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Hypertension is highly prevalent in the United States and worldwide. In the United States, 1 in 3 adults (≈73 million people) have hypertension. The financial and human costs of hypertension are substantial. The estimated costs attributable to hypertension in the United States alone in 2010 were $70 billion direct costs for medical treatments and $24 billion indirect costs because of lost productivity.1 Awareness and treatment of hypertension have improved considerably during the past several decades: >80% of all people with hypertension are aware of the condition and 75% are using antihypertensive medications.2,3 However, despite enormous effort and treatment-related expenditures, only 53% of those with documented hypertension have their blood pressure controlled.3 Older age (Figure 1), left ventricular hypertrophy, and obesity are important risk factors for uncontrolled or drug-resistant hypertension.4 Importantly, the foregoing are also risk factors for increased aortic stiffness.5 Figure 1. Hypertension prevalence and control rates as a function of age in the National Health and Nutrition Examination Survey 2003 to 2004. Data derived from Wong et al.44 During the past 2 decades, measures of aortic stiffness have emerged as important risk factors for progression of blood pressure and incident cardiovascular disease. Carotid femoral pulse wave velocity (CFPWV), a measure of aortic wall stiffness, increases markedly with age, particularly after midlife. For example, Framingham Heart Study investigators have shown that the prevalence of CFPWV ≥12 m/s increases from a few percentage before 50 years of age to ≥60% after 70 years of age (Figure 2A). Importantly, elevated CFPWV is associated with high risk for incident hypertension (Figure 2B) and cardiovascular disease (Figure 2C). Figure 2C should be interpreted within the context that a normal value (<95th percentile) for CFPWV in a healthy Framingham Heart Study reference sample <50 years of age was 8.1 …

Increasing adiposity with a clinical endpoint of obesity is associated with a progressive increase in risk of cardiovascular diseases (CVD). However, we are only beginning to understand the physiological and pathophysiological mechanisms by which excessive body fat storage causes CVD. In this issue of Hypertension , the findings of Wildman et al1 extend and strengthen previous observations2–5 by showing that changes in arterial stiffness, as measured by aortic pulse-wave velocity (PWV), are related to changes in body weight over a 2-year follow-up period in healthy men and women aged 20 to 40 years. Weight gain was associated with an increase, whereas weight loss was associated with a decrease in aortic PWV, independent of changes in blood pressure. The greatest increases in aortic PWV were observed in those subjects who gained the most weight (≥4.5 kg). Importantly, the changes in aortic PWV were related to changes in body mass index (BMI), indicating that the associations with body weight were mediated by changes in adiposity (rather than fat-free mass). Changes in aortic PWV over time also were related to baseline body weight, BMI, and waist circumference (an indirect measure of abdominal adiposity), providing evidence that higher initial levels of total and abdominal body fatness are associated with greater future increases in arterial stiffness. The study included subjects with a broad range of BMI, suggesting that the findings are applicable to both nonobese and obese adults in the general population. An important finding was that black adults demonstrated greater increases in weight gain-adjusted and blood pressure-adjusted aortic PWV over time, suggesting that they undergo greater increases in arterial stiffness in response to the same age-associated weight gain compared with whites. These observations provide further support for the emerging concept that adiposity-driven weight gain during adulthood contributes significantly to the adverse changes …

High systolic blood pressure is one of the leading global risk factors for mortality, contributing significantly to cardiovascular diseases. Despite advances in treatment, a large proportion of patients with hypertension do not achieve optimal blood pressure control. Arterial stiffness (AS), measured by pulse wave velocity (PWV), is an independent predictor of cardiovascular events and overall mortality. Various antihypertensive drugs exhibit differential effects on PWV, but the extent to which these effects vary depending on individual patient characteristics is not well understood. Given the complexity of selecting the most appropriate antihypertensive medication for reducing PWV, machine learning (ML) techniques offer an opportunity to improve personalized treatment recommendations. This study aims to develop an ML model that provides personalized recommendations for antihypertensive medications aimed at reducing PWV. The model considers individual patient characteristics, such as demographic factors, clinical data, and cardiovascular measurements, to identify the most suitable antihypertensive agent for improving AS. This study, known as the RIGIPREV study, used data from the EVA, LOD-DIABETES, and EVIDENT studies involving individuals with hypertension with baseline and follow-up measurements. Antihypertensive drugs were grouped into classes such as angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), β-blockers, diuretics, and combinations of diuretics with ACEIs or ARBs. The primary outcomes were carotid-femoral and brachial-ankle PWV, while the secondary outcomes included various cardiovascular, anthropometric, and biochemical parameters. A multioutput regressor using 6 random forest models was used to predict the impact of each antihypertensive class on PWV reduction. Model performance was evaluated using the coefficient of determination (R2) and mean squared error. The random forest models exhibited strong predictive capabilities, with internal validation yielding R2 values between 0.61 and 0.74, while external validation showed a range of 0.26 to 0.46. The mean squared values ranged from 0.08 to 0.22 for internal validation and from 0.29 to 0.45 for external validation. Variable importance analysis revealed that glycated hemoglobin and weight were the most critical predictors for ACEIs, while carotid-femoral PWV and total cholesterol were key variables for ARBs. The decision tree model achieved an accuracy of 84.02% in identifying the most suitable antihypertensive drug based on individual patient characteristics. Furthermore, the system's recommendations for ARBs matched 55.3% of patients' original prescriptions. This study demonstrates the utility of ML techniques in providing personalized treatment recommendations for antihypertensive therapy. By accounting for individual patient characteristics, the model improves the selection of drugs that control blood pressure and reduce AS. These findings could significantly aid clinicians in optimizing hypertension management and reducing cardiovascular risk. However, further studies with larger and more diverse populations are necessary to validate these results and extend the model's applicability.

