n = 3745) or lisinopril (n = 2294), with genetic data available from a prior genetic association study. The current study's objective was to examine the association of the SBP PRS to AHT response over 6 months, as well as to examine the predictive accuracy of the SBP PRS with aTRH. The current analysis took place in February 2023, with additional analyses conducted in July 2024.

Exposure

An SBP PRS (comprising 1 084 157 genetic variants) stratified as quintiles and per SD.

Main Outcomes and Measures

The primary outcome was change in SBP (ΔSBP) and diastolic BP (ΔDBP) over 6 months. aTRH was defined as the use of 3 AHTs with uncontrolled HTN at year 3 of follow-up or taking 4 or more AHTs at year 3 of follow-up, regardless of BP. Baseline demographics were compared across PRS quintiles using Kruskal-Wallis or χ2 tests as appropriate. The least-square means of BP response were calculated through multivariable adjusted linear regression, and multivariable adjusted logistic regression was used to calculate the odds ratios and 95% confidence intervals for aTRH.

Results

Among 3745 Black GenHAT participants randomized to chlorthalidone treatment, median (IQR) participant age was 65 (60-71) years, and 2064 participants (55.1%) were female. Each increasing quintile of the SBP PRS from 1 to 5 was associated with a reduced BP response to treatment over 6 months. Participants in the lowest quintile experienced a mean ΔSBP of -10.01 mm Hg (95% CI, -11.11 to -8.90) compared to -6.57 mm Hg (95% CI, -7.67 to -5.48) for participants in the median quintile. No associations were observed between the SBP PRS and BP response to lisinopril. Participants in the highest PRS quintile had 67% higher odds of aTRH compared to those in the median quintile (odds ratio, 1.67; 95% CI, 1.19-2.36). These associations were independently validated.

Conclusions and Relevance

In this genetic association study, Black individuals with HTN at a lower genetic risk of elevated BP experienced an approximately 3.5 mm Hg-greater response to chlorthalidone compared with those at an intermediate genetic risk of elevated BP. SBP PRS may also identify individuals with HTN harboring a higher risk of treatment-resistant HTN. Overall, SBP PRS demonstrates potential to identify those who may have greater benefit from chlorthalidone, but future research is needed to determine if PRS can inform initiation and choice of treatment among individuals with HTN.

Intrauterine chromium exposure and cognitive developmental delay: The modifying effect of genetic predisposition

There is limited evidence on the effects of intrauterine chromium (Cr) exposure on children's cognitive developmental delay (CDD). Further, little is known about the genetic factors in modifying the association between intrauterine Cr exposure and CDD. The present study involved 2361 mother-child pairs, in which maternal plasma Cr concentrations were assessed, a polygenic risk score for the child was constructed, and the child's cognitive development was evaluated using the Bayley Scales of Infant Development. The risks of CDD conferred by intrauterine Cr exposure in children with different genetic backgrounds were evaluated by logistic regression. The additive interaction between intrauterine Cr exposure and genetic factors was evaluated by calculating the relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (SI). According to present study, higher intrauterine Cr exposure was significantly associated with increased CDD risk [each unit increase in ln-transformed maternal plasma Cr concentration (ln-Cr): adjusted OR (95 % CI), 1.18 (1.04-1.35); highest vs lowest quartile: adjusted OR (95 % CI), 1.57 (1.10-2.23)]. The dose-response relationship of intrauterine Cr exposure and CDD for children with high genetic risk was more prominent [each unit increased ln-Cr: adjusted OR (95 % CI), 1.36 (1.09-1.70)]. Joint effects between intrauterine Cr exposure and genetic factors were found. Specifically, for high genetic risk carriers, the association between intrauterine Cr exposure and CDD was more evident [highest vs lowest quartile: adjusted OR (95 % CI), 2.33 (1.43-3.80)]. For those children with high intrauterine Cr exposure and high genetic risk, the adjusted AP was 0.39 (95 % CI, 0.07-0.72). Conclusively, intrauterine Cr exposure was a high-risk factor for CDD in children, particularly for those with high genetic risk. Intrauterine Cr exposure and one's adverse genetic background jointly contribute to an increased risk of CDD in children.

Reducing bias in healthcare artificial intelligence: A white paper

Objective: Mitigation of racism in artificial intelligence (AI) is needed to improve health outcomes, yet no consensus exists on how this might be achieved. Methods: At an international conference in 2022, experts gathered to discuss strategies for reducing bias in healthcare AI. Results: This paper delineates these strategies along with their corresponding strengths and weaknesses and reviews the existing literature on these strategies. Conclusions: Five major themes resulted: reducing dataset bias, accurate modeling of existing data, transparency of artificial intelligence, regulation of artificial intelligence and the people who develop it, and bringing stakeholders to the table.

Obstructive sleep apnea mediates genetic risk of Diabetes Mellitus: The Hispanic Community Health Study/Study of Latinos

Objective

We sought to evaluate whether obstructive sleep apnea (OSA), and other sleep disorders, increase genetic risk of developing diabetes mellitus (DM).