Implication of Inherited Genetic Variants Associated with Follicular Lymphoma Susceptibility in the Prognosis of Patients Treated By Immunochemotherapy in PRIMA Study

Background:Components of arterial stiffness (structural stiffness from aging and load-dependent stiffness from high blood pressure) are markers for cardiovascular disease risk. We evaluated relationships between polygenic risk scores (PRS) for blood pressure (BP) and arterial stiffness.Methods:Participants (n=5942) are from the Multi-Ethnic Study of Atherosclerosis with measures of carotid ultrasound and genomic DNA. Pulse wave velocity (PWV) was calculated from carotid ultrasound representing total stiffness. Structural stiffness was calculated by adjusting PWV to a 120/80 mmHg blood pressure with participant-specific models. Load-dependent stiffness was the difference of total and structural stiffness. Blood pressure PRSs were previously derived from a genome-wide association study. Associations between BP PRSs and components of carotid arterial stiffness were assessed with multivariable-adjusted regression models. Mendelian Randomization (MR) was performed with two-stage least squares to 1) confirm that BP PRSs strongly predict MAP and 2) estimate the effect of MAP on arterial stiffness.Results:Participants (n=5,942) were mean (standard deviation) age of 62.8 (10.3) years (52% female; 39% White, 25% African-American, 13% Asian, 23% Hispanic). Systolic BP (SBP) was 133.5 (20.5) mmHg, diastolic BP (DBP) was 74.27 (10.28) mmHg and pulse pressure (PP) was 59.22 (16.17) mmHg. Total carotid stiffness was associated with the SBP PRS (β=0.10 +/− 0.02, p=5.25E-08) and the DBP PRS (β: 0.08 +/− 0.02, p=7.26E-07) but not PP PRS (β=0.04 +/− 0.02, P=0.05). Load dependent carotid stiffness was associated with the SBP PRS β=0.09 +/− 0.01, p=7.93*10−30) the DBP PRS (β=0.09 +/− 0.01, p= 4.92*10−34) and the PP PRS (β=0.03 +/− 0.01, p= 8.18*10−5). Structural carotid stiffness was not associated with any BP PRSs. MR showed that SBP and DPB PRS strongly predict MAP (p<0.001) and each 1-mmHg increase in MAP raised load dependent stiffness by 0.0494 m/s (p<0.001).Conclusion:There are key differences in the genetic underpinnings of the mechanistic components of arterial stiffness.

Background/Objectives: Mexicans are disproportionately affected by dyslipidemia, specifically low high-density lipoprotein (HDL-C) and high triglyceride (TG) concentrations. Research on the genetic contributions to dyslipidemia, conducted primarily among European populations, has identified numerous single-nucleotide polymorphisms (SNPs) with small effect sizes and low replication rates. A genetic risk score (GRS) can examine the cumulative effects of multiple SNPs and potentially explain greater phenotypic variability than individual SNPs. GRS in Mexican populations and those without diagnosed dyslipidemia are limited. This study aims to construct a GRS from lipid metabolism-related SNPs and determine its associations with blood lipid concentrations in young Mexican college students. Methods: Adults (ages 18-25 years, n = 580) provided a fasting blood sample to determine TG and HDL-C concentrations. DNA was genotyped for 14 SNPs in lipid metabolism pathways (reverse cholesterol transport [RCT], cellular lipid uptake, and lipoprotein formation and transport). Additive (number of risk alleles) and weighted (regression-derived β coefficients) GRS were calculated for individual pathways, and their sum (total GRS) was explored. Associations among individual SNPs, GRS, and blood lipids were determined through general linear models in SAS. Results: The additive RCT and total GRS were associated with TG (both p < 0.05). The RCT pathway explained 3.4% of the variability in TG concentrations, and the total GRS explained 6.1%. The weighted RCT GRS was associated with HDL-C (p = 0.007). The ATP-binding cassette protein (ABCA1) rs9282541 variant was most strongly associated with HDL-C (p = 0.016). When this SNP was removed from the GRS, the association became non-significant. Conclusions: SNPs in lipoprotein metabolism pathways cumulatively associate with blood lipid concentrations in young Mexican adults. The ABCA1-rs9282541 variant, previously shown to be positively associated with low HDL-C concentrations in Amerindian populations, had the strongest association with HDL-C. Further work is needed to elucidate the roles of genetic admixture and lifestyle risk factors in dyslipidemia in this population.

Background/Objectives: We aimed to predict the incidence of obesity in a Korean population using a genetic risk score (GRS) constructed with obesity-related single nucleotide polymorphisms (SNPs) along with an oxidative stress score (OSS). Subjects/Methods: A total of 9,460 Korean subjects and 356,974 SNPs were included. The GRS was constructed using three significant obesity-related SNP loci, and the OSS was calculated with three reliable oxidative stress biomarkers. Results: In an association study, the top three SNPs, rs2846412, rs58424360, and rs591120, showed profound associations with obesity in a Korean population (all FDR &amp;lt; 0.1). The GRS showed a more significant association with increased obesity (OR = 2.879, 95% CI = 2.283-3.630) than individual SNPs did after adjusting for age and sex. Three oxidative stress biomarkers, including malondialdehyde (MDA), oxidized low-density lipoprotein (ox-LDL), and 8-epi-prostaglandin F2α (8-epi-PGF2α), showed significantly high levels in the obese group. Moreover, the OSS, which was the sum of each oxidative stress biomarker score, showed a markedly high association with the incidence of obesity, with an OR of 3.213 (p = 3.57E-18). Based on the results from the regression tests and a receiver-operating characteristic (ROC) curve analysis, we found that HOMA-IR, hs-CRP, the GRS, and the OSS were the most relevant factors for the incremental risk of obesity and were significantly associated with the incidence of obesity. The area under the ROC curve (AUROC) was improved when the GRS was added to the model with the OSS and two obesity-related parameters (from 74.2% to 75.1%). Conclusions: We first identified that subjects with an obesity GRS and a high OSS may have a higher risk of obesity. Our findings and weighting approaches were effective in predicting the incidence of obesity; furthermore, the GRS is a relevant factor that significantly predicts the risk of obesity. Disclosure M. Kim: None. H. Yoo: None. Y. Han: None. J. Lee: None. Funding Korean Ministry of Health and Welfare (HI13C0715); Korean National Research Foundation (NRF-2017R1C1B2007195, NRF-2012M3A9C4048762)

Funding AcknowledgementsType of funding sources: Other. Main funding source(s): Carlos III Health Institute and the European Regional Development Fund Government of Catalonia through the Agency for Management of University and Research Grants Crue-CSIC-Santander FONDO SUPERA COVID-19BackgroundThe disease presentation of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ranges from asymptomatic to fatal. COVID-19 patients with pre-existing coronary artery disease (CAD) risk factors or overt cardiovascular disease are at particular risk of severe disease. We hypothesised that a specific genetic risk score (GRS) based on single nucleotide polymorphisms (SNPs) allele count to score COVID-19 severity might include SNPs counts related to CAD incidence and to thrombosis, inflammation, and viral infectivity determinants involved in the severity of SARS-CoV-2. Such GRS could improve the early risk stratification of COVID-19 patients and optimize treatment strategies.PurposeTo evaluate the capacity of a genetic risk score (GRS) with candidate genes to predict COVID-19 severity.MethodsWe conducted an age- and sex-matched case-control study with 1:2 ratio recruitment involving 2454 patients from Catalan hospitals and primary care. Cases were hospitalized severe (requiring at least oxygen treatment) or fatal COVID-19 patients; and controls were moderate-symptom and asymptomatic patients treated at home. Standard parametric and non-parametric methods, as required, were used to compare patient characteristics by severity. Individual genotypes for 33 CAD, 14 thrombosis, 22 inflammation, 15 viral infectivity SNPs and 2 COVID-19 SNPs already published were tested for association with severity with Cochran-Armitage statistics and p-values corrected for multiple comparisons. GRS was computed as the unweighted count of adverse alleles (0, 1 or 2). The odds ratio of severe COVID-19 was analysed for GRS (and its component SNPs) with logistic regression models adjusted for potential confounding factors. Area under the curve (AUC) improvement and net reclassification index (NRI) for GRS was estimated from a basic model including CAD and COVID-19 severity risk factors. Models’ performance was measured with the Akaike information criterion.ResultsSNPs identifications are not shown to prevent patent conflict. Cases and control characteristics are compared in Table 1. Cases had a more adverse cardiovascular and anthropometric risk profile. After correcting for multiple testing by Benjamini-Hochberg criteria, we observed 13 SNPs to be significantly associated with severity. After excluding the close SNPs in linkage disequilibrium, 7 were retained in the GRS model, which yielded the discrimination and reclassification characteristics described in Table 2.ConclusionA GRS with 7 SNPs related to CAD, thrombosis and inflammation significantly improves the severe COVID-19 risk assessment done with age, sex, comorbidity, and anthropometry alone.CV and anthropometric risk profileOR of COVID-19 severity for a 7-SNP GRS