Research Design and Methods

Using GWAS summary statistics from the DIAGRAM consortium and Million Veteran Program, we developed multi-ancestry Type 2 Diabetes (T2D) polygenic risk scores (T2D-PRSs) useful in admixed Hispanic/Latino individuals. We estimated the association of the T2D-PRS with cross-sectional and incident DM in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We conducted a mediation analysis with T2D-PRSs as an exposure, incident DM as an outcome, and OSA as a mediator. Additionally, we performed Mendelian randomization (MR) analysis to assess the causal relationship between T2D and OSA.

Results

Of 12,342 HCHS/SOL participants, at baseline, 48.4% were normoglycemic, 36.6% were hyperglycemic, and 15% had diabetes, and 50.9% identified as female. Mean age was 41.5, and mean BMI was 29.4. T2D-PRSs was strongly associated with baseline DM and with incident DM. At baseline, a 1 SD increase in the primary T2D-PRS had DM adjusted odds ratio (OR) = 2.67, 95% CI [2.40; 2.97] and a higher incident DM rate (incident rate ratio (IRR) = 2.02, 95% CI [1.75; 2.33]). In a stratified analysis based on OSA severity categories the associations were stronger in individuals with mild OSA compared to those with moderate to severe OSA. Mediation analysis suggested that OSA mediates the T2D-PRS association with DM. In two-sample MR analysis, T2D-PRS had a causal effect on OSA, OR = 1.03, 95% CI [1.01; 1.05], and OSA had a causal effect on T2D, with OR = 2.34, 95% CI [1.59; 3.44].

Conclusions

OSA likely mediates genetic effects on T2D.

Exploring the genetics of airflow limitation in lung function across the lifespan - a polygenic risk score study

Background

Chronic obstructive pulmonary disease (COPD) is caused by interactions between many factors across the life course, including genetics. A proportion of COPD may be due to reduced lung growth in childhood. We hypothesized that a polygenic risk score (PRS) for COPD is associated with lower lung function already in childhood and up to adulthood.

Methods

A weighted PRS was calculated based on the 82 association signals (p ≤ 5 × 10-8) revealed by the largest GWAS of airflow limitation (defined as COPD) to date. This PRS was tested in association with lung function measures (FEV1, FVC, and FEV1/FVC) in subjects aged 4-50 years from 16 independent cohorts participating in the Chronic Airway Diseases Early Stratification (CADSET) Clinical Research Collaboration. Age-stratified meta-analyses were conducted combining the results from each cohort (n = 45,406). These findings were validated in subjects >50 years old.

Findings

We found significant associations between the PRS for airflow limitation and: (1) lower pre-bronchodilator FEV1/FVC from school age (7-10 years; β: -0.13 z-scores per one PRS z-score increase [-0.15, -0.11], q-value = 7.04 × 10-53) to adulthood (41-50 years; β: -0.16 [-0.19, -0.13], q-value = 1.31 × 10-24); and (2) lower FEV1 (from school age: 7-10 years; β: -0.07 [-0.09, -0.05], q-value = 1.65 × 10-9, to adulthood: 41-50 years; β: -0.17 [-0.20, -0.13], q-value = 4.48 x 10-20). No effect modification by smoking, sex, or a diagnosis of asthma was observed.

Interpretation

We provide evidence that a higher genetic risk for COPD is linked to lower lung function from childhood onwards.

Funding

This study was supported by CADSET, a Clinical Research Collaboration of the European Respiratory Society.

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.

Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits

Hypertension affects more than one billion people worldwide. Here we identify 113 novel loci, reporting a total of 2,103 independent genetic signals (P < 5 × 10-8) from the largest single-stage blood pressure (BP) genome-wide association study to date (n = 1,028,980 European individuals). These associations explain more than 60% of single nucleotide polymorphism-based BP heritability. Comparing top versus bottom deciles of polygenic risk scores (PRSs) reveals clinically meaningful differences in BP (16.9 mmHg systolic BP, 95% CI, 15.5-18.2 mmHg, P = 2.22 × 10-126) and more than a sevenfold higher odds of hypertension risk (odds ratio, 7.33; 95% CI, 5.54-9.70; P = 4.13 × 10-44) in an independent dataset. Adding PRS into hypertension-prediction models increased the area under the receiver operating characteristic curve (AUROC) from 0.791 (95% CI, 0.781-0.801) to 0.826 (95% CI, 0.817-0.836, ∆AUROC, 0.035, P = 1.98 × 10-34). We compare the 2,103 loci results in non-European ancestries and show significant PRS associations in a large African-American sample. Secondary analyses implicate 500 genes previously unreported for BP. Our study highlights the role of increasingly large genomic studies for precision health research.

Genomic data in the All of Us Research Program

Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics1-4. The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health5,6. Here we describe the programme's genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.

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.