Polygenic Risk Scores for Follow Up After Colonoscopy and Polypectomy: Another Tool for Risk Stratification and Planning Surveillance?

A Blueprint for Genetic Determinism

Genetic markers associated with cardiovascular disease, which may be predictive, have been identified by of genome-wide association studies, but their predictive ability has not been tested. The evaluation of a literature-based genetic risk score for cardiovascular disease is now possible using the online catalog maintained by the National Human Genome Research Institute of all genetic markers identified in genome-wide association studies. This prospective cohort study investigated the predictive ability of literature-based genetic risk scores for cardiovascular disease, and tested their relationship to incident cardiovascular events and their potential to improve prediction. The participants—a cohort of 19,313 initially healthy females enrolled in the Women's Genome Health Study—were followed for a median of 12.3 years. Genetic risk scores were constructed using the online National Human Genome Research Institute catalog of studies published between 2005 and 2009. The scores were based on the selection of single nucleotide polymorphisms (SNPs) that were known markers associated with either cardiovascular disease (myocardial infarction, stroke, coronary disease, or cardiovascular death), or an intermediate phenotype (total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, hypertension, and other phenotypes). A total of 101 SNPs were identified with a published risk allele and a P value of less than or equal to 10−7 for the association with cardiovascular disease or at least 1 intermediate phenotype; the addition of risk alleles created the genetic risk score. During the follow-up period, there were 777 incident cardiovascular events (199 myocardial infarctions, 203 strokes, 63 cardiovascular deaths, and 312 revascularizations). Adjustment for age showed that the 101 SNP genetic risk scores were associated with increased risk of cardiovascular disease: the age-adjusted hazard ratio for cardiovascular disease per allele for the101 SNP genetic risk scores was 1.02 per risk allele, with a 95% confidence interval of 1.00 to 1.03 per risk allele; P = 0.006). Over a 10-year study period, this represented an absolute cardiovascular disease risk of 3% in the lowest tertile of genetic risk (73–99 risk alleles) and 3.7% in the highest tertile (106–125 risk alleles). However, after adjusting for traditional risk factors, the ability of the risk score alone to discriminate between women at risk for cardiovascular events and those not at risk was minimal with a c index of 0.52 in the ATP III (Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults) risk score; the ATP III–adjusted hazard ratio per allele was 1.00, with a 95% CI of 0.99 to 1.01. In contrast, self-reported family history remained an independent risk factor in multivariate models for incident cardiovascular disease. These findings indicate that a genetic risk score comprised of 101 SNPs is not significantly associated with incident cardiovascular disease, after adjustment for traditional risk factors, and has no significant effect on discrimination or reclassification.

Autonomic nervous system (ANS) dysfunction and vascular stiffness increase cardiovascular risk in people with chronic kidney disease (CKD). Chronic elevations in sympathetic activity can lead to increased arterial stiffness; however, the relationship between these variables is unknown in CKD. To explore the association between measures of autonomic function and arterial stiffness in patients with moderate-to-severe CKD. This study was a prespecified secondary analysis of a randomized controlled trial. This included the following measures: 24-hour ambulatory blood pressure (BP), carotid-femoral and carotid-radial pulse wave velocity (PWV), and postexercise heart rate recovery (HRR). We used mixed effect linear regression models with Bayesian information criteria (BIC) to assess the contribution of ANS measurements. Forty-four patients were included in the analysis. Mean carotid-femoral and carotid-radial PWV were 7.12 m/s (95% CI 6.13, 8.12) and 8.51 m/s (7.90, 9.11), respectively. Mean systolic dipping, calculated as percentage change in mean systolic readings from day to night, was 10.0% (95% CI 7.79, 12.18). Systolic dipping was independently associated with carotid-radial PWV, MD -0.09 m/s (95% CI -0.15, -0.02) and had the lowest BIC. Systolic dipping was associated with carotid-radial PWV in people with moderate-to-severe CKD; however, there was no association with carotid-femoral PWV. Systolic dipping may be a feasible surrogate of ANS function, as the association with carotid-radial PWV was consistent with the minimal clinically important difference (MCID). Future studies are needed to define the relationship between ANS function, arterial stiffness, and CV events over time in people with CKD.

Conditional Inference Tree for Multiple Gene-Environment Interactions on Myocardial Infarction

Background To investigate the difference of carotid structural abnormality and stiffness, and the relationship between carotid structural abnormality and stiffness in prehypertensive and normotensive subjects. Methods A total of 581 participants (270 with prehypertension, 311 normotensive control subjects) from the First Affiliated Hospital of Fujian Medical University were enrolled from January 2017 to March 2019. Body height/weight, resting heart rate, and blood pressure were recorded. Blood biochemical indexes and carotid–femoral pulse wave velocity were determined, and carotid ultrasonography was performed. Carotid intima–media thickness ≥1.0 mm and carotid–femoral pulse wave velocity ≥10.0 m/s were defined as carotid structural abnormality and arterial stiffness, respectively. Results The percentage of patients with carotid structural abnormality (60.7% vs. 51.4%), carotid–femoral pulse wave velocity [(8.78 ± 1.48) vs. (7.92 ± 1.30) m/s], and the percentage of patients with arterial stiffness (13.7% vs. 5.8%) were increased in the prehypertension group compared with the control group (all P &amp;lt; 0.05). In the prehypertension group, the percentage of patients with arterial stiffness in the abnormal carotid structure subgroup (n = 164) was higher than that in the normal carotid structure subgroup (n = 106) (20.1% vs. 3.8%, χ2 = 14.551, P &amp;lt; 0.001). Furthermore, carotid structural abnormality, age, diabetes mellitus, resting heart rate, systolic blood pressure, fasting blood glucose, glycosylated hemoglobin A1c, and hypoglycemic therapy were all correlated with arterial stiffness (all P &amp;lt; 0.05). After adjusting for other cardiovascular risk factors, multiple logistic regression analysis showed that carotid structural abnormality, age, diabetes mellitus, and resting heart rate were independently correlated with arterial stiffness, and patients with carotid structural abnormality had 5.25-fold higher risk of arterial stiffness than those with a normal carotid arterial structure (odds ratio = 5.250, 95% confidence interval 1.580–17.448, P = 0.007). However, in the normotensive group, no such relationships were observed between carotid artery structural abnormality and stiffness. Conclusions In prehypertensives but not normotensives, carotid artery structural abnormalities and stiffness are common, and independently correlated.

HMG-CoA-Reductase inhibitors (HMGRIs) are currently the most widely used group of drugs in patients with coronary artery disease (CAD) and are given preemptively to patients with high levels of cholesterol, including those with diabetes mellitus (DM). However, intake of HMGRIs also increases the progression of coronary artery calcification (CAC) and the risk of developing DM. This study aimed to investigate whether HMGRI intake interacts with the diabetes-associated genetic risk score (GRS) to affect CAC progression using data from the population-based Heinz Nixdorf Recall (HNR) study. CAC was measured in 3157 participants using electron-beam computed tomography twice, at baseline (CACb) and 5 years later (CAC5y). CAC progression was classified as slow, expected, or rapid based on predicted values. Weighted DM GRS was constructed using 100 diabetes mellitus–associated single nucleotide polymorphisms (SNPs). We used log-linear regression to evaluate the interaction of HMGRI intake with diabetes-associated GRS and individual SNPs on CAC progression (rapid vs. expected/slow), adjusting for age, sex, and log(CACb + 1). The prevalence of rapid CAC progression in the HNR study was 19.6%. We did not observe any association of the weighted diabetes mellitus GRS with the rapid progression of CAC (relative risk (RR) [95% confidence interval (95% CI)]: 1.01 [0.94; 1.10]). Furthermore, no indication of an interaction between GRS and HMGRI intake was observed (1.08 [0.83; 1.41]). Our analyses showed no indication that the impact of HMGRIs on CAC progression is significantly more severe in patients with a high genetic risk of developing DM than in those with a low GRS.